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1. Mortality surveillance in Brazil: factors associated with certification of unspecified external cause of death

Author: Soares Filho, AM, Duarte, E, Adair, T, Diaz Bermudez, XP, Merchan-Hamann, E, Soares Filho, AM, Duarte, E, Adair, T, Diaz Bermudez, XP, and Merchan-Hamann, E
Abstract: This article aims to analyze the association between characteristics of death - type of certifier and place of death - and the odds of an external cause death being certified as unspecified in Brazil. Cross-sectional study of deaths due to external causes from the Mortality Information System, 2017. Unspecified external cause (UEC) is the outcome variable in the models. Type of certifier physician, place of death and the interaction of these variables were the explanatory variables. Confounders were controlled by multiple logistic regression. UEC were the initial underlying cause for 22% of the 159,720 deaths from external causes in Brazil and 31% of hospital deaths issued by coroners. After adjustment for confounders, the odds of UEC in a hospital death certified by a coroner was 98% greater (OR=1.98; 95%CI: 1.53; 2.56) than in a home/street death issued by another certifier. This was greater than the odds for certifications by coroners (OR=1.23; 95%CI: 1.14; 1.33) and hospital deaths (OR=1.44; 95%CI: 1.32; 1.58). External causes certified by coroners and/or occurring in hospitals have a higher presence of UEC than other deaths; and indicate the need for coordinated initiatives by the health and public security sectors.
Published: 2022

2. Burden of injury along the development spectrum: associations between the Socio-demographic Index and disability-adjusted life year estimates from the Global Burden of Disease Study 2017

Author: Haagsma, JA, James, SL, Castle, CD, Dingels, Z, Fox, JT, Hamilton, EB, Liu, Z, Lucchesi, LR, Roberts, NLS, Sylte, DO, Adebayo, OM, Ahmadi, A, Ahmed, MB, Aichour, MTE, Alahdab, F, Alghnam, SA, Aljunid, SM, Al-Raddadi, RM, Alsharif, U, Altirkawi, K, Anjomshoa, M, Antonio, CAT, Appiah, SCY, Aremu, O, Arora, A, Asayesh, H, Assadi, R, Awasthi, A, Ayala Quintanilla, BP, Balalla, S, Banstola, A, Barker-Collo, SL, Baernighausen, TW, Bazargan-Hejazi, S, Bedi, N, Behzadifar, M, Benjet, C, Bennett, DA, Bensenor, IM, Bhaumik, S, Bhutta, ZA, Bijani, A, Borges, G, Borschmann, R, Bose, D, Boufous, S, Brazinova, A, Rincon, JCC, Cardenas, R, Carrero, JJ, Carvalho, F, Castaneda-Orjuela, CA, Catala-Lopez, F, Choi, J-YJ, Christopher, DJ, Crowe, CS, Dalal, K, Daryani, A, Davitoiu, DV, Degenhardt, L, De Leo, D, De Neve, J-W, Deribe, K, Dessie, GA, deVeber, GA, Dharmaratne, SD, Linh, PD, Dolan, KA, Driscoll, TR, Dubey, M, El-Khatib, Z, Ellingsen, CL, Zaki, MES, Endries, AY, Eskandarieh, S, Faro, A, Fereshtehnejad, S-M, Fernandes, E, Filip, I, Fischer, F, Franklin, RC, Fukumoto, T, Gezae, KE, Gill, TK, Goulart, AC, Grada, A, Guo, Y, Gupta, R, Bidgoli, HH, Haj-Mirzaian, A, Hamadeh, RR, Hamidi, S, Maria Haro, J, Hassankhani, H, Hassen, HY, Havmoeller, R, Hendrie, D, Henok, A, Hijar, M, Hole, MK, Rad, EH, Hossain, N, Hostiuc, S, Hu, G, Igumbor, EU, Ilesanmi, OS, Irvani, SSN, Islam, SMS, Ivers, RQ, Jacobsen, KH, Jahanmehr, N, Jakovljevic, M, Jayatilleke, AU, Jha, RP, Jonas, JB, Shushtari, ZJ, Jozwiak, JJ, Jurisson, M, Kabir, A, Kalani, R, Kasaeian, A, Kelbore, AG, Kengne, AP, Khader, YS, Khafaie, MA, Khalid, N, Khan, EA, Khoja, AT, Kiadaliri, AA, Kim, Y-E, Kim, D, Kisa, A, Koyanagi, A, Defo, BK, Bicer, BK, Kumar, M, Lalloo, R, Lam, H, Lami, FH, Lansingh, VC, Leasher, JL, Li, S, Linn, S, Lunevicius, R, Machado, FR, Abd El Razek, HM, Abd El Razek, MM, Mahotra, NB, Majdan, M, Majeed, A, Malekzadeh, R, Malik, MA, Malta, DC, Manda, A-L, Mansournia, MA, Massenburg, BB, Maulik, PK, Meheretu, HAA, Mehndiratta, MM, Melese, A, Mendoza, W, Mengesha, MM, Meretoja, TJ, Meretoja, A, Mestrovic, T, Miazgowski, T, Miller, TR, Mini, GK, Mirrakhimov, EM, Moazen, B, Mezerji, NMG, Mohammadibakhsh, R, Mohammed, S, Molokhia, M, Monasta, L, Mondello, S, Montero-Zamora, PA, Moodley, Y, Moosazadeh, M, Moradi, G, Moradi-Lakeh, M, Morawska, L, Moreno Velasquez, I, Morrison, SD, Moschos, MM, Mousavi, SM, Murthy, S, Musa, KI, Naik, G, Najafi, F, Nangia, V, Nascimento, BR, Ndwandwe, DE, Negoi, I, Trang, HN, Son, HN, Long, HN, Huong, LTN, Ningrum, DNA, Nirayo, YL, Ofori-Asenso, R, Ogbo, FA, Oh, I-H, Oladimeji, O, Olagunju, AT, Olagunju, TO, Olivares, PR, Orpana, HM, Otstavnov, SS, Mahesh, PA, Pakhale, S, Park, E-K, Patton, GC, Pesudovs, K, Phillips, MR, Polinder, S, Prakash, S, Radfar, A, Rafay, A, Rafiei, A, Rahimi, S, Rahimi-Movaghar, V, Rahman, MA, Rai, RK, Ramezanzadeh, K, Rawaf, S, Rawaf, DL, Renzaho, AMN, Resnikoff, S, Rezaeian, S, Roever, L, Ronfani, L, Roshandel, G, Sabde, YD, Saddik, B, Salamati, P, Salimi, Y, Salz, I, Samy, AM, Sanabria, J, Riera, LS, Milicevic, MMS, Satpathy, M, Sawhney, M, Sawyer, SM, Saxena, S, Saylan, M, Schneider, IJC, Schwebel, DC, Seedat, S, Sepanlou, SG, Shaikh, MA, Shams-Beyranvand, M, Shamsizadeh, M, Sharif-Alhoseini, M, Sheikh, A, Shen, J, Shigematsu, M, Shiri, R, Shiue, I, Silva, JP, Singh, JA, Sinha, DN, Soares Filho, AM, Soriano, JB, Soshnikov, S, Soyiri, IN, Starodubov, V, Stein, DJ, Stokes, MA, Sufiyan, MB, Sunshine, JE, Sykes, BL, Tabares-Seisdedos, R, Tabb, KM, Tehrani-Banihashemi, A, Tessema, GA, Thakur, JS, Khanh, BT, Bach, XT, Car, LT, Uthman, OA, Uzochukwu, BSC, Valdez, PR, Varavikova, E, Nogales Vasconcelos, AM, Venketasubramanian, N, Violante, FS, Vlassov, V, Waheed, Y, Wang, Y-P, Wijeratne, T, Winkler, AS, Yadav, P, Yano, Y, Yenesew, MA, Yip, P, Yisma, E, Yonemoto, N, Younis, MZ, Yu, C, Zafar, S, Zaidi, Z, Bin Zaman, S, Zamani, M, Zhao, Y, Zodpey, S, Hay, S, Lopez, AD, Mokdad, AH, Vos, T, Haagsma, JA, James, SL, Castle, CD, Dingels, Z, Fox, JT, Hamilton, EB, Liu, Z, Lucchesi, LR, Roberts, NLS, Sylte, DO, Adebayo, OM, Ahmadi, A, Ahmed, MB, Aichour, MTE, Alahdab, F, Alghnam, SA, Aljunid, SM, Al-Raddadi, RM, Alsharif, U, Altirkawi, K, Anjomshoa, M, Antonio, CAT, Appiah, SCY, Aremu, O, Arora, A, Asayesh, H, Assadi, R, Awasthi, A, Ayala Quintanilla, BP, Balalla, S, Banstola, A, Barker-Collo, SL, Baernighausen, TW, Bazargan-Hejazi, S, Bedi, N, Behzadifar, M, Benjet, C, Bennett, DA, Bensenor, IM, Bhaumik, S, Bhutta, ZA, Bijani, A, Borges, G, Borschmann, R, Bose, D, Boufous, S, Brazinova, A, Rincon, JCC, Cardenas, R, Carrero, JJ, Carvalho, F, Castaneda-Orjuela, CA, Catala-Lopez, F, Choi, J-YJ, Christopher, DJ, Crowe, CS, Dalal, K, Daryani, A, Davitoiu, DV, Degenhardt, L, De Leo, D, De Neve, J-W, Deribe, K, Dessie, GA, deVeber, GA, Dharmaratne, SD, Linh, PD, Dolan, KA, Driscoll, TR, Dubey, M, El-Khatib, Z, Ellingsen, CL, Zaki, MES, Endries, AY, Eskandarieh, S, Faro, A, Fereshtehnejad, S-M, Fernandes, E, Filip, I, Fischer, F, Franklin, RC, Fukumoto, T, Gezae, KE, Gill, TK, Goulart, AC, Grada, A, Guo, Y, Gupta, R, Bidgoli, HH, Haj-Mirzaian, A, Hamadeh, RR, Hamidi, S, Maria Haro, J, Hassankhani, H, Hassen, HY, Havmoeller, R, Hendrie, D, Henok, A, Hijar, M, Hole, MK, Rad, EH, Hossain, N, Hostiuc, S, Hu, G, Igumbor, EU, Ilesanmi, OS, Irvani, SSN, Islam, SMS, Ivers, RQ, Jacobsen, KH, Jahanmehr, N, Jakovljevic, M, Jayatilleke, AU, Jha, RP, Jonas, JB, Shushtari, ZJ, Jozwiak, JJ, Jurisson, M, Kabir, A, Kalani, R, Kasaeian, A, Kelbore, AG, Kengne, AP, Khader, YS, Khafaie, MA, Khalid, N, Khan, EA, Khoja, AT, Kiadaliri, AA, Kim, Y-E, Kim, D, Kisa, A, Koyanagi, A, Defo, BK, Bicer, BK, Kumar, M, Lalloo, R, Lam, H, Lami, FH, Lansingh, VC, Leasher, JL, Li, S, Linn, S, Lunevicius, R, Machado, FR, Abd El Razek, HM, Abd El Razek, MM, Mahotra, NB, Majdan, M, Majeed, A, Malekzadeh, R, Malik, MA, Malta, DC, Manda, A-L, Mansournia, MA, Massenburg, BB, Maulik, PK, Meheretu, HAA, Mehndiratta, MM, Melese, A, Mendoza, W, Mengesha, MM, Meretoja, TJ, Meretoja, A, Mestrovic, T, Miazgowski, T, Miller, TR, Mini, GK, Mirrakhimov, EM, Moazen, B, Mezerji, NMG, Mohammadibakhsh, R, Mohammed, S, Molokhia, M, Monasta, L, Mondello, S, Montero-Zamora, PA, Moodley, Y, Moosazadeh, M, Moradi, G, Moradi-Lakeh, M, Morawska, L, Moreno Velasquez, I, Morrison, SD, Moschos, MM, Mousavi, SM, Murthy, S, Musa, KI, Naik, G, Najafi, F, Nangia, V, Nascimento, BR, Ndwandwe, DE, Negoi, I, Trang, HN, Son, HN, Long, HN, Huong, LTN, Ningrum, DNA, Nirayo, YL, Ofori-Asenso, R, Ogbo, FA, Oh, I-H, Oladimeji, O, Olagunju, AT, Olagunju, TO, Olivares, PR, Orpana, HM, Otstavnov, SS, Mahesh, PA, Pakhale, S, Park, E-K, Patton, GC, Pesudovs, K, Phillips, MR, Polinder, S, Prakash, S, Radfar, A, Rafay, A, Rafiei, A, Rahimi, S, Rahimi-Movaghar, V, Rahman, MA, Rai, RK, Ramezanzadeh, K, Rawaf, S, Rawaf, DL, Renzaho, AMN, Resnikoff, S, Rezaeian, S, Roever, L, Ronfani, L, Roshandel, G, Sabde, YD, Saddik, B, Salamati, P, Salimi, Y, Salz, I, Samy, AM, Sanabria, J, Riera, LS, Milicevic, MMS, Satpathy, M, Sawhney, M, Sawyer, SM, Saxena, S, Saylan, M, Schneider, IJC, Schwebel, DC, Seedat, S, Sepanlou, SG, Shaikh, MA, Shams-Beyranvand, M, Shamsizadeh, M, Sharif-Alhoseini, M, Sheikh, A, Shen, J, Shigematsu, M, Shiri, R, Shiue, I, Silva, JP, Singh, JA, Sinha, DN, Soares Filho, AM, Soriano, JB, Soshnikov, S, Soyiri, IN, Starodubov, V, Stein, DJ, Stokes, MA, Sufiyan, MB, Sunshine, JE, Sykes, BL, Tabares-Seisdedos, R, Tabb, KM, Tehrani-Banihashemi, A, Tessema, GA, Thakur, JS, Khanh, BT, Bach, XT, Car, LT, Uthman, OA, Uzochukwu, BSC, Valdez, PR, Varavikova, E, Nogales Vasconcelos, AM, Venketasubramanian, N, Violante, FS, Vlassov, V, Waheed, Y, Wang, Y-P, Wijeratne, T, Winkler, AS, Yadav, P, Yano, Y, Yenesew, MA, Yip, P, Yisma, E, Yonemoto, N, Younis, MZ, Yu, C, Zafar, S, Zaidi, Z, Bin Zaman, S, Zamani, M, Zhao, Y, Zodpey, S, Hay, S, Lopez, AD, Mokdad, AH, and Vos, T
Abstract: BACKGROUND: The epidemiological transition of non-communicable diseases replacing infectious diseases as the main contributors to disease burden has been well documented in global health literature. Less focus, however, has been given to the relationship between sociodemographic changes and injury. The aim of this study was to examine the association between disability-adjusted life years (DALYs) from injury for 195 countries and territories at different levels along the development spectrum between 1990 and 2017 based on the Global Burden of Disease (GBD) 2017 estimates. METHODS: Injury mortality was estimated using the GBD mortality database, corrections for garbage coding and CODEm-the cause of death ensemble modelling tool. Morbidity estimation was based on surveys and inpatient and outpatient data sets for 30 cause-of-injury with 47 nature-of-injury categories each. The Socio-demographic Index (SDI) is a composite indicator that includes lagged income per capita, average educational attainment over age 15 years and total fertility rate. RESULTS: For many causes of injury, age-standardised DALY rates declined with increasing SDI, although road injury, interpersonal violence and self-harm did not follow this pattern. Particularly for self-harm opposing patterns were observed in regions with similar SDI levels. For road injuries, this effect was less pronounced. CONCLUSIONS: The overall global pattern is that of declining injury burden with increasing SDI. However, not all injuries follow this pattern, which suggests multiple underlying mechanisms influencing injury DALYs. There is a need for a detailed understanding of these patterns to help to inform national and global efforts to address injury-related health outcomes across the development spectrum.
Published: 2020

