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5. Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Author

Burstein, Roy, Henry, Nathaniel J., Collison, Michael L., Marczak, Laurie B., Sligar, Amber, Watson, Stefanie, Marquez, Neal, Abbasalizad-Farhangi, Mahdieh, Abbasi, Masoumeh, Abd-Allah, Foad, Abdoli, Amir, Abdollahi, Mohammad, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abrigo, Michael R. M., Acharya, Dilaram, Adebayo, Oladimeji M., Adekanmbi, Victor, Adham, Davoud, Afshari, Mahdi, Aghaali, Mohammad, Ahmadi, Keivan, Ahmadi, Mehdi, Ahmadpour, Ehsan, Ahmed, Rushdia, Akal, Chalachew Genet, Akinyemi, Joshua O., Alahdab, Fares, Alam, Noore, Alamene, Genet Melak, Alene, Kefyalew Addis, Alijanzadeh, Mehran, Alinia, Cyrus, Alipour, Vahid, Aljunid, Syed Mohamed, Almalki, Mohammed J., Al-Mekhlafi, Hesham M., Altirkawi, Khalid, Alvis-Guzman, Nelson, Amegah, Adeladza Kofi, Amini, Saeed, Amit, Arianna Maever Loreche, Anbari, Zohreh, Androudi, Sofia, Anjomshoa, Mina, Ansari, Fereshteh, Antonio, Carl Abelardo T., Arabloo, Jalal, Arefi, Zohreh, Aremu, Olatunde, Armoon, Bahram, Arora, Amit, Artaman, Al, Asadi, Anvar, Asadi-Aliabadi, Mehran, Ashraf-Ganjouei, Amir, Assadi, Reza, Ataeinia, Bahar, Atre, Sachin R., Quintanilla, Beatriz Paulina Ayala, Ayanore, Martin Amogre, Azari, Samad, Babaee, Ebrahim, Babazadeh, Arefeh, Badawi, Alaa, Bagheri, Soghra, Bagherzadeh, Mojtaba, Baheiraei, Nafiseh, Balouchi, Abbas, Barac, Aleksandra, Bassat, Quique, Baune, Bernhard T., Bayati, Mohsen, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Belay, Yared Belete, Bell, Brent, Bell, Michelle L., Berbada, Dessalegn Ajema, Bernstein, Robert S., Bhattacharjee, Natalia V., Bhattarai, Suraj, Bhutta, Zulfiqar A., Bijani, Ali, Bohlouli, Somayeh, Breitborde, Nicholas J. K., Britton, Gabrielle, Browne, Annie J., Nagaraja, Sharath Burugina, Busse, Reinhard, Butt, Zahid A., Car, Josip, Cárdenas, Rosario, Castañeda-Orjuela, Carlos A., Cerin, Ester, Chanie, Wagaye Fentahun, Chatterjee, Pranab, Chu, Dinh-Toi, Cooper, Cyrus, Costa, Vera M., Dalal, Koustuv, Dandona, Lalit, Dandona, Rakhi, Daoud, Farah, Daryani, Ahmad, Das Gupta, Rajat, Davis, Ian, Davis Weaver, Nicole, Davitoiu, Dragos Virgil, De Neve, Jan-Walter, Demeke, Feleke Mekonnen, Demoz, Gebre Teklemariam, Deribe, Kebede, Desai, Rupak, Deshpande, Aniruddha, Desyibelew, Hanna Demelash, Dey, Sagnik, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Diaz, Daniel, Doshmangir, Leila, Duraes, Andre R., Dwyer-Lindgren, Laura, Earl, Lucas, Ebrahimi, Roya, Ebrahimpour, Soheil, Effiong, Andem, Eftekhari, Aziz, Ehsani-Chimeh, Elham, El Sayed, Iman, El Sayed Zaki, Maysaa, El Tantawi, Maha, El-Khatib, Ziad, Emamian, Mohammad Hassan, Enany, Shymaa, Eskandarieh, Sharareh, Eyawo, Oghenowede, Ezalarab, Maha, Faramarzi, Mahbobeh, Fareed, Mohammad, Faridnia, Roghiyeh, Faro, Andre, Fazaeli, Ali Akbar, Fazlzadeh, Mehdi, Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, João C., Filip, Irina, Fischer, Florian, Foigt, Nataliya A., Foroutan, Masoud, Francis, Joel Msafiri, Fukumoto, Takeshi, Fullman, Nancy, Gallus, Silvano, Gebre, Destallem Gebremedhin, Gebrehiwot, Tsegaye Tewelde, Gebremeskel, Gebreamlak Gebremedhn, Gessner, Bradford D., Geta, Birhanu, Gething, Peter W., Ghadimi, Reza, Ghadiri, Keyghobad, Ghajarzadeh, Mahsa, Ghashghaee, Ahmad, Gill, Paramjit Singh, Gill, Tiffany K., Golding, Nick, Gomes, Nelson G. M., Gona, Philimon N., Gopalani, Sameer Vali, Gorini, Giuseppe, Goulart, Bárbara Niegia Garcia, Graetz, Nicholas, Greaves, Felix, Green, Manfred S., Guo, Yuming, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hall, Brian James, Hamidi, Samer, Haririan, Hamidreza, Haro, Josep Maria, Hasankhani, Milad, Hasanpoor, Edris, Hasanzadeh, Amir, Hassankhani, Hadi, Hassen, Hamid Yimam, Hegazy, Mohamed I., Hendrie, Delia, Heydarpour, Fatemeh, Hird, Thomas R., Hoang, Chi Linh, Hollerich, Gillian, Rad, Enayatollah Homaie, Hoseini-Ghahfarokhi, Mojtaba, Hossain, Naznin, Hosseini, Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Househ, Mowafa, Hsairi, Mohamed, Ilesanmi, Olayinka Stephen, Imani-Nasab, Mohammad Hasan, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Islam, Nazrul, Islam, Sheikh Mohammed Shariful, Jürisson, Mikk, Balalami, Nader Jafari, Jalali, Amir, Javidnia, Javad, Jayatilleke, Achala Upendra, Jenabi, Ensiyeh, Ji, John S., Jobanputra, Yash B., Johnson, Kimberly, Jonas, Jost B., Shushtari, Zahra Jorjoran, Jozwiak, Jacek Jerzy, Kabir, Ali, Kahsay, Amaha, Kalani, Hamed, Kalhor, Rohollah, Karami, Manoochehr, Karki, Surendra, Kasaeian, Amir, Kassebaum, Nicholas J., Keiyoro, Peter Njenga, Kemp, Grant Rodgers, Khabiri, Roghayeh, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khan, Ejaz Ahmad, Khan, Junaid, Khan, Muhammad Shahzeb, Khang, Young-Ho, Khatab, Khaled, Khater, Amir, Khater, Mona M., Khatony, Alireza, Khazaei, Mohammad, Khazaei, Salman, Khazaei-Pool, Maryam, Khubchandani, Jagdish, Kianipour, Neda, Kim, Yun Jin, Kimokoti, Ruth W., Kinyoki, Damaris K., Kisa, Adnan, Kisa, Sezer, Kolola, Tufa, Kosen, Soewarta, Koul, Parvaiz A., Koyanagi, Ai, Kraemer, Moritz U. G., Krishan, Kewal, Krohn, Kris J., Kugbey, Nuworza, Kumar, G. Anil, Kumar, Manasi, Kumar, Pushpendra, Kuupiel, Desmond, Lacey, Ben, Lad, Sheetal D., Lami, Faris Hasan, Larsson, Anders O., Lee, Paul H., Leili, Mostafa, Levine, Aubrey J., Li, Shanshan, Lim, Lee-Ling, Listl, Stefan, Longbottom, Joshua, Lopez, Jaifred Christian F., Lorkowski, Stefan, Magdeldin, Sameh, Abd El Razek, Hassan Magdy, Abd El Razek, Muhammed