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19 results on '"Babalola, Tk"'

1. Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Author: Haeuser, E, Serfes, AL, Cork, MA, Yang, M, Abbastabar, H, Abhilash, ES, Adabi, M, Adebayo, OM, Adekanmbi, V, Adeyinka, DA, Afzal, S, Ahinkorah, BO, Ahmadi, K, Ahmed, MB, Akalu, Y, Akinyemi, RO, Akunna, CJ, Alahdab, F, Alanezi, FM, Alanzi, TM, Alene, KA, Alhassan, RK, Alipour, V, Almasi-Hashiani, A, Alvis-Guzman, N, Ameyaw, EK, Amini, S, Amugsi, DA, Ancuceanu, R, Anvari, D, Appiah, SCY, Arabloo, J, Aremu, O, Asemahagn, MA, Jafarabadi, MA, Awedew, AF, Quintanilla, BPA, Ayanore, MA, Aynalem, YA, Azari, S, Azene, ZN, Darshan, BB, Babalola, TK, Baig, AA, Banach, M, Bärnighausen, TW, Bell, AW, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhattacharyya, K, Bijani, A, Bitew, ZW, Bohlouli, S, Bolarinwa, OA, Boloor, A, Bozicevic, I, Butt, ZA, Cárdenas, R, Carvalho, F, Charan, J, Chattu, VK, Chowdhury, MAK, Chu, D-T, Cowden, RG, Dahlawi, SMA, Damiani, G, Darteh, EKM, Darwesh, AM, das Neves, J, Weaver, ND, De Leo, D, De Neve, J-W, Deribe, K, Deuba, K, Dharmaratne, S, Dianatinasab, M, Diaz, D, Didarloo, A, Djalalinia, S, Dorostkar, F, Dubljanin, E, Duko, B, El Tantawi, M, El-Jaafary, SI, Eshrati, B, Eskandarieh, S, Eyawo, O, Ezeonwumelu, IJ, Ezzikouri, S, Farzadfar, F, Fattahi, N, Fauk, NK, Fernandes, E, Filip, I, Fischer, F, Foigt, NA, Foroutan, M, Fukumoto, T, Gad, MM, Gaidhane, AM, Gebregiorgis, BG, Gebremedhin, KB, Getacher, L, Ghadiri, K, Ghashghaee, A, Golechha, M, Gubari, MIM, Gugnani, HC, Guimarães, RA, Haider, MR, Haj-Mirzaian, A, Hamidi, S, Hashi, A, Hassanipour, S, Hassankhani, H, Hayat, K, Herteliu, C, Ho, HC, Holla, R, Hosseini, M, Hosseinzadeh, M, Hwang, B-F, Ibitoye, SE, Ilesanmi, OS, Ilic, IM, Ilic, MD, Islam, RM, Iwu, CCD, Jakovljevic, M, Jha, RP, Ji, JS, Johnson, KB, Joseph, N, Joshua, V, Joukar, F, Jozwiak, JJ, Kalankesh, LR, Kalhor, R, Kamyari, N, Kanchan, T, Matin, BK, Karimi, SE, Kayode, GA, Karyani, AK, Keramati, M, Khan, EA, Khan, G, Khan, MN, Khatab, K, Khubchandani, J, Kim, YJ, Kisa, A, Kisa, S, Kopec, JA, Kosen, S, Laxminarayana, SLK, Koyanagi, A, Krishan, K, Defo, BK, Kugbey, N, Kulkarni, V, Kumar, M, Kumar, N, Kusuma, D, La Vecchia, C, Lal, DK, Landires, I, Larson, HJ, Lasrado, S, Lee, PH, Li, S, Liu, X, Maleki, A, Malik, P, Mansournia, MA, Martins-Melo, FR, Mendoza, W, Menezes, RG, Mengesha, EW, Meretoja, TJ, Mestrovic, T, Mirica, A, Moazen, B, Mohamad, O, Mohammad, Y, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, S, Mokdad, AH, Moradi, M, Moraga, P, Mubarik, S, Mulu, GBB, Mwanri, L, Nagarajan, AJ, Naimzada, MD, Naveed, M, Nazari, J, Ndejjo, R, Negoi, I, Ngalesoni, FN, Nguefack-Tsague, G, Ngunjiri, JW, Nguyen, CT, Nguyen, HLT, Nnaji, CA, Noubiap, JJ, Nuñez-Samudio, V, Nwatah, VE, Oancea, B, Odukoya, OO, Olagunju, AT, Olakunde, BO, Olusanya, BO, Olusanya, JO, Bali, AO, Onwujekwe, OE, Orisakwe, OE, Otstavnov, N, Otstavnov, SS, Owolabi, MO, Mahesh, PA, Padubidri, JR, Pana, A, Pandey, A, Pandi-Perumal, SR, Kan, FP, Patton, GC, Pawar, S, Peprah, EK, Postma, MJ, Preotescu, L, Syed, ZQ, Rabiee, N, Radfar, A, Rafiei, A, Rahim, F, Rahimi-Movaghar, V, Rahmani, AM, Ramezanzadeh, K, Rana, J, Ranabhat, CL, Rao, SJ, Rawaf, DL, Rawaf, S, Rawassizadeh, R, Regassa, LD, Rezaei, N, Rezapour, A, Riaz, MA, Ribeiro, AI, Ross, JM, Rubagotti, E, Rumisha, SF, Rwegerera, GM, Moghaddam, SS, Sagar, R, Sahiledengle, B, Sahu, M, Salem, MR, Kafil, HS, Samy, AM, Sartorius, B, Sathian, B, Seidu, A-A, Shaheen, AA, Shaikh, MA, Shamsizadeh, M, Shiferaw, WS, Shin, JI, Shrestha, R, Singh, JA, Skryabin, VY, Skryabina, AA, Soltani, S, Sufiyan, MB, Tabuchi, T, Tadesse, EG, Taveira, N, Tesfay, FH, Thapar, R, Tovani-Palone, MR, Tsegaye, GW, Umeokonkwo, CD, Unnikrishnan, B, Villafañe, JH, Violante, FS, Vo, B, Vu, GT, Wado, YD, Waheed, Y, Wamai, RG, Wang, Y, Ward, P, Wickramasinghe, ND, Wilson, K, Yaya, S, Yip, P, Yonemoto, N, Yu, C, Zastrozhin, MS, Zhang, Y, Zhang, Z-J, Hay, SI, Dwyer-Lindgren, L, Local Burden of Disease sub-Saharan Africa HIV Prevalence Collaborators, Value, Affordability and Sustainability (VALUE), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Microbes in Health and Disease (MHD), Haeuser, Emily, Serfes, Audrey L., Cork, Michael A., Yang, Mingyou, Ahmed, Muktar Beshir, Dwyer Lindgrena, Laura, HUS Comprehensive Cancer Center, Helsinki University Hospital Area, Department of Oncology, Epidemiologie, and RS: NUTRIM - R3 - Respiratory & Age-related Health
Subjects: Male, Adult, Settore MED/17 - Malattie Infettive, Adolescent, Settore MED/42 - Igiene Generale e Applicata, HIV Infections, Settore MED/01 - Statistica Medica, Young Adult, HIV Infections/prevention & control, Pregnancy, Seroepidemiologic Studies, General & Internal Medicine, Prevalence, Humans, Geostatistics, Africa, Demographics, HIV, HIV prevalence, Mapping, Spatial statistics, 11 Medical and Health Sciences, Africa South of the Sahara, Acquired Immunodeficiency Syndrome, Acquired Immunodeficiency Syndrome/epidemiology, General Medicine, Middle Aged, Settore SECS-S/04 - Demografia, Africa South of the Sahara/epidemiology, 3142 Public health care science, environmental and occupational health, 3121 General medicine, internal medicine and other clinical medicine, Female
Abstract: Background Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is still among the leading causes of disease burden and mortality in sub-Saharan Africa (SSA), and the world is not on track to meet targets set for ending the epidemic by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United Nations Sustainable Development Goals (SDGs). Precise HIV burden information is critical for effective geographic and epidemiological targeting of prevention and treatment interventions. Age- and sex-specific HIV prevalence estimates are widely available at the national level, and region-wide local estimates were recently published for adults overall. We add further dimensionality to previous analyses by estimating HIV prevalence at local scales, stratified into sex-specific 5-year age groups for adults ages 15–59 years across SSA. Methods We analyzed data from 91 seroprevalence surveys and sentinel surveillance among antenatal care clinic (ANC) attendees using model-based geostatistical methods to produce estimates of HIV prevalence across 43 countries in SSA, from years 2000 to 2018, at a 5 × 5-km resolution and presented among second administrative level (typically districts or counties) units. Results We found substantial variation in HIV prevalence across localities, ages, and sexes that have been masked in earlier analyses. Within-country variation in prevalence in 2018 was a median 3.5 times greater across ages and sexes, compared to for all adults combined. We note large within-district prevalence differences between age groups: for men, 50% of districts displayed at least a 14-fold difference between age groups with the highest and lowest prevalence, and at least a 9-fold difference for women. Prevalence trends also varied over time; between 2000 and 2018, 70% of all districts saw a reduction in prevalence greater than five percentage points in at least one sex and age group. Meanwhile, over 30% of all districts saw at least a five percentage point prevalence increase in one or more sex and age group. Conclusions As the HIV epidemic persists and evolves in SSA, geographic and demographic shifts in prevention and treatment efforts are necessary. These estimates offer epidemiologically informative detail to better guide more targeted interventions, vital for combating HIV in SSA.
Published: 2022

2. Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990–2050

Author: Micah, AE, Cogswell, IE, Cunningham, B, Ezoe, S, Harle, AC, Maddison, ER, McCracken, D, Nomura, S, Simpson, KE, Stutzman, HN, Tsakalos, G, Wallace, LE, Zhao, Y, Zende, RR, Abbafati, C, Abdelmasseh, M, Abedi, A, Abegaz, KH, Abhilash, ES, Abolhassani, H, Abrigo, MRM, Adhikari, TB, Afzal, S, Ahinkorah, BO, Ahmadi, S, Ahmed, H, Ahmed, MB, Ahmed Rashid, T, Ajami, M, Aji, B, Akalu, Y, Akunna, CJ, Al Hamad, H, Alam, K, Alanezi, FM, Alanzi, TM, Alemayehu, Y, Alhassan, RK, Alinia, C, Aljunid, SM, Almustanyir, SA, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Amini, S, Amini-Rarani, M, Amu, H, Ancuceanu, R, Andrei, CL, Andrei, T, Angell, B, Anjomshoa, M, Antonio, CAT, Antony, CM, Aqeel, M, Arabloo, J, Arab-Zozani, M, Aripov, T, Arrigo, A, Ashraf, T, Atnafu, DD, Ausloos, M, Avila-Burgos, L, Awan, AT, Ayano, G, Ayanore, MA, Azari, S, Azhar, GS, Babalola, TK, Bahrami, MA, Baig, AA, Banach, M, Barati, N, Bärnighausen, TW, Barrow, A, Basu, S, Baune, BT, Bayati, M, Benzian, H, Berman, AE, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bibi, S, Bijani, A, Bodolica, V, Gupta, V, Islam, Shariful, Micah, AE, Cogswell, IE, Cunningham, B, Ezoe, S, Harle, AC, Maddison, ER, McCracken, D, Nomura, S, Simpson, KE, Stutzman, HN, Tsakalos, G, Wallace, LE, Zhao, Y, Zende, RR, Abbafati, C, Abdelmasseh, M, Abedi, A, Abegaz, KH, Abhilash, ES, Abolhassani, H, Abrigo, MRM, Adhikari, TB, Afzal, S, Ahinkorah, BO, Ahmadi, S, Ahmed, H, Ahmed, MB, Ahmed Rashid, T, Ajami, M, Aji, B, Akalu, Y, Akunna, CJ, Al Hamad, H, Alam, K, Alanezi, FM, Alanzi, TM, Alemayehu, Y, Alhassan, RK, Alinia, C, Aljunid, SM, Almustanyir, SA, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Amini, S, Amini-Rarani, M, Amu, H, Ancuceanu, R, Andrei, CL, Andrei, T, Angell, B, Anjomshoa, M, Antonio, CAT, Antony, CM, Aqeel, M, Arabloo, J, Arab-Zozani, M, Aripov, T, Arrigo, A, Ashraf, T, Atnafu, DD, Ausloos, M, Avila-Burgos, L, Awan, AT, Ayano, G, Ayanore, MA, Azari, S, Azhar, GS, Babalola, TK, Bahrami, MA, Baig, AA, Banach, M, Barati, N, Bärnighausen, TW, Barrow, A, Basu, S, Baune, BT, Bayati, M, Benzian, H, Berman, AE, Bhagavathula, AS, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bibi, S, Bijani, A, Bodolica, V, Gupta, V, and Islam, Shariful
Published: 2021

3. Mapping geographical inequalities in oral rehydration therapy coverage in low-income and middle-income countries, 2000-17