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

Author: James, SL, Theadom, A, Ellenbogen, RG, Bannick, MS, Mountjoy-Venning, WC, Lucchesi, LR, Abbasi, N, Abdulkader, R, Abraha, HN, Adsuar, JC, Afarideh, M, Agrawal, S, Ahmadi, A, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Akinyemi, RO, Akseer, N, Alahdab, F, Alebel, A, Alghnam, SA, Ali, BA, Alsharif, U, Altirkawi, K, Andrei, CL, Anjomshoa, M, Ansari, H, Ansha, MG, Antonio, CAT, Appiah, SCY, Ariani, F, Asefa, NG, Asgedom, SW, Atique, S, Awasthi, A, Quintanilla, BPA, Ayuk, TB, Azzopardi, PS, Badali, H, Badawi, A, Balalla, S, Banstola, A, Barker-Collo, SL, Barnighausen, TW, Bedi, N, Behzadifar, M, Bekele, BB, Belachew, AB, Belay, YA, Bennett, DA, Bensenor, IM, Berhane, A, Beuran, M, Bhalla, A, Bhaumik, S, Bhutta, ZA, Biadgo, B, Biffino, M, Bijani, A, Bililign, N, Birungi, C, Boufous, S, Brazinova, A, Brown, AW, Car, M, Cardenas, R, Carrero, JJ, Carvalho, F, Castaneda-Orjuela, CA, Catala-Lopez, F, Chaiah, Y, Champs, AP, Chang, J-C, Choi, J-YJ, Christopher, DJ, Cooper, C, Crowe, CS, Dandona, L, Dandona, R, Daryani, A, Davitoiu, DV, Degefa, MG, Demoz, GT, Deribe, K, Djalalinia, S, Huyen, PD, Doku, DT, Drake, TM, Dubey, M, Dubljanin, E, El-Khatib, Z, Eskandarieh, S, Esteghamati, A, Esteghamati, S, Faro, A, Farzadfar, F, Farzaei, MH, Fereshtehnejad, S-M, Fernandes, E, Feyissa, GT, Filip, I, Fischer, F, Fukumoto, T, Ganji, M, Gankpe, FG, Gebre, AK, Gebrehiwot, TT, Gezae, KE, Gopalkrishna, G, Goulart, AC, Haagsma, JA, Haj-Mirzaian, A, Hamadeh, RR, Hamidi, S, Haro, JM, Hassankhani, H, Hassen, HY, Havmoeller, R, Hawley, C, Hay, SI, Hegazy, MI, Hendrie, D, Henok, A, Hibstu, DT, Hoffman, HJ, Hole, MK, Rad, EH, Hosseini, SM, Hostiuc, S, Hu, G, Hussen, MA, Ilesanmi, OS, Irvani, SN, Jakovljevic, M, Jayaraman, S, Jha, RP, Jonas, JB, Jones, KM, Shushtari, ZJ, Jozwiak, JJ, Jurisson, M, Kabir, A, Kahsay, A, Kahssay, M, Kalani, R, Karch, A, Kasaeian, A, Kassa, GM, Kassa, TD, Kassa, ZY, Kengne, AP, Khader, YS, Khafaie, MA, Khalid, N, Khalil, I, Khan, EA, Khan, MS, Khang, Y-H, Khazaie, H, Khoja, AT, Khubchandani, J, Kiadaliri, AA, Kim, D, Kim, Y-E, Kisa, A, Koyanagi, A, Krohn, KJ, Defo, BK, Bicer, BK, Kumar, GA, Kumar, M, Lalloo, R, Lami, FH, Lansingh, VC, Laryea, DO, Latifi, A, Leshargie, CT, Levi, M, Li, S, Liben, ML, Lotufo, PA, Lunevicius, R, Mahotra, NB, Majdan, M, Majeed, A, Malekzadeh, R, Manda, A-L, Mansournia, MA, Massenburg, BB, Mate, KKV, Mehndiratta, MM, Mehta, V, Meles, H, Melese, A, Memiah, PTN, Mendoza, W, Mengistu, G, Meretoja, A, Meretoja, TJ, Mestrovic, T, Miazgowski, T, Miller, TR, Mini, GK, Mirica, A, Mirrakhimov, EM, Moazen, B, Mohammadi, M, Mokdad, AH, Molokhia, M, Monasta, L, Mondello, S, Moosazadeh, M, Moradi, G, Moradi, M, Moradi-Lakeh, M, Moradinazar, M, Morrison, SD, Moschos, MM, Mousavi, SM, Murthy, S, Musa, KI, Mustafa, G, Naghavi, M, Naik, G, Najafi, F, Nangia, V, Nascimento, BR, Negoi, I, Trang, HN, Nichols, E, Ningrum, DNA, Nirayo, YL, Nyasulu, PS, Ofori-Asenso, R, Ogbo, FA, Oh, I-H, Okoro, A, Olagunju, AT, Olagunju, TO, Olivares, PR, Otstavnov, SS, Owolabi, MO, Mahesh, PA, Pakhale, S, Pandey, AR, Pesudovs, K, Pinilla-Monsalve, GD, Polinder, S, Poustchi, H, Prakash, S, Qorbani, M, Radfar, A, Rafay, A, Rafiei, A, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rahman, MA, Rai, RK, Rajati, F, Ram, U, Rawaf, DL, Rawaf, S, Reiner, RC, Reis, C, Renzaho, AMN, Resnikoff, S, Rezaei, S, Rezaeian, S, Roever, L, Ronfani, L, Roshandel, G, Roy, N, Ruhago, GM, Saddik, B, Safari, H, Safiri, S, Sahraian, MA, Salamati, P, Saldanha, RDF, Samy, AM, Sanabria, J, Santos, JV, Milicevic, MMMS, Sartorius, B, Satpathy, M, Schneider, IJC, Schwebel, DC, Sepanlou, SG, Shabaninejad, H, Shaikh, MAA, Shams-Beyranvand, M, Sharif, M, Sharif-Alhoseini, M, Islam, SMS, She, J, Sheikh, A, Shen, J, Sheth, KN, Shibuya, K, Shiferaw, MS, Shigematsu, M, Shiri, R, Shiue, I, Shoman, H, Siabani, S, Siddiqi, TJ, Silva, JP, Silveira, DGA, Sinha, DN, Smith, M, Soares Filho, AM, Sobhani, S, Soofi, M, Soriano, JB, Soyiri, IN, Stein, DJ, Stokes, MA, Sufiyan, MB, Sunguya, BF, Sunshine, JE, Sykes, BL, Szoeke, CEI, Tabares-Seisdedos, R, Ao, BJT, Tehrani-Banihashemi, A, Tekle, MG, Temsah, M-H, Temsah, O, Topor-Madry, R, Tortajada-Girbes, M, Bach, XT, Khanh, BT, Car, LT, Ukwaja, KN, Ullah, I, Usman, MS, Uthman, OA, Valdez, PR, Vasankari, TJ, Venketasubramanian, N, Violante, FS, Wagnew, FS, Waheed, Y, Wang, Y-P, Weldegwergs, KG, Werdecker, A, Wijeratne, T, Winkler, AS, Wyper, GMA, Yano, Y, Yaseri, M, Yasin, YJ, Ye, P, Yimer, EM, Yip, P, Yisma, E, Yonemoto, N, Yoon, S-J, Yost, MG, Younis, MZ, Yousefifard, M, Yu, C, Zaidi, Z, Bin Zaman, S, Zamani, M, Zenebe, ZM, Zodpey, S, Feigin, VL, Vos, T, Murray, CJL, James, SL, Theadom, A, Ellenbogen, RG, Bannick, MS, Mountjoy-Venning, WC, Lucchesi, LR, Abbasi, N, Abdulkader, R, Abraha, HN, Adsuar, JC, Afarideh, M, Agrawal, S, Ahmadi, A, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Akinyemi, RO, Akseer, N, Alahdab, F, Alebel, A, Alghnam, SA, Ali, BA, Alsharif, U, Altirkawi, K, Andrei, CL, Anjomshoa, M, Ansari, H, Ansha, MG, Antonio, CAT, Appiah, SCY, Ariani, F, Asefa, NG, Asgedom, SW, Atique, S, Awasthi, A, Quintanilla, BPA, Ayuk, TB, Azzopardi, PS, Badali, H, Badawi, A, Balalla, S, Banstola, A, Barker-Collo, SL, Barnighausen, TW, Bedi, N, Behzadifar, M, Bekele, BB, Belachew, AB, Belay, YA, Bennett, DA, Bensenor, IM, Berhane, A, Beuran, M, Bhalla, A, Bhaumik, S, Bhutta, ZA, Biadgo, B, Biffino, M, Bijani, A, Bililign, N, Birungi, C, Boufous, S, Brazinova, A, Brown, AW, Car, M, Cardenas, R, Carrero, JJ, Carvalho, F, Castaneda-Orjuela, CA, Catala-Lopez, F, Chaiah, Y, Champs, AP, Chang, J-C, Choi, J-YJ, Christopher, DJ, Cooper, C, Crowe, CS, Dandona, L, Dandona, R, Daryani, A, Davitoiu, DV, Degefa, MG, Demoz, GT, Deribe, K, Djalalinia, S, Huyen, PD, Doku, DT, Drake, TM, Dubey, M, Dubljanin, E, El-Khatib, Z, Eskandarieh, S, Esteghamati, A, Esteghamati, S, Faro, A, Farzadfar, F, Farzaei, MH, Fereshtehnejad, S-M, Fernandes, E, Feyissa, GT, Filip, I, Fischer, F, Fukumoto, T, Ganji, M, Gankpe, FG, Gebre, AK, Gebrehiwot, TT, Gezae, KE, Gopalkrishna, G, Goulart, AC, Haagsma, JA, Haj-Mirzaian, A, Hamadeh, RR, Hamidi, S, Haro, JM, Hassankhani, H, Hassen, HY, Havmoeller, R, Hawley, C, Hay, SI, Hegazy, MI, Hendrie, D, Henok, A, Hibstu, DT, Hoffman, HJ, Hole, MK, Rad, EH, Hosseini, SM, Hostiuc, S, Hu, G, Hussen, MA, Ilesanmi, OS, Irvani, SN, Jakovljevic, M, Jayaraman, S, Jha, RP, Jonas, JB, Jones, KM, Shushtari, ZJ, Jozwiak, JJ, Jurisson, M, Kabir, A, Kahsay, A, Kahssay, M, Kalani, R, Karch, A, Kasaeian, A, Kassa, GM, Kassa, TD, Kassa, ZY, Kengne, AP, Khader, YS, Khafaie, MA, Khalid, N, Khalil, I, Khan, EA, Khan, MS, Khang, Y-H, Khazaie, H, Khoja, AT, Khubchandani, J, Kiadaliri, AA, Kim, D, Kim, Y-E, Kisa, A, Koyanagi, A, Krohn, KJ, Defo, BK, Bicer, BK, Kumar, GA, Kumar, M, Lalloo, R, Lami, FH, Lansingh, VC, Laryea, DO, Latifi, A, Leshargie, CT, Levi, M, Li, S, Liben, ML, Lotufo, PA, Lunevicius, R, Mahotra, NB, Majdan, M, Majeed, A, Malekzadeh, R, Manda, A-L, Mansournia, MA, Massenburg, BB, Mate, KKV, Mehndiratta, MM, Mehta, V, Meles, H, Melese, A, Memiah, PTN, Mendoza, W, Mengistu, G, Meretoja, A, Meretoja, TJ, Mestrovic, T, Miazgowski, T, Miller, TR, Mini, GK, Mirica, A, Mirrakhimov, EM, Moazen, B, Mohammadi, M, Mokdad, AH, Molokhia, M, Monasta, L, Mondello, S, Moosazadeh, M, Moradi, G, Moradi, M, Moradi-Lakeh, M, Moradinazar, M, Morrison, SD, Moschos, MM, Mousavi, SM, Murthy, S, Musa, KI, Mustafa, G, Naghavi, M, Naik, G, Najafi, F, Nangia, V, Nascimento, BR, Negoi, I, Trang, HN, Nichols, E, Ningrum, DNA, Nirayo, YL, Nyasulu, PS, Ofori-Asenso, R, Ogbo, FA, Oh, I-H, Okoro, A, Olagunju, AT, Olagunju, TO, Olivares, PR, Otstavnov, SS, Owolabi, MO, Mahesh, PA, Pakhale, S, Pandey, AR, Pesudovs, K, Pinilla-Monsalve, GD, Polinder, S, Poustchi, H, Prakash, S, Qorbani, M, Radfar, A, Rafay, A, Rafiei, A, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rahman, MA, Rai, RK, Rajati, F, Ram, U, Rawaf, DL, Rawaf, S, Reiner, RC, Reis, C, Renzaho, AMN, Resnikoff, S, Rezaei, S, Rezaeian, S, Roever, L, Ronfani, L, Roshandel, G, Roy, N, Ruhago, GM, Saddik, B, Safari, H, Safiri, S, Sahraian, MA, Salamati, P, Saldanha, RDF, Samy, AM, Sanabria, J, Santos, JV, Milicevic, MMMS, Sartorius, B, Satpathy, M, Schneider, IJC, Schwebel, DC, Sepanlou, SG, Shabaninejad, H, Shaikh, MAA, Shams-Beyranvand, M, Sharif, M, Sharif-Alhoseini, M, Islam, SMS, She, J, Sheikh, A, Shen, J, Sheth, KN, Shibuya, K, Shiferaw, MS, Shigematsu, M, Shiri, R, Shiue, I, Shoman, H, Siabani, S, Siddiqi, TJ, Silva, JP, Silveira, DGA, Sinha, DN, Smith, M, Soares Filho, AM, Sobhani, S, Soofi, M, Soriano, JB, Soyiri, IN, Stein, DJ, Stokes, MA, Sufiyan, MB, Sunguya, BF, Sunshine, JE, Sykes, BL, Szoeke, CEI, Tabares-Seisdedos, R, Ao, BJT, Tehrani-Banihashemi, A, Tekle, MG, Temsah, M-H, Temsah, O, Topor-Madry, R, Tortajada-Girbes, M, Bach, XT, Khanh, BT, Car, LT, Ukwaja, KN, Ullah, I, Usman, MS, Uthman, OA, Valdez, PR, Vasankari, TJ, Venketasubramanian, N, Violante, FS, Wagnew, FS, Waheed, Y, Wang, Y-P, Weldegwergs, KG, Werdecker, A, Wijeratne, T, Winkler, AS, Wyper, GMA, Yano, Y, Yaseri, M, Yasin, YJ, Ye, P, Yimer, EM, Yip, P, Yisma, E, Yonemoto, N, Yoon, S-J, Yost, MG, Younis, MZ, Yousefifard, M, Yu, C, Zaidi, Z, Bin Zaman, S, Zamani, M, Zenebe, ZM, Zodpey, S, Feigin, VL, Vos, T, and Murray, CJL
Abstract: BACKGROUND: Traumatic brain injury (TBI) and spinal cord injury (SCI) are increasingly recognised as global health priorities in view of the preventability of most injuries and the complex and expensive medical care they necessitate. We aimed to measure the incidence, prevalence, and years of life lived with disability (YLDs) for TBI and SCI from all causes of injury in every country, to describe how these measures have changed between 1990 and 2016, and to estimate the proportion of TBI and SCI cases caused by different types of injury. METHODS: We used results from the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016 to measure the global, regional, and national burden of TBI and SCI by age and sex. We measured the incidence and prevalence of all causes of injury requiring medical care in inpatient and outpatient records, literature studies, and survey data. By use of clinical record data, we estimated the proportion of each cause of injury that required medical care that would result in TBI or SCI being considered as the nature of injury. We used literature studies to establish standardised mortality ratios and applied differential equations to convert incidence to prevalence of long-term disability. Finally, we applied GBD disability weights to calculate YLDs. We used a Bayesian meta-regression tool for epidemiological modelling, used cause-specific mortality rates for non-fatal estimation, and adjusted our results for disability experienced with comorbid conditions. We also analysed results on the basis of the Socio-demographic Index, a compound measure of income per capita, education, and fertility. FINDINGS: In 2016, there were 27·08 million (95% uncertainty interval [UI] 24·30-30·30 million) new cases of TBI and 0·93 million (0·78-1·16 million) new cases of SCI, with age-standardised incidence rates of 369 (331-412) per 100 000 population for TBI and 13 (11-16) per 100 000 for SCI. In 2016, the number of prevalent cases of TBI was 55·50
Published: 2019