Magdy, Majeed, Azeem, Maleki, Afshin, Malekzadeh, Reza, Malta, Deborah Carvalho, Mamun, Abdullah A., Manafi, Navid, Manda, Ana-Laura, Mansourian, Morteza, Martins-Melo, Francisco Rogerlândio, Masaka, Anthony, Massenburg, Benjamin Ballard, Maulik, Pallab K., Mayala, Benjamin K., Mazidi, Mohsen, McKee, Martin, Mehrotra, Ravi, Mehta, Kala M., Meles, Gebrekiros Gebremichael, Mendoza, Walter, Menezes, Ritesh G., Meretoja, Atte, Meretoja, Tuomo J., Mestrovic, Tomislav, Miller, Ted R., Miller-Petrie, Molly K., Mills, Edward J., Milne, George J., Mini, G. K., Mir, Seyed Mostafa, Mirjalali, Hamed, Mirrakhimov, Erkin M., Mohamadi, Efat, Mohammad, Dara K., Darwesh, Aso Mohammad, Mezerji, Naser Mohammad Gholi, Mohammed, Ammas Siraj, Mohammed, Shafiu, Mokdad, Ali H., Molokhia, Mariam, Monasta, Lorenzo, Moodley, Yoshan, Moosazadeh, Mahmood, Moradi, Ghobad, Moradi, Masoud, Moradi, Yousef, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morawska, Lidia, Mosapour, Abbas, Mousavi, Seyyed Meysam, Mueller, Ulrich Otto, Muluneh, Atalay Goshu, Mustafa, Ghulam, Nabavizadeh, Behnam, Naderi, Mehdi, Nagarajan, Ahamarshan Jayaraman, Nahvijou, Azin, Najafi, Farid, Nangia, Vinay, Ndwandwe, Duduzile Edith, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Ngunjiri, Josephine W., Thi Nguyen, Huong Lan, Nguyen, Long Hoang, Nguyen, Son Hoang, Nielsen, Katie R., Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nixon, Molly R., Nnaji, Chukwudi A., Nojomi, Marzieh, Noroozi, Mehdi, Nosratnejad, Shirin, Noubiap, Jean Jacques, Motlagh, Soraya Nouraei, Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oladimeji, Kelechi E., Olagunju, Andrew T., Olfatifar, Meysam, Olum, Solomon, Olusanya, Bolajoko Olubukunola, Oluwasanu, Mojisola Morenike, Onwujekwe, Obinna E., Oren, Eyal, Ortega-Altamirano, Doris D. V., Ortiz, Alberto, Osarenotor, Osayomwanbo, Osei, Frank B., Osgood-Zimmerman, Aaron E., Otstavnov, Stanislav S., Owolabi, Mayowa Ojo, P. A., Mahesh, Pagheh, Abdol Sattar, Pakhale, Smita, Panda-Jonas, Songhomitra, Pandey, Animika, Park, Eun-Kee, Parsian, Hadi, Pashaei, Tahereh, Patel, Sangram Kishor, Pepito, Veincent Christian Filipino, Pereira, Alexandre, Perkins, Samantha, Pickering, Brandon V., Pilgrim, Thomas, Pirestani, Majid, Piroozi, Bakhtiar, Pirsaheb, Meghdad, Plana-Ripoll, Oleguer, Pourjafar, Hadi, Puri, Parul, Qorbani, Mostafa, Quintana, Hedley, Rabiee, Mohammad, Rabiee, Navid, Radfar, Amir, Rafiei, Alireza, Rahim, Fakher, Rahimi, Zohreh, Rahimi-Movaghar, Vafa, Rahimzadeh, Shadi, Rajati, Fatemeh, Raju, Sree Bhushan, Ramezankhani, Azra, Ranabhat, Chhabi Lal, Rasella, Davide, Rashedi, Vahid, Rawal, Lal, Reiner Jr, Robert C., Renzaho, Andre M. N., Rezaei, Satar, Rezapour, Aziz, Riahi, Seyed Mohammad, Ribeiro, Ana Isabel, Roever, Leonardo, Roro, Elias Merdassa, Roser, Max, Roshandel, Gholamreza, Roshani, Daem, Rostami, Ali, Rubagotti, Enrico, Rubino, Salvatore, Sabour, Siamak, Sadat, Nafis, Sadeghi, Ehsan, Saeedi, Reza, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Sahebkar, Amirhossein, Salahshoor, Mohammad Reza, Salam, Nasir, Salamati, Payman, Salehi, Farkhonde, Zahabi, Saleh Salehi, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A., Sambala, Evanson Zondani, Samy, Abdallah M., Santric Milicevic, Milena M., Jose, Bruno Piassi Sao, Saraswathy, Sivan Yegnanarayana Iyer, Sarmiento-Suárez, Rodrigo, Sartorius, Benn, Sathian, Brijesh, Saxena, Sonia, Sbarra, Alyssa N., Schaeffer, Lauren E., Schwebel, David C., Sepanlou, Sadaf G., Seyedmousavi, Seyedmojtaba, Shaahmadi, Faramarz, Shaikh, Masood Ali, Shams-Beyranvand, Mehran, Shamshirian, Amir, Shamsizadeh, Morteza, Sharafi, Kiomars, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharifi, Hamid, Sharma, Jayendra, Sharma, Rajesh, Sheikh, Aziz, Shields, Chloe, Shigematsu, Mika, Shiri, Rahman, Shiue, Ivy, Shuval, Kerem, Siddiqi, Tariq J., Silva, João Pedro, Singh, Jasvinder A., Sinha, Dhirendra Narain, Sisay, Malede Mequanent, Sisay, Solomon, Sliwa, Karen, Smith, David L., Somayaji, Ranjani, Soofi, Moslem, Soriano, Joan B., Sreeramareddy, Chandrashekhar T., Sudaryanto, Agus, Sufiyan, Mu’awiyyah Babale, Sykes, Bryan L., Sylaja, P. N., Tabarés-Seisdedos, Rafael, Tabb, Karen M., Tabuchi, Takahiro, Taveira, Nuno, Temsah, Mohamad-Hani, Terkawi, Abdullah Sulieman, Tessema, Zemenu Tadesse, Thankappan, Kavumpurathu Raman, Thirunavukkarasu, Sathish, To, Quyen G., Tovani-Palone, Marcos Roberto, Tran, Bach Xuan, Tran, Khanh Bao, Ullah, Irfan, Usman, Muhammad Shariq, Uthman, Olalekan A., Vahedian-Azimi, Amir, Valdez, Pascual R., van Boven, Job F. M., Vasankari, Tommi Juhani, Vasseghian, Yasser, Veisani, Yousef, Venketasubramanian, Narayanaswamy, Violante, Francesco S., Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vos, Theo, Vu, Giang Thu, Vujcic, Isidora S., Waheed, Yasir, Wakefield, Jon, Wang, Haidong, Wang, Yafeng, Wang, Yuan-Pang, Ward, Joseph L., Weintraub, Robert G., Weldegwergs, Kidu Gidey, Weldesamuel, Girmay Teklay, Westerman, Ronny, Wiysonge, Charles Shey, Wondafrash, Dawit Zewdu, Woyczynski, Lauren, Wu, Ai-Min, Xu, Gelin, Yadegar, Abbas, Yamada, Tomohide, Yazdi-Feyzabadi, Vahid, Yilgwan, Christopher Sabo, Yip, Paul, Yonemoto, Naohiro, Lebni, Javad Yoosefi, Younis, Mustafa Z., Yousefifard, Mahmoud, Yousof, Hebat-Allah Salah A., Yu, Chuanhua, Yusefzadeh, Hasan, Zabeh, Erfan, Moghadam, Telma Zahirian, Bin Zaman, Sojib, Zamani, Mohammad, Zandian, Hamed, Zangeneh, Alireza, Zerfu, Taddese Alemu, Zhang, Yunquan, Ziapour, Arash, Zodpey, Sanjay, Murray, Christopher J. L., and Hay, Simon I.

