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2. Burden of disease scenarios for 204 countries and territories, 2022-2050: a forecasting analysis for the Global Burden of Disease Study 2021
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Vollset, SE, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbastabar, H, Abd Al Magied, AHA, Abd ElHafeez, S, Abdelkader, A, Abdelmasseh, M, Abd-Elsalam, S, Abdi, P, Abdollahi, M, Abdoun, M, Abdullahi, A, Abebe, M, Abiodun, O, Aboagye, RG, Abolhassani, H, Abouzid, M, Aboye, GB, Abreu, LG, Absalan, A, Abualruz, H, Abubakar, B, Abukhadijah, HJJ, Addolorato, G, Adekanmbi, V, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adha, R, Adhikary, RK, Adnani, QES, Adzigbli, LA, Afrashteh, F, Afzal, MS, Afzal, S, Agbozo, F, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahlstrom, AJ, Ahmad, A, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmed, A, Ahmed, H, Ahmed, S, Ahmed, SA, Akinosoglou, K, Akkaif, MA, Akrami, AE, Akter, E, Al Awaidy, S, Al Hasan, SM, Al Mosa, AS, Al Ta'ani, O, Al Zaabi, OAM, Alahdab, F, Alajlani, MM, Al-Ajlouni, Y, Alalalmeh, SO, Al-Aly, Z, Alam, K, Alam, N, Alam, T, Alam, Z, Al-Amer, RM, Alanezi, FM, Alanzi, TM, Albakri, A, Aldhaleei, WA, Aldridge, RW, Alemohammad, SY, Alemu, YM, Al-Gheethi, AAS, Al-Hanawi, MK, Ali, A, Ali, I, Ali, MU, Ali, R, Ali, SSS, Ali, VE, Ali, W, Al-Ibraheem, A, Alicandro, G, Alif, SM, Aljunid, SM, Alla, F, Almazan, JU, Al-Mekhlafi, HM, Alqutaibi, AY, Alrawashdeh, A, Alrousan, SM, Al-Sabah, SK, Alsabri, MA, Altaany, Z, Al-Tammemi, AB, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Al-Wardat, MS, Al-Worafi, YM, Aly, H, Alyahya, MS, Alzoubi, KH, Al-Zyoud, W, Amani, R, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, MH, Amu, H, Amugsi, DA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Andrei, T, Andrews, EA, Anil, A, Anil, S, Anoushiravani, A, Antony, CM, Antriyandarti, E, Anuoluwa, BS, Anvari, S, Anyasodor, AE, Appiah, F, Aquilano, M, Arab, JP, Arabloo, J, Arafa, EA, Arafat, M, Aravkin, AY, Ardekani, A, Areda, D, Aregawi, BB, Aremu, A, Ariffin, H, Arkew, M, Armani, K, Artamonov, AA, Arumugam, A, Asghari-Jafarabadi, M, Ashbaugh, C, Astell-Burt, T, Athari, SS, Atorkey, P, Atout, MMW, Aujayeb, A, Ausloos, M, Awad, H, Awotidebe, AW, Ayatollahi, H, Ayuso-Mateos, JL, Azadnajafabad, S, Azeez, FK, Azevedo, RMS, Badar, M, Baghdadi, S, Bagheri, M, Bagheri, N, Bai, R, Baker, JL, Bako, AT, Balakrishnan, S, Balcha, WF, Baltatu, OC, Barchitta, M, Bardideh, E, Barker-Collo, SL, Barnighausen, TW, Barqawi, HJ, Barteit, S, Basiru, A, Basso, JD, Bastan, M-M, Basu, S, Bauckneht, M, Baune, BT, Bayati, M, Bayileyegn, NS, Behnoush, AH, Behzadi, P, Beiranvand, M, Bello, OO, Belo, L, Beloukas, A, Bemanalizadeh, M, Bensenor, IM, Benzian, H, Beran, A, Berezvai, Z, Bernstein, RS, Bettencourt, PJG, Beyene, KA, Beyene, MG, Bhagat, DS, Bhagavathula, AS, Bhala, N, Bhandari, D, Bharadwaj, R, Bhardwaj, N, Bhardwaj, P, Bhargava, A, Bhaskar, S, Bhat, V, Bhattacharjee, NV, Bhatti, GK, Bhatti, JS, Bhatti, MS, Bhuiyan, MA, Bisignano, C, Biswas, B, Bjorge, T, Bodolica, V, Bodunrin, AO, Hashemi, MB, Basara, BB, Borhany, H, Bosoka, SA, Carvajal, AB, Bouaoud, S, Boufous, S, Boxe, C, Boyko, EJ, Brady, OJ, Braithwaite, D, Brauer, M, Brazo-Sayavera, J, Brenner, H, Brown, CS, Browne, AJ, Brugha, T, Bryazka, D, Bulamu, NB, Buonsenso, D, Burkart, K, Burns, RA, Busse, R, Bustanji, Y, Butt, ZA, dos Santos, FLC, Barsbay, MC, Calina, D, Campos, LA, Cao, S, Capodici, A, Cardenas, R, Carreras, G, Carugno, A, Carvalho, M, Castaldelli-Maia, JM, Castelpietra, G, Cattaruzza, MS, Caye, A, Cegolon, L, Cembranel, F, Cenko, E, Cerin, E, Chadban, SJ, Chadwick, J, Chakraborty, C, Chakraborty, S, Chalek, J, Chan, JSK, Chandika, RM, Chandy, S, Charan, J, Chaudhary, AA, Chaurasia, A, Chen, A-T, Chen, H, Chen, MX, Chen, S, Cherbuin, N, Chi, G, Chichagi, F, Chimed-Ochir, O, Chimoriya, R, Ching, PR, Chirinos-Caceres, JL, Chitheer, A, Cho, DY, Cho, WCS, Choi, D-W, Chong, B, Chong, CL, Chopra, H, Chu, D-T, Chung, E, Chutiyami, M, Clayton, JT, Cogen, RM, Cohen, AJ, Columbus, A, Comfort, H, Conde, J, Connolly, JT, Cooper, EEK, Cortese, S, Cruz-Martins, N, da Silva, AG, Dadras, O, Dai, X, Dai, Z, Dalton, BE, Damiani, G, Dandona, L, Dandona, R, Das, JK, Das, S, Dash, NR, Davletov, K, De la Hoz, FP, De Leo, D, Debopadhaya, S, Delgado-Enciso, I, Denova-Gutierrez, E, Dervenis, N, Desai, HD, Devanbu, VGC, Dewan, SMR, Dhama, K, Dhane, AS, Dhingra, S, da Silva, DD, Diaz, D, Diaz, LA, Diaz, MJ, Dima, A, Ding, DD, Do, THP, Do Prado, CB, Dodangeh, M, Doegah, PT, Dohare, S, Dong, W, D'Oria, M, Doshi, R, Dowou, RK, Dsouza, HL, Dsouza, V, Dube, J, Dumith, SC, Duncan, BB, Duraes, AR, Duraisamy, S, Durojaiye, OC, Dushpanova, A, Dutta, S, Dzianach, PA, Dziedzic, AM, Eboreime, E, Ebrahimi, A, Kalan, ME, Edinur, HA, Efendi, F, Eikemo, TA, Eini, E, Ekundayo, TC, El Arab, RA, El Sayed, I, Elamin, O, Elemam, NM, ElGohary, GMT, Elhadi, M, Elmeligy, OAA, Elmoselhi, AB, Elshaer, M, Elsohaby, I, Eltahir, ME, Emeto, TI, Eshrati, B, Eslami, M, Esmaeili, Z, Fabin, N, Fagbamigbe, AF, Fagbule, OF, Falzone, L, Fareed, M, Farinha, CSES, Faris, MEM, Faro, A, Fasihi, K, Fatehizadeh, A, Fauk, NK, Fazylov, T, Feigin, VL, Fekadu, G, Feng, X, Fereshtehnejad, S-M, Ferrara, P, Ferreira, N, Firew, BS, Fischer, F, Fitriana, I, Flavel, J, Flor, LS, Folayan, MO, Foley, KM, Fonzo, M, Force, LM, Foschi, M, Freitas, A, Fridayani, NKY, Fukutaki, KG, Furtado, JM, Fux, B, Gaal, PA, Gadanya, MA, Gallus, S, Ganesan, B, Ganiyani, MA, Gautam, RK, Gebi, TG, Gebregergis, MW, Gebrehiwot, M, Getacher, L, Getahun, GKA, Gething, PW, Ghadimi, DJ, Ghadirian, F, Ghafarian, S, Ghailan, KY, Ghasemi, M, Dabaghi, GG, Ghazy, RM, Ghoba, S, Gholami, E, Gholamrezanezhad, A, Gholizadeh, N, Ghorbani, M, Vajargah, PG, Ghotbi, E, Gil, AU, Gill, TK, Girmay, A, Glasbey, JC, Glushkova, EV, Gnedovskaya, EV, Gobolos, L, Goldust, M, Goleij, P, Golinelli, D, Gopalani, SV, Goulart, AC, Gouravani, M, Goyal, A, Grivna, M, Grosso, G, Guarducci, G, Gubari, MIM, Guicciardi, S, Guimaraes, RA, Gulati, S, Gulisashvili, D, Gunawardane, DA, Guo, C, Gupta, AK, Gupta, R, Gupta, S, Gupta, VK, Haakenstad, A, Hadi, NR, Haep, N, Hafiz, A, Haghmorad, D, Haile, D, Ali, AH, Ali, HA, Haj-Mirzaian, A, Halboub, ES, Haller, S, Halwani, R, Abdullah, KH, Hamdy, NM, Hamoudi, R, Hanifi, N, Hankey, GJ, Haq, ZA, Haque, MR, Harapan, H, Hargono, A, Haro, JM, Hasaballah, AI, Hasan, SMM, Hasanian, M, Hasnain, MS, Hassan, A, Haubold, J, Hay, SI, Hebert, JJ, Hegazi, OE, Heidari, M, Hemmati, M, Henson, CA, Herrera-Serna, BY, Herteliu, C, Heydari, M, Hezam, K, Hidayana, I, Hiraike, Y, Hoan, NQ, Holla, R, Hoogar, P, Horita, N, Hossain, MM, Hosseinzadeh, H, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hu, C, Huang, J, Hultstrom, M, Hundie, TG, Hunt, AJ, Hushmandi, K, Hussain, J, Hussain, MA, Hussein, NR, Huynh, H-H, Hwang, B-F, Ibitoye, SE, Iftikhar, PM, Ikiroma, AI, Ikwegbue, PC, Ilic, IM, Ilic, MD, Immurana, M, Isa, MA, Islam, MR, Islam, SMS, Ismail, F, Ismail, NE, Isola, G, Iwagami, M, Iyamu, IO, Jacob, L, Jacobsen, KH, Jafarinia, M, Jahankhani, K, Jahanmehr, N, Jain, N, Jairoun, AA, Mani, DRJ, Jamil, S, Jamora, RDG, Jatau, AI, Javadov, S, Javaheri, T, Jayaram, S, Jee, SH, Jeganathan, J, Jiang, H, Jokar, M, Jonas, JB, Joseph, N, Joshua, CE, Jurisson, M, Vaishali, K, Kabir, A, Kabir, Z, Kadashetti, V, Kalankesh, LR, Kalra, S, Kamath, A, Kamath, R, Kamireddy, A, Kanaan, M, Kanchan, T, Kanmiki, EW, Kanmodi, KK, Kansal, SK, Karim, A, Karkhah, S, Kashoo, FZ, Kasraei, H, Kassel, MB, Katikireddi, SV, Kauppila, JH, Kaur, H, Kayode, GA, Kazemi, F, Kazemian, S, Kebede, F, Kendal, ES, Kesse-Guyot, E, Khademvatan, S, Khajuria, H, Khalaji, A, Khalid, A, Khalid, N, Khalilian, A, Khamesipour, F, Khan, F, Khan, MJ, Khan, MAB, Khanmohammadi, S, Khatab, K, Khatatbeh, H, Khatatbeh, MM, Khatib, MN, Kashani, HRK, Kheirallah, KA, Khokhar, M, Khormali, M, Khorrami, Z, Khosla, AA, Khosravi, M, Khosrowjerdi, M, Khubchandani, J, Kifle, ZD, Kim, G, Kim, JS, Kim, MS, Kim, YJ, Kimokoti, RW, Kisa, A, Kisa, S, Knibbs, LD, Knudsen, AKS, Kochhar, S, Kolahi, A-A, Kompani, F, Koren, G, Korzh, O, Krishan, K, Krishna, V, Krishnamoorthy, V, Bicer, BK, Kuddus, MA, Kuddus, M, Kuitunen, I, Kujan, O, Kulimbet, M, Kulkarni, V, Kumar, GA, Kumar, H, Kumar, N, Kumar, R, Kumar, V, Kundu, A, Kusuma, D, Kyei-Arthur, F, Kyto, V, Kyu, HH, La