4. Global Mortality From Firearms, 1990-2016

Author: Global Burden of Disease 2016 Injury Collaborators, Naghavi M, Marczak LB, Kutz M, Shackelford KA, Arora M, Miller-Petrie M, Aichour MTE, Akseer N, Al-Raddadi RM, Alam K, Alghnam SA, Antonio CAT, Aremu O, Arora A, Asadi-Lari M, Assadi R, Atey TM, Avila-Burgos L, Awasthi A, Ayala Quintanilla BP, Barker-Collo SL, Bärnighausen TW, Bazargan-Hejazi S, Behzadifar M, Bennett JR, Bhalla A, Bhutta ZA, Bilal AI, Borges G, Borschmann R, Brazinova A, Campuzano Rincon JC, Carvalho F, Castañeda-Orjuela CA, Dandona L, Dandona R, Dargan PI, De Leo D, Dharmaratne SD, Ding EL, Phuc Do H, Doku DT, Doyle KE, Driscoll TR, Edessa D, El-Khatib Z, Endries AY, Esteghamati A, Faro A, Farzadfar F, Feigin VL, Fischer F, Foreman KJ, Franklin RC, Fullman N, Futran ND, Gebrehiwot TT, Gutiérrez RA, Hafezi-Nejad N, Haghparast Bidgoli H, Hailu GB, Haro JM, Hassen HY, Hawley C, Hendrie D, Híjar M, Hu G, Ilesanmi OS, Jakovljevic M, James SL, Jayaraman S, Jonas JB, Kahsay A, Kasaeian A, Keiyoro PN, Khader Y, Khalil IA, Khang YH, Khubchandani J, Ahmad Kiadaliri A, Kieling C, Kim YJ, Kosen S, Krohn KJ, Kumar GA, Lami FH, Lansingh VC, Larson HJ, Linn S, Lunevicius R, Magdy Abd El Razek H, Magdy Abd El Razek M, Malekzadeh R, Carvalho Malta D, Mason-Jones AJ, Matzopoulos R, Memiah PTN, Mendoza W, Meretoja TJ, Mezgebe HB, Miller TR, Mohammed S, Moradi-Lakeh M, Mori R, Nand D, Tat Nguyen C, Le Nguyen Q, Ningrum DNA, Akpojene Ogbo F, Olagunju AT, Patton GC, Phillips MR, Polinder S, Pourmalek F, Qorbani M, Rahimi-Movaghar A, Rahimi-Movaghar V, Rahman M, Rai RK, Ranabhat CL, Rawaf DL, Rawaf S, Rowhani-Rahbar A, Safdarian M, Safiri S, Sagar R, Salama JS, Sanabria J, Santric Milicevic MM, Sarmiento-Suárez R, Sartorius B, Satpathy M, Schwebel DC, Seedat S, Sepanlou SG, Shaikh MA, Sharew NT, Shiue I, Singh JA, Sisay M, Skirbekk V, Soares Filho AM, Stein DJ, Stokes MA, Sufiyan MB, Swaroop M, Sykes BL, Tabarés-Seisdedos R, Tadese F, Tran BX, Thanh Tran T, Ukwaja KN, Vasankari TJ, Vlassov V, Werdecker A, Ye P, Yip P, Yonemoto N, Younis MZ, Zaidi Z, El Sayed Zaki M, Hay SI, Lim SS, Lopez AD, Mokdad AH, Vos T, and Murray CJL
Abstract: IMPORTANCE: Understanding global variation in firearm mortality rates could guide prevention policies and interventions. OBJECTIVE: To estimate mortality due to firearm injury deaths from 1990 to 2016 in 195 countries and territories. DESIGN, SETTING, AND PARTICIPANTS: This study used deidentified aggregated data including 13?812 location-years of vital registration data to generate estimates of levels and rates of death by age-sex-year-location. The proportion of suicides in which a firearm was the lethal means was combined with an estimate of per capita gun ownership in a revised proxy measure used to evaluate the relationship between availability or access to firearms and firearm injury deaths. EXPOSURES: Firearm ownership and access. MAIN OUTCOMES AND MEASURES: Cause-specific deaths by age, sex, location, and year. RESULTS: Worldwide, it was estimated that 251?000 (95% uncertainty interval [UI], 195?000-276?000) people died from firearm injuries in 2016, with 6 countries (Brazil, United States, Mexico, Colombia, Venezuela, and Guatemala) accounting for 50.5% (95% UI, 42.2%-54.8%) of those deaths. In 1990, there were an estimated 209?000 (95% UI, 172?000 to 235?000) deaths from firearm injuries. Globally, the majority of firearm injury deaths in 2016 were homicides (64.0% [95% UI, 54.2%-68.0%]; absolute value, 161?000 deaths [95% UI, 107?000-182?000]); additionally, 27% were firearm suicide deaths (67?500 [95% UI, 55?400-84?100]) and 9% were unintentional firearm deaths (23?000 [95% UI, 18?200-24?800]). From 1990 to 2016, there was no significant decrease in the estimated global age-standardized firearm homicide rate (-0.2% [95% UI, -0.8% to 0.2%]). Firearm suicide rates decreased globally at an annualized rate of 1.6% (95% UI, 1.1-2.0), but in 124 of 195 countries and territories included in this study, these levels were either constant or significant increases were estimated. There was an annualized decrease of 0.9% (95% UI, 0.5%-1.3%) in the global rate of age-standardized firearm deaths from 1990 to 2016. Aggregate firearm injury deaths in 2016 were highest among persons aged 20 to 24 years (for men, an estimated 34?700 deaths [95% UI, 24?900-39?700] and for women, an estimated 3580 deaths [95% UI, 2810-4210]). Estimates of the number of firearms by country were associated with higher rates of firearm suicide (P < .001; R2 = 0.21) and homicide (P < .001; R2 = 0.35). CONCLUSIONS AND RELEVANCE: This study estimated between 195?000 and 276?000 firearm injury deaths globally in 2016, the majority of which were firearm homicides. Despite an overall decrease in rates of firearm injury death since 1990, there was variation among countries and across demographic subgroups.
Published: 2018

5. Global mortality from firearms, 1990-2016

Author: Naghavi, M, Marczak, LB, Kutz, M, Shackelford, KA, Arora, M, Miller-Petrie, M, Aichour, MTE, Akseer, N, Al-Raddadi, RM, Alam, K, Alghnam, SA, Antonio, CAT, Aremu, O, Arora, A, Asadi-Lari, M, Assadi, R, Atey, TM, Avila-Burgos, L, Awasthi, A, Ayala Quintanilla, BP, Barker-Collo, SL, Bärnighausen, TW, Bazargan-Hejazi, S, Behzadifar, M, Bennett, JR, Bhalla, A, Bhutta, ZA, Bilal, AI, Borges, G, Borschmann, R, Brazinova, A, Campuzano Rincon, JC, Carvalho, F, Castañeda-Orjuela, CA, Dandona, L, Dandona, R, Dargan, PI, De Leo, D, Dharmaratne, SD, Ding, EL, Phuc Do, H, Doku, DT, Doyle, KE, Driscoll, TR, Edessa, D, El-Khatib, Z, Endries, AY, Esteghamati, A, Faro, A, Farzadfar, F, Feigin, VL, Fischer, F, Foreman, KJ, Franklin, RC, Fullman, N, Futran, ND, Gebrehiwot, TT, Gutiérrez, RA, Hafezi-Nejad, N, Haghparast Bidgoli, H, Hailu, GB, Haro, JM, Hassen, HY, Hawley, C, Hendrie, D, Híjar, M, Hu, G, Ilesanmi, OS, Jakovljevic, M, James, SL, Jayaraman, S, Jonas, JB, Kahsay, A, Kasaeian, A, Keiyoro, PN, Khader, Y, Khalil, IA, Khang, Y-H, Khubchandani, J, Ahmad Kiadaliri, A, Kieling, C, Kim, YJ, Kosen, S, Krohn, KJ, Kumar, GA, Lami, FH, Lansingh, VC, Larson, HJ, Linn, S, Lunevicius, R, Magdy Abd El Razek, H, Magdy Abd El Razek, M, Malekzadeh, R, Carvalho Malta, D, Mason-Jones, AJ, Matzopoulos, R, Memiah, PTN, Mendoza, W, Meretoja, TJ, Mezgebe, HB, Miller, TR, Mohammed, S, Moradi-Lakeh, M, Mori, R, Nand, D, Tat Nguyen, C, Le Nguyen, Q, Ningrum, DNA, Akpojene Ogbo, F, Olagunju, AT, Patton, GC, Phillips, MR, Polinder, S, Pourmalek, F, Qorbani, M, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rai, RK, Ranabhat, CL, Rawaf, DL, Rawaf, S, Rowhani-Rahbar, A, Safdarian, M, Safiri, S, Sagar, R, Salama, JS, Sanabria, J, Santric Milicevic, MM, Sarmiento-Suárez, R, Sartorius, B, Satpathy, M, Schwebel, DC, Seedat, S, Sepanlou, SG, Shaikh, MA, Sharew, NT, Shiue, I, Singh, JA, Sisay, M, Skirbekk, V, Soares Filho, AM, Stein, DJ, Stokes, Mark Andrew, Sufiyan, MB, Swaroop, M, Sykes, BL, Tabarés-Seisdedos, R, Tadese, F, Tran, BX, Thanh Tran, T, Ukwaja, KN, Vasankari, TJ, Vlassov, V, Werdecker, A, Ye, P, Yip, P, Yonemoto, N, Younis, MZ, Zaidi, Z, El Sayed Zaki, M, Hay, SI, Lim, SS, Lopez, AD, Mokdad, AH, Vos, T, Murray, CJL, Naghavi, M, Marczak, LB, Kutz, M, Shackelford, KA, Arora, M, Miller-Petrie, M, Aichour, MTE, Akseer, N, Al-Raddadi, RM, Alam, K, Alghnam, SA, Antonio, CAT, Aremu, O, Arora, A, Asadi-Lari, M, Assadi, R, Atey, TM, Avila-Burgos, L, Awasthi, A, Ayala Quintanilla, BP, Barker-Collo, SL, Bärnighausen, TW, Bazargan-Hejazi, S, Behzadifar, M, Bennett, JR, Bhalla, A, Bhutta, ZA, Bilal, AI, Borges, G, Borschmann, R, Brazinova, A, Campuzano Rincon, JC, Carvalho, F, Castañeda-Orjuela, CA, Dandona, L, Dandona, R, Dargan, PI, De Leo, D, Dharmaratne, SD, Ding, EL, Phuc Do, H, Doku, DT, Doyle, KE, Driscoll, TR, Edessa, D, El-Khatib, Z, Endries, AY, Esteghamati, A, Faro, A, Farzadfar, F, Feigin, VL, Fischer, F, Foreman, KJ, Franklin, RC, Fullman, N, Futran, ND, Gebrehiwot, TT, Gutiérrez, RA, Hafezi-Nejad, N, Haghparast Bidgoli, H, Hailu, GB, Haro, JM, Hassen, HY, Hawley, C, Hendrie, D, Híjar, M, Hu, G, Ilesanmi, OS, Jakovljevic, M, James, SL, Jayaraman, S, Jonas, JB, Kahsay, A, Kasaeian, A, Keiyoro, PN, Khader, Y, Khalil, IA, Khang, Y-H, Khubchandani, J, Ahmad Kiadaliri, A, Kieling, C, Kim, YJ, Kosen, S, Krohn, KJ, Kumar, GA, Lami, FH, Lansingh, VC, Larson, HJ, Linn, S, Lunevicius, R, Magdy Abd El Razek, H, Magdy Abd El Razek, M, Malekzadeh, R, Carvalho Malta, D, Mason-Jones, AJ, Matzopoulos, R, Memiah, PTN, Mendoza, W, Meretoja, TJ, Mezgebe, HB, Miller, TR, Mohammed, S, Moradi-Lakeh, M, Mori, R, Nand, D, Tat Nguyen, C, Le Nguyen, Q, Ningrum, DNA, Akpojene Ogbo, F, Olagunju, AT, Patton, GC, Phillips, MR, Polinder, S, Pourmalek, F, Qorbani, M, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rai, RK, Ranabhat, CL, Rawaf, DL, Rawaf, S, Rowhani-Rahbar, A, Safdarian, M, Safiri, S, Sagar, R, Salama, JS, Sanabria, J, Santric Milicevic, MM, Sarmiento-Suárez, R, Sartorius, B, Satpathy, M, Schwebel, DC, Seedat, S, Sepanlou, SG, Shaikh, MA, Sharew, NT, Shiue, I, Singh, JA, Sisay, M, Skirbekk, V, Soares Filho, AM, Stein, DJ, Stokes, Mark Andrew, Sufiyan, MB, Swaroop, M, Sykes, BL, Tabarés-Seisdedos, R, Tadese, F, Tran, BX, Thanh Tran, T, Ukwaja, KN, Vasankari, TJ, Vlassov, V, Werdecker, A, Ye, P, Yip, P, Yonemoto, N, Younis, MZ, Zaidi, Z, El Sayed Zaki, M, Hay, SI, Lim, SS, Lopez, AD, Mokdad, AH, Vos, T, and Murray, CJL
Published: 2018