Published
2019
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10. The Efficacy of Written Information Intervention in Reduction of Hospital Re-admission Cost in Patients With Heart Failure; A Systematic Review and Meta-Analysis

Author

Zarea Gavgani Vahideh, Kazemi Majd Faranak, Nosratnejad Shirin, Golmohammadi Ali, and Sadeghi Bazargani Homayoun

Subjects
Heart Failure, Patient Admission, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Objective: To assess the efficacy of written information versus non written information intervention in reducing hospital readmission cost, if prescribed or presented to the patients with HF. Methods: The study was a systematic review and meta-analysis. We searched Medline (Ovid)and Cochrane library during the past 20 years from 1993 to 2013. We also conducted a manual search through Google Scholar and a direct search in the group of related journals in Black Welland Science Direct trough their websites. Two reviewers appraised the identified studies, and meta-analysis was done to estimate the mean saving cost of patient readmission. All the included studies must have been done by randomization to be eligible for study. Result: We assessed the full-texts 3 out of 65 studies with 754 patients and average age of 74.33.The mean of estimated saving readmission cost in intervention group versus control group was US $2751 (95% CI: 2708 – 2794) and the mean of total saving cost in intervention group versus control group was US $2047 (base year 2010) with (95% CI: 2004 – 2089). No publication bias was found by testing the heterogeneity of studies. Conclusion: One of the effective factors in minimizing the healthcare cost and preventing from hospital re-admission is providing the patients with information prescription in a written format.It is suggested that hospital management, Medicare organizations, policy makers and individual physicians consider the prescription of appropriate medical information as the indispensable part of patient’s care process.

Published
2015
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12. Direct and Indirect Costs Associated with Coronary Artery (Heart) Disease in Tabriz, Iran

Author

Darba,Shahla, Safaei,Naser, Mahboub–Ahari,Alireza, Nosratnejad,Shirin, Alizadeh,Gisoo, Ameri,Hosein, and Yousefi,Mahmood

Subjects
Risk Management and Healthcare Policy
Abstract

Shahla Darba,1 Naser Safaei,2 Alireza Mahboub–Ahari,3 Shirin Nosratnejad,3 Gisoo Alizadeh,4 Hosein Ameri,5 Mahmood Yousefi3 1Department of Health Economics, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran; 2Madani Heart Center, Tabriz University of Medical Sciences, Tabriz, Iran; 3Department of Health Economics, Iranian Center of Excellence in Health Services Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran; 4Department of Health Policy and Management, Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran; 5Health Policy and Management Research, Department of Health Care Management, School of Public Health, Shahid Sadoughi University of Medical Science, Yazd, IranCorrespondence: Mahmood Yousefi Department of Health EconomicsIranian Center of Excellence in Health Services Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, IranTel +98 912 175 5785Fax +98 41-33251362Email mahmoodyousefi59@gmail.comPurpose: Cardiovascular diseases (CVDs) are the major causes of mortalities worldwide. This study was conducted to evaluate the direct and indirect costs of coronary artery disease (CAD) in Iran.Patients and Methods: This is a prevalence-based cost-of-illness (COI) study that estimates the direct and indirect costs of CAD. The study conducted over a six-month period from April to September in 2017. Patients were recruited from Madani hospital in Tabriz, Iran. A total of 379 patients were investigated from societal perspective. Direct costs were estimated using the bottom-up costing approach and indirect costs were estimated using the Human Capital (HC) approach. A generalized linear model of regression was used to explore the relation between total cost and socio-demographic variables. The total annual mean cost was compared to Gross Domestic Product (GDP)per capita which was reported in the form of Purchasing Power Parity (PPP) index. To deal with uncertainty, one-way sensitivity analysis was performed.Results: Total costs per patient in one year were estimated to be IRR 63452290.17 ($PPP 7736.19) at a 95% confidence interval (58191511.73– 68713068.60), the biggest part of which is related to direct medical costs with IRR 33884019.53 per year ($PPP 4131.18) (54%). Direct non-medical costs were estimated IRR 1655936.68 ($PPP 201.89) per patient (2%) and indirect costs were estimated IRR 27912333.97 per patient ($PPP 3403.11) (44%), which 62% of indirect costs is related to patients’ work absenteeism.Conclusion: This study estimates the direct (56%) and indirect (44%) costs associated with CAD. The study explores the essential drivers of the costs and provides the magnitude of the burden in terms of the share of GDP. The outcomes can be used in priority setting, in particular for cost benefit analysis, and adopting new policies regarding insurance coverage and equity issues.Keywords: coronary heart disease (CHD), direct cost, indirect cost, cost-disease analysis

