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1. Glycopeptides as Potential Interventions for COVID-19

Author: Demsie DG, Gebre AK, Yimer EM, Alema NM, Araya EM, Bantie AT, Allene MD, Gebremedhin H, Yehualaw A, Tafere C, Tadese HT, Amare B, Weldekidan E, and Gebrie D
Subjects: corona virus, sars-cov, sars-cov2, covid19, glycopeptides, Medicine (General), R5-920
Abstract: Desalegn Getnet Demsie, 1 Abadi Kahsu Gebre, 1, 2 Ebrahim M Yimer, 1– 3 Niguse Meles Alema, 1 Ephrem Mebrahtu Araya, 1– 3 Abere Tilahun Bantie, 1– 4 Mengesha Dessie Allene, 1– 5 Hagazi Gebremedhin, 2 Adane Yehualaw, 6 Chernet Tafere, 6 Haileslassie Tesfay Tadese, 1– 5, 7 Bekalu Amare, 2 Etsay Weldekidan, 1 Desye Gebrie 8, 9 1Adigrat University, College of Medicine and Health Sciences, Department of Pharmacy, Adigrat, Ethiopia; 2Mekelle University, College of Health Sciences, Department of Pharmacology and Toxicology, Mekelle, Ethiopia; 3Wollo University, College of Medicine and Health Sciences, Department of Pharmacy, Dessie, Ethiopia; 4Adigrat University, College of Medicine and Health Sciences, Department of Anesthesia, Adigrat, Ethiopia; 5Debre Berhan University, School of Medicine, College of Medicine, Department of Anesthesia, Debre Berhan, Ethiopia; 6Bahir Dar University, College of Health Sciences, Department of Pharmacy, Bahir Dar, Ethiopia; 7Adigrat University, College of Medicine and Health Sciences, Department of Nursing, Adigrat, Ethiopia; 8Mekelle University, College of Health Sciences, Department of Social Pharmacy and Pharmacoepidemiology, Mekelle, Ethiopia; 9Addis Ababa University, College of Health Sciences, Center for Innovative Drug Development and Therapeutic Trials for Africa, Addis Ababa, EthiopiaCorrespondence: Desalegn Getnet Demsie Email desget361@gmail.comAbstract: Coronavirus disease 2019 (COVID-19), an infectious disease that primarily attacks the human pulmonary system, is caused by a viral strain called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak emerged from Wuhan, China, and later spread throughout the world. Until the first week of May 2020, over 3.7 million cases had been reported worldwide and more than 258,000 had died due to the disease. So far, off label use of various drugs has been tried in many clinical settings, however, at present, there is no vaccine or antiviral treatment for human and animal coronaviruses. Therefore, repurposing of the available drugs may be promising to control emerging infections of SARS-COV2; however, new interventions are likely to require months to years to develop. Glycopeptides, which are active against gram-positive bacteria, have demonstrated significant activity against viral infections including SARS-COV and MERS-COV and have a high resemblance of sequence homology with SARS-COV2. Recent in vitro studies have also shown promising activities of aglycon derivative of glycopeptides and teicoplanin against SARS-COV2. Hydrophobic aglycon derivatives and teicoplanin, with minimal toxicity to human cell lines, inhibit entry and replication of SARS-COV2. These drugs block proteolysis of polyprotein a/b with replicase and transcription domains. Teicoplanin use was associated with complete viral clearance in a cohort of patients with severe COVID-19 symptoms. This review attempts to describe the activity, elucidate the possible mechanisms and potential clinical applications of existing glycopeptides against corona viruses, specifically SARS-COV2.Keywords: corona virus, SARS-COV, SARS-COV2, COVID19, glycopeptides
Published: 2020

2. In vitro Antioxidant and in vivo Hepatoprotective Activities of Root Bark Extract and Solvent Fractions of Croton macrostachyus Hochst. Ex Del. (Euphorbiaceae) on Paracetamol-Induced Liver Damage in Mice

Author: Tafere GG, Tuem KB, Gebre AK, and Balasubramaniam R
Subjects: antioxidant, hepatoprotective, croton macrostachyus, paracetamol, biochemical parameters, liver damage., Therapeutics. Pharmacology, RM1-950
Abstract: Gebrehiwot Gebremedhin Tafere,1 Kald Beshir Tuem,1 Abadi Kahsu Gebre,1 Rajkapoor Balasubramaniam1,2 1Department of Pharmacology and Toxicology, School of Pharmacy, College of Health Sciences, Mekelle University, Mekelle, Ethiopia; 2Department of Pharmacology, JKK Nattraja College of Pharmacy, Komarapalayam 638 183, Tamilnadu, IndiaCorrespondence: Kald Beshir Tuem Tel +251920425426Email kalizad2003@gmail.comBackground: Liver disease is a major public health threat, particularly in developing countries. Several medicinal plants and formulations have been claimed to have liver protective activities. The present study aimed to evaluate in vitro antioxidant and in vivo hepatoprotective activities of root bark extracts of Croton macrostachyus (Euphorbiaceae).Methods: Free radical scavenging activity of crude extract and solvent fractions of the plant was conducted using the DPPH assay method. Hepatoprotective activities of the crude extract and solvent fractions of the plant were carried out based on paracetamol-induced liver damage in mice. Serum biomarkers (AST, ALT, ALP, total bilirubin and total protein) were assessed to find out the effect. Histopathological examination was also carried out for all groups of mice to further confirm the findings.Results: Antioxidant assay revealed that the crude extract, aqueous fraction and chloroform fraction of Croton macrostachyus exhibited free radical scavenging activity with IC50 values of 128.6, 168.9, and 406 μg/mL, respectively. Pretreatment of the mice with the crude extract and solvent fractions of Croton macrostachyus significantly reduced ALP (p< 0.001), ALT (p< 0.001), and AST (p< 0.001) levels at all the administered doses compared to the toxic group. The crude extract and chloroform fraction decreased total bilirubin level at doses of 200 mg/kg (P< 0.05) and 400 mg/kg (P< 0.001). Pretreatment of the mice with 400 mg/kg of the crude extract and aqueous fraction elevated total protein value compared to the paracetamol treated group (P< 0.05). The hepatoprotective activities of the plant extracts were confirmed by histopathological studies.Conclusion: From this study, it can be concluded that the crude extract and solvent fractions of Croton macrostachyus demonstrated antioxidant and hepatoprotective activities.Keywords: antioxidant, hepatoprotective, Croton macrostachyus, paracetamol, biochemical parameters, liver damage
Published: 2020

3. Risk factors for COVID-19 infection, disease severity and related deaths in Africa: a systematic review

Author: Gesesew, HA, Koye, DN, Fetene, DM, Woldegiorgis, M, Kinfu, Y, Geleto, AB, Melaku, YA, Mohammed, H, Alene, KA, Awoke, MA, Birhanu, MM, Gebremedhin, AT, Gelaw, YA, Shifti, DM, Muluneh, MD, Tegegne, TK, Abrha, S, Aregay, AF, Ayalew, MB, Gebre, AK, Gebremariam, KT, Gebremedhin, T, Gebremichael, L, Leshargie, CT, Kibret, GD, Meazaw, MW, Mekonnen, AB, Tekle, DY, Tesema, AG, Tesfay, FH, Tesfaye, W, Wubishet, BL, Dachew, BA, Adane, AA, Gesesew, HA, Koye, DN, Fetene, DM, Woldegiorgis, M, Kinfu, Y, Geleto, AB, Melaku, YA, Mohammed, H, Alene, KA, Awoke, MA, Birhanu, MM, Gebremedhin, AT, Gelaw, YA, Shifti, DM, Muluneh, MD, Tegegne, TK, Abrha, S, Aregay, AF, Ayalew, MB, Gebre, AK, Gebremariam, KT, Gebremedhin, T, Gebremichael, L, Leshargie, CT, Kibret, GD, Meazaw, MW, Mekonnen, AB, Tekle, DY, Tesema, AG, Tesfay, FH, Tesfaye, W, Wubishet, BL, Dachew, BA, and Adane, AA
Abstract: Objective The aim of this study was to provide a comprehensive evidence on risk factors for transmission, disease severity and COVID-19 related deaths in Africa. Design A systematic review has been conducted to synthesise existing evidence on risk factors affecting COVID-19 outcomes across Africa. Data sources Data were systematically searched from MEDLINE, Scopus, MedRxiv and BioRxiv. Eligibility criteria Studies for review were included if they were published in English and reported at least one risk factor and/or one health outcome. We included all relevant literature published up until 11 August 2020. Data extraction and synthesis We performed a systematic narrative synthesis to describe the available studies for each outcome. Data were extracted using a standardised Joanna Briggs Institute data extraction form. Results Fifteen articles met the inclusion criteria of which four were exclusively on Africa and the remaining 11 papers had a global focus with some data from Africa. Higher rates of infection in Africa are associated with high population density, urbanisation, transport connectivity, high volume of tourism and international trade, and high level of economic and political openness. Limited or poor access to healthcare are also associated with higher COVID-19 infection rates. Older people and individuals with chronic conditions such as HIV, tuberculosis and anaemia experience severe forms COVID-19 leading to hospitalisation and death. Similarly, high burden of chronic obstructive pulmonary disease, high prevalence of tobacco consumption and low levels of expenditure on health and low levels of global health security score contribute to COVID-19 related deaths. Conclusions Demographic, institutional, ecological, health system and politico-economic factors influenced the spectrum of COVID-19 infection, severity and death. We recommend multidisciplinary and integrated approaches to mitigate the identified factors and strengthen effective prevention strategi
Published: 2021

4. Global injury morbidity and mortality from 1990 to 2017: results from the Global Burden of Disease Study 2017

Author: James, SL, Castle, CD, Dingels, ZV, Fox, JT, Hamilton, EB, Liu, Z, S Roberts, NL, Sylte, DO, Henry, NJ, LeGrand, KE, Abdelalim, A, Abdoli, A, Abdollahpour, I, Abdulkader, RS, Abedi, A, Abosetugn, AE, Abushouk, AI, Adebayo, OM, Agudelo-Botero, M, Ahmad, T, Ahmed, R, Ahmed, MB, Eddine Aichour, MT, Alahdab, F, Alamene, GM, Alanezi, FM, Alebel, A, Alema, NM, Alghnam, SA, Al-Hajj, S, Ali, BA, Ali, S, Alikhani, M, Alinia, C, Alipour, V, Aljunid, SM, Almasi-Hashiani, A, Almasri, NA, Altirkawi, K, Abdeldayem Amer, YS, Amini, S, Loreche Amit, AM, Andrei, CL, Ansari-Moghaddam, A, T Antonio, CA, Yaw Appiah, SC, Arabloo, J, Arab-Zozani, M, Arefi, Z, Aremu, O, Ariani, F, Arora, A, Asaad, M, Asghari, B, Awoke, N, Ayala Quintanilla, BP, Ayano, G, Ayanore, MA, Azari, S, Azarian, G, Badawi, A, Badiye, AD, Bagli, E, Baig, AA, Bairwa, M, Bakhtiari, A, Balachandran, A, Banach, M, Banerjee, SK, Banik, PC, Banstola, A, Barker-Collo, SL, Bärnighausen, TW, Barrero, LH, Barzegar, A, Bayati, M, Baye, BA, Bedi, N, Behzadifar, M, Bekuma, TT, Belete, H, Benjet, C, Bennett, DA, Bensenor, IM, Berhe, K, Bhardwaj, P, Bhat, AG, Bhattacharyya, K, Bibi, S, Bijani, A, Bin Sayeed, MS, Borges, G, Borzì, AM, Boufous, S, Brazinova, A, Briko, NI, Budhathoki, SS, Car, J, Cárdenas, R, Carvalho, F, Castaldelli-Maia, JM, Castañeda-Orjuela, CA, Castelpietra, G, Catalá-López, F, Cerin, E, Chandan, JS, Chanie, WF, Chattu, SK, Chattu, VK, Chatziralli, I, Chaudhary, N, Cho, DY, Kabir Chowdhury, MA, Chu, D-T, Colquhoun, SM, Constantin, M-M, Costa, VM, Damiani, G, Daryani, A, Dávila-Cervantes, CA, Demeke, FM, Demis, AB, Demoz, GT, Demsie, DG, Derakhshani, A, Deribe, K, Desai, R, Nasab, MD, da Silva, DD, Dibaji Forooshani, ZS, Doyle, KE, Driscoll, TR, Dubljanin, E, Adema, BD, Eagan, AW, Eftekhari, A, Ehsani-Chimeh, E, Sayed Zaki, ME, Elemineh, DA, El-Jaafary, SI, El-Khatib, Z, Ellingsen, CL, Emamian, MH, Endalew, DA, Eskandarieh, S, Faris, PS, Faro, A, Farzadfar, F, Fatahi, Y, Fekadu, W, Ferede, TY, Fereshtehnejad, S-M, Fernandes, E, Ferrara, P, Feyissa, GT, Filip, I, Fischer, F, Folayan, MO, Foroutan, M, Francis, JM, Franklin, RC, Fukumoto, T, Geberemariyam, BS, Gebre, AK, Gebremedhin, KB, Gebremeskel, GG, Gebremichael, B, Gedefaw, GA, Geta, B, Ghafourifard, M, Ghamari, F, Ghashghaee, A, Gholamian, A, Gill, TK, Goulart, AC, Grada, A, Grivna, M, Mohialdeen Gubari, MI, Guimarães, RA, Guo, Y, Gupta, G, Haagsma, JA, Hafezi-Nejad, N, Bidgoli, HH, Hall, BJ, Hamadeh, RR, Hamidi, S, Haro, JM, Hasan, MM, Hasanzadeh, A, Hassanipour, S, Hassankhani, H, Hassen, HY, Havmoeller, R, Hayat, K, Hendrie, D, Heydarpour, F, Híjar, M, Ho, HC, Hoang, CL, Hole, MK, Holla, R, Hossain, N, Hosseinzadeh, M, Hostiuc, S, Hu, G, Ibitoye, SE, Ilesanmi, OS, Ilic, I, Ilic, MD, Inbaraj, LR, Indriasih, E, Naghibi Irvani, SS, Shariful Islam, SM, Islam, MM, Ivers, RQ, Jacobsen, KH, Jahani, MA, Jahanmehr, N, Jakovljevic, M, Jalilian, F, Jayaraman, S, Jayatilleke, AU, Jha, RP, John-Akinola, YO, Jonas, JB, Joseph, N, Joukar, F, Jozwiak, JJ, Jungari, SB, Jürisson, M, Kabir, A, Kadel, R, Kahsay, A, Kalankesh, LR, Kalhor, R, Kamil, TA, Kanchan, T, Kapoor, N, Karami, M, Kasaeian, A, Kassaye, HG, Kavetskyy, T, Kebede, HK, Keiyoro, PN, Kelbore, AG, Kelkay, B, Khader, YS, Khafaie, MA, Khalid, N, Khalil, IA, Khalilov, R, Khammarnia, M, Khan, EA, Khan, M, Khanna, T, Khazaie, H, Shadmani, FK, Khundkar, R, Kiirithio, DN, Kim, Y-E, Kim, D, Kim, YJ, Kisa, A, Kisa, S, Komaki, H, M Kondlahalli, SK, Korshunov, VA, Koyanagi, A, G Kraemer, MU, Krishan, K, Bicer, BK, Kugbey, N, Kumar, V, Kumar, N, Kumar, GA, Kumar, M, Kumaresh, G, Kurmi, OP, Kuti, O, Vecchia, CL, Lami, FH, Lamichhane, P, Lang, JJ, Lansingh, VC, Laryea, DO, Lasrado, S, Latifi, A, Lauriola, P, Leasher, JL, Huey Lee, SW, Lenjebo, TL, Levi, M, Li, S, Linn, S, Liu, X, Lopez, AD, Lotufo, PA, Lunevicius, R, Lyons, RA, Madadin, M, El Razek, MMA, Mahotra, NB, Majdan, M, Majeed, A, Malagon-Rojas, JN, Maled, V, Malekzadeh, R, Malta, DC, Manafi, N, Manafi, A, Manda, A-L, Manjunatha, N, Mansour-Ghanaei, F, Mansouri, B, Mansournia, MA, Maravilla, JC, March, LM, Mason-Jones, AJ, Masoumi, SZ, Massenburg, BB, Maulik, PK, Meles, GG, Melese, A, Melketsedik, ZA, N Memiah, PT, Mendoza, W, Menezes, RG, Mengesha, MB, Mengesha, MM, Meretoja, TJ, Meretoja, A, Merie, HE, Mestrovic, T, Miazgowski, B, Miazgowski, T, Miller, TR, Mini, GK, Mirica, A, Mirrakhimov, EM, Mirzaei-Alavijeh, M, Mithra, P, Moazen, B, Moghadaszadeh, M, Mohamadi, E, Mohammad, Y, Mohammad, KA, Darwesh, AM, Gholi Mezerji, NM, Mohammadian-Hafshejani, A, Mohammadoo-Khorasani, M, Mohammadpourhodki, R, Mohammed, S, Mohammed, JA, Mohebi, F, Molokhia, M, Monasta, L, Moodley, Y, Moosazadeh, M, Moradi, M, Moradi, G, Moradi-Lakeh, M, Moradpour, F, Morawska, L, Velásquez, IM, Morisaki, N, Morrison, SD, Mossie, TB, Muluneh, AG, Murthy, S, Musa, KI, Mustafa, G, Nabhan, AF, Nagarajan, AJ, Naik, G, Naimzada, MD, Najafi, F, Nangia, V, Nascimento, BR, Naserbakht, M, Nayak, V, Ndwandwe, DE, Negoi, I, Ngunjiri, JW, Nguyen, CT, Thi Nguyen, HL, Nikbakhsh, R, Anggraini Ningrum, DN, Nnaji, CA, Nyasulu, PS, Ogbo, FA, Oghenetega, OB, Oh, I-H, Okunga, EW, Olagunju, AT, Olagunju, TO, Bali, AO, Onwujekwe, OE, Asante, KO, Orpana, HM, Ota, E, Otstavnov, N, Otstavnov, SS, A, MP, Padubidri, JR, Pakhale, S, Pakshir, K, Panda-Jonas, S, Park, E-K, Patel, SK, Pathak, A, Pati, S, Patton, GC, Paulos, K, Peden, AE, Filipino Pepito, VC, Pereira, J, Pham, HQ, Phillips, MR, Pinheiro, M, Polibin, RV, Polinder, S, Poustchi, H, Prakash, S, Angga Pribadi, DR, Puri, P, Syed, ZQ, Rabiee, M, Rabiee, N, Radfar, A, Rafay, A, Rafiee, A, Rafiei, A, Rahim, F, Rahimi, S, Rahimi-Movaghar, V, Rahman, MA, Rajabpour-Sanati, A, Rajati, F, Rakovac, I, Ranganathan, K, Rao, SJ, Rashedi, V, Rastogi, P, Rathi, P, Rawaf, S, Rawal, L, Rawassizadeh, R, Renjith, V, N Renzaho, AM, Resnikoff, S, Rezapour, A, Ribeiro, AI, Rickard, J, Rios González, CM, Ronfani, L, Roshandel, G, Saad, AM, Sabde, YD, Sabour, S, Saddik, B, Safari, S, Safari-Faramani, R, Safarpour, H, Safdarian, M, Sajadi, SM, Salamati, P, Salehi, F, Zahabi, SS, Rashad Salem, MR, Salem, H, Salman, O, Salz, I, Samy, AM, Sanabria, J, Riera, LS, Santric Milicevic, MM, Sarker, AR, Sarveazad, A, Sathian, B, Sawhney, M, Sawyer, SM, Saxena, S, Sayyah, M, Schwebel, DC, Seedat, S, Senthilkumaran, S, Sepanlou, SG, Seyedmousavi, S, Sha, F, Shaahmadi, F, Shahabi, S, Shaikh, MA, Shams-Beyranvand, M, Shamsizadeh, M, Sharif-Alhoseini, M, Sharifi, H, Sheikh, A, Shigematsu, M, Shin, JI, Shiri, R, Siabani, S, Sigfusdottir, ID, Singh, PK, Singh, JA, Sinha, DN, Smarandache, C-G, R Smith, EU, Soheili, A, Soleymani, B, Soltanian, AR, Soriano, JB, Sorrie, MB, Soyiri, IN, Stein, DJ, Stokes, MA, Sufiyan, MB, Rasul Suleria, HA, Sykes, BL, Tabarés-Seisdedos, R, Tabb, KM, Taddele, BW, Tadesse, DB, Tamiru, AT, Tarigan, IU, Tefera, YM, Tehrani-Banihashemi, A, Tekle, MG, Tekulu, GH, Tesema, AK, Tesfay, BE, Thapar, R, Tilahune, AB, Tlaye, KG, Tohidinik, HR, Topor-Madry, R, Tran, BX, Tran, KB, Tripathy, JP, Tsai, AC, Car, LT, Ullah, S, Ullah, I, Umar, M, Unnikrishnan, B, Upadhyay, E, Uthman, OA, Valdez, PR, Vasankari, TJ, Venketasubramanian, N, Violante, FS, Vlassov, V, Waheed, Y, Weldesamuel, GT, Werdecker, A, Wiangkham, T, Wolde, HF, Woldeyes, DH, Wondafrash, DZ, Wondmeneh, TG, Wondmieneh, AB, Wu, A-M, Yadav, R, Yadollahpour, A, Yano, Y, Yaya, S, Yazdi-Feyzabadi, V, Yip, P, Yisma, E, Yonemoto, N, Yoon, S-J, Youm, Y, Younis, MZ, Yousefi, Z, Yu, Y, Yu, C, Yusefzadeh, H, Moghadam, TZ, Zaidi, Z, Zaman, SB, Zamani, M, Zamanian, M, Zandian, H, Zarei, A, Zare, F, Zhang, Z-J, Zhang, Y, Zodpey, S, Dandona, L, Dandona, R, Degenhardt, L, Dharmaratne, SD, Hay, SI, Mokdad, AH, Reiner, RC, Sartorius, B, and Vos, T
Subjects: Life Expectancy, Incidence, burden of disease, descriptive epidemiology, global, Humans, Wounds and Injuries, Public Health, Quality-Adjusted Life Years, Morbidity, Global Health, 1106 Human Movement and Sports Sciences, 1117 Public Health and Health Services, 1701 Psychology, Global Burden of Disease
Abstract: BACKGROUND: Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries.; METHODS: We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs).; FINDINGS: In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100000. In 1990, there were 354064302 (95% uncertainty interval: 338174876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100000 to 3267 (3058 to 3505).; INTERPRETATION: Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.
Published: 2020

5. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019

Author: Roth, GA, Mensah, GA, Johnson, CO, Addolorato, G, Ammirati, E, Baddour, LM, Barengo, NC, Beaton, AZ, Benjamin, EJ, Benziger, CP, Bonny, A, Brauer, M, Brodmann, M, Cahill, TJ, Carapetis, J, Catapano, AL, Chugh, SS, Cooper, LT, Coresh, J, Criqui, M, DeCleene, N, Eagle, KA, Emmons-Bell, S, Feigin, VL, Fernández-Solà, J, Fowkes, G, Gakidou, E, Grundy, SM, He, FJ, Howard, G, Hu, F, Inker, L, Karthikeyan, G, Kassebaum, N, Koroshetz, W, Lavie, C, Lloyd-Jones, D, Lu, HS, Mirijello, A, Temesgen, AM, Mokdad, A, Moran, AE, Muntner, P, Narula, J, Neal, B, Ntsekhe, M, Moraes de Oliveira, G, Otto, C, Owolabi, M, Pratt, M, Rajagopalan, S, Reitsma, M, Ribeiro, ALP, Rigotti, N, Rodgers, A, Sable, C, Shakil, S, Sliwa-Hahnle, K, Stark, B, Sundström, J, Timpel, P, Tleyjeh, IM, Valgimigli, M, Vos, T, Whelton, PK, Yacoub, M, Zuhlke, L, Murray, C, Fuster, V, Beaton, A, Carapetis, JR, Chugh, S, Criqui, MH, DeCleene, NK, Fernández-Sola, J, Fowkes, FGR, Kassebaum, NJ, Koroshetz, WJ, Misganaw, AT, Mokdad, AH, Oliveira, GMM, Otto, CM, Owolabi, MO, Reitsma, MB, Rigotti, NA, Sable, CA, Shakil, SS, Sliwa, K, Stark, BA, Tleyjeh, II, Zuhlke, LJ, Abbasi-Kangevari, M, Abdi, A, Abedi, A, Aboyans, V, Abrha, WA, Abu-Gharbieh, E, Abushouk, AI, Acharya, D, Adair, T, Adebayo, OM, Ademi, Z, Advani, SM, Afshari, K, Afshin, A, Agarwal, G, Agasthi, P, Ahmad, S, Ahmadi, S, Ahmed, MB, Aji, B, Akalu, Y, Akande-Sholabi, W, Aklilu, A, Akunna, CJ, Alahdab, F, Al-Eyadhy, A, Alhabib, KF, Alif, SM, Alipour, V, Aljunid, SM, Alla, F, Almasi-Hashiani, A, Almustanyir, S, Al-Raddadi, RM, Amegah, AK, Amini, S, Aminorroaya, A, Amu, H, Amugsi, DA, Ancuceanu, R, Anderlini, D, Andrei, T, Andrei, CL, Ansari-Moghaddam, A, Anteneh, ZA, Antonazzo, IC, Antony, B, Anwer, R, Appiah, LT, Arabloo, J, Ärnlöv, J, Artanti, KD, Ataro, Z, Ausloos, M, Avila-Burgos, L, Awan, AT, Awoke, MA, Ayele, HT, Ayza, MA, Azari, S, B, DB, Baheiraei, N, Baig, AA, Bakhtiari, A, Banach, M, Banik, PC, Baptista, EA, Barboza, MA, Barua, L, Basu, S, Bedi, N, Béjot, Y, Bennett, DA, Bensenor, IM, Berman, AE, Bezabih, YM, Bhagavathula, AS, Bhaskar, S, Bhattacharyya, K, Bijani, A, Bikbov, B, Birhanu, MM, Boloor, A, Brant, LC, Brenner, H, Briko, NI, Butt, ZA, Caetano dos Santos, FL, Cahill, LE, Cahuana-Hurtado, L, Cámera, LA, Campos-Nonato, IR, Cantu-Brito, C, Car, J, Carrero, JJ, Carvalho, F, Castañeda-Orjuela, CA, Catalá-López, F, Cerin, E, Charan, J, Chattu, VK, Chen, S, Chin, KL, Choi, J-YJ, Chu, D-T, Chung, S-C, Cirillo, M, Coffey, S, Conti, S, Costa, VM, Cundiff, DK, Dadras, O, Dagnew, B, Dai, X, Damasceno, AAM, Dandona, L, Dandona, R, Davletov, K, De la Cruz-Góngora, V, De la Hoz, FP, De Neve, J-W, Denova-Gutiérrez, E, Derbew Molla, M, Derseh, BT, Desai, R, Deuschl, G, Dharmaratne, SD, Dhimal, M, Dhungana, RR, Dianatinasab, M, Diaz, D, Djalalinia, S, Dokova, K, Douiri, A, Duncan, BB, Duraes, AR, Eagan, AW, Ebtehaj, S, Eftekhari, A, Eftekharzadeh, S, Ekholuenetale, M, El Nahas, N, Elgendy, IY, Elhadi, M, El-Jaafary, SI, 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Zhong, C, Zou, Z, Zuniga, YMH, and Murray, CJL
Abstract: Cardiovascular diseases (CVDs), principally ischemic heart disease (IHD) and stroke, are the leading cause of global mortality and a major contributor to disability. This paper reviews the magnitude of total CVD burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019. GBD, an ongoing multinational collaboration to provide comparable and consistent estimates of population health over time, used all available population-level data sources on incidence, prevalence, case fatality, mortality, and health risks to produce estimates for 204 countries and territories from 1990 to 2019.Prevalent cases of total CVD nearly doubled from 271 million (95% uncertainty interval [UI]: 257 to 285 million) in 1990 to 523 million (95% UI: 497 to 550 million) in 2019, and the number of CVD deaths steadily increased from 12.1 million (95% UI:11.4 to 12.6 million) in 1990, reaching 18.6 million (95% UI: 17.1 to 19.7 million) in 2019. The global trends for disability-adjusted life years (DALYs) and years of life lost also increased significantly, and years lived with disability doubled from 17.7 million (95% UI: 12.9 to 22.5 million) to 34.4 million (95% UI:24.9 to 43.6 million) over that period. The total number of DALYs due to IHD has risen steadily since 1990, reaching 182 million (95% UI: 170 to 194 million) DALYs, 9.14 million (95% UI: 8.40 to 9.74 million) deaths in the year 2019, and 197 million (95% UI: 178 to 220 million) prevalent cases of IHD in 2019. The total number of DALYs due to stroke has risen steadily since 1990, reaching 143 million (95% UI: 133 to 153 million) DALYs, 6.55 million (95% UI: 6.00 to 7.02 million) deaths in the year 2019, and 101 million (95% UI: 93.2 to 111 million) prevalent cases of stroke in 2019.Cardiovascular diseases remain the leading cause of disease burden in the world. CVD burden continues its decades-long rise for almost all countries outside hi
Published: 2020

6. Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017

Author: Soriano, JB, Kendrick, PJ, Paulson, KR, Gupta, V, Vos, T, Abrams, EM, Adedoyin, RA, Adhikari, TB, Advani, SM, Agrawal, A, Ahmadian, E, Alahdab, F, Aljunid, SM, Altirkawi, KA, Alvis-Guzman, N, Anber, NH, Andrei, CL, Anjomshoa, M, Ansari, F, Anto, JM, Arabloo, J, Athari, SM, Athari, SS, Awoke, N, Badawi, A, Banoub, JAM, Bennett, DA, Bensenor, IM, Berfield, KS, Bernstein, RS, Bhattacharyya, K, Bijani, A, Brauer, M, Bukhman, G, Butt, ZA, Camera, LA, Car, J, Carrero, JJ, Carvalho, F, Castaneda-Orjuela, CA, Choi, J-YJ, Christopher, DJ, Cohen, AJ, Dandona, L, Dandona, R, Dang, AK, Daryani, A, de Courten, B, Demeke, FM, Demoz, GT, De Neve, J-W, Desai, R, Dharmaratne, SD, Diaz, D, Douiri, A, Driscoll, TR, Duken, EE, Eftekhari, A, Elkout, H, Endries, AY, Fadhil, I, Faro, A, Farzadfar, F, Fernandes, E, Filip, I, Fischer, F, Foroutan, M, Garcia-Gordillo, MA, Gebre, AK, Gebremedhin, KB, Gebremeskel, GG, Gezae, KE, Ghoshal, AG, Gill, PS, Gillum, RF, Goudarzi, H, Guo, Y, Gupta, R, Hailu, GB, Hasanzadeh, A, Hassen, HY, Hay, SI, Hoang, CL, Hole, MK, Horita, N, Hosgood, HD, Hostiuc, M, Househ, M, Ilesanmi, OS, Ilic, MD, Irvani, SSN, Islam, SMS, Jakovljevic, M, Jamal, AA, Jha, RP, Jonas, JB, Kabir, Z, Kasaeian, A, Kasahun, GG, Kassa, GM, Kefale, AT, Kengne, AP, Khader, YS, Khafaie, MA, Khan, EA, Khan, J, Khubchandani, J, Kim, Y-E, Kim, YJ, Kisa, S, Kisa, A, Knibbs, LD, Komaki, H, Koul, PA, Koyanagi, A, Kumar, GA, Lan, Q, Lasrado, S, Lauriola, P, La Vecchia, C, Tham, TL, Leigh, J, Levi, M, Li, S, Lopez, AD, Lotufo, PA, Madotto, F, Mahotra, NB, Majdan, M, Majeed, A, Malekzadeh, R, Mamun, AA, Manafi, N, Manafi, F, Mantovani, LG, Meharie, BG, Meles, HG, Meles, GG, Menezes, RG, Mestrovic, T, Miller, TR, Mini, GK, Mirrakhimov, EM, Moazen, B, Mohammad, KA, Mohammed, S, Mohebi, F, Mokdad, AH, Molokhia, M, Monasta, L, Moradi, M, Moradi, G, Morawska, L, Mousavi, SM, Musa, KI, Mustafa, G, Naderi, M, Naghavi, M, Naik, G, Nair, S, Nangia, V, Nansseu, JR, Nazari, J, Ndwandwe, DE, Negoi, RI, Trang, HN, Cuong, TN, Huong, LTN, Nixon, MR, Ofori-Asenso, R, Ogbo, FA, Olagunju, AT, Olagunju, TO, Oren, E, Ortiz, JR, Owolabi, MO, Mahesh, PA, Pakhale, S, Pana, A, Panda-Jonas, S, Park, E-K, Hai, QP, Postma, MJ, Pourjafar, H, Poustchi, H, Radfar, A, Rafiei, A, Rahim, F, Rahman, MHU, Rahman, MA, Rawaf, S, Rawaf, DL, Rawal, L, Reiner, RC, Reitsma, MB, Roever, L, Ronfani, L, Roro, EM, Roshandel, G, Rudd, KE, Sabde, YD, Sabour, S, Saddik, B, Safari, S, Saleem, K, Samy, AM, Santric-Milicevic, MM, Jose, BPS, Sartorius, B, Satpathy, M, Savic, M, Sawhney, M, Sepanlou, SG, Shaikh, MA, Sheikh, A, Shigematsu, M, Shirkoohi, R, Si, S, Siabani, S, Singh, V, Singh, JA, Soljak, M, Somayaji, R, Soofi, M, Soyiri, IN, Tefera, YM, Temsah, M-H, Tesfay, BE, Thakur, JS, Toma, AT, Tortajada-Girbes, M, Khanh, BT, Bach, XT, Car, LT, Ullah, I, Vacante, M, Valdez, PR, van Boven, JFM, Vasankari, TJ, Veisani, Y, Violante, FS, Wagner, GR, Westerman, R, Wolfe, CDA, Wondafrash, DZ, Wondmieneh, AB, Yonemoto, N, Yoon, S-J, Zaidi, Z, Zamani, M, Zar, HJ, Zhang, Y, Soriano, JB, Kendrick, PJ, Paulson, KR, Gupta, V, Vos, T, Abrams, EM, Adedoyin, RA, Adhikari, TB, Advani, SM, Agrawal, A, Ahmadian, E, Alahdab, F, Aljunid, SM, Altirkawi, KA, Alvis-Guzman, N, Anber, NH, Andrei, CL, Anjomshoa, M, Ansari, F, Anto, JM, Arabloo, J, Athari, SM, Athari, SS, Awoke, N, Badawi, A, Banoub, JAM, Bennett, DA, Bensenor, IM, Berfield, KS, Bernstein, RS, Bhattacharyya, K, Bijani, A, Brauer, M, Bukhman, G, Butt, ZA, Camera, LA, Car, J, Carrero, JJ, Carvalho, F, Castaneda-Orjuela, CA, Choi, J-YJ, Christopher, DJ, Cohen, AJ, Dandona, L, Dandona, R, Dang, AK, Daryani, A, de Courten, B, Demeke, FM, Demoz, GT, De Neve, J-W, Desai, R, Dharmaratne, SD, Diaz, D, Douiri, A, Driscoll, TR, Duken, EE, Eftekhari, A, Elkout, H, Endries, AY, Fadhil, I, Faro, A, Farzadfar, F, Fernandes, E, Filip, I, Fischer, F, Foroutan, M, Garcia-Gordillo, MA, Gebre, AK, Gebremedhin, KB, Gebremeskel, GG, Gezae, KE, Ghoshal, AG, Gill, PS, Gillum, RF, Goudarzi, H, Guo, Y, Gupta, R, Hailu, GB, Hasanzadeh, A, Hassen, HY, Hay, SI, Hoang, CL, Hole, MK, Horita, N, Hosgood, HD, Hostiuc, M, Househ, M, Ilesanmi, OS, Ilic, MD, Irvani, SSN, Islam, SMS, Jakovljevic, M, Jamal, AA, Jha, RP, Jonas, JB, Kabir, Z, Kasaeian, A, Kasahun, GG, Kassa, GM, Kefale, AT, Kengne, AP, Khader, YS, Khafaie, MA, Khan, EA, Khan, J, Khubchandani, J, Kim, Y-E, Kim, YJ, Kisa, S, Kisa, A, Knibbs, LD, Komaki, H, Koul, PA, Koyanagi, A, Kumar, GA, Lan, Q, Lasrado, S, Lauriola, P, La Vecchia, C, Tham, TL, Leigh, J, Levi, M, Li, S, Lopez, AD, Lotufo, PA, Madotto, F, Mahotra, NB, Majdan, M, Majeed, A, Malekzadeh, R, Mamun, AA, Manafi, N, Manafi, F, Mantovani, LG, Meharie, BG, Meles, HG, Meles, GG, Menezes, RG, Mestrovic, T, Miller, TR, Mini, GK, Mirrakhimov, EM, Moazen, B, Mohammad, KA, Mohammed, S, Mohebi, F, Mokdad, AH, Molokhia, M, Monasta, L, Moradi, M, Moradi, G, Morawska, L, Mousavi, SM, Musa, KI, Mustafa, G, Naderi, M, Naghavi, M, Naik, G, Nair, S, Nangia, V, Nansseu, JR, Nazari, J, Ndwandwe, DE, Negoi, RI, Trang, HN, Cuong, TN, Huong, LTN, Nixon, MR, Ofori-Asenso, R, Ogbo, FA, Olagunju, AT, Olagunju, TO, Oren, E, Ortiz, JR, Owolabi, MO, Mahesh, PA, Pakhale, S, Pana, A, Panda-Jonas, S, Park, E-K, Hai, QP, Postma, MJ, Pourjafar, H, Poustchi, H, Radfar, A, Rafiei, A, Rahim, F, Rahman, MHU, Rahman, MA, Rawaf, S, Rawaf, DL, Rawal, L, Reiner, RC, Reitsma, MB, Roever, L, Ronfani, L, Roro, EM, Roshandel, G, Rudd, KE, Sabde, YD, Sabour, S, Saddik, B, Safari, S, Saleem, K, Samy, AM, Santric-Milicevic, MM, Jose, BPS, Sartorius, B, Satpathy, M, Savic, M, Sawhney, M, Sepanlou, SG, Shaikh, MA, Sheikh, A, Shigematsu, M, Shirkoohi, R, Si, S, Siabani, S, Singh, V, Singh, JA, Soljak, M, Somayaji, R, Soofi, M, Soyiri, IN, Tefera, YM, Temsah, M-H, Tesfay, BE, Thakur, JS, Toma, AT, Tortajada-Girbes, M, Khanh, BT, Bach, XT, Car, LT, Ullah, I, Vacante, M, Valdez, PR, van Boven, JFM, Vasankari, TJ, Veisani, Y, Violante, FS, Wagner, GR, Westerman, R, Wolfe, CDA, Wondafrash, DZ, Wondmieneh, AB, Yonemoto, N, Yoon, S-J, Zaidi, Z, Zamani, M, Zar, HJ, and Zhang, Y
Abstract: BACKGROUND: Previous attempts to characterise the burden of chronic respiratory diseases have focused only on specific disease conditions, such as chronic obstructive pulmonary disease (COPD) or asthma. In this study, we aimed to characterise the burden of chronic respiratory diseases globally, providing a comprehensive and up-to-date analysis on geographical and time trends from 1990 to 2017. METHODS: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we estimated the prevalence, morbidity, and mortality attributable to chronic respiratory diseases through an analysis of deaths, disability-adjusted life-years (DALYs), and years of life lost (YLL) by GBD super-region, from 1990 to 2017, stratified by age and sex. Specific diseases analysed included asthma, COPD, interstitial lung disease and pulmonary sarcoidosis, pneumoconiosis, and other chronic respiratory diseases. We also assessed the contribution of risk factors (smoking, second-hand smoke, ambient particulate matter and ozone pollution, household air pollution from solid fuels, and occupational risks) to chronic respiratory disease-attributable DALYs. FINDINGS: In 2017, 544·9 million people (95% uncertainty interval [UI] 506·9-584·8) worldwide had a chronic respiratory disease, representing an increase of 39·8% compared with 1990. Chronic respiratory disease prevalence showed wide variability across GBD super-regions, with the highest prevalence among both males and females in high-income regions, and the lowest prevalence in sub-Saharan Africa and south Asia. The age-sex-specific prevalence of each chronic respiratory disease in 2017 was also highly variable geographically. Chronic respiratory diseases were the third leading cause of death in 2017 (7·0% [95% UI 6·8-7·2] of all deaths), behind cardiovascular diseases and neoplasms. Deaths due to chronic respiratory diseases numbered 3 914 196 (95% UI 3 790 578-4 044 819) in 2017, an increase of 18·0% since 1990, while
Published: 2020