Vecchia, C, Lacey, B, Ladan, MA, Laflamme, L, Lahariya, C, Lai, DTC, Lalloo, R, Lallukka, T, Lam, J, Lan, Q, Lan, T, Landires, I, Lanfranchi, F, Langguth, B, Lansingh, VC, Laplante-Levesque, A, Larijani, B, Larsson, AO, Lasrado, S, Lauriola, P, Lawlor, HR, Le, H-H, Le, LKD, Le, NHH, Le, TTT, Le, TDT, Leasher, JL, Lee, DW, Lee, M, Lee, PH, Lee, S-W, Lee, SW, Lee, SWH, Lee, YH, Leigh, J, Leong, E, Li, M-C, Libra, M, Ligade, VS, Lim, L-L, Lim, SS, Limenh, LW, Lindholm, D, Lindstedt, PA, Listl, S, Liu, G, Liu, S, Liu, X, Llanaj, E, Lopez-Bueno, R, Lopez-Gil, JF, Loreche, AM, Lotufo, PA, Lozano, R, Lubinda, J, Lucchetti, G, Luo, L, Lusk, JB, Lv, L, Amin, HIM, Ma, ZF, Maass, KL, Machairas, N, Machoy, M, Madureira-Carvalho, AM, Abd El Razek, HM, Maghazachi, AA, Prasad, DRM, Mahalleh, M, Mahasha, PW, Mahmoud, MA, Mahmoudi, E, Mahmoudvand, G, Makama, M, Rad, EM, Malhotra, K, Malik, AA, Malta, DC, Manla, Y, Mansour, A, Mansouri, MH, Mansouri, P, Mansouri, V, Mansourian, M, Mansournia, MA, Marasini, BP, Marateb, HR, Maravilla, JC, Mardi, P, Marjani, A, Moghadam, HM, Arnedo, CAM, Martinez, G, Martinez-Piedra, R, Martins-Melo, FR, Martorell, M, Marx, W, Marzo, RR, Masoudi, S, Mathangasinghe, Y, Mathioudakis, AG, Mathur, M, Mathur, N, Matozinhos, FP, Mattumpuram, J, Maude, RJ, Maugeri, A, Mayeli, M, Mazidi, M, Mazzotti, A, McGrath, JJ, Mckee, M, McKowen, ALW, McPhail, MA, McPhail, SM, Mehmood, A, Mehrabani-Zeinabad, K, Mehravar, S, Meto, TM, Melese, EB, Mendez-Lopez, MAM, Mendoza, W, Menezes, RG, Mensah, GA, Mensah, LG, Mentis, A-FA, Meo, SA, Meretoja, A, Meretoja, TJ, Mersha, AM, Mestrovic, T, Mettananda, KCD, Mettananda, S, Mhlanga, A, Mhlanga, L, Miazgowski, T, Michalek, IM, de Sa, ACMGN, Miller, TR, Minh, LHN, Mirahmadi, A, Mirijello, A, Mirrakhimov, EM, Mirzaei, R, Mitchell, PB, Mittal, C, Moberg, ME, Fard, AM, Mohajelin, S, Mohamadkhani, A, Mohamed, AI, Mohamed, J, Mohamed, MFH, Mohamed, NS, Mohammad, AM, Mohammadi, S, Mohammed, H, Mohammed, M, Mohammed, S, Mokdad, AH, Molokhia, M, Momani, SM, Momtazmanesh, S, Monasta, L, Mondello, S, Moni, MA, Montazeri, F, Ghalibaf, AM, Moradi, M, Moradi, Y, Moraga, P, Morawska, L, Moreira, RS, Morovatdar, N, Morrison, SD, Mosapour, A, Mosser, JF, Mossialos, E, Motappa, R, Mougin, V, Mousavi, P, Mrejen, M, Mubarik, S, Mueller, UO, Mulita, F, Munjal, K, Murillo-Zamora, E, Musallam, KM, Musina, A-M, Mustafa, G, Myung, W, Nafei, A, Nagarajan, AJ, Naghavi, P, Naik, GR, Naik, G, Nainu, F, Najdaghi, S, Ansari, NN, Nangia, V, Swamy, SN, Nargus, S, Davani, DN, Nascimento, BR, Nascimento, GG, Naser, AY, Nashwan, AJ, Natto, ZS, Nauman, J, Navaratna, SNK, Naveed, M, Nawsherwan, Nayak, BP, Nayak, VC, Negash, H, Negoi, I, Negoi, RI, Nejadghaderi, SA, Nejjari, C, Nematollahi, S, Netsere, HB, Ng, M, Nguefack-Tsague, G, Ngunjiri, JW, Nguyen, AH, Nguyen, DH, Nguyen, HTH, Nguyen, N, Nguyen, NNY, Nguyen, PT, Nguyen, QP, Nguyen, VT, Minh, DNT, Niazi, RK, Nigatu, YT, Niknam, M, Nikoobar, A, Nikpoor, AR, Nikravangolsefid, N, Noman, EA, Nomura, S, Noor, STA, Noroozi, N, Nouri, M, Nozari, M, Nri-Ezedi, CA, Ntaios, G, Nunemo, MH, Nurrika, D, Nutor, JJ, Nzoputam, CI, Nzoputam, OJ, Oancea, B, Obamiro, KO, Odetokun, IA, Oduro, MS, Ogundijo, OA, Ogunfowokan, AA, Ogunkoya, A, Oguntade, AS, Oh, I-H, Ojo-Akosile, TR, Okati-Aliabad, H, Okekunle, AP, Okonji, OC, Olagunju, AT, Olatubi, MI, Oliveira, GMM, Olusanya, BO, Olusanya, JO, Oluwafemi, YD, Omar, HA, Omer, GL, Ong, S, Onie, S, Onwujekwe, OE, Opejin, AO, Ordak, M, Orish, VN, Ortiz, A, Ortiz-Prado, E, Osman, WMS, Ostojic, SM, Ostroff, SM, Osuagwu, UL, Otoiu, A, Otstavnov, SS, Ouyahia, A, Owolabi, MO, Oyeyemi, OT, Ozair, A, Padukudru, MPA, Padron-Monedero, A, Padubidri, JR, Pal, PK, Palicz, T, Pan, F, Pan, H-F, Panda-Jonas, S, Pandey, A, Pando-Robles, V, Pangaribuan, HU, Panos, GD, Panos, LD, Pantazopoulos, I, 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- Abstract
BACKGROUND: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. METHODS: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects m
- Published
- 2024
3. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
- Author
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Badiye, AD, Bagga, A, Baghdadi, S, Bagheri, N, Bagherieh, S, Bahrami Taghanaki, P, Bai, R, Abeldaño Zuñiga, RA, Baig, AA, Moni, MA, Baker, JL, Bakkannavar, SM, Balasubramanian, M, Baltatu, OC, Bam, K, Bandyopadhyay, S, Banik, B, Banik, PC, Banke-Thomas, A, Abiodun, O, Moodi Ghalibaf, A, Bansal, H, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Bärnighausen, TW, Barone-Adesi, F, Barqawi, HJ, Barrero, LH, Barrow, A, Moore, CE, Abiso, TL, Barteit, S, Basharat, Z, Basiru, A, Basso, JD, Bastan, M-M, Basu, S, Batchu, S, Batra, K, Batra, R, Moradi, M, Baune, BT, Aboagye, RG, Bayati, M, Bayileyegn, NS, Beaney, T, Behnoush, AH, Beiranvand, M, Béjot, Y, Bekele, A, Belgaumi, UI, Bell, AW, Bell, ML, Abolhassani, H, Bello, MB, Bello, OO, Belo, L, Beloukas, A, Bendak, S, Bennett, DA, and Bennitt, FB
- Published
- 2024
4. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
- Author
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Naghavi, M, Ong, KL, Aali, A, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbasi-Kangevari, M, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdelwahab, A, Abdollahi, M, Abdollahifar, M-A, Abdoun, M, Abdulah, DM, Abdullahi, A, Abebe, M, Abebe, SS, Abedi, A, Abegaz, KH, Abhilash, ES, Abidi, H, Abiodun, O, Aboagye, RG, Abolhassani, H, Abolmaali, M, Abouzid, M, Aboye, GB, Abreu, LG, Abrha, WA, Abtahi, D, Abu Rumeileh, S, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Abu-Rmeileh, NME, Aburuz, S, Abu-Zaid, A, Accrombessi, MMK, Adal, TG, Adamu, AA, Addo, IY, Addolorato, G, Adebiyi, AO, Adekanmbi, V, Adepoju, AV, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adeyinka, DA, Adeyomoye, OI, Admass, BAA, Adnani, QES, Adra, S, Afolabi, AA, Afzal, MS, Afzal, S, Agampodi, SB, Agasthi, P, Aggarwal, M, Aghamiri, S, Agide, FD, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmad, T, Ahmadi, K, Ahmadzade, 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Zyoud, SH, Wool, EE, and Murray, CJL
- Published
- 2024
5. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
- Author
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BA, Zamora, N, Zand, R, Zandieh, GGZ, Zar, HJ, Zarrintan, A, Zastrozhin, MS, Zhang, H, Zhang, N, Zhang, Y, Zhao, H, Zhong, C, Zhong, P, Zhou, J, Zhu, Z, Ziafati, M, Zielińska, M, Zimsen, SRM, Zoladl, M, Zumla, A, Zyoud, SH, Vos, T, and Murray, CJL
- Published
- 2024
6. EP-245 A snapshot review of the use of prophylactic antibiotics for emergency gastrointestinal surgeries: a tertiary care centre experience
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Nour, Hussameldin Mohamed, primary, Peristeri, Dmitria V, additional, Shafique, Shehram, additional, Khan, Prof Mansoor, additional, Singh, Krishna K, additional, and Sajid, Mr Muhammad S, additional
- Published
- 2022
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7. The global burden of cancer attributable to risk factors, 2010-19: a systematic analysis for the Global Burden of Disease Study 2019
- Author
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Khanh, BT, Lang, JJ, Compton, K, Xu, R, Acheson, AR, Henrikson, HJ, Kocarnik, JM, Penberthy, L, Aali, A, Abbas, Q, Abbasi, B, Abbasi-Kangevari, M, Abbasi-Kangevari, Z, Abbastabar, H, Abdelmasseh, M, Abd-Elsalam, S, Abdelwahab, AA, Abdoli, G, Abdulkadir, HA, Abedi, A, Abegaz, KH, Abidi, H, Aboagye, RG, Abolhassani, H, Absalan, A, Abtew, YD, Ali, HA, Abu-Gharbieh, E, Achappa, B, Acuna, JM, Addison, D, Addo, IY, Adegboye, OA, Adesina, MA, Adnan, M, Adnani, QES, Advani, SM, Afrin, S, Afzal, MS, Aggarwal, M, Ahinkorah, BO, Ahmad, AR, Ahmad, R, Ahmad, S, Ahmadi, S, Ahmed, H, Ahmed, LA, Ahmed, MB, Rashid, TA, Aiman, W, Ajami, M, Akalu, GT, Akbarzadeh-Khiavi, M, Aklilu, A, Akonde, M, Akunna, CJ, Al