Published
2020

18. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017

Author

Fitzmaurice, Christina, Abate, Degu, Abbasi, Naghmeh, Abbastabar, Hedayat, Abd-Allah, Foad, Abdel-Rahman, Omar, Abdelalim, Ahmed, Abdoli, Amir, Abdollahpour, Ibrahim, Abdulle, Abdishakur SM, Abebe, Nebiyu Dereje, Abraha, Haftom Niguse, Abu-Raddad, Laith Jamal, Abualhasan, Ahmed, Adedeji, Isaac Akinkunmi, Advani, Shailesh M, Afarideh, Mohsen, Afshari, Mahdi, Aghaali, Mohammad, Agius, Dominic, Agrawal, Sutapa, Ahmadi, Ayat, Ahmadian, Elham, Ahmadpour, Ehsan, Ahmed, Muktar Beshir, Akbari, Mohammad Esmaeil, Akinyemiju, Tomi, Al-Aly, Ziyad, AlAbdulKader, Assim M, Alahdab, Fares, Alam, Tahiya, Alamene, Genet Melak, Alemnew, Birhan Tamene T, Alene, Kefyalew Addis, Alinia, Cyrus, Alipour, Vahid, Aljunid, Syed Mohamed, Bakeshei, Fatemeh Allah, Almadi, Majid Abdulrahman Hamad, Almasi-Hashiani, Amir, Alsharif, Ubai, Alsowaidi, Shirina, Alvis-Guzman, Nelson, Amini, Erfan, Amini, Saeed, Amoako, Yaw Ampem, Anbari, Zohreh, Anber, Nahla Hamed, Andrei, Catalina Liliana, Anjomshoa, Mina, Ansari, Fereshteh, Ansariadi, Ansariadi, Appiah, Seth Christopher Yaw, Arab-Zozani, Morteza, Arabloo, Jalal, Arefi, Zohreh, Aremu, Olatunde, Areri, Habtamu Abera, Artaman, Al, Asayesh, Hamid, Asfaw, Ephrem Tsegay, Ashagre, Alebachew Fasil, Assadi, Reza, Ataeinia, Bahar, Atalay, Hagos Tasew, Ataro, Zerihun, Atique, Suleman, Ausloos, Marcel, Avila-Burgos, Leticia, Avokpaho, Euripide FGA, Awasthi, Ashish, Awoke, Nefsu, Ayala Quintanilla, Beatriz Paulina, Ayanore, Martin Amogre, Ayele, Henok Tadesse, Babaee, Ebrahim, Bacha, Umar, Badawi, Alaa, Bagherzadeh, Mojtaba, Bagli, Eleni, Balakrishnan, Senthilkuimar, Balouchi, Abbas, Bärnighausen, Till Winfried, Battista, Robert J, Behzadifar, Masoud, Behzadifar, Meysam, Bekele, Bayu Begashaw, Belay, Yared Belete, Belayneh, Yaschilal Muche, Berfield, Kathleen Kim Sachiko, Berhane, Adugnaw, Bernabe, Eduardo, Beuran, Mircea, Bhakta, Nickhill, Bhattacharyya, Krittika, Biadgo, Belete, Bijani, Ali, Bin Sayeed, Muhammad Shahdaat, Birungi, Charles, Bisignano, Catherine, Bitew, Helen, Bjørge, Tone, Bleyer, Archie, Bogale, Kassawmar Angaw, Bojia, Hunduma Amensisa, Borzì, Antonio M, Bosetti, Cristina, Bou-Orm, Ibrahim R, Brenner, Hermann, Brewer, Jerry D, Briko, Andrey Nikolaevich, Briko, Nikolay Ivanovich, Bustamante-Teixeira, Maria Teresa, Butt, Zahid A, Carreras, Giulia, Carrero, Juan J, Carvalho, Félix, Castro, Clara, Castro, Franz, Catalá-López, Ferrán, Cerin, Ester, Chaiah, Yazan, Chanie, Wagaye Fentahun, Chattu, Vijay Kumar, Chaturvedi, Pankaj, Chauhan, Neelima Singh, Chehrazi, Mohammad, Chiang, Peggy Pei-Chia, Chichiabellu, Tesfaye Yitna, Chido-Amajuoyi, Onyema Greg, Chimed-Ochir, Odgerel, Choi, Jee-Young J, Christopher, Devasahayam J, Chu, Dinh-Toi, Constantin, Maria-Magdalena, Costa, Vera M, Crocetti, Emanuele, Crowe, Christopher Stephen, Curado, Maria Paula, Dahlawi, Saad MA, Damiani, Giovanni, Darwish, Amira Hamed, Daryani, Ahmad, das Neves, José, Demeke, Feleke Mekonnen, Demis, Asmamaw Bizuneh, Demissie, Birhanu Wondimeneh, Demoz, Gebre Teklemariam, Denova-Gutiérrez, Edgar, Derakhshani, Afshin, Deribe, Kalkidan Solomon, Desai, Rupak, Desalegn, Beruk Berhanu, Desta, Melaku, Dey, Subhojit, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Diaz, Daniel, Dinberu, Mesfin Tadese Tadese, Djalalinia, Shirin, Doku, David Teye, Drake, Thomas M, Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Ebrahimi, Hedyeh, Effiong, Andem, Eftekhari, Aziz, El Sayed, Iman, Zaki, Maysaa El Sayed, El-Jaafary, Shaimaa I, El-Khatib, Ziad, Elemineh, Demelash Abewa, Elkout, Hajer, Ellenbogen, Richard G, Elsharkawy, Aisha, Emamian, Mohammad Hassan, Endalew, Daniel Adane, Endries, Aman Yesuf, Eshrati, Babak, Fadhil, Ibtihal, Fallah, Vahid, Faramarzi, Mahbobeh, Farhangi, Mahdieh Abbasalizad, Farioli, Andrea, Farzadfar, Farshad, Fentahun, Netsanet, Fernandes, Eduarda, Feyissa, Garumma Tolu, Filip, Irina, Fischer, Florian, Fisher, James L, Force, Lisa M, Foroutan, Masoud, Freitas, Marisa, Fukumoto, Takeshi, Futran, Neal D, Gallus, Silvano, Gankpe, Fortune Gbetoho, Gayesa, Reta Tsegaye, Gebrehiwot, Tsegaye