1
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Bhattacharjee NV, Schaeffer LE, Marczak LB, Ross JM, Swartz SJ, Albright J, Gardner WM, Shields C, Sligar A, Schipp MF, Pickering BV, Henry NJ, Johnson KB, Louie C, Cork MA, Steuben KM, Lazzar-Atwood A, Lu D, Kinyoki DK, Osgood-Zimmerman A, Earl L, Mosser JF, Deshpande A, Burstein R, Woyczynski LP, Wilson KF, VanderHeide JD, Wiens KE, Reiner RC, Piwoz EG, Rawat R, Sartorius B, Davis Weaver N, Nixon MR, Smith DL, Kassebaum NJ, Gakidou E, Lim SS, Mokdad AH, Murray CJL, Dwyer-Lindgren L, Hay SI. Mapping exclusive breastfeeding in Africa between 2000 and 2017. Nat Med 2019; 25:1205-1212. [PMID: 31332393 PMCID: PMC6749549 DOI: 10.1038/s41591-019-0525-0] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 06/17/2019] [Indexed: 11/30/2022]
Abstract
Exclusive breastfeeding (EBF)—giving infants only breast-milk (and medications, oral rehydration salts and vitamins as needed) with no additional food or drink for their first six months of life—is one of the most effective strategies for preventing child mortality1–4. Despite these advantages, only 37% of infants under 6 months of age in Africa were exclusively breastfed in 20175, and the practice of EBF varies by population. Here, we present a fine-scale geospatial analysis of EBF prevalence and trends in 49 African countries from 2000–2017, providing policy-relevant administrative- and national-level estimates. Previous national-level analyses found that most countries will not meet the World Health Organization’s Global Nutrition Target of 50% EBF prevalence by 20256. Our analyses show that even fewer will achieve this ambition in all subnational areas. Our estimates provide the ability to visualize subnational EBF variability and identify populations in need of additional breastfeeding support. Exclusive breastfeeding in Africa is highly varied within and between countries, with many countries unlikely to reach World Health Organization 2025 targets without urgent action.
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Affiliation(s)
| | - Lauren E Schaeffer
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Laurie B Marczak
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jennifer M Ross
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Global Health, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Scott J Swartz
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - James Albright
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - William M Gardner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Chloe Shields
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Amber Sligar
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Megan F Schipp
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Brandon V Pickering
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nathaniel J Henry
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Kimberly B Johnson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Celia Louie
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Michael A Cork
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Krista M Steuben
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Alice Lazzar-Atwood
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Dan Lu
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Damaris K Kinyoki
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jonathan F Mosser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Lauren P Woyczynski
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Katherine F Wilson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - John D VanderHeide
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Kirsten E Wiens
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | | | - Rahul Rawat
- Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Benn Sartorius
- Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.,Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Nicole Davis Weaver
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Molly R Nixon
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Nicholas J Kassebaum
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
| | - Emmanuela Gakidou
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Laura Dwyer-Lindgren
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. .,Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
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2
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Dwyer-Lindgren L, Cork MA, Sligar A, Steuben KM, Wilson KF, Provost NR, Mayala BK, VanderHeide JD, Collison ML, Hall JB, Biehl MH, Carter A, Frank T, Douwes-Schultz D, Burstein R, Casey DC, Deshpande A, Earl L, El Bcheraoui C, Farag TH, Henry NJ, Kinyoki D, Marczak LB, Nixon MR, Osgood-Zimmerman A, Pigott D, Reiner RC, Ross JM, Schaeffer LE, Smith DL, Davis Weaver N, Wiens KE, Eaton JW, Justman JE, Opio A, Sartorius B, Tanser F, Wabiri N, Piot P, Murray CJL, Hay SI. Mapping HIV prevalence in sub-Saharan Africa between 2000 and 2017. Nature 2019; 570:189-193. [PMID: 31092927 PMCID: PMC6601349 DOI: 10.1038/s41586-019-1200-9] [Citation(s) in RCA: 243] [Impact Index Per Article: 48.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 04/10/2019] [Indexed: 12/16/2022]
Abstract
HIV/AIDS is a leading cause of disease burden in sub-Saharan Africa. Existing evidence has demonstrated that there is substantial local variation in the prevalence of HIV; however, subnational variation has not been investigated at a high spatial resolution across the continent. Here we explore within-country variation at a 5 × 5-km resolution in sub-Saharan Africa by estimating the prevalence of HIV among adults (aged 15–49 years) and the corresponding number of people living with HIV from 2000 to 2017. Our analysis reveals substantial within-country variation in the prevalence of HIV throughout sub-Saharan Africa and local differences in both the direction and rate of change in HIV prevalence between 2000 and 2017, highlighting the degree to which important local differences are masked when examining trends at the country level. These fine-scale estimates of HIV prevalence across space and time provide an important tool for precisely targeting the interventions that are necessary to bringing HIV infections under control in sub-Saharan Africa. Fine-scale estimates of the prevalence of HIV in adults across sub-Saharan Africa reveal substantial within-country variation and local differences in both the direction and rate of change in the prevalence of HIV between 2000 and 2017.