Hamad, H, Alahdab, F, Alanezi, FM, Alanzi, TM, Alessy, SA, Algammal, AM, Al-Hanawi, MK, Alhassan, RK, Ali, BA, Ali, L, Ali, SS, Alimohamadi, Y, Alipour, V, Aljunid, SM, Alkhayyat, M, Al-Maweri, SAA, Almustanyir, S, Alonso, N, Alqalyoobi, S, Al-Raddadi, RM, Al-Rifai, RHH, Al-Sabah, SK, Al-Tammemi, AB, Altawalah, H, Alvis-Guzman, N, Amare, F, Ameyaw, EK, Dehkordi, JJA, Amirzade-Iranaq, MH, Amu, H, Amusa, GA, Ancuceanu, R, Anderson, JA, Animut, YA, Anoushiravani, A, Anoushirvani, AA, Ansari-Moghaddam, A, Ansha, MG, Antony, B, Antwi, MH, Anwar, SL, Anwer, R, Anyasodor, AE, Arabloo, J, Arab-Zozani, M, Aremu, O, Argaw, AM, Ariffin, H, Aripov, T, Arshad, M, Al, A, Arulappan, J, Aruleba, RT, Aryannejad, A, Asaad, M, Asemahagn, MA, Asemi, Z, Asghari-Jafarabadi, M, Ashraf, T, Assadi, R, Athar, M, Athari, SS, Null, MMWA, Attia, S, Aujayeb, A, Ausloos, M, Avila-Burgos, L, Awedew, AF, Awoke, MA, Awoke, T, Quintanilla, BPA, Ayana, TM, Ayen, SS, Azadi, D, Null, SA, Azami-Aghdash, S, Azanaw, MM, Azangou-Khyavy, M, Jafari, AA, Azizi, H, Azzam, AYY, Babajani, A, Badar, M, Badiye, AD, Baghcheghi, N, Bagheri, N, Bagherieh, S, Bahadory, S, Baig, AA, Baker, JL, Bakhtiari, A, Bakshi, RK, Banach, M, Banerjee, I, Bardhan, M, Barone-Adesi, F, Barra, F, Barrow, A, Bashir, NZ, Bashiri, A, Basu, S, Batiha, A-MM, Begum, A, Bekele, AB, Belay, AS, Belete, MA, Belgaumi, UI, Bell, AW, Belo, L, Benzian, H, Berhie, AY, Bermudez, ANC, Bernabe, E, Bhagavathula, AS, Bhala, N, Bhandari, BB, Bhardwaj, N, Bhardwaj, P, Bhattacharyya, K, Bhojaraja, VS, Bhuyan, SS, Bibi, S, Bilchut, AH, Bintoro, BS, Biondi, A, Birega, MGB, Birhan, HE, Bjorge, T, Blyuss, O, Bodicha, BBA, Bolla, SR, Boloor, A, Bosetti, C, Braithwaite, D, Brauer, M, Brenner, H, Briko, AN, Briko, NI, Buchanan, CM, Bulamu, NB, Bustamante-Teixeira, MT, Butt, MH, Butt, NS, Butt, ZA, Caetano dos Santos, FL, Camera, LA, Cao, C, Cao, Y, Carreras, G, Carvalho, M, Cembranel, F, Cerin, E, Chakraborty, PA, Charalampous, P, Chattu, VK, Chimed-Ochir, O, Chirinos-Caceres, JL, Cho, DY, Cho, WCS, Christopher, DJ, Chu, D-T, Chukwu, IS, Cohen, AJ, Conde, J, Cortas, S, Costa, VM, Cruz-Martins, N, Culbreth, GT, Dadras, O, Dagnaw, FT, Dahlawi, SMA, Dai, X, Dandona, L, Dandona, R, Daneshpajouhnejad, P, Danielewicz, A, An, TMD, Soltani, RDC, Darwesh, AM, Das, S, Davitoiu, DV, Esmaeili, ED, De la Hoz, FP, Debela, SA, Dehghan, A, Demisse, B, Demisse, FW, DenovaGutiA, E, Derakhshani, A, Molla, MD, Dereje, D, Deribe, KS, Desai, R, Desalegn, MD, Dessalegn, FN, Dessalegni, SAA, Dessie, G, Desta, AA, Dewan, SMR, Dharmaratne, SD, Dhimal, M, Dianatinasab, M, Diao, N, Diaz, D, Digesa, LE, Dixit, SG, Doaei, S, Linh, PD, Doku, PN, Dongarwar, D, dos Santos, WM, Driscoll, TR, Dsouza, HL, Durojaiye, OC, Edalati, S, Eghbalian, F, Ehsani-Chimeh, E, Eini, E, Ekholuenetale, M, Ekundayo, TC, Ekwueme, DU, El Tantawi, M, Elbahnasawy, MA, Elbarazi, I, Elghazaly, H, Elhadi, M, El-Huneidi, W, Emamian, MH, Bain, LE, Enyew, DB, Erkhembayar, R, Eshetu, T, Eshrati, B, Eskandarieh, S, Espinosa-Montero, J, Etaee, F, Etemadimanesh, A, Eyayu, T, Ezeonwumelu, IJ, Ezzikouri, S, Fagbamigbe, AF, Fahimi, S, Fakhradiyev, IR, Faraon, EJA, Fares, J, Farmany, A, Farooque, U, Farrokhpour, H, Fasanmi, AO, Fatehizadeh, A, Fatima, W, Fattahi, H, Fekadu, G, Feleke, BE, Ferrari, AA, Ferrero, S, Desideri, LF, Filip, I, Fischer, F, Foroumadi, R, Foroutan, M, Fukumoto, T, Gaal, PA, Gad, MM, Gadanya, MA, Gaipov, A, Galehdar, N, Gallus, S, Garg, T, Fonseca, MG, Gebremariam, YH, Gebremeskel, TG, Gebremichael, MA, Geda, YF, Gela, YY, Gemeda, BNB, Getachew, M, Getachew, ME, Ghaffari, K, Ghafourifard, M, Ghamari, S-H, Nour, MG, Ghassemi, F, Ghimire, A, Ghith, N, Gholamalizadeh, M, Navashenaq, JG, Ghozy, S, Gilani, SA, Gill, PS, Ginindza, TG, Gizaw, ATT, Glasbey, JC, Godos, J, Goel, A, Golechha, M, Goleij, P, Golinelli, D, Golitaleb, M, Gorini, G, Goulart, BNG, Grosso, G, Guadie, HA, Gubari, MIM, Gudayu, TW, Guerra, MR, Gunawardane, DA, Gupta, B, Gupta, S, Gupta, V, Gupta, VK, Gurara, MK, Guta, A, Habibzadeh, P, Avval, AH, Hafezi-Nejad, N, Ali, AH, Haj-Mirzaian, A, Halboub, ES, Halimi, A, Halwani, R, Hamadeh, RR, Hameed, S, Hamidi, S, Hanif, A, Hariri, S, Harlianto, N, Haro, JM, Hartono, RK, Hasaballah, A, Hasan, SMM, Hasani, H, Hashemi, SM, Hassan, AM, Hassanipour, S, Hayat, K, Heidari, G, Heidari, M, Heidarymeybodi, Z, Herrera-Serna, BY, Herteliu, C, Hezam, K, Hiraike, Y, Hlongwa, MM, Holla, R, Holm, M, Horita, N, Hoseini, M, Hossain, MM, Hossain, MBH, Hosseini, M-S, Hosseinzadeh, A, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Huang, J, Hugo, FN, Humayun, A, Hussain, S, Hussein, NR, Hwang, B-F, Ibitoye, SE, Iftikhar, PM, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Immurana, M, Innos, K, Iranpour, P, Irham, LM, Islam, MS, Islam, RM, Islami, F, Ismail, NE, Isola, G, Iwagami, M, Merin, LJ, Jaiswal, A, Jakovljevic, M, Jalili, M, Jalilian, S, Jamshidi, E, Jang, S-I, Jani, CT, Javaheri, T, Jayarajah, UU, Jayaram, S, Jazayeri, SB, Jebai, R, Jemal, B, Jeong, W, Jha, RP, Jindal, HA, John-Akinola, YO, Jonas, JB, Joo, T, Joseph, N, Joukar, F, Jozwiak, JJ, Jarisson, M, Kabir, A, Kacimi, SEO, Kadashetti, V, Kahe, F, Kakodkar, PV, Kalankesh, LR, Kalhor, R, Kamal, VK, Kamangar, F, Kamath, A, Kanchan, T, Kandaswamy, E, Kandel, H, Kang, H, Kanno, GG, Kapoor, N, Kar, SS, Karanth, SD, Karaye, IM, Karch, A, Karimi, A, Kassa, BG, Katoto, PDMC, Kauppila, JH, Kaur, H, Kebede, AG, Keikavoosi-Arani, L, Kejela, GG, Bohan, PMK, Keramati, M, Keykhaei, M, Khajuria, H, Khan, A, Khan, AAK, Khan, EA, Khan, G, Khan, MN, Ab Khan, M, Khanali, J, Khatab, K, Khatatbeh, MM, Khatib, MN, Khayamzadeh, M, Kashani, HRK, Tabari, MAK, Khezeli, M, Khodadost, M, Kim, MS, Kim, YJ, Kisa, A, Kisa, S, Klugar, M, Klugarova, J, Kolahi, A-A, Kolkhir, P, Kompani, F, Koul, PA, Laxminarayana, SLK, Koyanagi, A, Krishan, K, Krishnamoorthy, Y, Bicer, BK, Kugbey, N, Kulimbet, M, Kumar, A, Kumar, GA, Kumar, N, Kurmi, OP, Kuttikkattu, A, La Vecchia, C, Lahiri, A, Lal, DK, Lam, J, Lan, Q, Landires, I, Larijani, B, Lasrado, S, Lau, J, Lauriola, P, Ledda, C, Lee, S-W, Lee, SWH, Lee, W-C, Lee, YY, Lee, YH, Legesse, SM, Leigh, J, Leong, E, Li, M-C, Lim, SS, Liu, G, Liu, J, Lo, C-H, Lohiya, A, Lopukhov, PD, Lorenzovici, L, Lotfi, M, Loureiro, JA, Lunevicius, R, Madadizadeh, F, Mafi, AR, Magdeldin, S, Mahjoub, S, Mahmoodpoor, A, Mahmoudi, M, Mahmoudimanesh, M, Mahumud, RA, Majeed, A, Majidpoor, J, Makki, A, Makris, KC, Rad, EM, Malekpour, M-R, Malekzadeh, R, Malik, AA, Mallhi, TH, Mallya, SD, Mamun, MA, Manda, AL, Mansour-Ghanaei, F, Mansouri, B, Mansournia, MA, Mantovani, LG, Martini, S, Martorell, M, Masoudi, S, Masoumi, SZ, Matei, CN, Mathews, E, Mathur, MR, Mathur, V, McKee, M, Meena, JK, Mehmood, K, Nasab, EM, Mehrotra, R, Melese, A, Mendoza, W, Menezes, RG, Mengesha, SD, Mensah, LG, Mentis, A-FA, Mera-Mamian, AYM, Meretoja, TJ, Merid, MW, Mersha, AG, Meselu, BT, Meshkat, M, Mestrovic, T, Jonasson, JM, Miazgowski, T, Michalek, IM, Mijena, GFW, Miller, TR, Mir, SA, Mirinezhad, SK, Mirmoeeni, S, Mirza-Aghazadeh-Attari, M, Mirzaei, H, Mirzaei, HR, Misganaw, AS, Misra, S, AbdulmuhsinMohammad, K, Mohammadi, E, Mohammadi, M, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, A, Mohammed, S, Mohan, S, Mohseni, M, Moka, N, Mokdad, AH, Molassiotis, A, Molokhia, M, Momenzadeh, K, Momtazmanesh, S, Monasta, L, Mons, U, Al Montasir, A, Montazeri, F, Montero, A, Moosavi, MA, Moradi, A, Moradi, Y, Sarabi, MM, Moraga, P, Morawska, L, Morrison, SD, Morze, J, Mosapour, A, Mostafavi, E, Mousavi, SM, Isfahani, HM, Khaneghah, AM, Mpundu-Kaambwa, C, Mubarik, S, Mulita, F, Munblit, D, Munro, SB, Murillo-Zamora, E, Musa, J, Nabhan, AF, Nagarajan, AJ, Nagaraju, SP, Nagel, G, Naghipour, M, Naimzada, MD, Nair, TS, Naqvi, AA, Swamy, SN, Narayana, AI, Nassereldine, H, Natto, ZS, Nayak, BP, Ndejjo, R, Nduaguba, SO, Negash, WW, Nejadghaderi, SA, Nejati, K, Kandel, SN, Huy, VNN, Niazi, RK, Noor, NM, Noori, M, Noroozi, N, Nouraei, H, Nowroozi, A, Nunez-Samudio, V, Nzoputam, CI, Nzoputam, OJ, Oancea, B, Odukoya, OO, Oghenetega, OB, Ogunsakin, RE, Oguntade, AS, Oh, I-H, Okati-Aliabad, H, Okekunle, AP, Olagunju, AT, Olagunju, TO, Olakunde, BO, Olufadewa, II, Omer, E, Omonisi, AEE, Ong, S, Onwujekwe, OE, Orru, H, Otstavnov, SS, Oulhaj, A, Oumer, B, Owopetu, OF, Oyinloye, BE, Mahesh, PA, Padron-Monedero, A, Padubidri, JR, Pakbin, B, Pakshir, K, Pakzad, R, Palicz, T, Pana, A, Pandey, A, Pant, S, Pardhan, S, Park, E-K, Park, S, Patel, J, Pati, S, Paudel, R, Paudel, U, Paun, M, Toroudi, HP, Peng, M, Pereira, J, Pereira, RB, Perna, S, Perumalsamy, N, Pestell, RG, Pezzani, R, Piccinelli, C, Pillay, JD, Piracha, ZZ, Pischon, T, Postma, MJ, Langroudi, AP, Pourshams, A, Pourtaheri, N, Prashant, A, Qadir, MMF, Syed, ZQ, Rabiee, M, Rabiee, N, Radfar, A, Radhakrishnan, RA, Radhakrishnan, V, Raeisi, M, Rafiee, A, Rafiei, A, Raheem, N, Rahim, F, Rahman, MO, Rahman, M, Rahman, MA, Rahmani, AM, Rahmani, S, Rahmanian, V, Rajai, N, Rajesh, A, Ram, P, Ramezanzadeh, K, Rana, J, Ranabhat, K, Ranasinghe, P, Rao, CR, Rao, SJ, Rashedi, S, Rashidi, A, Rashidi, M-M, Ratan, ZA, Rawaf, DL, Rawaf, S, Rawal, L, Rawassizadeh, R, Razeghinia, MS, Rehman, AU, Rehman, IU, Reitsma, MB, Renzaho, AMN, Rezaei, M, Rezaei, N, Rezaei, S, Rezaeian, M, Rezapour, A, Riad, A, Rikhtegar, R, Rios-Blancas, M, Roberts, TJ, Rohloff, P, Romero-Rodriguez, E, Roshandel, G, Rwegerera, GM, Manjula, S, Saber-Ayad, MM, Saberzadeh-Ardestani, B, Sabour, S, Saddik, B, Sadeghi, E, Saeb, MR, Saeed, U, Safaei, M, Safary, A, Sahebazzamani, M, Sahebkar, A, Sahoo, H, Sajid, MR, Salari, H, Salehi, S, Salem, MR, Salimzadeh, H, Samodra, YL, Samy, AM, Sanabria, J, Sankararaman, S, Sanmarchi, F, Santric-Milicevic, MM, Saqib, MAN, Sarveazad, A, Sarvi, F, Sathian, B, Satpathy, M, Sayegh, N, Schneider, IJC, Schwarzinger, M, Sekerija, M, Senthilkumaran, S, Sepanlou, SG, Seylani, A, Seyoum, K, Sha, F, Shafaat, O, Shah, PA, Shahabi, S, Shahid, I, Shahrbaf, MA, Shahsavari, HR, Shaikh, MA, Shaka, MF, Shaker, E, Shannawaz, M, Sharew, MMS, Sharifi, A, Sharifi-Rad, J, Sharma, P, Shashamo, BB, Sheikh, A, Sheikh, M, Sheikhbahaei, S, Sheikhi, RA, Sheikhy, A, Shepherd, PR, Shetty, A, Shetty, JK, Shetty, RS, Shibuya, K, Shirkoohi, R, Shirzad-Aski, H, Shivakumar, KM, Shivalli, S, Shivarov, V, Shobeiri, P, Varniab, ZS, Shorofi, SA, Shrestha, S, Sibhat, MM, Malleshappa, SS, Sidemo, NB, Silva, DAS, Silva, LMLR, Julian, GS, Silvestris, N, Simegn, W, Singh, AD, Singh, A, Singh, G, Singh, H, Singh, JA, Singh, JK, Singh, P, Singh, S, Sinha, DN, Sinke, AH, Siraj, MS, Sitas, F, Siwal, SS, Skryabin, VY, Skryabina, AA, Socea, B, Soeberg, MJ, Sofi-Mahmudi, A, Solomon, Y, Soltani-Zangbar, MS, Song, S, Song, Y, Sorensen, RJD, Soshnikov, S, Sotoudeh, H, Sowe, A, Sufiyan, MB, Suk, R, Suleman, M, Abdulkader, RS, Sultana, S, Sur, D, Szacska, M, Tabaeian, SP, Tabares-Seisdedos, R, Tabatabaei, SM, Tabuchi, T, Tadbiri, H, Taheri, E, Taheri, M, Soodejani, MT, Takahashi, K, Talaat, IM, Tampa, M, Tan, K-K, Tat, NY, Tat, VY, Tavakoli, A, Tehrani-Banihashemi, A, Tekalegn, Y, Tesfay, FH, Thapar, R, Thavamani, A, Chandrasekar, VT, Thomas, N, Thomas, NK, Ticoalu, JHV, Tiyuri, A, Tollosa, DN, Topor-Madry, R, Touvier, M, Tovani-Palone, MR, Traini, E, Mai, TNT, Tripathy, JP, Ukke, GG, Ullah, I, Ullah, S, Unnikrishnan, B, Vacante, M, Vaezi, M, Tahbaz, SV, Valdez, PR, Vardavas, C, Varthya, SB, Vaziri, S, Velazquez, DZ, Veroux, M, Villeneuve, PJ, Violante, FS, Vladimirov, SK, Vlassov, V, Vo, B, Vu, LG, Wadood, AW, Waheed, Y, Walde, MT, Wamai, RG, Wang, C, Wang, F, Wang, N, Wang, Y, Ward, P, Waris, A, Westerman, R, Wickramasinghe, ND, Woldemariam, M, Woldu, B, Xiao, H, Xu, S, Xu, X, Yadav, L, Jabbari, SHY, Yang, L, Yazdanpanah, F, Yeshaw, Y, Yismaw, Y, Yonemoto, N, Younis, MZ, Yousefi, Z, Yousefian, F, Yu, C, Yu, Y, Yunusa, I, Zahir, M, Zaki, N, Zaman, BA, Zangiabadian, M, Zare, F, Zare, I, Zareshahrabadi, Z, Zarrintan, A, Zastrozhin, MS, Zeineddine, MA, Zhang, D, Zhang, J, Zhang, Y, Zhang, Z-J, Zhou, L, Zodpey, S, Zoladl, M, Vos, T, Hay, S, Force, LM, Murray, CJL, Khanh, BT, Lang, JJ, Compton, K, Xu, R, Acheson, AR, Henrikson, HJ, Kocarnik, JM, Penberthy, L, Aali, A, Abbas, Q, Abbasi, B, Abbasi-Kangevari, M, Abbasi-Kangevari, Z, Abbastabar, H, Abdelmasseh, M, Abd-Elsalam, S, Abdelwahab, AA, Abdoli, G, Abdulkadir, HA, Abedi, A, Abegaz, KH, Abidi, H, Aboagye, RG, Abolhassani, H, Absalan, A, Abtew, YD, Ali, HA, Abu-Gharbieh, E, Achappa, B, Acuna, JM, Addison, D, Addo, IY, Adegboye, OA, Adesina, MA, Adnan, M, Adnani, QES, Advani, SM, Afrin, S, Afzal, MS, Aggarwal, M, Ahinkorah, BO, Ahmad, AR, Ahmad, R, Ahmad, S, Ahmadi, S, Ahmed, H, Ahmed, LA, Ahmed, MB, Rashid, TA, Aiman, W, Ajami, M, Akalu, GT, Akbarzadeh-Khiavi, M, Aklilu, A, Akonde, M, Akunna, CJ, Al Hamad, H, Alahdab, F, Alanezi, FM, Alanzi, TM, Alessy, SA, Algammal, AM, Al-Hanawi, MK, Alhassan, RK, Ali, BA, Ali, L, Ali, SS, Alimohamadi, Y, Alipour, V, Aljunid, SM, Alkhayyat, M, Al-Maweri, SAA, Almustanyir, S, Alonso, N, Alqalyoobi, S, Al-Raddadi, RM, Al-Rifai, RHH, Al-Sabah, SK, Al-Tammemi, AB, Altawalah, H, Alvis-Guzman, N, Amare, F, Ameyaw, EK, Dehkordi, JJA, Amirzade-Iranaq, MH, Amu, H, Amusa, GA, Ancuceanu, R, Anderson, JA, Animut, YA, Anoushiravani, A, Anoushirvani, AA, Ansari-Moghaddam, A, Ansha, MG, Antony, B, Antwi, MH, Anwar, SL, Anwer, R, Anyasodor, AE, Arabloo, J, Arab-Zozani, M, Aremu, O, Argaw, AM, Ariffin, H, Aripov, T, Arshad, M, Al, A, Arulappan, J, Aruleba, RT, Aryannejad, A, Asaad, M, Asemahagn, MA, Asemi, Z, Asghari-Jafarabadi, M, Ashraf, T, Assadi, R, Athar, M, Athari, SS, Null, MMWA, Attia, S, Aujayeb, A, Ausloos, M, Avila-Burgos, L, Awedew, AF, Awoke, MA, Awoke, T, Quintanilla, BPA, Ayana, TM, Ayen, SS, Azadi, D, Null, SA, Azami-Aghdash, S, Azanaw, MM, Azangou-Khyavy, M, Jafari, AA, Azizi, H, Azzam, AYY, Babajani, A, Badar, M, Badiye, AD, Baghcheghi, N, Bagheri, N, Bagherieh, S, Bahadory, S, Baig, AA, Baker, JL, Bakhtiari, A, Bakshi, RK, Banach, M, Banerjee, I, Bardhan, M, Barone-Adesi, F, Barra, F, Barrow, A, Bashir, NZ, Bashiri, A, Basu, S, Batiha, A-MM, Begum, A, Bekele, AB, Belay, AS, Belete, MA, Belgaumi, UI, Bell, AW, Belo, L, Benzian, H, Berhie, AY, Bermudez, ANC, Bernabe, E, Bhagavathula, AS, Bhala, N, Bhandari, BB, Bhardwaj, N, Bhardwaj, P, Bhattacharyya, K, Bhojaraja, VS, Bhuyan, SS, Bibi, S, Bilchut, AH, Bintoro, BS, Biondi, A, Birega, MGB, Birhan, HE, Bjorge, T, Blyuss, O, Bodicha, BBA, Bolla, SR, Boloor, A, Bosetti, C, Braithwaite, D, Brauer, M, Brenner, H, Briko, AN, Briko, NI, Buchanan, CM, Bulamu, NB, Bustamante-Teixeira, MT, Butt, MH, Butt, NS, Butt, ZA, Caetano dos Santos, FL, Camera, LA, Cao, C, Cao, Y, Carreras, G, Carvalho, M, Cembranel, F, Cerin, E, Chakraborty, PA, Charalampous, P, Chattu, VK, Chimed-Ochir, O, Chirinos-Caceres, JL, Cho, DY, Cho, WCS, Christopher, DJ, Chu, D-T, Chukwu, IS, Cohen, AJ, Conde, J, Cortas, S, Costa, VM, Cruz-Martins, N, Culbreth, GT, Dadras, O, Dagnaw, FT, Dahlawi, SMA, Dai, X, Dandona, L, Dandona, R, Daneshpajouhnejad, P, Danielewicz, A, An, TMD, Soltani, RDC, Darwesh, AM, Das, S, Davitoiu, DV, Esmaeili, ED, De la Hoz, FP, Debela, SA, Dehghan, A, Demisse, B, Demisse, FW, DenovaGutiA, E, Derakhshani, A, Molla, MD, Dereje, D, Deribe, KS, Desai, R, Desalegn, MD, Dessalegn, FN, Dessalegni, SAA, Dessie, G, Desta, AA, Dewan, SMR, Dharmaratne, SD, Dhimal, M, Dianatinasab, M, Diao, N, Diaz, D, Digesa, LE, Dixit, SG, Doaei, S, Linh, PD, Doku, PN, Dongarwar, D, dos Santos, WM, Driscoll, TR, Dsouza, HL, Durojaiye, OC, Edalati, S, Eghbalian, F, Ehsani-Chimeh, E, Eini, E, Ekholuenetale, M, Ekundayo, TC, Ekwueme, DU, El Tantawi, M, Elbahnasawy, MA, Elbarazi, I, Elghazaly, H, Elhadi, M, El-Huneidi, W, Emamian, MH, Bain, LE, Enyew, DB, Erkhembayar, R, Eshetu, T, Eshrati, B, Eskandarieh, S, Espinosa-Montero, J, Etaee, F, Etemadimanesh, A, Eyayu, T, Ezeonwumelu, IJ, Ezzikouri, S, Fagbamigbe, AF, Fahimi, S, Fakhradiyev, IR, Faraon, EJA, Fares, J, Farmany, A, Farooque, U, Farrokhpour, H, Fasanmi, AO, Fatehizadeh, A, Fatima, W, Fattahi, H, Fekadu, G, Feleke, BE, Ferrari, AA, Ferrero, S, Desideri, LF, Filip, I, Fischer, F, Foroumadi, R, Foroutan, M, Fukumoto, T, Gaal, PA, Gad, MM, Gadanya, MA, Gaipov, A, Galehdar, N, Gallus, S, Garg, T, Fonseca, MG, Gebremariam, YH, Gebremeskel, TG, Gebremichael, MA, Geda, YF, Gela, YY, Gemeda, BNB, Getachew, M, Getachew, ME, Ghaffari, K, Ghafourifard, M, Ghamari, S-H, Nour, MG, Ghassemi, F, Ghimire, A, Ghith, N, Gholamalizadeh, M, Navashenaq, JG, Ghozy, S, Gilani, SA, Gill, PS, Ginindza, TG, Gizaw, ATT, Glasbey, JC, Godos, J, Goel, A, Golechha, M, Goleij, P, Golinelli, D, Golitaleb, M, Gorini, G, Goulart, BNG, Grosso, G, Guadie, HA, Gubari, MIM, Gudayu, TW, Guerra, MR, Gunawardane, DA, Gupta, B, Gupta, S, Gupta, V, Gupta, VK, Gurara, MK, Guta, A, Habibzadeh, P, Avval, AH, Hafezi-Nejad, N, Ali, AH, Haj-Mirzaian, A, Halboub, ES, Halimi, A, Halwani, R, Hamadeh, RR, Hameed, S, Hamidi, S, Hanif, A, Hariri, S, Harlianto, N, Haro, JM, Hartono, RK, Hasaballah, A, Hasan, SMM, Hasani, H, Hashemi, SM, Hassan, AM, Hassanipour, S, Hayat, K, Heidari, G, Heidari, M, Heidarymeybodi, Z, Herrera-Serna, BY, Herteliu, C, Hezam, K, Hiraike, Y, Hlongwa, MM, Holla, R, Holm, M, Horita, N, Hoseini, M, Hossain, MM, Hossain, MBH, Hosseini, M-S, Hosseinzadeh, A, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Househ, M, Huang, J, Hugo, FN, Humayun, A, Hussain, S, Hussein, NR, Hwang, B-F, Ibitoye, SE, Iftikhar, PM, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Immurana, M, Innos, K, Iranpour, P, Irham, LM, Islam, MS, Islam, RM, Islami, F, Ismail, NE, Isola, G, Iwagami, M, Merin, LJ, Jaiswal, A, Jakovljevic, M, Jalili, M, Jalilian, S, Jamshidi, E, Jang, S-I, Jani, CT, Javaheri, T, Jayarajah, UU, Jayaram, S, Jazayeri, SB, Jebai, R, Jemal, B, Jeong, W, Jha, RP, Jindal, HA, John-Akinola, YO, Jonas, JB, Joo, T, Joseph, N, Joukar, F, Jozwiak, JJ, Jarisson, M, Kabir, A, Kacimi, SEO, Kadashetti, V, Kahe, F, Kakodkar, PV, Kalankesh, LR, Kalhor, R, Kamal, VK, Kamangar, F, Kamath, A, Kanchan, T, Kandaswamy, E, Kandel, H, Kang, H, Kanno, GG, Kapoor, N, Kar, SS, Karanth, SD, Karaye, IM, Karch, A, Karimi, A, Kassa, BG, Katoto, PDMC, Kauppila, JH, Kaur, H, Kebede, AG, Keikavoosi-Arani, L, Kejela, GG, Bohan, PMK, Keramati, M, Keykhaei, M, Khajuria, H, Khan, A, Khan, AAK, Khan, EA, Khan, G, Khan, MN, Ab