Tewelde, Gebremeskel, Gebreamlak Gebremedhn, Gedefaw, Getnet Azeze, Gelaw, Belayneh K, Geta, Birhanu, Getachew, Sefonias, Gezae, Kebede Embaye, Ghafourifard, Mansour, Ghajar, Alireza, Ghashghaee, Ahmad, Gholamian, Asadollah, Gill, Paramjit Singh, Ginindza, Themba TG, Girmay, Alem, Gizaw, Muluken, Gomez, Ricardo Santiago, Gopalani, Sameer Vali, Gorini, Giuseppe, Goulart, Bárbara Niegia Garcia, Grada, Ayman, Ribeiro Guerra, Maximiliano, Guimaraes, Andre Luiz Sena, Gupta, Prakash C, Gupta, Rahul, Hadkhale, Kishor, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hamadeh, Randah R, Hamidi, Samer, Hanfore, Lolemo Kelbiso, Haro, Josep Maria, Hasankhani, Milad, Hasanzadeh, Amir, Hassen, Hamid Yimam, Hay, Roderick J, Hay, Simon I, Henok, Andualem, Henry, Nathaniel J, Herteliu, Claudiu, Hidru, Hagos D, Hoang, Chi Linh, Hole, Michael K, Hoogar, Praveen, Horita, Nobuyuki, Hosgood, H Dean, Hosseini, Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Househ, Mowafa, Hussen, Mohammedaman Mama, Ileanu, Bogdan, Ilic, Milena D, Innos, Kaire, Irvani, Seyed Sina Naghibi, Iseh, Kufre Robert, Islam, Sheikh Mohammed Shariful, Islami, Farhad, Jafari Balalami, Nader, Jafarinia, Morteza, Jahangiry, Leila, Jahani, Mohammad Ali, Jahanmehr, Nader, Jakovljevic, Mihajlo, James, Spencer L, Javanbakht, Mehdi, Jayaraman, Sudha, Jee, Sun Ha, Jenabi, Ensiyeh, Jha, Ravi Prakash, Jonas, Jost B, Jonnagaddala, Jitendra, Joo, Tamas, Jungari, Suresh Banayya, Jürisson, Mikk, Kabir, Ali, Kamangar, Farin, Karch, André, Karimi, Narges, Karimian, Ansar, Kasaeian, Amir, Kasahun, Gebremicheal Gebreslassie, Kassa, Belete, Kassa, Tesfaye Dessale, Kassaw, Mesfin Wudu, Kaul, Anil, Keiyoro, Peter Njenga, Kelbore, Abraham Getachew, Kerbo, Amene Abebe, Khader, Yousef Saleh, Khalilarjmandi, Maryam, Khan, Ejaz Ahmad, Khan, Gulfaraz, Khang, Young-Ho, Khatab, Khaled, Khater, Amir, Khayamzadeh, Maryam, Khazaee-Pool, Maryam, Khazaei, Salman, Khoja, Abdullah T, Khosravi, Mohammad Hossein, Khubchandani, Jagdish, Kianipour, Neda, Kim, Daniel, Kim, Yun Jin, Kisa, Adnan, Kisa, Sezer, Kissimova-Skarbek, Katarzyna, Komaki, Hamidreza, Koyanagi, Ai, Krohn, Kristopher J, Bicer, Burcu Kucuk, Kugbey, Nuworza, Kumar, Vivek, Kuupiel, Desmond, La Vecchia, Carlo, Lad, Deepesh P, Lake, Eyasu Alem, Lakew, Ayenew Molla, Lal, Dharmesh Kumar, Lami, Faris Hasan, Lan, Qing, Lasrado, Savita, Lauriola, Paolo, Lazarus, Jeffrey V, Leigh, James, Leshargie, Cheru Tesema, Liao, Yu, Limenih, Miteku Andualem, Listl, Stefan, Lopez, Alan D, Lopukhov, Platon D, Lunevicius, Raimundas, Madadin, Mohammed, Magdeldin, Sameh, El Razek, Hassan Magdy Abd, Majeed, Azeem, Maleki, Afshin, Malekzadeh, Reza, Manafi, Ali, Manafi, Navid, Manamo, Wondimu Ayele, Mansourian, Morteza, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Maroufizadeh, Saman, Martini, Santi Martini S, Mashamba-Thompson, Tivani Phosa, Massenburg, Benjamin Ballard, Maswabi, Motswadi Titus, Mathur, Manu Raj, McAlinden, Colm, McKee, Martin, Meheretu, Hailemariam Abiy Alemu, Mehrotra, Ravi, Mehta, Varshil, Meier, Toni, Melaku, Yohannes A, Meles, Gebrekiros Gebremichael, Meles, Hagazi Gebre, Melese, Addisu, Melku, Mulugeta, Memiah, Peter TN, Mendoza, Walter, Menezes, Ritesh G, Merat, Shahin, Meretoja, Tuomo J, Mestrovic, Tomislav, Miazgowski, Bartosz, Miazgowski, Tomasz, Mihretie, Kebadnew Mulatu M, Miller, Ted R, Mills, Edward J, Mir, Seyed Mostafa, Mirzaei, Hamed, Mirzaei, Hamid Reza, Mishra, Rashmi, Moazen, Babak, Mohammad, Dara K, Mohammad, Karzan Abdulmuhsin, Mohammad, Yousef, Darwesh, Aso Mohammad, Mohammadbeigi, Abolfazl, Mohammadi, Hiwa, Mohammadi, Moslem, Mohammadian, Mahdi, Mohammadian-Hafshejani, Abdollah, Mohammadoo-Khorasani, Milad, Mohammadpourhodki, Reza, Mohammed, Ammas Siraj, Mohammed, Jemal Abdu, Mohammed, Shafiu, Mohebi, Farnam, Mokdad, Ali H, Monasta, Lorenzo, Moodley, Yoshan, Moosazadeh, Mahmood, Moossavi, Maryam, Moradi, Ghobad, Moradi-Joo, Mohammad, Moradi-Lakeh, Maziar, Moradpour, Farhad, Morawska, Lidia, Morgado-da-Costa, Joana, Morisaki, Naho, Morrison, Shane Douglas, Mosapour, Abbas, Mousavi, Seyyed Meysam, Muche, Achenef Asmamaw, Muhammed, Oumer Sada S, Musa, Jonah, Nabhan, Ashraf