Author: Wiens, KE, Lindstedt, PA, Blacker, BF, Johnson, KB, Baumann, MM, Schaeffer, LE, Abbastabar, H, Abd-Allah, F, Abdelalim, A, Abdollahpour, I, Abegaz, KH, Abejie, AN, Abreu, LG, Abrigo, MRM, Abualhasan, A, Accrombessi, MMK, Acharya, D, Adabi, M, Adamu, AA, Adebayo, OM, Adedoyin, RA, Adekanmbi, V, Adetokunboh, OO, Adhena, BM, Afarideh, M, Ahmad, S, Ahmadi, K, Ahmed, AE, Ahmed, MB, Ahmed, R, Akalu, TY, Alahdab, F, Al-Aly, Z, Alam, N, Alam, S, Alamene, GM, Alanzi, TM, Alcalde-Rabanal, JE, Ali, BA, Alijanzadeh, M, Alipour, V, Aljunid, SM, Almasi, A, Almasi-Hashiani, A, Al-Mekhlafi, HM, Altirkawi, KA, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Amini, S, Amit, AML, Andrei, CL, Anjomshoa, M, Anoushiravani, A, Ansari, F, Antonio, CAT, Antony, B, Antriyandarti, E, Arabloo, J, Aref, HMA, Aremu, O, Armoon, B, Arora, A, Aryal, KK, Arzani, A, Asadi-Aliabadi, M, Atalay, HT, Athari, SS, Athari, SM, Atre, SR, Ausloos, M, Awoke, N, Ayala Quintanilla, BP, Ayano, G, Ayanore, MA, Aynalem, YA, Azari, S, Azzopardi, PS, Babaee, E, Babalola, TK, Badawi, A, Bairwa, M, Bakkannavar, SM, Balakrishnan, S, Bali, AG, Banach, M, Banoub, JAM, Barac, A, Bärnighausen, TW, Basaleem, H, Basu, S, Bay, VD, Bayati, M, Baye, E, Bedi, N, Beheshti, M, Behzadifar, M, Bekele, BB, Belayneh, YM, Bell, ML, Wiens, KE, Lindstedt, PA, Blacker, BF, Johnson, KB, Baumann, MM, Schaeffer, LE, Abbastabar, H, Abd-Allah, F, Abdelalim, A, Abdollahpour, I, Abegaz, KH, Abejie, AN, Abreu, LG, Abrigo, MRM, Abualhasan, A, Accrombessi, MMK, Acharya, D, Adabi, M, Adamu, AA, Adebayo, OM, Adedoyin, RA, Adekanmbi, V, Adetokunboh, OO, Adhena, BM, Afarideh, M, Ahmad, S, Ahmadi, K, Ahmed, AE, Ahmed, MB, Ahmed, R, Akalu, TY, Alahdab, F, Al-Aly, Z, Alam, N, Alam, S, Alamene, GM, Alanzi, TM, Alcalde-Rabanal, JE, Ali, BA, Alijanzadeh, M, Alipour, V, Aljunid, SM, Almasi, A, Almasi-Hashiani, A, Al-Mekhlafi, HM, Altirkawi, KA, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Amini, S, Amit, AML, Andrei, CL, Anjomshoa, M, Anoushiravani, A, Ansari, F, Antonio, CAT, Antony, B, Antriyandarti, E, Arabloo, J, Aref, HMA, Aremu, O, Armoon, B, Arora, A, Aryal, KK, Arzani, A, Asadi-Aliabadi, M, Atalay, HT, Athari, SS, Athari, SM, Atre, SR, Ausloos, M, Awoke, N, Ayala Quintanilla, BP, Ayano, G, Ayanore, MA, Aynalem, YA, Azari, S, Azzopardi, PS, Babaee, E, Babalola, TK, Badawi, A, Bairwa, M, Bakkannavar, SM, Balakrishnan, S, Bali, AG, Banach, M, Banoub, JAM, Barac, A, Bärnighausen, TW, Basaleem, H, Basu, S, Bay, VD, Bayati, M, Baye, E, Bedi, N, Beheshti, M, Behzadifar, M, Bekele, BB, Belayneh, YM, and Bell, ML
Abstract: Background: Oral rehydration solution (ORS) is a form of oral rehydration therapy (ORT) for diarrhoea that has the potential to drastically reduce child mortality; yet, according to UNICEF estimates, less than half of children younger than 5 years with diarrhoea in low-income and middle-income countries (LMICs) received ORS in 2016. A variety of recommended home fluids (RHF) exist as alternative forms of ORT; however, it is unclear whether RHF prevent child mortality. Previous studies have shown considerable variation between countries in ORS and RHF use, but subnational variation is unknown. This study aims to produce high-resolution geospatial estimates of relative and absolute coverage of ORS, RHF, and ORT (use of either ORS or RHF) in LMICs. Methods: We used a Bayesian geostatistical model including 15 spatial covariates and data from 385 household surveys across 94 LMICs to estimate annual proportions of children younger than 5 years of age with diarrhoea who received ORS or RHF (or both) on continuous continent-wide surfaces in 2000–17, and aggregated results to policy-relevant administrative units. Additionally, we analysed geographical inequality in coverage across administrative units and estimated the number of diarrhoeal deaths averted by increased coverage over the study period. Uncertainty in the mean coverage estimates was calculated by taking 250 draws from the posterior joint distribution of the model and creating uncertainty intervals (UIs) with the 2·5th and 97·5th percentiles of those 250 draws. Findings: While ORS use among children with diarrhoea increased in some countries from 2000 to 2017, coverage remained below 50% in the majority (62·6%; 12 417 of 19 823) of second administrative-level units and an estimated 6 519 000 children (95% UI 5 254 000–7 733 000) with diarrhoea were not treated with any form of ORT in 2017. Increases in ORS use corresponded with declines in RHF in many locations, resulting in relatively constant overall ORT covera
Published: 2020

4. Health sector spending and spending on HIV/AIDS, tuberculosis, and malaria, and development assistance for health: progress towards Sustainable Development Goal 3

Author: Micah, AE, Su, Y, Bachmeier, SD, Chapin, A, Cogswell, IE, Crosby, SW, Cunningham, B, Harle, AC, Maddison, ER, Moitra, M, Sahu, M, Schneider, MT, Simpson, KE, Stutzman, HN, Tsakalos, G, Zende, RR, Zlavog, BS, Abbafati, C, Abebo, ZH, Abolhassani, H, Abrigo, MRM, Ahmed, MB, Akinyemi, RO, Alam, K, Ali, S, Alinia, C, Alipour, V, Aljunid, SM, Almasi, A, Alvis-Guzman, N, Ancuceanu, R, Andrei, T, Andrei, CL, Anjomshoa, M, Antonio, CAT, Arabloo, J, Arab-Zozani, M, Aremu, O, Atnafu, DD, Ausloos, M, Avila-Burgos, L, Ayanore, MA, Azari, S, Babalola, TK, Bagherzadeh, M, Baig, AA, Bakhtiari, A, Banach, M, Banerjee, SK, Barnighausen, TW, Basu, S, Baune, BT, Bayati, M, Berman, AE, Bhageerathy, R, Bhardwaj, P, Bohluli, M, Busse, R, Cahuana-Hurtado, L, Camera, LLAA, Castaneda-Orjuela, CA, Catala-Lopez, F, Cevik, M, Chattu, VK, Dandona, L, Dandona, R, Dianatinasab, M, Hoa, TD, Doshmangir, L, El Tantawi, M, Eskandarieh, S, Esmaeilzadeh, F, Faraj, A, Farzadfar, F, Fischer, F, Foigt, NA, Fullman, N, Gad, MM, Ghafourifard, M, Ghashghaee, A, Gholamian, A, Goharinezhad, S, Grada, A, Bidgoli, HH, Hamidi, S, Harb, HL, Hasanpoor, E, Hay, SI, Hendrie, D, Henry, NJ, Herteliu, C, Hole, MK, Hosseinzadeh, M, Hostiuc, S, Huda, TM, Humayun, A, Hwang, B-F, Ilesanmi, OS, Iqbal, U, Irvani, SS, Islam, SMS, Islam, MM, Jahani, MA, Jakovljevic, M, James, SL, Javaheri, Z, Jonas, JB, Joukar, F, Jozwiak, JJ, Jurisson, M, Kalhor, R, Matin, BK, Karimi, SE, Kayode, GA, Karyani, AK, Kinfu, Y, Kisa, A, Kohler, S, Komaki, H, Kosen, S, Kotlo, A, Koyanagi, A, Kumar, GA, Kusuma, D, Lansingh, VC, Larsson, AO, Lasrado, S, Lee, SWH, Lim, L-L, Lozano, R, Abd El Razek, HM, Mahdavi, MM, Maleki, S, Malekzadeh, R, Mansour-Ghanaei, F, Mansournia, MA, Mantovani, LG, Martinez, G, Masoumi, SZ, Massenburg, BB, Menezes, RG, Mengesha, EW, Meretoja, TJ, Meretoja, A, Mestrovic, T, Kostova, NM, Miller, TR, Mirica, A, Mirrakhimov, EM, Moghadaszadeh, M, Mohajer, B, Mohamadi, E, Darwesh, AM, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, S, Mohebi, F, Mokdad, AH, Morrison, SD, Mosser, JF, Mousavi, SM, Muriithi, MK, Muthupandian, S, Myint, C-Y, Naderi, M, Nagarajan, AJ, Cuong, TN, Huong, LTN, Nonvignon, J, Noubiap, JJ, Oh, I-H, Olagunju, AT, Olusanya, JO, Olusanya, BO, Bali, AO, Onwujekwe, OE, Otstavnov, SS, Otstavnov, N, Owolabi, MO, Padubidri, JR, Palladino, R, Panda-Jonas, S, Pandey, A, Postma, MJ, Prada, SI, Pribadi, DRA, Rabiee, M, Rabiee, N, Rahim, F, Ranabhat, CL, Rao, SJ, Rathi, P, Rawaf, S, Rawaf, DL, Rawal, L, Rawassizadeh, R, Rezapour, A, Sabour, S, Sahraian, MA, Salman, OM, Salomon, JA, Samy, AM, Sanabria, J, Santos, JV, Milicevic, MMS, Jose, BPS, Savic, M, Schwendicke, F, Senthilkumaran, S, Sepanlou, SG, Servan-Mori, E, Setayesh, H, Shaikh, MA, Sheikh, A, Shibuya, K, Shrime, MG, Simonetti, B, Singh, JA, Singh, P, Skryabin, VY, Soheili, A, Soltani, S, Stefan, SC, Tabares-Seisdedos, R, Topor-Madry, R, Tovani-Palone, MR, Tran, BX, Travillian, R, Undurraga, EA, Valdez, PR, van Boven, JFM, Vasankari, TJ, Violante, FS, Vlassov, V, Vos, T, Wolfe, CDA, Wu, J, Yaya, S, Yazdi-Feyzabadi, V, Yip, P, Yonemoto, N, Younis, MZ, Yu, C, Zaidi, Z, Bin Zaman, S, Zastrozhin, MS, Zhang, Z-J, Zhao, Y, Murray, CJL, Dieleman, JL, Micah, AE, Su, Y, Bachmeier, SD, Chapin, A, Cogswell, IE, Crosby, SW, Cunningham, B, Harle, AC, Maddison, ER, Moitra, M, Sahu, M, Schneider, MT, Simpson, KE, Stutzman, HN, Tsakalos, G, Zende, RR, Zlavog, BS, Abbafati, C, Abebo, ZH, Abolhassani, H, Abrigo, MRM, Ahmed, MB, Akinyemi, RO, Alam, K, Ali, S, Alinia, C, Alipour, V, Aljunid, SM, Almasi, A, Alvis-Guzman, N, Ancuceanu, R, Andrei, T, Andrei, CL, Anjomshoa, M, Antonio, CAT, Arabloo, J, Arab-Zozani, M, Aremu, O, Atnafu, DD, Ausloos, M, Avila-Burgos, L, Ayanore, MA, Azari, S, Babalola, TK, Bagherzadeh, M, Baig, AA, Bakhtiari, A, Banach, M, Banerjee, SK, Barnighausen, TW, Basu, S, Baune, BT, Bayati, M, Berman, AE, Bhageerathy, R, Bhardwaj, P, Bohluli, M, Busse, R, Cahuana-Hurtado, L, Camera, LLAA, Castaneda-Orjuela, CA, Catala-Lopez, F, Cevik, M, Chattu, VK, Dandona, L, Dandona, R, Dianatinasab, M, Hoa, TD, Doshmangir, L, El Tantawi, M, Eskandarieh, S, Esmaeilzadeh, F, Faraj, A, Farzadfar, F, Fischer, F, Foigt, NA, Fullman, N, Gad, MM, Ghafourifard, M, Ghashghaee, A, Gholamian, A, Goharinezhad, S, Grada, A, Bidgoli, HH, Hamidi, S, Harb, HL, Hasanpoor, E, Hay, SI, Hendrie, D, Henry, NJ, Herteliu, C, Hole, MK, Hosseinzadeh, M, Hostiuc, S, Huda, TM, Humayun, A, Hwang, B-F, Ilesanmi, OS, Iqbal, U, Irvani, SS, Islam, SMS, Islam, MM, Jahani, MA, Jakovljevic, M, James, SL, Javaheri, Z, Jonas, JB, Joukar, F, Jozwiak, JJ, Jurisson, M, Kalhor, R, Matin, BK, Karimi, SE, Kayode, GA, Karyani, AK, Kinfu, Y, Kisa, A, Kohler, S, Komaki, H, Kosen, S, Kotlo, A, Koyanagi, A, Kumar, GA, Kusuma, D, Lansingh, VC, Larsson, AO, Lasrado, S, Lee, SWH, Lim, L-L, Lozano, R, Abd El Razek, HM, Mahdavi, MM, Maleki, S, Malekzadeh, R, Mansour-Ghanaei, F, Mansournia, MA, Mantovani, LG, Martinez, G, Masoumi, SZ, Massenburg, BB, Menezes, RG, Mengesha, EW, Meretoja, TJ, Meretoja, A, Mestrovic, T, Kostova, NM, Miller, TR, Mirica, A, Mirrakhimov, EM, Moghadaszadeh, M, Mohajer, B, Mohamadi, E, Darwesh, AM, Mohammadian-Hafshejani, A, Mohammadpourhodki, R, Mohammed, S, Mohebi, F, Mokdad, AH, Morrison, SD, Mosser, JF, Mousavi, SM, Muriithi, MK, Muthupandian, S, Myint, C-Y, Naderi, M, Nagarajan, AJ, Cuong, TN, Huong, LTN, Nonvignon, J, Noubiap, JJ, Oh, I-H, Olagunju, AT, Olusanya, JO, Olusanya, BO, Bali, AO, Onwujekwe, OE, Otstavnov, SS, Otstavnov, N, Owolabi, MO, Padubidri, JR, Palladino, R, Panda-Jonas, S, Pandey, A, Postma, MJ, Prada, SI, Pribadi, DRA, Rabiee, M, Rabiee, N, Rahim, F, Ranabhat, CL, Rao, SJ, Rathi, P, Rawaf, S, Rawaf, DL, Rawal, L, Rawassizadeh, R, Rezapour, A, Sabour, S, Sahraian, MA, Salman, OM, Salomon, JA, Samy, AM, Sanabria, J, Santos, JV, Milicevic, MMS, Jose, BPS, Savic, M, Schwendicke, F, Senthilkumaran, S, Sepanlou, SG, Servan-Mori, E, Setayesh, H, Shaikh, MA, Sheikh, A, Shibuya, K, Shrime, MG, Simonetti, B, Singh, JA, Singh, P, Skryabin, VY, Soheili, A, Soltani, S, Stefan, SC, Tabares-Seisdedos, R, Topor-Madry, R, Tovani-Palone, MR, Tran, BX, Travillian, R, Undurraga, EA, Valdez, PR, van Boven, JFM, Vasankari, TJ, Violante, FS, Vlassov, V, Vos, T, Wolfe, CDA, Wu, J, Yaya, S, Yazdi-Feyzabadi, V, Yip, P, Yonemoto, N, Younis, MZ, Yu, C, Zaidi, Z, Bin Zaman, S, Zastrozhin, MS, Zhang, Z-J, Zhao, Y, Murray, CJL, and Dieleman, JL
Abstract: BACKGROUND: Sustainable Development Goal (SDG) 3 aims to "ensure healthy lives and promote well-being for all at all ages". While a substantial effort has been made to quantify progress towards SDG3, less research has focused on tracking spending towards this goal. We used spending estimates to measure progress in financing the priority areas of SDG3, examine the association between outcomes and financing, and identify where resource gains are most needed to achieve the SDG3 indicators for which data are available. METHODS: We estimated domestic health spending, disaggregated by source (government, out-of-pocket, and prepaid private) from 1995 to 2017 for 195 countries and territories. For disease-specific health spending, we estimated spending for HIV/AIDS and tuberculosis for 135 low-income and middle-income countries, and malaria in 106 malaria-endemic countries, from 2000 to 2017. We also estimated development assistance for health (DAH) from 1990 to 2019, by source, disbursing development agency, recipient, and health focus area, including DAH for pandemic preparedness. Finally, we estimated future health spending for 195 countries and territories from 2018 until 2030. We report all spending estimates in inflation-adjusted 2019 US$, unless otherwise stated. FINDINGS: Since the development and implementation of the SDGs in 2015, global health spending has increased, reaching $7·9 trillion (95% uncertainty interval 7·8-8·0) in 2017 and is expected to increase to $11·0 trillion (10·7-11·2) by 2030. In 2017, in low-income and middle-income countries spending on HIV/AIDS was $20·2 billion (17·0-25·0) and on tuberculosis it was $10·9 billion (10·3-11·8), and in malaria-endemic countries spending on malaria was $5·1 billion (4·9-5·4). Development assistance for health was $40·6 billion in 2019 and HIV/AIDS has been the health focus area to receive the highest contribution since 2004. In 2019, $374 million of DAH was provided for pandemic preparedness, less than 1% of
Published: 2020