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Affiliation(s)
- Laura Dwyer-Lindgren
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Michael A Cork
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Amber Sligar
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Krista M Steuben
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Kate F Wilson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Naomi R Provost
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - John D VanderHeide
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Michael L Collison
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jason B Hall
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Molly H Biehl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Austin Carter
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Tahvi Frank
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Dirk Douwes-Schultz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Charbel El Bcheraoui
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Tamer H Farag
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nathaniel J Henry
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Damaris Kinyoki
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Laurie B Marczak
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Molly R Nixon
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - David Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jennifer M Ross
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Global Health, University of Washington, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Lauren E Schaeffer
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nicole Davis Weaver
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Kirsten E Wiens
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jeffrey W Eaton
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Jessica E Justman
- ICAP, Mailman School of Public Health, Columbia University, New York, NY, USA.,Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Alex Opio
- Medireal Investment Uganda, Entebbe, Uganda
| | - Benn Sartorius
- Public Health Medicine, School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Frank Tanser
- School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa.,Africa Health Research Institute, KwaZulu-Natal, South Africa.,Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa.,Research Department of Infection & Population Health, University College London, London, UK
| | - Njeri Wabiri
- HIV/AIDS, STIs & TB Research Programme, Human Sciences Research Council, Pretoria, South Africa
| | - Peter Piot
- London School of Hygiene & Tropical Medicine, London, UK
| | | | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
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3
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Mosser JF, Gagne-Maynard W, Rao PC, Osgood-Zimmerman A, Fullman N, Graetz N, Burstein R, Updike RL, Liu PY, Ray SE, Earl L, Deshpande A, Casey DC, Dwyer-Lindgren L, Cromwell EA, Pigott DM, Shearer FM, Larson HJ, Weiss DJ, Bhatt S, Gething PW, Murray CJL, Lim SS, Reiner RC, Hay SI. Mapping diphtheria-pertussis-tetanus vaccine coverage in Africa, 2000-2016: a spatial and temporal modelling study. Lancet 2019; 393:1843-1855. [PMID: 30961907 PMCID: PMC6497987 DOI: 10.1016/s0140-6736(19)30226-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Revised: 12/19/2018] [Accepted: 01/15/2019] [Indexed: 01/29/2023]
Abstract
BACKGROUND Routine childhood vaccination is among the most cost-effective, successful public health interventions available. Amid substantial investments to expand vaccine delivery throughout Africa and strengthen administrative reporting systems, most countries still require robust measures of local routine vaccine coverage and changes in geographical inequalities over time. METHODS This analysis drew from 183 surveys done between 2000 and 2016, including data from 881 268 children in 49 African countries. We used a Bayesian geostatistical model calibrated to results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017, to produce annual estimates with high-spatial resolution (5 × 5 km) of diphtheria-pertussis-tetanus (DPT) vaccine coverage and dropout for children aged 12-23 months in 52 African countries from 2000 to 2016. FINDINGS Estimated third-dose (DPT3) coverage increased in 72·3% (95% uncertainty interval [UI] 64·6-80·3) of second-level administrative units in Africa from 2000 to 2016, but substantial geographical inequalities in DPT coverage remained across and within African countries. In 2016, DPT3 coverage at the second administrative (ie, district) level varied by more than 25% in 29 of 52 countries, with only two (Morocco and Rwanda) of 52 countries meeting the Global Vaccine Action Plan target of 80% DPT3 coverage or higher in all second-level administrative units with high confidence (posterior probability ≥95%). Large areas of low DPT3 coverage (≤50%) were identified in the Sahel, Somalia, eastern Ethiopia, and in Angola. Low first-dose (DPT1) coverage (≤50%) and high relative dropout (≥30%) together drove low DPT3 coverage across the Sahel, Somalia, eastern Ethiopia, Guinea, and Angola. INTERPRETATION Despite substantial progress in Africa, marked national and subnational inequalities in DPT coverage persist throughout the continent. These results can help identify areas of low coverage and vaccine delivery system vulnerabilities and can ultimately support more precise targeting of resources to improve vaccine coverage and health outcomes for African children. FUNDING Bill & Melinda Gates Foundation.