Khan, M, Khanali, J, Khatab, K, Khatatbeh, MM, Khatib, MN, Khayamzadeh, M, Kashani, HRK, Tabari, MAK, Khezeli, M, Khodadost, M, Kim, MS, Kim, YJ, Kisa, A, Kisa, S, Klugar, M, Klugarova, J, Kolahi, A-A, Kolkhir, P, Kompani, F, Koul, PA, Laxminarayana, SLK, Koyanagi, A, Krishan, K, Krishnamoorthy, Y, Bicer, BK, Kugbey, N, Kulimbet, M, Kumar, A, Kumar, GA, Kumar, N, Kurmi, OP, Kuttikkattu, A, La Vecchia, C, Lahiri, A, Lal, DK, Lam, J, Lan, Q, Landires, I, Larijani, B, Lasrado, S, Lau, J, Lauriola, P, Ledda, C, Lee, S-W, Lee, SWH, Lee, W-C, Lee, YY, Lee, YH, Legesse, SM, Leigh, J, Leong, E, Li, M-C, Lim, SS, Liu, G, Liu, J, Lo, C-H, Lohiya, A, Lopukhov, PD, Lorenzovici, L, Lotfi, M, Loureiro, JA, Lunevicius, R, Madadizadeh, F, Mafi, AR, Magdeldin, S, Mahjoub, S, Mahmoodpoor, A, Mahmoudi, M, Mahmoudimanesh, M, Mahumud, RA, Majeed, A, Majidpoor, J, Makki, A, Makris, KC, Rad, EM, Malekpour, M-R, Malekzadeh, R, Malik, AA, Mallhi, TH, Mallya, SD, Mamun, MA, Manda, AL, Mansour-Ghanaei, F, Mansouri, B, Mansournia, MA, Mantovani, LG, Martini, S, Martorell, M, Masoudi, S, Masoumi, SZ, Matei, CN, Mathews, E, Mathur, MR, Mathur, V, McKee, M, Meena, JK, Mehmood, K, Nasab, EM, Mehrotra, R, Melese, A, Mendoza, W, Menezes, RG, Mengesha, SD, Mensah, LG, Mentis, A-FA, Mera-Mamian, AYM, Meretoja, TJ, Merid, MW, Mersha, AG, Meselu, BT, Meshkat, M, Mestrovic, T, Jonasson, JM, Miazgowski, T, Michalek, IM, Mijena, GFW, Miller, TR, Mir, SA, Mirinezhad, SK, Mirmoeeni, S, Mirza-Aghazadeh-Attari, M, Mirzaei, H, Mirzaei, HR, Misganaw, AS, Misra, S, AbdulmuhsinMohammad, K, Mohammadi, E, Mohammadi, M, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, A, Mohammed, S, Mohan, S, Mohseni, M, Moka, N, Mokdad, AH, Molassiotis, A, Molokhia, M, Momenzadeh, K, Momtazmanesh, S, Monasta, L, Mons, U, Al Montasir, A, Montazeri, F, Montero, A, Moosavi, MA, Moradi, A, Moradi, Y, Sarabi, MM, Moraga, P, Morawska, L, Morrison, SD, Morze, J, Mosapour, A, Mostafavi, E, Mousavi, SM, Isfahani, HM, Khaneghah, AM, Mpundu-Kaambwa, C, Mubarik, S, Mulita, F, Munblit, D, Munro, SB, Murillo-Zamora, E, Musa, J, Nabhan, AF, Nagarajan, AJ, Nagaraju, SP, Nagel, G, Naghipour, M, Naimzada, MD, Nair, TS, Naqvi, AA, Swamy, SN, Narayana, AI, Nassereldine, H, Natto, ZS, Nayak, BP, Ndejjo, R, Nduaguba, SO, Negash, WW, Nejadghaderi, SA, Nejati, K, Kandel, SN, Huy, VNN, Niazi, RK, Noor, NM, Noori, M, Noroozi, N, Nouraei, H, Nowroozi, A, Nunez-Samudio, V, Nzoputam, CI, Nzoputam, OJ, Oancea, B, Odukoya, OO, Oghenetega, OB, Ogunsakin, RE, Oguntade, AS, Oh, I-H, Okati-Aliabad, H, Okekunle, AP, Olagunju, AT, Olagunju, TO, Olakunde, BO, Olufadewa, II, Omer, E, Omonisi, AEE, Ong, S, Onwujekwe, OE, Orru, H, Otstavnov, SS, Oulhaj, A, Oumer, B, Owopetu, OF, Oyinloye, BE, Mahesh, PA, Padron-Monedero, A, Padubidri, JR, Pakbin, B, Pakshir, K, Pakzad, R, Palicz, T, Pana, A, Pandey, A, Pant, S, Pardhan, S, Park, E-K, Park, S, Patel, J, Pati, S, Paudel, R, Paudel, U, Paun, M, Toroudi, HP, Peng, M, Pereira, J, Pereira, RB, Perna, S, Perumalsamy, N, Pestell, RG, Pezzani, R, Piccinelli, C, Pillay, JD, Piracha, ZZ, Pischon, T, Postma, MJ, Langroudi, AP, Pourshams, A, Pourtaheri, N, Prashant, A, Qadir, MMF, Syed, ZQ, Rabiee, M, Rabiee, N, Radfar, A, Radhakrishnan, RA, Radhakrishnan, V, Raeisi, M, Rafiee, A, Rafiei, A, Raheem, N, Rahim, F, Rahman, MO, Rahman, M, Rahman, MA, Rahmani, AM, Rahmani, S, Rahmanian, V, Rajai, N, Rajesh, A, Ram, P, Ramezanzadeh, K, Rana, J, Ranabhat, K, Ranasinghe, P, Rao, CR, Rao, SJ, Rashedi, S, Rashidi, A, Rashidi, M-M, Ratan, ZA, Rawaf, DL, Rawaf, S, Rawal, L, Rawassizadeh, R, Razeghinia, MS, Rehman, AU, Rehman, IU, Reitsma, MB, Renzaho, AMN, Rezaei, M, Rezaei, N, Rezaei, S, Rezaeian, M, Rezapour, A, Riad, A, Rikhtegar, R, Rios-Blancas, M, Roberts, TJ, Rohloff, P, Romero-Rodriguez, E, Roshandel, G, Rwegerera, GM, Manjula, S, Saber-Ayad, MM, Saberzadeh-Ardestani, B, Sabour, S, Saddik, B, Sadeghi, E, Saeb, MR, Saeed, U, Safaei, M, Safary, A, Sahebazzamani, M, Sahebkar, A, Sahoo, H, Sajid, MR, Salari, H, Salehi, S, Salem, MR, Salimzadeh, H, Samodra, YL, Samy, AM, Sanabria, J, Sankararaman, S, Sanmarchi, F, Santric-Milicevic, MM, Saqib, MAN, Sarveazad, A, Sarvi, F, Sathian, B, Satpathy, M, Sayegh, N, Schneider, IJC, Schwarzinger, M, Sekerija, M, Senthilkumaran, S, Sepanlou, SG, Seylani, A, Seyoum, K, Sha, F, Shafaat, O, Shah, PA, Shahabi, S, Shahid, I, Shahrbaf, MA, Shahsavari, HR, Shaikh, MA, Shaka, MF, Shaker, E, Shannawaz, M, Sharew, MMS, Sharifi, A, Sharifi-Rad, J, Sharma, P, Shashamo, BB, Sheikh, A, Sheikh, M, Sheikhbahaei, S, Sheikhi, RA, Sheikhy, A, Shepherd, PR, Shetty, A, Shetty, JK, Shetty, RS, Shibuya, K, Shirkoohi, R, Shirzad-Aski, H, Shivakumar, KM, Shivalli, S, Shivarov, V, Shobeiri, P, Varniab, ZS, Shorofi, SA, Shrestha, S, Sibhat, MM, Malleshappa, SS, Sidemo, NB, Silva, DAS, Silva, LMLR, Julian, GS, Silvestris, N, Simegn, W, Singh, AD, Singh, A, Singh, G, Singh, H, Singh, JA, Singh, JK, Singh, P, Singh, S, Sinha, DN, Sinke, AH, Siraj, MS, Sitas, F, Siwal, SS, Skryabin, VY, Skryabina, AA, Socea, B, Soeberg, MJ, Sofi-Mahmudi, A, Solomon, Y, Soltani-Zangbar, MS, Song, S, Song, Y, Sorensen, RJD, Soshnikov, S, Sotoudeh, H, Sowe, A, Sufiyan, MB, Suk, R, Suleman, M, Abdulkader, RS, Sultana, S, Sur, D, Szacska, M, Tabaeian, SP, Tabares-Seisdedos, R, Tabatabaei, SM, Tabuchi, T, Tadbiri, H, Taheri, E, Taheri, M, Soodejani, MT, Takahashi, K, Talaat, IM, Tampa, M, Tan, K-K, Tat, NY, Tat, VY, Tavakoli, A, Tehrani-Banihashemi, A, Tekalegn, Y, Tesfay, FH, Thapar, R, Thavamani, A, Chandrasekar, VT, Thomas, N, Thomas, NK, Ticoalu, JHV, Tiyuri, A, Tollosa, DN, Topor-Madry, R, Touvier, M, Tovani-Palone, MR, Traini, E, Mai, TNT, Tripathy, JP, Ukke, GG, Ullah, I, Ullah, S, Unnikrishnan, B, Vacante, M, Vaezi, M, Tahbaz, SV, Valdez, PR, Vardavas, C, Varthya, SB, Vaziri, S, Velazquez, DZ, Veroux, M, Villeneuve, PJ, Violante, FS, Vladimirov, SK, Vlassov, V, Vo, B, Vu, LG, Wadood, AW, Waheed, Y, Walde, MT, Wamai, RG, Wang, C, Wang, F, Wang, N, Wang, Y, Ward, P, Waris, A, Westerman, R, Wickramasinghe, ND, Woldemariam, M, Woldu, B, Xiao, H, Xu, S, Xu, X, Yadav, L, Jabbari, SHY, Yang, L, Yazdanpanah, F, Yeshaw, Y, Yismaw, Y, Yonemoto, N, Younis, MZ, Yousefi, Z, Yousefian, F, Yu, C, Yu, Y, Yunusa, I, Zahir, M, Zaki, N, Zaman, BA, Zangiabadian, M, Zare, F, Zare, I, Zareshahrabadi, Z, Zarrintan, A, Zastrozhin, MS, Zeineddine, MA, Zhang, D, Zhang, J, Zhang, Y, Zhang, Z-J, Zhou, L, Zodpey, S, Zoladl, M, Vos, T, Hay, S, Force, LM, and Murray, CJL
- Abstract
BACKGROUND: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01-4·94) deaths and 105 million (95·0-116) DALYs for both sexes combined, representing 44·4% (41·3-48·4) of all cancer deaths and 42·0% (39·1-45·6) of all DALYs. There were 2·88 million (2·60-3·18) risk-attributable cancer deaths in males (50·6% [47·8-54·1] of all male cancer deaths) and 1·58 million (1·36-1·84) risk-attributable cancer deaths in females (36·3% [32·5-41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6-28·4) and DALYs by 16·8% (8·8-25·0), with the greatest percentage increase in metabolic risks (34·7% [2
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- 2022
8. Non Clinical Risk Factors of Myocardial Infarction: A Meta-Analysis Approach
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Waheed K, Hanif A, Sajid Mr, Ashraf T, Tufail S, Butt A, and Ansar A
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medicine.medical_specialty ,Heart disease ,business.industry ,Odds ratio ,medicine.disease ,Random effects model ,Omics ,Confidence interval ,Internal medicine ,Meta-analysis ,medicine ,Myocardial infarction ,Family history ,business - Abstract