7. The global, regional, and national burden of oesophageal cancer and its attributable risk factors in 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017

Author: Kamangar, F, Nasrollahzadeh, D, Safiri, S, Sepanlou, SG, Fitzmaurice, C, Ikuta, KS, Bisignano, C, Islami, F, Roshandel, G, Lim, SS, Abolhassani, H, Abu-Gharbieh, E, Adedoyin, RA, Advani, SM, Ahmed, MB, Aichour, MTE, Akinyemiju, T, Akunna, CJ, Alahdab, F, Alipour, V, Almasi-Hashiani, A, Almulhim, AM, Anber, NH, Ansari-Moghaddam, A, Arabloo, J, Arab-Zozani, M, Awedew, AF, Badawi, A, Berfield, KSS, Berhe, K, Bhattacharyya, K, Biondi, A, Bjorge, T, Borzi, AM, Bosetti, C, Carreras, G, Carvalho, F, Castro, C, Chu, D-T, Costa, VM, Dagnew, B, Gela, JD, Daryani, A, Demeke, FM, Demoz, GT, Dianatinasab, M, Elbarazi, I, Emamian, MH, Etemadi, A, Faris, PS, Fernandes, E, Filip, I, Fischer, F, Gad, MM, Gallus, S, Gebre, AK, Gebrehiwot, TT, Gebremeskel, GG, Gebresillassie, BM, Ghasemi-kebria, F, Ghashghaee, A, Ghith, N, Golechha, M, Gorini, G, Gupta, R, Hafezi-Nejad, N, Haj-Mirzaian, A, Harvey, JD, Hashemian, M, Hassen, HY, Hay, SI, Henok, A, Hoang, CL, Hosgood, HD, Househ, M, Ilesanmi, OS, Ilic, MD, Irvani, SSN, Jain, C, James, SL, Jee, SH, Jha, RP, Joukar, F, Kabir, A, Kasaeian, A, Kassaw, MW, Kaur, S, Kengne, AP, Kerboua, E, Khader, YS, Khalilov, R, Khan, EA, Khoja, AT, Kocarnik, JM, Komaki, H, Kumar, V, La Vecchia, C, Lasrado, S, Li, B, Lopez, AD, Majeed, A, Manafi, N, Manda, AL, Mansour-Ghanaei, F, Mathur, MR, Mehta, V, Mehta, D, Mendoza, W, Mithra, P, Mohammad, KA, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, JA, Mohebi, F, Mokdad, AH, Monasta, L, Moosavi, D, Moosazadeh, M, Moradi, G, Moradpour, F, Moradzadeh, R, Naik, G, Negoi, I, Nggada, HA, Nguyen, HLT, Nikbakhsh, R, Nixon, MR, Olagunju, AT, Olagunju, TO, Padubidri, JR, Pakshir, K, Patel, S, Pathak, M, Pham, HQ, Pourshams, A, Rabiee, N, Rabiee, M, Radfar, A, Rafiei, A, Ramezanzadeh, K, Rath, GK, Rathi, P, Rawaf, S, Rawaf, DL, Rezaei, N, Roro, EM, Saad, AM, Salimzadeh, H, Samy, AM, Sartorius, B, Sarveazad, A, Sekerija, M, Sha, F, Shamsizadeh, M, Sheikhbahaei, S, Shirkoohi, R, Malleshappa, SKS, Singh, JA, Sinha, DN, Smarandache, C-G, Soshnikov, S, Suleria, HAR, Tadesse, DB, Tesfay, BE, Thakur, B, Traini, E, Tran, KB, Tran, BX, Ullah, I, Vacante, M, Veisani, Y, Vujcic, IS, Weldesamuel, GT, Xu, R, Yazdi-Feyzabadi, V, Yuce, D, Zadnik, V, Zaidi, Z, Zhang, Z-J, Malekzadeh, R, Naghavi, M, Kamangar, F, Nasrollahzadeh, D, Safiri, S, Sepanlou, SG, Fitzmaurice, C, Ikuta, KS, Bisignano, C, Islami, F, Roshandel, G, Lim, SS, Abolhassani, H, Abu-Gharbieh, E, Adedoyin, RA, Advani, SM, Ahmed, MB, Aichour, MTE, Akinyemiju, T, Akunna, CJ, Alahdab, F, Alipour, V, Almasi-Hashiani, A, Almulhim, AM, Anber, NH, Ansari-Moghaddam, A, Arabloo, J, Arab-Zozani, M, Awedew, AF, Badawi, A, Berfield, KSS, Berhe, K, Bhattacharyya, K, Biondi, A, Bjorge, T, Borzi, AM, Bosetti, C, Carreras, G, Carvalho, F, Castro, C, Chu, D-T, Costa, VM, Dagnew, B, Gela, JD, Daryani, A, Demeke, FM, Demoz, GT, Dianatinasab, M, Elbarazi, I, Emamian, MH, Etemadi, A, Faris, PS, Fernandes, E, Filip, I, Fischer, F, Gad, MM, Gallus, S, Gebre, AK, Gebrehiwot, TT, Gebremeskel, GG, Gebresillassie, BM, Ghasemi-kebria, F, Ghashghaee, A, Ghith, N, Golechha, M, Gorini, G, Gupta, R, Hafezi-Nejad, N, Haj-Mirzaian, A, Harvey, JD, Hashemian, M, Hassen, HY, Hay, SI, Henok, A, Hoang, CL, Hosgood, HD, Househ, M, Ilesanmi, OS, Ilic, MD, Irvani, SSN, Jain, C, James, SL, Jee, SH, Jha, RP, Joukar, F, Kabir, A, Kasaeian, A, Kassaw, MW, Kaur, S, Kengne, AP, Kerboua, E, Khader, YS, Khalilov, R, Khan, EA, Khoja, AT, Kocarnik, JM, Komaki, H, Kumar, V, La Vecchia, C, Lasrado, S, Li, B, Lopez, AD, Majeed, A, Manafi, N, Manda, AL, Mansour-Ghanaei, F, Mathur, MR, Mehta, V, Mehta, D, Mendoza, W, Mithra, P, Mohammad, KA, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, JA, Mohebi, F, Mokdad, AH, Monasta, L, Moosavi, D, Moosazadeh, M, Moradi, G, Moradpour, F, Moradzadeh, R, Naik, G, Negoi, I, Nggada, HA, Nguyen, HLT, Nikbakhsh, R, Nixon, MR, Olagunju, AT, Olagunju, TO, Padubidri, JR, Pakshir, K, Patel, S, Pathak, M, Pham, HQ, Pourshams, A, Rabiee, N, Rabiee, M, Radfar, A, Rafiei, A, Ramezanzadeh, K, Rath, GK, Rathi, P, Rawaf, S, Rawaf, DL, Rezaei, N, Roro, EM, Saad, AM, Salimzadeh, H, Samy, AM, Sartorius, B, Sarveazad, A, Sekerija, M, Sha, F, Shamsizadeh, M, Sheikhbahaei, S, Shirkoohi, R, Malleshappa, SKS, Singh, JA, Sinha, DN, Smarandache, C-G, Soshnikov, S, Suleria, HAR, Tadesse, DB, Tesfay, BE, Thakur, B, Traini, E, Tran, KB, Tran, BX, Ullah, I, Vacante, M, Veisani, Y, Vujcic, IS, Weldesamuel, GT, Xu, R, Yazdi-Feyzabadi, V, Yuce, D, Zadnik, V, Zaidi, Z, Zhang, Z-J, Malekzadeh, R, and Naghavi, M
Abstract: BACKGROUND: Oesophageal cancer is a common and often fatal cancer that has two main histological subtypes: oesophageal squamous cell carcinoma and oesophageal adenocarcinoma. Updated statistics on the incidence and mortality of oesophageal cancer, and on the disability-adjusted life-years (DALYs) caused by the disease, can assist policy makers in allocating resources for prevention, treatment, and care of oesophageal cancer. We report the latest estimates of these statistics for 195 countries and territories between 1990 and 2017, by age, sex, and Socio-demographic Index (SDI), using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD). METHODS: We used data from vital registration systems, vital registration-samples, verbal autopsy records, and cancer registries, combined with relevant modelling, to estimate the mortality, incidence, and burden of oesophageal cancer from 1990 to 2017. Mortality-to-incidence ratios (MIRs) were estimated and fed into a Cause of Death Ensemble model (CODEm) including risk factors. MIRs were used for mortality and non-fatal modelling. Estimates of DALYs attributable to the main risk factors of oesophageal cancer available in GBD were also calculated. The proportion of oesophageal squamous cell carcinoma to all oesophageal cancers was extracted by use of publicly available data, and its variation was examined against SDI, the Healthcare Access and Quality (HAQ) Index, and available risk factors in GBD that are specific for oesophageal squamous cell carcinoma (eg, unimproved water source and indoor air pollution) and for oesophageal adenocarcinoma (gastro-oesophageal reflux disease). FINDINGS: There were 473 000 (95% uncertainty interval [95% UI] 459 000-485 000) new cases of oesophageal cancer and 436 000 (425 000-448 000) deaths due to oesophageal cancer in 2017. Age-standardised incidence was 5·9 (5·7-6·1) per 100 000 population and age-standardised mortality was 5·5 (5·3-5·6) per 100 000. Oesophageal c
Published: 2020

8. Global trends of hand and wrist trauma: a systematic analysis of fracture and digit amputation using the Global Burden of Disease 2017 Study

Author: Crowe, CS, Massenburg, BB, Morrison, SD, Chang, J, Friedrich, JB, Abady, GG, Alahdab, F, Alipour, V, Arabloo, J, Asaad, M, Banach, M, Bijani, A, Borzi, AM, Briko, NI, Castle, CD, Cho, DY, Chung, MT, Daryani, A, Demoz, GT, Dingels, ZV, Do, HTP, Fischer, F, Fox, JT, Fukumoto, T, Gebre, AK, Gebremichael, B, Haagsma, Juanita, Haj-Mirzaian, A, Handiso, DW, Hay, SI, Hoang, CL, Irvani, SSN, Jozwiak, JJ, Kalhor, R, Kasaeian, A, Khader, YS, Khalilov, R, Khan, EA, Khundkar, R, Kisa, S, Kisa, A, Liu, ZC, Majdan, M, Manafi, N, Manafi, A, Manda, AL, Meretoja, TJ, Miller, TR, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Bandpei, MAM, Mokdad, AH, Naimzada, MD, Ndwandwe, DE, Nguyen, CT, Nguyen, HLT, Olagunju, AT, Olagunju, TO, Pham, HQ, Pribadi, DRA, Rabiee, N, Ramezanzadeh, K, Ranganathan, K, Roberts, NLS, Roever, L, Safari, S, Samy, AM, Riera, LS, Shahabi, S, Smarandache, CG, Sylte, DO, Tesfay, BE, Tran, BX, Ullah, I, Vahedi, P, Vahedian-Azimi, A, Vos, T, Woldeyes, DH, Wondmieneh, AB, Zhang, ZJ, James, SL, Crowe, CS, Massenburg, BB, Morrison, SD, Chang, J, Friedrich, JB, Abady, GG, Alahdab, F, Alipour, V, Arabloo, J, Asaad, M, Banach, M, Bijani, A, Borzi, AM, Briko, NI, Castle, CD, Cho, DY, Chung, MT, Daryani, A, Demoz, GT, Dingels, ZV, Do, HTP, Fischer, F, Fox, JT, Fukumoto, T, Gebre, AK, Gebremichael, B, Haagsma, Juanita, Haj-Mirzaian, A, Handiso, DW, Hay, SI, Hoang, CL, Irvani, SSN, Jozwiak, JJ, Kalhor, R, Kasaeian, A, Khader, YS, Khalilov, R, Khan, EA, Khundkar, R, Kisa, S, Kisa, A, Liu, ZC, Majdan, M, Manafi, N, Manafi, A, Manda, AL, Meretoja, TJ, Miller, TR, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Bandpei, MAM, Mokdad, AH, Naimzada, MD, Ndwandwe, DE, Nguyen, CT, Nguyen, HLT, Olagunju, AT, Olagunju, TO, Pham, HQ, Pribadi, DRA, Rabiee, N, Ramezanzadeh, K, Ranganathan, K, Roberts, NLS, Roever, L, Safari, S, Samy, AM, Riera, LS, Shahabi, S, Smarandache, CG, Sylte, DO, Tesfay, BE, Tran, BX, Ullah, I, Vahedi, P, Vahedian-Azimi, A, Vos, T, Woldeyes, DH, Wondmieneh, AB, Zhang, ZJ, and James, SL
Published: 2020

9. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017

Author: Kyu, Hh, Abate, D, Abate, Kh, Abay, Sm, Abbafati, C, Abbasi, N, Abbastabar, H, Abd-Allah, F, Abdela, J, Abdelalim, A, Abdollahpour, I, Abdulkader, Rs, Abebe, M, Abebe, Z, Abil, Oz, Aboyans, V, Abrham, Ar, Abu-Raddad, Lj, Abu-Rmeileh, Nme, Accrombessi, Mmk, Acharya, D, Acharya, P, Ackerman, In, Adamu, Aa, Adebayo, Om, Adekanmbi, V, Ademi, Z, Adetokunboh, Oo, Adib, Mg, Adsuar, Jc, Afanvi, Ka, Afarideh, M, Afshin, A, Agarwal, G, Agesa, Km, Aggarwal, R, Aghayan, Sa, Agrawal, A, Ahmadi, A, Ahmadi, M, Ahmadieh, H, Ahmed, Mb, Ahmed, S, Aichour, An, Aichour, I, Aichour, Mte, Akinyemiju, T, Akseer, N, Al-Aly, Z, Al-Eyadhy, A, Al-Mekhlafi, Hm, Al-Raddadi, Rm, Alahdab, F, Alam, K, Alam, T, Alashi, A, Alavian, Sm, Alene, Ka, Alijanzadeh, M, Alizadeh-Navaei, R, Aljunid, Sm, Alkerwi, A, Alla, F, Allebeck, P, Alonso, J, Alsharif, U, Altirkawi, K, Alvis-Guzman, N, Aminde, Ln, Amini, E, Amiresmaili, M, Ammar, W, Amoako, Ya, Anber, Nh, Andrei, Cl, Androudi, S, Animut, Md, Anjomshoa, M, Ansha, Mg, Antonio, Cat, Anwari, P, Arabloo, J, Aremu, O, Ärnlöv, J, Arora, A, Arora, M, Artaman, A, Aryal, Kk, Asayesh, H, Ataro, Z, Ausloos, M, Avila-Burgos, L, Avokpaho, Efga, Awasthi, A, Ayala, Quintanilla, Ayer, R, Azzopardi, Ps, Babazadeh, A, Badali, H, Balakrishnan, K, Bali, Ag, Banach, M, Banoub, Jam, Barac, A, Barboza, Ma, Barker-Collo, Sl, Bärnighausen, Tw, Barquera, S, Barrero, Lh, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Behzadifar, M, Bekele, Bb, Bekru, Et, Belachew, Ab, Belay, Ya, Bell, Ml, Bello, Ak, Bennett, Da, Bensenor, Im, Berhane, A, Bernabe, E, Bernstein, Rs, Beuran, M, Beyranvand, T, Bhala, N, Bhatt, S, Bhaumik, S, Bhutta, Za, Biadgo, B, Biehl, Mh, Bijani, A, Bikbov, B, Bilano, V, Bililign, N, Bin, Sayeed, Bisanzio, D, Bjørge, T, Bleyer, A, Bobasa, Em, Bou-Orm, Ir, Boufous, S, Bourne, R, Brady, Oj, Brant, Lc, Brayne, C, Brazinova, A, Breitborde, Njk, Brenner, H, Briant, Ps, Briko, An, Britton, G, Brugha, T, Buchbinder, R, Busse, R, Butt, Za, Cahuana-Hurtado, L, Campuzano, Rincon, Cano, J, Cárdenas, R, Carrero, Jj, Carter, A, Carvalho, F, Castañeda-Orjuela, Ca, Castillo, Rivas, J, Castro, F, Catalá-López, F, Cercy, Km, Cerin, E, Chaiah, Y, Chang, Jc, Charlson, Fj, Chattu, Vk, Chiang, Pp, Chitheer, A, Choi, Jj, Christensen, H, Christopher, Dj, Chung, Sc, Cicuttini, Fm, Cirillo, Massimo, Collado-Mateo, D, Cooper, C, Cortesi, Pa, Cortinovis, M, Cousin, E, Criqui, Mh, Cromwell, Ea, Cross, M, Crump, Ja, Daba, Ak, Dachew, Ba, Dadi, Af, Dandona, L, Dandona, R, Dargan, Pi, Daryani, A, Das, Gupta, Das, R, Neves, J, Dasa, Tt, Davitoiu, Dv, De, La, Hoz, Fp, Leo, De, D, De, Neve, Jw, Steur, De, H, Degefa, Mg, Degenhardt, L, Deiparine, S, Demoz, Gt, Denova-Gutiérrez, E, Deribe, K, Dervenis, N, Des, Jarlais, Dey, S, Dharmaratne, Sd, Dhimal, M, Dinberu, Mt, Dirac, Ma, Djalalinia, S, Doan, L, Dokova, K, Doku, Dt, Dorsey, Er, Doyle, Ke, Driscoll, Tr, Dubey, M, Dubljanin, E, Duken, Ee, Duncan, Bb, Duraes, Ar, Ebrahimi, H, Ebrahimpour, S, Echko, Mm, Edessa, D, Edvardsson, D, Effiong, A, Eggen, Ae, Ehrlich, Jr, Bcheraoui, El, C, El-Khatib, Z, Elyazar, Irf, Enayati, A, Endalifer, Ml, Endries, Ay, Er, B, Erskine, He, Eskandarieh, S, Esteghamati, A, Esteghamati, S, Fakhim, H, Faramarzi, M, Fareed, M, Farhadi, F, Farid, Ta, Farinha, Cses, Farioli, A, Faro, A, Farzadfar, F, Fazaeli, Aa, Feigin, Vl, Fentahun, N, Fereshtehnejad, Sm, Fernandes, E, Fernandes, Jc, Ferrari, Aj, Ferreira, Ml, Filip, I, Fischer, F, Fitzmaurice, C, Foigt, Na, Foreman, Kj, Frank, Td, Fukumoto, T, Fullman, N, Fürst, T, Furtado, Jm, Gakidou, E, Gall, S, Gallus, S, Ganji, M, Garcia-Basteiro, Al, Gardner, Wm, Gebre, Ak, Gebremedhin, At, Gebremichael, Tg, Gelano, Tf, Geleijnse, Jm, Genova-Maleras, R, Geramo, Ycd, Gething, Pw, Gezae, Ke, Ghadami, Mr, Ghadiri, K, Ghasemi-Kasman, M, Ghimire, M, Ghoshal, Ag, Gill, Ps, Gill, Tk, Ginawi, Ia, Giussani, G, Gnedovskaya, Ev, Goldberg, Em, Goli, S, Gómez-Dantés, H, Gona, Pn, Gopalani, Sv, Gorman, Tm, Goulart, Ac, Goulart, Bng, Grada, A, Grosso, G, Gugnani, Hc, Guillemin, F, Guo, Y, Gupta, Pc, Gupta, R, Gupta, T, Gutiérrez, Ra, Gyawali, B, Haagsma, Ja, Hachinski, V, Hafezi-Nejad, N, Haghparast, Bidgoli, Hagos, Tb, Hailegiyorgis, Tt, Haj-Mirzaian, A, Hamadeh, Rr, Hamidi, S, Handal, Aj, Hankey, Gj, Hao, Y, Harb, Hl, Harikrishnan, S, Haririan, H, Haro, Jm, Hassankhani, H, Hassen, Hy, Havmoeller, R, Hay, Rj, Hay, Si, Hedayatizadeh-Omran, A, Heibati, B, Hendrie, D, Henok, A, Heredia-Pi, I, Herteliu, C, Heydarpour, F, Heydarpour, P, Hibstu, Dt, Hoek, Hw, Hoffman, Hj, Hole, Mk, Homaie, Rad, E, Hoogar, P, Hosgood, Hd, Hosseini, Sm, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hotez, Pj, Hoy, Dg, Hsairi, M, Htet, As, Huang, Jj, Iburg, Km, Ikeda, Ct, Ilesanmi, Os, Irvani, Ssn, Irvine, Cms, Islam, Sms, Islami, F, Jacobsen, Kh, Jahangiry, L, Jahanmehr, N, Jain, Sk, Jakovljevic, M, James, Sl, Jayatilleke, Au, Jeemon, P, Jha, Rp, Jha, V, Js, Ji, Johnson, Co, Jonas, Jb, Jonnagaddala, J, Jorjoran, Shushtari, Z, Joshi, A, Jozwiak, Jj, Jungari, Sb, Jürisson, M, Kabir, Z, Kadel, R, Kahsay, A, Kalani, R, Kanchan, T, Kar, C, Karami, M, Karami, Matin, B, Karch, A, Karema, C, Karimi, N, Karimi, Sm, Kasaeian, A, Kassa, Dh, Kassa, Gm, Kassa, Td, Kassebaum, Nj, Katikireddi, Sv, Kaul, A, Kawakami, N, Kazemi, Z, Karyani, Ak, Keighobadi, Mm, Keiyoro, Pn, Kemmer, L, Kemp, Gr, Kengne, Ap, Keren, A, Khader, Ys, Khafaei, B, Khafaie, Ma, Khajavi, A, Khalid, N, Khalil, Ia, Khan, Ea, Khan, Ms, Khan, Ma, Khang, Yh, Khater, Mm, Khazaei, M, Khoja, At, Khosravi, A, Khosravi, Mh, Kiadaliri, Aa, Kidanemariam, Zt, Kiirithio, Dn, Kim, Ci, Kim, D, Kim, Ye, Kim, Yj, Kimokoti, Rw, Kinfu, Y, Kisa, A, Kissimova-Skarbek, K, Knudsen, Aks, Kocarnik, Jm, Kochhar, S, Kokubo, Y, Kolola, T, Kopec, Ja, Kosen, S, Kotsakis, Ga, Koul, Pa, Koyanagi, A, Krishan, K, Krishnaswami, S, Krohn, Kj, Kuate, Defo, Kucuk, B, Bicer, B, Kumar, Ga, Kumar, M, Kuzin, I, Lad, Dp, Lad, Sd, Lafranconi, A, Lalloo, R, Lallukka, T, Lami, Fh, Lang, Jj, Langan, Sm, Lansingh, Vc, Latifi, A, Lau, Km, Lazarus, Jv, Leasher, Jl, Ledesma, Jr, Lee, Ph, Leigh, J, Leili, M, Leshargie, Ct, Leung, J, Levi, M, Lewycka, S, Li, S, Li, Y, Liang, X, Liao, Y, Liben, Ml, Lim, Ll, Lim, Ss, Limenih, Ma, Linn, S, Liu, S, Looker, Kj, Lopez, Ad, Lorkowski, S, Lotufo, Pa, Lozano, R, Lucas, Tcd, Lunevicius, R, Lyons, Ra, Ma, S, Macarayan, Erk, Mackay, Mt, Maddison, Er, Madotto, F, Maghavani, Dp, Mai, Ht, Majdan, M, Majdzadeh, R, Majeed, A, Malekzadeh, R, Malta, Dc, Mamun, Aa, Manda, Al, Manguerra, H, Mansournia, Ma, Mantilla, Herrera, Mantovani, Lg, Maravilla, Jc, Marcenes, W, Marks, A, Martins-Melo, Fr, Martopullo, I, März, W, Marzan, Mb, Massano, J, Massenburg, Bb, Mathur, Mr, Maulik, Pk, Mazidi, M, Mcalinden, C, Mcgrath, Jj, Mckee, M, Mcmahon, Bj, Mehata, S, Mehrotra, R, Mehta, Km, Mehta, V, Mejia-Rodriguez, F, Mekonen, T, Melese, A, Melku, M, Memiah, Ptn, Memish, Za, Mendoza, W, Mengistu, G, Mensah, Ga, Mereta, St, Meretoja, A, Meretoja, Tj, Mestrovic, T, Miazgowski, B, Miazgowski, T, Millear, Ai, Miller, Tr, Mini, Gk, Mirarefin, M, Mirica, A, Mirrakhimov, Em, Misganaw, At, Mitchell, Pb, Mitiku, H, Moazen, B, Mohajer, B, Mohammad, Ka, Mohammadi, M, Mohammadifard, N, Mohammadnia-Afrouzi, M, Mohammed, Ma, Mohammed, S, Mohebi, F, Mokdad, Ah, Molokhia, M, Monasta, L, Montañez, Jc, Moosazadeh, M, Moradi, G, Moradi, M, Moradi-Lakeh, M, Moradinazar, M, Moraga, P, Morawska, L, Moreno, Velásquez, I, Morgado-Da-Costa, J, Morrison, Sd, Moschos, Mm, Mousavi, Sm, Mruts, Kb, Muche, Aa, Muchie, Kf, Mueller, Uo, Muhammed, Os, Mukhopadhyay, S, Muller, K, Mumford, Je, Murthy, Gvs, Musa, Ki, Mustafa, G, Nabhan, Af, Nagata, C, Nagel, G, Naghavi, M, Naheed, A, Nahvijou, A, Naik, G, Najafi, F, Nam, Hs, Nangia, V, Nansseu, Jr, Neamati, N, Negoi, I, Negoi, Ri, Neupane, S, Newton, Crj, Ngunjiri, Jw, Nguyen, Aq, Nguyen, G, Nguyen, Ht, Nguyen, Hlt, Nguyen, Lh, Nguyen, M, Nguyen, Nb, Nguyen, Sh, Nichols, E, Ningrum, Dna, Nixon, Mr, Nomura, S, Noroozi, M, Norrving, B, Noubiap, Jj, Nouri, Hr, Shiadeh, Mn, Nowroozi, Mr, Nsoesie, Eo, Nyasulu, Ps, Odell, Cm, Ofori-Asenso, R, Ogbo, Fa, Ih, Oh, Oladimeji, O, Olagunju, At, Olagunju, To, Olivares, Pr, Olsen, He, Olusanya, Bo, Olusanya, Jo, Ong, Kl, Ong, Sk, Oren, E, Ortiz, A, Ota, E, Otstavnov, Ss, Øverland, S, Owolabi, Mo, P, A, M, Pacella, R, Pakhare, Ap, Pakpour, Ah, Pana, A, Panda-Jonas, S, Park, Ek, Park, J, Parry, Cdh, Parsian, H, Pasdar, Y, Patel, S, Patil, St, Patle, A, Patton, Gc, Paturi, Vr, Paudel, D, Paulson, Kr, Pearce, N, Pereira, A, Pereira, Dm, Perico, N, Pesudovs, K, Petzold, M, Pham, Hq, Phillips, Mr, Pigott, Dm, Pillay, Jd, Piradov, Ma, Pirsaheb, M, Pishgar, F, Plana-Ripoll, O, Polinder, S, Popova, S, Postma, Mj, Pourshams, A, Poustchi, H, Prabhakaran, D, Prakash, S, Prakash, V, Prasad, N, Purcell, Ca, Qorbani, M, Quistberg, Da, Radfar, A, Rafay, A, Rafiei, A, Rahim, F, Rahimi, K, Rahimi, Z, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rahman, Mhu, Rahman, Ma, Rahman, Su, Rai, Rk, Rajati, F, Ranjan, P, Rao, Pc, Rasella, D, Rawaf, Dl, Rawaf, S, Reddy, Ks, Reiner, Rc, Reitsma, Mb, Remuzzi, G, Renzaho, Amn, Resnikoff, S, Rezaei, S, Rezai, Ms, Ribeiro, Alp, Roberts, Nls, Robinson, Sr, Roever, L, Ronfani, L, Roshandel, G, Rostami, A, Roth, Ga, Rothenbacher, D, Rubagotti, E, Sachdev, Ps, Sadat, N, Sadeghi, E, Saeedi, Moghaddam, S, Safari, H, Safari, Y, Safari-Faramani, R, Safdarian, M, Safi, S, Safiri, S, Sagar, R, Sahebkar, A, Sahraian, Ma, Sajadi, Hs, Salam, N, Salama, Js, Salamati, P, Saleem, Z, Salimi, Y, Salimzadeh, H, Salomon, Ja, Salvi, Ss, Salz, I, Samy, Am, Sanabria, J, Sanchez-Niño, Md, Santomauro, Df, Santos, Is, Santos, Jv, Santric, Milicevic, Sao, Mm, Jose, Bp, Sardana, M, Sarker, Ar, Sarmiento-Suárez, R, Sarrafzadegan, N, Sartorius, B, Sarvi, S, Sathian, B, Satpathy, M, Sawant, Ar, Sawhney, M, Saxena, S, Schaeffner, E, Schmidt, Mi, Schneider, Ijc, Schutte, Ae, Schwebel, Dc, Schwendicke, F, Scott, Jg, Sekerija, M, Sepanlou, Sg, Serván-Mori, E, Seyedmousavi, S, Shabaninejad, H, Shafieesabet, A, Shahbazi, M, Shaheen, Aa, Shaikh, Ma, Shams-Beyranvand, M, Shamsi, M, Sharafi, H, Sharafi, K, Sharif, M, Sharif-Alhoseini, M, Sharma, J, Sharma, R, She, J, Sheikh, A, Shi, P, Shibuya, K, Shiferaw, Ms, Shigematsu, M, Shiri, R, Shirkoohi, R, Shiue, I, Shokoohinia, Y, Shokraneh, F, Shoman, H, Shrime, Mg, Si, S, Siabani, S, Sibai, Am, Siddiqi, Tj, Sigfusdottir, Id, Sigurvinsdottir, R, Silva, Das, Silva, Jp, Silveira, Dga, Singam, Nsv, Singh, Ja, Singh, Np, Singh, V, Sinha, Dn, Skiadaresi, E, Skirbekk, V, Sliwa, K, Smith, Dl, Smith, M, Soares, Filho, Sobaih, Bh, Sobhani, S, Soofi, M, Sorensen, Rjd, Soyiri, In, Sposato, La, Sreeramareddy, Ct, Srinivasan, V, Stanaway, Jd, Starodubov, Vi, Stein, Dj, Steiner, C, Steiner, Tj, Stokes, Ma, Stovner, Lj, Subart, Ml, Sudaryanto, A, Sufiyan, Mb, Sulo, G, Sunguya, Bf, Sur, Pj, Sykes, Bl, Sylaja, Pn, Sylte, Do, Szoeke, Cei, Tabarés-Seisdedos, R, Tabuchi, T, Tadakamadla, Sk, Tandon, N, Tassew, Sg, Tavakkoli, M, Taveira, N, Taylor, Hr, Tehrani-Banihashemi, A, Tekalign, Tg, Tekelemedhin, Sw, Tekle, Mg, Temsah, Mh, Temsah, O, Terkawi, As, Tessema, B, Teweldemedhin, M, Thankappan, Kr, Theis, A, Thirunavukkarasu, S, Thomas, N, Tilahun, B, Qg, To, Tonelli, M, Topor-Madry, R, Torre, Ae, Tortajada-Girbés, M, Touvier, M, Tovani-Palone, Mr, Towbin, Ja, Tran, Bx, Tran, Kb, Troeger, Ce, Tsadik, Ag, Tsoi, D, Tudor, Car, L, Tyrovolas, S, Ukwaja, Kn, Ullah, I, Undurraga, Ea, Updike, Rl, Usman, Ms, Uthman, Oa, Vaduganathan, M, Vaezi, A, Valdez, Pr, Varavikova, E, Varughese, S, Vasankari, Tj, Venketasubramanian, N, Villafaina, S, Violante, Fs, Vladimirov, Sk, Vlassov, V, Vollset, Se, Vos, T, Vosoughi, K, Vujcic, Is, Wagnew, Fs, Waheed, Y, Wang, Y, Wang, Yp, Weiderpass, E, Weintraub, Rg, Weiss, Dj, Weldegebreal, F, Weldegwergs, Kg, Werdecker, A, West, Te, Westerman, R, Whiteford, Ha, Widecka, J, Wijeratne, T, Williams, Hc, Wilner, Lb, Wilson, S, Winkler, As, Wiyeh, Ab, Wiysonge, Cs, Wolfe, Cda, Woolf, Ad, Wyper, Gma, Xavier, D, Xu, G, Yadgir, S, Yahyazadeh, Jabbari, Yamada, T, Yan, Ll, Yano, Y, Yaseri, M, Yasin, Yj, Yeshaneh, A, Yimer, Em, Yip, P, Yisma, E, Yonemoto, N, Yoon, Sj, Yotebieng, M, Younis, Mz, Yousefifard, M, Yu, C, Zadnik, V, Zaidi, Z, Zaman, Sb, Zamani, M, Zandian, H, Zar, Hj, Zenebe, Zm, Zhou, M, Zipkin, B, Zodpey, S, Zucker, I, Zuhlke, Lj, Murray, Cjl., GBD 2017 DALYs & HALE Coll, Kyu, Hh, Abate, D, Abate, Kh, Abay, Sm, Abbafati, C, Abbasi, N, Abbastabar, H, Abd-Allah, F, Abdela, J, Abdelalim, A, Abdollahpour, I, Abdulkader, R, Abebe, M, Abebe, Z, Abil, Oz, Aboyans, V, Abrham, Ar, Abu-Raddad, Lj, Abu-Rmeileh, Nme, Accrombessi, Mmk, Acharya, D, Acharya, P, Ackerman, In, Adamu, Aa, Adebayo, Om, Adekanmbi, V, Ademi, Z, Adetokunboh, Oo, Adib, Mg, Adsuar, Jc, Afanvi, Ka, Afarideh, M, Afshin, A, Agarwal, G, Agesa, Km, Aggarwal, R, Aghayan, Sa, Agrawal, A, Ahmadi, A, Ahmadi, M, Ahmadieh, H, Ahmed, Mb, Ahmed, S, Aichour, An, Aichour, I, Aichour, Mte, Akinyemiju, T, Akseer, N, Al-Aly, Z, Al-Eyadhy, A, Al-Mekhlafi, Hm, Al-Raddadi, Rm, Alahdab, F, Alam, K, Alam, T, Alashi, A, Alavian, Sm, Alene, Ka, Alijanzadeh, M, Alizadeh-Navaei, R, Aljunid, Sm, Alkerwi, A, Alla, F, Allebeck, P, Alonso, J, Alsharif, U, Altirkawi, K, Alvis-Guzman, N, Aminde, Ln, Amini, E, Amiresmaili, M, Ammar, W, Amoako, Ya, Anber, Nh, Andrei, Cl, Androudi, S, Animut, Md, Anjomshoa, M, Ansha, Mg, Antonio, Cat, Anwari, P, Arabloo, J, Aremu, O, Ärnlöv, J, Arora, A, Arora, M, Artaman, A, Aryal, Kk, Asayesh, H, Ataro, Z, Ausloos, M, Avila-Burgos, L, Avokpaho, Efga, Awasthi, A, Ayala Quintanilla, Bp, Ayer, R, Azzopardi, P, Babazadeh, A, Badali, H, Balakrishnan, K, Bali, Ag, Banach, M, Banoub, Jam, Barac, A, Barboza, Ma, Barker-Collo, Sl, Bärnighausen, Tw, Barquera, S, Barrero, Lh, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Behzadifar, M, Bekele, Bb, Bekru, Et, Belachew, Ab, Belay, Ya, Bell, Ml, Bello, Ak, Bennett, Da, Bensenor, Im, Berhane, A, Bernabe, E, Bernstein, R, Beuran, M, Beyranvand, T, Bhala, N, Bhatt, S, Bhaumik, 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Alve, Singam, Narayana Sarma Venkata, Singh, Jasvinder A, Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Skiadaresi, Eirini, Skirbekk, Vegard, Sliwa, Karen, Smith, David L, Smith, Mari, Soares Filho, Adauto Martin, Sobaih, Badr Hasan, Sobhani, Soheila, Soofi, Moslem, Sorensen, Reed J D, Soyiri, Ireneous N, Sposato, Luciano A, Sreeramareddy, Chandrashekhar T, Srinivasan, Vinay, Stanaway, Jeffrey D, Starodubov, Vladimir I, Stein, Dan J, Steiner, Caitlyn, Steiner, Timothy J, Stokes, Mark A, Stovner, Lars Jacob, Subart, Michelle L, Sudaryanto, Agu, Sufiyan, Mu'awiyyah Babale, Sulo, Gerhard, Sunguya, Bruno F, Sur, Patrick John, Sykes, Bryan L, Sylaja, P.N., Sylte, Dillon O, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Tabuchi, Takahiro, Tadakamadla, Santosh Kumar, Tandon, Nikhil, Tassew, Segen Gebremeskel, Tavakkoli, Mohammad, Taveira, Nuno, Taylor, Hugh R, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekelemedhin, Shishay Wahdey, Tekle, Merhawi Gebremedhin, Temsah, Mohamad-Hani, Temsah, Omar, Terkawi, Abdullah Sulieman, Tessema, Belay, Teweldemedhin, Mebrahtu, Thankappan, Kavumpurathu Raman, Theis, Andrew, Thirunavukkarasu, Sathish, Thomas, Nihal, Tilahun, Binyam, To, Quyen G, Tonelli, Marcello, Topor-Madry, Roman, Torre, Anna E, Tortajada-Girbés, Miguel, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Towbin, Jeffrey A, Tran, Bach Xuan, Tran, Khanh Bao, Troeger, Christopher E, Tsadik, Afewerki Gebremeskel, Tsoi, Derrick, Tudor Car, Lorainne, Tyrovolas, Stefano, Ukwaja, Kingsley Nnanna, Ullah, Irfan, Undurraga, Eduardo A, Updike, Rachel L, Usman, Muhammad Shariq, Uthman, Olalekan A, Vaduganathan, Muthiah, Vaezi, Afsane, Valdez, Pascual R, Varavikova, Elena, Varughese, Santosh, Vasankari, Tommi Juhani, Venketasubramanian, Narayanaswamy, Villafaina, Santo, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vujcic, Isidora S, Wagnew, Fasil Shiferaw, Waheed, Yasir, Wang, Yafeng, Wang, Yuan-Pang, Weiderpass, Elisabete, Weintraub, Robert G, Weiss, Daniel J, Weldegebreal, Fitsum, Weldegwergs, Kidu Gidey, Werdecker, Andrea, West, T Eoin, Westerman, Ronny, Whiteford, Harvey A, Widecka, Justyna, Wijeratne, Tissa, Williams, Hywel C, Wilner, Lauren B, Wilson, Shadrach, Winkler, Andrea Sylvia, Wiyeh, Alison B, Wiysonge, Charles Shey, Wolfe, Charles D A, Woolf, Anthony D, Wyper, Grant M A, Xavier, Deni, Xu, Gelin, Yadgir, Simon, Yahyazadeh Jabbari, Seyed Hossein, Yamada, Tomohide, Yan, Lijing L, Yano, Yuichiro, Yaseri, Mehdi, Yasin, Yasin Jemal, Yeshaneh, Alex, Yimer, Ebrahim M, Yip, Paul, Yisma, Engida, Yonemoto, Naohiro, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zadnik, Vesna, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zandian, Hamed, Zar, Heather J, Zenebe, Zerihun Menlkalew, Zhou, Maigeng, Zipkin, Ben, Zodpey, Sanjay, Zucker, Inbar, Zuhlke, Liesl Joanna, and Murray, Christopher J L
Subjects: Male, Health Status, health statu, HALE, mortality rate, communicable disease, DALYs, Global Burden of Disease, Risk Factors, Prevalence, Medicine and Health Sciences, Singapore, OUTCOMES, disability-adjusted life year, international cooperation, Medicine (all), 11 Medical And Health Sciences, Central African Republic, newborn disease, IRON-DEFICIENCY, AIDS, priority journal, Bahrain, disease severity, Female, Quality-Adjusted Life Years, cerebrovascular accident, Ukraine, Life Sciences & Biomedicine, INTERVENTIONS, Slovakia, DISORDERS, Burundi, WEIGHTS, GBD 2017 DALYs and HALE Collaborators, Communicable Diseases, Article, STYLE, Medicine, General & Internal, Life Expectancy, General & Internal Medicine, Humans, Disabled Persons, human, Healthy Lifestyle, HALE, DALYs, global burden of disease, Mortality, Aged, Science & Technology, Mortality, Premature, HIV, ischemic heart disease, major clinical study, PREVENTION, non communicable disease, GBD, disability-adjusted life-years, age, sex factor, Socioeconomic Factors, Algeria, Federation of Bosnia and Herzegovina, lower respiratory tract infection, Wounds and Injuries, GENDER, Human medicine, trend study, chronic obstructive lung disease
Abstract: Background How long one lives, how many years of life are spent in good and poor health, and how the population's state of health and leading causes of disability change over time all have implications for policy, planning, and provision of services. We comparatively assessed the patterns and trends of healthy life expectancy (HALE), which quantifies the number of years of life expected to be lived in good health, and the complementary measure of disability-adjusted life years (DALYs), a composite measure of disease burden capturing both premature mortality and prevalence and severity of ill health, for 359 diseases and injuries for 195 countries and territories over the past 28 years. Methods We used data for age-specific mortality rates, years of life lost (YLLs) due to premature mortality, and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to calculate HALE and DALYs from 1990 to 2017. We calculated HALE using age-specific mortality rates and YLDs per capita for each location, age, sex, and year. We calculated DALYs for 359 causes as the sum of YLLs and YLDs. We assessed how observed HALE and DALYs differed by country and sex from expected trends based on Sociodemographic Index (SDI). We also analysed HALE by decomposing years of life gained into years spent in good health and in poor health, between 1990 and 2017, and extra years lived by females compared with males. Findings Globally, from 1990 to 2017, life expectancy at birth increased by 7.4 years (95% uncertainty interval 74-7.8), from 65.6 years (65.3-65- 8) in 1990 to 73.0 years (72.7-73.3) in 2017. The increase in years of life varied from 5.1 years (5.0-5.3) in high SDI countries to 12.0 years (11.3-12.8) in low SDI countries. Of the additional years of life expected at birth, 26.3% (20.1-33.1) were expected to be spent in poor health in high SDI countries compared with 11.7% (8.8-15.1) in low-middle SDI countries. HALE at birth increased by 6.3 years (5.9-6.7), from 57.0 years (54.6-59.1) in 1990 to 63.3 years (60.5-65.7) in 2017. The increase varied from 3.8 years (3.4-4.1) in high SDI countries to 10.5 years (9.8-11.2) in low SDI countries. Even larger variations in HALE than these were observed between countries, ranging from 1.0 year (0.4-1.7) in Saint Vincent and the Grenadines (62.4 years [59.9-64.7] in 1990 to 63.5 years [60.9-65.8] in 2017) to 23.7 years (21.9-25.6) in Eritrea (30.7 years [28.9-32.2] in 1990 to 54.4 years [51.5-57.1] in 2017). In most countries, the increase in HALE was smaller than the increase in overall life expectancy, indicating more years lived in poor health. In 180 of 195 countries and territories, females were expected to live longer than males in 2017, with extra years lived varying from 1.4 years (0.6-2.3) in Algeria to 11.9 years (10.9-12.9) in Ukraine. Of the extra years gained, the proportion spent in poor health varied largely across countries, with less than 20% of additional years spent in poor health in Bosnia and Herzegovina, Burundi, and Slovakia, whereas in Bahrain all the extra years were spent in poor health. In 2017, the highest estimate of HALE at birth was in Singapore for both females (75.8 years [72.4-78.7]) and males (72.6 years [69 " 8-75.0]) and the lowest estimates were in Central African Republic (47.0 years [43.7-50.2] for females and 42.8 years [40.1-45.6] for males). Globally, in 2017, the five leading causes of DALYs were neonatal disorders, ischaemic heart disease, stroke, lower respiratory infections, and chronic obstructive pulmonary disease. Between 1990 and 2017, age-standardised DALY rates decreased by 41.3% (38.8-43.5) for communicable diseases and by 49"8% (47.9-51.6) for neonatal disorders. For non-communicable diseases, global DALYs increased by 40.1% (36.8-43.0), although age-standardised DALY rates decreased by 18.1% (16.0-20.2). Interpretation With increasing life expectancy in most countries, the question of whether the additional years of life gained are spent in good health or poor health has been increasingly relevant because of the potential policy implications, such as health-care provisions and extending retirement ages. In some locations, a large proportion of those additional years are spent in poor health. Large inequalities in HALE and disease burden exist across countries in different SDI quintiles and between sexes. The burden of disabling conditions has serious implications for health system planning and health-related expenditures. Despite the progress made in reducing the burden of communicable diseases and neonatal disorders in low S DI countries, the speed of this progress could be increased by scaling up proven interventions. The global trends among non-communicable diseases indicate that more effort is needed to maximise HALE, such as risk prevention and attention to upstream determinants of health. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.
Published: 2018

10. Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Author: Johnson, CO, Nguyen, M, Roth, GA, Nichols, E, Alam, T, Abate, D, Abd-Allah, F, Abdelalim, A, Abraha, HN, Abu-Rmeileh, NM, Adebayo, OM, Adeoye, AM, Agarwal, G, Agrawal, S, Aichour, AN, Aichour, I, Aichour, MTE, Alahdab, F, Ali, R, Alvis-Guzman, N, Anber, NH, Anjomshoa, M, Arabloo, J, Arauz, A, Ärnlöv, J, Arora, A, Awasthi, A, Banach, M, Barboza, MA, Barker-Collo, SL, Bärnighausen, TW, Basu, S, Belachew, AB, Belayneh, YM, Bennett, DA, Bensenor, IM, Bhattacharyya, K, Biadgo, B, Bijani, A, Bikbov, B, Bin Sayeed, MS, Butt, ZA, Cahuana-Hurtado, L, Carrero, JJ, Carvalho, F, Castañeda-Orjuela, CA, Castro, F, Catalá-López, F, Chaiah, Y, Chiang, PPC, Choi, JYJ, Christensen, H, Chu, DT, Cortinovis, M, Damasceno, AAM, Dandona, L, Dandona, R, Daryani, A, Davletov, K, De Courten, B, De la Cruz-Góngora, V, Degefa, MG, Dharmaratne, SD, Diaz, D, Dubey, M, Duken, EE, Edessa, D, Endres, M, Faraon, EJA, Farzadfar, F, Fernandes, E, Fischer, F, Flor, LS, Ganji, M, Gebre, AK, Gebremichael, TG, Geta, B, Gezae, KE, Gill, PS, Gnedovskaya, EV, Gómez-Dantés, H, Goulart, AC, Grosso, G, Guo, Y, Gupta, R, Haj-Mirzaian, A, Hamidi, S, Hankey, GJ, Hassen, HY, Hay, SI, Hegazy, MI, Heidari, B, Herial, NA, Hosseini, MA, Hostiuc, S, Irvani, SSN, Islam, SMS, Jahanmehr, N, and Javanbakht, M
Subjects: Neurology & Neurosurgery
Abstract: © 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Background: Stroke is a leading cause of mortality and disability worldwide and the economic costs of treatment and post-stroke care are substantial. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic, comparable method of quantifying health loss by disease, age, sex, year, and location to provide information to health systems and policy makers on more than 300 causes of disease and injury, including stroke. The results presented here are the estimates of burden due to overall stroke and ischaemic and haemorrhagic stroke from GBD 2016. Methods: We report estimates and corresponding uncertainty intervals (UIs), from 1990 to 2016, for incidence, prevalence, deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs). DALYs were generated by summing YLLs and YLDs. Cause-specific mortality was estimated using an ensemble modelling process with vital registration and verbal autopsy data as inputs. Non-fatal estimates were generated using Bayesian meta-regression incorporating data from registries, scientific literature, administrative records, and surveys. The Socio-demographic Index (SDI), a summary indicator generated using educational attainment, lagged distributed income, and total fertility rate, was used to group countries into quintiles. Findings: In 2016, there were 5·5 million (95% UI 5·3 to 5·7) deaths and 116·4 million (111·4 to 121·4) DALYs due to stroke. The global age-standardised mortality rate decreased by 36·2% (−39·3 to −33·6) from 1990 to 2016, with decreases in all SDI quintiles. Over the same period, the global age-standardised DALY rate declined by 34·2% (−37·2 to −31·5), also with decreases in all SDI quintiles. There were 13·7 million (12·7 to 14·7) new stroke cases in 2016. Global age-standardised incidence declined by 8·1% (−10·7 to −5·5) from 1990 to 2016 and decreased in all SDI quintiles except the middle SDI group. There were 80·1 million (74·1 to 86·3) prevalent cases of stroke globally in 2016; 41·1 million (38·0 to 44·3) in women and 39·0 million (36·1 to 42·1) in men. Interpretation: Although age-standardised mortality rates have decreased sharply from 1990 to 2016, the decrease in age-standardised incidence has been less steep, indicating that the burden of stroke is likely to remain high. Planned updates to future GBD iterations include generating separate estimates for subarachnoid haemorrhage and intracerebral haemorrhage, generating estimates of transient ischaemic attack, and including atrial fibrillation as a risk factor. Funding: Bill & Melinda Gates Foundation
Published: 2019

11. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990-2016 : a systematic analysis for the Global Burden of Disease Study 2016

Author: James, SL, Bannick, MS, Montjoy-Venning, WC, Lucchesi, LR, Dandona, L, Dandona, R, Hawley, C, Hay, SI, Jakovljevic, M, Khalil, I, Krohn, KJ, Mokdad, AH, Naghavi, M, Nichols, E, Reiner, RC, Smith, M, Feigin, VL, Vos, T, Murray, CJL, Sunshine, JE, Yost, MG, Ellenbogen, RG, Kalani, R, Morrison, SD, Crowe, CS, Massenburg, BB, Theadom, A, Te Ao, BJ, Balalla, S, Jones, KM, Ofori-Asenso, R, Li, S, Sobhani, S, Hosseini, SM, Mansournia, MA, Yaseri, M, Anjomshoa, M, Mousavi, SM, Haj-Mirzaian, A, Malekzadeh, R, Poustchi, H, Roshandel, G, Sepanlou, SG, Afarideh, M, Esteghamati, A, Esteghamati, S, Ganji, M, Kasaeian, A, Rahimi-Movaghar, A, Eskandarieh, S, Sahraian, MA, Shams-Beyranvand, M, Abbasi, N, Farzadfar, F, Irvani, SN, Rahimi-Movaghar, V, Salamati, P, Sharif-Alhoseini, M, Fereshtehnejad, SM, Mate, KKV, Abdulkader, R, Abraha, HN, Kassa, TD, Nirayo, YL, Weldegwergs, KG, Gezae, KE, Zenebe, ZM, Degefa, MG, Kahsay, A, Asgedom, SW, Gebre, AK, Yimer, EM, Belachew, AB, Meles, H, Adsuar, JC, Zodpey, S, Agrawal, S, Awasthi, A, Kumar, GA, Ahmadi, A, Najafi, F, Rajati, F, Khazaie, H, Farzaei, MH, Moradi, M, Rezaei, S, Soofi, M, Siabani, S, Rezaeian, S, Ahmed, MB, Gebrehiwot, TT, Feyissa, GT, Hussen, MA, Aichour, A, Aichour, I, Aichour, MTE, Akinyemi, RO, Owolabi, MO, Akseer, N, and Lee Kong Chian School of Medicine (LKCMedicine)
Subjects: Disease Study, Traumatic Brain Injury, Medicine [Science], Spinal Cord Injury
Abstract: Background Traumatic brain injury (TBI) and spinal cord injury (SCI) are increasingly recognised as global health priorities in view of the preventability of most injuries and the complex and expensive medical care they necessitate. We aimed to measure the incidence, prevalence, and years of life lived with disability (YLDs) for TBI and SCI from all causes of injury in every country, to describe how these measures have changed between 1990 and 2016, and to estimate the proportion of TBI and SCI cases caused by different types of injury. Methods We used results from the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016 to measure the global, regional, and national burden of TBI and SCI by age and sex. We measured the incidence and prevalence of all causes of injury requiring medical care in inpatient and outpatient records, literature studies, and survey data. By use of clinical record data, we estimated the proportion of each cause of injury that required medical care that would result in TBI or SCI being considered as the nature of injury. We used literature studies to establish standardised mortality ratios and applied differential equations to convert incidence to prevalence of long-term disability. Finally, we applied GBD disability weights to calculate YLDs. We used a Bayesian meta-regression tool for epidemiological modelling, used causespecific mortality rates for non-fatal estimation, and adjusted our results for disability experienced with comorbid conditions. We also analysed results on the basis of the Socio-demographic Index, a compound measure of income per capita, education, and fertility. Findings In 2016, there were 27.08 million (95% uncertainty interval [UI] 24.30-30.30 million) new cases of TBI and 0.93 million (0.78-1.16 million) new cases of SCI, with age-standardised incidence rates of 369 (331-412) per 100 000 population for TBI and 13 (11-16) per 100 000 for SCI. In 2016, the number of prevalent cases of TBI was 55.50 million (53.40-57.62 million) and of SCI was 27.04 million (24.98-30.15 million). From 1990 to 2016, the agestandardised prevalence of TBI increased by 8.4% (95% UI 7.7 to 9.2), whereas that of SCI did not change significantly (-0.2% [-2.1 to 2.7]). Age-standardised incidence rates increased by 3.6% (1.8 to 5.5) for TBI, but did not change significantly for SCI (-3.6% [-7.4 to 4.0]). TBI caused 8.1 million (95% UI 6.0-10.4 million) YLDs and SCI caused 9.5 million (6.7-12.4 million) YLDs in 2016, corresponding to age-standardised rates of 111 (82-141) per 100 000 for TBI and 130 (90-170) per 100 000 for SCI. Falls and road injuries were the leading causes of new cases of TBI and SCI in most regions. Interpretation TBI and SCI constitute a considerable portion of the global injury burden and are caused primarily by falls and road injuries. The increase in incidence of TBI over time might continue in view of increases in population density, population ageing, and increasing use of motor vehicles, motorcycles, and bicycles. The number of individuals living with SCI is expected to increase in view of population growth, which is concerning because of the specialised care that people with SCI can require. Our study was limited by data sparsity in some regions, and it will be important to invest greater resources in collection of data for TBI and SCI to improve the accuracy of future assessments. Published version We acknowledge the funding and support of the Bill & Melinda Gates Foundation. AK was supported by the Miguel Servet contract, which was financed by the CP13/00150 and PI15/00862 projects integrated into the National Research, Development, and Implementation, and funded by the Instituto de Salud Carlos III General Branch Evaluation and Promotion of Health Research and the European Regional Development Fund (ERDF-FEDER). AMS is supported by the Egyptian Fulbright Mission Program. AF acknowledges the Federal University of Sergipe (Sergipe, Brazil). AA received financial assistance from the Indian Department of Science and Technology (New Delhi, India) through the INSPIRE faculty programme. AS is supported by Health Data Research UK. DJS is supported by the South African Medical Research Council. AB is supported by the Public Health Agency of Canada. SMSI received a senior research fellowship from the Institute for Physical Activity and Nutrition, Deakin University (Waurn Ponds, VIC, Australia), and a career transition grant from the High Blood Pressure Research Council of Australia. FP and CF acknowledge support from the European Union (FEDER funds POCI/01/0145/FEDER/007728 and POCI/01/0145/FEDER/007265) and National Funds (FCT/MEC, Fundação para a Ciência e a Tecnologia, and Ministério da Educação e Ciência) under the Partnership Agreements PT2020 UID/MULTI/04378/2013 and PT2020 UID/QUI/50006/2013. TB acknowledges financial support from the Institute of Medical Research and Medicinal Plant Studies, Yaoundé, Cameroon. AM of Imperial College London is grateful for support from the Northwest London National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care and the Imperial NIHR Biomedical Research Centre. KD is funded by a Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medicine (grant number 201900). PSA is supported by an Australian National Health and Medical Research Council Early Career Fellowship. RT-S was supported in part by grant number PROMETEOII/2015/021 from Generalitat Valenciana and the national grant PI17/00719 from ISCIII-FEDER. The Serbian part of this contribution (by MJ) has been co-financed with grant OI175014 from the Serbian Ministry of Education, Science and Technological Development; publication of results was not contingent upon the Ministry’s approval. MMMSM acknowledges support from the Serbian Ministry of Education, Science and Technological Development (contract 175087). MM’s research was supported by the NIHR Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust (London, UK) and King’s College London. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, or the UK Department of Health. TWB was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt professor award, which was funded by the German Federal Ministry of Education and Research.
Published: 2019

12. Global, regional, and national burden of Alzheimer's disease and other dementias, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016

Author: Nichols, E, Szoeke, CEI, Vollset, SE, Abbasi, N, Abd-Allah, F, Abdela, J, Aichour, MTE, Akinyemi, RO, Alahdab, F, Asgedom, SW, Awasthi, A, Barker-Collo, SL, Baune, BT, Béjot, Y, Belachew, AB, Bennett, DA, Biadgo, B, Bijani, A, Bin Sayeed, MS, Brayne, C, Carpenter, DO, Carvalho, F, Catalá López, Ferrán, Cerin, E, Choi, JJ, Dang, AK, Degefa, MG, Djalalinia, S, Dubey, M, Duken, EE, Edvardsson, D, Endres, M, Eskandarieh, S, Faro, A, Farzadfar, F, Fereshtehnejad, SM, Fernandes, E, Filip, I, Fischer, F, and Gebre, AK
Subjects: Malalties mentals
Abstract: BACKGROUND: The number of individuals living with dementia is increasing, negatively affecting families, communities, and health-care systems around the world. A successful response to these challenges requires an accurate understanding of the dementia disease burden. We aimed to present the first detailed analysis of the global prevalence, mortality, and overall burden of dementia as captured by the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, and highlight the most important messages for clinicians and neurologists. METHODS: GBD 2016 obtained data on dementia from vital registration systems, published scientific literature and surveys, and data from health-service encounters on deaths, excess mortality, prevalence, and incidence from 195 countries and territories from 1990 to 2016, through systematic review and additional data-seeking efforts. To correct for differences in cause of death coding across time and locations, we modelled mortality due to dementia using prevalence data and estimates of excess mortality derived from countries that were most likely to code deaths to dementia relative to prevalence. Data were analysed by standardised methods to estimate deaths, prevalence, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs; computed as the sum of YLLs and YLDs), and the fractions of these metrics that were attributable to four risk factors that met GBD criteria for assessment (high body-mass index [BMI], high fasting plasma glucose, smoking, and a diet high in sugar-sweetened beverages). FINDINGS: In 2016, the global number of individuals who lived with dementia was 43·8 million (95% uncertainty interval [UI] 37·8-51·0), increased from 20.2 million (17·4-23·5) in 1990. This increase of 117% (95% UI 114-121) contrasted with a minor increase in age-standardised prevalence of 1·7% (1·0-2·4), from 701 cases (95% UI 602-815) per 100 000 population in 1990 to 712 cases (614-828) per 100 000 population in 2016. More women than men had dementia in 2016 (27·0 million, 95% UI 23·3-31·4, vs 16.8 million, 14.4-19.6), and dementia was the fifth leading cause of death globally, accounting for 2·4 million (95% UI 2·1-2·8) deaths. Overall, 28·8 million (95% UI 24·5-34·0) DALYs were attributed to dementia; 6·4 million (95% UI 3·4-10·5) of these could be attributed to the modifiable GBD risk factors of high BMI, high fasting plasma glucose, smoking, and a high intake of sugar-sweetened beverages. INTERPRETATION: The global number of people living with dementia more than doubled from 1990 to 2016, mainly due to increases in population ageing and growth. Although differences in coding for causes of death and the heterogeneity in case-ascertainment methods constitute major challenges to the estimation of the burden of dementia, future analyses should improve on the methods for the correction of these biases. Until breakthroughs are made in prevention or curative treatment, dementia will constitute an increasing challenge to health-care systems worldwide.
Published: 2019

13. Global, regional, and national burden of meningitis, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016

Author: Zunt, JR, Kassebaum, NJ, Blake, N, Glennie, L, Wright, C, Nichols, E, Abd-Allah, F, Abdela, J, Abdelalim, A, Adamu, AA, Adib, MG, Ahmadi, A, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Akseer, N, Al-Raddadi, RM, Alahdab, F, Alene, KA, Aljunid, SM, AlMazora, MA, Khalid, Alvis-Guzman, N, Animut, MD, Anjomshoa, M, Ansha, MG, Asghar, RJ, Avokpaho, EFGA, Awasthi, A, Badali, H, Barac, A, Baernighausen, TW, Bassat, Q, Bedi, N, Belachew, AB, Bhattacharyya, K, Bhutta, ZA, Bijani, A, Butt, ZA, Carvalho, F, Castaneda-Orjuela, CA, Chitheer, A, Choi, J-YJ, Christopher, DJ, Dang, AK, Daryani, A, Demoz, GT, Djalalinia, S, Huyen, PD, Dubey, M, Dubljanin, E, Duken, EE, Zaki, MES, Elyazar, IR, Fakhim, H, Fernandes, E, Fischer, F, Fukumoto, T, Ganji, M, Gebre, AK, Gebremeskel, A, Gessner, BD, Gopalani, SV, Guo, Y, Gupta, R, Hailu, GB, Haj-Mirzaian, A, Hamidi, S, Hay, S, Henok, A, Irvani, SSN, Jha, RP, Jurisson, M, Kahsay, A, Karami, M, Karch, A, Kasaeian, A, Kassa, TD, Kefale, AT, Khader, YS, Khalil, IA, Khan, EA, Khang, Y-H, Khubchandani, J, Kimokoti, RW, Kisa, A, Lami, FH, Levi, M, Li, S, Loy, CT, Majdan, M, Majeed, A, Mantovani, LG, Martins-Melo, FR, McAlinden, C, Mehta, V, Melese, A, Memish, ZA, Mengistu, G, Mestrovic, T, Mezgebe, HB, Miazgowski, B, Milosevic, B, Mokdad, AH, Monasta, L, Moradi, G, Moraga, P, Mousavi, SM, Mueller, UO, Murthy, S, Mustafa, G, Naheed, A, Naik, G, Newton, CRJ, Nirayo, YL, Nixon, MR, Ofori-Asenso, R, Ogbo, FA, Olagunju, TO, Olusanya, BO, Ortiz, JR, Owolabi, MO, Patel, S, Pinilla-Monsalve, GD, Postma, MJ, Qorbani, M, Rafiei, A, Rahimi-Movaghar, V, Reiner, RC, Renzaho, AMN, Rezai, MS, Roba, KT, Ronfani, L, Roshandel, G, Rostami, A, Safari, S, Safiri, S, Sagar, R, Samy, AM, Milicevic, MMS, Sartorius, B, Sarvi, S, Sawhney, M, Saxena, S, Shafieesabet, A, Shaikh, MA, Sharif, M, Shigematsu, M, Si, S, Skiadaresi, E, Smith, M, Somayaji, R, Sufiyan, MB, Tawye, NY, Temsah, M-H, Tortajada-Girbes, M, Khanh, BT, Ukwaja, KN, Ullah, I, Vujcic, IS, Wagnew, F, Waheed, Y, Weldegwergs, KG, Winkler, AS, Wiyeh, AB, Wiysonge, CS, Wyper, GMA, Yimer, EM, Yonemoto, N, Zaidi, Z, Zenebe, ZM, Feigin, VL, Vos, T, and Murray, CJL
Subjects: BACTERIAL-MENINGITIS, RISK, AFRICA, Science & Technology, Neurology & Neurosurgery, IMPACT, Clinical Neurology, UNITED-STATES, CHILDREN, 1103 Clinical Sciences, GBD 2016 Meningitis Collaborators, PNEUMOCOCCAL CONJUGATE VACCINE, HEALTH-CARE, EPIDEMIOLOGY, Neurosciences & Neurology, MENINGOCOCCAL DISEASE, 1109 Neurosciences, Life Sciences & Biomedicine
Abstract: Background Acute meningitis has a high case-fatality rate and survivors can have severe lifelong disability. We aimed to provide a comprehensive assessment of the levels and trends of global meningitis burden that could help to guide introduction, continuation, and ongoing development of vaccines and treatment programmes. Methods The Global Burden of Diseases, Injuries, and Risk Factors (GBD) 2016 study estimated meningitis burden due to one of four types of cause: pneumococcal, meningococcal, Haemophilus influenzae type b, and a residual category of other causes. Cause-specific mortality estimates were generated via cause of death ensemble modelling of vital registration and verbal autopsy data that were subject to standardised data processing algorithms. Deaths were multiplied by the GBD standard life expectancy at age of death to estimate years of life lost, the mortality component of disability-adjusted life-years (DALYs). A systematic analysis of relevant publications and hospital and claims data was used to estimate meningitis incidence via a Bayesian meta-regression tool. Meningitis deaths and cases were split between causes with meta-regressions of aetiological proportions of mortality and incidence, respectively. Probabilities of long-term impairment by cause of meningitis were applied to survivors and used to estimate years of life lived with disability (YLDs). We assessed the relationship between burden metrics and Socio-demographic Index (SDI), a composite measure of development based on fertility, income, and education. Findings Global meningitis deaths decreased by 21·0% from 1990 to 2016, from 403 012 (95% uncertainty interval [UI] 319 426–458 514) to 318 400 (265 218–408 705). Incident cases globally increased from 2·50 million (95% UI 2·19–2·91) in 1990 to 2·82 million (2·46–3·31) in 2016. Meningitis mortality and incidence were closely related to SDI. The highest mortality rates and incidence rates were found in the peri-Sahelian countries that comprise the African meningitis belt, with six of the ten countries with the largest number of cases and deaths being located within this region. Haemophilus influenzae type b was the most common cause of incident meningitis in 1990, at 780 070 cases (95% UI 613 585–978 219) globally, but decreased the most (–49·1%) to become the least common cause in 2016, with 397 297 cases (291 076–533 662). Meningococcus was the leading cause of meningitis mortality in 1990 (192 833 deaths [95% UI 153 358–221 503] globally), whereas other meningitis was the leading cause for both deaths (136 423 [112 682–178 022]) and incident cases (1·25 million [1·06–1·49]) in 2016. Pneumococcus caused the largest number of YLDs (634 458 [444 787–839 749]) in 2016, owing to its more severe long-term effects on survivors. Globally in 2016, 1·48 million (1·04—1·96) YLDs were due to meningitis compared with 21·87 million (18·20—28·28) DALYs, indicating that the contribution of mortality to meningitis burden is far greater than the contribution of disabling outcomes. Interpretation Meningitis burden remains high and progress lags substantially behind that of other vaccine-preventable diseases. Particular attention should be given to developing vaccines with broader coverage against the causes of meningitis, making these vaccines affordable in the most affected countries, improving vaccine uptake, improving access to low-cost diagnostics and therapeutics, and improving support for disabled survivors. Substantial uncertainty remains around pathogenic causes and risk factors for meningitis. Ongoing, active cause-specific surveillance of meningitis is crucial to continue and to improve monitoring of meningitis burdens and trends throughout the world.
Published: 2018

14. Global, regional, and national burden of suicide mortality 1990 to 2016: Systematic analysis for the Global Burden of Disease Study 2016

Author: Orpana, HM, Marczak, LB, Arora, M, Abbasi, N, Abdulkader, RS, Abebe, Z, Abraha, HN, Afarideh, M, Afshari, M, Ahmadi, A, Aichour, AN, Aichour, I, Aichour, MTE, Akseer, N, Al‐raddadi, RM, Alahdab, F, Alkerwi, A, Allebeck, P, Alvis‐guzman, N, Anber, NH, Anjomshoa, M, Antonio, CAT, Arora, A, Aryal, KK, Asgedom, SW, Awasthi, A, Quintanilla, BPA, Badali, H, Barker‐collo, SL, Bärnighausen, TW, Bazargan‐hejazi, S, Benjet, C, Bensenor, IM, Berfeld, N, Beuran, M, Bhutta, ZA, Biadgo, B, Bililign, N, Borges, G, Borschmann, R, Brazinova, A, Breitborde, NJK, Brugha, T, Butt, ZA, Carrero, JJ, Carvalho, F, Malta, DC, Castañeda‐orjuela, CA, Catalá‐lópez, F, Ciobanu, LG, Dachew, BA, Dandona, L, Dandona, R, Dargan, PI, Daryani, A, Davitoiu, DV, Davletov, K, Degenhardt, L, Demoz, GT, Jarlais, DCD, Dharmaratne, SD, Djalalinia, S, Doan, L, Doku, DT, Dubey, M, El‐khatib, Z, Eskandarieh, S, Esteghamati, A, Esteghamati, S, Faro, A, Farzadfar, F, Fekadu, W, Fernandes, E, Ferrari, AJ, Filip, I, Fischer, F, Foreman, KJ, Fukumoto, T, Gebre, AK, Grosso, G, Gupta, R, Haagsma, JA, Bidgoli, HH, Haj‐mirzaian, A, Hamidi, S, Hankey, GJ, Haro, JM, Hassen, HY, Hay, SI, Heidari, B, Hendrie, D, Rad, EH, Hosseini, SM, Hostiuc, S, Irvani, SSN, Islam, SMS, Jakovljevic, M, James, S, Jayatilleke, AU, Jha, RP, Orpana, HM, Marczak, LB, Arora, M, Abbasi, N, Abdulkader, RS, Abebe, Z, Abraha, HN, Afarideh, M, Afshari, M, Ahmadi, A, Aichour, AN, Aichour, I, Aichour, MTE, Akseer, N, Al‐raddadi, RM, Alahdab, F, Alkerwi, A, Allebeck, P, Alvis‐guzman, N, Anber, NH, Anjomshoa, M, Antonio, CAT, Arora, A, Aryal, KK, Asgedom, SW, Awasthi, A, Quintanilla, BPA, Badali, H, Barker‐collo, SL, Bärnighausen, TW, Bazargan‐hejazi, S, Benjet, C, Bensenor, IM, Berfeld, N, Beuran, M, Bhutta, ZA, Biadgo, B, Bililign, N, Borges, G, Borschmann, R, Brazinova, A, Breitborde, NJK, Brugha, T, Butt, ZA, Carrero, JJ, Carvalho, F, Malta, DC, Castañeda‐orjuela, CA, Catalá‐lópez, F, Ciobanu, LG, Dachew, BA, Dandona, L, Dandona, R, Dargan, PI, Daryani, A, Davitoiu, DV, Davletov, K, Degenhardt, L, Demoz, GT, Jarlais, DCD, Dharmaratne, SD, Djalalinia, S, Doan, L, Doku, DT, Dubey, M, El‐khatib, Z, Eskandarieh, S, Esteghamati, A, Esteghamati, S, Faro, A, Farzadfar, F, Fekadu, W, Fernandes, E, Ferrari, AJ, Filip, I, Fischer, F, Foreman, KJ, Fukumoto, T, Gebre, AK, Grosso, G, Gupta, R, Haagsma, JA, Bidgoli, HH, Haj‐mirzaian, A, Hamidi, S, Hankey, GJ, Haro, JM, Hassen, HY, Hay, SI, Heidari, B, Hendrie, D, Rad, EH, Hosseini, SM, Hostiuc, S, Irvani, SSN, Islam, SMS, Jakovljevic, M, James, S, Jayatilleke, AU, and Jha, RP
Abstract: Objectives To use the estimates from the Global Burden of Disease Study 2016 to describe patterns of suicide mortality globally, regionally, and for 195 countries and territories by age, sex, and Socio-demographic index, and to describe temporal trends between 1990 and 2016. Design Systematic analysis. Main outcome measures Crude and age standardised rates from suicide mortality and years of life lost were compared across regions and countries, and by age, sex, and Socio-demographic index (a composite measure of fertility, income, and education). Results The total number of deaths from suicide increased by 6.7% (95% uncertainty interval 0.4% to 15.6%) globally over the 27 year study period to 817 000 (762 000 to 884 000) deaths in 2016. However, the age standardised mortality rate for suicide decreased by 32.7% (27.2% to 36.6%) worldwide between 1990 and 2016, similar to the decline in the global age standardised mortality rate of 30.6%. Suicide was the leading cause of age standardised years of life lost in the Global Burden of Disease region of high income Asia Pacific and was among the top 10 leading causes in eastern Europe, central Europe, western Europe, central Asia, Australasia, southern Latin America, and high income North America. Rates for men were higher than for women across regions, countries, and age groups, except for the 15 to 19 age group. There was variation in the female to male ratio, with higher ratios at lower levels of Socio-demographic index. Women experienced greater decreases in mortality rates (49.0%, 95% uncertainty interval 42.6% to 54.6%) than men (23.8%, 15.6% to 32.7%). Conclusions Age standardised mortality rates for suicide have greatly reduced since 1990, but suicide remains an important contributor to mortality worldwide. Suicide mortality was variable across locations, between sexes, and between age groups. Suicide prevention strategies can be targeted towards vulnerable populations if they are informed by variations in mortalit
Published: 2019