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

Author: Fullman, N, Yearwood, J, Abay, SM, Abbafati, C, Abd-Allah, F, Abdela, J, Abdelalim, A, Abebe, Z, Abebo, TA, Aboyans, V, Abraha, HN, Abreu, DMX, Abu-Raddad, LJ, Adane, AA, Adedoyin, RA, Adetokunboh, O, Adhikari, TB, Afarideh, M, Afshin, A, Agarwal, G, Agius, D, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Aichour, MTE, Akibu, M, Akinyemi, RO, Akinyemiju, TF, Akseer, N, Al Lami, FH, Alahdab, F, Al-Aly, Z, Alam, K, Alam, T, Alasfoor, D, Albittar, MI, Alene, KA, Al-Eyadhy, A, Ali, SD, Alijanzadeh, M, Aljunid, SM, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Alomari, MA, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amenu, K, Ammar, W, Amoako, YA, Anber, N, Andrei, CL, Androudi, S, Antonio, CAT, Araújo, VEM, Aremu, O, Ärnlöv, J, Artaman, A, Aryal, KK, Asayesh, H, Asfaw, ET, Asgedom, SW, Asghar, RJ, Ashebir, MM, Asseffa, NA, Atey, TM, Atre, SR, Atteraya, MS, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Ayala Quintanilla, BP, Ayalew, AA, Ayele, HT, Ayer, R, Ayuk, TB, Azzopardi, P, Azzopardi-Muscat, N, Babalola, TK, Badali, H, Badawi, A, Banach, M, Banerjee, A, Banstola, A, Barber, RM, Barboza, MA, Barker-Collo, SL, Bärnighausen, T, Barquera, S, Barrero, LH, Bassat, Q, Basu, S, Baune, BT, Bazargan-Hejazi, S, Bedi, N, and Beghi, E
Subjects: General & Internal Medicine, Humans, Wounds and Injuries, Noncommunicable Diseases, Communicable Diseases, Health Services Accessibility, Quality of Health Care, Global Burden of Disease
Abstract: Copyright © 2018 The Author(s). Published by Elsevier Ltd. Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0-100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0-100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97·1 (95% UI 95·8-98·1) in Iceland, followed by 96·6 (94·9-97·9) in Norway and 96·1 (94·5-97·3) in the Netherlands, to values as low as 18·6 (13·1-24·4) in the Central African Republic, 19·0 (14·3-23·7) in Somalia, and 23·4 (20·2-26·8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91·5 (89·1-93·6) in Beijing to 48·0 (43·4-53·2) in Tibet (a 43·5-point difference), while India saw a 30·8-point disparity, from 64·8 (59·6-68·8) in Goa to 34·0 (30·3-38·1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4·8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20·9-point to 17·0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17·2-point to 20·4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle- SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view - and subsequent provision - of quality health care for all populations.
Published: 2018

6. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the Global Burden of Disease Study 2016

Author: Wang, H, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abera, SF, Abraha, HN, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adedoyin, RA, Adetifa, IMO, Adetokunboh, O, Afshin, A, Aggarwal, R, Agrawal, A, Agrawal, S, Kiadaliri, AA, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Aiyar, S, Akanda, S, Akinyemiju, TF, Akseer, N, Al-Eyadhy, A, Al Lami, FH, Alabed, S, Alahdab, F, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Aldridge, RW, Alene, KA, Alhabib, S, Ali, R, Alizadeh-Navaei, R, Aljunid, SM, Alkaabi, JM, Alkerwi, A, Alla, F, Allam, SD, Allebeck, P, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Martin, EA, Alvis-Guzman, N, Amare, AT, Ameh, EA, Amini, E, Ammar, W, Amoako, YA, Anber, N, Andrei, CL, Androudi, S, Ansari, H, Ansha, MG, Antonio, CAT, Anwari, P, Arnlov, J, Arora, M, Al, A, Aryal, KK, Asayesh, H, Asgedom, SW, Asghar, RJ, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Quintanilla, BPA, Babalola, TK, Bacha, U, Badawi, A, Balakrishnan, K, Balalla, S, Barac, A, Barber, RM, Barboza, MA, Barker-Collo, SL, Barnighausen, T, Barquera, S, Barregard, L, Barrero, LH, Baune, BT, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Bejot, Y, Bekele, BB, Bell, ML, Bello, AK, Bennett, DA, Bennett, JR, Bensenor, IM, Benson, J, Berhane, A, Berhe, DF, Bernabe, E, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhaumik, S, Bhutta, ZA, Bikbov, B, Birungi, C, Biryukov, S, Bisanzio, D, Bizuayehu, HM, Bjerregaard, P, Blosser, CD, Boneya, DJ, Boufous, S, Bourne, RRA, Brazinova, A, Breitborde, NJK, Brenner, H, Brugha, TS, Bukhman, G, Negesa, L, Bulto, B, Bumgarner, BR, Burch, M, Butt, ZA, Cahill, LE, Cahuana-Hurtado, L, Campos-Nonato, IR, Car, J, Car, M, Crdenas, R, Carpenter, DO, Carrero, JJ, Carter, A, Castaneda-Orjuela, CA, Rivas, JC, Castro, FF, Castro, RE, Catala-Lopez, F, Chen, H, Chiang, PP-C, Chibalabala, M, Chisumpa, VH, Chitheer, AA, Choi, J-YJ, Christensen, H, Christopher, DJ, Ciobanu, LG, Cirillo, M, Cohen, AJ, Colquhoun, SM, Coresh, J, Criqui, MH, Cromwell, EA, Crump, JA, Dandona, L, Dandona, R, Dargan, PI, das Neves, J, Davey, G, Davitoiu, DV, Davletov, K, de Courten, B, De Leo, D, Degenhardt, L, Deiparine, S, Dellavalle, RP, Deribe, K, Deribew, A, Des Jarlais, DC, Dey, S, Dharmaratne, SD, Dherani, MK, Diaz-Torne, C, Ding, EL, Dixit, P, Djalalinia, S, Huyen, PD, Doku, DT, Donnelly, CA, Priscila, K, dos Santos, B, Douwes-Schultz, D, Driscoll, TR, Duan, L, Dubey, M, Duncan, BB, Dwivedi, LK, Ebrahimi, H, El Bcheraoui, C, Ellingsen, CL, Enayati, A, Endries, AY, Ermakov, SP, Eshetie, S, Eshrati, B, Eskandarieh, S, Esteghamati, A, Estep, K, Fanuel, BBF, Faro, A, Farvid, MS, Farzadfar, F, Feigin, VL, Fereshtehnejad, S-M, Fernandes, JG, Fernandes, JC, Feyissa, TR, Filip, I, Fischer, F, Foigt, N, Foreman, KJ, Frank, T, Franklin, RC, Fraser, M, Friedman, J, Frostad, JJ, Fullman, N, Furst, T, Furtado, JM, Futran, ND, Gakidou, E, Gambashidze, K, Gamkrelidze, A, Gankpe, FG, Garcia-Basteiro, AL, Gebregergs, GB, Gebrehiwot, TT, Gebrekidan, KG, Gebremichael, MW, Gelaye, AA, Geleijnse, JM, Gemechu, BL, Gemechu, KS, Genova-Maleras, R, Gesesew, HA, Gething, PW, Gibney, KB, Gill, PS, Gillum, RF, Giref, AZ, Girma, BW, Giussani, G, Goenka, S, Gomez, B, Gona, PN, Gopalani, SV, Goulart, AC, Graetz, N, Gugnani, HC, Gupta, PC, Gupta, R, Gupta, T, Gupta, V, Haagsma, JA, Hafezi-Nejad, N, Bidgoli, HH, Hakuzimana, A, Halasa, YA, Hamadeh, RR, Hambisa, MT, Hamidi, S, Hammami, M, Hancock, J, Handal, AJ, Hankey, GJ, Hao, Y, Harb, HL, Hareri, HA, Harikrishnan, S, Haro, JM, Hassanvand, MS, Havmoeller, R, Hay, RJ, Hay, SI, He, F, Heredia-Pi, IB, Herteliu, C, Hilawe, EH, Hoek, HW, Horita, N, Hosgood, HD, Hostiuc, S, Hotez, PJ, Hoy, DG, Hsairi, M, Htet, AS, Hu, G, Huang, H, Huang, JJ, Iburg, KM, Igumbor, EU, Ileanu, BV, Inoue, M, Irenso, AA, Irvine, CMS, Islam, N, Jacobsen, KH, Jaenisch, T, Jahanmehr, N, Jakovljevic, MB, Javanbakht, M, Jayatilleke, AU, Jeemon, P, Jensen, PN, Jha, V, Jin, Y, John, D, John, O, Johnson, SC, Jonas, JB, Jurisson, M, Kabir, Z, Kadel, R, Kahsay, A, Kalkonde, Y, Kamal, R, Kan, H, Karch, A, Karema, CK, Karimi, SM, Karthikeyan, G, Kasaeian, A, Kassaw, NA, Kassebaum, NJ, Kastor, A, Katikireddi, SV, Kaul, A, Kawakami, N, Kazanjan, K, Keiyoro, PN, Kelbore, SG, Kemp, AH, Kengne, AP, Keren, A, Kereselidze, M, Kesavachandran, CN, Ketema, EB, Khader, YS, Khalil, IA, Khan, EA, Khan, G, Khang, Y-H, Khera, S, Khoja, ATA, Khosravi, MH, Kibret, GD, Kieling, C, Kim, C-I, Kim, D, Kim, P, Kim, S, Kim, YJ, Kimokoti, RW, Kinfu, Y, Kishawi, S, Kissimova-Skarbek, KA, Kissoon, N, Kivimaki, M, Knudsen, AK, Kokubo, Y, Kopec, JA, Kosen, S, Koul, PA, Koyanagi, A, Kravchenko, M, Krohn, KJ, Defo, BK, Bicer, BK, Kuipers, EJ, Kulikoff, XR, Kulkarni, VS, Kumar, GA, Kumar, P, Kumsa, FA, Kutz, M, Lachat, C, Lagat, AK, Lager, ACJ, Lal, DK, Lalloo, R, Lambert, N, Lan, Q, Lansingh, VC, Larson, HJ, Larsson, A, Laryea, DO, Lavados, PM, Laxmaiah, A, Lee, PH, Leigh, J, Leung, J, Leung, R, Levi, M, Li, Y, Liao, Y, Liben, ML, Lim, SS, Linn, S, Lipshultz, SE, Liu, S, Lodha, R, Logroscino, G, Lorch, SA, Lorkowski, S, Lotufo, PA, Lozano, R, Lunevicius, R, Lyons, RA, Ma, S, Macarayan, ERK, Machado, IE, Mackay, MT, Abd el Razek, MM, Magis-Rodriguez, C, Mahdavi, M, Majdan, M, Majdzadeh, R, Majeed, A, Malekzadeh, R, Malhotra, R, Malta, DC, Mantovani, LG, Manyazewal, T, Mapoma, CC, Marczak, LB, Marks, GB, Martinez-Raga, J, Martins-Melo, FR, Massano, J, Maulik, PK, Mayosi, BM, Mazidi, M, McAlinden, C, McGarvey, ST, McGrath, JJ, Mckee, M, Mehata, S, Mehndiratta, MM, Mehta, KM, Meier, T, Mekonnen, TC, Meles, KG, Memiah, P, Memish, ZA, Mendoza, W, Mengesha, MM, Mengistie, MA, Tadese, D, Menon, MGR, Menota, BG, Mensah, GA, Meretoja, A, Meretoja, TJ, Mezgebe, HB, Micha, R, Mikesell, J, Miller, TR, Mills, EJ, Minnig, S, Mirarefin, M, Mirrakhimov, EM, Misganaw, A, Mishra, SR, Mohammad, KA, Mohammadi, A, Mohammed, KESM, Mohan, MBV, Mohanty, SK, Mokdad, AH, Assaye, AM, Mollenkopf, SK, Molokhia, M, Monasta, L, Hernandez, JCM, Montico, M, Mooney, MD, Moore, AR, Moradi-Lakeh, M, Moraga, P, Morawska, L, Velasquez, IM, Mori, R, Morrison, SD, Mruts, KB, Mueller, UO, Mullany, E, Muller, K, Venkata, G, Murthy, S, Musa, KI, Nachega, JB, Nagata, C, Nagel, G, Naghavi, M, Naidoo, KS, Nanda, L, Nangia, V, Nascimento, BR, Natarajan, G, Negoi, I, Cuong, TN, Ningrum, DNA, Nisar, MI, Nomura, M, Vuong, MN, Norheim, OF, Norrving, B, Noubiap, JJN, Nyakarahuka, L, Obermeyer, CM, O'Donnell, MJ, Ogbo, FA, Oh, I-H, Okoro, A, Oladimeji, O, Olagunju, AT, Olusanya, BO, Olusanya, JO, Oren, E, Ortiz, A, Osgood-Zimmerman, A, Ota, E, Owolabi, MO, Oyekale, AS, Pa, M, Pacella, RE, Pakhale, S, Pana, A, Panda, BK, Panda-Jonas, S, Park, E-K, Parsaeian, M, Patel, T, Patten, SB, Patton, GC, Paudel, D, Pereira, DM, Perez-Padilla, R, Perez-Ruiz, F, Perico, N, Pervaiz, A, Pesudovs, K, Peterson, CB, Petri, WA, Petzold, M, Phillips, MR, Piel, FB, Pigott, DM, Pishgar, F, Plass, D, Polinder, S, Popova, S, Postma, MJ, Poulton, RG, 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Werdecker, A, Wesana, J, Wijeratne, T, Wilkinson, JD, Wiysonge, CS, Woldeyes, BG, Wolfe, CDA, Workicho, A, Workie, SB, Xavier, D, Xu, G, Yaghoubi, M, Yakob, B, Yalew, AZ, Yan, LL, Yano, Y, Yaseri, M, Ye, P, Yimam, HH, Yip, P, Yirsaw, BD, Yonemoto, N, Yoon, S-J, Yotebieng, M, Younis, MZ, Zaidi, Z, Zaki, MES, Zeeb, H, Zenebe, ZM, Zerfu, TA, Zhang, AL, Zhang, X, Zodpey, S, Zuhlke, LJ, Lopez, AD, and Murray, CJL
Published: 2017

7. Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

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

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

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Sayem, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akanda, Ali S, Akbari, Mohammad Esmaeil, Akibu, Mohammed, Akinyemi, Rufus Olusola, Akinyemiju, Tomi, Akseer, Nadia, Alahdab, Fares, Al-Aly, Ziyad, Alam, Khurshid, Alebel, Animut, Aleman, Alicia V, Alene, Kefyalew Addis, Al-Eyadhy, Ayman, Ali, Raghib, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Almasi, Ali, Alonso, Jordi, Al-Raddadi, Rajaa M, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Ammar, Walid, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Ansari, Hossein, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T, Appiah, Seth Christopher Yaw, Aremu, Olatunde, Areri, Habtamu Abera, Arian, Nicholas, Ärnlöv, Johan, Artaman, Al, Aryal, Krishna K, Asayesh, Hamid, Asfaw, Ephrem Tsegay, Asgedom, Solomon Weldegebreal, Assadi, Reza, Atey, Tesfay Mehari Mehari, Atique, Suleman, 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Subjects: Adult, Male, Population Density, GBD, population, fertility, Adolescent, Medicine (all), Infant, Newborn, Infant, 11 Medical And Health Sciences, Middle Aged, Global Health, Global Burden of Disease, Young Adult, Child, Preschool, General & Internal Medicine, Female, Human medicine, Mortality, Child, Population Growth, Birth Rate, Aged, Human, Maternal Age
Abstract: Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10-54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10-14 years and 50-54 years was estimated from data on fertility in women aged 15-19 years and 45-49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-cotnponent method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings From 1950 to 2017, TFRs decreased by 49.4% (95% uncertainty interval [UI] 46.4-52.0). The TFR decreased from 4.7 livebirths (4.5-4.9) to 2.4 livebirths (2.2-2.5), and the ASFR of mothers aged 10-19 years decreased from 37 livebirths (34-40) to 22 livebirths (19-24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83.8 million people per year since 1985. The global population increased by 197-2% (193.3-200.8) since 1950, from 2.6 billion (2.5-2.6) to 7.6 billion (7.4-7.9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2.0%; this rate then remained nearly constant until 1970 and then decreased to 1.1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2.5% in 1963 to O7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2.7%. The global average age increased from 26.6 years in 1950 to 32.1 years in 2017, and the proportion of the population that is of working age (age 15-64 years) increased from 59.9% to 65.3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1.0 livebirths (95% UI 0. 9-1.2) in Cyprus to a high of 7.1 livebirths (6.8-7.4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0.08 livebirths (0.07-0.09) in South Korea to 2.4 livebirths (2.2-2.6) in Niger, and the TFR over age 30 years (I F030; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0.3 livebirths (0.3-0-4) in Puerto Rico to a high of 3.1 livebirths (3.0-3.2) in Niger. TF030 was higher than TFU25 in 145 countries and territories in 2017.33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2.0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.
Published: 2018