Background: Myocardial Infarction (MI) is a coronary heart disease that is one of the main causes of the mortality over the globe. There are various clinical and non-clinical risk factors that can be further classified as modifiable and non-modifiable. This study has explored the role of the some Non Clinical factors like; Gender, Education and Family History with MI using Meta-analysis approach. Methods: The published literature from 1990 to 2015 on MI was collected by using several databases and search engines. A review of the collected literature (28 studies) showed that the studies under analysis were of different origins and had different objectives. For each study, Odds Ratio and 95% confidence intervals was extracted and pooled with a random effect model, weighting for the inverse of the variance. Meta-analysis software version 2.0 was used to analyze heterogeneity analysis and estimate pooled estimates through random effect model. Results: The study has showed that gender (OR=1.391 and 95% C.I.: 1.140, 1.697), family history of heart diseases (OR=3.206 95% C.I.:2.064, 4.981) and low education level or illiteracy (OR=1.552 and 95%C.I.: 1.132, 2.128) are the significant risk factors in developing Myocardial Infarction. Conclusion: This study has concluded that included factors in this study are significantly related to the Myocardial Infarction. Gender difference, family history of heart disease and low education are the important risk factors in causing this fatal disease.
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- 2017
9. Beyond the curriculum: unveiling medical students' drivers and barriers to research participation at Alfaisal University.
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Sabbah BN, Shah HY, Baqal EJ, Arabi TZ, Shakir IM, Obeidat A, AlKattan K, and Sajid MR
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- Humans, Universities, Motivation, Curriculum, Students, Medical, Education, Medical
- Abstract
Medical education continually adapts to the needs of future health care professionals, with student motivation in research being a pivotal aspect. This study at Alfaisal University aimed to explore the motivations, benefits, and challenges faced by medical students in extracurricular research activities. Using a mixed-method approach, we combined quantitative surveys with qualitative group interviews. Findings revealed that both extrinsic (e.g., enhancing postgraduate training prospects) and intrinsic (e.g., personal interest and skill refinement) factors significantly motivate students to be involved in research activities. Participants unanimously acknowledged skill enhancement, particularly in literature comprehension, creative ideation, and networking. However, challenges such as conflicts with course scheduling, lack of hands-on experiences, and mentorship issues were identified as potential barriers to research participation. Addressing these barriers and understanding motivations can inform the design of research programs, enhancing the overall student research experience. This study underscores the importance of research in medical education, emphasizing the need for institutions to prioritize addressing challenges and leveraging benefits to prepare medical students for a research-integrated clinical future. NEW & NOTEWORTHY This article examines the motivating factors and obstacles of extracurricular research in Alfaisal University, Saudi Arabia. The study utilizes a mixed methodology of online surveys and in-person group interviews to gain insights from the medical students of the university. We revealed several extrinsic and intrinsic motivators that drove the students; however, there remain several challenges to students during their research journey. Addressing these challenges will help the students obtain a more fruitful, educational research experience.
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- 2024
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10. Polyherbal extract improves glycometabolic control in alloxan-induced diabetic rats via down-regulating the MAPK/JNK pathway, modulating Nrf-2/Keap-1 expression, and stimulating insulin signaling.
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Aslam B, Hussain A, Faisal MN, Kousar S, Roobi A, Sajid MR, and Gul A
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Objectives: This study focused on the evaluation of antioxidant and antidiabetic activities of polyherbal extract (PHE), containing Cassia absus (L.), Gymnema sylvestre (R. Br.), Nigella sativa (L.), and Piper nigrum (L.), in alloxan-induced diabetes model., Materials and Methods: In vitro , HPLC characterization, DPPH scavenging assay, and α-amylase inhibition test were conducted. In vivo , acute oral toxicity of PHE was assessed. Alloxan-induced diabetic Wistar rats (n=6) were orally treated with PHE (200, 400, and 600 mg/kg/day) and glibenclamide (GLB; 10 mg/kg/day) for six consecutive weeks. Then, biochemical biomarkers, oxidative stress parameters, histopathological examination, and mRNA expression levels (RT-qPCR) were determined., Results: The presence of polyphenols in PHE was confirmed in correlation to marked DPPH scavenging (IC
50 : 1.60 mg/ml) and α-amylase inhibition (IC50 : 0.82 mg/ml). PHE demonstrated no toxicity in rats up to a dose of 2000 mg/kg. In diabetic rats, PHE dose-dependently ameliorated the serum levels of glucose, insulin, glycated hemoglobin A1c (HbA1c), leptin, and glucokinase (GCK). Also, PHE substantially alleviated serum inflammatory markers (TNF-α and CRP) and oxidative stress indicators (MDA, SOD, and CAT) in pancreatic tissues. PHE, particularly at 600 mg/kg, attenuated cellular oxidative stress via modulating the mRNA expression levels of genes regulating MAPK/JNK (Mapk-8, Traf-4, and Traf-6) and Nrf-2/Keap-1 pathways and promoted insulin signaling through up-regulating insulin signaling cascade (Pdx-1, Ins-1, and Ins-2), as compared to GLB. Furthermore, histopathological findings supported the aforementioned results., Conclusion: Our study suggests that polyherbal extract has promising antioxidant and antidiabetic activities by modulating the MAPK/JNK, Nrf-2/Keap-1, and insulin signaling pathways., Competing Interests: None.- Published
- 2024
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11. A retrospective analysis of the frequency of heparin-induced thrombocytopenia in the intensive care unit at a tertiary care center in Riyadh, Saudi Arabia.
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Mushtaq AH, Rasheed AW, Jamil MG, Maghrabi K, Khoja O, Sajid MR, Tamim H, Hijazi M, and Owaidah T
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Background: Heparin-induced thrombocytopenia (HIT) is an extremely serious and potentially fatal condition that can develop in patients taking heparin-based medications, such as unfractionated heparin (UFH) or low-molecular-weight heparin (LMWH). The incidence and risk factors for HIT in critically ill patients, however, are not well defined., Methods: We retrospectively collected data on HIT test results, route of heparin administration, age, sex, heparin type (UFH or LMWH), and date of illness from patients admitted to the intensive care unit (ICU) and regular nursing floor (non-ICU) at our hospital between January 2011 and December 2014. We screened patients for HIT using the 4T score and confirmed the diagnosis through laboratory testing (direct enzyme immunoassay immunoglobulin G [IgG] or a platelet-activating antibody)., Results: We screened a total of 946 patients, 56 (5.9%) of whom were positive for HIT. Among 776 patients receiving UFH and 180 receiving LMWH, 2.8 and 6.6% developed HIT, respectively ( P = 0.051). We then classified our patients into two groups: ICU, and non-ICU. In the non-ICU group (n = 317), 4 (2.7%) patients receiving LMWH and 25 (5.1%) receiving UFH were positive for HIT ( P = 0.221). In the ICU group (n = 639), 1 (3.1%) patient receiving LMWH and 26 (9.1%) receiving UFH were positive for HIT ( P = 0.249). The ICU group, therefore, had a higher cumulative incidence rate of HIT than the non-ICU group (8.5 vs. 4.5%)., Conclusion: HIT was more common in ICU patients than non-ICU patients and in more patients receiving UFH than LMWH, although the differences were not statistically significant. Early diagnosis and appropriate treatment are essential to prevent adverse outcomes in patients with HIT., Competing Interests: None., (AJBR Copyright © 2023.)