15. Global, regional, and national burden of epilepsy, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016

Author: Beghi, E, Giussani, G, Abd-Allah, F, Abdela, J, Abdelalim, A, Abraha, HN, Adib, MG, Agrawal, S, Alandab, F, Awasthi, A, Ayele, Y, Barboza, MA, Belachew, AB, Biadgo, B, Bijani, A, Bitew, H, Carvalho, F, Chaiah, Y, Daryani, A, Huyen, PD, Dubey, M, Endries, AYY, Eskandarieh, S, Faro, A, Farzadfar, F, Fereshtehnejad, S-M, Fernandes, E, Fijabi, DO, Filip, I, Fischer, F, Gebre, AK, Tsadik, AG, Gebremichael, TG, Gezae, KE, Ghasemi-Kasman, M, Weldegwergs, KG, Degefa, MG, Gnedovskaya, E, Hagos, TB, Haj-Mirzaian, A, Hassen, HY, Hay, S, Jakovljevic, M, Kasaeian, A, Kassa, TD, Khader, YS, Khalil, I, Khan, EA, Khubchandani, J, Kisa, A, Krohn, KJ, Kulkarni, C, Nirayo, YL, Mackay, MT, Majdan, M, Majeed, A, Manhertz, T, Mehndiratta, MM, Mekonen, T, Meles, HG, Mengistu, G, Mohammed, S, Naghavi, M, Mokdad, AH, Mustafa, G, Irvani, SSN, Long, HN, Nichols, E, Nixon, MR, Ogbo, FA, Olagunju, AT, Olagunju, TO, Owolabi, MO, Philips, MR, Pinilla-Monsalve, GD, Qorbani, M, Radfar, A, Rafay, A, Rahimi-Movaghar, V, Reinig, N, Sachdev, PS, Safari, H, Safari, S, Safiri, S, Sahraian, MA, Samy, AM, Sarvi, S, Sawhney, M, Shaikh, MA, Sharif, M, Singh, G, Smith, M, Szoeke, CE, Tabares-Seisdedos, R, Temsah, M-H, Temsah, O, Tortajada-Girbes, M, Bach, XT, Tsegay, AAT, Ullah, I, Venketasubramanian, N, Westerman, R, Winkler, AS, Yimer, EM, Yonemoto, N, Feigin, VL, Vos, T, Murray, CJL, Beghi, E, Giussani, G, Abd-Allah, F, Abdela, J, Abdelalim, A, Abraha, HN, Adib, MG, Agrawal, S, Alandab, F, Awasthi, A, Ayele, Y, Barboza, MA, Belachew, AB, Biadgo, B, Bijani, A, Bitew, H, Carvalho, F, Chaiah, Y, Daryani, A, Huyen, PD, Dubey, M, Endries, AYY, Eskandarieh, S, Faro, A, Farzadfar, F, Fereshtehnejad, S-M, Fernandes, E, Fijabi, DO, Filip, I, Fischer, F, Gebre, AK, Tsadik, AG, Gebremichael, TG, Gezae, KE, Ghasemi-Kasman, M, Weldegwergs, KG, Degefa, MG, Gnedovskaya, E, Hagos, TB, Haj-Mirzaian, A, Hassen, HY, Hay, S, Jakovljevic, M, Kasaeian, A, Kassa, TD, Khader, YS, Khalil, I, Khan, EA, Khubchandani, J, Kisa, A, Krohn, KJ, Kulkarni, C, Nirayo, YL, Mackay, MT, Majdan, M, Majeed, A, Manhertz, T, Mehndiratta, MM, Mekonen, T, Meles, HG, Mengistu, G, Mohammed, S, Naghavi, M, Mokdad, AH, Mustafa, G, Irvani, SSN, Long, HN, Nichols, E, Nixon, MR, Ogbo, FA, Olagunju, AT, Olagunju, TO, Owolabi, MO, Philips, MR, Pinilla-Monsalve, GD, Qorbani, M, Radfar, A, Rafay, A, Rahimi-Movaghar, V, Reinig, N, Sachdev, PS, Safari, H, Safari, S, Safiri, S, Sahraian, MA, Samy, AM, Sarvi, S, Sawhney, M, Shaikh, MA, Sharif, M, Singh, G, Smith, M, Szoeke, CE, Tabares-Seisdedos, R, Temsah, M-H, Temsah, O, Tortajada-Girbes, M, Bach, XT, Tsegay, AAT, Ullah, I, Venketasubramanian, N, Westerman, R, Winkler, AS, Yimer, EM, Yonemoto, N, Feigin, VL, Vos, T, and Murray, CJL
Abstract: BACKGROUND: Seizures and their consequences contribute to the burden of epilepsy because they can cause health loss (premature mortality and residual disability). Data on the burden of epilepsy are needed for health-care planning and resource allocation. The aim of this study was to quantify health loss due to epilepsy by age, sex, year, and location using data from the Global Burden of Diseases, Injuries, and Risk Factors Study. METHODS: We assessed the burden of epilepsy in 195 countries and territories from 1990 to 2016. Burden was measured as deaths, prevalence, and disability-adjusted life-years (DALYs; a summary measure of health loss defined by the sum of years of life lost [YLLs] for premature mortality and years lived with disability), by age, sex, year, location, and Socio-demographic Index (SDI; a compound measure of income per capita, education, and fertility). Vital registrations and verbal autopsies provided information about deaths, and data on the prevalence and severity of epilepsy largely came from population representative surveys. All estimates were calculated with 95% uncertainty intervals (UIs). FINDINGS: In 2016, there were 45·9 million (95% UI 39·9-54·6) patients with all-active epilepsy (both idiopathic and secondary epilepsy globally; age-standardised prevalence 621·5 per 100 000 population; 540·1-737·0). Of these patients, 24·0 million (20·4-27·7) had active idiopathic epilepsy (prevalence 326·7 per 100 000 population; 278·4-378·1). Prevalence of active epilepsy increased with age, with peaks at 5-9 years (374·8 [280·1-490·0]) and at older than 80 years of age (545·1 [444·2-652·0]). Age-standardised prevalence of active idiopathic epilepsy was 329·3 per 100 000 population (280·3-381·2) in men and 318·9 per 100 000 population (271·1-369·4) in women, and was similar among SDI quintiles. Global age-standardised mortality rates of idiopathic epilepsy were 1·74 per 100 000 population (1·64-1·87; 1·40 per 100 000 population [1·23-1·54] for women
Published: 2019

16. Adipose tissue ATP binding cassette transporter A1 contributes to high-density lipoprotein biogenesis in vivo.

Author: Chung S, Sawyer JK, Gebre AK, Maeda N, Parks JS, Chung, Soonkyu, Sawyer, Janet K, Gebre, Abraham K, Maeda, Nobuyo, and Parks, John S
Published: 2011
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17. Role of LDL subfraction heterogeneity in the reduced binding of low density lipoproteins to arterial proteoglycans in cynomolgus monkeys fed a fish oil diet.

Author: Parks, JS, primary, Gebre, AK, additional, Edwards, IJ, additional, and Wagner, WD, additional
Published: 1991
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18. Studies on the effect of dietary fish oil on the physical and chemical properties of low density lipoproteins in cynomolgus monkeys.

Author: Parks, JS, primary and Gebre, AK, additional
Published: 1991
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19. Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Author: Lozano, Rafael, Fullman, Nancy, Abate, Degu, Abay, Solomon M, Abbafati, Cristiana, Abbasi, Nooshin, Abbastabar, Hedayat, Abd-Allah, Foad, Abdela, Jemal, Abdelalim, Ahmed, Abdel-Rahman, Omar, Abdi, Alireza, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Nebiyu Dereje, Abebe, Zegeye, Abejie, Ayenew Negesse, Abera, Semaw F, Abil, Olifan Zewdie, Aboyans, Victor, Abraha, Haftom Niguse, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen Me, Abyu, Gebre Y, Accrombessi, Manfred Mario Kokou, Acharya, Dilaram, Acharya, Pawan, Adamu, Abdu A, Adebayo, Oladimeji M, Adedeji, Isaac Akinkunmi, Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Adetokunboh, Olatunji O, Adhena, Beyene Meressa, Adhikari, Tara Ballav, Adib, Mina G, Adou, Arsène Kouablan, Adsuar, Jose C, Afarideh, Mohsen, Afshari, Mahdi, Afshin, Ashkan, Agarwal, Gina, Aghayan, Sargis Aghasi, Agius, Dominic, Agrawal, Anurag, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmadieh, Hamid, Ahmed, Muktar Beshir, Ahmed, Sayem, Akalu, Temesgen Yihunie, Akanda, Ali S, Akbari, Mohammad Esmaeil, Akibu, Mohammed, Akinyemi, Rufus Olusola, Akinyemiju, Tomi, Akseer, Nadia, Alahdab, Fares, Al-Aly, Ziyad, Alam, Khurshid, Alam, Tahiya, Albujeer, Ammar, Alebel, Animut, Alene, Kefyalew Addis, Al-Eyadhy, Ayman, Alhabib, Samia, Ali, Raghib, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Allen, Christine A, Almasi, Ali, Al-Maskari, Fatma, Al-Mekhlafi, Hesham M, Alonso, Jordi, Al-Raddadi, Rajaa M, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Amenu, Kebede, Amini, Erfan, Ammar, Walid, Anber, Nahla Hamed, Anderson, Jason A, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Anjomshoa, Mina, Ansari, Hossein, Ansariadi, Ansariadi, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T, Anwari, Palwasha, Appiah, Lambert Tetteh, Aremu, Olatunde, Areri, Habtamu Abera, Ärnlöv, Johan, Arora, Monika, Aryal, Krishna K, Asayesh, Hamid, Asfaw, Ephrem Tsegay, Asgedom, Solomon Weldegebreal, Asghar, Rana Jawad, Assadi, Reza, Ataro, Zerihun, Atique, Suleman, Atre, Sachin R, Atteraya, Madhu Sudhan, Ausloos, Marcel, Avila-Burgos, Leticia, Avokpaho, Euripide F G A, Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayele, Henok Tadesse, Ayele, Yohanes, Ayer, Rakesh, Azarpazhooh, Mahmoud Reza, Azzopardi, Peter S, Azzopardi-Muscat, Natasha, Babalola, Tesleem Kayode, Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Balakrishnan, Kalpana, Bali, Ayele Geleto, Banach, Maciej, Banerjee, Amitava, Banoub, Joseph Adel Mattar, Banstola, Amrit, Barac, Aleksandra, Barboza, Miguel A, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H, Barthelemy, Celine M, Bassat, Quique, Basu, Arindam, Basu, Sanjay, Battista, Robert J, Baune, Bernhard T, Baynes, Habtamu Wondifraw, Bazargan-Hejazi, Shahrzad, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Béjot, Yannick, Bekele, Bayu Begashaw, Belachew, Abate Bekele, Belay, Aregawi Gebreyesus, Belay, Saba Abraham, Belay, Yihalem Abebe, Bell, Michelle L, Bello, Aminu K, Bennett, Derrick A, Bensenor, Isabela M, Benzian, Habib, Berhane, Adugnaw, Berhe, Abadi Kidanemariam, Berman, Adam E, Bernabe, Eduardo, Bernstein, Robert S, Bertolacci, Gregory J, Beuran, Mircea, Beyranvand, Tina, Bhala, Neeraj, Bhalla, Ashish, Bhansali, Anil, Bhattarai, Suraj, Bhaumik, Soumyadeep, Bhutta, Zulfiqar A, Biadgo, Belete, Biehl, Molly H, Bijani, Ali, Bikbov, Boris, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Birlik, Sait Mentes, Birungi, Charles, Bisanzio, Donal, Biswas, Tuhin, Bitew, Helen, Bizuneh, Hailemichael, Bjertness, Espen, Bobasa, Eshetu Mulisa, Boufous, Soufiane, Bourne, Rupert, Bozorgmehr, Kayvan, Bragazzi, Nicola Luigi, Brainin, Michael, Brant, Luisa C, Brauer, Michael, Brazinova, Alexandra, Breitborde, Nicholas J K, Briant, Paul Svitil, Britton, Gabrielle, Brugha, Traolach, Bukhman, Gene, Busse, Reinhard, Butt, Zahid A, Cahuana-Hurtado, Lucero, Callender, Charlton SKH, Campos-Nonato, Ismael R, Campuzano Rincon, Julio Cesar, Cano, Jorge, Car, Josip, Car, Mate, Cárdenas, Rosario, Carrero, Juan J, Carter, Austin, Carvalho, Félix, Castañeda-Orjuela, Carlos A, Castillo Rivas, Jacqueline, Castro, Franz, Causey, Kate, Çavlin, Alanur, Cercy, Kelly M, Cerin, Ester, Chaiah, Yazan, Chalek, Julian, Chang, Hsing-Yi, Chang, Jung-Chen, Chattopadhyay, Aparajita, Chattu, Vijay Kumar, Chaturvedi, Pankaj, Chiang, Peggy Pei-Chia, Chin, Ken Lee, Chisumpa, Vesper Hichilombwe, Chitheer, Abdulaal, Choi, Jee-Young J, Chowdhury, Rajiv, Christensen, Hanne, Christopher, Devasahayam J, Chung, Sheng-Chia, Cicuttini, Flavia M, Ciobanu, Liliana G, Cirillo, Massimo, Claro, Rafael M, Claßen, Thomas Khaled Dwayne, Cohen, Aaron J, Collado-Mateo, Daniel, Cooper, Cyrus, Cooper, Leslie Trumbull, Cornaby, Leslie, Cortinovis, Monica, Costa, Megan, Cousin, Ewerton, Cromwell, Elizabeth A, Crowe, Christopher Stephen, Cunningham, Matthew, 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Subjects: GENDER-DIFFERENCES, SUICIDE, Medicine (all), Burden of Disease Study 2017, SDG, CONSUMPTION, 11 Medical And Health Sciences, GBD, Sustainable Development Goals, PREVENTION, ALCOHOL-USE, CARDIOVASCULAR-DISEASE, SMOKING PREVALENCE, General & Internal Medicine, factors risk, Medicine and Health Sciences, Human medicine, CESSATION, PERSPECTIVE, TRANSITION
Abstract: Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of "leaving no one behind", it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health -related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990-2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related S DG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health related SDG index, we transformed the value for each indicator on a scale of 0-100, with 0 as the 2.5th percentile and 100 as the 97.5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings The global median health-related SDG index in 2017 was 59.4 (IQR 35.4-67.3), ranging from a low of 11.6 (95% uncertainty interval 9.6-14.0) to a high of 84.9 (83.1-86.7). SDG index values in countries assessed at the subnational level varied substantially particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attaimnent by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation The GBD study offers a unique, robust platform for monitoring the health -related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health -related SDG indicators, NCDs, NCD-related risks, and violence -related indicators will require a concerted shift away from what might have driven past gains curative interventions in the case of NCDs towards multisectoral, prevention -oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the S DGs. What is clear is that our actions or inaction today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
Published: 2018

20. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017

Author: Roth, Gregory A., Collaborotors, G. B. 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Dean, Hosseini, Seyed Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hotez, Peter J., Hoy, Damian C., Hsiao, Thomas, Hu, Guoqing, Huang, John J., Husseini, Abdullatif, Hussen, Mohammedaman Mama, Hutfless, Susan, Idrisov, Bulat, Ilesanmi, Olayinka Stephen, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Irvine, Caleb Mackay Salpeter, Islam, Nazrul, Islam, Sheikh Mohammed Shariful, Islami, Farhad, Jacobsen, Kathryn H., Jahangiry, Leila, Jahanmehr, Nader, Jain, Sudhir Kumar, Jakovlievic, Mihajlo, Jalu, Moti Tolera, James, Spencer L., Javanbakht, Mehdi, Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jenkins, Kathy J., Jha, Ravi Prakash, Jha, Vivekanand, Johnson, Catherine O., Johnson, Sarah C., Jonas, Jost B., Joshi, Ankur, Jozwiak, Jacek Jerzy, Jungari, Suresh Banayya, Jurisson, Mikk, Kabir, Zubair, Kadel, Rajendra, Kahsay, Amaha, Kalani, Rizwan, Karami, Manoochehr, Matin, Behzad Karami, Karch, Andre, Karema, Corine, Karimi-Sari, Hamidreza, Kasaeian, Amir, Kassa, Dessalegn H., Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassebaum, Nicholas J., Katikireddi, Srinivasa Vittal, Kaul, Anil, Kazemi, Zhila, Karyani, Ali Kazemi, Kazi, Dhruv Satish, Kefale, Adane Teshome, Keiyoro, Peter Njenga, Kemp, Grant Rodgers, Kengne, Andre Pascal, Keren, Andre, Kesavachandran, Chandrasekharan Nair, Khader, Yousef Saleh, Khafaei, Behzad, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalid, Nauman, Khalil, Ibrahim A., Khan, Ejaz Ahmad, Khan, Muhammad Shahzeb, Khan, Muhammad Ali, Khang, Young-Ho, Khater, Mona M., Khoja, Abdullah T., Khosravi, Ardeshir, Khosravi, Mohammad Hossein, Khubchandani, Jagdish, Kiadaliri, Aliasghar A., Kibret, Getiye D., Kidanemariam, Zelalem Teklemariam, Kiirithio, Daniel N., Kim, Daniel, Kim, Young-Eun, Kim, Yun Jin, Kimokoti, Ruth W., Kinfu, Yohannes, Kisa, Adnan, Kissimova-Skarbek, Katarzyna, Kivimaki, Mika, Knudsen, Ann Kristin Skrindo, Kocarnik, Jonathan M., Kochhar, Sonali, Kokubo, Yoshihiro, Kolola, Tufa, Kopec, Jacek A., Koul, Parvaiz A., Koyanagi, Ai, Kravchenko, Michael A., Krishan, Kewal, Defo, Barthelemy Kuate, Bicer, Burcu Kinuk, Kumar, G. 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N., Memish, Ziad A., Mendoza, Walter, Mengistu, Desalegn Tadese, Mengistu, Getnet, Mensah, George A., Mereta, Seid Tiku, Meretoja, Atte, Meretoja, Tuomo J., Meshovic, Tbmislav, Mezgebe, Haftay Berhane, Miazgowski, Bartosz, Miazgowski, Tomasz, Millear, Anoushka I., Miller, Ted R., Katherine, Molly, Petrie, Miller, Mini, G. K., Mirabi, Parvaneh, Mirarefin, Mojde, Mirica, Andreea, Mirrakhimov, Erkin M., Misganaw, Awoke Temesgen, Mitiku, Habtamu, Moazen, Babak, Mohammad, Karzan Abdulmuhsin, Mohammad, Moslem, Mohammadifard, Noushin, Mohammed, A. 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Jc, Ferrari, Aj, Feyissa, Gt, Foigt, Na, Foreman, Kj, Frank, Td, Fuller, Je, Fürst, T, Furtado, Jm, Futran, Nd, Garcia-Basteiro, Al, Garcia-Gordillo, Ma, Gardner, Wm, Gebre, Ak, Gebrehiwot, Tt, Gebremedhin, At, Gebremichael, Tg, Gelano, Tf, Geleijnse, Jm, Geramo, Ycd, Gething, Pw, Gezae, Ke, Ghadami, Mr, Gibney, Kb, Gill, Tk, Gillum, Rf, Ginawi, Ia, Goldberg, Em, Gómez-Dantés, H, Gona, Pn, Gopalani, Sv, Gorman, Tm, Goulart, Ac, Gnedovskaya, Ev, Gugnani, Hc, Guimaraes, Al, Gupta, Pc, Gutiérrez, Ra, Haagsma, Ja, Hagos, Tb, Hailegiyorgis, Tt, Hailu, Gb, Hamadeh, Rr, Handal, Aj, Hankey, Gj, Harb, Hl, Haro, Jm, Hassen, Hy, Hay, Rj, Hay, Si, Hegazy, Mi, Henry, Nj, Hibstu, Dt, Hoek, Hw, Hole, Mk, Hosgood, Hd, Hosseini, Sm, Hotez, Pj, Hoy, Dg, Huang, Jj, Hussen, Mm, Irvani, Ssn, Irvine, Cm, Islam, Sm, Jacobsen, Kh, Jain, Sk, Jalu, Mt, James, Sl, Jayatilleke, Au, Jenkins, Kj, Jha, Rp, Johnson, Co, Johnson, Sc, Jonas, Jb, Jozwiak, Jj, Jungari, Sb, Jürisson, M, Kassa, Dh, Kassa, Gm, Kassa, Td, Kassebaum, Nj, Katikireddi, Sv, Karyani, Ak, Kefale, At, Keiyoro, Pn, Kemp, Gr, Kengne, Ap, Kesavachandran, Cn, Khafaie, Ma, Khalil, Ia, Khan, Ea, Khan, Ma, Khang, Yh, Khater, Mm, Khoja, At, Khosravi, Mh, Kiadaliri, Aa, Kibret, Gd, Kidanemariam, Zt, Kiirithio, Dn, Kim, Ye, Kim, Yj, Kimokoti, Rw, Kivimäki, M, Knudsen, Ak, Kocarnik, Jm, Kopec, Ja, Koul, Pa, Kravchenko, Ma, Kumar, Ga, Kutz, Mj, Kyu, Hh, Lad, Dp, Lad, Sd, Lal, Dk, Lam, Jo, Lami, Fh, Lansingh, Vc, Larson, Hj, Lau, Km, Lazarus, Jv, Lee, Ph, Leshargie, Ct, Lim, Ll, Limenih, Ma, Lopez, Ad, Lotufo, Pa, Macarayan, Erk, Mackay, Mt, Maclachlan, Jh, Maddison, Er, Maghavani, Dp, Malta, Dc, Manda, Al, Mandarano-Filho, Lg, Mansournia, Ma, Mapoma, Cc, Maravilla, Jc, Marks, Gb, Martins-Melo, Fr, März, W, Marzan, Mb, Masci, Jr, Massenburg, Bb, Mathur, Mr, Maulik, Pk, Mcgrath, Jj, Mcmahon, Bj, Mehndiratta, Mm, Mehta, Km, Mekonnen, Tc, Memiah, Ptn, Memish, Za, Mengistu, Dt, Mensah, Ga, Mereta, St, Meretoja, Tj, Mezgebe, Hb, Millear, Ai, Miller, Tr, Miller-Petrie, Mk, Mini, Gk, Mirrakhimov, Em, Misganaw, At, Mohammad, Ka, Mohammed, Ma, Mokdad, Ah, Moreno Velásquez, I, Morrison, Sd, Moschos, Mm, Mousavi, Sm, Muchie, Kf, Mueller, Uo, Mumford, Je, Musa, Ki, Nachega, Jb, Nansseu, Jr, Nascimento, Br, Negoi, Ri, Newton, Crj, Ngalesoni, Fn, Ngunjiri, Jw, Nguyen, Aq, Nguyen, Ht, Nguyen, Lh, Nguyen, Th, Ningrum, Dna, Nirayo, Yl, Nixon, Mr, Norheim, Of, Noubiap, Jj, Nouri, Hr, Nourollahpour Shiadeh, M, Nowroozi, Mr, Odell, Cm, Ogbo, Fa, Oh, Ih, Olagunju, At, Olivares, Pr, Olsen, He, Olusanya, Bo, Olusanya, Jo, Ong, Kl, Ong, Sk, Orpana, Hm, Ortiz, Jr, Øverland, S, Owolabi, Mo, Özdemir, R, P A, M, Pakhare, Ap, Pakpour, Ah, Pandian, Jd, Park, Ek, Parry, Cdh, Patton, Gc, Paturi, Vr, Paulson, Kr, Pereira, Dm, Phillips, Mr, Piel, Fb, Pigott, Dm, Pillay, Jd, Postma, Mj, Purcell, Ca, Quistberg, Da, Rade, Kw, Radfar, A, Rafay, A, Rafiei, A, Rahim, F, Rahman, Mhu, Rahman, Ma, Rai, Rk, Ranabhat, Cl, Rao, Pc, Rawaf, Dl, Razo-García, C, Reiner, Rc, Reitsma, Mb, Renzaho, Amn, Riahi, Sm, Ribeiro, Alp, Rios-Blancas, Mj, Roba, Kt, Roberts, Nl, Robinson, Sr, Sahraian, Ma, Saldanha, Rf, Sambala, Ez, Samy, Am, Sanchez-Niño, Md, Santomauro, Df, Santos, Jv, Milicevic, Mm, Sao Jose, Bp, Sarker, Ar, Sarmiento-Suárez, R, Sawant, Ar, Schmidt, Mi, Schneider, Ijc, Schöttker, B, Schutte, Ae, Schwebel, Dc, Scott, Jg, Sepanlou, Sg, Serván-Mori, E, Shackelford, Ka, Shaheen, Aa, Shaikh, Ma, Shrime, Mg, Siddiqi, Tj, Sigfusdottir, Id, Silberberg, Dh, Silva, Da, Silva, Jp, Silva, Ntd, Silveira, Dga, Singh, Ja, Singh, Np, Singh, Pk, Sinha, Dn, Sobaih, Bh, Sorensen, Rjd, Soriano, Jb, Soyiri, In, Sposato, La, Sreeramareddy, Ct, Stanaway, Jd, Starodubov, Vi, Stein, Dj, Stewart, Lg, Stokes, Ma, Subart, Ml, Sufiyan, Mb, Sur, Pj, Sykes, Bl, Sylaja, Pn, Sylte, Do, Szoeke, Cei, Tabarés-Seisdedos, R, Tadakamadla, Sk, Tassew, Sg, Tekalign, Tg, Tekle, Mg, Temsah, Mh, Teshale, My, Tessema, Ga, Thankappan, Kr, Thrift, Ag, Thurston, Gd, To, Qg, Torre, Ae, Tortajada-Girbés, M, Tovani-Palone, Mr, Tran, Bx, Tran, Kb, Troeger, Ce, Truelsen, Tc, Truong, Nt, Tsadik, Ag, Tuzcu, Em, Ukwaja, Kn, Undurraga, Ea, Updike, Rl, Uthman, Oa, Uzun, Sb, Valdez, Pr, Vasankari, Tj, Vladimirov, Sk, Vollset, Se, Wagner, Gr, Wallin, Mt, Walson, Jl, Wang, Yp, Wassie, Mm, Weintraub, Rg, Weldegwergs, Kg, Werkneh, Aa, West, Te, Whiteford, Ha, Wilner, Lb, Wolfe, Cda, Wu, Yc, Wyper, Gma, Yahyazadeh Jabbari, Sh, Yan, Ll, Yasin, Yj, Yentür, Gk, Yimer, Em, Yirsaw, Bd, Yoon, Sj, Younis, Mz, Zaman, Sb, Zeleke, Aj, Zenebe, Zm, Zhang, Al, Zuhlke, Lj, and Murray, Cjl.
Subjects: Adult, Male, Adolescent, national age-sex-specific mortality, UNITED-STATES, Global Health, Socioeconomic Factor, Global Burden of Disease, GBD, causes of death, Young Adult, Age Distribution, Life Expectancy, General & Internal Medicine, Cause of Death, PUBLIC-HEALTH PROBLEM, mortality, 282 causes global burden of disease, Sex Distribution, Child, Aged, Aged, 80 and over, BARRIERS, DEMENTIA, Medicine (all), Infant, Newborn, Infant, 11 Medical And Health Sciences, Middle Aged, TRENDS, PREVENTION, PREVALENCE, ALZHEIMERS-DISEASE, INDIVIDUALS, 3121 General medicine, internal medicine and other clinical medicine, Child, Preschool, REGISTRATION, Female, Human medicine, Human
Abstract: Background Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODErn), to generate cause fractions and cause specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings At the broadest grouping of causes of death (Level 1), non-communicable diseases (NC Ds) comprised the greatest fraction of deaths, contributing to 73.4% (95% uncertainty interval [UI] 72.5-74.1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 186% (17.9-19.6), and injuries 8.0% (7.7-8.2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22.7% (21.5-23.9), representing an additional 7.61 million (7. 20-8.01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7.9% (7.08.8). The number of deaths for CMNN causes decreased by 222% (20.0-24.0) and the death rate by 31.8% (30.1-33.3). Total deaths from injuries increased by 2.3% (0-5-4-0) between 2007 and 2017, and the death rate from injuries decreased by 13.7% (12.2-15.1) to 57.9 deaths (55.9-59.2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000-289 000) globally in 2007 to 352 000 (334 000-363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118.0% (88.8-148.6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36.4% (32.2-40.6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33.6% (31.2-36.1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respirator}, infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990 neonatal disorders, lower respiratory infections, and diarrhoeal diseases were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.
Published: 2018

21. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017

Author: Stanaway, Jeffrey D., Afshin, Ashkan, Gakidou, Emmanuela, Lim, Stephen S., Abate, Degu, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbasi, Nooshin, Abbastabar, Hedayat, Abd-Allah, Foad, Abdela, Jemal, Abdelalim, Ahmed, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Molla, Abebe, Zegeye, Abera, Semaw F., Abil, Olifan Zewdie, Abraha, Haftom Niguse, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen ME, Accrombessi, Manfred Mario Kokou, Acharya, Dilaram, Acharya, Pawan, Adamu, Abdu A., Adane, Akilew Awoke, Adebayo, Oladimeji M., Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Ademi, Zanfina, Adetokunboh, Olatunji O., Adib, Mina G., Admasie, Amha, Adsuar, Jose C., Afanvi, Kossivi Agbelenko, Afarideh, Mohsen, Agarwal, Gina, Aggarwal, Anju, Aghayan, Sargis Aghasi, Agrawal, Anurag, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmadieh, Hamid, Ahmed, Muktar Beshir, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akbari, Mohammad Esmaeil, Akinyemiju, Tomi, Akseer, Nadia, Al-Aly, Ziyad, Al-Eyadhy, Ayman, Al-Mekhlafi, Hesham M., Alahdab, Fares, Alam, Khurshid, Alam, Samiah, Alam, Tahiya, Alashi, Alaa, Alavian, Seyed Moayed, Alene, Kefyalew Addis, Ali, Komal, Ali, Syed Mustafa, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T., Ammar, Walid, Anber, Nahla Hamed, Anderson, Jason A., Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Anjomshoa, Mina, Ansha, Mustafa Geleto, Antó, Josep M., Antonio, Carl Abelardo T., Anwari, Palwasha, Appiah, Lambert Tetteh, Appiah, Seth Christopher Yaw, Arabloo, Jalal, Aremu, Olatunde, Ärnlöv, Johan, Artaman, Al, Aryal, Krishna K., Asayesh, Hamid, Ataro, Zerihun, Ausloos, Marcel, Avokpaho, Euripide F.G.A., Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayer, Rakesh, Ayuk, Tambe B., Azzopardi, Peter S., Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Balakrishnan, Kalpana, Bali, Ayele Geleto, Ball, Kylie, Ballew, Shoshana H., Banach, Maciej, Banoub, Joseph Adel Mattar, Barac, Aleksandra, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H., Basu, Sanjay, Baune, Bernhard T., Bazargan-Hejazi, Shahrzad, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Béjot, Yannick, Bekele, Bayu Begashaw, Bekru, Eyasu Tamru, Belay, Ezra, Belay, Yihalem Abebe, Bell, Michelle L., Bello, Aminu K., Bennett, Derrick A., Bensenor, Isabela M., Bergeron, Gilles, Berhane, Adugnaw, Bernabe, Eduardo, Bernstein, Robert S., Beuran, Mircea, Beyranvand, Tina, Bhala, Neeraj, Bhalla, Ashish, Bhattarai, Suraj, Bhutta, Zulfiqar A., Biadgo, Belete, Bijani, Ali, Bikbov, Boris, Bilano, Ver, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Bisanzio, Donal, Biswas, Tuhin, Bjørge, Tone, Blacker, Brigette F., Bleyer, Archie, Borschmann, Rohan, Bou-Orm, Ibrahim R., Boufous, Soufiane, Bourne, Rupert, Brady, Oliver J., Brauer, Michael, Brazinova, Alexandra, Breitborde, Nicholas J.K., Brenner, Hermann, Briko, Andrey Nikolaevich, Britton, Gabrielle, Brugha, Traolach, Buchbinder, Rachelle, Burnett, Richard T., Busse, Reinhard, Butt, Zahid A., Cahill, Leah E., Cahuana-Hurtado, Lucero, Campos-Nonato, Ismael R., Cárdenas, Rosario, Carreras, Giulia, Carrero, Juan J., Carvalho, Félix, Castañeda-Orjuela, Carlos A., Castillo Rivas, Jacqueline, Castro, Franz, Catalá-López, Ferrán, Causey, Kate, Cercy, Kelly M., Cerin, Ester, Chaiah, Yazan, Chang, Hsing Yi, Chang, Jung Chen, Chang, Kai Lan, Charlson, Fiona J., Chattopadhyay, Aparajita, Chattu, Vijay Kumar, Chee, Miao Li, Cheng, Ching Yu, Chew, Adrienne, Chiang, Peggy Pei Chia, Chimed-Ochir, Odgerel, Chin, Ken Lee, Chitheer, Abdulaal, Choi, Jee Young J., Chowdhury, Rajiv, Christensen, Hanne, Christopher, Devasahayam J., Chung, Sheng Chia, Cicuttini, Flavia M., Cirillo, Massimo, Cohen, Aaron J., Collado-Mateo, Daniel, Cooper, Cyrus, Cooper, Owen R., Coresh, Josef, Cornaby, Leslie, Cortesi, Paolo Angelo, Cortinovis, Monica, Costa, Megan, Cousin, Ewerton, Criqui, Michael H., Cromwell, Elizabeth A., Cundiff, David K., Daba, Alemneh Kabeta, Dachew, Berihun Assefa, Dadi, Abel Fekadu, Damasceno, Albertino Antonio Moura, Dandona, Lalit, Dandona, Rakhi, Darby, Sarah C., Dargan, Paul I., Daryani, Ahmad, Das Gupta, Rajat, Das Neves, José, Dasa, Tamirat Tesfaye, Dash, Aditya Prasad, Davitoiu, Dragos Virgil, Davletov, Kairat, De la Cruz-Góngora, Vanessa, De La Hoz, Fernando Pio, De Leo, Diego, De Neve, Jan Walter, Degenhardt, Louisa, Deiparine, Selina, Dellavalle, Robert P., Demoz, Gebre Teklemariam, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolaos, Deshpande, Aniruddha, Des Jarlais, Don C., Dessie, Getenet Ayalew, Deveber, Gabrielle Aline, Dey, Subhojit, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Dinberu, Mesfin Tadese, Ding, Eric L., Diro, Helen Derara, Djalalinia, Shirin, Do, Huyen Phuc, Dokova, Klara, Doku, David Teye, Doyle, Kerrie E., Driscoll, Tim R., Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Duncan, Bruce B., Duraes, Andre R., Ebert, Natalie, Ebrahimi, Hedyeh, Ebrahimpour, Soheil, Edvardsson, David, Effiong, Andem, Eggen, Anne Elise, El Bcheraoui, Charbel, El-Khatib, Ziad, Elyazar, Iqbal Rf, Enayati, Ahmadali, Endries, Aman Yesuf, Er, Benjamin, Erskine, Holly E., Eskandarieh, Sharareh, Esteghamati, Alireza, Estep, Kara, Fakhim, Hamed, Faramarzi, Mahbobeh, Fareed, Mohammad, Farid, Talha A., Farinha, Carla Sofia E.sá, Farioli, Andrea, Faro, Andre, Farvid, Maryam S., Farzaei, Mohammad Hosein, Fatima, Batool, Fay, Kairsten A., Fazaeli, Ali Akbar, Feigin, Valery L., Feigl, Andrea B., Fereshtehnejad, Seyed Mohammad, Fernandes, Eduarda, Fernandes, Joao C., Ferrara, Giannina, Ferrari, Alize J., Ferreira, Manuela L., Filip, Irina, Finger, Jonas David, Fischer, Florian, Foigt, Nataliya A., Foreman, Kyle J., Fukumoto, Takeshi, Fullman, Nancy, Fürst, Thomas, Furtado, João M., Futran, Neal D., Gall, Seana, Gallus, Silvano, Gamkrelidze, Amiran, Ganji, Morsaleh, Garcia-Basteiro, Alberto L., Gardner, William M., Gebre, Abadi Kahsu, Gebremedhin, Amanuel Tesfay, Gebremichael, Teklu Gebrehiwo, Gelano, Tilayie Feto, Geleijnse, Johanna M., Geramo, Yilma Chisha Dea, Gething, Peter W., Gezae, Kebede Embaye, Ghadimi, Reza, Ghadiri, Keyghobad, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Ghimire, Mamata, Ghosh, Rakesh, Ghoshal, Aloke Gopal, Giampaoli, Simona, Gill, Paramjit Singh, Gill, Tiffany K., Gillum, Richard F., Ginawi, Ibrahim Abdelmageed, Giussani, Giorgia, Gnedovskaya, Elena V., Godwin, William W., Goli, Srinivas, Gómez-Dantés, Hector, Gona, Philimon N., Gopalani, Sameer Vali, Goulart, Alessandra C., Grada, Ayman, Grams, Morgan E., Grosso, Giuseppe, Gugnani, Harish Chander, Guo, Yuming, Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Gutiérrez, Reyna Alma, Gutiérrez-Torres, Daniela S., Haagsma, Juanita A., Habtewold, Tesfa Dejenie, Hachinski, Vladimir, Hafezi-Nejad, Nima, Hagos, Tekleberhan B., Hailegiyorgis, Tewodros Tesfa, Hailu, Gessessew Bugssa, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hamadeh, Randah R., Hamidi, Samer, Handal, Alexis J., Hankey, Graeme J., Hao, Yuantao, Harb, Hilda L., Harikrishnan, Sivadasanpillai, Haro, Josep Maria, Hassankhani, Hadi, Hassen, Hamid Yimam, Havmoeller, Rasmus, Hawley, Caitlin N., Hay, Simon I., Hedayatizadeh-Omran, Akbar, Heibati, Behzad, Heidari, Behnam, Heidari, Mohsen, Hendrie, Delia, Henok, Andualem, Heredia-Pi, Ileana, Herteliu, Claudiu, Heydarpour, Fatemeh, Heydarpour, Sousan, Hibstu, Desalegn T., Higazi, Tarig B., Hilawe, Esayas Haregot, Hoek, Hans W., Hoffman, Howard J., Hole, Michael K., Homaie Rad, Enayatollah, Hoogar, Praveen, Hosgood, H. Dean, Hosseini, Seyed Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hoy, Damian G., Hsairi, Mohamed, Hsiao, Thomas, Hu, Guoqing, Hu, Howard, Huang, John J., Hussen, Mamusha Aman, Huynh, Chantal K., Iburg, Kim Moesgaard, Ikeda, Nayu, Ilesanmi, Olayinka Stephen, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Irvine, Caleb Mackay Salpeter, Islam, Sheikh Mohammed Shariful, Islami, Farhad, Jackson, Maria D., Jacobsen, Kathryn H., Jahangiry, Leila, Jahanmehr, Nader, Jain, Sudhir Kumar, Jakovljevic, Mihajlo, James, Spencer L., Jassal, Simerjot K., Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Ji, John S., Jonas, Jost B., Jonnagaddala, Jitendra, Jorjoran Shushtari, Zahra, Joshi, Ankur, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kabir, Zubair, Kahsay, Amaha, Kalani, Rizwan, Kanchan, Tanuj, Kant, Surya, Kar, Chittaranjan, Karami, Manoochehr, Karami Matin, Behzad, Karch, André, Karema, Corine, Karimi, Narges, Karimi, Seyed M., Kasaeian, Amir, Kassa, Dessalegn H., Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassebaum, Nicholas J., Katikireddi, Srinivasa Vittal, Kaul, Anil, Kawakami, Norito, Kazemi, Zhila, Karyani, Ali Kazemi, Kefale, Adane Teshome, Keiyoro, Peter Njenga, Kemp, Grant Rodgers, Kengne, Andre Pascal, Keren, Andre, Kesavachandran, Chandrasekharan Nair, Khader, Yousef Saleh, Khafaei, Behzad, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalid, Nauman, Khalil, Ibrahim A., Khan, Gulfaraz, Khan, Muhammad Shahzeb, Khan, Muhammad Ali, Khang, Young Ho, Khater, Mona M., Khazaei, Mohammad, Khazaie, Habibolah, Khoja, Abdullah T., Khosravi, Ardeshir, Khosravi, Mohammad Hossein, Kiadaliri, Aliasghar A., Kiirithio, Daniel N., Kim, Cho Il, Kim, Daniel, Kim, Young Eun, Kim, Yun Jin, Kimokoti, Ruth W., Kinfu, Yohannes, Kisa, Adnan, Kissimova-Skarbek, Katarzyna, Kivimäki, Mika, Knibbs, Luke D., Knudsen, Ann Kristin Skrindo, Kochhar, Sonali, Kokubo, Yoshihiro, Kolola, Tufa, Kopec, Jacek A., Kosen, Soewarta, Koul, Parvaiz A., Koyanagi, Ai, Kravchenko, Michael A., Krishan, Kewal, Krohn, Kristopher J., Kromhout, Hans, Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kumar, G. 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Sk, Vollset, Se, Vu, Gt, Waller, Sg, Walson, Jl, Wang, Yp, Weintraub, Rg, Werkneh, Aa, West, Jj, Whiteford, Ha, Wiyeh, Ab, Wolfe, Cda, Wong, Ty, Yahyazadeh Jabbari, Sh, Yan, Ll, Yasin, Yj, Yimer, Em, Yoon, Sj, Younis, Mz, Zaman, Sb, Zhang, Al, Zimsen, Srm, Murray, Cjl., Stanaway, Jeffrey D, Afshin, Ashkan, Gakidou, Emmanuela, Lim, Stephen S, Abate, Degu, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbasi, Nooshin, Abbastabar, Hedayat, Abd-Allah, Foad, Abdela, Jemal, Abdelalim, Ahmed, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Molla, Abebe, Zegeye, Abera, Semaw F, Abil, Olifan Zewdie, Abraha, Haftom Niguse, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen ME, Accrombessi, Manfred Mario Kokou, Acharya, Dilaram, Acharya, Pawan, Adamu, Abdu A, Adane, Akilew Awoke, Adebayo, Oladimeji M, Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Ademi, Zanfina, Adetokunboh, Olatunji O, Adib, Mina G, Admasie, Amha, Adsuar, Jose C, Afanvi, Kossivi Agbelenko, Afarideh, 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Kumar, Takahashi, Ken, Tandon, Nikhil, Tassew, Segen Gebremeskel, Tavakkoli, Mohammad, Taveira, Nuno, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekelemedhin, Shishay Wahdey, Tekle, Merhawi Gebremedhin, Temesgen, Habtamu, Temsah, Mohamad-Hani, Temsah, Omar, Terkawi, Abdullah Sulieman, Tessema, Belay, Teweldemedhin, Mebrahtu, Thankappan, Kavumpurathu Raman, Theis, Andrew, Thirunavukkarasu, Sathish, Thomas, Hannah J, Thomas, Matthew Lloyd, Thomas, Nihal, Thurston, George D, Tilahun, Binyam, Tillmann, Taavi, To, Quyen G, Tobollik, Myriam, Tonelli, Marcello, Topor-Madry, Roman, Torre, Anna E, Tortajada-Girbés, Miguel, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Towbin, Jeffrey A, Tran, Bach Xuan, Tran, Khanh Bao, Truelsen, Thomas Clement, Truong, Nu Thi, Tsadik, Afewerki Gebremeskel, Tudor Car, Lorainne, Tuzcu, E Murat, Tymeson, Hayley D, Tyrovolas, Stefano, Ukwaja, Kingsley N, Ullah, Irfan, Updike, Rachel L, Usman, Muhammad Shariq, Uthman, Olalekan A, Vaduganathan, Muthiah, Vaezi, Afsane, Valdez, Pascual R, Van Donkelaar, Aaron, Varavikova, Elena, Varughese, Santosh, Vasankari, Tommi Juhani, Venkateswaran, Vidhya, Venketasubramanian, Narayanaswamy, Villafaina, Santo, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vu, Giang Thu, Vujcic, Isidora S, Wagnew, Fasil Shiferaw, Waheed, Yasir, Waller, Stephen G, Walson, Judd L, Wang, Yafeng, Wang, Yanping, Wang, Yuan-Pang, Weiderpass, Elisabete, Weintraub, Robert G, Weldegebreal, Fitsum, Werdecker, Andrea, Werkneh, Adhena Ayaliew, West, J Jason, Westerman, Ronny, Whiteford, Harvey A, Widecka, Justyna, Wijeratne, Tissa, Winkler, Andrea Sylvia, Wiyeh, Alison B, Wiysonge, Charles Shey, Wolfe, Charles D A, Wong, Tien Yin, Wu, Shouling, Xavier, Deni, Xu, Gelin, Yadgir, Simon, Yadollahpour, Ali, Yahyazadeh Jabbari, Seyed Hossein, Yamada, Tomohide, Yan, Lijing L, Yano, Yuichiro, Yaseri, Mehdi, Yasin, Yasin Jemal, Yeshaneh, Alex, Yimer, Ebrahim M, Yip, Paul, Yisma, Engida, Yonemoto, Naohiro, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zavala-Arciniega, Lui, Zhang, Anthony Lin, Zhang, Hao, Zhang, Kai, Zhou, Maigeng, Zimsen, Stephanie R M, Zodpey, Sanjay, and Murray, Christopher J L
Subjects: Science & Technology, GBD, risks, risk factors, comparative risk assesment, Dalys, Medicine (all), CHOLESTEROL, BLOOD-PRESSURE, 11 Medical And Health Sciences, ILLNESS, TRENDS, humanities, Medicine, General & Internal, TOBACCO CONTROL, General & Internal Medicine, parasitic diseases, Medicine and Health Sciences, CARDIOVASCULAR-DISEASES, TRIAL, Human medicine, Life Sciences & Biomedicine, METAANALYSIS
Abstract: Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk– outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Lancet 2018; 392: 1923–94 *Collaborators listed at the end of the paper Correspondence to: Prof Christopher J L Murray, Institute for Health Metrics and Evalution, Seattle, WA 98121, USA cjlm@uw.edu Global Health Metrics 1924 www.thelancet.com Vol 392 November 10, 2018 Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning.
Published: 2018

22. Population and fertility by age and sex for 195 countries and territories, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017

Author: Murray, Christopher J L, Callender, Charlton S K H, Kulikoff, Xie Rachel, Srinivasan, Vinay, Abate, Degu, Abate, Kalkidan Hassen, Abay, Solomon M, Abbasi, Nooshin, Abbastabar, Hedayat, Abdela, Jemal, Abdelalim, Ahmed, Abdel-Rahman, Omar, Abdi, Alireza, Abdoli, Nasrin, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Haftom Temesgen, Abebe, Molla, Abebe, Zegeye, Abebo, Teshome Abuka, Abejie, Ayenew Negesse, Aboyans, Victor, Abraha, Haftom Niguse, Abreu, Daisy Maria Xavier, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen M E, Accrombessi, Manfred Mario Kokou, Acharya, Pawan, Adamu, Abdu A, Adebayo, Oladimeji M, Adedeji, Isaac Akinkunmi, Adekanmbi, Victor, Adetokunboh, Olatunji O, Adhena, Beyene Meressa, Adhikari, Tara Ballav, Adib, Mina G, Adou, Arsène Kouablan, Adsuar, Jose C, Afarideh, Mohsen, Afshin, Ashkan, Agarwal, Gina, Agesa, Kareha M, Aghayan, Sargis Aghasi, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmed, Muktar Beshir, Ahmed, Sayem, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akanda, Ali S, Akbari, Mohammad Esmaeil, Akibu, Mohammed, Akinyemi, Rufus Olusola, Akinyemiju, Tomi, Akseer, Nadia, Alahdab, Fares, Al-Aly, Ziyad, Alam, Khurshid, Alebel, Animut, Aleman, Alicia V, Alene, Kefyalew Addis, Al-Eyadhy, Ayman, Ali, Raghib, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Almasi, Ali, Alonso, Jordi, Al-Raddadi, Rajaa M, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Ammar, Walid, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Ansari, Hossein, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T, Appiah, Seth Christopher Yaw, Aremu, Olatunde, Areri, Habtamu Abera, Arian, Nicholas, Ärnlöv, Johan, Artaman, Al, Aryal, Krishna K, Asayesh, Hamid, Asfaw, Ephrem Tsegay, Asgedom, Solomon Weldegebreal, Assadi, Reza, Atey, Tesfay Mehari Mehari, Atique, Suleman, Atteraya, Madhu Sudhan, Ausloos, Marcel, Avokpaho, Euripide F G A, Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayele, Yohanes, Ayer, Rakesh, Ayuk, Tambe B, Azzopardi, Peter S, Babalola, Tesleem Kayode, Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Bali, Ayele Geleto, Banach, Maciej, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H, Basaleem, Huda, Bassat, Quique, Basu, Arindam, Baune, Bernhard T, Baynes, Habtamu Wondifraw, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Bekele, Bayu Begashaw, Belachew, Abate Bekele, Belay, Aregawi Gebreyesus, Belay, Ezra, Belay, Saba Abraham, Belay, Yihalem Abebe, Bell, Michelle L, Bello, Aminu K, Bennett, Derrick A, Bensenor, Isabela M, Bergeron, Gilles, Berhane, Adugnaw, Berman, Adam E, Bernabe, Eduardo, Bernstein, Robert S, Bertolacci, Gregory J, Beuran, Mircea, Bhattarai, Suraj, Bhaumik, Soumyadeep, Bhutta, Zulfiqar A, Biadgo, Belete, Bijani, Ali, Bikbov, Boris, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Birlik, Sait Mentes, Birungi, Charles, Biswas, Tuhin, Bizuneh, Hailemichael, Bleyer, Archie, Basara, Berrak Bora, Bosetti, Cristina, Boufous, Soufiane, Brady, Oliver J, Bragazzi, Nicola Luigi, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J K, Brenner, Hermann, Brewer, Jerry D, Briant, Paul Svitil, Britton, Gabrielle, Burstein, Roy, Busse, Reinhard, Butt, Zahid A, Cahuana-Hurtado, Lucero, Campos-Nonato, Ismael R, Campuzano Rincon, Julio Cesar, Cano, Jorge, Car, Mate, Cárdenas, Rosario, Carrero, Juan J, Carvalho, Félix, Castañeda-Orjuela, Carlos A, Castillo Rivas, Jacqueline, Castro, Franz, Catalá-López, Ferrán, Çavlin, Alanur, Cerin, Ester, Chalek, Julian, Chang, Hsing-Yi, Chang, Jung-Chen, Chattopadhyay, Aparajita, Chaturvedi, Pankaj, Chiang, Peggy Pei-Chia, Chin, Ken Lee, Chisumpa, Vesper Hichilombwe, Chitheer, Abdulaal, Choi, Jee-Young J, Chowdhury, Rajiv, Christopher, Devasahayam J, Cicuttini, Flavia M, Ciobanu, Liliana G, Cirillo, Massimo, Claro, Rafael M, Collado-Mateo, Daniel, Constantin, Maria-Magdalena, Conti, Sara, Cooper, Cyrus, Cooper, Leslie Trumbull, Cornaby, Leslie, Cortesi, Paolo Angelo, Cortinovis, Monica, Costa, Megan, Cromwell, Elizabeth, Crowe, Christopher Stephen, Cukelj, Petra, Cunningham, Matthew, Daba, Alemneh Kabeta, Dachew, Berihun Assefa, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I, Daryani, Ahmad, Das Gupta, Rajat, Das Neves, José, Dasa, Tamirat Tesfaye, Dash, Aditya Prasad, Weaver, Nicole Davis, Davitoiu, Dragos Virgil, Davletov, Kairat, De Leo, Diego, De Neve, Jan-Walter, Degefa, Meaza Girma, Degenhardt, Louisa, Degfie, Tizta Tilahun, Deiparine, Selina, Demoz, Gebre Teklemariam, Demtsu, Balem, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolaos, Des Jarlais, Don C, Dessie, Getenet Ayalew, Dharmaratne, Samath D, Dhimal, Meghnath, Dicker, Daniel, Ding, Eric L, Dinsa, Girmaye Deye, Djalalinia, Shirin, Do, Huyen Phuc, Dokova, Klara, Doku, David Teye, Dolan, Kate A, Doyle, Kerrie E, Driscoll, Tim R, Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Duraes, Andre R, Ebrahimpour, Soheil, Edvardsson, David, El Bcheraoui, Charbel, El-Khatib, Ziad, Elyazar, Iqbal Rf, Enayati, Ahmadali, Endries, Aman Yesuf, Ermakov, Sergey Petrovich, Eshrati, Babak, Eskandarieh, Sharareh, Esmaeili, Reza, Esteghamati, Alireza, Esteghamati, Sadaf, Estep, Kara, Fakhim, Hamed, Farag, Tamer, Faramarzi, Mahbobeh, Fareed, Mohammad, Farinha, Carla Sofia E Sá, Faro, Andre, Farvid, Maryam S, Farzadfar, Farshad, Farzaei, Mohammad Hosein, Fay, Kairsten A, Fazeli, Mir Sohail, Feigin, Valery L, Feigl, Andrea B, Feizy, Fariba, Fenny, Ama P, Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, Eduarda, Feyissa, Garumma Tolu, Filip, Irina, Finegold, Samuel, Fischer, Florian, Flor, Luisa Sorio, Foigt, Nataliya A, Foreman, Kyle J, Fornari, Carla, Fürst, Thomas, Fukumoto, Takeshi, Fuller, John E, Fullman, Nancy, Gakidou, Emmanuela, Gallus, Silvano, Gamkrelidze, Amiran, Ganji, Morsaleh, Gankpe, Fortune Gbetoho, Garcia, Gregory M, Garcia-Gordillo, Miguel Á, Gebre, Abadi Kahsu, Gebre, Teshome, Gebregergs, Gebremedhin Berhe, Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Gelano, Tilayie Feto, Gelaw, Yalemzewod Assefa, Geleijnse, Johanna M, Genova-Maleras, Ricard, Gething, Peter, Gezae, Kebede Embaye, Ghadami, Mohammad Rasoul, Ghadimi, Reza, Ghadiri, Keyghobad, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Ghiasvand, Hesam, Ghimire, Mamata, Ghoshal, Aloke Gopal, Gill, Paramjit Singh, Gill, Tiffany K, Giussani, Giorgia, Gnedovskaya, Elena V, Goli, Srinivas, Gomez, Ricardo Santiago, Gómez-Dantés, Hector, Gona, Philimon N, Goodridge, Amador, Gopalani, Sameer Vali, Goulart, Alessandra C, Goulart, Bárbara Niegia Garcia, Grada, Ayman, Grosso, Giuseppe, Gugnani, Harish Chander C, Guo, Jingwen, Guo, Yuming, Gupta, Prakash C, Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Haagsma, Juanita A, Hachinski, Vladimir, Hafezi-Nejad, Nima, Hagos, Tekleberhan B, Hailegiyorgis, Tewodros Tesfa, Hailu, Gessessew Bugssa, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hamadeh, Randah R, Hamidi, Samer, Handal, Alexis J, Hankey, Graeme J, Hao, Yuantao, Harb, Hilda L, Haririan, Hamidreza, Haro, Josep Maria, Hasan, Mehedi, Hassankhani, Hadi, Hassen, Hamid Yimam, Havmoeller, Rasmus, Hay, Simon I, He, Yihua, Hedayatizadeh-Omran, Akbar, Hegazy, Mohamed I, Heibati, Behzad, Heidari, Behnam, Hendrie, Delia, Henok, Andualem, Henry, Nathaniel J, Herteliu, Claudiu, Heydarpour, Fatemeh, Hibstu, Desalegn T, Hole, Michael K, Homaie Rad, Enayatollah, Hoogar, Praveen, Hosgood, H Dean, Hosseini, Seyed Mostafa, Hosseini Chavoshi, Meimanat M, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hsairi, Mohamed, Hsiao, Thomas, Hu, Guoqing, Huang, John J, Iburg, Kim Moesgaard, Igumbor, Ehimario U, Ikeda, Chad Thomas, Ilesanmi, Olayinka Stephen, Iqbal, Usman, Irenso, Asnake Ararsa, Irvani, Seyed Sina Naghibi, Isehunwa, Oluwaseyi Oluwakemi, Islam, Sheikh Mohammed Shariful, Jahangiry, Leila, Jahanmehr, Nader, Jain, Sudhir Kumar, Jakovljevic, Mihajlo, Jalu, Moti Tolera, James, Spencer L, Jassal, Simerjot K, Javanbakht, Mehdi, Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Ji, John S, Jonas, Jost B, Jozwiak, Jacek Jerzy, Jungari, Suresh Banayya, Jürisson, Mikk, Kabir, Zubair, Kadel, Rajendra, Kahsay, Amaha, Kalani, Rizwan, Kapil, Umesh, Karami, Manoochehr, Matin, Behzad Karami, Karch, André, Karema, Corine, Karimi, Seyed M, Kasaeian, Amir, Kassa, Dessalegn H, Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassa, Zemenu Yohannes, Kassebaum, Nicholas J, Kastor, Anshul, Katikireddi, Srinivasa Vittal, Kaul, Anil, Kawakami, Norito, Karyani, Ali Kazemi, Kebede, Seifu, Keiyoro, Peter Njenga, Kemp, Grant Rodgers, Kengne, Andre Pascal, Keren, Andre, Kereselidze, Maia, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalid, Nauman, Khalil, Ibrahim A, Khan, Ejaz Ahmad, Khan, Muhammad Shahzeb, Khang, Young-Ho, Khanna, Tripti, Khater, Mona M, Khatony, Alireza, Khazaeipour, Zahra, Khazaie, Habibolah, Khoja, Abdullah T, Khosravi, Ardeshir, Khosravi, Mohammad Hossein, Kibret, Getiye D, Kidanemariam, Zelalem Teklemariam, Kiirithio, Daniel N, Kilgore, Paul Evan, Kim, Daniel, Kim, Jun Y, Kim, Young-Eun, Kim, Yun Jin, Kimokoti, Ruth W, Kinfu, Yohannes, Kinra, Sanjay, Kisa, Adnan, Kivimäki, Mika, Kochhar, Sonali, Kokubo, Yoshihiro, Kolola, Tufa, Kopec, Jacek A, Kosek, Margaret N, Kosen, Soewarta, Koul, Parvaiz A, Koyanagi, Ai, Krishan, Kewal, Krishnaswami, Sanjay, Krohn, Kristopher J, Defo, Barthelemy Kuate, Bicer, Burcu Kucuk, Kumar, G Anil, Kumar, Manasi, Kumar, Pushpendra, Kumsa, Fekede Asefa, Kutz, Michael J, Lad, Sheetal D, Lafranconi, Alessandra, Lal, Dharmesh Kumar, Lalloo, Ratilal, Lam, Hilton, Lami, Faris Hasan, Lang, Justin J, Lanksy, Sonia, Lansingh, Van C, Laryea, Dennis Odai, Lassi, Zohra S, Latifi, Arman, Laxmaiah, Avula, Lazarus, Jeffrey V, Lee, James B, Lee, Paul H, Leigh, James, Leshargie, Cheru Tesema, Leta, Samson, Levi, Miriam, Li, Shanshan, Li, Xiaohong, Li, Yichong, Liang, Juan, Liang, Xiaofeng, Liben, Misgan Legesse, Lim, Lee-Ling, Limenih, Miteku Andualem, Linn, Shai, Liu, Shiwei, Lorkowski, Stefan, Lotufo, Paulo A, Lozano, Rafael, Lunevicius, Raimundas, Mabika, Crispin Mabika, Macarayan, Erlyn Rachelle King, Mackay, Mark T, Madotto, Fabiana, Mahmood, Tarek Abd Elaziz, Mahotra, Narayan Bahadur, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malik, Manzoor Ahmad, Mamun, Abdullah A, Manamo, Wondimu Ayele, Manda, Ana-Laura, Mangalam, Srikanth, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Mapoma, Chabila Christopher, Marami, Dadi, Maravilla, Joemer C, Marcenes, Wagner, Marina, Shakhnazarova, Martins-Melo, Francisco Rogerlândio, März, Winfried, Marzan, Melvin B, Mashamba-Thompson, Tivani Phosa, Masiye, Felix, Mason-Jones, Amanda J, Massenburg, Benjamin Ballard, Mathur, Manu Raj, Maulik, Pallab K, Mazidi, Mohsen, McGrath, John J, Mehata, Suresh, Mehendale, Sanjay Madhav, Mehndiratta, Man Mohan, Mehrotra, Ravi, Mehrzadi, Saeed, Mehta, Kala M, Mehta, Varshil, Mekonnen, Tefera C, Meles, Hagazi Gebre, Meles, Kidanu Gebremariam, Melese, Addisu, Melku, Mulugeta, Memiah, Peter T N, Memish, Ziad A, Mendoza, Walter, Mengesha, Melkamu Merid, Mengistu, Desalegn Tadese, Mengistu, Getnet, Mensah, George A, Mereta, Seid Tiku, Meretoja, Atte, Meretoja, Tuomo J, Mestrovic, Tomislav, Mezgebe, Haftay Berhane, Miangotar, Yode, Miazgowski, Bartosz, Miazgowski, Tomasz, Miller, Ted R, Miller-Petrie, Molly Katherine, Mini, G K, Mirabi, Parvaneh, Mirica, Andreea, Mirrakhimov, Erkin M, Misganaw, Awoke Temesgen, Moazen, Babak, Mohammad, Karzan Abdulmuhsin, Mohammadi, Moslem, Mohammadifard, Noushin, Mohammadi-Khanaposhtani, Maryam, Mohammed, Mohammed A, Mohammed, Shafiu, Mokdad, Ali H, Mola, Glen Dl, Molokhia, Mariam, Monasta, Lorenzo, Montañez, Julio Cesar, Moradi, Ghobad, Moradi, Mahmoudreza, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morgado-Da-Costa, Joana, Mori, Rintaro, Morrison, Shane Douglas, Mosapour, Abbas, Moschos, Marilita M, Mousavi, Seyyed Meysam, Muche, Achenef Asmamaw, Muchie, Kindie Fentahun, Mueller, Ulrich Otto, Mukhopadhyay, Satinath, Muller, Kate, Murphy, Tasha B, Murthy, G V S, Musa, Jonah, Musa, Kamarul Imran, Mustafa, Ghulam, Muthupandian, Saravanan, Nachega, Jean B, Nagel, Gabriele, Naghavi, Mohsen, Naheed, Aliya, Nahvijou, Azin, Naik, Gurudatta, Naik, Paulami, Najafi, Farid, Naldi, Luigi, Nangia, Vinay, Nansseu, Jobert Richie, Nascimento, Bruno Ramos, Nawaz, Haseeb, Ncama, Busisiwe P, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Neupane, Subas, Newton, Charles Richard James, Ngalesoni, Frida N, Ngunjiri, Josephine W, Nguyen, Grant, Nguyen, Long Hoang, Nguyen, Trang Huyen, Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nisar, Muhammad Imran, Nixon, Molly R, Nomura, Shuhei, Noroozi, Mehdi, Noubiap, Jean Jacques, Nouri, Hamid Reza, Shiadeh, Malihe Nourollahpour, Nowroozi, Mohammad Reza, Nyandwi, Alypio, Nyasulu, Peter S, Odell, Christopher M, Ofori-Asenso, Richard, Ogah, Okechukwu Samuel, Ogbo, Felix Akpojene, Oh, In-Hwan, Okoro, Anselm, Oladimeji, Olanrewaju, Olagunju, Andrew T, Olagunju, Tinuke O, Olivares, Pedro R, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Sok King, Ortiz, Alberto, Osgood-Zimmerman, Aaron, Ota, Erika, Otieno, Brenda Achieng, Otstavnov, Stanislav S, Owolabi, Mayowa Ojo, Oyekale, Abayomi Samuel, P A, Mahesh, Pakhale, Smita, Pakhare, Abhijit P, Pana, Adrian, Panda, Basant Kumar, Panda-Jonas, Songhomitra, Pandey, Achyut Raj, Park, Eun-Kee, Parsian, Hadi, Patel, Shanti, Patil, Snehal T, Patle, Ajay, Patton, George C, Paturi, Vishnupriya Rao, Paudel, Deepak, Pedroso, Marcel Moraes, Peprah, Emmanuel K, Pereira, David M, Perico, Norberto, Pesudovs, Konrad, Petri, William A, Petzold, Max, Pierce, Maxwell, Pigott, David M, Pillay, Julian David, Pirsaheb, Meghdad, Polanczyk, Guilherme V, Postma, Maarten J, Pourmalek, Farshad, Pourshams, Akram, Poustchi, Hossein, Prakash, Swayam, Prasad, Narayan, Purcell, Caroline A, Purwar, Manorama B, Qorbani, Mostafa, Quansah, Reginald, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Md Shafiur, Rahman, Mohammad Hifz Ur, Rahman, Muhammad Aziz, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Rajati, Fatemeh, Rajsic, Sasa, Ram, Usha, Ranabhat, Chhabi Lal, Ranjan, Prabhat, Rawaf, David Laith, Rawaf, Salman, Ray, Sarah E, Razo-García, Christian, Reiner, Robert C, Reis, Cesar, Remuzzi, Giuseppe, Renzaho, Andre M N, Resnikoff, Serge, Rezaei, Satar, Rezaeian, Shahab, Rezai, Mohammad Sadegh, Riahi, Seyed Mohammad, Rios-Blancas, Maria Jesus, Roba, Kedir Teji, Roberts, Nicholas L S, Roever, Leonardo, Ronfani, Luca, Roshandel, Gholamreza, Rostami, Ali, Rubagotti, Enrico, Ruhago, George Mugambage, Sabde, Yogesh Damodar, Sachdev, Perminder S, Saddik, Basema, Saeedi Moghaddam, Sahar, Safari, Hosein, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Safi, Sare, Safiri, Saeid, Sagar, Rajesh, Sahebkar, Amirhossein, Sahraian, Mohammad Ali, Sajadi, Haniye Sadat, Salahshoor, Mohamadreza, Salam, Nasir, Salama, Joseph S, Salamati, Payman, Saldanha, Raphael De Freitas, Saleem, Zikria, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A, Salvi, Sundeep Santosh, Salz, Inbal, Sambala, Evanson Zondani, Samy, Abdallah M, Sanabria, Juan, Sanchez-Niño, Maria Dolores, Santos, Itamar S, Santric Milicevic, Milena M, Sao Jose, Bruno Piassi, Sardana, Mayank, Sarker, Abdur Razzaque, Sarmiento-Suárez, Rodrigo, Saroshe, Satish, Sarrafzadegan, Nizal, Sartorius, Benn, Sarvi, Shahabeddin, Sathian, Brijesh, Satpathy, Maheswar, Sawant, Arundhati R, Sawhney, Monika, Saxena, Sonia, Schaeffner, Elke, Schelonka, Kathryn, Schneider, Ione J C, Schwebel, David C, Schwendicke, Falk, Seedat, Soraya, Sekerija, Mario, Sepanlou, Sadaf G, Serván-Mori, Edson, Shabaninejad, Hosein, Shackelford, Katya Anne, Shafieesabet, Azadeh, Shaheen, Amira A, Shaikh, Masood Ali, Shakir, Raad A, Shams-Beyranvand, Mehran, Shamsi, Mohammadbagher, Shamsizadeh, Morteza, Sharafi, Heidar, Sharafi, Kiomars, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharma, Jayendra, Sharma, Rajesh, She, Jun, Sheikh, Aziz, Shi, Peilin, Shibuya, Kenji, Shigematsu, Mika, Shiri, Rahman, Shirkoohi, Reza, Shiue, Ivy, Shokraneh, Farhad, Shukla, Sharvari Rahul, Si, Si, Siabani, Soraya, Sibai, Abla Mehio, Siddiqi, Tariq J, Sigfusdottir, Inga Dora, Sigurvinsdottir, Rannveig, Silpakit, Naris, Silva, Diego Augusto Santos, Silva, João Pedro, Silveira, Dayane Gabriele Alves, Singam, Narayana Sarma Venkata, Singh, Jasvinder A, Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Sliwa, Karen, Soares Filho, Adauto Martins, Sobaih, Badr Hasan, Sobhani, Soheila, Soofi, Moslem, Soriano, Joan B, Soyiri, Ireneous N, Sreeramareddy, Chandrashekhar T, Starodubov, Vladimir I, Steiner, Caitlyn, Stewart, Leo G, Stokes, Mark A, Strong, Mark, Subart, Michelle L, Sufiyan, Mu'awiyyah Babale, Sulo, Gerhard, Sunguya, Bruno F, Sur, Patrick John, Sutradhar, Ipsita, Sykes, Bryan L, Sylaja, P N, Sylte, Dillon O, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Tabb, Karen M, Tadakamadla, Santosh Kumar, Tandon, Nikhil, Tassew, Aberash Abay, Tassew, Segen Gebremeskel, Taveira, Nuno, Tawye, Nega Yimer, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekle, Merhawi Gebremedhin, Temsah, Mohamad-Hani, Terkawi, Abdullah Sulieman, Teshale, Manaye Yihune, Tessema, Belay, Teweldemedhin, Mebrahtu, Thakur, Jarnail Singh, Thankappan, Kavumpurathu Raman, Thirunavukkarasu, Sathish, Thomas, Nihal, Thomson, Alan J, Tilahun, Binyam, To, Quyen G, Tonelli, Marcello, Topor-Madry, Roman, Torre, Anna E, Tortajada-Girbés, Miguel, Tovani-Palone, Marcos Roberto, Toyoshima, Hideaki, Tran, Bach Xuan, Tran, Khanh Bao, Tripathy, Srikanth Prasad, Truelsen, Thomas Clement, Truong, Nu Thi, Tsadik, Afewerki Gebremeskel, Tsegay, Amanuel, Tsilimparis, Nikolaos, Tudor Car, Lorainne, Ukwaja, Kingsley N, Ullah, Irfan, Usman, Muhammad Shariq, Uthman, Olalekan A, Uzun, Selen Begüm, Vaduganathan, Muthiah, Vaezi, Afsane, Vaidya, Gaurang, Valdez, Pascual R, Varavikova, Elena, Varughese, Santosh, Vasankari, Tommi Juhani, Vasconcelos, Ana Maria Nogales, Venketasubramanian, Narayanaswamy, Villafaina, Santos, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vujcic, Isidora S, Wagnew, Fasil Shiferaw, Waheed, Yasir, Walson, Judd L, Wang, Yanping, Wang, Yuan-Pang, Weiderpass, Elisabete, Weintraub, Robert G, Weldegwergs, Kidu Gidey, Werdecker, Andrea, Westerman, Ronny, Whiteford, Harvey, Widecka, Justyna, Widecka, Katarzyna, Wijeratne, Tissa, Winkler, Andrea Sylvia, Wiysonge, Charles Shey, Wolfe, Charles D A, Wu, Shouling, Wyper, Grant M A, Xu, Gelin, Yamada, Tomohide, Yano, Yuichiro, Yaseri, Mehdi, Yasin, Yasin Jemal, Ye, Pengpeng, Yentür, Gökalp Kadri, Yeshaneh, Alex, Yimer, Ebrahim M, Yip, Paul, Yisma, Engida, Yonemoto, Naohiro, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zadnik, Vesna, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zare, Zohreh, Zeleke, Mulugeta Molla, Zenebe, Zerihun Menlkalew, Zerfu, Taddese Alemu, Zhang, Xueying, Zhao, Xiu-Ju, Zhou, Maigeng, Zhu, Jun, Zimsen, Stephanie R M, Zodpey, Sanjay, Zoeckler, Leo, Lopez, Alan D, Lim, Stephen S, Halk Sağlığı, GBD 2017 Population Fertility Coll, Murray, Cjl, Callender, Cskh, Kulikoff, Xr, Srinivasan, V, Abate, D, Abate, Kh, Abay, Sm, Abbasi, N, Abbastabar, H, Abdela, J, Abdelalim, A, Abdel-Rahman, O, Abdi, A, Abdoli, N, Abdollahpour, I, Abdulkader, R, Abebe, Ht, Abebe, M, Abebe, Z, Abebo, Ta, Abejie, An, Aboyans, V, Abraha, Hn, Abreu, Dmx, Abrham, Ar, Abu-Raddad, Lj, Abu-Rmeileh, Nme, Accrombessi, Mmk, Acharya, P, Adamu, Aa, Adebayo, Om, Adedeji, Ia, Adekanmbi, V, Adetokunboh, Oo, Adhena, Bm, Adhikari, Tb, Adib, Mg, Adou, Ak, Adsuar, Jc, Afarideh, M, Afshin, A, 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Vasconcelos, A, Vladimirov, S, Vollset, S, Walson, J, Weintraub, R, Weldegwergs, K, Wolfe, C, Wyper, G, Yasin, Y, Yentür, G, Yimer, E, Yoon, S, Younis, M, Zaman, S, Zeleke, M, Zenebe, Z, Zerfu, T, Zhao, X, Zimsen, S, Lopez, A, and Lim, S
Subjects: Adult, Male, Population Density, GBD, population, fertility, Adolescent, Medicine (all), Infant, Newborn, Infant, 11 Medical And Health Sciences, Middle Aged, Global Health, Global Burden of Disease, Young Adult, Child, Preschool, General & Internal Medicine, Female, Human medicine, Mortality, Child, Population Growth, Birth Rate, Aged, Human, Maternal Age
Abstract: Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10-54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10-14 years and 50-54 years was estimated from data on fertility in women aged 15-19 years and 45-49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-cotnponent method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings From 1950 to 2017, TFRs decreased by 49.4% (95% uncertainty interval [UI] 46.4-52.0). The TFR decreased from 4.7 livebirths (4.5-4.9) to 2.4 livebirths (2.2-2.5), and the ASFR of mothers aged 10-19 years decreased from 37 livebirths (34-40) to 22 livebirths (19-24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83.8 million people per year since 1985. The global population increased by 197-2% (193.3-200.8) since 1950, from 2.6 billion (2.5-2.6) to 7.6 billion (7.4-7.9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2.0%; this rate then remained nearly constant until 1970 and then decreased to 1.1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2.5% in 1963 to O7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2.7%. The global average age increased from 26.6 years in 1950 to 32.1 years in 2017, and the proportion of the population that is of working age (age 15-64 years) increased from 59.9% to 65.3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1.0 livebirths (95% UI 0. 9-1.2) in Cyprus to a high of 7.1 livebirths (6.8-7.4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0.08 livebirths (0.07-0.09) in South Korea to 2.4 livebirths (2.2-2.6) in Niger, and the TFR over age 30 years (I F030; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0.3 livebirths (0.3-0-4) in Puerto Rico to a high of 3.1 livebirths (3.0-3.2) in Niger. TF030 was higher than TFU25 in 145 countries and territories in 2017.33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2.0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.
Published: 2018

23. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017

Author: James, Spencer L., Abate, Degu, Abate, Kalkidan Hassen, Abay, Solomon M., Abbafati, Cristiana, Abbasi, Nooshin, Abbastabar, Hedayat, Abd-Allah, Foad, Abdela, Jemal, Abdelalim, Ahmed, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Zegeye, Abera, Semaw F., Abil, Olifan Zewdie, Abraha, Haftom Niguse, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen M. E., Accrombessi, Manfred Mario Kokou, Acharya, Dilaram, Acharya, Pawan, Ackerman, Ilana N., Adamu, Abdu A., Adebayo, Oladimeji M., Adekanmbi, Victor, Adetokunboh, Olatunji O., Adib, Mina G., Adsuar, Jose C., Afanvi, Kossivi Agbelenko, Afarideh, Mohsen, Afshin, Ashkan, Agarwal, Gina, Agesa, Kareha M., Aggarwal, Rakesh, Aghayan, Sargis Aghasi, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmadieh, Hamid, Ahmed, Muktar Beshir, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akinyemiju, Tomi, Akseer, Nadia, Al-Aly, Ziyad, Al-Eyadhy, Ayman, Al-Mekhlafi, Hesham M., Al-Raddadi, Rajaa M., Alahdab, Fares, Alam, Khurshid, Alam, Tahiya, Alashi, Alaa, Alavian, Seyed Moayed, Alene, Kefyalew Addis, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Alouani, Mohamed M. L., Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T., Aminde, Leopold N., Ammar, Walid, Amoako, Yaw Ampem, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Anjomshoa, Mina, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T., Anwari, Palwasha, Arabloo, Jalal, Arauz, Antonio, Aremu, Olatunde, Ariani, Filippo, Armoon, Bahroom, Ärnlöv, Johan, Arora, Amit, Artaman, Al, Aryal, Krishna K., Asayesh, Hamid, Asghar, Rana Jawad, Ataro, Zerihun, Atre, Sachin R., Ausloos, Marcel, Avila-Burgos, Leticia, Avokpaho, Euripide F. G. A., Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayer, Rakesh, Azzopardi, Peter S., Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Bali, Ayele Geleto, Ballesteros, Katherine E., Ballew, Shoshana H., Banach, Maciej, Banoub, Joseph Adel Mattar, Banstola, Amrit, Barac, Aleksandra, Barboza, Miguel A., Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H., Baune, Bernhard T., Bazargan-Hejazi, Shahrzad, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Béjot, Yannick, Belachew, Abate Bekele, Belay, Yihalem Abebe, Bell, Michelle L., Bello, Aminu K., Bensenor, Isabela M., Bernabe, Eduardo, Bernstein, Robert S., Beuran, Mircea, Beyranvand, Tina, Bhala, Neeraj, Bhattarai, Suraj, Bhaumik, Soumyadeep, Bhutta, Zulfiqar A., Biadgo, Belete, Bijani, Ali, Bikbov, Boris, Bilano, Ver, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Bisanzio, Donal, Blacker, Brigette F., Blyth, Fiona M., Bou-Orm, Ibrahim R., Boufous, Soufiane, Bourne, Rupert, Brady, Oliver J., Brainin, Michael, Brant, Luisa C., Brazinova, Alexandra, Breitborde, Nicholas J. 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Marzan, Mb, Mashamba-Thompson, Tp, Massenburg, Bb, Mathur, Mr, Maulik, Pk, Mcgrath, Jj, Mehndiratta, Mm, Mehta, Km, Memiah, Ptn, Memish, Za, Mengistu, Dt, Mensah, Ga, Mereta, St, Meretoja, Tj, Mezerji, Nmg, Millear, Ai, Miller, Tr, Mini, Gk, Mirrakhimov, Em, Misganaw, At, Mitchell, Pb, Mohammad, Ka, Mohammed, Ma, Mokdad, Ah, Morrison, Sd, Moschos, Mm, Mousavi, Sm, Mruts, Kb, Muche, Aa, Muchie, Kf, Mueller, Uo, Mumford, Je, Musa, Ki, Nabhan, Af, Nansseu, Jr, Nascimento, Br, Negoi, Ri, Newton, Crj, Ngunjiri, Jw, Nguyen, Aq, Nguyen, Ht, Nguyen, Hlt, Nguyen, Lh, Nguyen, Nb, Nguyen, Sh, Ningrum, Dna, Nixon, Mr, Norheim, Of, Noubiap, Jj, Nouri, Hr, Nowroozi, Mr, Nsoesie, Eo, Odell, Cm, Ogbo, Fa, Oh, Ih, Olagunju, At, Olagunju, To, Olivares, Pr, Olsen, He, Olusanya, Bo, Ong, Kl, Ong, Sk, Owolabi, Mo, Pakpour, Ah, Park, Ek, Parry, Cdh, Patil, St, Patton, Gc, Paturi, Vr, Paulson, Kr, Pereira, Dm, Pham, Hq, Phillips, Mr, Pigott, Dm, Pillay, Jd, Piradov, Ma, Postma, Mj, Purcell, Ca, Purwar, Mb, 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Sharafi, Kiomar, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharma, Meenakshi, Sharma, Rajesh, She, Jun, Sheikh, Aziz, Shi, Peilin, Shibuya, Kenji, Shigematsu, Mika, Shiri, Rahman, Shirkoohi, Reza, Shishani, Kawkab, Shiue, Ivy, Shokraneh, Farhad, Shoman, Haitham, Shrime, Mark G, Si, Si, Siabani, Soraya, Siddiqi, Tariq J, Sigfusdottir, Inga Dora, Sigurvinsdottir, Rannveig, Silva, João Pedro, Silveira, Dayane Gabriele Alve, Singam, Narayana Sarma Venkata, Singh, Jasvinder A, Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Skiadaresi, Eirini, Slepak, Erica Leigh N, Sliwa, Karen, Smith, David L, Smith, Mari, Soares Filho, Adauto Martin, Sobaih, Badr Hasan, Sobhani, Soheila, Sobngwi, Eugène, Soneji, Samir S, Soofi, Moslem, Soosaraei, Masoud, Sorensen, Reed J D, Soriano, Joan B, Soyiri, Ireneous N, Sposato, Luciano A, Sreeramareddy, Chandrashekhar T, Srinivasan, Vinay, Stanaway, Jeffrey D, Stein, Dan J, Steiner, Caitlyn, Steiner, Timothy J, Stokes, Mark A, Stovner, Lars Jacob, Subart, Michelle L, Sudaryanto, Agu, Sufiyan, Mu'awiyyah Babale, Sunguya, Bruno F, Sur, Patrick John, Sutradhar, Ipsita, Sykes, Bryan L, Sylte, Dillon O, Tabarés-Seisdedos, Rafael, Tadakamadla, Santosh Kumar, Tadesse, Birkneh Tilahun, Tandon, Nikhil, Tassew, Segen Gebremeskel, Tavakkoli, Mohammad, Taveira, Nuno, Taylor, Hugh R, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekelemedhin, Shishay Wahdey, Tekle, Merhawi Gebremedhin, Temesgen, Habtamu, Temsah, Mohamad-Hani, Temsah, Omar, Terkawi, Abdullah Sulieman, Teweldemedhin, Mebrahtu, Thankappan, Kavumpurathu Raman, Thomas, Nihal, Tilahun, Binyam, To, Quyen G, Tonelli, Marcello, Topor-Madry, Roman, Topouzis, Foti, Torre, Anna E, Tortajada-Girbés, Miguel, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Towbin, Jeffrey A, Tran, Bach Xuan, Tran, Khanh Bao, Troeger, Christopher E, Truelsen, Thomas Clement, Tsilimbaris, Miltiadis K, Tsoi, Derrick, Tudor Car, Lorainne, Tuzcu, E Murat, Ukwaja, Kingsley N, Ullah, Irfan, Undurraga, Eduardo A, Unutzer, Jurgen, Updike, Rachel L, Usman, Muhammad Shariq, Uthman, Olalekan A, Vaduganathan, Muthiah, Vaezi, Afsane, Valdez, Pascual R, Varughese, Santosh, Vasankari, Tommi Juhani, Venketasubramanian, Narayanaswamy, Villafaina, Santo, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vosoughi, Kia, Vujcic, Isidora S, Wagnew, Fasil Shiferaw, Waheed, Yasir, Waller, Stephen G, Wang, Yafeng, Wang, Yuan-Pang, Weiderpass, Elisabete, Weintraub, Robert G, Weiss, Daniel J, Weldegebreal, Fitsum, Weldegwergs, Kidu Gidey, Werdecker, Andrea, West, T Eoin, Whiteford, Harvey A, Widecka, Justyna, Wijeratne, Tissa, Wilner, Lauren B, Wilson, Shadrach, Winkler, Andrea Sylvia, Wiyeh, Alison B, Wiysonge, Charles Shey, Wolfe, Charles D A, Woolf, Anthony D, Wu, Shouling, Wu, Yun-Chun, Wyper, Grant M A, Xavier, Deni, Xu, Gelin, Yadgir, Simon, Yadollahpour, Ali, Yahyazadeh Jabbari, Seyed Hossein, Yamada, Tomohide, Yan, Lijing L, Yano, Yuichiro, Yaseri, Mehdi, Yasin, Yasin Jemal, Yeshaneh, Alex, Yimer, Ebrahim M, Yip, Paul, Yisma, Engida, Yonemoto, Naohiro, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zadnik, Vesna, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zare, Zohreh, Zeleke, Ayalew Jejaw, Zenebe, Zerihun Menlkalew, Zhang, Kai, Zhao, Zheng, Zhou, Maigeng, Zodpey, Sanjay, Zucker, Inbar, Vos, Theo, and Murray, Christopher J L
Subjects: YLDs, global burden of disease, General & Internal Medicine, Medicine (all), prevalence, incidence, Human medicine, global burden of disease, YLDs, incidence, prevalence, GBD, years lived with disability
Abstract: Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 39% (95% uncertainty interval [UI] 3.1-4. 6) from 1990 to 2017; however, the all-age YLD rate increased by 7.2% (6.0-8.4) while the total sum of global YLDs increased from 562 million (421-723) to 853 million (642-1100). The increases for males and females were similar, with increases in all-age YLD rates of 7.9% (6 6-9. 2) for males and 6.5% (5.4-7.7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782-3252] per 100 000 in males vs 1400 [1279-1524] per 100 000 in females), transport injuries (3322 [3082-3583] vs 2336 [2154-2535]), and self-hatin and interpersonal violence (3265 [2943-3630] vs 5643 [5057-6302]). Interpretation Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.
Published: 2018

24. Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016

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Sd, Aljunid, Sm, Alomari, Ma, Altirkawi, Ka, Amare, At, Amoako, Ya, Andrei, Cl, Antonio, Cat, Araújo, Vem, Aryal, Kk, Asfaw, Et, Asgedom, Sw, Asghar, Rj, Ashebir, Mm, Asseffa, Na, Atey, Tm, Atre, Sr, Avokpaho, Efga, Ayala Quintanilla, Bp, Ayalew, Aa, Ayele, Ht, Ayuk, Tb, Babalola, Tk, Barber, Rm, Barboza, Ma, Barker-Collo, Sl, Barrero, Lh, Baune, Bt, Bekele, Bb, Belachew, Ab, Belay, Sa, Belay, Ya, Bell, Ml, Bello, Ak, Bennett, Da, Bennett, Jr, Bensenor, Im, Berhe, Df, Bhutta, Za, Bolliger, Iw, Bou-Orm, Ir, Brady, Oj, Breitborde, Njk, Butt, Za, Campos-Nonato, Ir, Campuzano, Jc, Carrero, Jj, Castañeda-Orjuela, Ca, Chang, Hy, Chang, Jc, Chiang, Pp, Chisumpa, Vh, Choi, Jj, Christopher, Dj, Chung, Sc, Ciobanu, Lg, Cortesi, Pa, Criqui, Mh, Cromwell, Ea, Crump, Ja, Daba, Ak, Dachew, Ba, Dadi, Af, Dargan, Pi, Das, Sk, De Neve, Jw, Dellavalle, Rp, Des Jarlais, Dc, Dharmaratne, Sd, Doku, Dt, Dorsey, Er, Dos Santos, Kpb, Doyle, Ke, Driscoll, Tr, Duncan, Bb, Ehrlich, Jr, El-Khatib, Zz, Endries, Ay, Faraon, Eja, Feigin, Vl, Feigl, Ab, Fereshtehnejad, Sm, Fernandes, Jc, Fijabi, Do, Flaxman, Ad, Foreman, Kj, Frostad, Jj, Futran, Nd, Gebre, Ak, Gebrehiwot, Tt, Gebremedhin, At, Gelaw, Ya, Geleijnse, Jm, Gething, Pw, Giref, Az, Gishu, Md, Godwin, Ww, Gona, Pn, Gopalani, Sv, Goulart, Ac, Gugnani, Hc, Haagsma, Ja, Hailu, Gb, Hamadeh, Rr, Hankey, Gj, Harb, Hl, Harewood, Hc, Haro, Jm, Hassen, Hy, Hay, Si, Hearps, Sjc, Hegazy, Mi, Henry, Nj, Herrera Ballesteros, Vh, Hibstu, Dt, Hiluf, Mk, Hoek, Hw, Hosgood, Hd, Hosseini, Sr, Hoy, Dg, Huang, Jj, Iburg, Km, Igumbor, Eu, Ileanu, Bv, Irvani, Ssn, Irvine, Cm, Jacobs, Ta, Jacobsen, Kh, Jain, Sk, Jakovljevic, Mb, Jalu, Mt, Jamal, Aa, Jayatilleke, Au, Jha, Rp, Johnson, Sc, Jonas, Jb, Karema, Ck, Karimi, Sm, Kassa, Dh, Kassa, Gm, Kassa, Td, Kassebaum, Nj, Katikireddi, Sv, Keiyoro, Pn, Kemp, Gr, Kengne, Ap, Ketema, Eb, Khafaie, Ma, Khalil, Ia, Khan, Ea, Khan, Mn, Khan, Ma, Khanal, Mn, Khang, Yh, Khater, Mm, Khoja, Ata, Kibret, Gd, Kiirithio, Dn, Kim, Yj, Kimokoti, Rw, Kopec, Ja, Koul, Pa, Krohn, Kj, Kumar, Ga, Kyu, Hh, Lad, Dp, Lal, Dk, Lang, Jj, Lansingh, Vc, Lazarus, Jv, Leasher, Jl, Lee, Ph, Leshargie, Ct, Liben, Ml, Lim, Ll, Lopez, Ad, Lorch, Sa, Lotufo, Pa, Lucas, Tcd, Lyons, Ra, Macarayan, Erk, Mackay, Mt, Maddison, Er, Malta, Dc, Mamun, Aa, Mansournia, Ma, Mantovani, Lg, Mapoma, Cc, Martins, Sco, Martins-Melo, Fr, Massenburg, Bb, Mathur, Mr, Maulik, Pk, Mcgrath, Jj, Mehta, Km, Meles, Kg, Memish, Za, Mengiste, Da, Mengistu, Dt, Menota, Bg, Mensah, Ga, Meretoja, Tj, Mezgebe, Hb, Miller, Tr, Mini, Gk, Mirrakhimov, Em, Mitchell, Pb, Mlashu, Fw, Mohammad, Ka, Mohammed, Ma, Mokdad, Ah, Mola, Gl, Montañez Hernandez, Jc, Morrison, Sd, Mousavi, Sm, Mueller, Uo, Murthy, Gv, Musa, Ki, Naik, Ga, Nansseu, Jrn, Narayan, Kv, Nascimento, Br, Negoi, Ri, Newton, Cr, Ngunjiri, Jw, Nguyen, Th, Ningrum, Dna, Nong, Vm, Norheim, Of, Noubiap, Jjn, Obermeyer, Cm, Ogbo, Fa, Oh, Ih, Olagunju, At, Olagunju, To, Olivares, Pr, Oliveira, Ppv, Olsen, He, Olusanya, Bo, Olusanya, Jo, Opio, Jn, Ortega-Altamirano, Dv, Pain, Aw, Palone, Mrt, Pandian, Jd, Park, Ek, Patil, St, Patton, Gc, Paturi, Vr, Pedroso, Mm, Pedroza, Sp, Pereira, Dm, Phillips, Mr, Piel, Fb, Pigott, Dm, Pillay, Jd, Piradov, Ma, Pond, Cd, Postma, Mj, Prasad, Nm, Quintana, Hk, Rahman, Ma, Rahman, Su, Rai, Rk, Raju, Sb, Rana, Sm, Rawaf, Dl, Ray, Se, Razo-García, Ca, Reiner, Rc, Reitsma, Mb, Renzaho, Amn, Ribeiro, Al, Rios Blancas, Mj, Rivera, Ja, Roth, Ga, Ruhago, Gm, Sabde, Yd, Sahraian, Ma, Saldanha, Rf, Salomon, Ja, Samy, Am, Sanabria, Jr, Sancheti, Pk, Sanchez-Niño, Md, Santric Milicevic, Mm, Sarker, Ar, Saylan, Mi, Schmidt, Mi, Schneider, Ijc, Schumacher, Ae, Schutte, Ae, Schwebel, Dc, Sepanlou, Sg, Servan-Mori, Ee, Shaikh, Ma, Shariful Islam, Sm, Shfare, Mt, Shrime, Mg, Shukla, Sr, Sigfusdottir, Id, Silberberg, Dh, Silva, Da, Silva, Jp, Silveira, Dga, Singh, Ja, Singh, Np, Sinha, Dn, Sinke, Ah, Soares Filho, Am, Sobaih, Bha, Sorensen, Rjd, Soriano, Jb, Soyiri, In, Sposato, La, Sreeramareddy, Ct, Stanaway, Jd, Stein, Dj, Stokes, Ma, Sufiyan, Mb, Suliankatchi, Ra, Sunguya, Bf, Sur, Pj, Sykes, Bl, Sylaja, Pn, Tadakamadla, Sk, Tadesse, Ah, Taffere, Gr, Tariku, At, Temsah, Mh, Tesema, Ag, Tesfaye, Dj, Thompson, Mj, To, Qg, Tran, Bx, Tran, Kb, Tripathy, Sp, Tuem, Kb, Ukwaja, Kn, Uthman, Oa, Uzochukwu, Bsc, Valdez, Pr, van Boven, Jfm, Vladimirov, Sk, Vlassov, Vv, Vollset, Se, Wallin, Mt, Walson, Jl, Wang, Yp, Wassie, Mm, Weaver, Mr, Weintraub, Rg, Weldegwergs, Kg, West, Te, White, Rg, Whiteford, Ha, Wolfe, Cd, Wondimkun, Ya, Wyper, Gma, Yan, Ll, Yimer, Nb, Yirsaw, Bd, Yoon, Sj, Younis, Mz, Zaki, Me, Zaman, Sb, Zenebe, Zm, Zimsen, Srm, Zuhlke, Lj, Murray, Cjl, and Lozano, R.
Subjects: Peformance, Coverage, Dánartíðni, Lífslíkur, Life expectancy, GBD, Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0–100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best), universal health coverage, Article, access quality health care, health care access and quality index, Nations, Healthcare Acce, Cause-specific mortality, Psychology, Healthcare Access and Quality Index, Mælitæki, States, Medicine (all), Health care, Þjóðir, Public Health, Global Health, Social Medicine and Epidemiology, Quality, Heilbrigðisþjónusta, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Sálfræði, Indicator, Amenable mortality, Transition, we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0–100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97·1 (95% UI 95·8–98·1) in Iceland, followed by 96·6 (94·9–97·9) in Norway and 96·1 (94·5–97·3) in the Netherlands, to values as low as 18·6 (13·1–24·4) in the Central African Republic, 19·0 (14·3–23·7) in Somalia, and 23·4 (20·2–26·8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91·5 (89·1–93·6) in Beijing to 48·0 (43·4–53·2) in Tibet (a 43·5-point difference), while India saw a 30·8-point disparity, from 64·8 (59·6–68·8) in Goa to 34·0 (30·3–38·1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4·8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20·9-point to 17·0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17·2-point to 20·4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle-SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view—and subsequent provision—of quality health care for all populations, Trends, Inequalities
Abstract: Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0-100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0-100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97.1 (95% UI 95.8-98.1) in Iceland, followed by 96.6 (94.9-97.9) in Norway and 96.1 (94.5-97.3) in the Netherlands, to values as low as 18.6 (13.1-24.4) in the Central African Republic, 19.0 (14.3-23.7) in Somalia, and 23.4 (20.2-26.8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91.5 (89.1-936) in Beijing to 48.0 (43.4-53.2) in Tibet (a 43.5-point difference), while India saw a 30.8-point disparity, from 64.8 (59.6-68.8) in Goa to 34.0 (30.3-38.1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4.8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20.9-point to 17.0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17.2-point to 20.4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle-SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view and subsequent provision of quality health care for all populations., Bill & Melinda Gates Foundation. Barbora de Courten is supported by a National Heart Foundation Future Leader Fellowship (100864). Ai Koyanagi’s work is supported by the Miguel Servet contract financed by the CP13/00150 and PI15/00862 projects, integrated into the National R + D + I and funded by the ISCIII —General Branch Evaluation and Promotion of Health Research—and the European Regional Development Fund (ERDF-FEDER). Alberto Ortiz was supported by Spanish Government (Instituto de Salud Carlos III RETIC REDINREN RD16/0019 FEDER funds). Ashish Awasthi acknowledges funding support from Department of Science and Technology, Government of India through INSPIRE Faculty scheme Boris Bikbov has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No. 703226. Boris Bikbov acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Panniyammakal Jeemon acknowledges support from the clinical and public health intermediate fellowship from the Wellcome Trust and Department of Biotechnology, India Alliance (2015–20). Job F M van Boven was supported by the Department of Clinical Pharmacy & Pharmacology of the University Medical Center Groningen, University of Groningen, Netherlands. Olanrewaju Oladimeji is an African Research Fellow hosted by Human Sciences Research Council (HSRC), South Africa and he also has honorary affiliations with Walter Sisulu University (WSU), Eastern Cape, South Africa and School of Public Health, University of Namibia (UNAM), Namibia. He is indeed grateful for support from HSRC, WSU and UNAM. EUI is supported in part by the South African National Research Foundation (NRF UID: 86003). Ulrich Mueller acknowledges funding by the German National Cohort Study grant No 01ER1511/D, Gabrielle B Britton is supported by Secretaría Nacional de Ciencia, Tecnología e Innovación and Sistema Nacional de Investigación de Panamá. Giuseppe Remuzzi acknowledges that the work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group. Behzad Heibati would like to acknowledge Air pollution Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran. Syed Aljunid acknowledges the National University of Malaysia for providing the approval to participate in this GBD Project. Azeem Majeed and Imperial College London are grateful for support from the Northwest London National Insititute of Health Research (NIHR) Collaboration for Leadership in Applied Health Research & Care. Tambe Ayuk acknowledges the Institute of Medical Research and Medicinal Plant Studies for office space provided. José das Neves was supported in his contribution to this work by a Fellowship from Fundação para a Ciência e a Tecnologia, Portugal (SFRH/BPD/92934/2013). João Fernandes gratefully acknowledges funding from FCT–Fundação para a Ciência e a Tecnologia (grant number UID/Multi/50016/2013). Jan-Walter De Neve was supported by the Alexander von Humboldt Foundation. Kebede Deribe is funded by a Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medicine (201900). Kazem Rahimi was supported by grants from the Oxford Martin School, the NIHR Oxford BRC and the RCUK Global Challenges Research Fund. Laith J Abu-Raddad acknowledges the support of Qatar National Research Fund (NPRP 9-040-3-008) who provided the main funding for generating the data provided to the GBD-IHME effort. Liesl Zuhlke is funded by the national research foundation of South Africa and the Medical Research Council of South Africa. Monica Cortinovis acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Chuanhua Yu acknowleges support from the National Natural Science Foundation of China (grant number 81773552 and grant number 81273179) Norberto Perico acknowledges that work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group. Charles Shey Wiysonge’s work is supported by the South African Medical Research Council and the National Research Foundation of South Africa (grant numbers 106035 and 108571). John J McGrath is supported by grant APP1056929 from the John Cade Fellowship from the National Health and Medical Research Council and the Danish National Research Foundation (Niels Bohr Professorship). Quique Bassat is an ICREA (Catalan Institution for Research and Advanced Studies) research professor at ISGlobal. Richard G White is funded by the UK MRC and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement that is also part of the EDCTP2 programme supported by the European Union (MR/P002404/1), the Bill & Melinda Gates Foundation (TB Modelling and Analysis Consortium: OPP1084276/OPP1135288, CORTIS: OPP1137034/OPP1151915, Vaccines: OPP1160830), and UNITAID (4214-LSHTM-Sept15; PO 8477-0-600). Rafael Tabarés-Seisdedos was supported in part by grant number PROMETEOII/2015/021 from Generalitat Valenciana and the national grant PI17/00719 from ISCIII-FEDER. Mihajlo Jakovljevic acknowleges contribution from the Serbian Ministry of Education Science and Technological Development of the Republic of Serbia (grant OI 175 014). Shariful Islam is funded by a Senior Fellowship from Institute for Physical Activity and Nutrition, Deakin University and received career transition grants from High Blood Pressure Research Council of Australia. Sonia Saxena is funded by various grants from the NIHR. Stefanos Tyrovolas was supported by the Foundation for Education and European Culture, the Sara Borrell postdoctoral program (reference number CD15/00019 from the Instituto de Salud Carlos III (ISCIII–Spain) and the Fondos Europeo de Desarrollo Regional. Stefanos was awarded with a 6 months visiting fellowship funding at IHME from M-AES (reference no. MV16/00035 from the Instituto de Salud Carlos III). S Vittal Katikreddi was funded by a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the MRC (MC_UU_12017/13 & MC_ UU_12017/15) and the Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15). Traolach S Brugha has received funding from NHS Digital UK to collect data used in this study. The work of Hamid Badali was financially supported by Mazandaran University of Medical Sciences, Sari, Iran. The work of Stefan Lorkowski is funded by the German Federal Ministry of Education and Research (nutriCARD, Grant agreement number 01EA1411A). Mariam Molokhia’s research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We also thank the countless individuals who have contributed to GBD 2016 in various capacities.
Published: 2018

25. Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Author: Dicker, Daniel, Nguyen, Grant, Abate, Degu, Abate, Kalkidan Hassen, Abay, Solomon M, Abbafati, Cristiana, Abbasi, Nooshin, Abbastabar, Hedayat, Abd-Allah, Foad, Abdela, Jemal, Abdelalim, Ahmed, Abdel-Rahman, Omar, Abdi, Alireza, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abdurahman, Ahmed Abdulahi, Abebe, Haftom Temesgen, Abebe, Molla, Abebe, Zegeye, Abebo, Teshome Abuka, Aboyans, Victor, Abraha, Haftom Niguse, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen ME, Accrombessi, Manfred Mario Kokou, Acharya, Pawan, Adebayo, Oladimeji M, Adedeji, Isaac Akinkunmi, Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Adetokunboh, Olatunji O, Adhena, Beyene Meressa, Adhikari, Tara Ballav, Adib, Mina G, Adou, Arsène Kouablan, Adsuar, Jose C, Afarideh, Mohsen, Afshin, Ashkan, Agarwal, Gina, Aggarwal, Rakesh, Aghayan, Sargis Aghasi, Agrawal, Sutapa, Agrawal, Anurag, Ahmadi, Mehdi, Ahmadi, Alireza, Ahmadieh, Hamid, Ahmed, Mohamed Lemine Cheikh brahim, Ahmed, Sayem, Ahmed, Muktar Beshir, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akanda, Ali S, Akbari, Mohammad Esmaeil, Akibu, Mohammed, Akinyemi, Rufus Olusola, Akinyemiju, Tomi, Akseer, Nadia, Alahdab, Fares, Al-Aly, Ziyad, Alam, Khurshid, Alebel, Animut, Aleman, Alicia V, Alene, Kefyalew Addis, Al-Eyadhy, Ayman, Ali, Raghib, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Allen, Christine A, Alonso, Jordi, Al-Raddadi, Rajaa M, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Amini, Erfan, Ammar, Walid, Amoako, Yaw Ampem, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Anjomshoa, Mina, Anlay, Degefaye Zelalem, Ansari, Hossein, Ansariadi, Ansariadi, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T, Appiah, Seth Christopher Yaw, Aremu, Olatunde, Areri, Habtamu Abera, Ärnlöv, Johan, Arora, Megha, Artaman, Al, Aryal, Krishna K, Asadi-Lari, Mohsen, Asayesh, Hamid, Asfaw, Ephrem Tsegay, Asgedom, Solomon Weldegebreal, Assadi, Reza, Ataro, Zerihun, Atey, Tesfay Mehari Mehari, Athari, Seyyed Shamsadin, Atique, Suleman, Atre, Sachin R, Atteraya, Madhu Sudhan, Attia, Engi F, Ausloos, Marcel, Avila-Burgos, Leticia, Avokpaho, Euripide F G A, Awasthi, Ashish, Awuah, Baffour, Ayala Quintanilla, Beatriz Paulina, Ayele, Henok Tadesse, Ayele, Yohanes, Ayer, Rakesh, Ayuk, Tambe B, Azzopardi, Peter S, Azzopardi-Muscat, Natasha, Badali, Hamid, Badawi, Alaa, Balakrishnan, Kalpana, Bali, Ayele Geleto, Banach, Maciej, Banstola, Amrit, Barac, Aleksandra, Barboza, Miguel A, Barquera, Simon, Barrero, Lope H, Basaleem, Huda, Bassat, Quique, Basu, Arindam, Basu, Sanjay, Baune, Bernhard T, Bazargan-Hejazi, Shahrzad, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Béjot, Yannick, Bekele, Bayu Begashaw, Belachew, Abate Bekele, Belay, Aregawi Gebreyesus, Belay, Ezra, Belay, Saba Abraham, Belay, Yihalem Abebe, Bell, Michelle L, Bello, Aminu K, Bennett, Derrick A, Bensenor, Isabela M, Berhane, Adugnaw, Berman, Adam E, Bernabe, Eduardo, Bernstein, Robert S, Bertolacci, Gregory J, Beuran, Mircea, Beyranvand, Tina, Bhala, Neeraj, Bhatia, Eesh, Bhatt, Samir, Bhattarai, Suraj, Bhaumik, Soumyadeeep, Bhutta, Zulfiqar A, Biadgo, Belete, Bijani, Ali, Bikbov, Boris, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Birlik, Sait Mentes, Birungi, Charles, Bisanzio, Donal, Biswas, Tuhin, Bjørge, Tone, Bleyer, Archie, Basara, Berrak Bora, Bose, Dipan, Bosetti, Cristina, Boufous, Soufiane, Bourne, Rupert, Brady, Oliver J, Bragazzi, Nicola Luigi, Brant, Luisa C, Brazinova, Alexandra, Breitborde, Nicholas J K, Brenner, Hermann, Britton, Gabrielle, Brugha, Traolach, Burke, Kristin E, Busse, Reinhard, Butt, Zahid A, Cahuana-Hurtado, Lucero, Callender, Charlton S K H, Campos-Nonato, Ismael R, Campuzano Rincon, Julio Cesar, Cano, Jorge, Car, Mate, Cárdenas, Rosario, Carreras, Giulia, Carrero, Juan J, Carter, Austin, Carvalho, Félix, Castañeda-Orjuela, Carlos A, Castillo Rivas, Jacqueline, Castro, Franz, Catalá-López, Ferrán, Çavlin, Alanur, Cerin, Ester, Chaiah, Yazan, Champs, Ana Paula, Chang, Hsing-Yi, Chang, Jung-Chen, Chattopadhyay, Aparajita, Chaturvedi, Pankaj, Chen, Wanqing, Chiang, Peggy Pei-Chia, Chimed-Ochir, Odgerel, Chin, Ken Lee, Chisumpa, Vesper Hichilombwe, Chitheer, Abdulaal, Choi, Jee-Young J, Christensen, Hanne, Christopher, Devasahayam J, Chung, Sheng-Chia, Cicuttini, Flavia M, Ciobanu, Liliana G, Cirillo, Massimo, Claro, Rafael M, Cohen, Aaron J, Collado-Mateo, Daniel, Constantin, Maria-Magdalena, Conti, Sara, Cooper, Cyrus, Cooper, Leslie Trumbull, Cortesi, Paolo Angelo, Cortinovis, Monica, Cousin, Ewerton, Criqui, Michael H, Cromwell, Elizabeth A, Crowe, Christopher Stephen, Crump, John A, Cucu, Alexandra, Cunningham, Matthew, Daba, Alemneh Kabeta, Dachew, Berihun Assefa, Dadi, Abel Fekadu, Dandona, Lalit, Dandona, Rakhi, Dang, Anh Kim, Dargan, Paul I, Daryani, Ahmad, Das, Siddharth K, Das Gupta, Rajat, das Neves, José, Dasa, Tamirat Tesfaye, Dash, Aditya Prasad, Weaver, Nicole Davis, Davitoiu, Dragos Virgil, Davletov, Kairat, Dayama, Anand, Courten, Barbora de, De la Hoz, Fernando Pio, De leo, Diego, De Neve, Jan-Walter, Degefa, Meaza Girma, Degenhardt, Louisa, Degfie, Tizta T, Deiparine, Selina, Dellavalle, Robert P, Demoz, Gebre Teklemariam, Demtsu, Balem Betsu, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolaos, Des Jarlais, Don C, Dessie, Getenet Ayalew, Dey, Subhojit, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Ding, Eric L, Djalalinia, Shirin, Doku, David Teye, Dolan, Kate A, Donnelly, Christl A, Dorsey, E Ray, Douwes-Schultz, Dirk, Doyle, Kerrie E, Drake, Thomas M, Driscoll, Tim Robert, Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Duncan, Bruce B, Duraes, Andre R, Ebrahimi, Hedyeh, Ebrahimpour, Soheil, Edessa, Dumessa, Edvardsson, David, Eggen, Anne Elise, El Bcheraoui, Charbel, El Sayed Zaki, Maysaa, Elfaramawi, Mohammed, El-Khatib, Ziad, Ellingsen, Christian Lycke, Elyazar, Iqbal R F, Enayati, Ahmadali, Endries, Aman Yesuf Yesuf, Er, Benjamin, Ermakov, Sergey Petrovich, Eshrati, Babak, Eskandarieh, Sharareh, Esmaeili, Reza, Esteghamati, Alireza, Esteghamati, Sadaf, Fakhar, Mahdi, Fakhim, Hamed, Farag, Tamer, Faramarzi, Mahbobeh, Fareed, Mohammad, Farhadi, Farzaneh, Farid, Talha A, Farinha, Carla Sofia e Sá, Farioli, Andrea, Faro, Andre, Farvid, Maryam S, Farzadfar, Farshad, Farzaei, Mohammad Hosein, Fazeli, Mir Sohail, Feigin, Valery L, Feigl, Andrea B, Feizy, Fariba, Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, Eduarda, Fernandes, Joao C, Feyissa, Garumma Tolu, Fijabi, Daniel Obadare, Filip, Irina, Finegold, Samuel, Fischer, Florian, Flor, Luisa Sorio, Foigt, Nataliya A, Ford, John A, Foreman, Kyle J, Fornari, Carla, Frank, Tahvi D, Franklin, Richard Charles, Fukumoto, Takeshi, Fuller, John E, Fullman, Nancy, Fürst, Thomas, Furtado, João M, Futran, Neal D, Galan, Adriana, Gallus, Silvano, Gambashidze, Ketevan, Gamkrelidze, Amiran, Gankpe, Fortune Gbetoho, Garcia-Basteiro, Alberto L, Garcia-Gordillo, Miguel A, Gebre, Teshome, Gebre, Abadi Kahsu, Gebregergs, Gebremedhin Berhe, Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Gelano, Tilayie Feto, Gelaw, Yalemzewod Assefa, Geleijnse, Johanna M, Genova-Maleras, Ricard, Gessner, Bradford D, Getachew, Sefonias, Gething, Peter W, Gezae, Kebede Embaye, Ghadami, Mohammad Rasoul, Ghadimi, Reza, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Ghiasvand, Hesam, Ghimire, Mamata, Ghoshal, Aloke Gopal, Gill, Paramjit Singh, Gill, Tiffany K, Gillum, Richard F, Giussani, Giorgia, Goenka, Shifalika, Goli, Srinivas, Gomez, Ricardo Santiago, Gomez-Cabrera, Mari Carmen, Gómez-Dantés, Hector, Gona, Philimon N, Goodridge, Amador, Gopalani, Sameer Vali, Goto, Atsushi, Goulart, Alessandra C, Goulart, Bárbara Niegia Garcia, Grada, Ayman, Grosso, Giuseppe, Gugnani, Harish Chander, Guimaraes, Andre Luiz Sena, Guo, Yuming, Gupta, Prakash C, Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Gyawali, Bishal, Haagsma, Juanita A, Hachinski, Vladimir, Hafezi-Nejad, Nima, Hagos, Tekleberhan B, Hailegiyorgis, Tewodros Tesfa, Hailu, Gessessew Bugssa, Haj-Mirzaian, Arya, Haj-Mirzaian, Arvin, Hamadeh, Randah R, Hamidi, Samer, Handal, Alexis J, Hankey, Graeme J, Harb, Hilda L, Harikrishnan, Sivadasanpillai, Haririan, Hamidreza, Haro, Josep Maria, Hasan, Mehedi, Hassankhani, Hadi, Hassen, Hamid Yimam, Havmoeller, Rasmus, Hay, Roderick J, Hay, Simon I, He, Yihua, Hedayatizadeh-Omran, Akbar, Hegazy, Mohamed I, Heibati, Behzad, Heidari, Mohsen, Hendrie, Delia, Henok, Andualem, Henry, Nathaniel J, Heredia-Pi, Ileana, Herteliu, Claudiu, Heydarpour, Fatemeh, Heydarpour, Pouria, Heydarpour, Sousan, Hibstu, Desalegn Tsegaw, Hoek, Hans W, Hole, Michael K, Homaie Rad, Enayatollah, Hoogar, Praveen, Horino, Masako, Hosgood, H Dean, Hosseini, Seyed Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Sorin, Hostiuc, Mihaela, Hotez, Peter J, Hoy, Damian G, Hsairi, Mohamed, Htet, Aung Soe, Hu, Guoqing, Huang, John J, Husseini, Abdullatif, Hussen, Mohammedaman Mama, Hutfless, Susan, Iburg, Kim Moesgaard, Igumbor, Ehimario U, Ikeda, Chad Thomas, Ilesanmi, Olayinka Stephen, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Isehunwa, Oluwaseyi Oluwakemi, Islam, Sheikh Mohammed Shariful, Islami, Farhad, Jahangiry, Leila, Jahanmehr, Nader, Jain, Rajesh, Jain, Sudhir Kumar, Jakovljevic, Mihajlo, James, Spencer L, Javanbakht, Mehdi, Jayaraman, Sudha, Jayatilleke, Achala Upendra, Jee, Sun Ha, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Ji, John S, Johnson, Sarah Charlotte, Jonas, Jost B, Joshi, Ankur, Jozwiak, Jacek Jerzy, Jungari, Suresh Banayya, Jürisson, Mikk, K, Madhanraj, Kabir, Zubair, Kadel, Rajendra, Kahsay, Amaha, Kahssay, Molla, Kalani, Rizwan, Kapil, Umesh, Karami, Manoochehr, Karami Matin, Behzad, Karch, André, Karema, Corine, Karimi, Narges, Karimi, Seyed M, Karimi-Sari, Hamidreza, Kasaeian, Amir, Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassa, Zemenu Yohannes, Kassebaum, Nicholas J, Katibeh, Marzieh, Katikireddi, Srinivasa Vittal, Kaul, Anil, Kawakami, Norito, Kazemeini, Hossein, Kazemi, Zhila, Karyani, Ali Kazemi, K C, Prakash, Kebede, Seifu, Keiyoro, Peter Njenga, Kemp, Grant Rodgers, Kengne, Andre Pascal, Keren, Andre, Kereselidze, Maia, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalid, Nauman, Khalil, Ibrahim A, Khan, Ejaz Ahmad, Khan, Gulfaraz, Khan, Muhammad Shahzeb, Khan, Muhammad Ali, Khang, Young-Ho, Khanna, Tripti, Khater, Mona M, Khatony, Alireza, Khazaie, Habibolah, Khoja, Abdullah T, Khosravi, Ardeshir, Khosravi, Mohammad Hossein, Khubchandani, Jagdish, Kiadaliri, Aliasghar A, Kibret, Getiye D Dejenu, Kim, Cho-il, Kim, Daniel, Kim, Jun Y, Kim, Young-Eun, Kimokoti, Ruth W, Kinfu, Yohannes, Kinra, Sanjay, Kisa, Adnan, Kissimova-Skarbek, Katarzyna, Kissoon, Niranjan, Kivimäki, Mika, Kleber, Marcus E, Knibbs, Luke D, Knudsen, Ann Kristin Skrindo, Kochhar, Sonali, Kokubo, Yoshihiro, Kolola, Tufa, Kopec, Jacek A, Kosek, Margaret N, Kosen, Soewarta, Koul, Parvaiz A, Koyanagi, Ai, Kravchenko, Michael A, Krishan, Kewal, Krishnaswami, Sanjay, Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kudom, Andreas A, Kuipers, Ernst J, Kulikoff, Xie Rachel, Kumar, G Anil, Kumar, Manasi, Kumar, Pushpendra, Kumsa, Fekede Asefa, Kutz, Michael J, Lad, Sheetal D, Lafranconi, Alessandra, Lal, Dharmesh Kumar, Lalloo, Ratilal, Lam, Hilton, Lami, Faris Hasan, Lan, Qing, Langan, Sinéad M, Lansingh, Van C, Lansky, Sonia, Larson, Heidi Jane, Laryea, Dennis Odai, Lassi, Zohra S, Latifi, Arman, Lavados, Pablo M, Laxmaiah, Avula, Lazarus, Jeffrey V, Lebedev, Georgy, Lee, Paul H, Leigh, James, Leshargie, Cheru Tesema, Leta, Samson, Levi, Miriam, Li, Shanshan, Li, Yichong, Li, Xiaohong, Liang, Juan, Liang, Xiaofeng, Liben, Misgan Legesse, Lim, Lee-Ling, Lim, Stephen S, Limenih, Miteku Andualem, Linn, Shai, Liu, Shiwei, Liu, Yang, Lodha, Rakesh, Logroscino, Giancarlo, Lonsdale, Chris, Lorch, Scott A, Lorkowski, Stefan, Lotufo, Paulo A, Lozano, Rafael, Lucas, Tim C D, Lunevicius, Raimundas, Lyons, Ronan A, Ma, Stefan, Mabika, Crispin, Macarayan, Erlyn Rachelle King, Mackay, Mark T, Maddison, Emilie R, Maddison, Ralph, Madotto, Fabiana, Magdy Abd El Razek, Hassan, Magdy Abd El Razek, Muhammed, Maghavani, Dhaval P, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malik, Manzoor Ahmad, Malta, Deborah Carvalho, Mamun, Abdullah A, Manamo, Wondimu Ayele, Manda, Ana-Laura, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Mapoma, Chabila Christopher, Marami, Dadi, Maravilla, Joemer C, Marcenes, Wagner, Marina, Shakhnazarova, Martinez-Raga, Jose, Martins, Sheila C O, Martins-Melo, Francisco Rogerlândio, März, Winfried, Marzan, Melvin B, Mashamba-Thompson, Tivani Phosa, Masiye, Felix, Massenburg, Benjamin Ballard, Maulik, Pallab K, Mazidi, Mohsen, McGrath, John J, McKee, Martin, Mehata, Suresh, Mehendale, Sanjay Madhav, Mehndiratta, Man Mohan, Mehrotra, Ravi, Mehta, Kala M, Mehta, Varshil, Mekonen, Tesfa, Mekonnen, Tefera Chane, Meles, Hagazi Gebre, Meles, Kidanu Gebremariam, Melese, Addisu, Melku, Mulugeta, Memiah, Peter T N, Memish, Ziad A, Mendoza, Walter, Mengistu, Desalegn Tadese, Mengistu, Getnet, Mensah, George A, Mereta, Seid Tiku, Meretoja, Atte, Meretoja, Tuomo J, Mestrovic, Tomislav, Mezgebe, Haftay Berhane, Miangotar, Yode, Miazgowski, Bartosz, Miazgowski, Tomasz, Miller, Ted R, Mini, G K, Mirica, Andreea, Mirrakhimov, Erkin M, Misganaw, Awoke Temesgen, Moazen, Babak, Moges, Nurilign Abebe, Mohammad, Karzan Abdulmuhsin, Mohammadi, Moslem, Mohammadifard, Noushin, Mohammadi-Khanaposhtani, Maryam, Mohammadnia-Afrouzi, Mousa, Mohammed, Shafiu, Mohammed, Mohammed A, Mohan, Viswanathan, Mokdad, Ali H, Molokhia, Mariam, Monasta, Lorenzo, Moradi, Ghobad, Moradi, Mahmoudreza, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morawska, Lidia, Moreno Velásquez, Ilais, Morgado-da-Costa, Joana, Morrison, Shane Douglas, Mosapour, Abbas, Moschos, Marilita M, Mousavi, Seyyed Meysam, Muche, Achenef Asmamaw, Muchie, Kindie Fentahun, Mueller, Ulrich Otto, Mukhopadhyay, Satinath, Mullany, Erin C, Muller, Kate, Murhekar, Manoj, Murphy, Tasha B, Murthy, G V S, Murthy, Srinivas, Musa, Jonah, Musa, Kamarul Imran, Mustafa, Ghulam, Muthupandian, Saravanan, Nachega, Jean B, Nagel, Gabriele, Naghavi, Mohsen, Naheed, Aliya, Nahvijou, Azin, Naik, Gurudatta, Nair, Sanjeev, Najafi, Farid, Nangia, Vinay, Nansseu, Jobert Richie, Nascimento, Bruno Ramos, Nawaz, Haseeb, Ncama, Busisiwe P, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Neupane, Subas, Newton, Charles Richard James, Ngalesoni, Frida N, Ngunjiri, Josephine W, Nguyen, Ha Thu, Nguyen, Huong Thanh, Nguyen, Long Hoang, Nguyen, Michele, Nguyen, Trang Huyen, Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nisar, Muhammad Imran, Nixon, Molly R, Nolutshungu, Nomonde, Nomura, Shuhei, Norheim, Ole F, Noroozi, Mehdi, Norrving, Bo, Noubiap, Jean Jacques, Nouri, Hamid Reza, Nourollahpour Shiadeh, Malihe, Nowroozi, Mohammad Reza, Nsoesie, Elaine O, Nyasulu, Peter S, Ofori-Asenso, Richard, Ogah, Okechukwu Samuel, Ogbo, Felix Akpojene, Oh, In-Hwan, Okoro, Anselm, Oladimeji, Olanrewaju, Olagunju, Andrew T, Olagunju, Tinuke O, Olivares, Pedro R, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Sok King, Opio, John Nelson, Oren, Eyal, Ortiz, Justin R, Ortiz, Alberto, Ota, Erika, Otstavnov, Stanislav S, Øverland, Simon, Owolabi, Mayowa Ojo, Oyekale, Abayomi Samuel, P A, Mahesh, Pacella, Rosana, Pakhale, Smita, Pakhare, Abhijit P, Pana, Adrian, Panda, Basant Kumar, Panda-Jonas, Songhomitra, Pandey, Achyut Raj, Pandian, Jeyaraj Durai, Parisi, Andrea, Park, Eun-Kee, Parry, Charles D H, Parsian, Hadi, Patel, Shanti, Patle, Ajay, Patten, Scott B, Patton, George C, Paudel, Deepak, Pearce, Neil, Peprah, Emmanuel K, Pereira, Alexandre, Pereira, David M, Perez, Krystle M, Perico, Norberto, Pervaiz, Aslam, Pesudovs, Konrad, Petri, William A, Petzold, Max, Phillips, Michael R, Pigott, David M, Pillay, Julian David, Pirsaheb, Meghdad, Pishgar, Farhad, Plass, Dietrich, Polinder, Suzanne, Pond, Constance Dimity, Popova, Svetlana, Postma, Maarten J, Pourmalek, Farshad, Pourshams, Akram, Poustchi, Hossein, Prabhakaran, Dorairaj, Prakash, V, Prakash, Swayam, Prasad, Narayan, Qorbani, Mostafa, Quistberg, D Alex, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi, Kazem, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Mohammad Hifz Ur, Rahman, Muhammad Aziz, Rahman, Sajjad ur, Rai, Rajesh Kumar, Rajati, Fatemeh, Rajsic, Sasa, Raju, Sree Bhushan, Ram, Usha, Ranabhat, Chhabi Lal, Ranjan, Prabhat, Ranta, Anna, Rasella, Davide, Rawaf, David Laith, Rawaf, Salman, Ray, Sarah E, Razo-García, Christian, Rego, Maria Albertina Santiago, Rehm, Jürgen, Reiner, Robert C, Reinig, Nickolas, Reis, Cesar, Remuzzi, Giuseppe, Renzaho, Andre M N, Resnikoff, Serge, Rezaei, Satar, Rezaeian, Shahab, Rezai, 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Subjects: Child mortality, Alnæmi, Dánartíðni, Heilsufar, GBD, Börn, Dauði, Almennar lyflækningar, Fullorðnir, General & Internal Medicine, parasitic diseases, Psychology, age-specific mortality, Estimating adult mortality, Medicine (all), HIV, 11 Medical And Health Sciences, General Medicine, All-cause mortality, humanities, Countries, Death, mortality, national, Sálfræði, Health, GBD, mortality, Human medicine, Trends
Abstract: The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Data for this research was provided by MEASURE Evaluation, funded by the United States Agency for International Development (USAID). Views expressed do not necessarily reflect those of USAID, the US Government, or MEASURE Evaluation. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with licence no. SLN2014-3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law-2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data., Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Findings Globally, 18·7% (95% uncertainty interval 18·4–19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2–59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5–49·6) to 70·5 years (70·1–70·8) for men and from 52·9 years (51·7–54·0) to 75·6 years (75·3–75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5–51·7) for men in the Central African Republic to 87·6 years (86·9–88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3–238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6–42·83) per 1000 livebirths in 2017, with huge reductions across countries. Nevertheless, there were still 5·4 million (5·2–5·6) deaths among children younger than 5 years in the world in 2017. Progress has been less pronounced and more variable for adults, especially for adult males, who had stagnant or increasing mortality rates in several countries. The gap between male and female life expectancy between 1950 and 2017, while relatively stable at the global level, shows distinctive patterns across super-regions and has consistently been the largest in central Europe, eastern Europe, and central Asia, and smallest in south Asia. Performance was also variable across countries and time in observed mortality rates compared with those expected on the basis of development. Interpretation This analysis of age-sex-specific mortality shows that there are remarkably complex patterns in population mortality across countries. The findings of this study highlight global successes, such as the large decline in under-5 mortality, which reflects significant local, national, and global commitment and investment over several decades. However, they also bring attention to mortality patterns that are a cause for concern, particularly among adult men and, to a lesser extent, women, whose mortality rates have stagnated in many countries over the time period of this study, and in some cases are increasing., Research reported in this publication was supported by the Bill & Melinda Gates Foundation, the University of Melbourne, Public Health England, the Norwegian Institute of Public Health, St. Jude Children's Research Hospital, the National Institute on Aging of the National Institutes of Health (award P30AG047845), and the National Institute of Mental Health of the National Institutes of Health (award R01MH110163).
Published: 2018

26. Impact of provision of abdominal aortic calcification results on fruit and vegetable intake: 12-week randomized phase 2 controlled trial.

Author: Radavelli-Bagatini S, Bondonno CP, Dalla Via J, Sim M, Gebre AK, Blekkenhorst LC, Connolly EL, Bondonno NP, Schousboe JT, Woodman RJ, Zhu K, Mullin S, Szulc P, Jackson B, Dimmock J, Schlaich MP, Cox KL, Kiel DP, Lim WH, Stanley M, Devine A, Thompson PL, Williams EJ, Wood LG, Sim M, Daly RM, Hodgson JM, and Lewis JR
Subjects: Humans, Female, Male, Aged, Middle Aged, Aged, 80 and over, Single-Blind Method, Diet, Vascular Calcification diagnostic imaging, Vascular Calcification prevention & control, Vascular Calcification blood, Cardiovascular Diseases prevention & control, Cardiovascular Diseases epidemiology, Vegetables, Fruit, Aorta, Abdominal diagnostic imaging, Aorta, Abdominal pathology, Carotenoids blood
Abstract: Provision of non-invasive vascular imaging results to individuals has been shown to improve cardiovascular disease risk factor control: its impact on diet remains uncertain. In this two-arm, single-blind, parallel, 12-week randomized controlled trial, 240 participants, 57.5% females aged 60-80 y had abdominal aortic calcification and clinical assessments performed at a hospital clinic. Participants were randomized 1:1 to receive (intervention n = 121) or not (control n = 119) their calcification results. Both groups received educational resources on cardiovascular disease risk control and were unblinded to the intervention. Outcome measures were performed at baseline and 12 weeks. The primary outcomes of the study were changes in fruit and vegetable intake measures over 12 weeks assessed using plasma carotenoid concentrations (biomarkers of FV intake) and a food frequency questionnaire. Secondary outcomes included 12-week changes in other aspects of the diet, physical activity, body weight, blood pressure, heart rate, lipid profile, glucose concentrations, estimated cardiovascular disease risk score, and medication use. Between-group differences were tested using linear mixed-effects regression. There were no between-group differences in the primary outcomes at 12 weeks: plasma carotenoids (mean difference +0.03 µg/mL [95%CI -0.06, 0.13]) and fruit and vegetable intakes (+18 g/d [-37, 72]). However, the provision of calcification results led to between-group differences in serum total (-0.22 mmol/L [-0.41, -0.04]) and non-HDL (-0.19 mmol/L [-0.35, -0.03]) cholesterol, and estimated cardiovascular disease risk score (-0.24% [-0.47, -0.02]). No between-group differences were seen for other secondary outcomes. In this work, providing vascular imaging results did not improve diet but did improve some cardiovascular disease risk factors (Australian and New Zealand Clinical Trials Registry ACTRN12618001087246)., (© 2024. The Author(s).)
Published: 2024
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27. Extent of Abdominal Aortic Calcification Is Associated With Incident Rapid Weight Loss Over 5 Years: The Perth Longitudinal Study of Ageing Women.

Author: Smith C, Sim M, Dalla Via J, Gebre AK, Zhu K, Lim WH, Teh R, Kiel DP, Schousboe JT, Levinger I, von Haehling S, Woodman R, Coats AJS, Prince RL, and Lewis JR
Subjects: Humans, Female, Aged, Risk Factors, Longitudinal Studies, Aging, Body Weight, Weight Loss, Aorta, Abdominal diagnostic imaging, Vascular Calcification etiology, Vascular Diseases, Aortic Diseases etiology
Abstract: Background: Abdominal aortic calcification (AAC), a marker of vascular disease, is associated with disease in other vascular beds including gastrointestinal arteries. We investigated whether AAC is related to rapid weight loss over 5 years and whether rapid weight loss is associated with 9.5-year all-cause mortality in community-dwelling older women., Methods: Lateral spine images from dual-energy x-ray absorptiometry (1998/1999) were used to assess AAC (24-point AAC scoring method) in 929 older women. Over 5 years, body weight was assessed at 12-month intervals. Rapid weight loss was defined as >5% decrease in body weight within any 12-month interval. Multivariable-adjusted logistic regression was used to assess AAC and rapid weight loss and Cox regression to assess the relationship between rapid weight loss and 9.5-year all-cause mortality., Results: Mean±SD age of women was 75.0±2.6 years. During the initial 5 years, 366 (39%) women presented with rapid weight loss. Compared with women with low AAC (24-point AAC score 0-1), those with moderate (24-point AAC score 2-5: odds ratio, 1.36 [95% CI, 1.00-1.85]) and extensive (24-point AAC score 6+: odds ratio, 1.59 [95% CI, 1.10-2.31]) AAC had higher odds for presenting with rapid weight loss. Results remained similar after further adjustment for dietary factors (alcohol, protein, fat, and carbohydrates), diet quality, blood pressure, and cholesterol measures. The estimates were similar in subgroups of women who met protein intake (n=599) and physical activity (n=735) recommendations (extensive AAC: odds ratios, 1.81 [95% CI, 1.12-2.92] and 1.58 [95% CI, 1.02-2.44], respectively). Rapid weight loss was associated with all-cause mortality over the next 9.5 years (hazard ratio, 1.49 [95% CI, 1.17-1.89]; P =0.001)., Conclusions: AAC extent was associated with greater risk for rapid weight loss over 5 years in older women, a risk for all-cause mortality. Since the association was unchanged after taking nutritional intakes into account, these data support the possibility that vascular disease may play a role in the maintenance of body weight., Competing Interests: Disclosures None.
Published: 2024
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28. Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women.

Author: Dalla Via J, Gebre AK, Smith C, Gilani Z, Suter D, Sharif N, Szulc P, Schousboe JT, Kiel DP, Zhu K, Leslie WD, Prince RL, Lewis JR, and Sim M
Subjects: Humans, Female, Aged, Prospective Studies, Longitudinal Studies, Independent Living, Risk Factors, Australia, Bone Density, Absorptiometry, Photon methods, Minerals, Vascular Calcification complications, Vascular Calcification diagnostic imaging, Vascular Calcification epidemiology, Fractures, Bone complications
Abstract: Abdominal aortic calcification (AAC), a recognized measure of advanced vascular disease, is associated with higher cardiovascular risk and poorer long-term prognosis. AAC can be assessed on dual-energy X-ray absorptiometry (DXA)-derived lateral spine images used for vertebral fracture assessment at the time of bone density screening using a validated 24-point scoring method (AAC-24). Previous studies have identified robust associations between AAC-24 score, incident falls, and fractures. However, a major limitation of manual AAC assessment is that it requires a trained expert. Hence, we have developed an automated machine-learning algorithm for assessing AAC-24 scores (ML-AAC24). In this prospective study, we evaluated the association between ML-AAC24 and long-term incident falls and fractures in 1023 community-dwelling older women (mean age, 75 ± 3 years) from the Perth Longitudinal Study of Ageing Women. Over 10 years of follow-up, 253 (24.7%) women experienced a clinical fracture identified via self-report every 4-6 months and verified by X-ray, and 169 (16.5%) women had a fracture hospitalization identified from linked hospital discharge data. Over 14.5 years, 393 (38.4%) women experienced an injurious fall requiring hospitalization identified from linked hospital discharge data. After adjusting for baseline fracture risk, women with moderate to extensive AAC (ML-AAC24 ≥ 2) had a greater risk of clinical fractures (hazard ratio [HR] 1.42; 95% confidence interval [CI], 1.10-1.85) and fall-related hospitalization (HR 1.35; 95% CI, 1.09-1.66), compared to those with low AAC (ML-AAC24 ≤ 1). Similar to manually assessed AAC-24, ML-AAC24 was not associated with fracture hospitalizations. The relative hazard estimates obtained using machine learning were similar to those using manually assessed AAC-24 scores. In conclusion, this novel automated method for assessing AAC, that can be easily and seamlessly captured at the time of bone density testing, has robust associations with long-term incident clinical fractures and injurious falls. However, the performance of the ML-AAC24 algorithm needs to be verified in independent cohorts. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)., (© 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).)
Published: 2023
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29. Provision of non-invasive coronary and carotid vascular imaging results on changes in diet and physical activity in asymptomatic adults: A scoping review.

Author: Radavelli-Bagatini S, Gebre AK, Kennedy MA, Sim M, Blekkenhorst LC, Bondonno CP, Jackson B, Dimmock J, Schlaich MP, Hodgson JM, and Lewis JR
Abstract: Background: Although a healthy diet and physical activity have been shown to prevent or delay cardiovascular disease (CVD) hospitalizations and deaths, most adults do not meet current guidelines. Provision of coronary artery calcification (CAC) and carotid ultrasound (CUS) imaging results may motivate beneficial lifestyle changes. We scoped the existing literature for studies providing non-invasive vascular imaging results and reporting diet, physical activity, and/or anthropometric measures to identify knowledge gaps and opportunities for further research., Methods: A systematic search was performed across three electronic databases, in line with PRISMA ScR guidelines and Arksey and O'Malley's scoping review framework., Results: Twenty studies (thirteen observational and seven randomized controlled trials) examining the impact of provision of CAC/CUS imaging results on diet and/or physical activity behaviors were included. Nearly half the studies did not clearly state whether participants received dietary and physical activity advice along with vascular imaging results, and these were secondary outcomes in most studies, with data assessment and reporting being inconsistent., Conclusion: Well-designed clinical trials with consistent and clear messaging based on detailed subjective and objective measures of diet and physical activity are needed to determine whether this approach may stimulate long-term dietary and physical activity change., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Radavelli-Bagatini, Gebre, Kennedy, Sim, Blekkenhorst, Bondonno, Jackson, Dimmock, Schlaich, Hodgson and Lewis.)
Published: 2022
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30. Assessment of drug use pattern using WHO core drug use indicators in selected general hospitals: a cross-sectional study in Tigray region, Ethiopia.

Author: Tassew SG, Abraha HN, Gidey K, and Gebre AK
Subjects: Cross-Sectional Studies, Drug Prescriptions, Ethiopia, Humans, Practice Patterns, Physicians', World Health Organization, Drugs, Essential, Hospitals, General
Abstract: Objective: Inappropriate use of medicine is a global challenge with greater impact on developing countries. Assessment of drug use pattern is used to identify gaps in medicine utilisation to implement strategies for promoting rational drug use. This study aimed to assess drug use pattern using the WHO drug use indicators in selected general hospitals in Tigray region, Ethiopia., Design: A cross-sectional study was conducted using WHO drug use indicators in two public hospitals located in Tigray., Setting: Prescriptions recorded from 1 January 2017 to 1 June 2019 were randomly selected, and participants who visited the public hospitals from 1 March 2019 to 30 August 2019 and hospital pharmacies were interviewed., Participants: 100 patients who visited both outpatient clinics and hospital pharmacy departments of the public hospitals., Results: The average number of medicines per prescription was 1.69 (±0.81). Prescriptions containing antibiotics and injectables were 58.2% and 15.9%, respectively. The percentages of medicines prescribed with a generic name from essential medicines list of Ethiopia were 97.5% (974) and 88.1% (970) in Mekelle Hospital and Quiha Hospital, respectively. The patients spent an average of 6.6(±3.5) min with their general practitioners, while only 22.8 (±21.7) s with their pharmacists. Of the patients interviewed, 56.9% knew their dosing regimen and 32.7% of them had their medication labelled., Conclusion: The finding of the present study revealed deviation of drug use pattern from the WHO optimal levels suggesting the hospitals had limitations in appropriate utilisation of medicines. Understanding the factors attributed to the observed gaps and implementing corrective measures are required to conform with the recommended standards of appropriate drug utilisation., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Published: 2021
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31. Antimalarial Effect of the Root of Silene macrosolen A. Rich (Caryophyllaceae) on Plasmodium - berghei -Infected Mice.

Author: Atsbha GH, Balasubramanian R, and Gebre AK
Abstract: Background: Malaria remains a major public health problem globally. Poor access to antimalarial drugs compounded with rapidly evolving drug resistance encourages researchers to continuously look for new drugs. Of importance, traditionally used medicines of plant origin are the highest priority as the ethnobotanical claim can be used as an important clue for its safety and efficacy profiles. Silene macrosolen A. Rich (Caryophyllaceae) has been traditionally used for malaria treatment in Ethiopia. Therefore, this study was aimed to evaluate the in vivo antimalarial activity of the plant against Plasmodium - berghei -infected (ANKA strain) Swiss albino mice., Methods: The dried powdered root of Silene macrosolen was extracted using 80% methanol. The crude extract was fractionated using chloroform, ethyl acetate, and distilled water that have different affinities to plant phytoconstituents. The in vivo antimalarial activities of the crude extract were evaluated using 4-day suppressive, prophylactic, and curative tests. The antimalarial activity of the solvent fractions was evaluated in a 4-day suppressive test. The oral acute toxicity of the crude extract was also determined according to the OECD guidelines., Results: The percentage of parasite suppression on the crude extract was 31.02%, 35.82%, and 39.23% in prophylactic, curative, and 4-day suppressive tests, respectively, at the tested dose level of 400 mg/kg. The percentages of chemosuppression of the solvent fractions (400 mg/kg) were 43.07%, 42.61%, and 38.38% in aqueous, ethyl acetate, and chloroform fractions, respectively. Both the crude extract and solvent fractions also significantly prolonged survival time except in the prophylactic test. In addition, prevention of weight loss and reduction in temperature and packed cell volume (PCV) were observed in crude extract as well as solvent fractions. The acute toxicity test of the plant extract also exhibited no sign of toxicity., Conclusion: The result indicated that Silene macrosolen has a significant antimalarial activity, justifying the traditional use of the plant material for treatment of malaria., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this research paper., (Copyright © 2021 Gebru Hagos Atsbha et al.)
Published: 2021
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32. Risk factors for COVID-19 infection, disease severity and related deaths in Africa: a systematic review.

Author: Gesesew HA, Koye DN, Fetene DM, Woldegiorgis M, Kinfu Y, Geleto AB, Melaku YA, Mohammed H, Alene KA, Awoke MA, Birhanu MM, Gebremedhin AT, Gelaw YA, Shifti DM, Muluneh MD, Tegegne TK, Abrha S, Aregay AF, Ayalew MB, Gebre AK, Gebremariam KT, Gebremedhin T, Gebremichael L, Leshargie CT, Kibret GD, Meazaw MW, Mekonnen AB, Tekle DY, Tesema AG, Tesfay FH, Tesfaye W, Wubishet BL, Dachew BA, and Adane AA
Subjects: Africa epidemiology, COVID-19 mortality, Humans, Risk Factors, Severity of Illness Index, COVID-19 epidemiology
Abstract: Objective: The aim of this study was to provide a comprehensive evidence on risk factors for transmission, disease severity and COVID-19 related deaths in Africa., Design: A systematic review has been conducted to synthesise existing evidence on risk factors affecting COVID-19 outcomes across Africa., Data Sources: Data were systematically searched from MEDLINE, Scopus, MedRxiv and BioRxiv., Eligibility Criteria: Studies for review were included if they were published in English and reported at least one risk factor and/or one health outcome. We included all relevant literature published up until 11 August 2020., Data Extraction and Synthesis: We performed a systematic narrative synthesis to describe the available studies for each outcome. Data were extracted using a standardised Joanna Briggs Institute data extraction form., Results: Fifteen articles met the inclusion criteria of which four were exclusively on Africa and the remaining 11 papers had a global focus with some data from Africa. Higher rates of infection in Africa are associated with high population density, urbanisation, transport connectivity, high volume of tourism and international trade, and high level of economic and political openness. Limited or poor access to healthcare are also associated with higher COVID-19 infection rates. Older people and individuals with chronic conditions such as HIV, tuberculosis and anaemia experience severe forms COVID-19 leading to hospitalisation and death. Similarly, high burden of chronic obstructive pulmonary disease, high prevalence of tobacco consumption and low levels of expenditure on health and low levels of global health security score contribute to COVID-19 related deaths., Conclusions: Demographic, institutional, ecological, health system and politico-economic factors influenced the spectrum of COVID-19 infection, severity and death. We recommend multidisciplinary and integrated approaches to mitigate the identified factors and strengthen effective prevention strategies., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Published: 2021
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33. Plasma apoM and S1P levels are inversely associated with mortality in African Americans with type 2 diabetes mellitus.

Author: Liu M, Frej C, Langefeld CD, Divers J, Bowden DW, Carr JJ, Gebre AK, Xu J, Larsson B, Dahlbäck B, Freedman BI, and Parks JS
Subjects: Biomarkers blood, Disease-Free Survival, Female, Humans, Male, Middle Aged, Sphingosine blood, Survival Rate, Black or African American, Apolipoproteins M blood, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 mortality, Lysophospholipids blood, Sphingosine analogs & derivatives
Abstract: apoM is a minor HDL apolipoprotein and carrier for sphingosine-1-phosphate (S1P). HDL apoM and S1P concentrations are inversely associated with atherosclerosis progression in rodents. We evaluated associations between plasma concentrations of S1P, plasma concentrations of apoM, and HDL apoM levels with prevalent subclinical atherosclerosis and mortality in the African American-Diabetes Heart Study participants (N = 545). Associations between plasma S1P, plasma apoM, and HDL apoM with subclinical atherosclerosis and mortality were assessed using multivariate parametric, nonparametric, and Cox proportional hazards models. At baseline, participants' median (25th percentile, 75th percentile) age was 55 (49, 62) years old and their coronary artery calcium (CAC) mass score was 26.5 (0.0, 346.5). Plasma S1P, plasma apoM, and HDL apoM were not associated with CAC. After 64 (57.6, 70.3) months of follow-up, 81 deaths were recorded. Higher concentrations of plasma S1P [odds ratio (OR) = 0.14, P = 0.01] and plasma apoM (OR = 0.10, P = 0.02), but not HDL apoM ( P = 0.89), were associated with lower mortality after adjusting for age, sex, statin use, CAC, kidney function, and albuminuria. We conclude that plasma S1P and apoM concentrations are inversely and independently associated with mortality, but not CAC, in African Americans with type 2 diabetes after accounting for conventional risk factors., (Copyright © 2019 Liu et al.)
Published: 2019
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34. The Effect of Metformin in Experimentally Induced Animal Models of Epileptic Seizure.

Author: Yimer EM, Surur A, Wondafrash DZ, and Gebre AK
Subjects: Animals, Behavior drug effects, Humans, Models, Animal, Anticonvulsants therapeutic use, Epilepsy drug therapy, Metformin pharmacology, Seizures drug therapy
Abstract: Background: Epilepsy is one of the common neurological illnesses which affects millions of individuals globally. Although the majority of epileptic patients have a good response for the currently available antiepileptic drugs (AEDs), about 30-40% of epileptic patients are developing resistance. In addition to low safety profiles of most of existing AEDs, there is no AED available for curative or disease-modifying actions for epilepsy so far., Objectives: This systematic review is intended to evaluate the effect of metformin in acute and chronic animal models of an epileptic seizure., Methods: We searched PubMed, SCOPUS, Sciences Direct, and grey literature in order to explore articles published in English from January 2010 to November 2018, using key terms "epilepsy," "seizure," "metformin," "oral hypoglycemic agents," and "oral antidiabetic drugs". The qualities of all the included articles were assessed according to the Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies (CAMARADES)., Results: Out of six hundred fifty original articles retrieved, eleven of them fulfilled the inclusion criteria and were included for final qualitative analysis. In these studies, metformin showed to control seizure attacks by attenuating seizure generation, delaying the onset of epilepsy, reducing hippocampal neuronal loss, and averting cognitive impairments in both acute and chronic models of an epileptic seizure. The possible mechanisms for its antiseizure or antiepileptic action might be due to activation of AMPK, antiapoptotic, antineuroinflammatory, and antioxidant properties, which possibly modify disease progression through affecting epileptogenesis., Conclusion: This review revealed the benefits of metformin in alleviating symptoms of epileptic seizure and modifying different cellular and molecular changes that affect the natural history of the disease in addition to its good safety profile.
Published: 2019
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35. Sociodemographic, Clinical Variables, and Quality of Life in Patients with Epilepsy in Mekelle City, Northern Ethiopia.