R, Naderi, Mehdi, Nagarajan, Ahamarshan Jayaraman, Nagel, Gabriele, Nahvijou, Azin, Naik, Gurudatta, Najafi, Farid, Naldi, Luigi, Nam, Hae Sung, Nasiri, Naser, Nazari, Javad, Negoi, Ionut, Neupane, Subas, Newcomb, Polly A, Nggada, Haruna Asura, Ngunjiri, Josephine W, Nguyen, Cuong Tat, Nikniaz, Leila, Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nixon, Molly R, Nnaji, Chukwudi A, Nojomi, Marzieh, Nosratnejad, Shirin, Shiadeh, Malihe Nourollahpour, Obsa, Mohammed Suleiman, Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oh, In-Hwan, Olagunju, Andrew T, Olagunju, Tinuke O, Oluwasanu, Mojisola Morenike, Omonisi, Abidemi E, Onwujekwe, Obinna E, Oommen, Anu Mary, Oren, Eyal, Ortega-Altamirano, Doris DV, Ota, Erika, Otstavnov, Stanislav S, Owolabi, Mayowa Ojo, P A, Mahesh, Padubidri, Jagadish Rao, Pakhale, Smita, Pakpour, Amir H, Pana, Adrian, Park, Eun-Kee, Parsian, Hadi, Pashaei, Tahereh, Patel, Shanti, Patil, Snehal T, Pennini, Alyssa, Pereira, David M, Piccinelli, Cristiano, Pillay, Julian David, Pirestani, Majid, Pishgar, Farhad, Postma, Maarten J, Pourjafar, Hadi, Pourmalek, Farshad, Pourshams, Akram, Prakash, Swayam, Prasad, Narayan, Qorbani, Mostafa, Rabiee, Mohammad, Rabiee, Navid, Radfar, Amir, Rafiei, Alireza, Rahim, Fakher, Rahimi, Mahdi, Rahman, Muhammad Aziz, Rajati, Fatemeh, Rana, Saleem M, Raoofi, Samira, Rath, Goura Kishor, Rawaf, David Laith, Rawaf, Salman, Reiner, Robert C, Renzaho, Andre MN, Rezaei, Nima, Rezapour, Aziz, Ribeiro, Ana Isabel, Ribeiro, Daniela, Ronfani, Luca, Roro, Elias Merdassa, Roshandel, Gholamreza, Rostami, Ali, Saad, Ragy Safwat, Sabbagh, Parisa, Sabour, Siamak, Saddik, Basema, Safiri, Saeid, Sahebkar, Amirhossein, Salahshoor, Mohammad Reza, Salehi, Farkhonde, Salem, Hosni, Salem, Marwa Rashad, Salimzadeh, Hamideh, Salomon, Joshua A, Samy, Abdallah M, Sanabria, Juan, Santric Milicevic, Milena M, Sartorius, Benn, Sarveazad, Arash, Sathian, Brijesh, Satpathy, Maheswar, Savic, Miloje, Sawhney, Monika, Sayyah, Mehdi, Schneider, Ione JC, Schöttker, Ben, Sekerija, Mario, Sepanlou, Sadaf G, Sepehrimanesh, Masood, Seyedmousavi, Seyedmojtaba, Shaahmadi, Faramarz, Shabaninejad, Hosein, Shahbaz, Mohammad, Shaikh, Masood Ali, Shamshirian, Amir, Shamsizadeh, Morteza, Sharafi, Heidar, Sharafi, Zeinab, Sharif, Mehdi, Sharifi, Ali, Sharifi, Hamid, Sharma, Rajesh, Sheikh, Aziz, Shirkoohi, Reza, Shukla, Sharvari Rahul, Si, Si, Siabani, Soraya, Silva, Diego Augusto Santos, Silveira, Dayane Gabriele Alves, Singh, Ambrish, Singh, Jasvinder A, Sisay, Solomon, Sitas, Freddy, Sobngwi, Eugène, Soofi, Moslem, Soriano, Joan B, Stathopoulou, Vasiliki, Sufiyan, Mu’awiyyah Babale, Tabarés-Seisdedos, Rafael, Tabuchi, Takahiro, Takahashi, Ken, Tamtaji, Omid Reza, Tarawneh, Mohammed Rasoul, Tassew, Segen Gebremeskel, Taymoori, Parvaneh, Tehrani-Banihashemi, Arash, Temsah, Mohamad-Hani, Temsah, Omar, Tesfay, Berhe Etsay, Tesfay, Fisaha Haile, Teshale, Manaye Yihune, Tessema, Gizachew Assefa, Thapa, Subash, Tlaye, Kenean Getaneh, Topor-Madry, Roman, Tovani-Palone, Marcos Roberto, Traini, Eugenio, Tran, Bach Xuan, Tran, Khanh Bao, Tsadik, Afewerki Gebremeskel, Ullah, Irfan, Uthman, Olalekan A, Vacante, Marco, Vaezi, Maryam, Varona Pérez, Patricia, Veisani, Yousef, Vidale, Simone, Violante, Francesco S, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vu, Giang Thu, Vujcic, Isidora S, Wabinga, Henry, Wachamo, Tesfahun Mulatu, Wagnew, Fasil Shiferaw, Waheed, Yasir, Weldegebreal, Fitsum, Weldesamuel, Girmay Teklay, Wijeratne, Tissa, Wondafrash, Dawit Zewdu, Wonde, Tewodros Eshete, Wondmieneh, Adam Belay, Workie, Hailemariam Mekonnen, Yadav, Rajaram, Yadegar, Abbas, Yadollahpour, Ali, Yaseri, Mehdi, Yazdi-Feyzabadi, Vahid, Yeshaneh, Alex, Yimam, Mohammed Ahmed, Yimer, Ebrahim M, Yisma, Engida, Yonemoto, Naohiro, Younis, Mustafa Z, Yousefi, Bahman, Yousefifard, Mahmoud, Yu, Chuanhua, Zabeh, Erfan, Zadnik, Vesna, Moghadam, Telma Zahirian, Zaidi, Zoubida, Zamani, Mohammad, Zandian, Hamed, Zangeneh, Alireza, Zaki, Leila, Zendehdel, Kazem, Zenebe, Zerihun Menlkalew, Zewale, Taye Abuhay, Ziapour, Arash, Zodpey, Sanjay, and Murray, Christopher JL