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

Author: Nancy, Fullman, Jamal, Yearwood, Solomon, M, Abay, Cristiana, Abbafati, Foad, Abd-Allah, Jemal, Abdela, Ahmed, Abdelalim, Zegeye, Abebe, Teshome, Abuka, Abebo, Victor, Aboyans, Haftom, Niguse, Abraha, Daisy, M, X, Abreu, Laith, Abu-Raddad, J, Akilew, Awoke, Adane, Rufus, Adesoji, Adedoyin, Olatunji, Adetokunboh, Tara, Ballav, Adhikari, Mohsen, Afarideh, Ashkan, Afshin, Gina, Agarwal, Dominic, Agius, Anurag, Agrawal, Sutapa, Agrawal, Aliasghar, Ahmad, Kiadaliri, Miloud, Taki, Eddine, Aichour, Mohammed, Akibu, Rufus, Olusola, Akinyemi, Tomi, Akinyemiju, F, Nadia, Akseer, Faris, Hasan, Lami, Al, Fares, Alahdab, Ziyad, Al-Aly, Khurshid, Alam, Tahiya, Alam, Deena, Alasfoor, Mohammed, I, Albittar, Kefyalew, Addis, Alene, Ayman, Al-Eyadhy, Syed, Danish, Ali, Mehran, Alijanzadeh, Syed, M, Aljunid, Ala’A, Alkerwi, François, Alla, Peter, Allebeck, Christine, Allen, Mahmoud, A, Alomari, Rajaa, Al-Raddadi, Ubai, Alsharif, Khalid, A, Altirkawi, Nelson, Alvis-Guzman, Azmeraw, T, Amare, Kebede, Amenu, Walid, Ammar, Yaw, Ampem, Amoako, Nahla, Anber, Catalina, Liliana, Andrei, Sofia, Androudi, Carl, Abelardo, Antonio, T, Valdelaine, E, Araújo, M, Olatunde, Aremu, Johan, Ärnlöv, Artaman, Al, Krishna, Kumar, Aryal, Hamid, Asayesh, Ephrem, Tsegay, Asfaw, Solomon, Weldegebreal, Asgedom, Rana, Jawad, Asghar, Mengistu, Mitiku, Ashebir, Netsanet, Abera, Asseffa, Tesfay, Mehari, Atey, Sachin, R, Atre, Madhu, Atteraya, S, Leticia, Avila-Burgos, Euripide, Frinel, Arthur, G, Avokpaho, Ashish, Awasthi, Beatriz, Paulina, Ayala, Quintanilla, Animut, Alebel, Ayalew, Henok, Tadesse, Ayele, Rakesh, Ayer, Tambe, Betrand, Ayuk, Peter, Azzopardi, Natasha, Azzopardi-Muscat, Tesleem, Kayode, Babalola, Hamid, Badali, Alaa, Badawi, Maciej, Banach, Amitava, Banerjee, Amrit, Banstola, Ryan, M, Barber, Miguel, Barboza, A, Suzanne, L, Barker-Collo, Till, Bärnighausen, Simon, Barquera, Lope, H, Barrero, Quique, Bassat, Sanjay, Basu, Bernhard, T, Baune, Shahrzad, Bazargan-Hejazi, Neeraj, Bedi, Ettore, Beghi, Masoud, Behzadifar, Meysam, Behzadifar, Bayu, Begashaw, Bekele, Abate, Bekele, Belachew, Saba, Abraham, Belay, Yihalem, Abebe, Belay, Michelle, L, Bell, Aminu, Bello, K, Derrick, A, Bennett, James, Bennett, R, Isabela, M, Bensenor, Derbew, Fikadu, Berhe, Eduardo, Bernabé, Robert, Steven, Bernstein, Mircea, Beuran, Ashish, Bhalla, Paurvi, Bhatt, Soumyadeep, Bhaumik, Zulfiqar, A, Bhutta, Belete, Biadgo, Ali, Bijani, Boris, Bikbov, Charles, Birungi, Stan, Biryukov, Hailemichael, Bizuneh, Ian, W, Bolliger, Kaylin, Bolt, Ibrahim, R, Bou-Orm, Kayvan, Bozorgmehr, Oliver, Jerome, Brady, Alexandra, Brazinova, Nicholas, J, Breitborde, K, Hermann, Brenner, Gabrielle, Britton, Traolach, S, Brugha, Zahid, Butt, A, Lucero, Cahuana-Hurtado, Ismael, Ricardo, Campos-Nonato, Julio, Cesar, Campuzano, Josip, Car, Mate, Car, Rosario, Cárdenas, Juan, Jesus, Carrero, Felix, Carvalho, Carlos, A, Castañeda-Orjuela, Jacqueline, Castillo, Rivas, Ferrán, Catalá-López, Kelly, Cercy, Julian, Chalek, Hsing-Yi, Chang, Jung-Chen, Chang, Aparajita, Chattopadhyay, Pankaj, Chaturvedi, Peggy, Pei-Chia, Chiang, Vesper, Hichilombwe, Chisumpa, Jee-Young, J, Choi, Hanne, Christensen, Devasahayam, Jesudas, Christopher, Sheng-Chia, Chung, Liliana, G, Ciobanu, Cirillo, Massimo, Danny, Colombara, Sara, Conti, Cyrus, Cooper, Leslie, Cornaby, Paolo, Angelo, Cortesi, Monica, Cortinovis, Alexandre, Costa, Pereira, Ewerton, Cousin, Michael, H, Criqui, Elizabeth, Cromwell, A, Christopher, Stephen, Crowe, John, Crump, A, Alemneh, Kabeta, Daba, Berihun, Assefa, Dachew, Abel, Fekadu, Dadi, Lalit, Dandona, Rakhi, Dandona, Paul, I, Dargan, Ahmad, Daryani, Maryam, Daryani, Jai, Das, Siddharth, Kumar, Das, José, Das, Neves, Nicole, Davis, Weaver, Kairat, Davletov, Barbora, De, Courten, Diego, Leo, De, Jan-Walter, De, Neve, Robert, Dellavalle, P, Gebre, Demoz, Kebede, Deribe, Don, C, Des, Jarlais, Subhojit, Dey, Samath, D, Dharmaratne, Meghnath, Dhimal, Shirin, Djalalinia, David, Teye, Doku, Kate, Dolan, Ray, E, Dorsey, Kadine, Priscila, Bender, Dos, Santos, Kerrie, E, Doyle, Tim, Driscoll, R, Manisha, Dubey, Eleonora, Dubljanin, Bruce, Bartholow, Duncan, Michelle, Echko, Dumessa, Edessa, David, Edvardsson, Joshua, R, Ehrlich, Erika, Eldrenkamp, Ziad, El-Khatib, Matthias, Endres, Aman, Yesuf, Endries, Babak, Eshrati, Sharareh, Eskandarieh, Alireza, Esteghamati, Mahdi, Fakhar, Tamer, Farag, Mahbobeh, Faramarzi, Emerito, Jose, Aquino, Faraon, André, Faro, Farshad, Farzadfar, Adesegun, Fatusi, Mir, Sohail, Fazeli, Valery, Feigin, L, Andrea, B, Feigl, Netsanet, Fentahun, Seyed-Mohammad, Fereshtehnejad, Eduarda, Fernandes, João, C, Fernandes, Daniel, Obadare, Fijabi, Irina, Filip, Florian, Fischer, Christina, Fitzmaurice, Abraham, D, Flaxman, Luisa, Sorio, Flor, Nataliya, Foigt, Kyle, J, Foreman, Joseph, Frostad, J, Thomas, Fürst, Neal, D, Futran, Emmanuela, Gakidou, Silvano, Gallus, Ketevan, Gambashidze, Amiran, Gamkrelidze, Morsaleh, Ganji, Abadi, Kahsu, Gebre, Tsegaye, Tewelde, Gebrehiwot, Amanuel, Tesfay, Gebremedhin, Yalemzewod, Assefa, Gelaw, Johanna, M, Geleijnse, Demeke, Geremew, Peter, W, Gething, Reza, Ghadimi, Khalil, Ghasemi, Falavarjani, Maryam, Ghasemi-Kasman, Paramjit, Singh, Gill, Ababi, Zergaw, Giref, Maurice, Giroud, Melkamu, Dedefo, Gishu, Giorgia, Giussani, William, W, Godwin, Srinivas, Goli, Hector, Gomez-Dantes, Philimon, N, Gona, Amador, Goodridge, Sameer, Vali, Gopalani, Yevgeniy, Goryakin, Alessandra, Carvalho, Goulart, Ayman, Grada, Max, Griswold, Giuseppe, Grosso, Harish, Chander, Gugnani, Yuming, Guo, Rahul, Gupta, Rajeev, Gupta, Tanush, Gupta, Tarun, Gupta, Vipin, Gupta, Juanita, A, Haagsma, Vladimir, Hachinski, Nima, Hafezi-Nejad, Gessessew, Bugssa, Hailu, Randah, Ribhi, Hamadeh, Samer, Hamidi, Graeme, J, Hankey, Hilda, Harb, L, Heather, C, Harewood, Sivadasanpillai, Harikrishnan, Josep, Maria, Haro, Hamid, Yimam, Hassen, Rasmus, Havmoeller, Caitlin, Hawley, Simon, I, Hay, Jiawei, He, Stephen, J, Hearps, C, Mohamed, I, Hegazy, Behzad, Heibati, Mohsen, Heidari, Delia, Hendrie, Nathaniel, J, Henry, Victor, Hugo, Herrera, Ballesteros, Claudiu, Herteliu, Desalegn, Tsegaw, Hibstu, Molla, Kahssay, Hiluf, Hans, W, Hoek, Enayatollah, Homaie, Rad, Nobuyuki, Horita, Dean, H, Hosgood, Mostafa, Hosseini, Seyed, Reza, Hosseini, Mihaela, Hostiuc, Sorin, Hostiuc, Damian, G, Hoy, Mohamed, Hsairi, Aung, Soe, Htet, Guoqing, Hu, John, J, Huang, Kim, Moesgaard, Iburg, Fachmi, Idris, Ehimario, Uche, Igumbor, Chad, Ikeda, Bogdan, Vasile, Ileanu, Olayinka, Ilesanmi, S, Kaire, Innos, Seyed, Sina, Naghibi, Irvani, Caleb, M, Irvine, S, Farhad, Islami, Troy, A, Jacobs, Kathryn, Jacobsen, H, Nader, Jahanmehr, Rajesh, Jain, Sudhir, Kumar, Jain, Mihajlo, Jakovljevic, M, Moti, Tolera, Jalu, Amr, Jamal, A, Mehdi, Javanbakht, Achala, Upendra, Jayatilleke, Panniyammakal, Jeemon, Ravi, Prakash, Jha, Vivekanand, Jha, Jacek, Józwiak, Oommen, John, Sarah, Charlotte, Johnson, Jost, Jonas, B, Vasna, Joshua, Mikk, Jürisson, Zubair, Kabir, Rajendra, Kadel, Amaha, Kahsay, Rizwan, Kalani, Chittaranjan, Kar, Marina, Karanikolos, André, Karch, Corine, Kakizi, Karema, Seyed, Karimi, M, Amir, Kasaeian, Dessalegn, Haile, Kassa, Getachew, Mullu, Kassa, Tesfaye, Dessale, Kassa, Nicholas, Kassebaum, J, Srinivasa, Vittal, Katikireddi, Anil, Kaul, Norito, Kawakami, Konstantin, Kazanjan, Seifu, Kebede, Peter, Njenga, Keiyoro, Grant, Rodgers, Kemp, Andre, Pascal, Kengne, Maia, Kereselidze, Ezra, Belay, Ketema, Yousef, Saleh, Khader, Morteza, Abdullatif, Khafaie, Alireza, Khajavi, Ibrahim, A, Khalil, Ejaz, Ahmad, Khan, Gulfaraz, Khan, Nuruzzaman, Md, Khan, Muhammad, Ali, Khan, Mukti, Nath, Khanal, Young-Ho, Khang, Mona, M, Khater, Abdullah, Tawfih, Abdullah, Khoja, Ardeshir, Khosravi, Jagdish, Khubchandani, Getiye, Dejenu, Kibret, Daniel, Ngari, Kiirithio, Daniel, Kim, Yun, Jin, Kim, Ruth, Kimokoti, W, Yohannes, Kinfu, Sanjay, Kinra, Adnan, Kisa, Niranjan, Kissoon, Sonali, Kochhar, Yoshihiro, Kokubo, Jacek, A, Kopec, Soewarta, Kosen, Parvaiz, A, Koul, 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Avila-Burgos, Leticia, Avokpaho, Euripide Frinel G. 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Ray, Dos Santos, Kadine Priscila Bender, Doyle, Kerrie E., Driscoll, Tim R., Dubey, Manisha, Dubljanin, Eleonora, Duncan, Bruce Bartholow, Echko, Michelle, Edessa, Dumessa, Edvardsson, David, Ehrlich, Joshua R., Eldrenkamp, Erika, El-Khatib, Ziad, Endres, Matthia, Endries, Aman Yesuf, Eshrati, Babak, Eskandarieh, Sharareh, Esteghamati, Alireza, Fakhar, Mahdi, Farag, Tamer, Faramarzi, Mahbobeh, Faraon, Emerito Jose Aquino, Faro, André, Farzadfar, Farshad, Fatusi, Adesegun, Fazeli, Mir Sohail, Feigin, Valery L., Feigl, Andrea B., Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, Eduarda, Fernandes, João C., Fijabi, Daniel Obadare, Filip, Irina, Fischer, Florian, Fitzmaurice, Christina, Flaxman, Abraham D., Flor, Luisa Sorio, Foigt, Nataliya, Foreman, Kyle J., Frostad, Joseph J., Fürst, Thoma, Futran, Neal D., Gakidou, Emmanuela, Gallus, Silvano, Gambashidze, Ketevan, Gamkrelidze, Amiran, Ganji, Morsaleh, Gebre, Abadi Kahsu, Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Gelaw, Yalemzewod Assefa, Geleijnse, Johanna M., Geremew, Demeke, Gething, Peter W., Ghadimi, Reza, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Gill, Paramjit Singh, Giref, Ababi Zergaw, Giroud, Maurice, Gishu, Melkamu Dedefo, Giussani, Giorgia, Godwin, William W., Goli, Sriniva, Gomez-Dantes, Hector, Gona, Philimon N., Goodridge, Amador, Gopalani, Sameer Vali, Goryakin, Yevgeniy, Goulart, Alessandra Carvalho, Grada, Ayman, Griswold, Max, Grosso, Giuseppe, Gugnani, Harish Chander, Guo, Yuming, Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Gupta, Tarun, Gupta, Vipin, Haagsma, Juanita A., Hachinski, Vladimir, Hafezi-Nejad, Nima, Hailu, Gessessew Bugssa, Hamadeh, Randah Ribhi, Hamidi, Samer, Hankey, Graeme J., Harb, Hilda L., Harewood, Heather C., Harikrishnan, Sivadasanpillai, Haro, Josep Maria, Hassen, Hamid Yimam, Havmoeller, Rasmu, Hawley, Caitlin, Hay, Simon I., He, Jiawei, Hearps, Stephen J.C., Hegazy, Mohamed I., Heibati, Behzad, Heidari, Mohsen, Hendrie, Delia, Henry, Nathaniel J., Herrera Ballesteros, Victor Hugo, Herteliu, Claudiu, Hibstu, Desalegn Tsegaw, Hiluf, Molla Kahssay, Hoek, Hans W., Homaie Rad, Enayatollah, Horita, Nobuyuki, Hosgood, H. Dean, Hosseini, Mostafa, Hosseini, Seyed Reza, Hostiuc, Mihaela, Hostiuc, Sorin, Hoy, Damian G., Hsairi, Mohamed, Htet, Aung Soe, Hu, Guoqing, Huang, John J., Iburg, Kim Moesgaard, Idris, Fachmi, Igumbor, Ehimario Uche, Ikeda, Chad, Ileanu, Bogdan Vasile, Ilesanmi, Olayinka S., Innos, Kaire, Irvani, Seyed Sina Naghibi, Irvine, Caleb M.S., Islami, Farhad, Jacobs, Troy A., Jacobsen, Kathryn H., Jahanmehr, Nader, Jain, Rajesh, Jain, Sudhir Kumar, Jakovljevic, Mihajlo M., Jalu, Moti Tolera, Jamal, Amr A., Javanbakht, Mehdi, Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Józwiak, Jacek, John, Oommen, Johnson, Sarah Charlotte, Jonas, Jost B., Joshua, Vasna, Jürisson, Mikk, Kabir, Zubair, Kadel, Rajendra, Kahsay, Amaha, Kalani, Rizwan, Kar, Chittaranjan, Karanikolos, Marina, Karch, André, Karema, Corine Kakizi, Karimi, Seyed M., Kasaeian, Amir, Kassa, Dessalegn Haile, Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassebaum, Nicholas J., Katikireddi, Srinivasa Vittal, Kaul, Anil, Kawakami, Norito, Kazanjan, Konstantin, Kebede, Seifu, Keiyoro, Peter Njenga, Kemp, Grant Rodger, Kengne, Andre Pascal, Kereselidze, Maia, Ketema, Ezra Belay, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalil, Ibrahim A., Khan, Ejaz Ahmad, Khan, Gulfaraz, Khan, Md Nuruzzaman, Khan, Muhammad Ali, Khanal, Mukti Nath, Khang, Young-Ho, Khater, Mona M., Khoja, Abdullah Tawfih Abdullah, Khosravi, Ardeshir, Khubchandani, Jagdish, Kibret, Getiye Dejenu, Kiirithio, Daniel Ngari, Kim, Daniel, Kim, Yun Jin, Kimokoti, Ruth W., Kinfu, Yohanne, Kinra, Sanjay, Kisa, Adnan, Kissoon, Niranjan, Kochhar, Sonali, Kokubo, Yoshihiro, Kopec, Jacek A., Kosen, Soewarta, Koul, Parvaiz A., Koyanagi, Ai, Kravchenko, Michael, Krishan, Kewal, Krohn, Kristopher J., Kuate Defo, Barthelemy, Kumar, G. Anil, Kumar, Pushpendra, Kutz, Michael, Kuzin, Igor, Kyu, Hmwe H., Lad, Deepesh Pravinkumar, Lafranconi, Alessandra, Lal, Dharmesh Kumar, Lalloo, Ratilal, Lam, Hilton, Lan, Qing, Lang, Justin J., Lansingh, Van C., Lansky, Sonia, Larsson, Ander, Latifi, Arman, Lazarus, Jeffrey Victor, Leasher, Janet L., Lee, Paul H., Legesse, Yirga, Leigh, Jame, Leshargie, Cheru Tesema, Leta, Samson, Leung, Janni, Leung, Ricky, Levi, Miriam, Li, Yongmei, Liang, Juan, Liben, Misgan Legesse, Lim, Lee-Ling, Lim, Stephen S., Lind, Margaret, Linn, Shai, Listl, Stefan, Liu, Patrick Y., Liu, Shiwei, Lodha, Rakesh, Lopez, Alan D., Lorch, Scott A., Lorkowski, Stefan, Lotufo, Paulo A., Lucas, Timothy C.D., Lunevicius, Raimunda, Lurton, Grégoire, Lyons, Ronan A., Maalouf, Fadi, Macarayan, Erlyn Rachelle King, Mackay, Mark T., Maddison, Emilie R., Madotto, Fabiana, Magdy Abd El Razek, Hassan, Magdy Abd El Razek, Mohammed, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malhotra, Rajesh, Malta, Deborah Carvalho, Mamun, Abdullah A., Manguerra, Helena, Manhertz, Treh, Mansournia, Mohammad Ali, Mantovani, Lorenzo G., Manyazewal, Tsegahun, Mapoma, Chabila C., Margono, Christopher, Martinez-Raga, Jose, Martins, Sheila Cristina Ourique, Martins-Melo, Francisco Rogerlândio, Martopullo, Ira, März, Winfried, Massenburg, Benjamin Ballard, Mathur, Manu Raj, Maulik, Pallab K., Mazidi, Mohsen, McAlinden, Colm, McGrath, John J., McKee, Martin, Mehata, Suresh, Mehrotra, Ravi, Mehta, Kala M., Mehta, Varshil, Meier, Toni, Mejia-Rodriguez, Fabiola, Meles, Kidanu Gebremariam, Melku, Mulugeta, Memiah, Peter, Memish, Ziad A., Mendoza, Walter, Mengiste, Degu Abate, Mengistu, Desalegn Tadese, Menota, Bereket Gebremichael, Mensah, George A., Meretoja, Atte, Meretoja, Tuomo J., Mezgebe, Haftay Berhane, Miazgowski, Tomasz, Micha, Renata, Milam, Robert, Millear, Anoushka, Miller, Ted R., Mini, G.K., Minnig, Shawn, Mirica, Andreea, Mirrakhimov, Erkin M., Misganaw, Awoke, Mitchell, Philip B., Mlashu, Fitsum Weldegebreal, Moazen, Babak, Mohammad, Karzan Abdulmuhsin, Mohammadibakhsh, Roghayeh, Mohammed, Ebrahim, Mohammed, Mohammed A., Mohammed, Shafiu, Mokdad, Ali H., Mola, Glen Liddell D., Molokhia, Mariam, Momeniha, Fatemeh, Monasta, Lorenzo, Montañez Hernandez, Julio Cesar, Moosazadeh, Mahmood, Moradi-Lakeh, Maziar, Moraga, Paula, Morawska, Lidia, Moreno Velasquez, Ilai, Mori, Rintaro, Morrison, Shane D., Moses, Mark, Mousavi, Seyyed Meysam, Mueller, Ulrich O., Murhekar, Manoj, Murthy, Gudlavalleti Venkata Satyanarayana, Murthy, Sriniva, Musa, Jonah, Musa, Kamarul Imran, Mustafa, Ghulam, Muthupandian, Saravanan, Nagata, Chie, Nagel, Gabriele, Naghavi, Mohsen, Naheed, Aliya, Naik, Gurudatta A., Naik, Nitish, Najafi, Farid, Naldi, Luigi, Nangia, Vinay, Nansseu, Jobert Richie Njingang, Narayan, K.M. Venkat, Nascimento, Bruno Ramo, Negoi, Ionut, Negoi, Ruxandra Irina, Newton, Charles R., Ngunjiri, Josephine Wanjiku, Nguyen, Grant, Nguyen, Long, Nguyen, Trang Huyen, Nichols, Emma, Ningrum, Dina Nur Anggraini, Nolte, Ellen, Nong, Vuong Minh, Norheim, Ole F., Norrving, Bo, Noubiap, Jean Jacques N., Nyandwi, Alypio, Obermeyer, Carla Makhlouf, Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oh, In-Hwan, Oladimeji, Olanrewaju, Olagunju, Andrew Toyin, Olagunju, Tinuke Oluwasefunmi, Olivares, Pedro R., De Oliveira, Patricia Pereira Vasconcelo, Olsen, Helen E., Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Kanyin, Opio, John Nelson, Oren, Eyal, Ortega-Altamirano, Doris V., Ortiz, Alberto, Ozdemir, Raziye, Pa, Mahesh, Pain, Amanda W., Palone, Marcos Roberto Tovani, Pana, Adrian, Panda-Jonas, Songhomitra, Pandian, Jeyaraj D., Park, Eun-Kee, Parsian, Hadi, Patel, Teja, Pati, Sanghamitra, Patil, Snehal T., Patle, Ajay, Patton, George C., Paturi, Vishnupriya Rao, Paudel, Deepak, De Moares Pedroso, Marcel, Pedroza, Sandra P., Pereira, David M., Perico, Norberto, Peterson, Hannah, Petzold, Max, Peykari, Niloofar, Phillips, Michael Robert, Piel, Frédéric B., Pigott, David M., Pillay, Julian David, Piradov, Michael A., Polinder, Suzanne, Pond, Constance D., Postma, Maarten J., Pourmalek, Farshad, Prakash, Swayam, Prakash, V., Prasad, Narayan, Prasad, Noela Marie, Purcell, Caroline, Qorbani, Mostafa, Quintana, Hedley Knewjen, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahimi, Kazem, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Muhammad Aziz, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Raju, Sree Bhushan, Ram, Usha, Rana, Saleem M., Rankin, Zane, Rasella, Davide, Rawaf, David Laith, Rawaf, Salman, Ray, Sarah E., Razo-García, Christian Aspacia, Reddy, Priscilla, Reiner, Robert C., Reis, Cesar, Reitsma, Marissa B., Remuzzi, Giuseppe, Renzaho, Andre M.N., Resnikoff, Serge, Rezaei, Satar, Rezai, Mohammad Sadegh, Ribeiro, Antonio L., Rios Blancas, Maria Jesu, Rivera, Juan A., Roever, Leonardo, Ronfani, Luca, Roshandel, Gholamreza, Rostami, Ali, Roth, Gregory A., Rothenbacher, Dietrich, Roy, Ambuj, Roy, Nobhojit, Ruhago, George Mugambage, Sabde, Yogesh Damodar, Sachdev, Perminder S., Sadat, Nafi, Safdarian, Mahdi, Safiri, Saeid, Sagar, Rajesh, Sahebkar, Amirhossein, Sahraian, Mohammad Ali, Sajadi, Haniye Sadat, Salama, Joseph, Salamati, Payman, De Freitas Saldanha, Raphael, Salimzadeh, Hamideh, Salomon, Joshua A., Samy, Abdallah M., Sanabria, Juan Ramon, Sancheti, Parag K., Sanchez-Niño, Maria Dolore, Santomauro, Damian, Santos, Itamar S., Santric Milicevic, Milena M., Sarker, Abdur