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- 2023
12. Alfaisal University's Academic Success Center: An Individualized Peer-Assisted Learning Program for Mutual Tutor-Student Advancement.
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Abdul Muthalib H, Syed F, Raziq T, Wilson EM, and Sajid MR
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Introduction: Peer-assisted learning (PAL) is an educational strategy whereby students teach other students. PAL presents many advantages to the institution, tutors, and tutees. It can benefit the university by presenting a cost-effective approach where the efforts and time of faculty are reduced. We describe a retrospective analysis detailing the structure, function, and effectiveness of the Academic Success Center (ASC) at Alfaisal University, Riyadh, Saudi Arabia, since 2020. The ASC encompasses various types of PAL methods. It is the only PAL program in Saudi Arabia whereby its tutors, referred to as student consultants, are compensated financially., Methods: We retrospectively analyzed the total number of appointments, the total number of students who accessed the center, as well as the breakdown between different colleges and academic years, and their satisfaction level with the PAL mode of teaching., Results: Our results indicate a high level of appointments for these PAL sessions increasing on a yearly basis and a high level of satisfaction from both the tutors and the tutees., Conclusion: PAL is an effective teaching and mentoring modality with high retention rates that has contributed to an increased sense of well-being among students who have utilized these services., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Abdul Muthalib et al.)
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- 2023
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13. Elevating athletic performance: Maximizing strength and power in long jumpers through combined low-intensity blood flow restriction and high-intensity resistance training.
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Sarfabadi P, Rizvi MR, Sharma A, Sami W, Sajid MR, Arora S, Anand A, and Bin Ab Hamid MR
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Purpose: This study aimed to evaluate the effects of low-intensity blood flow restriction (BFR) training and high-intensity resistance training (HI-RT) on the leaping performance of long-jumpers., Materials and Methods: Long jump players were divided into two groups; one group (group A) receiving HI-RT (n = 8) and the other group (group B) receiving combined low-intensity BFR training plus HI-RT (n = 8). Muscle power and knee muscle strength was assessed at baseline, 3 weeks and 6 weeks of intervention., Results: 1-RM was found to be significantly different between Group A and Group B at 3 and 6 weeks. Further, IKDQR, IKDHR and IKDQL was significantly improved in group B as compared to group A both at 3 and 6 weeks. There was significant time effect, group effect and time-group interaction in the strength of quadriceps and hamstring of both left and right leg measured through isokinetic device. Post-hoc analysis for 1-RM in group B showed a significant improvement at baseline and 6 weeks and the broad jump was significant at baseline and 3 weeks and at baseline and 6 weeks., Conclusion: The combined effects of low-intensity BFR training and HI-RT is effective in improving the muscle strength and power of lower limbs in long jumpers., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)
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- 2023
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14. Utility of multi-omics data to inform genomic prediction of heifer fertility traits.
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Tahir MS, Porto-Neto LR, Reverter-Gomez T, Olasege BS, Sajid MR, Wockner KB, Tan AWL, and Fortes MRS
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- Pregnancy, Cattle genetics, Animals, Female, Genotype, Genomics, Phenotype, Fertility genetics, Polymorphism, Single Nucleotide, Multiomics, Genome
- Abstract
Biologically informed single nucleotide polymorphisms (SNPs) impact genomic prediction accuracy of the target traits. Our previous genomics, proteomics, and transcriptomics work identified candidate genes related to puberty and fertility in Brahman heifers. We aimed to test this biological information for capturing heritability and predicting heifer fertility traits in another breed i.e., Tropical Composite. The SNP from the identified genes including 10 kilobases (kb) region on either side were selected as biologically informed SNP set. The SNP from the rest of the Bos taurus genes including 10-kb region on either side were selected as biologically uninformed SNP set. Bovine high-density (HD) complete SNP set (628,323 SNP) was used as a control. Two populations-Tropical Composites (N = 1331) and Brahman (N = 2310)-had records for three traits: pregnancy after first mating season (PREG1, binary), first conception score (FCS, score 1 to 3), and rebreeding score (REB, score 1 to 3.5). Using the best linear unbiased prediction method, effectiveness of each SNP set to predict the traits was tested in two scenarios: a 5-fold cross-validation within Tropical Composites using biological information from Brahman studies, and application of prediction equations from one breed to the other. The accuracy of prediction was calculated as the correlation between genomic estimated breeding values and adjusted phenotypes. Results show that biologically informed SNP set estimated heritabilities not significantly better than the control HD complete SNP set in Tropical Composites; however, it captured all the observed genetic variance in PREG1 and FCS when modeled together with the biologically uninformed SNP set. In 5-fold cross-validation within Tropical Composites, the biologically informed SNP set performed marginally better (statistically insignificant) in terms of prediction accuracies (PREG1: 0.20, FCS: 0.13, and REB: 0.12) as compared to HD complete SNP set (PREG1: 0.17, FCS: 0.10, and REB: 0.11), and biologically uninformed SNP set (PREG1: 0.16, FCS: 0.10, and REB: 0.11). Across-breed use of prediction equations still remained a challenge: accuracies by all SNP sets dropped to around zero for all traits. The performance of biologically informed SNP was not significantly better than other sets in Tropical Composites. However, results indicate that biological information obtained from Brahman was successful to predict the fertility traits in Tropical Composite population., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
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- 2022
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15. The Perceptions of Non-native Medical Students Towards Language Barrier on Clinical Teaching and Learning: a Qualitative Study from Saudi Arabia.
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Sheikh AM, Sajid MR, Bakshi EN, Khan AU, Wahed MM, Sohail F, and Sethi A
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Background: Patient-doctor communication is essential for achieving the best healthcare quality for the patients. Saudi Arabia hosts a variety of healthcare providers from diverse cultures and languages, making language barriers distinctive towards effective communication for a predominantly Arab population. There is limited research on the challenges associated with language barriers among non-native medical students. The current qualitative study aims to explore the perceptions and experiences of non-native medical students during clinical encounters with Arabic speaking patient population at a private university in Riyadh, Saudi Arabia., Methods: This is a qualitative case study employing non-native medical students in clinical years to explore their perceptions and experiences towards language barriers. Participants attended four focus group discussions, following a brief regarding the concept of the challenges that language barriers pose. All interviews were recorded and transcribed verbatim. A thematic framework analysis was employed to analyze the data., Results: Twenty-two non-native medical students participated in the study. We identified four broad themes constituting problems in patient interaction, development of clinical skills, managing and overcoming language barriers, and student recommendations to overcome language barrier. Participants suggested improvements in available Arabic language courses and the introduction of a professional skills course in Arabic for non-native students., Conclusion: Non-Native medical students perceived language barriers as an obstacle to a meaningful clinical experience. They identified implications for clinical learning environment, professionalism, empathy, and patient care. Recommendations for advanced occupation-related Arabic language courses to facilitate doctor-patient interactions were made to improve patient-student interactions between native patients and non-native medical students., Competing Interests: Conflict of InterestThe authors declare no competing interests., (© The Author(s) under exclusive licence to International Association of Medical Science Educators 2022.)
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- 2022
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16. Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score.
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Sajid MR, Khan AA, Albar HM, Muhammad N, Sami W, Bukhari SAC, and Wajahat I
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- Humans, Machine Learning, Neural Networks, Computer, Risk Factors, Support Vector Machine, Cardiovascular Diseases, Supervised Machine Learning
- Abstract
Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Mirza Rizwan Sajid et al.)
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- 2022
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17. Scientific premise for the involvement of neutrophil extracellular traps (NETs) in vaccine-induced thrombotic thrombocytopenia (VITT).
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Kashir J, Ambia AR, Shafqat A, Sajid MR, AlKattan K, and Yaqinuddin A
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- Animals, COVID-19 immunology, COVID-19 Vaccines therapeutic use, Humans, Inflammasomes immunology, Interleukin-1beta immunology, NLR Family, Pyrin Domain-Containing 3 Protein immunology, Thrombocytopenia immunology, Thrombocytopenia prevention & control, Thrombocytopenia therapy, COVID-19 prevention & control, COVID-19 Vaccines adverse effects, Extracellular Traps immunology, Thrombocytopenia etiology
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Following on from the devastating spread of COVID-19, a major global priority has been the production, procurement, and distribution of effective vaccines to ensure that the global pandemic reaches an end. However, concerns were raised about worrying side effects, particularly the occurrence of thrombosis and thrombocytopenia after administration of the Oxford/AstraZeneca and Johnson & Johnson's Janssen COVID-19 vaccine, in a phenomenon being termed vaccine-induced thrombotic thrombocytopenia (VITT). Similar to heparin-induced thrombocytopenia (HIT), this condition has been associated with the development of anti-platelet factor 4 antibodies, purportedly leading to neutrophil-platelet aggregate formation. Although thrombosis has also been a common association with COVID-19, the precise molecular mechanisms governing its occurrence are yet to be established. Recently, increasing evidence highlights the NLRP3 (NOD-like, leucine-rich repeat domains, and pyrin domain-containing protein) inflammasome complex along with IL-1β and effete neutrophils producing neutrophil extracellular traps (NETs) through NETosis. Herein, we propose and discuss that perhaps the incidence of VITT may be due to inflammatory reactions mediated via IL-1β/NLRP3 inflammasome activation and consequent overproduction of NETs, where similar autoimmune mechanisms are observed in HIT. We also discuss avenues by which such modalities could be treated to prevent the occurrence of adverse events and ensure vaccine rollouts remain safe and on target to end the current pandemic., (©2021 Society for Leukocyte Biology.)
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- 2022
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18. Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches.
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Sajid MR, Almehmadi BA, Sami W, Alzahrani MK, Muhammad N, Chesneau C, Hanif A, Khan AA, and Shahbaz A
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- Adult, Aged, Case-Control Studies, Humans, Machine Learning, Middle Aged, Reproducibility of Results, Risk Assessment, Cardiovascular Diseases epidemiology
- Abstract
Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low-middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case-control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities.
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- 2021
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19. Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach.
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Sajid MR, Muhammad N, Zakaria R, Shahbaz A, Bukhari SAC, Kadry S, and Suresh A
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- Case-Control Studies, Humans, Logistic Models, Machine Learning, Cardiovascular Diseases
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Background: In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms., Methods: A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches., Results: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation., Conclusion: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.
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- 2021
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20. Students' and Faculty Perspectives Toward the Role and Value of the Hidden Curriculum in Undergraduate Medical Education: a Qualitative Study from Saudi Arabia.
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Almairi SOA, Sajid MR, Azouz R, Mohamed RR, Almairi M, and Fadul T
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A hidden medical curriculum is defined as the unwritten, unofficial, and unintended learning that students experience in medical school along with more formal aspects of education. This term describes the behaviours, attitudes, assumptions and beliefs conveyed by teachers, peers and the surrounding environment. However, more research is needed to evaluate its impact on student and faculty interactions in this context. We conducted this qualitative study utilizing focus group and semi-structured interviews of students and faculty to evaluate the perspectives of medical students and faculty toward the role and impact of the hidden medical curriculum in medical education at Alfaisal University, Riyadh, Saudi Arabia. Data was analysed using open-, axial- and selective-coding using thematic framework analysis. Interviewees consisted of 24 students in years 1-3 during the spring semester 2018-2019, 8 faculty members and 4 teaching assistants. We identified six core themes of hidden curriculum at Alfaisal University (Appendix). Role and behavioural modelling, value-based teaching, interpersonal faculty-student interactions, effects of diversity and socialization, teaching methodologies and hidden curriculum, mentoring and student support systems. Although some of the themes identified in these focus group interviews were similar to previously published studies, the novel themes that we identified were diversity, socialization and interpersonal faculty-student interactions. We conclude that identifying the issues pertaining to hidden curriculum is important for the development of medical students and for nurturing and upholding the values that we want to instil in our future physicians., Competing Interests: Competing InterestsThe authors declare that they have no conflict of interests., (© International Association of Medical Science Educators 2021.)
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- 2021
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21. Evaluation of COVID-19 myths in Saudi Arabia.