6. 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

7. 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

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

Author: James, Spencer L., Abate, Degu, Abate, Kalkidan Hassen, Abay, Solomon M., Abbafati, Cristiana, Abbasi, Nooshin, Abbastabar, Hedayat, Abd-Allah, Foad, Abdela, Jemal, Abdelalim, Ahmed, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Zegeye, Abera, Semaw F., Abil, Olifan Zewdie, Abraha, Haftom Niguse, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen M. E., Accrombessi, Manfred Mario Kokou, Acharya, Dilaram, Acharya, Pawan, Ackerman, Ilana N., Adamu, Abdu A., Adebayo, Oladimeji M., Adekanmbi, Victor, Adetokunboh, Olatunji O., Adib, Mina G., Adsuar, Jose C., Afanvi, Kossivi Agbelenko, Afarideh, Mohsen, Afshin, Ashkan, Agarwal, Gina, Agesa, Kareha M., Aggarwal, Rakesh, Aghayan, Sargis Aghasi, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmadieh, Hamid, Ahmed, Muktar Beshir, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akinyemiju, Tomi, Akseer, Nadia, Al-Aly, Ziyad, Al-Eyadhy, Ayman, Al-Mekhlafi, Hesham M., Al-Raddadi, Rajaa M., Alahdab, Fares, Alam, Khurshid, Alam, Tahiya, Alashi, Alaa, Alavian, Seyed Moayed, Alene, Kefyalew Addis, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Alouani, Mohamed M. L., Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T., Aminde, Leopold N., Ammar, Walid, Amoako, Yaw Ampem, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Anjomshoa, Mina, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T., Anwari, Palwasha, Arabloo, Jalal, Arauz, Antonio, Aremu, Olatunde, Ariani, Filippo, Armoon, Bahroom, Ärnlöv, Johan, Arora, Amit, Artaman, Al, Aryal, Krishna K., Asayesh, Hamid, Asghar, Rana Jawad, Ataro, Zerihun, Atre, Sachin R., Ausloos, Marcel, Avila-Burgos, Leticia, Avokpaho, Euripide F. G. A., Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayer, Rakesh, Azzopardi, Peter S., Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Bali, Ayele Geleto, Ballesteros, Katherine E., Ballew, Shoshana H., Banach, Maciej, Banoub, Joseph Adel Mattar, Banstola, Amrit, Barac, Aleksandra, Barboza, Miguel A., Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H., Baune, Bernhard T., Bazargan-Hejazi, Shahrzad, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Béjot, Yannick, Belachew, Abate Bekele, Belay, Yihalem Abebe, Bell, Michelle L., Bello, Aminu K., Bensenor, Isabela M., Bernabe, Eduardo, Bernstein, Robert S., Beuran, Mircea, Beyranvand, Tina, Bhala, Neeraj, Bhattarai, Suraj, Bhaumik, Soumyadeep, Bhutta, Zulfiqar A., Biadgo, Belete, Bijani, Ali, Bikbov, Boris, Bilano, Ver, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Bisanzio, Donal, Blacker, Brigette F., Blyth, Fiona M., Bou-Orm, Ibrahim R., Boufous, Soufiane, Bourne, Rupert, Brady, Oliver J., Brainin, Michael, Brant, Luisa C., Brazinova, Alexandra, Breitborde, Nicholas J. K., Brenner, Hermann, Briant, Paul Svitil, Briggs, Andrew M., Briko, Andrey Nikolaevich, Britton, Gabrielle, Brugha, Traolach, Buchbinder, Rachelle, Busse, Reinhard, Butt, Zahid A., Cahuana-Hurtado, Lucero, Cano, Jorge, Cárdenas, Rosario, Carrero, Juan J., Carter, Austin, Carvalho, Félix, Castañeda-Orjuela, Carlos A., Castillo Rivas, Jacqueline, Castro, Franz, Catalá-López, Ferrán, Cercy, Kelly M., Cerin, Ester, Chaiah, Yazan, Chang, Alex R., Chang, Hsing-Yi, Chang, Jung-Chen, Charlson, Fiona J., Chattopadhyay, Aparajita, Chattu, Vijay Kumar, Chaturvedi, Pankaj, Chiang, Peggy Pei-Chia, Chin, Ken Lee, Chitheer, Abdulaal, Choi, Jee-Young J., Chowdhury, Rajiv, Christensen, Hanne, Christopher, Devasahayam J., Cicuttini, Flavia M., Ciobanu, Liliana G., Cirillo, Massimo, Claro, Rafael M., Collado-Mateo, Daniel, Cooper, Cyrus, Coresh, Josef, Cortesi, Paolo Angelo, Cortinovis, Monica, Costa, Megan, Cousin, Ewerton, Criqui, Michael H., Cromwell, Elizabeth A., Cross, Marita, Crump, John A., Dadi, Abel Fekadu, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I., Daryani, Ahmad, Das Gupta, Rajat, Das Neves, José, Dasa, Tamirat Tesfaye, Davey, Gail, Davis, Adrian C., Davitoiu, Dragos Virgil, De Courten, Barbora, De La Hoz, Fernando Pio, De Leo, Diego, De Neve, Jan-Walter, Degefa, Meaza Girma, Degenhardt, Louisa, Deiparine, Selina, Dellavalle, Robert P., Demoz, Gebre Teklemariam, Deribe, Kebede, Dervenis, Nikolaos, Des Jarlais, Don C., Dessie, Getenet Ayalew, Dey, Subhojit, Dharmaratne, Samath Dhamminda, Dinberu, Mesfin Tadese, Dirac, M. Ashworth, Djalalinia, Shirin, Doan, Linh, Dokova, Klara, Doku, David Teye, Dorsey, E. Ray, Doyle, Kerrie E., Driscoll, Tim Robert, Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Duncan, Bruce B., Duraes, Andre R., Ebrahimi, Hedyeh, Ebrahimpour, Soheil, Echko, Michelle Marie, Edvardsson, David, Effiong, Andem, Ehrlich, Joshua R., El Bcheraoui, Charbel, El Sayed Zaki, Maysaa, El-Khatib, Ziad, Elkout, Hajer, Elyazar, Iqbal R. F., Enayati, Ahmadali, Endries, Aman Yesuf, Er, Benjamin, Erskine, Holly E., Eshrati, Babak, Eskandarieh, Sharareh, Esteghamati, Alireza, Esteghamati, Sadaf, Fakhim, Hamed, Fallah Omrani, Vahid, Faramarzi, Mahbobeh, Fareed, Mohammad, Farhadi, Farzaneh, Farid, Talha A., Farinha, Carla Sofia E. sá, Farioli, Andrea, Faro, Andre, Farvid, Maryam S., Farzadfar, Farshad, Feigin, Valery L., Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, Eduarda, Fernandes, Joao C., Ferrari, Alize J., Feyissa, Garumma Tolu, Filip, Irina, Fischer, Florian, Fitzmaurice, Christina, Foigt, Nataliya A., Foreman, Kyle J., Fox, Jack, Frank, Tahvi D., Fukumoto, Takeshi, Fullman, Nancy, Fürst, Thomas, Furtado, João M., Futran, Neal D., Gall, Seana, Ganji, Morsaleh, Gankpe, Fortune Gbetoho, Garcia-Basteiro, Alberto L., Gardner, William M., Gebre, Abadi Kahsu, Gebremedhin, Amanuel Tesfay, Gebremichael, Teklu Gebrehiwo, Gelano, Tilayie Feto, Geleijnse, Johanna M., Genova-Maleras, Ricard, Geramo, Yilma Chisha Dea, Gething, Peter W., Gezae, Kebede Embaye, Ghadiri, Keyghobad, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Ghimire, Mamata, Ghosh, Rakesh, Ghoshal, Aloke Gopal, Giampaoli, Simona, Gill, Paramjit Singh, Gill, Tiffany K., Ginawi, Ibrahim Abdelmageed, Giussani, Giorgia, Gnedovskaya, Elena V., Goldberg, Ellen M., Goli, Srinivas, Gómez-Dantés, Hector, Gona, Philimon N., Gopalani, Sameer Vali, Gorman, Taren M., Goulart, Alessandra C., Goulart, Bárbara Niegia Garcia, Grada, Ayman, Grams, Morgan E., Grosso, Giuseppe, Gugnani, Harish Chander, Guo, Yuming, Gupta, Prakash C., Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Gyawali, Bishal, Haagsma, Juanita A., Hachinski, Vladimir, Hafezi-Nejad, Nima, Haghparast Bidgoli, Hassan, Hagos, Tekleberhan B., Hailu, Gessessew Bugssa, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hamadeh, Randah R., Hamidi, Samer, Handal, Alexis J., Hankey, Graeme J., Hao, Yuantao, Harb, Hilda L., Harikrishnan, Sivadasanpillai, Haro, Josep Maria, Hasan, Mehedi, Hassankhani, Hadi, Hassen, Hamid Yimam, Havmoeller, Rasmus, Hawley, Caitlin N., Hay, Roderick J., Hay, Simon I., Hedayatizadeh-Omran, Akbar, Heibati, Behzad, Hendrie, Delia, Henok, Andualem, Herteliu, Claudiu, Heydarpour, Sousan, Hibstu, Desalegn Tsegaw, Hoang, Huong Thanh, Hoek, Hans W., Hoffman, Howard J., Hole, Michael K., Homaie Rad, Enayatollah, Hoogar, Praveen, Hosgood, H. Dean, Hosseini, Seyed Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hotez, Peter J., Hoy, Damian G., Hsairi, Mohamed, Htet, Aung Soe, Hu, Guoqing, Huang, John J., Huynh, Chantal K., Iburg, Kim Moesgaard, Ikeda, Chad Thomas, Ileanu, Bogdan, Ilesanmi, Olayinka Stephen, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Irvine, Caleb Mackay Salpeter, Islam, Sheikh Mohammed Shariful, Islami, Farhad, Jacobsen, Kathryn H., Jahangiry, Leila, Jahanmehr, Nader, Jain, Sudhir Kumar, Jakovljevic, Mihajlo, Javanbakht, Mehdi, Jayatilleke, Achala Upendra, Jeemon, Panniyammakal, Jha, Ravi Prakash, Jha, Vivekanand, Ji, John S., Johnson, Catherine O., Jonas, Jost B., Jozwiak, Jacek Jerzy, Jungari, Suresh Banayya, Jürisson, Mikk, Kabir, Zubair, Kadel, Rajendra, Kahsay, Amaha, Kalani, Rizwan, Kanchan, Tanuj, Karami, Manoochehr, Karami Matin, Behzad, Karch, André, Karema, Corine, Karimi, Narges, Karimi, Seyed M., Kasaeian, Amir, Kassa, Dessalegn H., Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kassebaum, Nicholas J., Katikireddi, Srinivasa Vittal, Kawakami, Norito, Karyani, Ali Kazemi, Keighobadi, Masoud Masoud, Keiyoro, Peter Njenga, Kemmer, Laura, Kemp, Grant Rodgers, Kengne, Andre Pascal, Keren, Andre, Khader, Yousef Saleh, Khafaei, Behzad, Khafaie, Morteza Abdullatif, Khajavi, Alireza, Khalil, Ibrahim A., Khan, Ejaz Ahmad, Khan, Muhammad Shahzeb, Khan, Muhammad Ali, Khang, Young-Ho, Khazaei, Mohammad, Khoja, Abdullah T., Khosravi, Ardeshir, Khosravi, Mohammad Hossein, Kiadaliri, Aliasghar A., Kiirithio, Daniel N., Kim, Cho-Il, Kim, Daniel, Kim, Pauline, Kim, Young-Eun, Kim, Yun Jin, Kimokoti, Ruth W., Kinfu, Yohannes, Kisa, Adnan, Kissimova-Skarbek, Katarzyna, Kivimäki, Mika, Knudsen, Ann Kristin Skrindo, Kocarnik, Jonathan M., Kochhar, Sonali, Kokubo, Yoshihiro, Kolola, Tufa, Kopec, Jacek A., Kosen, Soewarta, Kotsakis, Georgios A., Koul, Parvaiz A., Koyanagi, Ai, Kravchenko, Michael A., Krishan, Kewal, Krohn, Kristopher J., Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kumar, G. Anil, Kumar, Manasi, Kyu, Hmwe Hmwe, Lad, Deepesh P., Lad, Sheetal D., Lafranconi, Alessandra, Lalloo, Ratilal, Lallukka, Tea, Lami, Faris Hasan, Lansingh, Van C., Latifi, Arman, Lau, Kathryn Mei-Ming, Lazarus, Jeffrey V., Leasher, Janet L., Ledesma, Jorge R., Lee, Paul H., Leigh, James, Leung, Janni, Levi, Miriam, Lewycka, Sonia, Li, Shanshan, Li, Yichong, Liao, Yu, Liben, Misgan Legesse, Lim, Lee-Ling, Lim, Stephen S., Liu, Shiwei, Lodha, Rakesh, Looker, Katharine J., Lopez, Alan D., Lorkowski, Stefan, Lotufo, Paulo A., Low, Nicola, Lozano, Rafael, Lucas, Tim C. D., Lucchesi, Lydia R., Lunevicius, Raimundas, Lyons, Ronan A., Ma, Stefan, Macarayan, Erlyn Rachelle King, Mackay, Mark T., Madotto, Fabiana, Magdy Abd El Razek, Hassan, Magdy Abd El Razek, Muhammed, Maghavani, Dhaval P., Mahotra, Narayan Bahadur, Mai, Hue Thi, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malta, Deborah Carvalho, Mamun, Abdullah A., Manda, Ana-Laura, Manguerra, Helena, Manhertz, Treh, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Mapoma, Chabila Christopher, Maravilla, Joemer C., Marcenes, Wagner, Marks, Ashley, Martins-Melo, Francisco Rogerlândio, Martopullo, Ira, März, Winfried, Marzan, Melvin B., Mashamba-Thompson, Tivani Phosa, Massenburg, Benjamin Ballard, Mathur, Manu Raj, Matsushita, Kunihiro, Maulik, Pallab K., Mazidi, Mohsen, McAlinden, Colm, McGrath, John J., McKee, Martin, Mehndiratta, Man Mohan, Mehrotra, Ravi, Mehta, Kala M., Mehta, Varshil, Mejia-Rodriguez, Fabiola, Mekonen, Tesfa, Melese, Addisu, Melku, Mulugeta, Meltzer, Michele, Memiah, Peter T. N., Memish, Ziad A., Mendoza, Walter, Mengistu, Desalegn Tadese, Mengistu, Getnet, Mensah, George A., Mereta, Seid Tiku, Meretoja, Atte, Meretoja, Tuomo J., Mestrovic, Tomislav, Mezerji, Naser Mohammad Gholi, Miazgowski, Bartosz, Miazgowski, Tomasz, Millear, Anoushka I., Miller, Ted R., Miltz, Benjamin, Mini, G. K., Mirarefin, Mojde, Mirrakhimov, Erkin M., Misganaw, Awoke Temesgen, Mitchell, Philip B., Mitiku, Habtamu, Moazen, Babak, Mohajer, Bahram, Mohammad, Karzan Abdulmuhsin, Mohammadifard, Noushin, Mohammadnia-Afrouzi, Mousa, Mohammed, Mohammed A., Mohammed, Shafiu, Mohebi, Farnam, Moitra, Modhurima, Mokdad, Ali H., Molokhia, Mariam, Monasta, Lorenzo, Moodley, Yoshan, Moosazadeh, Mahmood, Moradi, Ghobad, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morawska, Lidia, Moreno Velásquez, Ilais, Morgado-Da-Costa, Joana, Morrison, Shane Douglas, Moschos, Marilita M., Mountjoy-Venning, W. Cliff, Mousavi, Seyyed Meysam, Mruts, Kalayu Brhane, Muche, Achenef Asmamaw, Muchie, Kindie Fentahun, Mueller, Ulrich Otto, Muhammed, Oumer Sada, Mukhopadhyay, Satinath, Muller, Kate, Mumford, John Everett, Murhekar, Manoj, Musa, Jonah, Musa, Kamarul Imran, Mustafa, Ghulam, Nabhan, Ashraf F., Nagata, Chie, Naghavi, Mohsen, Naheed, Aliya, Nahvijou, Azin, Naik, Gurudatta, Naik, Nitish, Najafi, Farid, Naldi, Luigi, Nam, Hae Sung, Nangia, Vinay, Nansseu, Jobert Richie, Nascimento, Bruno Ramos, Natarajan, Gopalakrishnan, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Neupane, Subas, Newton, Charles Richard James, Ngunjiri, Josephine W., Nguyen, Anh Quynh, Nguyen, Ha Thu, Nguyen, Huong Lan Thi, Nguyen, Huong Thanh, Nguyen, Long Hoang, Nguyen, Minh, Nguyen, Nam Ba, Nguyen, Son Hoang, Nichols, Emma, Ningrum, Dina Nur Anggraini, Nixon, Molly R., Nolutshungu, Nomonde, Nomura, Shuhei, Norheim, Ole F., Noroozi, Mehdi, Norrving, Bo, Noubiap, Jean Jacques, Nouri, Hamid Reza, Nourollahpour Shiadeh, Malihe, Nowroozi, Mohammad Reza, Nsoesie, Elaine O., Nyasulu, Peter S., Odell, Christopher M., Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oh, In-Hwan, Oladimeji, Olanrewaju, Olagunju, Andrew T., Olagunju, Tinuke O., Olivares, Pedro R., Olsen, Helen Elizabeth, Olusanya, Bolajoko Olubukunola, Ong, Kanyin L., Ong, Sok King, Oren, Eyal, Ortiz, Alberto, Ota, Erika, Otstavnov, Stanislav S., Øverland, Simon, Owolabi, Mayowa Ojo, P A, Mahesh, Pacella, Rosana, Pakpour, Amir H., Pana, Adrian, Panda-Jonas, Songhomitra, Parisi, Andrea, Park, Eun-Kee, Parry, Charles D. H., Patel, Shanti, Pati, Sanghamitra, Patil, Snehal T., Patle, Ajay, Patton, George C., Paturi, Vishnupriya Rao, Paulson, Katherine R., Pearce, Neil, Pereira, David M., Perico, Norberto, Pesudovs, Konrad, Pham, Hai Quang, Phillips, Michael R., Pigott, David M., Pillay, Julian David, Piradov, Michael A., Pirsaheb, Meghdad, Pishgar, Farhad, Plana-Ripoll, Oleguer, Plass, Dietrich, Polinder, Suzanne, Popova, Svetlana, Postma, Maarten J., Pourshams, Akram, Poustchi, Hossein, Prabhakaran, Dorairaj, Prakash, Swayam, Prakash, V., Purcell, Caroline A., Purwar, Manorama B., Qorbani, Mostafa, Quistberg, D. AlexRadfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi, Kazem, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Mohammad Hifz ur, Rahman, Muhammad Aziz, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Rajati, Fatemeh, Ram, Usha, Ranjan, Prabhat, Ranta, Anna, Rao, Puja C., Rawaf, David Laith, Rawaf, Salman, Reddy, K. Srinath, Reiner, Robert C., Reinig, Nickolas, Reitsma, Marissa Bettay, Remuzzi, Giuseppe, Renzaho, Andre M. N., Resnikoff, Serge, Rezaei, Satar, Rezai, Mohammad Sadegh, Ribeiro, Antonio Luiz P., Roberts, Nicholas L. 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Subjects: YLDs, global burden of disease, General & Internal Medicine, Medicine (all), prevalence, incidence, Human medicine, global burden of disease, YLDs, incidence, prevalence, GBD, years lived with disability
Abstract: Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 39% (95% uncertainty interval [UI] 3.1-4. 6) from 1990 to 2017; however, the all-age YLD rate increased by 7.2% (6.0-8.4) while the total sum of global YLDs increased from 562 million (421-723) to 853 million (642-1100). The increases for males and females were similar, with increases in all-age YLD rates of 7.9% (6 6-9. 2) for males and 6.5% (5.4-7.7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782-3252] per 100 000 in males vs 1400 [1279-1524] per 100 000 in females), transport injuries (3322 [3082-3583] vs 2336 [2154-2535]), and self-hatin and interpersonal violence (3265 [2943-3630] vs 5643 [5057-6302]). Interpretation Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.
Published: 2018

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

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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 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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 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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. 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Sd, Aljunid, Sm, Alomari, Ma, Altirkawi, Ka, Amare, At, Amoako, Ya, Andrei, Cl, Antonio, Cat, Araújo, Vem, Aryal, Kk, Asfaw, Et, Asgedom, Sw, Asghar, Rj, Ashebir, Mm, Asseffa, Na, Atey, Tm, Atre, Sr, Avokpaho, Efga, Ayala Quintanilla, Bp, Ayalew, Aa, Ayele, Ht, Ayuk, Tb, Babalola, Tk, Barber, Rm, Barboza, Ma, Barker-Collo, Sl, Barrero, Lh, Baune, Bt, Bekele, Bb, Belachew, Ab, Belay, Sa, Belay, Ya, Bell, Ml, Bello, Ak, Bennett, Da, Bennett, Jr, Bensenor, Im, Berhe, Df, Bhutta, Za, Bolliger, Iw, Bou-Orm, Ir, Brady, Oj, Breitborde, Njk, Butt, Za, Campos-Nonato, Ir, Campuzano, Jc, Carrero, Jj, Castañeda-Orjuela, Ca, Chang, Hy, Chang, Jc, Chiang, Pp, Chisumpa, Vh, Choi, Jj, Christopher, Dj, Chung, Sc, Ciobanu, Lg, Cortesi, Pa, Criqui, Mh, Cromwell, Ea, Crump, Ja, Daba, Ak, Dachew, Ba, Dadi, Af, Dargan, Pi, Das, Sk, De Neve, Jw, Dellavalle, Rp, Des Jarlais, Dc, Dharmaratne, Sd, Doku, Dt, Dorsey, Er, Dos Santos, Kpb, Doyle, Ke, Driscoll, Tr, Duncan, Bb, Ehrlich, Jr, El-Khatib, Zz, Endries, Ay, Faraon, Eja, Feigin, Vl, Feigl, Ab, Fereshtehnejad, Sm, Fernandes, Jc, Fijabi, Do, Flaxman, Ad, Foreman, Kj, Frostad, Jj, Futran, Nd, Gebre, Ak, Gebrehiwot, Tt, Gebremedhin, At, Gelaw, Ya, Geleijnse, Jm, Gething, Pw, Giref, Az, Gishu, Md, Godwin, Ww, Gona, Pn, Gopalani, Sv, Goulart, Ac, Gugnani, Hc, Haagsma, Ja, Hailu, Gb, Hamadeh, Rr, Hankey, Gj, Harb, Hl, Harewood, Hc, Haro, Jm, Hassen, Hy, Hay, Si, Hearps, Sjc, Hegazy, Mi, Henry, Nj, Herrera Ballesteros, Vh, Hibstu, Dt, Hiluf, Mk, Hoek, Hw, Hosgood, Hd, Hosseini, Sr, Hoy, Dg, Huang, Jj, Iburg, Km, Igumbor, Eu, Ileanu, Bv, Irvani, Ssn, Irvine, Cm, Jacobs, Ta, Jacobsen, Kh, Jain, Sk, Jakovljevic, Mb, Jalu, Mt, Jamal, Aa, Jayatilleke, Au, Jha, Rp, Johnson, Sc, Jonas, Jb, Karema, Ck, Karimi, Sm, Kassa, Dh, Kassa, Gm, Kassa, Td, Kassebaum, Nj, Katikireddi, Sv, Keiyoro, Pn, Kemp, Gr, Kengne, Ap, Ketema, Eb, Khafaie, Ma, Khalil, Ia, Khan, Ea, Khan, Mn, Khan, Ma, Khanal, Mn, Khang, Yh, Khater, Mm, Khoja, Ata, Kibret, Gd, Kiirithio, Dn, Kim, Yj, Kimokoti, Rw, Kopec, Ja, Koul, Pa, Krohn, Kj, Kumar, Ga, Kyu, Hh, Lad, Dp, Lal, Dk, Lang, Jj, Lansingh, Vc, Lazarus, Jv, Leasher, Jl, Lee, Ph, Leshargie, Ct, Liben, Ml, Lim, Ll, Lopez, Ad, Lorch, Sa, Lotufo, Pa, Lucas, Tcd, Lyons, Ra, Macarayan, Erk, Mackay, Mt, Maddison, Er, Malta, Dc, Mamun, Aa, Mansournia, Ma, Mantovani, Lg, Mapoma, Cc, Martins, Sco, Martins-Melo, Fr, Massenburg, Bb, Mathur, Mr, Maulik, Pk, Mcgrath, Jj, Mehta, Km, Meles, Kg, Memish, Za, Mengiste, Da, Mengistu, Dt, Menota, Bg, Mensah, Ga, Meretoja, Tj, Mezgebe, Hb, Miller, Tr, Mini, Gk, Mirrakhimov, Em, Mitchell, Pb, Mlashu, Fw, Mohammad, Ka, Mohammed, Ma, Mokdad, Ah, Mola, Gl, Montañez Hernandez, Jc, Morrison, Sd, Mousavi, Sm, Mueller, Uo, Murthy, Gv, Musa, Ki, Naik, Ga, Nansseu, Jrn, Narayan, Kv, Nascimento, Br, Negoi, Ri, Newton, Cr, Ngunjiri, Jw, Nguyen, Th, Ningrum, Dna, Nong, Vm, Norheim, Of, Noubiap, Jjn, Obermeyer, Cm, Ogbo, Fa, Oh, Ih, Olagunju, At, Olagunju, To, Olivares, Pr, Oliveira, Ppv, Olsen, He, Olusanya, Bo, Olusanya, Jo, Opio, Jn, Ortega-Altamirano, Dv, Pain, Aw, Palone, Mrt, Pandian, Jd, Park, Ek, Patil, St, Patton, Gc, Paturi, Vr, Pedroso, Mm, Pedroza, Sp, Pereira, Dm, Phillips, Mr, Piel, Fb, Pigott, Dm, Pillay, Jd, Piradov, Ma, Pond, Cd, Postma, Mj, Prasad, Nm, Quintana, Hk, Rahman, Ma, Rahman, Su, Rai, Rk, Raju, Sb, Rana, Sm, Rawaf, Dl, Ray, Se, Razo-García, Ca, Reiner, Rc, Reitsma, Mb, Renzaho, Amn, Ribeiro, Al, Rios Blancas, Mj, Rivera, Ja, Roth, Ga, Ruhago, Gm, Sabde, Yd, Sahraian, Ma, Saldanha, Rf, Salomon, Ja, Samy, Am, Sanabria, Jr, Sancheti, Pk, Sanchez-Niño, Md, Santric Milicevic, Mm, Sarker, Ar, Saylan, Mi, Schmidt, Mi, Schneider, Ijc, Schumacher, Ae, Schutte, Ae, Schwebel, Dc, Sepanlou, Sg, Servan-Mori, Ee, Shaikh, Ma, Shariful Islam, Sm, Shfare, Mt, Shrime, Mg, Shukla, Sr, Sigfusdottir, Id, Silberberg, Dh, Silva, Da, Silva, Jp, Silveira, Dga, Singh, Ja, Singh, Np, Sinha, Dn, Sinke, Ah, Soares Filho, Am, Sobaih, Bha, Sorensen, Rjd, Soriano, Jb, Soyiri, In, 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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. Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