Razzaque, Sarrafzadegan, Nizal, Sartorius, Benn, Satpathy, Maheswar, Savic, Miloje, Sawhney, Monika, Saxena, Sonia, Saylan, Mete I., Schaeffner, Elke, Schmidhuber, Josef, Schmidt, Maria Inê, Schneider, Ione J.C., Schumacher, Austin E., Schutte, Aletta E., Schwebel, David C., Schwendicke, Falk, Sekerija, Mario, Sepanlou, Sadaf G., Servan-Mori, Edson E., Shafieesabet, Azadeh, Shaikh, Masood Ali, Shakh-Nazarova, Marina, Shams-Beyranvand, Mehran, Sharafi, Heidar, Sharif-Alhoseini, Mahdi, Shariful Islam, Sheikh Mohammed, Sharma, Meenakshi, Sharma, Rajesh, She, Jun, Sheikh, Aziz, Shfare, Mebrahtu Teweldemedhin, Shi, Peilin, Shields, Chloe, Shigematsu, Mika, Shinohara, Yukito, Shiri, Rahman, Shirkoohi, Reza, Shiue, Ivy, Shrime, Mark G., Shukla, Sharvari Rahul, Siabani, Soraya, Sigfusdottir, Inga Dora, Silberberg, Donald H., Silva, Diego Augusto Santo, Silva, João Pedro, Silveira, Dayane Gabriele Alve, Singh, Jasvinder A., Singh, Lavanya, Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Sinke, Abiy Hiruye, Sisay, Mekonnen, Skirbekk, Vegard, Sliwa, Karen, Smith, Alison, Soares Filho, Adauto Martin, Sobaih, Badr H.A., Somai, Melek, Soneji, Samir, Soofi, Moslem, Sorensen, Reed J.D., Soriano, Joan B., Soyiri, Ireneous N., Sposato, Luciano A., Sreeramareddy, Chandrashekhar T., Srinivasan, Vinay, Stanaway, Jeffrey D., Stathopoulou, Vasiliki, Steel, Nichola, Stein, Dan J., Stokes, Mark Andrew, Sturua, Lela, Sufiyan, Muawiyyah Babale, Suliankatchi, Rizwan Abdulkader, Sunguya, Bruno F., Sur, Patrick J., Sykes, Bryan L., Sylaja, P.N., Szoeke, Cassandra E.I., Tabarés-Seisdedos, Rafael, Tadakamadla, Santosh Kumar, Tadesse, Andualem Henok, Taffere, Getachew Redae, Tandon, Nikhil, Tariku, Amare Tariku, Taveira, Nuno, Tehrani-Banihashemi, Arash, Temam Shifa, Girma, Temsah, Mohamad-Hani, Terkawi, Abdullah Sulieman, Tesema, Azeb Gebresilassie, Tesfaye, Dawit Jember, Tessema, Belay, Thakur, J.S., Thomas, Nihal, Thompson, Matthew J., Tillmann, Taavi, To, Quyen G., Tobe-Gai, Ruoyan, Tonelli, Marcello, Topor-Madry, Roman, Topouzis, Foti, Torre, Anna, Tortajada, Miguel, Tran, Bach Xuan, Tran, Khanh Bao, Tripathi, Avnish, Tripathy, Srikanth Prasad, Troeger, Christopher, Truelsen, Thoma, Tsoi, Derrick, Tudor Car, Lorainne, Tuem, Kald Beshir, Tyrovolas, Stefano, Uchendu, Uche S., Ukwaja, Kingsley Nnanna, Ullah, Irfan, Updike, Rachel, Uthman, Olalekan A., Uzochukwu, Benjamin S. Chudi, Valdez, Pascual Rubén, Van Boven, Job F.M., Varughese, Santosh, Vasankari, Tommi, Venketasubramanian, Narayanaswamy, Violante, Francesco S., Vladimirov, Sergey K., Vlassov, Vasiliy Victorovich, Vollset, Stein Emil, Vos, Theo, Wagnew, Fasil, Waheed, Yasir, Wallin, Mitchell T., Walson, Judd L., Wang, Yafeng, Wang, Yuan-Pang, Wassie, Molla Mesele, Weaver, Marcia R., Weiderpass, Elisabete, Weintraub, Robert G., Weiss, Jordan, Weldegwergs, Kidu Gidey, Werdecker, Andrea, West, T. Eoin, Westerman, Ronny, White, Richard G., Whiteford, Harvey A., Widecka, Justyna, Winkler, Andrea Sylvia, Wiysonge, Charles Shey, Wolfe, Charles D.A., Wondimkun, Yohanes Ayele, Workicho, Abdulhalik, Wyper, Grant M.A., Xavier, Deni, Xu, Gelin, Yan, Lijing L., Yano, Yuichiro, Yaseri, Mehdi, Yimer, Nigus Bililign, Yin, Peng, Yip, Paul, Yirsaw, Biruck Desalegn, Yonemoto, Naohiro, Yonga, Gerald, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z., Yu, Chuanhua, Zadnik, Vesna, Zaidi, Zoubida, El Sayed Zaki, Maysaa, Zaman, Sojib Bin, Zamani, Mohammad, Zenebe, Zerihun Menlkalew, Zhou, Maigeng, Zhu, Jun, Zimsen, Stephanie R.M., Zipkin, Ben, Zodpey, Sanjay, Zuhlke, Liesl Joanna, Murray, Christopher J.L., Lozano, Rafael, Viðskiptadeild (HR), School of Business (RU), Háskólinn í Reykjavík, Reykjavik University, Abay, Sm, Abebo, Ta, Abraha, Hn, Abreu, Dmx, Abu-Raddad, Lj, Adane, Aa, Adedoyin, Ra, Adhikari, Tb, Aichour, Mte, Akinyemi, Ro, Akinyemiju, Tf, Al Lami, Fh, Albittar, Mi, Alene, Ka, Ali, Sd, Aljunid, Sm, Alomari, Ma, Altirkawi, Ka, Amare, At, Amoako, Ya, Andrei, Cl, Antonio, Cat, Araújo, Vem, Aryal, Kk, Asfaw, Et, Asgedom, Sw, Asghar, Rj, Ashebir, Mm, Asseffa, Na, Atey, Tm, Atre, Sr, Avokpaho, Efga, Ayala Quintanilla, Bp, Ayalew, Aa, Ayele, Ht, Ayuk, Tb, Babalola, Tk, Barber, Rm, Barboza, Ma, Barker-Collo, Sl, Barrero, Lh, Baune, Bt, Bekele, Bb, Belachew, Ab, Belay, Sa, Belay, Ya, Bell, Ml, Bello, Ak, Bennett, Da, Bennett, Jr, Bensenor, Im, Berhe, Df, Bhutta, Za, Bolliger, Iw, Bou-Orm, Ir, Brady, Oj, Breitborde, Njk, Butt, Za, Campos-Nonato, Ir, Campuzano, Jc, Carrero, Jj, Castañeda-Orjuela, Ca, Chang, Hy, Chang, Jc, Chiang, Pp, Chisumpa, Vh, Choi, Jj, Christopher, Dj, Chung, Sc, Ciobanu, Lg, Cortesi, Pa, Criqui, Mh, Cromwell, Ea, Crump, Ja, Daba, Ak, Dachew, Ba, Dadi, Af, Dargan, Pi, Das, Sk, De Neve, Jw, Dellavalle, Rp, Des Jarlais, Dc, Dharmaratne, Sd, Doku, Dt, Dorsey, Er, Dos Santos, Kpb, Doyle, Ke, Driscoll, Tr, Duncan, Bb, Ehrlich, Jr, El-Khatib, Zz, Endries, Ay, Faraon, Eja, Feigin, Vl, Feigl, Ab, Fereshtehnejad, Sm, Fernandes, Jc, Fijabi, Do, Flaxman, Ad, Foreman, Kj, Frostad, Jj, Futran, Nd, Gebre, Ak, Gebrehiwot, Tt, Gebremedhin, At, Gelaw, Ya, Geleijnse, Jm, Gething, Pw, Giref, Az, Gishu, Md, Godwin, Ww, Gona, Pn, Gopalani, Sv, Goulart, Ac, Gugnani, Hc, Haagsma, Ja, Hailu, Gb, Hamadeh, Rr, Hankey, Gj, Harb, Hl, Harewood, Hc, Haro, Jm, Hassen, Hy, Hay, Si, Hearps, Sjc, Hegazy, Mi, Henry, Nj, Herrera Ballesteros, Vh, Hibstu, Dt, Hiluf, Mk, Hoek, Hw, Hosgood, Hd, Hosseini, Sr, Hoy, Dg, Huang, Jj, Iburg, Km, Igumbor, Eu, Ileanu, Bv, Irvani, Ssn, Irvine, Cm, Jacobs, Ta, Jacobsen, Kh, Jain, Sk, Jakovljevic, Mb, Jalu, Mt, Jamal, Aa, Jayatilleke, Au, Jha, Rp, Johnson, Sc, Jonas, Jb, Karema, Ck, Karimi, Sm, Kassa, Dh, Kassa, Gm, Kassa, Td, Kassebaum, Nj, Katikireddi, Sv, Keiyoro, Pn, Kemp, Gr, Kengne, Ap, Ketema, Eb, Khafaie, Ma, Khalil, Ia, Khan, Ea, Khan, Mn, Khan, Ma, Khanal, Mn, Khang, Yh, Khater, Mm, Khoja, Ata, Kibret, Gd, Kiirithio, Dn, Kim, Yj, Kimokoti, Rw, Kopec, Ja, Koul, Pa, Krohn, Kj, Kumar, Ga, Kyu, Hh, Lad, Dp, Lal, Dk, Lang, Jj, Lansingh, Vc, Lazarus, Jv, Leasher, Jl, Lee, Ph, Leshargie, Ct, Liben, Ml, Lim, Ll, Lopez, Ad, Lorch, Sa, Lotufo, Pa, Lucas, Tcd, Lyons, Ra, Macarayan, Erk, Mackay, Mt, Maddison, Er, Malta, Dc, Mamun, Aa, Mansournia, Ma, Mantovani, Lg, Mapoma, Cc, Martins, Sco, Martins-Melo, Fr, Massenburg, Bb, Mathur, Mr, Maulik, Pk, Mcgrath, Jj, Mehta, Km, Meles, Kg, Memish, Za, Mengiste, Da, Mengistu, Dt, Menota, Bg, Mensah, Ga, Meretoja, Tj, Mezgebe, Hb, Miller, Tr, Mini, Gk, Mirrakhimov, Em, Mitchell, Pb, Mlashu, Fw, Mohammad, Ka, Mohammed, Ma, Mokdad, Ah, Mola, Gl, Montañez Hernandez, Jc, Morrison, Sd, Mousavi, Sm, Mueller, Uo, Murthy, Gv, Musa, Ki, Naik, Ga, Nansseu, Jrn, Narayan, Kv, Nascimento, Br, Negoi, Ri, Newton, Cr, Ngunjiri, Jw, Nguyen, Th, Ningrum, Dna, Nong, Vm, Norheim, Of, Noubiap, Jjn, Obermeyer, Cm, Ogbo, Fa, Oh, Ih, Olagunju, At, Olagunju, To, Olivares, Pr, Oliveira, Ppv, Olsen, He, Olusanya, Bo, Olusanya, Jo, Opio, Jn, Ortega-Altamirano, Dv, Pain, Aw, Palone, Mrt, Pandian, Jd, Park, Ek, Patil, St, Patton, Gc, Paturi, Vr, Pedroso, Mm, Pedroza, Sp, Pereira, Dm, Phillips, Mr, Piel, Fb, Pigott, Dm, Pillay, Jd, Piradov, Ma, Pond, Cd, Postma, Mj, Prasad, Nm, Quintana, Hk, Rahman, Ma, Rahman, Su, Rai, Rk, Raju, Sb, Rana, Sm, Rawaf, Dl, Ray, Se, Razo-García, Ca, Reiner, Rc, Reitsma, Mb, Renzaho, Amn, Ribeiro, Al, Rios Blancas, Mj, Rivera, Ja, Roth, Ga, Ruhago, Gm, Sabde, Yd, Sahraian, Ma, Saldanha, Rf, Salomon, Ja, Samy, Am, Sanabria, Jr, Sancheti, Pk, Sanchez-Niño, Md, Santric Milicevic, Mm, Sarker, Ar, Saylan, Mi, Schmidt, Mi, Schneider, Ijc, Schumacher, Ae, Schutte, Ae, Schwebel, Dc, Sepanlou, Sg, Servan-Mori, Ee, Shaikh, Ma, Shariful Islam, Sm, Shfare, Mt, Shrime, Mg, Shukla, Sr, Sigfusdottir, Id, Silberberg, Dh, Silva, Da, Silva, Jp, Silveira, Dga, Singh, Ja, Singh, Np, Sinha, Dn, Sinke, Ah, Soares Filho, Am, Sobaih, Bha, Sorensen, Rjd, Soriano, Jb, Soyiri, In, Sposato, La, Sreeramareddy, Ct, Stanaway, Jd, Stein, Dj, Stokes, Ma, Sufiyan, Mb, Suliankatchi, Ra, Sunguya, Bf, Sur, Pj, Sykes, Bl, Sylaja, Pn, Tadakamadla, Sk, Tadesse, Ah, Taffere, Gr, Tariku, At, Temsah, Mh, Tesema, Ag, Tesfaye, Dj, Thompson, Mj, To, Qg, Tran, Bx, Tran, Kb, Tripathy, Sp, Tuem, Kb, Ukwaja, Kn, Uthman, Oa, Uzochukwu, Bsc, Valdez, Pr, van Boven, Jfm, Vladimirov, Sk, Vlassov, Vv, Vollset, Se, Wallin, Mt, Walson, Jl, Wang, Yp, Wassie, Mm, Weaver, Mr, Weintraub, Rg, Weldegwergs, Kg, West, Te, White, Rg, Whiteford, Ha, Wolfe, Cd, Wondimkun, Ya, Wyper, Gma, Yan, Ll, Yimer, Nb, Yirsaw, Bd, Yoon, Sj, Younis, Mz, Zaki, Me, Zaman, Sb, Zenebe, Zm, Zimsen, Srm, Zuhlke, Lj, Murray, Cjl, and Lozano, R.
Subjects: Peformance, Coverage, Dánartíðni, Lífslíkur, Life expectancy, GBD, Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0–100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best), universal health coverage, Article, access quality health care, health care access and quality index, Nations, Healthcare Acce, Cause-specific mortality, Psychology, Healthcare Access and Quality Index, Mælitæki, States, Medicine (all), Health care, Þjóðir, Public Health, Global Health, Social Medicine and Epidemiology, Quality, Heilbrigðisþjónusta, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Sálfræði, Indicator, Amenable mortality, Transition, we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0–100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97·1 (95% UI 95·8–98·1) in Iceland, followed by 96·6 (94·9–97·9) in Norway and 96·1 (94·5–97·3) in the Netherlands, to values as low as 18·6 (13·1–24·4) in the Central African Republic, 19·0 (14·3–23·7) in Somalia, and 23·4 (20·2–26·8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91·5 (89·1–93·6) in Beijing to 48·0 (43·4–53·2) in Tibet (a 43·5-point difference), while India saw a 30·8-point disparity, from 64·8 (59·6–68·8) in Goa to 34·0 (30·3–38·1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4·8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20·9-point to 17·0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17·2-point to 20·4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle-SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage hinges upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view—and subsequent provision—of quality health care for all populations, Trends, Inequalities
Abstract: Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0-100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0-100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97.1 (95% UI 95.8-98.1) in Iceland, followed by 96.6 (94.9-97.9) in Norway and 96.1 (94.5-97.3) in the Netherlands, to values as low as 18.6 (13.1-24.4) in the Central African Republic, 19.0 (14.3-23.7) in Somalia, and 23.4 (20.2-26.8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91.5 (89.1-936) in Beijing to 48.0 (43.4-53.2) in Tibet (a 43.5-point difference), while India saw a 30.8-point disparity, from 64.8 (59.6-68.8) in Goa to 34.0 (30.3-38.1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4.8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20.9-point to 17.0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17.2-point to 20.4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle-SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view and subsequent provision of quality health care for all populations., Bill & Melinda Gates Foundation. Barbora de Courten is supported by a National Heart Foundation Future Leader Fellowship (100864). Ai Koyanagi’s work is supported by the Miguel Servet contract financed by the CP13/00150 and PI15/00862 projects, integrated into the National R + D + I and funded by the ISCIII —General Branch Evaluation and Promotion of Health Research—and the European Regional Development Fund (ERDF-FEDER). Alberto Ortiz was supported by Spanish Government (Instituto de Salud Carlos III RETIC REDINREN RD16/0019 FEDER funds). Ashish Awasthi acknowledges funding support from Department of Science and Technology, Government of India through INSPIRE Faculty scheme Boris Bikbov has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No. 703226. Boris Bikbov acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Panniyammakal Jeemon acknowledges support from the clinical and public health intermediate fellowship from the Wellcome Trust and Department of Biotechnology, India Alliance (2015–20). Job F M van Boven was supported by the Department of Clinical Pharmacy & Pharmacology of the University Medical Center Groningen, University of Groningen, Netherlands. Olanrewaju Oladimeji is an African Research Fellow hosted by Human Sciences Research Council (HSRC), South Africa and he also has honorary affiliations with Walter Sisulu University (WSU), Eastern Cape, South Africa and School of Public Health, University of Namibia (UNAM), Namibia. He is indeed grateful for support from HSRC, WSU and UNAM. EUI is supported in part by the South African National Research Foundation (NRF UID: 86003). Ulrich Mueller acknowledges funding by the German National Cohort Study grant No 01ER1511/D, Gabrielle B Britton is supported by Secretaría Nacional de Ciencia, Tecnología e Innovación and Sistema Nacional de Investigación de Panamá. Giuseppe Remuzzi acknowledges that the work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group. Behzad Heibati would like to acknowledge Air pollution Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran. Syed Aljunid acknowledges the National University of Malaysia for providing the approval to participate in this GBD Project. Azeem Majeed and Imperial College London are grateful for support from the Northwest London National Insititute of Health Research (NIHR) Collaboration for Leadership in Applied Health Research & Care. Tambe Ayuk acknowledges the Institute of Medical Research and Medicinal Plant Studies for office space provided. José das Neves was supported in his contribution to this work by a Fellowship from Fundação para a Ciência e a Tecnologia, Portugal (SFRH/BPD/92934/2013). João Fernandes gratefully acknowledges funding from FCT–Fundação para a Ciência e a Tecnologia (grant number UID/Multi/50016/2013). Jan-Walter De Neve was supported by the Alexander von Humboldt Foundation. Kebede Deribe is funded by a Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medicine (201900). Kazem Rahimi was supported by grants from the Oxford Martin School, the NIHR Oxford BRC and the RCUK Global Challenges Research Fund. Laith J Abu-Raddad acknowledges the support of Qatar National Research Fund (NPRP 9-040-3-008) who provided the main funding for generating the data provided to the GBD-IHME effort. Liesl Zuhlke is funded by the national research foundation of South Africa and the Medical Research Council of South Africa. Monica Cortinovis acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Chuanhua Yu acknowleges support from the National Natural Science Foundation of China (grant number 81773552 and grant number 81273179) Norberto Perico acknowledges that work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group. Charles Shey Wiysonge’s work is supported by the South African Medical Research Council and the National Research Foundation of South Africa (grant numbers 106035 and 108571). John J McGrath is supported by grant APP1056929 from the John Cade Fellowship from the National Health and Medical Research Council and the Danish National Research Foundation (Niels Bohr Professorship). Quique Bassat is an ICREA (Catalan Institution for Research and Advanced Studies) research professor at ISGlobal. Richard G White is funded by the UK MRC and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement that is also part of the EDCTP2 programme supported by the European Union (MR/P002404/1), the Bill & Melinda Gates Foundation (TB Modelling and Analysis Consortium: OPP1084276/OPP1135288, CORTIS: OPP1137034/OPP1151915, Vaccines: OPP1160830), and UNITAID (4214-LSHTM-Sept15; PO 8477-0-600). Rafael Tabarés-Seisdedos was supported in part by grant number PROMETEOII/2015/021 from Generalitat Valenciana and the national grant PI17/00719 from ISCIII-FEDER. Mihajlo Jakovljevic acknowleges contribution from the Serbian Ministry of Education Science and Technological Development of the Republic of Serbia (grant OI 175 014). Shariful Islam is funded by a Senior Fellowship from Institute for Physical Activity and Nutrition, Deakin University and received career transition grants from High Blood Pressure Research Council of Australia. Sonia Saxena is funded by various grants from the NIHR. Stefanos Tyrovolas was supported by the Foundation for Education and European Culture, the Sara Borrell postdoctoral program (reference number CD15/00019 from the Instituto de Salud Carlos III (ISCIII–Spain) and the Fondos Europeo de Desarrollo Regional. Stefanos was awarded with a 6 months visiting fellowship funding at IHME from M-AES (reference no. MV16/00035 from the Instituto de Salud Carlos III). S Vittal Katikreddi was funded by a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the MRC (MC_UU_12017/13 & MC_ UU_12017/15) and the Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15). Traolach S Brugha has received funding from NHS Digital UK to collect data used in this study. The work of Hamid Badali was financially supported by Mazandaran University of Medical Sciences, Sari, Iran. The work of Stefan Lorkowski is funded by the German Federal Ministry of Education and Research (nutriCARD, Grant agreement number 01EA1411A). Mariam Molokhia’s research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We also thank the countless individuals who have contributed to GBD 2016 in various capacities.
Published: 2018