Subjects
Global Burden of Disease Cancer Collaboration
Abstract

Importance Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data.\ud \ud Objective To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning.\ud \ud Evidence Review We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence.\ud \ud Findings In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572 000 deaths and 15.2 million DALYs), and stomach cancer (542 000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819 000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601 000 deaths and 17.4 million DALYs), TBL cancer (596 000 deaths and 12.6 million DALYs), and colorectal cancer (414 000 deaths and 8.3 million DALYs).\ud \ud Conclusions and Relevance The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer

Published
2019

22. 172: ECONOMIC BURDEN OF ROAD TRAFFIC INJURIES IN LOW AND MIDDLE INCOME COUNTRIES VERSUS HIGH INCOME COUNTRIES: A SYSTEMATIC REVIEW

Author

Zakeri, Raana and Nosratnejad, Shirin

Subjects
Oral, Abstracts from the 5th International Society for Evidence-Based Healthcare Congress, Kish Island, Ira, road traffic injuries, systematic review, low-and-middle-income-countries, economic burden, high-income-countries
Abstract

Background and aims Road traffic injury (RTI) as a major public health problem is the ninth leading cause of death globally. Cases of RTIs are increasing in most of the regions of the world. Research into RTIs indicates that there are distinct differences in the pattern of economic growth between high-income countries (HIC) and low-middle-income countries (LMIC). We aimed to address the economic burden of RTI among LMIC versus HIC; for this purpose we gathered the evidences through a review of the literature. Methods We systematically searched databases (PubMed, Web of Science, Science Direct, Scopus, ProQuest, SID, IranMedex and Magiran) up to February 2016 and included the original articles on economic burden of RTI in national levels. Two authors appraised the identified studies. We examined the direct, indirect and intangible costs of RTI as a percentage of GDP, and also as a percentage of household consumption expenditure in LMIC versus HIC. Results 20 studies from fifteen countries met the inclusion criteria, of which five are HIC and ten are LMIC. The mean cost of direct plus indirect costs of RTI as a percentage of GDP and as a percentage of households consumption expenditure in LMIC and HIC are respectively 1.29, 1.61 and 1.74, 3.50; and mean of total economic burden of RTI (direct+indirect+intangible cost) as a percentage of GDP and as a percentage of households consumption expenditure in LMIC versus HIC are respectively 2.35, 1.43 and 2.01, 2.90. Among LMIC, the economic burden of RTI in Iran is the highest (6.64% GDP) and among HIC, it's Australia (2.28% GDP). Conclusion The economic burden of RTI in LMIC is nearly a half of the health expenditure in these countries. Also the economic burden of RTI in HIC is a quarter of total health expenditure. This evidence emphasizes the importance of road safety interventions as cost-benefit interventions.

Published
2017

23. 70: HEALTH INSURANCE AND THE UTILIZATION OF HEALTH CARE: A SYSTEMATIC REVIEW

Author

Nosratnejad, Shirin and Shami, Elham

Subjects
Abstracts from the 5th International Society for Evidence-Based Healthcare Congress, Kish Island, Ira, Health insurance, Health care utilization, Systematic review, Poster
Abstract

Background and aims All the countries are moving towards universal coverage insurance. in their ability to achieve universal coverage, it is likely that they would have similar effects on the health care utilization of the people who gained coverage. This study aimed to examine the relationship between health insurance status and utilization of health services. Methods We conducted a systematic review using five electronic databases (PubMed, Scopus, and Web of science, ProQuest Medical Library, and Science Direct). We did the search up to April 2015. Two authors appraised the identified studies and then examined Factors associated with the coverage and utilization of health insurance of the study. We used two synthesis approaches: narrative synthesis and vote- counting. Results According to inclusion criteria, 19 studies were included in the study. All retrieved articles were qualitative study. age, Gender, Education, Marriage, Employment, Health status, insurance status, household size, Income, Inpatient and Outpatient services, Physician visit, and Emergency visit are important factors on the utilization and influence the utilization of health services positively. Also health services utilization among insured was significantly higher than that of the uninsured households. Conclusion health insurance increased access to health care and utilization of health services. Health insurance can also be induced demand too. But increasing utilization through health insurance doesn't improve health outcomes.

Published
2017

28. Cost-effectiveness of Anemia Screening in Vulnerable Groups: A Systematic Review.

Author

Nosratnejad, Shirin, Barfar, Eshagh, Hosseini, Hamed, Barooti, Esmat, and Rashidian, Arash

Subjects
*MEDICAL care costs, *ANEMIA prevention, *MEDICAL screening, *META-analysis, *BLOOD diseases, *MEDICAL databases
Abstract

Background: Anemia is the most common blood disorder observed in vulnerable groups and affects their efficiency in their everyday activities. Possible complications of the disease may be reduced or prevented by screening of patients. Screening programs impose certain costs upon the health system, which may offset their positive effects. Whether the positive impacts of screening outweigh its costs is a subject of debate among policy-makers. In this research, we have conducted a systematic review of the cost-effectiveness of anemia screening. Methods: The Pubmed, Science Direct, SCOPUS, EMBASE, and CINAHL databases were searched for relevant results dating between 1962-2010 using key words. The references of the related articles were gone over manually. In the end, Persian databases were also examined for results. Results: Using data from the four mentioned databases, a total of 722 articles were elected, which, after evaluation, were narrowed down to 4. Of these, 3 focused on newborns and infants. Disparity existed among obtained results, such that no two articles were similar, and this made making comparisons between them cumbersome and sometimes even impossible. Only one study evaluated cost-effectiveness of anemia screening in vulnerable target groups. Conclusions: Research findings show that there is not enough evidence of cost-effectiveness of screening for decision-making. Bearing in mind the importance of the matter to health policy-makers, due to high prevalence of iron-deficiency anemia in low- and middle-income countries, conduction of research in this field seems necessary. [ABSTRACT FROM AUTHOR]

Published
2014

29. Cost Effectiveness of Breast Cancer Screening Using Mammography; a Systematic Review.

Author

Rashidian, Arash, Barfar, Eshagh, Hosseini, Hamed, Nosratnejad, Shirin, and Barooti, Esmat

Abstract

Background: Breast cancer is the leading cause of malignancy among women. Screening using mammography is proposed as an effective intervention for reducing early deaths due to breast cancer. We conducted a systematic review to assess the cost-effectiveness of such screening programs. We searched Medline, Scopus and Google Scholar and complemented it by other searches using sensitive search terms from 1993-2010. We screened the titles and abstracts, assessed the full texts of the remaining studies, and extracted data to a pre-designed data extraction sheet. Studies were categorized according to the age groups of the target population. We used narrative synthesis approaches for analyzing the data. Twenty-eight articles met the minimum inclusion criteria, mostly from high income settings. All studies used secondary data, and a variety of modeling techniques, age groups, screening intervals and outcome measures. Cost per life year gained, ranging from $1,634 (once at the age of 50 in India) to $65,000 (extending the lower age limit of screening to 40 Australian study), was the most commonly used outcome measure. Biennial screening test for those aged 50-70 years seems to be the most cost-effective option ($2685). Biennial screening for aged 50-70 years is the most cost-effective option among alternative scenarios. Screening those aged less than 50 is not recommended. Further studies in low-income and middle-income countries, and cost effectiveness studies along with randomized trials are required. To improve the comparability of the findings, future studies should include biennial screening in 50-70 age groups as an alternative strategy. [ABSTRACT FROM AUTHOR]

Published
2013

30. ECONOMIC BURDEN OF ROAD TRAFFIC INJURIES IN LOW AND MIDDLE INCOME COUNTRIES VERSUS HIGH INCOME COUNTRIES: A SYSTEMATIC REVIEW.