Author: Gebre AK and Haylay A
Subjects: Adolescent, Adult, Cross-Sectional Studies, Ethiopia epidemiology, Female, Hospitals, Public statistics & numerical data, Humans, Male, Middle Aged, Young Adult, Anticonvulsants therapeutic use, Educational Status, Epilepsy drug therapy, Epilepsy epidemiology, Epilepsy physiopathology, Quality of Life
Abstract: Background: Epilepsy is a chronic neurological disorder characterized by unprovoked recurrent seizure episodes. The disease has detrimental effects on social, cognitive, psychological, and physical components of life consequently quality of life of the patients. The level of the effect of the disease on quality life is influenced by different factors including the use of antiepileptic medications., Objectives: The study was aimed at assessing quality of life in patients with epilepsy and the variables affecting it in Mekelle city, northern Ethiopia., Methods: 175 patients with epilepsy aging 18 years old and above attending neurologic clinics of the two governmental hospitals available in Mekelle city were interviewed using standard and validated Tigrigna version of Quality of Life in Epilepsy Scale-31 (QOLIE-31). One-way ANOVA and independent t -test and analysis of covariance were used for data analysis., Result: The mean age of the patients was 29.36 (standard deviation (SD) 12.77) years old, and 61% of them were males while 52% of the respondents were on phenobarbitone monotherapy. The mean total QOLIE-31 score was 77.97 (SD 20.78) with the highest subscale score for medication effects and the lowest for overall quality of life (QOL) functioning with a score of 86.2 (SD 22.12) and 70.97 (SD 26.43), respectively. The patients with high seizure frequency in the past month before the current visit had a significantly low quality of life 76.81 (SD 21.11). Conversely, patients with tertiary education and above had shown a significantly high quality of life 89.52 (SD 11.85)., Conclusion: The overall QOL of the patients was good. Seizure frequency and level of education were found significant predictors of QOL showing the necessity of seizure control and patient education for improving quality of life in patients with epilepsy.
Published: 2018
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36. Tpcn2 knockout mice have improved insulin sensitivity and are protected against high-fat diet-induced weight gain.

Author: He H, Holl K, DeBehnke S, Yeo CT, Hansen P, Gebre AK, Leone-Kabler S, Ruas M, Parks JS, Parrington J, and Solberg Woods LC
Subjects: Animals, Calcium Channels metabolism, Cholesterol blood, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 genetics, Diabetes Mellitus, Type 2 metabolism, Female, Glucose Tolerance Test, Male, Mice, 129 Strain, Mice, Inbred C57BL, Mice, Knockout, Obesity etiology, Obesity metabolism, Sex Factors, Triglycerides blood, Calcium Channels genetics, Diet, High-Fat adverse effects, Insulin Resistance genetics, Obesity genetics, Weight Gain genetics
Abstract: Type 2 diabetes is a complex disorder affected by multiple genes and the environment. Our laboratory has shown that in response to a glucose challenge, two-pore channel 2 ( Tpcn2) knockout mice exhibit a decreased insulin response but normal glucose clearance, suggesting they have improved insulin sensitivity compared with wild-type mice. We tested the hypothesis that improved insulin sensitivity in Tpcn2 knockout mice would protect against the negative effects of a high fat diet. Male and female Tpcn2 knockout (KO), heterozygous (Het), and wild-type (WT) mice were fed a low-fat (LF) or high-fat (HF) diet for 24 wk. HF diet significantly increases body weight in WT mice relative to those on the LF diet; this HF diet-induced increase in body weight is blunted in the Het and KO mice. Despite the protection against diet-induced weight gain, however, Tpcn2 KO mice are not protected against HF-diet-induced changes in glucose or insulin area under the curve during glucose tolerance tests in female mice, while HF diet has no significant effect on glucose tolerance in the male mice, regardless of genotype. Glucose disappearance during an insulin tolerance test is augmented in male KO mice, consistent with our previous findings suggesting enhanced insulin sensitivity in these mice. Male KO mice exhibit increased fasting plasma total cholesterol and triglyceride concentrations relative to WT mice on the LF diet, but this difference disappears in HF diet-fed mice where there is increased cholesterol and triglycerides across all genotypes. These data demonstrate that knockout of Tpcn2 may increase insulin action in male, but not female, mice. In addition, both male and female KO mice are protected against diet-induced weight gain, but this protection is likely independent from glucose tolerance, insulin sensitivity, and plasma lipid levels.
Published: 2018
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37. Reactive Astrocytes as Drug Target in Alzheimer's Disease.

Author: Assefa BT, Gebre AK, and Altaye BM
Subjects: Alzheimer Disease pathology, Amyloid beta-Peptides metabolism, Animals, Aquaporin 4 metabolism, Astrocytes pathology, Calcium metabolism, Energy Metabolism, Excitatory Amino Acid Transporter 2, Glutamate Plasma Membrane Transport Proteins metabolism, Humans, Inflammation Mediators metabolism, Monoamine Oxidase metabolism, Potassium metabolism, Synapses metabolism, Synapses pathology, gamma-Aminobutyric Acid metabolism, Alzheimer Disease drug therapy, Alzheimer Disease metabolism, Astrocytes metabolism
Abstract: Alzheimer's disease is a neurodegenerative disease characterized by deposition of extracellular amyloid- β , intracellular neurofibrillary tangles, and loss of cortical neurons. However, the mechanism underlying neurodegeneration in Alzheimer's disease (AD) remains to be explored. Many of the researches on AD have been primarily focused on neuronal changes. Current research, however, broadens to give emphasis on the importance of nonneuronal cells, such as astrocytes. Astrocytes play fundamental roles in several cerebral functions and their dysfunctions promote neurodegeneration and, eventually, retraction of neuronal synapses, which leads to cognitive deficits found in AD. Astrocytes become reactive as a result of deposition of A β , which in turn have detrimental consequences, including decreased glutamate uptake due to reduced expression of uptake transporters, altered energy metabolism, altered ion homeostasis (K + and Ca + ), increased tonic inhibition, and increased release of cytokines and inflammatory mediators. In this review, recent insights on the involvement of, tonic inhibition, astrocytic glutamate transporters and aquaporin in the pathogenesis of Alzheimer's disease are provided. Compounds which increase expression of GLT1 have showed efficacy for AD in preclinical studies. Tonic inhibition mediated by GABA could also be a promising target and drugs that block the GABA synthesizing enzyme, MAO-B, have shown efficacy. However, there are contradictory evidences on the role of AQP4 in AD.
Published: 2018
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38. Drug Resistance Patterns of Escherichia coli in Ethiopia: A Meta-Analysis.

Author: Tuem KB, Gebre AK, Atey TM, Bitew H, Yimer EM, and Berhe DF
Subjects: Anti-Bacterial Agents pharmacology, Confidence Intervals, Escherichia coli drug effects, Ethiopia, Geography, Humans, Microbial Sensitivity Tests, Drug Resistance, Bacterial drug effects, Escherichia coli physiology
Abstract: Background: Antimicrobial drug resistance is a global threat for treatment of infectious diseases and costs life and money and threatens health delivery system's effectiveness. The resistance of E. coli to frequently utilized antimicrobial drugs is becoming a major challenge in Ethiopia. However, there is no inclusive countrywide study. Therefore, this study intended to assess the prevalence of E. coli resistance and antimicrobial-specific resistance pattern among E. coli clinical isolates in Ethiopia., Methods: Articles were retrieved from PubMed, Embase, and grey literature from 2007 to 2017. The main outcome measures were overall E. coli and drug-specific resistance patterns. A random-effects model was used to determine pooled prevalence with 95% confidence interval (CI), using DerSimonian and Laird method. In addition, subgroup analysis was conducted to improve the outcome. The study bias was assessed by Begg's funnel plot. This study was registered in PROSPERO as follows: PROSPERO 2017: CRD42017070106., Results: Of 164 articles retrieved, 35 articles were included. A total of 19,235 study samples participated in the studies and 2,635 E. coli strains were isolated. Overall, E. coli antibacterial resistance was 45.38% (95% confidence interval (CI): 33.50 to 57.27). The resistance pattern ranges from 62.55% in Addis Ababa to 27.51% in Tigray region. The highest resistance of E. coli reported was to ampicillin (83.81%) and amoxicillin (75.79%), whereas only 13.55% of E. coli isolates showed resistance to nitrofurantoin., Conclusion: E. coli antimicrobial resistance remains high with disparities observed among regions. The bacterium was found to be highly resistant to aminopenicillins. The finding implies the need for effective prevention strategies for the E. coli drug resistance and calls for multifaceted approaches with full involvement of all stakeholders.
Published: 2018
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39. Targeting Renin-Angiotensin System Against Alzheimer's Disease.

Author: Gebre AK, Altaye BM, Atey TM, Tuem KB, and Berhe DF
Abstract: Renin Angiotensin System (RAS) is a hormonal system that regulates blood pressure and fluid balance through a coordinated action of renal, cardiovascular, and central nervous systems. In addition to its hemodynamic regulatory role, RAS involves in many brain activities, including memory acquisition and consolidation. This review has summarized the involvement of RAS in the pathology of Alzheimer's disease (AD), and the outcomes of treatment with RAS inhibitors. We have discussed the effect of brain RAS in the amyloid plaque (Aβ) deposition, oxidative stress, neuroinflammation, and vascular pathology which are directly and indirectly associated with AD. Angiotensin II (AngII) via AT1 receptor is reported to increase brain Aβ level via different mechanisms including increasing amyloid precursor protein (APP) mRNA, β-secretase activity, and presenilin expression. Similarly, it was associated with tau phosphorylation, and reactive oxygen species generation. However, these effects are counterbalanced by Ang II mediated AT2 signaling. The protective effect observed with angiotensin receptor blockers (ARBs) and angiotensin converting enzyme inhibitors (ACEIs) could be as the result of inhibition of Ang II signaling. ARBs also offer additional benefit by shifting the effect of Ang II toward AT2 receptor. To conclude, targeting RAS in the brain may benefit patients with AD though it still requires further in depth understanding.
Published: 2018
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40. Feeding of tobacco blend or nicotine induced weight loss associated with decreased adipocyte size and increased physical activity in male mice.

Author: Liu M, Chuang Key CC, Weckerle A, Boudyguina E, Sawyer JK, Gebre AK, Spoo W, Makwana O, and Parks JS
Subjects: Adipocytes cytology, Adipose Tissue metabolism, Animals, Blood Glucose metabolism, Cell Size, Feeding Behavior, Insulin blood, Insulin Resistance, Lipolysis, Male, Mice, Triglycerides metabolism, Adipocytes drug effects, Nicotine pharmacology, Physical Conditioning, Animal, Nicotiana, Weight Loss drug effects
Abstract: Although epidemiological data and results from rodent studies support an inverse relationship between nicotine consumption and body weight, the molecular mechanisms are poorly understood. CD-1 mice were fed a basal diet or a basal diet containing low or high dose smokeless tobacco blend or high dose nicotine tartrate for 14 weeks. High dose tobacco blend and nicotine tartrate diets vs. basal diet reduced mouse body weight (16.3% and 19.7%, respectively), epididymal (67.6% and 72.5%, respectively) and brown adipose weight (42% and 38%, respectively), epididymal adipocyte size (46.4% and 41.4%, respectively), and brown adipose tissue lipid droplet abundance, with no elevation of adipose tissue inflammation. High dose tobacco blend and nicotine diets also increased mouse physical activity and decreased respiratory exchange ratio, suggesting that high dose nicotine intake induces adipose tissue triglyceride lipolysis to provide fatty acids as an energy source. Both low and high dose tobacco blend and nicotine diet feeding vs. basal diet increased plasma insulin levels (2.9, 3.6 and 4.3-fold, respectively) and improved blood glucose disposal without affecting insulin sensitivity. Feeding of the high dose tobacco blend or nicotine feeding in mice induces body weight loss likely by increasing physical activity and stimulating adipose tissue triglyceride lipolysis., (Copyright © 2018. Published by Elsevier Ltd.)
Published: 2018
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41. Slfn2 mutation-induced loss of T-cell quiescence leads to elevated de novo sterol synthesis.

Author: Omar I, Rom O, Aviram M, Cohen-Daniel L, Gebre AK, Parks JS, and Berger M
Subjects: Animals, Cell Cycle Proteins genetics, Cholesterol Esters biosynthesis, Endoplasmic Reticulum Stress, Genotype, Hydroxymethylglutaryl CoA Reductases metabolism, Lipid Droplets metabolism, Membrane Microdomains metabolism, Mice, Inbred C57BL, Mice, Mutant Strains, Monocytes immunology, Monocytes metabolism, Phenotype, T-Lymphocytes immunology, Up-Regulation, Cell Cycle Proteins metabolism, Cell Proliferation, Cellular Senescence, Cholesterol biosynthesis, Lymphocyte Activation, Mutation, T-Lymphocytes metabolism
Abstract: Acquisition of a 'quiescence programme' by naive T cells is important to provide a stress-free environment and resistance to apoptosis while preserving their responsiveness to activating stimuli. Therefore, the survival and proper function of naive T cells depends on their ability to maintain quiescence. Recently we demonstrated that by preventing chronic unresolved endoplasmic reticulum (ER) stress, Schlafen2 (Slfn2) maintains a stress-free environment to conserve a pool of naive T cells ready to respond to a microbial invasion. These findings strongly suggest an intimate association between quiescence and stress signalling. However, the connection between ER stress conditions and loss of T-cell quiescence is unknown. Here we demonstrate that homeostasis of cholesterol and lipids, is disrupted in T cells and monocytes from Slfn2-mutant, elektra, mice with higher levels of lipid rafts and lipid droplets found in these cells. Moreover, elektra T cells had elevated levels of free cholesterol and cholesteryl ester due to increased de novo synthesis and higher levels of the enzyme HMG-CoA reductase. As cholesterol plays an important role in the transition of T cells from resting to active state, and ER regulates cholesterol and lipid synthesis, we suggest that regulation of cholesterol levels through the prevention of ER stress is an essential component of the mechanism by which Slfn2 regulates quiescence., (© 2017 John Wiley & Sons Ltd.)
Published: 2017
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42. Characterization of circulating APOL1 protein complexes in African Americans.

Author: Weckerle A, Snipes JA, Cheng D, Gebre AK, Reisz JA, Murea M, Shelness GS, Hawkins GA, Furdui CM, Freedman BI, Parks JS, and Ma L
Subjects: Adult, Apolipoprotein L1, Apolipoproteins genetics, Cardiovascular Diseases blood, Cardiovascular Diseases genetics, Female, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Humans, Kidney Diseases blood, Kidney Diseases genetics, Lipoproteins, HDL genetics, Male, Middle Aged, Proteomics, Risk Factors, Black or African American, Apolipoproteins blood, Lipoproteins, HDL blood
Abstract: APOL1 gene renal-risk variants are associated with nephropathy and CVD in African Americans; however, little is known about the circulating APOL1 variant proteins which reportedly bind to HDL. We examined whether APOL1 G1 and G2 renal-risk variant serum concentrations or lipoprotein distributions differed from nonrisk G0 APOL1 in African Americans without nephropathy. Serum APOL1 protein concentrations were similar regardless of APOL1 genotype. In addition, serum APOL1 protein was bound to protein complexes in two nonoverlapping peaks, herein referred to as APOL1 complex A (12.2 nm diameter) and complex B (20.0 nm diameter). Neither of these protein complexes associated with HDL or LDL. Proteomic analysis revealed that complex A was composed of APOA1, haptoglobin-related protein (HPR), and complement C3, whereas complex B contained APOA1, HPR, IgM, and fibronectin. Serum HPR was less abundant on complex B in individuals with G1 and G2 renal-risk variant genotypes, relative to G0 (P = 0.0002-0.037). These circulating complexes may play roles in HDL metabolism and susceptibility to CVD., (Copyright © 2016 by the American Society for Biochemistry and Molecular Biology, Inc.)
Published: 2016
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43. Uncleaved ApoM signal peptide is required for formation of large ApoM/sphingosine 1-phosphate (S1P)-enriched HDL particles.

Author: Liu M, Allegood J, Zhu X, Seo J, Gebre AK, Boudyguina E, Cheng D, Chuang CC, Shelness GS, Spiegel S, and Parks JS
Subjects: Animals, Apolipoproteins chemistry, Apolipoproteins M, Male, Mice, Mice, Inbred C57BL, Sphingosine chemistry, Apolipoproteins metabolism, Lipoproteins, HDL chemistry, Lysophospholipids chemistry, Protein Sorting Signals, Sphingosine analogs & derivatives
Abstract: Apolipoprotein M (apoM), a plasma sphingosine 1-phosphate (S1P) carrier, associates with plasma HDL via its uncleaved signal peptide. Hepatocyte-specific apoM overexpression in mice stimulates formation of both larger nascent HDL in hepatocytes and larger mature apoM/S1P-enriched HDL particles in plasma by enhancing hepatic S1P synthesis and secretion. Mutagenesis of apoM glutamine 22 to alanine (apoM(Q22A)) introduces a functional signal peptidase cleavage site. Expression of apoM(Q22A) in ABCA1-expressing HEK293 cells resulted in the formation of smaller nascent HDL particles compared with wild type apoM (apoM(WT)). When apoM(Q22A) was expressed in vivo, using recombinant adenoviruses, smaller plasma HDL particles and decreased plasma S1P and apoM were observed relative to expression of apoM(WT). Hepatocytes isolated from both apoM(WT)- and apoM(Q22A)-expressing mice displayed an equivalent increase in cellular levels of S1P, relative to LacZ controls; however, relative to apoM(WT), apoM(Q22A) hepatocytes displayed more rapid apoM and S1P secretion but minimal apoM(Q22A) bound to nascent lipoproteins. Pharmacologic inhibition of ceramide synthesis increased cellular sphingosine and S1P but not medium S1P in both apoM(WT) and apoM(Q22A) hepatocytes. We conclude that apoM secretion is rate-limiting for hepatocyte S1P secretion and that its uncleaved signal peptide delays apoM trafficking out of the cell, promoting formation of larger nascent apoM- and S1P-enriched HDL particles that are probably precursors of larger apoM/S1P-enriched plasma HDL., (© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.)
Published: 2015
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44. Myeloid cell-specific ATP-binding cassette transporter A1 deletion has minimal impact on atherogenesis in atherogenic diet-fed low-density lipoprotein receptor knockout mice.

Author: Bi X, Zhu X, Gao C, Shewale S, Cao Q, Liu M, Boudyguina E, Gebre AK, Wilson MD, Brown AL, and Parks JS
Subjects: Animals, Aorta metabolism, Aorta pathology, Atherosclerosis genetics, Atherosclerosis prevention & control, Bone Marrow Transplantation, Cholesterol, LDL blood, Cholesterol, VLDL blood, Female, Genotype, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Receptors, LDL deficiency, Receptors, LDL genetics, Triglycerides blood, ATP Binding Cassette Transporter 1 physiology, Atherosclerosis metabolism, Cholesterol metabolism, Diet, Atherogenic adverse effects, Macrophages, Peritoneal metabolism, Myeloid Cells metabolism
Abstract: Objective: Transplantation studies suggest that bone marrow cell ATP-binding cassette transporter A1 protects against atherosclerosis development. However, the in vivo effect of macrophage ATP-binding cassette transporter A1 expression on atherogenesis is not fully understood because bone marrow contains other leukocytes and hematopoietic stem and progenitor cells. Myeloid-specific ATP-binding cassette transporter A1 knockout mice in the low-density lipoprotein (LDL) receptor knockout C57BL/6 background were developed to address this question., Approach and Results: Chow-fed myeloid-specific ATP-binding cassette transporter A1 knockout/LDL receptor knockout (double knockout [DKO]) versus LDL receptor knockout (single knockout [SKO]) mice had similar plasma lipid concentrations, but atherogenic diet (AD)-fed DKO mice had reduced plasma very-LDL (VLDL)/LDL concentrations resulting from decreased hepatic VLDL triglyceride secretion. Resident peritoneal macrophages from AD-fed DKO versus SKO mice had significantly higher cholesterol content but similar proinflammatory gene expression. Atherosclerosis extent was similar between genotypes after 10 to 16 weeks of AD but increased modestly in DKO mice by 24 weeks of AD. Lesional macrophage content was similar, likely because of the higher monocyte flux through aortic root lesions in DKO versus SKO mice. After transplantation of DKO or SKO bone marrow into SKO mice and 16 weeks of AD feeding, atherosclerosis extent was similar and plasma apolipoprotein B lipoproteins were reduced in mice receiving DKO bone marrow. When differences in plasma VLDL/LDL concentrations were minimized by maintaining mice on chow for 24 weeks, DKO mice had modest, but significantly more, atherosclerosis compared with SKO mice., Conclusions: Myeloid cell ATP-binding cassette transporter A1 increases hepatic VLDL triglyceride secretion and plasma VLDL/LDL concentrations in AD-fed LDL receptor knockout mice, offsetting its atheroprotective role in decreasing macrophage cholesterol content, resulting in a minimal increase in atherosclerosis., (© 2014 American Heart Association, Inc.)
Published: 2014
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45. Hepatic apolipoprotein M (apoM) overexpression stimulates formation of larger apoM/sphingosine 1-phosphate-enriched plasma high density lipoprotein.

Author: Liu M, Seo J, Allegood J, Bi X, Zhu X, Boudyguina E, Gebre AK, Avni D, Shah D, Sorci-Thomas MG, Thomas MJ, Shelness GS, Spiegel S, and Parks JS
Subjects: Animals, Apolipoproteins E blood, Apolipoproteins M, Female, Humans, Lipoproteins, HDL blood, Lipoproteins, HDL chemistry, Liver cytology, Lysophospholipids biosynthesis, Macrophages metabolism, Male, Mice, Mice, Inbred C57BL, Mice, Inbred DBA, Mice, Transgenic, Particle Size, Phosphatidylcholine-Sterol O-Acyltransferase metabolism, Sphingosine biosynthesis, Sphingosine metabolism, Apolipoproteins genetics, Apolipoproteins metabolism, Hepatocytes metabolism, Lipocalins genetics, Lipocalins metabolism, Lipoproteins, HDL metabolism, Liver metabolism, Lysophospholipids metabolism, Sphingosine analogs & derivatives
Abstract: Apolipoprotein M (apoM), a lipocalin family member, preferentially associates with plasma HDL and binds plasma sphingosine 1-phosphate (S1P), a signaling molecule active in immune homeostasis and endothelial barrier function. ApoM overexpression in ABCA1-expressing HEK293 cells stimulated larger nascent HDL formation, compared with cells that did not express apoM; however, the in vivo role of apoM in HDL metabolism remains poorly understood. To test whether hepatic apoM overexpression increases plasma HDL size, we generated hepatocyte-specific apoM transgenic (APOM Tg) mice, which had an ∼3-5-fold increase in plasma apoM levels compared with wild-type mice. Although HDL cholesterol concentrations were similar to wild-type mice, APOM Tg mice had larger plasma HDLs enriched in apoM, cholesteryl ester, lecithin:cholesterol acyltransferase, and S1P. Despite the presence of larger plasma HDLs in APOM Tg mice, in vivo macrophage reverse cholesterol transport capacity was similar to that in wild-type mice. APOM Tg mice had an ∼5-fold increase in plasma S1P, which was predominantly associated with larger plasma HDLs. Primary hepatocytes from APOM Tg mice generated larger nascent HDLs and displayed increased sphingolipid synthesis and S1P secretion. Inhibition of ceramide synthases in hepatocytes increased cellular S1P levels but not S1P secretion, suggesting that apoM is rate-limiting in the export of hepatocyte S1P. Our data indicate that hepatocyte-specific apoM overexpression generates larger nascent HDLs and larger plasma HDLs, which preferentially bind apoM and S1P, and stimulates S1P biosynthesis for secretion. The unique apoM/S1P-enriched plasma HDL may serve to deliver S1P to extrahepatic tissues for atheroprotection and may have other as yet unidentified functions.
Published: 2014
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46. Liver ABCA1 deletion in LDLrKO mice does not impair macrophage reverse cholesterol transport or exacerbate atherogenesis.

Author: Bi X, Zhu X, Duong M, Boudyguina EY, Wilson MD, Gebre AK, and Parks JS
Subjects: ATP Binding Cassette Transporter 1 genetics, Animals, Aortic Diseases etiology, Aortic Diseases genetics, Aortic Diseases pathology, Apolipoprotein B-100, Apolipoproteins B blood, Atherosclerosis etiology, Atherosclerosis genetics, Atherosclerosis pathology, Bile metabolism, Biological Transport, Cell Line, Cholesterol blood, Cholesterol, HDL blood, Cholesterol, LDL blood, Cholesterol, VLDL blood, Diet, Atherogenic, Disease Models, Animal, Feces chemistry, Female, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Receptors, LDL genetics, Time Factors, ATP Binding Cassette Transporter 1 deficiency, Aortic Diseases metabolism, Atherosclerosis metabolism, Cholesterol metabolism, Liver metabolism, Macrophages, Peritoneal metabolism, Receptors, LDL deficiency
Abstract: Objective: Hepatic ATP binding cassette transporter A1 (ABCA1) expression is critical for maintaining plasma high-density lipoprotein (HDL) concentrations, but its role in macrophage reverse cholesterol transport and atherosclerosis is not fully understood. We investigated atherosclerosis development and reverse cholesterol transport in hepatocyte-specific ABCA1 knockout (HSKO) mice in the low-density lipoprotein (LDL) receptor KO (LDLrKO) C57BL/6 background., Approach and Results: Male and female LDLrKO and HSKO/LDLrKO mice were switched from chow at 8 weeks of age to an atherogenic diet (10% palm oil, 0.2% cholesterol) for 16 weeks. Chow-fed HSKO/LDLrKO mice had HDL concentrations 10% to 20% of LDLrKO mice, but similar very low-density lipoprotein and LDL concentrations. Surprisingly, HSKO/LDLrKO mice fed the atherogenic diet had significantly lower (40% to 60%) very low-density lipoprotein, LDL, and HDL concentrations (50%) compared with LDLrKO mice. Aortic surface lesion area and cholesterol content were similar for both genotypes of mice, but aortic root intimal area was significantly lower (20% to 40%) in HSKO/LDLrKO mice. Although macrophage (3)H-cholesterol efflux to apoB lipoprotein-depleted plasma was 24% lower for atherogenic diet-fed HSKO/LDLrKO versus LDLrKO mice, variation in percentage efflux among individual mice was <2-fold compared with a 10-fold variation in plasma HDL concentrations, suggesting that HDL levels, per se, were not the primary determinant of plasma efflux capacity. In vivo reverse cholesterol transport, resident peritoneal macrophage sterol content, biliary lipid composition, and fecal cholesterol mass were similar between both genotypes of mice., Conclusions: The markedly reduced plasma HDL pool in HSKO/LDLrKO mice is sufficient to maintain macrophage reverse cholesterol transport, which, along with reduced plasma very low-density lipoprotein and LDL concentrations, prevented the expected increase in atherosclerosis.
Published: 2013
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47. Myeloid cell-specific ABCA1 deletion does not worsen insulin resistance in HF diet-induced or genetically obese mouse models.

Author: Zhu X, Chung S, Bi X, Chuang CC, Brown AL, Liu M, Seo J, Cuffe H, Gebre AK, Boudyguina E, and Parks JS
Subjects: ATP Binding Cassette Transporter 1 deficiency, Adipose Tissue, White immunology, Adipose Tissue, White metabolism, Animals, Cells, Cultured, Chemokines metabolism, Cholesterol blood, Epididymis immunology, Epididymis metabolism, Inflammation Mediators metabolism, Lipid Metabolism, Liver metabolism, Male, Mice, Mice, Inbred C57BL, Mice, Obese, Mice, Transgenic, Obesity etiology, Obesity immunology, ATP Binding Cassette Transporter 1 genetics, Diet, High-Fat adverse effects, Insulin Resistance, Macrophages metabolism, Obesity metabolism
Abstract: Obesity-associated low-grade chronic inflammation plays an important role in the development of insulin resistance. The membrane lipid transporter ATP-binding cassette transporter A1 (ABCA1) promotes formation of nascent HDL particles. ABCA1 also dampens macrophage inflammation by reducing cellular membrane cholesterol and lipid raft content. We tested the hypothesis that myeloid-specific ABCA1 deletion may exacerbate insulin resistance by increasing the obesity-associated chronic low-grade inflammation. Myeloid cell-specific ABCA1 knockout (MSKO) and wild-type (WT) mice developed obesity, insulin resistance, mild hypercholesterolemia, and hepatic steatosis to a similar extent with a 45% high-fat (HF) diet feeding or after crossing into the ob/ob background. Resident peritoneal macrophages and stromal vascular cells from obese MSKO mice accumulated significantly more cholesterol. Relative to chow, HF diet markedly induced macrophage infiltration and inflammatory cytokine expression to a similar extent in adipose tissue of WT and MSKO mice. Among pro-inflammatory cytokines examined, only IL-6 was highly upregulated in MSKO-ob/ob versus ob/ob mouse peritoneal macrophages, indicating a nonsignificant effect of myeloid ABCA1 deficiency on obesity-associated chronic inflammation. In conclusion, myeloid-specific ABCA1 deficiency does not exacerbate obesity-associated low-grade chronic inflammation and has minimal impact on the pathogenesis of insulin resistance in both HF diet-induced and genetically obese mouse models.
Published: 2013
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48. Echium oil reduces plasma triglycerides by increasing intravascular lipolysis in apoB100-only low density lipoprotein (LDL) receptor knockout mice.

Author: Forrest LM, Lough CM, Chung S, Boudyguina EY, Gebre AK, Smith TL, Colvin PL, and Parks JS
Subjects: Animals, Cholesterol Esters blood, Cholesterol, VLDL blood, Fish Oils pharmacology, Gene Expression, Liver drug effects, Liver metabolism, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Palm Oil, RNA genetics, RNA isolation & purification, Apolipoprotein B-100 blood, Echium chemistry, Lipolysis drug effects, Plant Oils pharmacology, Receptors, LDL blood, Triglycerides blood
Abstract: Echium oil (EO), which is enriched in SDA (18:4 n-3), reduces plasma triglyceride (TG) concentrations in humans and mice. We compared mechanisms by which EO and fish oil (FO) reduce plasma TG concentrations in mildly hypertriglyceridemic male apoB100-only LDLrKO mice. Mice were fed one of three atherogenic diets containing 0.2% cholesterol and palm oil (PO; 20%), EO (10% EO + 10% PO), or FO (10% FO + 10% PO). Livers from PO- and EO-fed mice had similar TG and cholesteryl ester (CE) content, which was significantly higher than in FO-fed mice. Plasma TG secretion was reduced in FO vs. EO-fed mice. Plasma very low density lipoprotein (VLDL) particle size was ordered: PO (63 ± 4 nm) > EO (55 ± 3 nm) > FO (40 ± 2 nm). Post-heparin lipolytic activity was similar among groups, but TG hydrolysis by purified lipoprotein lipase was significantly greater for EO and FO VLDL compared to PO VLDL. Removal of VLDL tracer from plasma was marginally faster in EO vs. PO fed mice. Our results suggest that EO reduces plasma TG primarily through increased intravascular lipolysis of TG and VLDL clearance. Finally, EO may substitute for FO to reduce plasma TG concentrations, but not hepatic steatosis in this mouse model.
Published: 2013
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49. Increased cholesterol content in gammadelta (γδ) T lymphocytes differentially regulates their activation.

Author: Cheng HY, Wu R, Gebre AK, Hanna RN, Smith DJ, Parks JS, Ley K, and Hedrick CC
Subjects: Animals, Cell Proliferation, Extracellular Signal-Regulated MAP Kinases metabolism, Gene Expression Profiling, Gene Expression Regulation, Intracellular Space metabolism, Lymphocyte Activation genetics, Membrane Microdomains metabolism, Mice, Mice, Inbred C57BL, Phenotype, Phosphorylation, Receptors, Antigen, T-Cell, alpha-beta metabolism, T-Lymphocytes cytology, T-Lymphocytes enzymology, Cholesterol metabolism, Lymphocyte Activation immunology, Receptors, Antigen, T-Cell, gamma-delta metabolism, T-Lymphocytes metabolism
Abstract: Gammadelta (γδ) T lymphocytes respond quickly upon antigen encounter to produce a cytokine response. In this study, we sought to understand how functions of γδ T cells are differentially regulated compared to αβ T cells. We found that cholesterol, an integral component of the plasma membrane and a regulator of TCR signaling, is increased in γδ T cells compared to αβ T cells, and modulates their function. Higher levels of activation markers, and increased lipid raft content in γδ cells suggest that γδ T cells are more activated. Cholesterol depletion effectively decreased lipid raft formation and activation of γδ T cells, indicating that increased cholesterol content contributes to the hyper-activated phenotype of γδ T cells, possibly through enhanced clustering of TCR signals in lipid rafts. TCR stimulation assays and western blotting revealed that instead of a lower TCR threshold, enhanced TCR signaling through ERK1/2 activation is likely the cause for high cholesterol-induced rapid activation and proliferation in γδ T cells. Our data indicate that cholesterol metabolism is differentially regulated in γδ T cells. The high intracellular cholesterol content leads to enhanced TCR signaling and increases activation and proliferation of γδ T cells.
Published: 2013
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50. Myeloid cell-specific ABCA1 deletion protects mice from bacterial infection.

Author: Zhu X, Westcott MM, Bi X, Liu M, Gowdy KM, Seo J, Cao Q, Gebre AK, Fessler MB, Hiltbold EM, and Parks JS
Subjects: ATP Binding Cassette Transporter 1, ATP-Binding Cassette Transporters genetics, ATP-Binding Cassette Transporters metabolism, Animals, Apolipoproteins E genetics, Apolipoproteins E immunology, Apolipoproteins E metabolism, Apoptosis immunology, Blotting, Western, Bone Marrow Cells immunology, Bone Marrow Cells metabolism, Bone Marrow Cells pathology, Cells, Cultured, Chemokines blood, Chemokines genetics, Chemokines immunology, Cytokines blood, Cytokines genetics, Cytokines immunology, Disease Resistance genetics, Disease Resistance immunology, Female, Host-Pathogen Interactions immunology, Listeria monocytogenes physiology, Listeriosis genetics, Listeriosis microbiology, Liver immunology, Liver microbiology, Liver pathology, Macrophages immunology, Macrophages metabolism, Macrophages pathology, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Monocytes immunology, Monocytes metabolism, Monocytes pathology, Myeloid Cells metabolism, Reverse Transcriptase Polymerase Chain Reaction, ATP-Binding Cassette Transporters immunology, Listeria monocytogenes immunology, Listeriosis immunology, Myeloid Cells immunology
Abstract: Rationale: ATP-binding cassette transporter A1 (ABCA1) plays a critical role in eliminating excess free cholesterol from tissues by effluxing cellular free cholesterol and phospholipids to lipid-poor apolipoprotein AI. Macrophage ABCA1 also dampens proinflammatory myeloid differentiation primary-response protein 88-dependent toll-like receptor signaling by reducing cellular membrane free cholesterol and lipid raft content, indicating a role of ABCA1 in innate immunity. However, whether ABCA1 expression has a role in regulating macrophage function in vivo is unknown., Objective: We investigated whether macrophage ABCA1 expression impacts host defense function, including microbial killing and chemotaxis., Methods and Results: Myeloid cell-specific ABCA1 knockout (MSKO) vs wild-type mice were infected with Listeria monocytogenes (Lm) for 36 hours or 72 hours before euthanasia. Lm-induced monocytosis was similar for wild-type and MSKO mice; however, MSKO mice were more resistant to Lm infection, with significantly less body weight loss, less Lm burden in liver and spleen, and less hepatic damage 3 days postinfection. In addition, Lm-infected MSKO mouse livers had: (1) greater monocyte chemoattractant protein-1 and macrophage inflammatory protein-2 expression; (2) more monocyte/macrophage infiltration; (3) less neutral lipid accumulation; and (4) diminished expression of lipogenic genes. MSKO macrophages showed enhanced chemotaxis toward chemokines in vitro and increased migration from peritoneum in response to lipopolysaccharide in vivo. Lm infection of wild-type macrophages markedly reduced expression of ABCA1 protein, as well as other cholesterol export proteins (such as ATP-binding cassette transporter G1 and apolipoprotein E)., Conclusions: Myeloid-specific ABCA1 deletion favors host response to and clearance of Lm. Macrophage Lm infection reduces expression of cholesterol export proteins, suggesting that diminished cholesterol efflux enhances innate immune function of macrophages.
Published: 2012
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