10. Synergistic action of carvedilol and clomiphene in mitigating the behavioral phenotypes of letrozole-model of PCOS rats by modulating the NRF2/NFKB pathway.

Author: Akintoye OO, Ajibare AJ, Oriyomi IA, Olofinbiyi BA, Yusuf GO, Afuye DC, Babalola TK, Faturoti OE, Oludipe S, and Owoyele VB
Subjects: Animals, Female, Humans, Rats, Acetylcholine, Carvedilol pharmacology, Carvedilol therapeutic use, Catalase, Clomiphene pharmacology, Clomiphene therapeutic use, Esterases, Fertility Agents, Female pharmacology, Fertility Agents, Female therapeutic use, Letrozole pharmacology, NF-E2-Related Factor 2, Ovulation Induction, Phenotype, NF-kappa B drug effects, NF-kappa B metabolism, Infertility, Female drug therapy, Polycystic Ovary Syndrome metabolism
Abstract: Introduction: Psychiatric and cognitive impairment has been observed in premenopausal women with a hormonal disorder called polycystic ovary syndrome (PCOS). This study aimed to explore the possibility of combining pharmacological agents: Carvedilol and Clomiphene citrate, with antiestrogenic, antioxidant and anti-inflammatory properties in letrozole-induced PCOS rats., Methods: PCOS was induced in rats by the administration of letrozole (1 mg/kg) daily for 21 days. They were subsequently divided into four groups, each receiving either the vehicle or Clomiphene citrate (1 mg/kg) or Carvedilol or a combination of Clomiphene citrate and Carvedilol, respectively from days 22-36. Neurobehavioral studies were conducted on day 35 (Elevated plus maze and Y maze) and day 36 (Novel object recognition). The serum levels of the antioxidants Superoxide dismutase, Catalase, Interleukin 1B (IL-1B), and the gene expression of nuclear factor-erythroid factor 2-related factor 2 (Nrf2), Nuclear Factor k-Beta (NFKB), and acetylcholine esterase in the frontal brain homogenate was determined., Result: Both Carvedilol and the combination therapy reversed the anxiety-like behavior, while Clomiphene citrate and the combination therapy ameliorated the spatial and non-spatial memory impairment observed in PCOS rats. Carvedilol, Clomiphene citrate, and the combination therapy increased the serum concentration of SOD and Catalase and decreased the serum concentration of IL-1B. The combination therapy up-regulated the NRF-2, NFKB, and acetylcholine esterase gene expression., Conclusion: Study showed that the combination of carvedilol and clomiphene citrate has anxiolytic potential and improved cognitive functions in PCOS rats. This might have been achieved by carvedilol and clomiphene citrate's ability to modulate the cholinergic system and the Nrf2 pathway while downregulating the NFκB signaling pathway., Competing Interests: Declaration of competing interest The authors consisting of (Olabode Oluwadare Akintoye, Ayodeji Johnson Ajibare, Isaac Adeola Oriyomi, Babatunde Ajayi Olofinbiyi, Grace Oyiza Yusuf, Damilola Christanah Afuye, Temitope Kabirat Babalola, Oluwadamilola Esther Faturoti, Seun Oludipe, Victor Bamidele Owoyele) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)
Published: 2023
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11. Development, Validation, and Utilization of a Social Media Use and Mental Health Questionnaire among Middle Eastern and Western Adults: A Pilot Study from the UAE.

Author: Hegazi O, Alalalmeh S, Alfaresi A, Dashtinezhad S, Bahada A, Shahwan M, Jairoun AA, Babalola TK, and Yasin H
Subjects: Adult, Humans, Mental Health, Pilot Projects, Reproducibility of Results, Social Stigma, Surveys and Questionnaires, Social Media, Mental Disorders epidemiology, Mental Disorders psychology
Abstract: Objectives: We aimed to develop and validate a mental health stigma measurement tool for use within the social media context, utilizing the tool to assess whether the stigma shown in face-to-face interactions translates to social media, coupled with comparing whether social media use can cause the stigma among a sample of Middle Eastern and Western populations., Methods: The development and validation phase comprised a systematic process that was used to develop an assessment tool that could be used within the social media context and establish its validity and reliability. A 5-point Likert-type scale (1 = strongly disagree to 5 = strongly agree) was developed to assess mental health stigma. The anonymous questionnaire was distributed from June 2022 to August 2022 on various social media platforms and groups predominated by the two demographics of interest, enrolling 1328 participants (with only 1001 responses deemed valid). The utilization phase consisted of bivariate and multivariable analysis of the data. The cutoff points for low, medium, and high scores were the 25th, 50th, and 75th percentil, respectively., Results: The instrument comprised three dimensions: acceptance, intolerance, and digital care sentiment. In the Middle Eastern subset of participants, a higher score of intolerance (more stigma) toward mental illness was found in 72.4% of the participants, with a higher score of acceptance being 35.1% and of digital care sentiment being 46.4%. The mean scores for all the scales were as follows: intolerance (3.08 ± 0.64), acceptance (3.87 ± 0.71), and digital care sentiment (3.18 ± 0.69). For Westerners, a higher score of intolerance toward mental illness was found in 24.0% of the participants, with a higher score of acceptance being 56.8% and of digital care sentiment being 38.2%. The mean scores for all the scales were as follows: intolerance (2.28 ± 0.73), acceptance (4.21 ± 0.61), and digital care sentiment (3.08 ± 0.62). Various results were obtained regarding the effect of individual social media platforms on the different subscales., Conclusions: Stigma does follow people on social media, whether they are Middle Easterners or Westerners, although to varying degrees. The results of social media interaction and activity varied based on the group that used them, with some having an impact on one group but not the other. For these reasons, proper guidance is advised when utilizing and interacting with social media platforms.
Published: 2022
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12. Analysis of factors influencing technical efficiency of public district hospitals in KwaZulu-Natal province, South Africa.

Author: Babalola TK, Ojugbele HO, Shahwan M, and Moodley I
Subjects: Humans, South Africa, Health Expenditures, Health Facilities, Hospitals, District, Hospitals, Public
Abstract: Background: District hospitals are crucial in supporting primary health care and serve as a gateway to more specialist care through a referral system. Majority of South Africans access health care services through the public sector district health system. Given the enormous task assigned to the public district hospital within the country, this study examined factors influencing their technical efficiency., Method: Data were collected for 38 public district hospitals in KwaZulu-Natal province from 2014/15 to 2016/17. Data envelopment analysis (DEA) was used to determine the technical efficiency of the hospitals, adopting both the constant return to scale (CRS) and variable return to scale (VRS) models. Tobit regression model was used to determine factors related to the technical efficiency of the district hospitals., Results: This study showed that a significant proportion of the district hospitals were technically inefficient. The Tobit regression model identified catchment population, the proportion of inpatients treated per medical personnel, the proportion of inpatients treated per nursing personnel and expenditure per patient day equivalent as factors influencing technical efficiency of the district hospitals., Conclusion: Findings from this study suggest that the technical efficiency of the district hospitals can be enhanced through an effective referral system and improved peoples' health-seeking behaviour. In addition, a standard mix of clinical staff toward efficient service delivery and periodic cost analysis of health services with the view to saving cost and maintaining the quality of health care should be considered., (© 2022. The Author(s).)
Published: 2022
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13. Dietary diversity and impact of Moringa oleifera Lam. leaves supplemented - Diet on the nutritional status and CD4 cell counts of patients receiving antiretroviral therapy in Nigeria: A double - Blind randomized trial.