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Ahmed, Muktar Beshir, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akanda, Ali S, Akbari, Mohammad Esmaeil, Akibu, Mohammed, Akinyemi, Rufus Olusola, Akinyemiju, Tomi, Akseer, Nadia, Alahdab, Fares, Al-Aly, Ziyad, Alam, Khurshid, Alebel, Animut, Aleman, Alicia V, Alene, Kefyalew Addis, Al-Eyadhy, Ayman, Ali, Raghib, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Allen, Christine A, Alonso, Jordi, Al-Raddadi, Rajaa M, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Amini, Erfan, Ammar, Walid, Amoako, Yaw Ampem, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Anjomshoa, Mina, Anlay, Degefaye Zelalem, Ansari, Hossein, Ansariadi, Ansariadi, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T, Appiah, Seth Christopher Yaw, Aremu, Olatunde, Areri, Habtamu Abera, Ärnlöv, Johan, Arora, Megha, Artaman, Al, Aryal, Krishna K, Asadi-Lari, 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Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zachariah, Geevar, Zadnik, Vesna, Zafar, Shamsa, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zare, Zohreh, Zeeb, Hajo, Zeleke, Mulugeta Molla, Zenebe, Zerihun Menlkalew, Zerfu, Taddese Alemu, Zhang, Kai, Zhang, Xueying, Zhou, Maigeng, Zhu, Jun, Zodpey, Sanjay, Zucker, Inbar, Zuhlke, Liesl Joanna J, Lopez, Alan D, Gakidou, Emmanuela, and Murray, Christopher J L
Subjects: Child mortality, Alnæmi, Dánartíðni, Heilsufar, GBD, Börn, Dauði, Almennar lyflækningar, Fullorðnir, General & Internal Medicine, parasitic diseases, Psychology, age-specific mortality, Estimating adult mortality, Medicine (all), HIV, 11 Medical And Health Sciences, General Medicine, All-cause mortality, humanities, Countries, Death, mortality, national, Sálfræði, Health, GBD, mortality, Human medicine, Trends
Abstract: The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Data for this research was provided by MEASURE Evaluation, funded by the United States Agency for International Development (USAID). Views expressed do not necessarily reflect those of USAID, the US Government, or MEASURE Evaluation. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with licence no. SLN2014-3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law-2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data., Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Findings Globally, 18·7% (95% uncertainty interval 18·4–19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2–59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5–49·6) to 70·5 years (70·1–70·8) for men and from 52·9 years (51·7–54·0) to 75·6 years (75·3–75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5–51·7) for men in the Central African Republic to 87·6 years (86·9–88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3–238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6–42·83) per 1000 livebirths in 2017, with huge reductions across countries. Nevertheless, there were still 5·4 million (5·2–5·6) deaths among children younger than 5 years in the world in 2017. Progress has been less pronounced and more variable for adults, especially for adult males, who had stagnant or increasing mortality rates in several countries. The gap between male and female life expectancy between 1950 and 2017, while relatively stable at the global level, shows distinctive patterns across super-regions and has consistently been the largest in central Europe, eastern Europe, and central Asia, and smallest in south Asia. Performance was also variable across countries and time in observed mortality rates compared with those expected on the basis of development. Interpretation This analysis of age-sex-specific mortality shows that there are remarkably complex patterns in population mortality across countries. The findings of this study highlight global successes, such as the large decline in under-5 mortality, which reflects significant local, national, and global commitment and investment over several decades. However, they also bring attention to mortality patterns that are a cause for concern, particularly among adult men and, to a lesser extent, women, whose mortality rates have stagnated in many countries over the time period of this study, and in some cases are increasing., Research reported in this publication was supported by the Bill & Melinda Gates Foundation, the University of Melbourne, Public Health England, the Norwegian Institute of Public Health, St. Jude Children's Research Hospital, the National Institute on Aging of the National Institutes of Health (award P30AG047845), and the National Institute of Mental Health of the National Institutes of Health (award R01MH110163).
Published: 2018
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11. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017

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Sanjay, Krohn, Kristopher J, Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kumar, G Anil, Kumar, Manasi, Kuzin, Igor, Lad, Deepesh P, Lad, Sheetal D, Lafranconi, Alessandra, Lalloo, Ratilal, Lallukka, Tea, Lami, Faris Hasan, Lang, Justin J, Langan, Sinéad M, Lansingh, Van C, Latifi, Arman, Lau, Kathryn Mei-Ming, Lazarus, Jeffrey V, Leasher, Janet L, Ledesma, Jorge R, Lee, Paul H, Leigh, Jame, Leili, Mostafa, Leshargie, Cheru Tesema, Leung, Janni, Levi, Miriam, Lewycka, Sonia, Li, Shanshan, Li, Yichong, Liang, Xiaofeng, Liao, Yu, Liben, Misgan Legesse, Lim, Lee-Ling, Lim, Stephen S, Limenih, Miteku Andualem, Linn, Shai, Liu, Shiwei, Looker, Katharine J, Lopez, Alan D, Lorkowski, Stefan, Lotufo, Paulo A, Lozano, Rafael, Lucas, Tim C D, Lunevicius, Raimunda, Lyons, Ronan A, Ma, Stefan, Macarayan, Erlyn Rachelle King, Mackay, Mark T, Maddison, Emilie R, Madotto, Fabiana, Maghavani, Dhaval P, Mai, Hue Thi, Majdan, Marek, Majdzadeh, Reza, Majeed, Azeem, Malekzadeh, Reza, Malta, Deborah Carvalho, Mamun, Abdullah A, Manda, Ana-Laura, Manguerra, Helena, Mansournia, Mohammad Ali, Mantilla Herrera, Ana Maria, Mantovani, Lorenzo Giovanni, Maravilla, Joemer C, Marcenes, Wagner, Marks, Ashley, Martins-Melo, Francisco Rogerlândio, Martopullo, Ira, März, Winfried, Marzan, Melvin B, Massano, João, Massenburg, Benjamin Ballard, Mathur, Manu Raj, Maulik, Pallab K, Mazidi, Mohsen, McAlinden, Colm, McGrath, John J, McKee, Martin, McMahon, Brian J, Mehata, Suresh, Mehrotra, Ravi, Mehta, Kala M, Mehta, Varshil, Mejia-Rodriguez, Fabiola, Mekonen, Tesfa, Melese, Addisu, Melku, Mulugeta, Memiah, Peter T N, Memish, Ziad A, Mendoza, Walter, Mengistu, Getnet, Mensah, George A, Mereta, Seid Tiku, Meretoja, Atte, Meretoja, Tuomo J, Mestrovic, Tomislav, Miazgowski, Bartosz, Miazgowski, Tomasz, Millear, Anoushka I, Miller, Ted R, Mini, G.K., Mirarefin, Mojde, Mirica, Andreea, Mirrakhimov, Erkin M, Misganaw, Awoke Temesgen, Mitchell, Philip B, Mitiku, Habtamu, Moazen, Babak, Mohajer, Bahram, Mohammad, Karzan Abdulmuhsin, Mohammadi, Moslem, Mohammadifard, Noushin, Mohammadnia-Afrouzi, Mousa, Mohammed, Mohammed A, Mohammed, Shafiu, Mohebi, Farnam, Mokdad, Ali H, Molokhia, Mariam, Monasta, Lorenzo, Montañez, Julio Cesar, Moosazadeh, Mahmood, Moradi, Ghobad, Moradi, Mahmoudreza, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morawska, Lidia, Moreno Velásquez, Ilai, Morgado-Da-Costa, Joana, Morrison, Shane Dougla, Moschos, Marilita M, Mousavi, Seyyed Meysam, Mruts, Kalayu Brhane, Muche, Achenef Asmamaw, Muchie, Kindie Fentahun, Mueller, Ulrich Otto, Muhammed, Oumer Sada, Mukhopadhyay, Satinath, Muller, Kate, Mumford, John Everett, Murthy, G.V.S., Musa, Kamarul Imran, Mustafa, Ghulam, Nabhan, Ashraf F, Nagata, Chie, Nagel, Gabriele, Naghavi, Mohsen, Naheed, Aliya, Nahvijou, Azin, Naik, Gurudatta, Najafi, Farid, Nam, Hae Sung, Nangia, Vinay, Nansseu, Jobert Richie, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Neupane, Suba, Newton, Charles Richard Jame, Ngunjiri, Josephine W, Nguyen, Anh Quynh, Nguyen, Grant, Nguyen, Ha Thu, Nguyen, Huong Lan Thi, Nguyen, Huong Thanh, Nguyen, Long Hoang, Nguyen, Minh, Nguyen, Nam Ba, Nguyen, Son Hoang, Nichols, Emma, Ningrum, Dina Nur Anggraini, Nixon, Molly R, Nomura, Shuhei, Noroozi, Mehdi, Norrving, Bo, Noubiap, Jean Jacque, Nouri, Hamid Reza, Shiadeh, Malihe Nourollahpour, Nowroozi, Mohammad Reza, Nsoesie, Elaine O, Nyasulu, Peter S, Odell, Christopher M, Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oh, In-Hwan, Oladimeji, Olanrewaju, Olagunju, Andrew T, Olagunju, Tinuke O, Olivares, Pedro R, Olsen, Helen Elizabeth, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Kanyin L, Ong, Sok King, Oren, Eyal, Ortiz, Alberto, Ota, Erika, Otstavnov, Stanislav S, Øverland, Simon, Owolabi, Mayowa Ojo, P A, Mahesh, Pacella, Rosana, Pakhare, Abhijit P, Pakpour, Amir H, Pana, Adrian, Panda-Jonas, Songhomitra, Park, Eun-Kee, Park, Jame, Parry, Charles D H, Parsian, Hadi, Pasdar, Yahya, Patel, Shanti, Patil, Snehal T, Patle, Ajay, Patton, George C, Paturi, Vishnupriya Rao, Paudel, Deepak, Paulson, Katherine R, Pearce, Neil, Pereira, Alexandre, Pereira, David M, Perico, Norberto, Pesudovs, Konrad, Petzold, Max, Pham, Hai Quang, Phillips, Michael R, Pigott, David M, Pillay, Julian David, Piradov, Michael A, Pirsaheb, Meghdad, Pishgar, Farhad, Plana-Ripoll, Oleguer, Polinder, Suzanne, Popova, Svetlana, Postma, Maarten J, Pourshams, Akram, Poustchi, Hossein, Prabhakaran, Dorairaj, Prakash, Swayam, Prakash, V., Prasad, Narayan, Purcell, Caroline A, Qorbani, Mostafa, Quistberg, D Alex, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi, Kazem, Rahimi, Zohreh, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Mohammad Hifz Ur, Rahman, Muhammad Aziz, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Rajati, Fatemeh, Ranjan, Prabhat, Rao, Puja C, Rasella, Davide, Rawaf, David Laith, Rawaf, Salman, Reddy, K Srinath, Reiner, Robert C, Reitsma, Marissa Bettay, Remuzzi, Giuseppe, Renzaho, Andre M N, Resnikoff, Serge, Rezaei, Satar, Rezai, Mohammad Sadegh, Ribeiro, Antonio Luiz P, Roberts, Nicholas L S, Robinson, Stephen R, Roever, Leonardo, Ronfani, Luca, Roshandel, Gholamreza, Rostami, Ali, Roth, Gregory A, Rothenbacher, Dietrich, Rubagotti, Enrico, Sachdev, Perminder S, Sadat, Nafi, Sadeghi, Ehsan, Saeedi Moghaddam, Sahar, Safari, Hosein, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Safi, Sare, Safiri, Saeid, Sagar, Rajesh, Sahebkar, Amirhossein, Sahraian, Mohammad Ali, Sajadi, Haniye Sadat, Salam, Nasir, Salama, Joseph S, Salamati, Payman, Saleem, Zikria, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A, Salvi, Sundeep Santosh, Salz, Inbal, Samy, Abdallah M, Sanabria, Juan, Sanchez-Niño, Maria Dolore, Santomauro, Damian Francesco, Santos, Itamar S, Santos, João Vasco, Santric Milicevic, Milena M, Sao Jose, Bruno Piassi, Sardana, Mayank, Sarker, Abdur Razzaque, Sarmiento-Suárez, Rodrigo, Sarrafzadegan, Nizal, Sartorius, Benn, Sarvi, Shahabeddin, Sathian, Brijesh, Satpathy, Maheswar, Sawant, Arundhati R, Sawhney, Monika, Saxena, Sonia, Schaeffner, Elke, Schmidt, Maria Inê, Schneider, Ione J C, Schutte, Aletta Elisabeth, Schwebel, David C, Schwendicke, Falk, Scott, James G, Sekerija, Mario, Sepanlou, Sadaf G, Serván-Mori, Edson, Seyedmousavi, Seyedmojtaba, Shabaninejad, Hosein, Shafieesabet, Azadeh, Shahbazi, Mehdi, Shaheen, Amira A, Shaikh, Masood Ali, Shams-Beyranvand, Mehran, Shamsi, Mohammadbagher, Sharafi, Heidar, Sharafi, Kiomar, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharma, Jayendra, Sharma, Rajesh, She, Jun, Sheikh, Aziz, Shi, Peilin, Shibuya, Kenji, Shiferaw, Mekonnen Sisay, Shigematsu, Mika, Shiri, Rahman, Shirkoohi, Reza, Shiue, Ivy, Shokoohinia, Yalda, Shokraneh, Farhad, Shoman, Haitham, Shrime, Mark G, Si, Si, Siabani, Soraya, Sibai, Abla Mehio, Siddiqi, Tariq J, Sigfusdottir, Inga Dora, Sigurvinsdottir, Rannveig, Silva, Diego Augusto Santo, Silva, João Pedro, Silveira, Dayane Gabriele Alve, Singam, Narayana Sarma Venkata, Singh, Jasvinder A, Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Skiadaresi, Eirini, Skirbekk, Vegard, Sliwa, Karen, Smith, David L, Smith, Mari, Soares Filho, Adauto Martin, Sobaih, Badr Hasan, Sobhani, Soheila, Soofi, Moslem, Sorensen, Reed J D, Soyiri, Ireneous N, Sposato, Luciano A, Sreeramareddy, Chandrashekhar T, Srinivasan, Vinay, Stanaway, Jeffrey D, Starodubov, Vladimir I, Stein, Dan J, Steiner, Caitlyn, Steiner, Timothy J, Stokes, Mark A, Stovner, Lars Jacob, Subart, Michelle L, Sudaryanto, Agu, Sufiyan, Mu'awiyyah Babale, Sulo, Gerhard, Sunguya, Bruno F, Sur, Patrick John, Sykes, Bryan L, Sylaja, P.N., Sylte, Dillon O, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Tabuchi, Takahiro, Tadakamadla, Santosh Kumar, Tandon, Nikhil, Tassew, Segen Gebremeskel, Tavakkoli, Mohammad, Taveira, Nuno, Taylor, Hugh R, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekelemedhin, Shishay Wahdey, Tekle, Merhawi Gebremedhin, Temsah, Mohamad-Hani, Temsah, Omar, Terkawi, Abdullah Sulieman, Tessema, Belay, Teweldemedhin, Mebrahtu, Thankappan, Kavumpurathu Raman, Theis, Andrew, Thirunavukkarasu, Sathish, Thomas, Nihal, Tilahun, Binyam, To, Quyen G, Tonelli, Marcello, Topor-Madry, Roman, Torre, Anna E, Tortajada-Girbés, Miguel, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Towbin, Jeffrey A, Tran, Bach Xuan, Tran, Khanh Bao, Troeger, Christopher E, Tsadik, Afewerki Gebremeskel, Tsoi, Derrick, Tudor Car, Lorainne, Tyrovolas, Stefano, Ukwaja, Kingsley Nnanna, Ullah, Irfan, Undurraga, Eduardo A, Updike, Rachel L, Usman, Muhammad Shariq, Uthman, Olalekan A, Vaduganathan, Muthiah, Vaezi, Afsane, Valdez, Pascual R, Varavikova, Elena, Varughese, Santosh, Vasankari, Tommi Juhani, Venketasubramanian, Narayanaswamy, Villafaina, Santo, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vujcic, Isidora S, Wagnew, Fasil Shiferaw, Waheed, Yasir, Wang, Yafeng, Wang, Yuan-Pang, Weiderpass, Elisabete, Weintraub, Robert G, Weiss, Daniel J, Weldegebreal, Fitsum, Weldegwergs, Kidu Gidey, Werdecker, Andrea, West, T Eoin, Westerman, Ronny, Whiteford, Harvey A, Widecka, Justyna, Wijeratne, Tissa, Williams, Hywel C, Wilner, Lauren B, Wilson, Shadrach, Winkler, Andrea Sylvia, Wiyeh, Alison B, Wiysonge, Charles Shey, Wolfe, Charles D A, Woolf, Anthony D, Wyper, Grant M A, Xavier, Deni, Xu, Gelin, Yadgir, Simon, Yahyazadeh Jabbari, Seyed Hossein, Yamada, Tomohide, Yan, Lijing L, Yano, Yuichiro, Yaseri, Mehdi, Yasin, Yasin Jemal, Yeshaneh, Alex, Yimer, Ebrahim M, Yip, Paul, Yisma, Engida, Yonemoto, Naohiro, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zadnik, Vesna, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zandian, Hamed, Zar, Heather J, Zenebe, Zerihun Menlkalew, Zhou, Maigeng, Zipkin, Ben, Zodpey, Sanjay, Zucker, Inbar, Zuhlke, Liesl Joanna, and Murray, Christopher J L
Subjects: Male, Health Status, health statu, HALE, mortality rate, communicable disease, DALYs, Global Burden of Disease, Risk Factors, Prevalence, Medicine and Health Sciences, Singapore, OUTCOMES, disability-adjusted life year, international cooperation, Medicine (all), 11 Medical And Health Sciences, Central African Republic, newborn disease, IRON-DEFICIENCY, AIDS, priority journal, Bahrain, disease severity, Female, Quality-Adjusted Life Years, cerebrovascular accident, Ukraine, Life Sciences & Biomedicine, INTERVENTIONS, Slovakia, DISORDERS, Burundi, WEIGHTS, GBD 2017 DALYs and HALE Collaborators, Communicable Diseases, Article, STYLE, Medicine, General & Internal, Life Expectancy, General & Internal Medicine, Humans, Disabled Persons, human, Healthy Lifestyle, HALE, DALYs, global burden of disease, Mortality, Aged, Science & Technology, Mortality, Premature, HIV, ischemic heart disease, major clinical study, PREVENTION, non communicable disease, GBD, disability-adjusted life-years, age, sex factor, Socioeconomic Factors, Algeria, Federation of Bosnia and Herzegovina, lower respiratory tract infection, Wounds and Injuries, GENDER, Human medicine, trend study, chronic obstructive lung disease
Abstract: Background How long one lives, how many years of life are spent in good and poor health, and how the population's state of health and leading causes of disability change over time all have implications for policy, planning, and provision of services. We comparatively assessed the patterns and trends of healthy life expectancy (HALE), which quantifies the number of years of life expected to be lived in good health, and the complementary measure of disability-adjusted life years (DALYs), a composite measure of disease burden capturing both premature mortality and prevalence and severity of ill health, for 359 diseases and injuries for 195 countries and territories over the past 28 years. Methods We used data for age-specific mortality rates, years of life lost (YLLs) due to premature mortality, and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to calculate HALE and DALYs from 1990 to 2017. We calculated HALE using age-specific mortality rates and YLDs per capita for each location, age, sex, and year. We calculated DALYs for 359 causes as the sum of YLLs and YLDs. We assessed how observed HALE and DALYs differed by country and sex from expected trends based on Sociodemographic Index (SDI). We also analysed HALE by decomposing years of life gained into years spent in good health and in poor health, between 1990 and 2017, and extra years lived by females compared with males. Findings Globally, from 1990 to 2017, life expectancy at birth increased by 7.4 years (95% uncertainty interval 74-7.8), from 65.6 years (65.3-65- 8) in 1990 to 73.0 years (72.7-73.3) in 2017. The increase in years of life varied from 5.1 years (5.0-5.3) in high SDI countries to 12.0 years (11.3-12.8) in low SDI countries. Of the additional years of life expected at birth, 26.3% (20.1-33.1) were expected to be spent in poor health in high SDI countries compared with 11.7% (8.8-15.1) in low-middle SDI countries. HALE at birth increased by 6.3 years (5.9-6.7), from 57.0 years (54.6-59.1) in 1990 to 63.3 years (60.5-65.7) in 2017. The increase varied from 3.8 years (3.4-4.1) in high SDI countries to 10.5 years (9.8-11.2) in low SDI countries. Even larger variations in HALE than these were observed between countries, ranging from 1.0 year (0.4-1.7) in Saint Vincent and the Grenadines (62.4 years [59.9-64.7] in 1990 to 63.5 years [60.9-65.8] in 2017) to 23.7 years (21.9-25.6) in Eritrea (30.7 years [28.9-32.2] in 1990 to 54.4 years [51.5-57.1] in 2017). In most countries, the increase in HALE was smaller than the increase in overall life expectancy, indicating more years lived in poor health. In 180 of 195 countries and territories, females were expected to live longer than males in 2017, with extra years lived varying from 1.4 years (0.6-2.3) in Algeria to 11.9 years (10.9-12.9) in Ukraine. Of the extra years gained, the proportion spent in poor health varied largely across countries, with less than 20% of additional years spent in poor health in Bosnia and Herzegovina, Burundi, and Slovakia, whereas in Bahrain all the extra years were spent in poor health. In 2017, the highest estimate of HALE at birth was in Singapore for both females (75.8 years [72.4-78.7]) and males (72.6 years [69 " 8-75.0]) and the lowest estimates were in Central African Republic (47.0 years [43.7-50.2] for females and 42.8 years [40.1-45.6] for males). Globally, in 2017, the five leading causes of DALYs were neonatal disorders, ischaemic heart disease, stroke, lower respiratory infections, and chronic obstructive pulmonary disease. Between 1990 and 2017, age-standardised DALY rates decreased by 41.3% (38.8-43.5) for communicable diseases and by 49"8% (47.9-51.6) for neonatal disorders. For non-communicable diseases, global DALYs increased by 40.1% (36.8-43.0), although age-standardised DALY rates decreased by 18.1% (16.0-20.2). Interpretation With increasing life expectancy in most countries, the question of whether the additional years of life gained are spent in good health or poor health has been increasingly relevant because of the potential policy implications, such as health-care provisions and extending retirement ages. In some locations, a large proportion of those additional years are spent in poor health. Large inequalities in HALE and disease burden exist across countries in different SDI quintiles and between sexes. The burden of disabling conditions has serious implications for health system planning and health-related expenditures. Despite the progress made in reducing the burden of communicable diseases and neonatal disorders in low S DI countries, the speed of this progress could be increased by scaling up proven interventions. The global trends among non-communicable diseases indicate that more effort is needed to maximise HALE, such as risk prevention and attention to upstream determinants of health. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.
Published: 2018