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Affiliation(s)
- Jonathan F Mosser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - William Gagne-Maynard
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Puja C Rao
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Rachel L Updike
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Patrick Y Liu
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Sarah E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Laura Dwyer-Lindgren
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Elizabeth A Cromwell
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | | | - Heidi Jane Larson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Samir Bhatt
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | | | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
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4
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Brady OJ, Osgood-Zimmerman A, Kassebaum NJ, Ray SE, de Araújo VEM, da Nóbrega AA, Frutuoso LCV, Lecca RCR, Stevens A, Zoca de Oliveira B, de Lima JM, Bogoch II, Mayaud P, Jaenisch T, Mokdad AH, Murray CJL, Hay SI, Reiner RC, Marinho F. The association between Zika virus infection and microcephaly in Brazil 2015-2017: An observational analysis of over 4 million births. PLoS Med 2019; 16:e1002755. [PMID: 30835728 PMCID: PMC6400331 DOI: 10.1371/journal.pmed.1002755] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 01/28/2019] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND In 2015, high rates of microcephaly were reported in Northeast Brazil following the first South American Zika virus (ZIKV) outbreak. Reported microcephaly rates in other Zika-affected areas were significantly lower, suggesting alternate causes or the involvement of arboviral cofactors in exacerbating microcephaly rates. METHODS AND FINDINGS We merged data from multiple national reporting databases in Brazil to estimate exposure to 9 known or hypothesized causes of microcephaly for every pregnancy nationwide since the beginning of the ZIKV outbreak; this generated between 3.6 and 5.4 million cases (depending on analysis) over the time period 1 January 2015-23 May 2017. The association between ZIKV and microcephaly was statistically tested against models with alternative causes or with effect modifiers. We found no evidence for alternative non-ZIKV causes of the 2015-2017 microcephaly outbreak, nor that concurrent exposure to arbovirus infection or vaccination modified risk. We estimate an absolute risk of microcephaly of 40.8 (95% CI 34.2-49.3) per 10,000 births and a relative risk of 16.8 (95% CI 3.2-369.1) given ZIKV infection in the first or second trimester of pregnancy; however, because ZIKV infection rates were highly variable, most pregnant women in Brazil during the ZIKV outbreak will have been subject to lower risk levels. Statistically significant associations of ZIKV with other birth defects were also detected, but at lower relative risks than that of microcephaly (relative risk < 1.5). Our analysis was limited by missing data prior to the establishment of nationwide ZIKV surveillance, and its findings may be affected by unmeasured confounding causes of microcephaly not available in routinely collected surveillance data. CONCLUSIONS This study strengthens the evidence that congenital ZIKV infection, particularly in the first 2 trimesters of pregnancy, is associated with microcephaly and less frequently with other birth defects. The finding of no alternative causes for geographic differences in microcephaly rate leads us to hypothesize that the Northeast region was disproportionately affected by this Zika outbreak, with 94% of an estimated 8.5 million total cases occurring in this region, suggesting a need for seroprevalence surveys to determine the underlying reason.