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Alhusseini NK, Sajid MR, Alsheikh HA, Sriwi TH, Odeh NB, Elshaer RE, Altamimi RE, and Cahusac PMB
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- Adolescent, Adult, Aged, Cross-Sectional Studies, Female, Hand Disinfection, Humans, Male, Masks, Middle Aged, Risk Factors, SARS-CoV-2, Saudi Arabia, Surveys and Questionnaires, Young Adult, COVID-19 epidemiology, COVID-19 physiopathology, COVID-19 prevention & control, COVID-19 transmission, Health Knowledge, Attitudes, Practice
- Abstract
Objectives: To examine the awareness level of COVID-19 and to highlight the frequency of myths and misconceptions among Saudi Arabia's population., Methods: We conducted a cross-sectional study from June 13 to 20, 2020 by distributing a 16-item online Google forms questionnaire among adults (18-65 years old) living in Saudi Arabia. We utilized the convenience sampling. Data analysis was performed using Chi-square and multiple regression analysis on Jamovi., Results: A total of 1436 responses were analyzed with 43.5% males and 56.5% females. Most respondents (89.1%) thought that only the elderly above 60 years old are considered at high risk of contracting COVID-19. Most respondents (86.5%) agreed that people with other health conditions could be more affected by COVID-19. Approximately 97.2% agreed that wearing a mask and avoiding touching eyes, nose, and mouth with unwashed hands was the leading preventive action. Most participants (99.4%) chose fever as an associated symptom. Respondents from the healthcare sector presented statistically higher scores than those in non-healthcare sectors ( p <0.001). Higher education and higher salary were important predictors of better COVID-19 knowledge., Conclusion: Public health officials need to increase awareness measures on COVID-19 to limit myths and misconceptions and reduce psychological distress associated with it., (Copyright: © Saudi Medical Journal.)
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- 2021
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22. Iron Deficiency and Iron Deficiency Anemia Are Common Epidemiological Conditions in Saudi Arabia: Report of the National Epidemiological Survey.
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Owaidah T, Al-Numair N, Al-Suliman A, Zolaly M, Hasanato R, Al Zahrani F, Albalawi M, Bashawri L, Siddiqui K, Alalaf F, Almomen A, and Sajid MR
- Abstract
Iron deficiency is the most prevalent nutritional deficiency worldwide. According to an estimate by the World Health Organization, up to 27% of the world's population experience iron deficiency anemia (IDA). Studies conducted in the Middle East, including Saudi Arabia, have suggested that IDA is the most common cause of anemia, especially among females. This study aimed to determine the prevalence of IDA and iron deficiency (ID) among apparently healthy young university students from four regions in Saudi Arabia. Students were asked to complete a simple survey questionnaire; blood samples were then collected and analyzed after obtaining informed consent. A total of 981 students completed the survey, with 11% of the participants reporting symptoms of anemia; 34% of participants were diagnosed with IDA and 6% reported a diagnosis of hemoglobinopathy. Blood analysis confirmed the prevalence of ID and IDA in 28.6% and 10.7% of the participants, respectively; those with ID and IDA were mostly females (88.5% and 94%, resp.). Thalassemia trait and sickle cell trait were detected in 1.3% and 7% of participants, respectively. Our findings from a national survey among young university in Saudi Arabia indicate a high prevalence of ID and IDA., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Tarek Owaidah et al.)
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- 2020
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23. Modifiable risk factors and overall cardiovascular mortality: Moderation of urbanization.
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Sajid MR, Muhammad N, Zakaria R, and Bukhari SAC
- Abstract
Background: Modifiable risk factors are associated with cardiovascular mortality (CVM) which is a leading form of global mortality. However, diverse nature of urbanization and its objective measurement can modify their relationship. This study aims to investigate the moderating role of urbanization in the relationship of combined exposure (CE) of modifiable risk factors and CVM. Design and Methods: This is the first comprehensive study which considers different forms of urbanization to gauge its manifold impact. Therefore, in addition to existing original quantitative form and traditional two categories of urbanization, a new form consisted of four levels of urbanization was duly introduced. This study used data of 129 countries mainly retrieved from a WHO report, Non-Communicable Diseases Country Profile 2014. Factor scores obtained through confirmatory factor analysis were used to compute the CE. Age-income adjusted regression model for CVM was tested as a baseline with three bootstrap regression models developed for the three forms of urbanization. Results: Results revealed that the CE and CVM baseline relationship was significantly moderated through the original quantitative form of urbanization. Contrarily, the two traditional categories of urbanization could not capture the moderating impact. However, the four levels of urbanization were objectively estimated the urbanization impact and subsequently indicated that the CE was more alarming in causing the CVM in levels 2 and 3 urbanized countries, mainly from low-middle-income countries. Conclusion: This study concluded that the urbanization is a strong moderator and it could be gauged effectively through four levels whereas sufficiency of two traditional categories of urbanization is questionable., (©Copyright: the Author(s).)
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- 2020
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24. Implementation of structured team-based review enhances knowledge consolidation and academic performance of undergraduate medical students studying neuroscience.
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Anwar K, Kashir J, Sajid MR, Rasool AJ, Shaikh AA, Ikram MF, Yaqinuddin A, Alshedoukhy A, and Ganguly PK
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- Comprehension, Curriculum, Educational Measurement, Educational Status, Humans, Memory Consolidation, Program Evaluation, Students, Medical, Education, Medical, Undergraduate, Group Processes, Models, Educational, Neurosciences education
- Abstract
Team-based learning (TBL) provides a systematic approach to teaching and learning and promotes critical thinking and enhances medical educational activities and professional development. TBL-based didactic methodology has proven beneficial in enhancing learning and consolidating key educational concepts throughout educational curricula. Such areas of application include neuroscience, which is traditionally considered to be one of the most difficult disciplines to be taught in undergraduate medical courses to the point where the scientific literature reports "neurophobia" among undergraduate medical students. Herein, we report the design and application of a modified version of TBL, which we termed team-based review (TBR) throughout two cohorts of undergraduate medical students undertaking neuroscience. We show that our TBR methodology enhanced student understanding of neuroscience, increasing average marks and grades achieved in final exams, while also increasing the proportion of students obtaining higher grades. Application of TBR also improved marks obtained by students throughout continuous assessment (midterms, TBL, and problem-based learning grades). In surveys taken following final exams, students strongly felt that TBR enhanced their learning experience and aided knowledge acquisition, consolidation, and exam preparation. Collectively, we show that TBR-based methodology was effective in enhancing the student learning experience and performance in neuroscience and could potentially be successfully used to enhance performance and learning in other subjects in the undergraduate medical curriculum.
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- 2020
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25. Can Facebook pages be a mode of blended learning to supplement in-class teaching in Saudi Arabia?
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Anwar K, Sajid MR, Cahusac P, Shaikh AA, Elgammal A, Alshedoukhy A, and Kashir J
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- Humans, Learning, Neurosciences trends, Saudi Arabia, Social Support, Surveys and Questionnaires, Teaching trends, Internet, Neurosciences education, Teaching standards
- Abstract
The aim of this study was to examine the potential of a self-designed Facebook page on Neuroscience, to supplement in-class teaching as a mode of blended learning. Posts were split into multiple choice questions (MCQs), general interest articles, neuroscience-related external links and resources, and lecture notes and PowerPoint presentations. The study was divided into three distinct phases: before, during, and after the Neuroscience block. Student responses were evaluated via a self-developed questionnaire. Grades achieved by students undertaking the block in 2015 and 2014 were recorded, as were the grades achieved by the same cohort in concurrent blocks in the same year of study. Results showed that ~80% of students reported that use of the page enhanced their overall subject knowledge and exam preparation. Highest page activity occurred during the Neuroscience block. Peak activity occurred directly before summative assessments, with MCQ posts having the highest impact. The cohort of students with access to the Facebook page achieved better grades in the block compared with the previous cohort, despite similar average performance in other subjects. We demonstrate the utility of Facebook as a powerful tool for undergraduate education, supplementing in-class teaching, and assisting in exam preparation, potentially increasing average student performance., (Copyright © 2017 the American Physiological Society.)
- Published
- 2017
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26. Perspectives of students and mentors on a formal mentorship program in Saudi Arabia.
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Ghawji M, Sajid MR, Shaikh AA, Sheriff R, Cahusac P, and Alkattan K
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- Humans, Saudi Arabia, Vocational Guidance organization & administration, Education, Pharmacy organization & administration, Mentors, Students, Medical psychology
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- 2017
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27. Can blended learning and the flipped classroom improve student learning and satisfaction in Saudi Arabia?
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Sajid MR, Laheji AF, Abothenain F, Salam Y, AlJayar D, and Obeidat A
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- Adult, Curriculum, Educational Measurement, Feedback, Female, Humans, Male, Saudi Arabia, Students, Medical, Computer-Assisted Instruction methods, Learning, Personal Satisfaction, Problem-Based Learning methods
- Abstract
Objectives: To evaluate student academic performance and perception towards blended learning and flipped classrooms in comparison to traditional teaching., Methods: This study was conducted during the hematology block on year three students. Five lectures were delivered online only. Asynchronous discussion boards were created where students could interact with colleagues and instructors. A flipped classroom was introduced with application exercises. Summative assessment results were compared with previous year results as a historical control for statistical significance. Student feedback regarding their blended learning experience was collected., Results: A total of 127 responses were obtained. Approximately 22.8% students felt all lectures should be delivered through didactic lecturing, while almost 35% felt that 20% of total lectures should be given online. Students expressed satisfaction with blended learning as a new and effective learning approach. The majority of students reported blended learning was helpful for exam preparation and concept clarification. However, a comparison of grades did not show a statistically significant increase in the academic performance of students taught via the blended learning method., Conclusions: Learning experiences can be enriched by adopting a blended method of instruction at various stages of undergraduate and postgraduate education. Our results suggest that blended learning, a relatively new concept in Saudi Arabia, shows promising results with higher student satisfaction. Flipped classrooms replace passive lecturing with active student-centered learning that enhances critical thinking and application, including information retention.
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- 2016
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28. Tackling student neurophobia in neurosciences block with team-based learning.
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Anwar K, Shaikh AA, Sajid MR, Cahusac P, Alarifi NA, and Al Shedoukhy A
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- Cooperative Behavior, Curriculum, Educational Measurement, Female, Humans, Male, Sex Factors, Education, Medical, Undergraduate organization & administration, Group Processes, Neurosciences education, Problem-Based Learning organization & administration, Students, Medical psychology
- Abstract
Introduction: Traditionally, neurosciences is perceived as a difficult course in undergraduate medical education with literature suggesting use of the term "Neurophobia" (fear of neurology among medical students). Instructional strategies employed for the teaching of neurosciences in undergraduate curricula traditionally include a combination of lectures, demonstrations, practical classes, problem-based learning and clinico-pathological conferences. Recently, team-based learning (TBL), a student-centered instructional strategy, has increasingly been regarded by many undergraduate medical courses as an effective method to assist student learning., Methods: In this study, 156 students of year-three neuroscience block were divided into seven male and seven female groups, comprising 11-12 students in each group. TBL was introduced during the 6 weeks of this block, and a total of eight TBL sessions were conducted during this duration. We evaluated the effect of TBL on student learning and correlated it with the student's performance in summative assessment. Moreover, the students' perceptions regarding the process of TBL was assessed by online survey., Results: We found that students who attended TBL sessions performed better in the summative examinations as compared to those who did not. Furthermore, students performed better in team activities compared to individual testing, with male students performing better with a more favorable impact on their grades in the summative examination. There was an increase in the number of students achieving higher grades (grade B and above) in this block when compared to the previous block (51.7% vs. 25%). Moreover, the number of students at risk for lower grades (Grade B- and below) decreased in this block when compared to the previous block (30.6% vs. 55%). Students generally elicited a favorable response regarding the TBL process, as well as expressed satisfaction with the content covered and felt that such activities led to improvement in communication and interpersonal skills., Conclusion: We conclude that implementing TBL strategy increased students' responsibility for their own learning and helped the students in bridging the gap in their cognitive knowledge to tackle 'neurophobia' in a difficult neurosciences block evidenced by their improved performance in the summative assessment.
- Published
- 2015
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