12. Female homicides in Brazil: global burden of disease study, 2000-2018.

Author: Vasconcelos NM, Souza JB, Soares Filho AM, Coelho PH, Reinach S, Stein C, Gomes CS, Flor LS, Gakidou E, Ribeiro ALP, and Malta DC
Abstract: Background: Female homicides are a public health-relevant issue, and its spatial distribution may evidence socioeconomic vulnerabilities. This study aims to analyze the temporal and spatial trends of female homicides in Brazil and investigate socioeconomic-demographic factors associated with it., Methods: This is an ecological, descriptive, and analytical epidemiological study investigating the age-standardized female homicide rate in all Brazilian municipalities between 2000 and 2018, divided into three periods. Spatial and temporal analyses were conducted using the Global Moran's Index and LISA to identify clusters of high and low rates. Rates were also calculated by population size and means of violence across macro-regions. For the last period, a multivariable linear regression model analyzed the association of female homicide rates with social, economic, and geographic factors., Findings: Female homicide rates in Brazil remained high during the studied period, with differences in trends between regions. Among the potentially associated factors, it was observed that male homicide rate, the high percentage of violent deaths among black women and those with low levels of education, in addition to the low Gross Domestic Product (GDP) per capita , were positively associated with female homicide, whereas larger cities were negatively associated., Interpretation: These findings show that Brazil is a country with a high risk of female homicide. Nevertheless, the vulnerability of women is unequally distributed in the country. Female homicides are mostly caused by domestic conflicts but can also be influenced by changes in the urban and social contexts., Funding: This project is funded by the Bill & Melinda Gates Foundation., Competing Interests: The authors have no conflicts of interest in this work., (© 2024 The Authors.)
Published: 2024
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13. The unequal impact of the pandemic at subnational levels and educational attainment-related inequalities in COVID-19 mortality, Brazil, 2020-2021.

Author: Szwarcwald CL, Almeida WS, Boccolini CS, Soares Filho AM, and Malta DC
Subjects: Humans, Brazil epidemiology, Female, Male, Middle Aged, Adult, Health Status Disparities, Aged, Socioeconomic Factors, Young Adult, SARS-CoV-2, Pandemics, Adolescent, COVID-19 mortality, COVID-19 epidemiology, Educational Status
Abstract: Objectives: We estimated COVID-19 mortality indicators in 2020-2021 to show the epidemic's impact at subnational levels and to analyze educational attainment-related inequalities in COVID-19 mortality in Brazil., Study Design: This was an ecological study with secondary mortality information., Methods: Crude and age-standardized COVID-19 mortality rates were calculated by gender, major regions, and states. The COVID-19 proportional mortality (percentage) was estimated by gender and age in each region. Measures of education-related inequalities in COVID-19 mortality were calculated per state, in each of which the COVID-19 maternal mortality rate (MMR) was estimated by the number of COVID-19 maternal deaths per 100,000 live births (LBs)., Results: The analysis of mortality rates at subnational levels showed critical regional differences. The North region proved to be the most affected by the pandemic, followed by the Center-West, with age-standardized COVID-19 mortality rates above 2 per 1000 inhabitants. The peak of COVID-19 mortality occurred in mid-March/April 2021 in all regions. Great inequality by educational level was found, with the illiterate population being the most negatively impacted in all states. The proportional mortality showed that males and females aged 50-69 years were the most affected. The MMR reached critical values (>100/100,000 LB) in several states of the North, Northeast, Southeast, and Center-West regions., Conclusions: This study highlights stark regional and educational disparities in COVID-19 mortality in Brazil. Exacerbated by the pandemic, these inequalities reveal potential areas for intervention to reduce disparities. The results also revealed high MMRs in certain states, underscoring pre-existing healthcare access challenges that worsened during the pandemic., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Published: 2024
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14. Spatial association of homicide rate with violence, sociodemographic, and public security factors: global burden of disease study 2018 for municipalities in Brazil.

Author: Soares Filho AM, Vasconcelos CH, Cunningham M, Ribeiro ALP, Naghavi M, and Malta DC
Subjects: Humans, Cities epidemiology, Brazil epidemiology, Violence, Homicide, Global Burden of Disease
Abstract: Objectives: To analyse spatial-temporal changes and spatial association of homicide rates with violence, sociodemographic, public security and human rights indicators in Brazilian municipalities., Study Design: An ecological study using homicide estimates from the Global Burden of Disease and population from the Brazilian Ministry of Health, 2000 to 2018. The explanatory variables come from the systems of mortality, notifications of violence and security, and the Brazilian Institute of Geography and Statistics., Methods: Moran indices and maps identified clusters of high and low risk for homicides in three trienniums (p < 0.05). Multivariate linear and spatial regressions estimated explanatory factors' contributions for the last triennium., Results: Municipalities with high rates of homicides (>34/100,000) doubled, reaching 21.5 %. Those rates were concentrated in big cities, and increased in smaller municipalities. Increases in critical areas were found in the Northeast and North regions: more than 40 % in the states of Sergipe, Bahia, Ceará, Rio Grande do Norte and Roraima. Decreases occurred in the Southeast and Midwest regions: more than 35 % in São Paulo and Rio de Janeiro states. The spatial model, with an 18.9 % higher R 2 (0.706), showed a positive association for records of violence, Blacks, low-level education, municipalities >50,000 inhabitants and municipalities with homicide and municipal police., Conclusions: An increase in and the interiorisation of homicide risk areas in Brazil was observed, with displacement among regions (from the Southeast to the North/Northeast). The level of violence was the main explanatory factor for homicides. Territorial space proved to be important to understand and prevent lethal crime., (Copyright © 2023 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)
Published: 2024
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15. Mortality by avoidable causes in Brazil from 1990 to 2019: data from the Global Burden of Disease Study.

Author: Malta DC, Saltarelli RMF, Veloso GA, Gomes CS, Soares Filho AM, Vieira EWR, Felisbino-Mendes MS, Naghavi M, and Ribeiro ALP
Subjects: Child, Infant, Newborn, Humans, Cause of Death, Brazil epidemiology, Global Burden of Disease, Global Health, Mortality, Communicable Diseases, Noncommunicable Diseases
Abstract: Objectives: The aim of this study was to analyse the trends of avoidable mortality in Brazil from 1990 to 2019 and its correlation with sociodemographic indexes (SDIs)., Study Design: Epidemiological mortality trends., Methods: This study analysed data from the Global Burden of Disease database. The list of causes of avoidable death, as proposed by Nolte and McKee, was applied and included 32 causes. The current study used age-standardised mortality rates and the rates of change, in addition to a correlation analysis between avoidable death and the SDI., Results: Mortality rates decreased from 343.90/100,000 inhabitants in 1990 to 155.80/100,000 inhabitants in 2019. Infectious diseases showed the largest decline in mortality rates, but notable decreases were also found for diarrhoeal diseases (-94.9%), maternal conditions (-66.5%) and neonatal conditions (-60.5%). Mortality rates for non-communicable diseases (NCDs) also decreased (-48%) but maintained a similar absolute number of deaths in 2019 compared with 1990. Decreased mortality rates were also found for ischaemic heart disease (-49.1%), stroke (-61.4%) and deaths due to adverse effects caused by medical treatments (-26.2%). Avoidable mortality rates declined in all of the 27 Brazilian states, and a high correlation was found between deaths and SDI (R = -0.74; P < 0.000001)., Conclusions: A reduction in avoidable deaths was found throughout Brazil over the study period, although major regional inequalities were revealed. Richer states presented the best overall reduction in mortality rates. The biggest decreases in mortality were seen in maternal and paediatric infectious diseases in the poorest states due to the expansion of the Primary Health System and improvements in sanitation. Today, NCDs predominate and efforts should be made to formulate public policies for the prevention and control of NCDs., (Copyright © 2023 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)
Published: 2024
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16. Violence Against LGB+ people in Brazil: analysis of the 2019 National Survey of Health.

Author: Vasconcelos NM, Alves FTA, Andrade GN, Pinto IV, Soares Filho AM, Pereira CA, and Malta DC
Abstract: Objective: To analyze the association between self-reported sexual orientation and violence in the Brazilian population., Methods: This cross-sectional epidemiological study used the 2019 National Survey of Health database. Total violence and its subtypes (psychological, physical, and sexual) were analyzed in the previous 12 months. Prevalence and odds ratio adjusted for age group were estimated, with their respective 95% confidence intervals, according to the self-reported sexual orientation of the Brazilian population aged 18 years and older. Statistical significance was set at 5%., Results: Most of the Brazilian population self-identified as heterosexual (94.75%) and 1.89% as LGB+. This percentage was lower than that of respondents who refused to answer the question (2.28%). The prevalence of violence in the general population of Brazil was 18.27%, and the most common subtype was psychological violence (17.36%). The LGB+ population was more than twice as likely to experience any type of violence. LGB+ women had the highest prevalence in all violence subtypes, and heterosexual men had the lowest. LGB+ women were over three times more likely to experience physical violence compared to heterosexual ones. Meanwhile, the probability of LGB+ men experiencing sexual violence was almost eight times higher than in heterosexual men., Conclusion: The prevalence of violence against the LGB+ population was high in the country. Public policies aimed at this population are necessary to fight discrimination against sexual diversity and ensure the rights of non-heterosexual people.
Published: 2023
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17. COVID-19 mortality in Brazil, 2020-21: consequences of the pandemic inadequate management.