Author: Gambo A, Gqaleni N, and Babalola TK
Abstract: Background: To maintain an optimal nutritional status and immunological function in PLHIV, a diet with adequate nutrients is of utmost importance. This is a major challenge among poor populations in developing worlds like Nigeria, where malnutrition and food insecurity are endemic. This study aimed to assess the type of regular diet consumed and assess the impact of supplementation of the diet with Moringa oleifera Lam. leaves on the nutritional status and CD4 cell counts of PLHIV that are on ART in Nigeria., Method: A double-blind, randomized trial was conducted. Two hundred consented patients were randomly allocated to either the Moringa oleifera Lam. group (MOG) or the control group (COG). The FAO individual dietary diversity questionnaire was used. The regular diets of participants at baseline and six months were monitored. The measurements of weight, BMI, MUAC, and CD4 cell counts were obtained from baseline to six months of Moringa oleifera Lam. leaves supplementation., Results: One hundred and seventy-seven patients completed the six-month follow-up (89 MOG versus 88 COG). At both baseline and sixth month, the foods most commonly consumed by the participants in both MOG and COG were cereals, spices and condiments, oils, fats and palm oil, and dark green vegetables. At baseline, significantly higher consumption of legumes, nuts & seeds (p = 0.001) was observed in the MOG and higher consumption of other vegetables (p = 0.024) in COG. Consumption of cereals, roots, and tubers was significantly higher (p = 0.024; 0.045) in the COG in the sixth month. In both groups, participants were in the medium or low dietary diversity tercile. Throughout the study period, all the nutritional status variables observed were not significantly different between the two study groups [(p > 0.0001); weight; p = 0.5556; BMI; p = 0.5145; MUAC; p = 0.6456]. Over the study period, the treatment by time interaction shows a significant difference in CD4 counts by treatment group (p < 0.0001) and an estimate of fixed effects 10.33 folds greater in the MOG than COG. All tests were conducted at 95CI., Conclusion: This study revealed a poor dietary diversity amongst PLHIV. Supplementation of regular diet with Moringa oleifera Lam. leaves did not affect the nutritional status but could improve the immune response of HIV-positive adults attending the antiretroviral treatment centre in the present study area., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)
Published: 2022
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14. Health trends, inequalities and opportunities in South Africa's provinces, 1990-2019: findings from the Global Burden of Disease 2019 Study.

Author: Achoki T, Sartorius B, Watkins D, Glenn SD, Kengne AP, Oni T, Wiysonge CS, Walker A, Adetokunboh OO, Babalola TK, Bolarinwa OA, Claassens MM, Cowden RG, Day CT, Ezekannagha O, Ginindza TG, Iwu CCD, Iwu CJ, Karangwa I, Katoto PD, Kugbey N, Kuupiel D, Mahasha PW, Mashamba-Thompson TP, Mensah GA, Ndwandwe DE, Nnaji CA, Ntsekhe M, Nyirenda TE, Odhiambo JN, Oppong Asante K, Parry CDH, Pillay JD, Schutte AE, Seedat S, Sliwa K, Stein DJ, Tanser FC, Useh U, Zar HJ, Zühlke LJ, Mayosi BM, Hay SI, Murray CJL, and Naghavi M
Abstract: Background: Over the last 30 years, South Africa has experienced four 'colliding epidemics' of HIV and tuberculosis, chronic illness and mental health, injury and violence, and maternal, neonatal, and child mortality, which have had substantial effects on health and well-being. Using data from the 2019 Global Burden of Diseases, Injuries and Risk Factors Study (GBD 2019), we evaluated national and provincial health trends and progress towards important Sustainable Development Goal targets from 1990 to 2019., Methods: We analysed GBD 2019 estimates of mortality, non-fatal health loss, summary health measures and risk factor burden, comparing trends over 1990-2007 and 2007-2019. Additionally, we decomposed changes in life expectancy by cause of death and assessed healthcare system performance., Results: Across the nine provinces, inequalities in mortality and life expectancy increased over 1990-2007, largely due to differences in HIV/AIDS, then decreased over 2007-2019. Demographic change and increases in non-communicable diseases nearly doubled the number of years lived with disability between 1990 and 2019. From 1990 to 2019, risk factor burdens generally shifted from communicable and nutritional disease risks to non-communicable disease and injury risks; unsafe sex remained the top risk factor. Despite widespread improvements in healthcare system performance, the greatest gains were generally in economically advantaged provinces., Conclusions: Reductions in HIV/AIDS and related conditions have led to improved health since 2007, though most provinces still lag in key areas. To achieve health targets, provincial governments should enhance health investments and exchange of knowledge, resources and best practices alongside populations that have been left behind, especially following the COVID-19 pandemic., Competing Interests: Competing interests: AES reports personal fees from Servier, Novartis,Takeda, Omron Healthcare, and Abbott, outside the submitted work. DJS reports personal fees from Lundbeck, Takeda, Johnson & Johnson, and Servier, outside the submitted work., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)
Published: 2022
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15. A double-blind, randomized controlled trial to examine the effect of Moringa oleifera leaf powder supplementation on the immune status and anthropometric parameters of adult HIV patients on antiretroviral therapy in a resource-limited setting.

Author: Gambo A, Moodley I, Babashani M, Babalola TK, and Gqaleni N
Subjects: Humans, Male, Adult, Female, Double-Blind Method, CD4 Lymphocyte Count, Middle Aged, Nigeria, Anthropometry, Powders, HIV-1 drug effects, Resource-Limited Settings, Moringa oleifera chemistry, HIV Infections drug therapy, HIV Infections immunology, HIV Infections virology, Dietary Supplements, Plant Leaves chemistry, Viral Load drug effects
Abstract: Background: People living with HIV (PLHIV) in resource-limited settings are vulnerable to malnutrition. Nutritional interventions aimed at improving food insecurity and malnutrition, together with antiretroviral therapy (ART), could improve treatment outcomes. In Nigeria, there is a high awareness of the nutraceutical benefits of Moringa oleifera. Thus, this study aimed to evaluate the effects of Moringa oleifera leaf supplementation on the CD4 counts, viral load and anthropometric of HIV-positive adults on ART., Methods: This was a double-blind, randomized study. Two hundred HIV-positive patients were randomly allocated to either the Moringa Oleifera group (MOG) given Moringa oleifera leaf powder or the control group (COG) given a placebo. Changes in anthropometric parameters [weight; body mass index (BMI)] and CD4 cell counts were measured monthly for six months, while HIV-1 viral loads were measured at baseline and the end of the study for both groups., Results: Over the study period, the treatment by time interaction shows a significant difference in CD4 counts by treatment group (p<0.0001). A further estimate of fixed effects showed that the CD4 counts among MOG were 10.33 folds greater than COG over the study period. However, the viral load (p = 0.9558) and all the anthropometric parameters (weight; p = 0.5556 and BMI; p = 0.5145) between the two groups were not significantly different over time. All tests were conducted at 95CI., Conclusion: This study revealed that Moringa oleifera leaf supplementation was associated with increased CD4 cell counts of PLHIV on ART in a resource-limited setting. Programs in low-resource settings, such as Nigeria, should consider nutritional supplementation as part of a comprehensive approach to ensure optimal treatment outcomes in PLHIV., Competing Interests: The authors have declared that no competing interests exist.
Published: 2021
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16. Impact of Moringa Oleifera leaves supplementation on quality of life of people living with HIV: A double-blind randomized controlled trial.

Author: Gambo A, Moodley I, Babashani M, and Babalola TK
Subjects: Adult, Dietary Supplements, Humans, Plant Leaves, Quality of Life psychology, HIV Infections drug therapy, Moringa oleifera
Abstract: Purpose: Advances in antiretroviral therapy (ART) and increased interest on nutritional interventions have led to improvements in life expectancy of people living with HIV (PLHIV). These have led to greater emphasis on their quality of life (QoL). This study was aimed at exploring the effects of Moringa oleifera leaves supplementation on QoL of HIV-positive adults in Nigeria., Method: A double-blind randomized controlled trial was conducted to determine the six months' impact of consuming Moringa oleifera leaves powder as a nutritional supplement on the different domains of QoL of PLHIV that are on ART. Two hundred consented patients were randomly allocated to either Moringa Oleifera group (MOG) or control group (COG). The WHOQOL-HIV-BREF was used to assess QoL at baseline and at six-month follow-up., Results: One hundred and seventy seven patients completed the study. At six-month follow-up, a significant (p < 0.05) increase in the mean scores of all the six domains of QoL was observed in the MOG. However, in the COG, a significant increase was observed in the social relationship, environment, and spirituality/religion/personal beliefs domains. The comparison between the MOG and COG at 6 months' follow-up showed a significant mean score difference in the MOG in the physical, psychological, level of independence, and social relationships domains of QoL., Conclusion: This study shows that supplementation with Moringa oleifera leaves for PLHIV that are on ART improves the QoL domains of physical, psychological, level of independence, and social relationships. Clinical Trial Registry registration number: PACTR201811722056449., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Published: 2021
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17. Technical efficiency and productivity of public district hospitals in KwaZulu-Natal province, South Africa.

Author: Babalola TK and Moodley I
Abstract: Background: District hospitals (DHs) constitute a significant proportion of public hospitals and consume a more substantial percentage of the government's total hospital budget. With the level of resources disbursed to DHs, it is essential to ensure efficient allocation and utilization. Hence, this study set out to assess the technical efficiency and productivity of public DHs in KwaZulu- Natal province, South Africa. Methods: Data envelopment analysis (DEA) and Malmquist total factor productivity (MTFP) were used to assess technical efficiency, identify adjustments required to make inefficient facilities more efficient, and determine overall productivity growth. Input data such as medical personnel and output information such as outpatient visits were retrieved from the databases of the district health information system (DHIS), and personnel salary systems (PERSAL) for three consecutive financial years (2014/15, 2015/16 and 2016/17). A total of 38 district hospitals were included in the study. Results: The proportion of technically efficient facilities according to constant return to scale (CRS) were 12 (31.6%), 16 (42.1%) and 14 (36.8%) in 2014/15, 2015/16 and 2016/17 respectively while according to the variable return to scale (VRS) technically efficient facilities were 22 (57.9%), 19 (50.0) and 21 (55.2%) respectively for the three consecutive years. On average, the total productivity of DHs increased by 4.8 percent over the three years, which is attributed majorly to technical growth of 6.9 percent. Conclusion: This study showed that a significant proportion of the district hospitals were technically inefficiency. Also, steps that could enable more efficient use of healthcare resources to yield optimal health service delivery were recommended., (©Copyright: the Author(s).)
Published: 2020
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18. Assessing the Efficiency of Health-care Facilities in Sub-Saharan Africa: A Systematic Review.

Author: Babalola TK and Moodley I
Abstract: Background: The provision of health-care services is dependent on the effective and efficient functioning of various components of a health-care system. It is therefore important to evaluate the functioning of these various components. Hence, the aim of this study was to review studies on health-care facilities efficiency in sub-Saharan Africa (SSA) with respect to the methodologies used as well as outcomes and factors influencing efficiency., Methods: The review was conducted through a comprehensive search of electronic databases which included PubMed, Web of science, academic search complete via EBSCOhost, Science Direct, and Google scholar. A search was also conducted by looking into citations in the reference list of selected articles and through gray literature. Studies were screened by examining their titles, abstracts, and full-text based on stated inclusion and exclusion criteria. The concurrent screening and data extraction were conducted by the two authors., Results: A total of 40 studies were shortlisted for the review. The majority (90.0%) of the studies employed the data envelopment analysis technique for their efficiency measurements. The input and output variables utilized by most of the studies were predominantly human resources and health-related services respectively. The outcome from majority of the studies showed that less than 40% of the studied facilities were efficient. The leading influencing factors reported by the studies were catchment population, facility ownership, and location., Conclusions: The review showed that there was a marked degree of inefficiency across the health-care facilities. Consequently, due to severe resource constraints facing SSA, there is a need to determine how to use the available resources optimally to improve health systems performance., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2020.)
Published: 2020
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19. Protocol for Investigating the Technical Efficiency of District Hospitals in the Public Health Sector of KwaZulu-Natal, South Africa.

Author: Babalola TK and Moodley I
Abstract: Background: The central objective of policy makers and health managers is efficiency in the delivery of health care. With frequent reports of global economic crises, there is a need to continuously measure the performance of various sectors of the health care system. This can inform the decision-making process toward allocating scarce resources with the aim of maximizing output., Objective: The aim of this study is to determine the technical efficiency (TE) of public sector district hospitals in the province of KwaZulu-Natal, South Africa to provide information that will assist in policy formulation that may further assist in more efficient resource allocation decisions., Methods: This is a health system research based on a quantitative research approach. All 38 public district hospitals in the 11 municipalities of the province will be included in this study. The data for the study will include inputs from hospitals' operations that contribute toward subsequent outputs. The input data will include information such as the number of health professionals (doctors, nurses, and other personnel) and number of hospital beds, whereas the output data will include information such as outpatient visits and number of admissions or discharge. Other data categories to be included will be determined by data availability and will be uniform for all facilities. Data for each facility for a 3-year period from 2014 to 2017 will be obtained from databases of the district health information, basic accounting, and personnel salary systems. On the basis of the data obtained, a model will be developed that can be used to assess how TE of public districts hospitals may be improved. TE will be determined using Data Envelopment Analysis, and factors influencing efficiency will be computed using StataCorp statistical package., Results: As of February 2019, the study is at the data collection, data input, and analysis stages. The results are expected to be available from the second quarter of 2019., Conclusions: Findings from this study can add to tools available to policy makers, health planners, and managers in making decisions about resource allocation in health care systems. Moreover, these findings will be disseminated electronically and in print., International Registered Report Identifier (irrid): DERR1-10.2196/12037., (©Tesleem K Babalola, Indres Moodley. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 14.03.2019.)
Published: 2019
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19 results on '"Babalola, Tk"'

1. Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

2. Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990–2050

3. Mapping geographical inequalities in oral rehydration therapy coverage in low-income and middle-income countries, 2000-17

4. Health sector spending and spending on HIV/AIDS, tuberculosis, and malaria, and development assistance for health: progress towards Sustainable Development Goal 3

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

6. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the Global Burden of Disease Study 2016

7. Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

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

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

10. Synergistic action of carvedilol and clomiphene in mitigating the behavioral phenotypes of letrozole-model of PCOS rats by modulating the NRF2/NFKB pathway.

11. Development, Validation, and Utilization of a Social Media Use and Mental Health Questionnaire among Middle Eastern and Western Adults: A Pilot Study from the UAE.

12. Analysis of factors influencing technical efficiency of public district hospitals in KwaZulu-Natal province, South Africa.

13. Dietary diversity and impact of Moringa oleifera Lam. leaves supplemented - Diet on the nutritional status and CD4 cell counts of patients receiving antiretroviral therapy in Nigeria: A double - Blind randomized trial.

14. Health trends, inequalities and opportunities in South Africa's provinces, 1990-2019: findings from the Global Burden of Disease 2019 Study.

15. A double-blind, randomized controlled trial to examine the effect of Moringa oleifera leaf powder supplementation on the immune status and anthropometric parameters of adult HIV patients on antiretroviral therapy in a resource-limited setting.

16. Impact of Moringa Oleifera leaves supplementation on quality of life of people living with HIV: A double-blind randomized controlled trial.

17. Technical efficiency and productivity of public district hospitals in KwaZulu-Natal province, South Africa.

18. Assessing the Efficiency of Health-care Facilities in Sub-Saharan Africa: A Systematic Review.

19. Protocol for Investigating the Technical Efficiency of District Hospitals in the Public Health Sector of KwaZulu-Natal, South Africa.

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