Author

Zakeri, Raana and Nosratnejad, Shirin

Abstract

Background and aims: Road traffic injury (RTI) as a major public health problem is the ninth leading cause of death globally. Cases of RTIs are increasing in most of the regions of the world. Research into RTIs indicates that there are distinct differences in the pattern of economic growth between high-income countries (HIC) and low-middle-income countries (LMIC). We aimed to address the economic burden of RTI among LMIC versus HIC; for this purpose we gathered the evidences through a review of the literature. Methods: We systematically searched databases (PubMed, Web of Science, Science Direct, Scopus, ProQuest, SID, IranMedex and Magiran) up to February 2016 and included the original articles on economic burden of RTI in national levels. Two authors appraised the identified studies. We examined the direct, indirect and intangible costs of RTI as a percentage of GDP, and also as a percentage of household consumption expenditure in LMIC versus HIC. Results: 20 studies from fifteen countries met the inclusion criteria, of which five are HIC and ten are LMIC. The mean cost of direct plus indirect costs of RTI as a percentage of GDP and as a percentage of households consumption expenditure in LMIC and HIC are respectively 1.29, 1.61 and 1.74, 3.50; and mean of total economic burden of RTI (direct+indirect+intangible cost) as a percentage of GDP and as a percentage of households consumption expenditure in LMIC versus HIC are respectively 2.35, 1.43 and 2.01, 2.90. Among LMIC, the economic burden of RTI in Iran is the highest (6.64% GDP) and among HIC, it's Australia (2.28% GDP). Conclusion: The economic burden of RTI in LMIC is nearly a half of the health expenditure in these countries. Also the economic burden of RTI in HIC is a quarter of total health expenditure. This evidence emphasizes the importance of road safety interventions as cost-benefit interventions. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
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31. HEALTH INSURANCE AND THE UTILIZATION OF HEALTH CARE: A SYSTEMATIC REVIEW.

Author

Nosratnejad, Shirin and Shami, Elham

Abstract

Background and aims: All the countries are moving towards universal coverage insurance. in their ability to achieve universal coverage, it is likely that they would have similar effects on the health care utilization of the people who gained coverage. This study aimed to examine the relationship between health insurance status and utilization of health services. Methods: We conducted a systematic review using five electronic databases (PubMed, Scopus, and Web of science, ProQuest Medical Library, and Science Direct). We did the search up to April 2015. Two authors appraised the identified studies and then examined Factors associated with the coverage and utilization of health insurance of the study. We used two synthesis approaches: narrative synthesis and vote-counting. Results: According to inclusion criteria, 19 studies were included in the study. All retrieved articles were qualitative study. age, Gender, Education, Marriage, Employment, Health status, insurance status, household size, Income, Inpatient and Outpatient services, Physician visit, and Emergency visit are important factors on the utilization and influence the utilization of health services positively. Also health services utilization among insured was significantly higher than that of the uninsured households. Conclusion: health insurance increased access to health care and utilization of health services. Health insurance can also be induced demand too. But increasing utilization through health insurance doesn't improve health outcomes. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

32. Cost-Effectiveness of Mammography Screening for Breast Cancer in a Low Socioeconomic Group of Iranian Women.

Author

Barfar, Eshagh, Rashidian, Arash, Hosseini, Hamed, Nosratnejad, Shirin, Barooti, Esmat, and Zendehdel, Kazem

Abstract

Background: Breast cancer is the most common cancer among women. Mammography screening has been used in many countries to reduce early deaths caused by breast cancer. It is important to ensure that screening programs are effective and efficient. We conducted a study to assess the cost-effectiveness of a national breast cancer screening program implemented in Iran. Methods: The perspective of the present study was the health system. Over 26,000 women aged 35 and higher, of low socioeconomic background were recruited from ten cities in the program. We used case-finding as the outcome indicator for assessing effectiveness of the program. We measured the service provision costs, the coordination costs and supervision costs of the program that included the staff costs, and measured cost per detected case. We also conducted sensitivity analyses and calculated false-positive rates as a result of the screening program. Results: The total cost of breast cancer screening program was estimate at $377,797. The program resulted in the identification of 24 patients with breast cancers, not different from baseline expectations without a screening program. The cost per cancer detected was calculated $15,742. The minimum and maximum cost per breast cancer detected were about $13,524 and $16,947, respectively. We observed a false-positive rate of 7.5% among the target population. Conclusion: Our findings suggest that the mammography screening program was not cost-effective. Although there were technical efficiency issues in the conduct of the program, the findings do not support the implementation of national mammography screening programs in Iran in women aged less than 50 years. Careful studies of such programs for higher age groups are also recommended before they are rolled-out nationally. [ABSTRACT FROM AUTHOR]

Published
2014
Full Text
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33. Willingness to Pay for Complementary Health Care Insurance in Iran.

Author

Nosratnejad S, Rashidian A, Akbari Sari A, and Moradi N

Abstract

Background: Complementary health insurance is increasingly used to remedy the limitations and shortcomings of the basic health insurance benefit packages. Hence, it is essential to gather reliable information about the amount of Willingness to Pay (WTP) for health insurance. We assessed the WTP for health insurance in Iran in order to suggest an affordable complementary health insurance., Methods: The study sample consisted of 300 household heads all over provinces of Iran in 2013. The method applied was double bounded dichotomous choice and open-ended question approach of contingent valuation., Results: The average WTP for complementary health insurance per person per month by double bounded dichotomous choice and open-ended question method respectively was 199000 and 115300 Rials (8 and 4.6 USD, respectively). Household's heads with higher levels of income and those who worked had more WTP for the health insurance. Besides, the WTP increased in direct proportion to the number of insured members of each household and in inverse proportion to the family size., Conclusion: The WTP value can be used as a premium in a society. As an important finding, the study indicated that the households were willing to pay higher premiums than currently collected for the complementary health insurance coverage in Iran. This offers the policy makers the opportunity to increase the premium and provide good benefits package for insured people of country then better risk pooling., Competing Interests: Conflict of Interests The authors declare that they have no competing interests

Published
2017

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