Author: Szwarcwald CL, Boccolini CS, da Silva de Almeida W, Soares Filho AM, and Malta DC
Abstract: Background: The COVID-19 pandemic brought countless challenges to public health and highlighted the Brazilian health system vulnerabilities in facing the emergency. In this article, we analyze data on COVID-19-related deaths in 2020-21 to show the epidemic consequences in Brazil., Methods: The Mortality Information System and the Live Birth Information System were the primary information sources. We used population estimates in 2020-21 to calculate COVID-19 specific mortality rates by age, sex, and educational level. Considering the total number of COVID-19 deaths in 2020-21, the COVID-19 proportional mortality (%) was estimated for each age group and sex. A graph of the daily number of deaths from January 2020 to December 2021 by sex was elaborated to show the temporal evolution of COVID-19 deaths in Brazil. In addition, four indicators related to COVID-19 mortality were estimated: infant mortality rate (IMR); maternal mortality ratio (MMR); number and rate of orphans due to mother's COVID-19 death; the average number of years lost., Results: The overall COVID-19 mortality rate was 14.8 (/10,000). The mortality rates increase with age and show a decreasing gradient with higher schooling. The rate among illiterate people was 38.8/10,000, three times higher than a college education. Male mortality was 31% higher than female mortality. COVID-19 deaths represented 19.1% of all deaths, with the highest proportions in the age group of 40-59 years. The average number of years lost due to COVID-19 was 19 years. The MMR due to COVID-19 was 35.7 per 100,000 live births (LB), representing 37.4% of the overall MMR. Regarding the number of orphans due to COVID-19, we estimated that 40,830 children under 18 lost their mothers during the epidemic, with an orphans' rate of 7.5/10,000 children aged 0-17 years. The IMR was 11.7 per 1000 LB, with 0.2 caused by COVID-19. The peak of COVID-19 deaths occurred in March 2021, reaching almost 4000 COVID-19 deaths per day, higher than the average number of deaths per day from all causes in 2019., Conclusions: The delay in adopting public health measures necessary to control the epidemic has exacerbated the spread of the disease, resulting in several avoidable deaths., (© 2022. The Author(s).)
Published: 2022
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18. Mortality surveillance in Brazil: factors associated with certification of unspecified external cause of death.

Author: Soares Filho AM, Duarte E, Adair T, Bermúdez XPD, and Merchan-Hamann E
Subjects: Brazil epidemiology, Cause of Death, Cross-Sectional Studies, Humans, Certification, Death Certificates
Abstract: This article aims to analyze the association between characteristics of death - type of certifier and place of death - and the odds of an external cause death being certified as unspecified in Brazil. Cross-sectional study of deaths due to external causes from the Mortality Information System, 2017. Unspecified external cause (UEC) is the outcome variable in the models. Type of certifier physician, place of death and the interaction of these variables were the explanatory variables. Confounders were controlled by multiple logistic regression. UEC were the initial underlying cause for 22% of the 159,720 deaths from external causes in Brazil and 31% of hospital deaths issued by coroners. After adjustment for confounders, the odds of UEC in a hospital death certified by a coroner was 98% greater (OR=1.98; 95%CI: 1.53; 2.56) than in a home/street death issued by another certifier. This was greater than the odds for certifications by coroners (OR=1.23; 95%CI: 1.14; 1.33) and hospital deaths (OR=1.44; 95%CI: 1.32; 1.58). External causes certified by coroners and/or occurring in hospitals have a higher presence of UEC than other deaths; and indicate the need for coordinated initiatives by the health and public security sectors.
Published: 2022
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19. Primary Health Care in Northern and Northeastern Brazil: mapping team distribution disparities.

Author: Soares Filho AM, Vasconcelos CH, Dias AC, Souza ACC, Merchan-Hamann E, and Silva MRFD
Subjects: Brazil, Cities, Humans, Spatial Analysis, Family Health, Primary Health Care
Abstract: This study analyzes the spatial pattern of implementation of Primary Health Care (PHC) teams in Northern and Northeastern Brazil. This is an ecological study on the rates of Community Health Workers (ACS), Family Health Team (eSF), Oral Health Team (eSB), and Family Health Extended Center (NASF) based on data from the Ministry of Health (MoH). The analysis of the area data identified patterns of spatial dependence of the municipalities for the rates, using Moran indices and scatterplots to visualize critical areas' clusters (95% confidence). Municipalities of the North (n=450) and Northeast (n=1,794) had 132,174 ACS, 18,405 eSF, 13,017 eSB, and 2,205 NASF. The proportion of municipalities with rates within the recommended by the MoH were: ACS (>1.33), 96% in the North and 98.5% in the Northeast; eSF (>2.9/1,000), 54% and 80% in the respective regions; eSB (>2.9/10,000) 28% and 59% in these respective regions. NASF teams were deployed in 70% of the North and 89% of the Northeast. Except for ACS, the North was a critical team area, mainly in Pará, Rondônia, Amazonas, and Amapá. In the Northeast, these areas were smaller and concentrated mainly in western Bahia and eastern Maranhão. The Northeast showed a better composition of teams and a smaller extent of critical areas.
Published: 2022
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20. Frequency and factors associated with recording deaths due to unspecified external causes in Brazil: a cross-sectional study, 2017.

Author: Soares Filho AM, Bermudez XP, and Merchan-Hamann E
Subjects: Brazil epidemiology, Cause of Death, Cross-Sectional Studies, Humans, Coroners and Medical Examiners, Information Systems
Abstract: Objective: To analyze the frequency and factors associated with recording deaths due to unspecified external causes in Brazil., Methods: This was a cross-sectional study of Mortality Information System data for the year 2017. Univariate logistic regression was used to obtain odds ratios (OR) and 95% confidence intervals (95%CI) of nonspecific recording according to the explanatory variables - (hospital deaths and deaths certified by coroners)., Results: Brazil registered 159,720 deaths from external causes; 38.9% occurred in hospital, 83.4% were certified by coroners and 21.7% were from unspecified causes. Factors associated with the recording of unspecified external causes were hospital death (OR=2.00 - 95%CI 1.96;2.05) and the coroner's certification (OR=1.08 - 95%CI 1.04;1.11)., Conclusion: The frequency of recording unspecified external causes is greater for hospital deaths than for coroner's certification.
Published: 2021
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21. Burden of injury along the development spectrum: associations between the Socio-demographic Index and disability-adjusted life year estimates from the Global Burden of Disease Study 2017.

Author: Haagsma JA, James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, Liu Z, Lucchesi LR, Roberts NLS, Sylte DO, Adebayo OM, Ahmadi A, Ahmed MB, Aichour MTE, Alahdab F, Alghnam SA, Aljunid SM, Al-Raddadi RM, Alsharif U, Altirkawi K, Anjomshoa M, Antonio CAT, Appiah SCY, Aremu O, Arora A, Asayesh H, Assadi R, Awasthi A, Ayala Quintanilla BP, Balalla S, Banstola A, Barker-Collo SL, Bärnighausen TW, Bazargan-Hejazi S, Bedi N, Behzadifar M, Behzadifar M, Benjet C, Bennett DA, Bensenor IM, Bhaumik S, Bhutta ZA, Bijani A, Borges G, Borschmann R, Bose D, Boufous S, Brazinova A, Campuzano Rincon JC, Cárdenas R, Carrero JJ, Carvalho F, Castañeda-Orjuela CA, Catalá-López F, Choi JJ, Christopher DJ, Crowe CS, Dalal K, Daryani A, Davitoiu DV, Degenhardt L, De Leo D, De Neve JW, Deribe K, Dessie GA, deVeber GA, Dharmaratne SD, Doan LP, Dolan KA, Driscoll TR, Dubey M, El-Khatib Z, Ellingsen CL, El Sayed Zaki M, Endries AY, Eskandarieh S, Faro A, Fereshtehnejad SM, Fernandes E, Filip I, Fischer F, Franklin RC, Fukumoto T, Gezae KE, Gill TK, Goulart AC, Grada A, Guo Y, Gupta R, Haghparast Bidgoli H, Haj-Mirzaian A, Haj-Mirzaian A, Hamadeh RR, Hamidi S, Haro JM, Hassankhani H, Hassen HY, Havmoeller R, Hendrie D, Henok A, Híjar M, Hole MK, Homaie Rad E, Hossain N, Hostiuc S, Hu G, Igumbor EU, Ilesanmi OS, Irvani SSN, Islam SMS, Ivers RQ, Jacobsen KH, Jahanmehr N, Jakovljevic M, Jayatilleke AU, Jha RP, Jonas JB, Jorjoran Shushtari Z, Jozwiak JJ, Jürisson M, Kabir A, Kalani R, Kasaeian A, Kelbore AG, Kengne AP, Khader YS, Khafaie MA, Khalid N, Khan EA, Khoja AT, Kiadaliri AA, Kim YE, Kim D, Kisa A, Koyanagi A, Kuate Defo B, Kucuk Bicer B, Kumar M, Lalloo R, Lam H, Lami FH, Lansingh VC, Leasher JL, Li S, Linn S, Lunevicius R, Machado FR, Magdy Abd El Razek H, Magdy Abd El Razek M, Mahotra NB, Majdan M, Majeed A, Malekzadeh R, Malik MA, Malta DC, Manda AL, Mansournia MA, Massenburg BB, Maulik PK, Meheretu HAA, Mehndiratta MM, Melese A, Mendoza W, Mengesha MM, Meretoja TJ, Meretoja A, Mestrovic T, Miazgowski T, Miller TR, Mini GK, Mirrakhimov EM, Moazen B, Mohammad Gholi Mezerji N, Mohammadibakhsh R, Mohammed S, Molokhia M, Monasta L, Mondello S, Montero-Zamora PA, Moodley Y, Moosazadeh M, Moradi G, Moradi-Lakeh M, Morawska L, Velásquez IM, Morrison SD, Moschos MM, Mousavi SM, Murthy S, Musa KI, Naik G, Najafi F, Nangia V, Nascimento BR, Ndwandwe DE, Negoi I, Nguyen TH, Nguyen SH, Nguyen LH, Nguyen HLT, Ningrum DNA, Nirayo YL, Ofori-Asenso R, Ogbo FA, Oh IH, Oladimeji O, Olagunju AT, Olagunju TO, Olivares PR, Orpana HM, Otstavnov SS, P A M, Pakhale S, Park EK, Patton GC, Pesudovs K, Phillips MR, Polinder S, Prakash S, Radfar A, Rafay A, Rafiei A, Rahimi S, Rahimi-Movaghar V, Rahman MA, Rai RK, Ramezanzadeh K, Rawaf S, Rawaf DL, Renzaho AMN, Resnikoff S, Rezaeian S, Roever L, Ronfani L, Roshandel G, Sabde YD, Saddik B, Salamati P, Salimi Y, Salz I, Samy AM, Sanabria J, Sanchez Riera L, Santric Milicevic MM, Satpathy M, Sawhney M, Sawyer SM, Saxena S, Saylan M, Schneider IJC, Schwebel DC, Seedat S, Sepanlou SG, Shaikh MA, Shams-Beyranvand M, Shamsizadeh M, Sharif-Alhoseini M, Sheikh A, Shen J, Shigematsu M, Shiri R, Shiue I, Silva JP, Singh JA, Sinha DN, Soares Filho AM, Soriano JB, Soshnikov S, Soyiri IN, Starodubov VI, Stein DJ, Stokes MA, Sufiyan MB, Sunshine JE, Sykes BL, Tabarés-Seisdedos R, Tabb KM, Tehrani-Banihashemi A, Tessema GA, Thakur JS, Tran KB, Tran BX, Tudor Car L, Uthman OA, Uzochukwu BSC, Valdez PR, Varavikova E, Vasconcelos AMN, Venketasubramanian N, Violante FS, Vlassov V, Waheed Y, Wang YP, Wijeratne T, Winkler AS, Yadav P, Yano Y, Yenesew MA, Yip P, Yisma E, Yonemoto N, Younis MZ, Yu C, Zafar S, Zaidi Z, Zaman SB, Zamani M, Zhao Y, Zodpey S, Hay SI, Lopez AD, Mokdad AH, and Vos T
Subjects: Adolescent, Global Health, Humans, Life Expectancy, Disabled Persons, Global Burden of Disease, Quality-Adjusted Life Years, Wounds and Injuries
Abstract: Background: The epidemiological transition of non-communicable diseases replacing infectious diseases as the main contributors to disease burden has been well documented in global health literature. Less focus, however, has been given to the relationship between sociodemographic changes and injury. The aim of this study was to examine the association between disability-adjusted life years (DALYs) from injury for 195 countries and territories at different levels along the development spectrum between 1990 and 2017 based on the Global Burden of Disease (GBD) 2017 estimates., Methods: Injury mortality was estimated using the GBD mortality database, corrections for garbage coding and CODEm-the cause of death ensemble modelling tool. Morbidity estimation was based on surveys and inpatient and outpatient data sets for 30 cause-of-injury with 47 nature-of-injury categories each. The Socio-demographic Index (SDI) is a composite indicator that includes lagged income per capita, average educational attainment over age 15 years and total fertility rate., Results: For many causes of injury, age-standardised DALY rates declined with increasing SDI, although road injury, interpersonal violence and self-harm did not follow this pattern. Particularly for self-harm opposing patterns were observed in regions with similar SDI levels. For road injuries, this effect was less pronounced., Conclusions: The overall global pattern is that of declining injury burden with increasing SDI. However, not all injuries follow this pattern, which suggests multiple underlying mechanisms influencing injury DALYs. There is a need for a detailed understanding of these patterns to help to inform national and global efforts to address injury-related health outcomes across the development spectrum., Competing Interests: Competing interests: Dr. Carl Abelardo T Antonio reports personal fees from Johnson & Johnson (Philippines), Inc., outside the submitted work. Dr. Jasvinder Singh reports personal fees from Crealta/Horizon, Medisys, Fidia, UBM LLC, Medscape, WebMD, Clinical Care options, Clearview healthcare partners, Putnam associates, Spherix, the National Institutes of Health and the American College of Rheumatology, stock options in Amarin pharmaceuticals and Viking pharmaceuticals, participating in the steering committee of OMERACT, an international organization that develops measures for clinical trials and receives arm’s length funding from 12 pharmaceutical companies, including Amgen, Janssen, Novartis, Roche, UCB Group, Ardea/Astra Zeneca, Bristol Myers Squibb, Celgene, EliLilly, Horizon Pharma, Pfizer, and Centrexion. Dr. Josep Maria Haro reports personal fees from Roche and Lundbeck, and that the institute for which they work provides services to Eli Lilly and Co., outside the submitted work. Dr. Mete Saylan is an employee of Bayer AG, outside the submitted work. Dr Sheikh Mohammed Shariful Islam is funded by National Heart Foundation of Australia and supported by a senior research fellowship from Deakin University, outside the submitted work. Dr. Spencer James reports grants from Sanofi Pasteur, outside the submitted work., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)
Published: 2020
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22. Association between firearms and mortality in Brazil, 1990 to 2017: a global burden of disease Brazil study.

Author: Malta DC, Soares Filho AM, Pinto IV, de Souza Minayo MC, Lima CM, Machado ÍE, Teixeira RA, Neto OLM, Ladeira RM, Merchan-Hamann E, de Souza MFM, Vasconcelos CH, Vidotti CCF, Cousin E, Glenn S, Bisignano C, Chew A, Ribeiro AL, and Naghavi M
Subjects: Adolescent, Adult, Age Distribution, Aged, Brazil epidemiology, Child, Child, Preschool, Cost of Illness, Female, Global Health, Homicide statistics & numerical data, Humans, Life Expectancy, Male, Middle Aged, Quality-Adjusted Life Years, Residence Characteristics, Risk Factors, Sex Distribution, Socioeconomic Factors, Suicide statistics & numerical data, Wounds and Injuries epidemiology, Young Adult, Firearms statistics & numerical data, Global Burden of Disease statistics & numerical data, Mortality trends
Abstract: Background: Brazil leads the world in number of firearm deaths and ranks sixth by country in rate of firearm deaths per 100,000 people. This study aims to analyze trends in and burden of mortality by firearms, according to age and sex, for Brazil, and the association between these deaths and indicators of possession and carrying of weapons using data from the global burden of diseases, injuries, and risk factors study (GBD) 2017., Methods: We used GBD 2017 estimates of mortality due to physical violence and self-harm from firearms for Brazil to analyze the association between deaths by firearms and explanatory variables., Results: Deaths from firearms increased in Brazil from 25,819 in 1990 to 48,493 in 2017. Firearm mortality rates were higher among men and in the 20-24 age group; the rate was 20 times higher than for women in the same age group. Homicide rates increased during the study period, while mortality rates for suicides and accidental deaths decreased. The group of Brazilian federation units with the highest firearm collection rate (median = 7.5) showed reductions in the rate of total violent deaths by firearms. In contrast, the group with the lowest firearm collection rate (median = 2.0) showed an increase in firearm deaths from 2000 to 2017. An increase in the rate of voluntary return of firearms was associated with a reduction in mortality rates of unintentional firearm deaths (r = -0.364, p < 0.001). An increase in socio-demographic index (SDI) was associated with a reduction in all firearm death rates (r = -0.266, p = 0.008). An increase in the composite index of firearms seized or collected was associated with a reduction in rates of deaths by firearm in the subgroup of females, children, and the elderly (r = -0.269, p = 0.005)., Conclusions: There was a change in the trend of firearms deaths after the beginning of the collection of weapons in 2004. Federation units that collected more guns have reduced rates of violent firearm deaths.
Published: 2020
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23. Mapping disparities in homicide trends across Brazil: 2000-2014.

Author: Nsoesie EO, Lima Neto AS, Jay J, Wang H, Zinszer K, Saha S, Maharana A, Marinho F, and Soares Filho AM
Abstract: Background: Homicides are a major problem in Brazil. Drugs and arms trafficking, and land conflicts are three of the many factors driving homicide rates in Brazil. Understanding long-term spatiotemporal trends and social structural factors associated with homicides in Brazil would be useful for designing policies aimed at reducing homicide rates., Methods: We obtained data from 2000 to 2014 from the Brazil Ministry of Health (MOH) Mortality Information System and sociodemographic data from the Brazil Institute of Geography and Statistics (IBGE). First, we quantified the rate of change in homicides at the municipality and state levels. Second, we used principal component regression and k-medoids clustering to examine differences in temporal trends across municipalities. Lastly, we used Bayesian hierarchical space-time models to describe spatio-temporal patterns and to assess the contribution of structural factors., Results: There were significant variations in homicide rates across states and municipalities. We noted the largest decrease in homicide rates in the western and southeastern states of Sao Paulo, Rio de Janeiro and Espirito Santo, which coincided with an increase in homicide rates in the northeastern states of Ceará, Alagoas, Paraiba, Rio Grande Norte, Sergipe and Bahia during the fifteen-year period. The decrease in homicides in municipalities with populations of at least 250,000 coincided with an increase in municipalities with 25,000 people or less. Structural factors that predicted municipality-level homicide rates included crude domestic product, urbanization, border with neighboring countries and proportion of population aged fifteen to twenty-nine., Conclusions: Our findings support both a dissemination hypothesis and an interiorization hypothesis. These findings should be considered when designing interventions to curb homicide rates.
Published: 2020
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24. [Expansion, displacement and interiorization of homicides in Brazil, between 2000 and 2015: a spatial analysis].

Author: Soares Filho AM, Merchan-Hamann E, and Vasconcelos CH
Subjects: Brazil epidemiology, Environment, Humans, Spatial Analysis, Homicide, Poverty
Abstract: The scope of this paper is to analyze the variation of the spatial pattern of the homicide rate in Brazil between 2000 and 2015. It is an ecological study by micro-regions of homicides taken from the Mortality Information System, using Moran indexes, and critical area clusters (95% CI). The rate increased by 6% (to 29.1/100,000 inhabitants (95% CI 28.9, 29.4), and in 80% of the micro regions between 2000 and 2015. The areas with high rates (> 38.2/100,000) increased 2.7-fold. In 2000, the highest rates were concentrated in areas in Pernambuco, São Paulo, Mato Grosso and Rio de Janeiro; by 2015, it will affect most states in the North and Northeast. The coastal regions of the Northeast and borders of Pará and Maranhão in the Amazon are critical areas. The lowest rate (19.1/100,000) is in São Paulo and Santa Catarina micro regions, with less critical clusters in the Southeast and South regions. Homicides have expanded into the interior of Brazil, with displacement between regions, mainly gravitating towards the poorest, which exhibit more critical areas in several scenarios, such as state borders and the coast. Conversely, there is marked contraction of homicides in states of highly developed regions with the presence of less critical areas. Key words Homicide.
Published: 2020
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25. [Trend and distribution of the homicide mortality rate in accordance with the size of the population of Brazilian municipalities - 2000 and 2015].

Author: Soares Filho AM, Duarte EC, and Merchan-Hamann E
Subjects: Adolescent, Adult, Brazil, Child, Child, Preschool, Cities statistics & numerical data, Female, Humans, Infant, Male, Middle Aged, Time Factors, Young Adult, Homicide trends, Mortality trends, Population Density
Abstract: The scope of this study is to analyze the trends and distribution of homicide mortality rates (HMR) according to the population size of Brazilian municipalities between 2000 and 2015. It is an ecological study of deaths recorded in the Mortality Information System, with HMR standardized by the direct method and 95% confidence interval. HMR in Brazil grew 6% (to 29.1/100,000) in the period, with an increase in small municipalities (83%; 12.7 to 23.2/100,000) and mediumsized cities (52%; 19.7% to 30.1/100,000); which is true for both sexes, different ages, regions and firearm-related events. HMR decreased in major cities (19%; 40.6% to 32.9/100,000) and the Southeast region (55%; 45.6% to 20.6/100,000). The relative risk (RR) of small and medium-sized cities in relation to large cities is already greater than or close to 1 among women (RR 0.99; 1.03), people aged 60 years or older (RR 1.43; 1.36) and homicides by other means (RR 1.16; 1.18). The cities of São Paulo and Rio de Janeiro contributed the most to the reduction of HMR, especially in large cities (-37.6 and -22.3 homicides/100,000 inhabitants). Small and medium-sized municipalities have consistent trends of an increase in HMR even considering population subgroups.
Published: 2020
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26. Improvement of the unspecified external causes classification based on the investigation of death in Brazil in 2017.

Author: Soares Filho AM, Vasconcelos CH, Nóbrega AAD, Pinto IV, Merchan-Hamann E, Ishitani LH, and França EB
Subjects: Accidents mortality, Accidents, Traffic mortality, Adolescent, Adult, Age Distribution, Aged, Brazil epidemiology, Child, Child, Preschool, Cities epidemiology, Data Accuracy, Female, Homicide statistics & numerical data, Humans, Infant, Male, Medical Records, Middle Aged, Sex Distribution, Suicide statistics & numerical data, Violence statistics & numerical data, Young Adult, Cause of Death, Death Certificates, Information Systems standards
Abstract: Background: Unspecified causes of death are among the traditional indicators of quality of information., Objective: To verify the performance of the 60 cities in the Data for Health Initiative project and to analyze the reclassification of unspecified external causes of death (UEC)., Methods: Using the 2017 records from the Mortality Information System, the proportion and percent change in UEC were compared after investigation between project cities and other cities, and the percent of reclassification to specific external causes was calculated., Results: The project cities comprised 52% (n = 11,759) of the total UEC in Brazil, of which 64.5% were reclassified after investigation, whereas the other cities reclassified 31% of UEC. Results were similar for men, youth, blacks, metropolitan cities, the Southeast region, and deaths attested by forensic institutes. In the project cities, pedestrian traffic accidents were external causes with greater reclassification. In men, the UEC was reclassified to homicides (23.8%) and accident of terrestrial transportation (ATT) (11.1%), with motorcyclists (4.4%) and pedestrians (4.3%) being the most prominent. In women, these causes were changed to other accident causes (20.8%), ATT (10.6%) and homicides (7.9%). UEC changed to ATT (18.3%) in the age groups of 0-14 years old and to homicides (32.5%) in the age groups of 15-44 years., Conclusion: The project cities obtained better results after investigation of UEC, enabling analysis of the reclassification to specific causes by sex and age groups.
Published: 2019
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27. Global Mortality From Firearms, 1990-2016.

Author: Naghavi M, Marczak LB, Kutz M, Shackelford KA, Arora M, Miller-Petrie M, Aichour MTE, Akseer N, Al-Raddadi RM, Alam K, Alghnam SA, Antonio CAT, Aremu O, Arora A, Asadi-Lari M, Assadi R, Atey TM, Avila-Burgos L, Awasthi A, Ayala Quintanilla BP, Barker-Collo SL, Bärnighausen TW, Bazargan-Hejazi S, Behzadifar M, Behzadifar M, Bennett JR, Bhalla A, Bhutta ZA, Bilal AI, Borges G, Borschmann R, Brazinova A, Campuzano Rincon JC, Carvalho F, Castañeda-Orjuela CA, Dandona L, Dandona R, Dargan PI, De Leo D, Dharmaratne SD, Ding EL, Phuc Do H, Doku DT, Doyle KE, Driscoll TR, Edessa D, El-Khatib Z, Endries AY, Esteghamati A, Faro A, Farzadfar F, Feigin VL, Fischer F, Foreman KJ, Franklin RC, Fullman N, Futran ND, Gebrehiwot TT, Gutiérrez RA, Hafezi-Nejad N, Haghparast Bidgoli H, Hailu GB, Haro JM, Hassen HY, Hawley C, Hendrie D, Híjar M, Hu G, Ilesanmi OS, Jakovljevic M, James SL, Jayaraman S, Jonas JB, Kahsay A, Kasaeian A, Keiyoro PN, Khader Y, Khalil IA, Khang YH, Khubchandani J, Ahmad Kiadaliri A, Kieling C, Kim YJ, Kosen S, Krohn KJ, Kumar GA, Lami FH, Lansingh VC, Larson HJ, Linn S, Lunevicius R, Magdy Abd El Razek H, Magdy Abd El Razek M, Malekzadeh R, Carvalho Malta D, Mason-Jones AJ, Matzopoulos R, Memiah PTN, Mendoza W, Meretoja TJ, Mezgebe HB, Miller TR, Mohammed S, Moradi-Lakeh M, Mori R, Nand D, Tat Nguyen C, Le Nguyen Q, Ningrum DNA, Akpojene Ogbo F, Olagunju AT, Patton GC, Phillips MR, Polinder S, Pourmalek F, Qorbani M, Rahimi-Movaghar A, Rahimi-Movaghar V, Rahman M, Rai RK, Ranabhat CL, Rawaf DL, Rawaf S, Rowhani-Rahbar A, Safdarian M, Safiri S, Sagar R, Salama JS, Sanabria J, Santric Milicevic MM, Sarmiento-Suárez R, Sartorius B, Satpathy M, Schwebel DC, Seedat S, Sepanlou SG, Shaikh MA, Sharew NT, Shiue I, Singh JA, Sisay M, Skirbekk V, Soares Filho AM, Stein DJ, Stokes MA, Sufiyan MB, Swaroop M, Sykes BL, Tabarés-Seisdedos R, Tadese F, Tran BX, Thanh Tran T, Ukwaja KN, Vasankari TJ, Vlassov V, Werdecker A, Ye P, Yip P, Yonemoto N, Younis MZ, Zaidi Z, El Sayed Zaki M, Hay SI, Lim SS, Lopez AD, Mokdad AH, Vos T, and Murray CJL
Subjects: Adolescent, Adult, Age Distribution, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Global Health statistics & numerical data, Humans, Infant, Infant, Newborn, Male, Middle Aged, Mortality trends, Sex Distribution, Young Adult, Firearms statistics & numerical data, Homicide statistics & numerical data, Suicide statistics & numerical data, Wounds, Gunshot mortality
Abstract: Importance: Understanding global variation in firearm mortality rates could guide prevention policies and interventions., Objective: To estimate mortality due to firearm injury deaths from 1990 to 2016 in 195 countries and territories., Design, Setting, and Participants: This study used deidentified aggregated data including 13 812 location-years of vital registration data to generate estimates of levels and rates of death by age-sex-year-location. The proportion of suicides in which a firearm was the lethal means was combined with an estimate of per capita gun ownership in a revised proxy measure used to evaluate the relationship between availability or access to firearms and firearm injury deaths., Exposures: Firearm ownership and access., Main Outcomes and Measures: Cause-specific deaths by age, sex, location, and year., Results: Worldwide, it was estimated that 251 000 (95% uncertainty interval [UI], 195 000-276 000) people died from firearm injuries in 2016, with 6 countries (Brazil, United States, Mexico, Colombia, Venezuela, and Guatemala) accounting for 50.5% (95% UI, 42.2%-54.8%) of those deaths. In 1990, there were an estimated 209 000 (95% UI, 172 000 to 235 000) deaths from firearm injuries. Globally, the majority of firearm injury deaths in 2016 were homicides (64.0% [95% UI, 54.2%-68.0%]; absolute value, 161 000 deaths [95% UI, 107 000-182 000]); additionally, 27% were firearm suicide deaths (67 500 [95% UI, 55 400-84 100]) and 9% were unintentional firearm deaths (23 000 [95% UI, 18 200-24 800]). From 1990 to 2016, there was no significant decrease in the estimated global age-standardized firearm homicide rate (-0.2% [95% UI, -0.8% to 0.2%]). Firearm suicide rates decreased globally at an annualized rate of 1.6% (95% UI, 1.1-2.0), but in 124 of 195 countries and territories included in this study, these levels were either constant or significant increases were estimated. There was an annualized decrease of 0.9% (95% UI, 0.5%-1.3%) in the global rate of age-standardized firearm deaths from 1990 to 2016. Aggregate firearm injury deaths in 2016 were highest among persons aged 20 to 24 years (for men, an estimated 34 700 deaths [95% UI, 24 900-39 700] and for women, an estimated 3580 deaths [95% UI, 2810-4210]). Estimates of the number of firearms by country were associated with higher rates of firearm suicide (P < .001; R2 = 0.21) and homicide (P < .001; R2 = 0.35)., Conclusions and Relevance: This study estimated between 195 000 and 276 000 firearm injury deaths globally in 2016, the majority of which were firearm homicides. Despite an overall decrease in rates of firearm injury death since 1990, there was variation among countries and across demographic subgroups.
Published: 2018
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28. Homicide victimization according to racial characteristics in Brazil.

Author: Soares Filho AM
Subjects: Brazil epidemiology, Female, Homicide ethnology, Humans, Male, Models, Statistical, Risk Factors, Sex Distribution, Sex Factors, Socioeconomic Factors, Time Factors, Violence, Black People statistics & numerical data, Homicide statistics & numerical data, White People statistics & numerical data
Abstract: Objective: To describe the temporal patterns of mortality by homicide in Brazil., Methods: A series of homicides in Brazil from 2000 to 2009 were studied. The explanatory variables were race/skin color, gender and education. The death statistics were obtained from the Mortality Information System. A trend analysis was performed by means of a polynomial regression for a historic time series (p < 0.05, 95% confidence interval)., Results: The black population represented 69% of the homicide victims in 2009. The homicide rate increased in the black population, while it decreased in the white population in the period studied. The homicide rate increased in groups with both higher and lower education among blacks; among whites, the rate decreased for those with the lowest level of schooling and remained stable in the group with higher educational levels. In 2009, blacks had a higher risk of death than whites from homicide, regardless of education level. Between 2004 and 2009, the homicide rate decreased in the white population, while it increased in the black population., Conclusions: The relative risk of falling victim to homicide increased in the black population, suggesting an increase in inequality. The effect of the anti-gun measures implemented in Brazil in 2004 was positive in the white population and less pronounced in the black population. Overall, race/skin color predicted the occurrence of homicide.
Published: 2011
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29. [The historical evolution of dengue prevention and control programs in Brazil].

Author: Ferreira BJ, Souza Mde F, Soares Filho AM, and Carvalho AA
Subjects: Brazil, Dengue epidemiology, Dengue history, History, 20th Century, History, 21st Century, Program Evaluation, Dengue prevention & control, National Health Programs history
Abstract: An epidemiological analysis of the forms and distribution of Dengue fever in Brazil and worldwide was carried out. The National Program of Dengue Control (NPDC) was evaluated based on the data available at 'Diagdengue' an official computerized information system allowing to follow-up the implantation and impact of the Program. A factorial analysis was performed by means of a set of indicators contributing to variability. The indicator scores were added and the cities demanding for prior attention of the NPCD were classified for the construction of an index, which is presented three-monthly for every state, with intervals distributed between 0 and 9. The implantation of the NCPD is irregular. Cities with bad implantation are predominating in the north and northeast of the country. In the second analysis, socio-economic variables of the selected cities were added. For the association tests these cities were classified into two groups: 'Bad' and 'Good/Very Good', based on the association of the index of building infestation informed in the FAD (Yellow Fever and Dengue) database and the constructed index. Associations were verified based on variance analysis, trend test and trend estimate. It was observed that cities with low rates of illiteracy, efficient garbage collection and high ratio of sanitary installations are correlated with a good classification of the city according to Diagdengue.
Published: 2009
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29 results on '"Soares Filho AM"'

1. Mortality surveillance in Brazil: factors associated with certification of unspecified external cause of death

2. Burden of injury along the development spectrum: associations between the Socio-demographic Index and disability-adjusted life year estimates from the Global Burden of Disease Study 2017

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

4. Global Mortality From Firearms, 1990-2016

5. Global mortality from firearms, 1990-2016

6. 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

7. 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

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

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

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

12. Female homicides in Brazil: global burden of disease study, 2000-2018.

13. The unequal impact of the pandemic at subnational levels and educational attainment-related inequalities in COVID-19 mortality, Brazil, 2020-2021.

14. Spatial association of homicide rate with violence, sociodemographic, and public security factors: global burden of disease study 2018 for municipalities in Brazil.

15. Mortality by avoidable causes in Brazil from 1990 to 2019: data from the Global Burden of Disease Study.

16. Violence Against LGB+ people in Brazil: analysis of the 2019 National Survey of Health.

17. COVID-19 mortality in Brazil, 2020-21: consequences of the pandemic inadequate management.

18. Mortality surveillance in Brazil: factors associated with certification of unspecified external cause of death.

19. Primary Health Care in Northern and Northeastern Brazil: mapping team distribution disparities.

20. Frequency and factors associated with recording deaths due to unspecified external causes in Brazil: a cross-sectional study, 2017.

21. Burden of injury along the development spectrum: associations between the Socio-demographic Index and disability-adjusted life year estimates from the Global Burden of Disease Study 2017.

22. Association between firearms and mortality in Brazil, 1990 to 2017: a global burden of disease Brazil study.

23. Mapping disparities in homicide trends across Brazil: 2000-2014.

24. [Expansion, displacement and interiorization of homicides in Brazil, between 2000 and 2015: a spatial analysis].

25. [Trend and distribution of the homicide mortality rate in accordance with the size of the population of Brazilian municipalities - 2000 and 2015].

26. Improvement of the unspecified external causes classification based on the investigation of death in Brazil in 2017.

27. Global Mortality From Firearms, 1990-2016.

28. Homicide victimization according to racial characteristics in Brazil.

29. [The historical evolution of dengue prevention and control programs in Brazil].

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