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Affiliation(s)
- Oliver J. Brady
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- * E-mail: (OJB); (FM)
| | - Aaron Osgood-Zimmerman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Nicholas J. Kassebaum
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
- Department of Anesthesiology & Pain Medicine, University of Washington, Seattle, Washington, United States of America
| | - Sarah E. Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | | | - Aglaêr A. da Nóbrega
- Secretariat of Health Surveillance, Ministry of Health of Brazil, Brasília, Brazil
| | - Livia C. V. Frutuoso
- Secretariat of Health Surveillance, Ministry of Health of Brazil, Brasília, Brazil
| | - Roberto C. R. Lecca
- Secretariat of Health Surveillance, Ministry of Health of Brazil, Brasília, Brazil
| | - Antony Stevens
- Secretariat of Health Surveillance, Ministry of Health of Brazil, Brasília, Brazil
| | | | - José M. de Lima
- Secretariat of Health Surveillance, Ministry of Health of Brazil, Brasília, Brazil
| | - Isaac I. Bogoch
- Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of General Internal Medicine, University Health Network, Toronto, Ontario, Canada
- Division of Infectious Diseases, University Health Network, Toronto, Ontario, Canada
| | - Philippe Mayaud
- Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Thomas Jaenisch
- Section of Clinical Tropical Medicine, Department of Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Ali H. Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Christopher J. L. Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Simon I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Robert C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Fatima Marinho
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
- Secretariat of Health Surveillance, Ministry of Health of Brazil, Brasília, Brazil
- * E-mail: (OJB); (FM)
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5
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Kyu HH, Maddison ER, Henry NJ, Ledesma JR, Wiens KE, Reiner R, Biehl MH, Shields C, Osgood-Zimmerman A, Ross JM, Carter A, Frank TD, Wang H, Srinivasan V, Agarwal SK, Alahdab F, Alene KA, Ali BA, Alvis-Guzman N, Andrews JR, Antonio CAT, Atique S, Atre SR, Awasthi A, Ayele HT, Badali H, Badawi A, Barac A, Bedi N, Behzadifar M, Behzadifar M, Bekele BB, Belay SA, Bensenor IM, Butt ZA, Carvalho F, Cercy K, Christopher DJ, Daba AK, Dandona L, Dandona R, Daryani A, Demeke FM, Deribe K, Dharmaratne SD, Doku DT, Dubey M, Edessa D, El-Khatib Z, Enany S, Fernandes E, Fischer F, Garcia-Basteiro AL, Gebre AK, Gebregergs GB, Gebremichael TG, Gelano TF, Geremew D, Gona PN, Goodridge A, Gupta R, Haghparast Bidgoli H, Hailu GB, Hassen HY, Hedayati MTT, Henok A, Hostiuc S, Hussen MA, Ilesanmi OS, Irvani SSN, Jacobsen KH, Johnson SC, Jonas JB, Kahsay A, Kant S, Kasaeian A, Kassa TD, Khader YS, Khafaie MA, Khalil I, Khan EA, Khang YH, Kim YJ, Kochhar S, Koyanagi A, Krohn KJ, Kumar GA, Lakew AM, Leshargie CT, Lodha R, Macarayan ERK, Majdzadeh R, Martins-Melo FR, Melese A, Memish ZA, Mendoza W, Mengistu DT, Mengistu G, Mestrovic T, Moazen B, Mohammad KA, Mohammed S, Mokdad AH, Moosazadeh M, Mousavi SM, Mustafa G, Nachega JB, Nguyen LH, Nguyen SH, Nguyen TH, Ningrum DNA, Nirayo YL, Nong VM, Ofori-Asenso R, Ogbo FA, Oh IH, Oladimeji O, Olagunju AT, Oren E, Pereira DM, Prakash S, Qorbani M, Rafay A, Rai RK, Ram U, Rubino S, Safiri S, Salomon JA, Samy AM, Sartorius B, Satpathy M, Seyedmousavi S, Sharif M, Silva JP, Silveira DGA, Singh JA, Sreeramareddy CT, Tran BX, Tsadik AG, Ukwaja KN, Ullah I, Uthman OA, Vlassov V, Vollset SE, Vu G, Weldegebreal F, Werdecker A, Yimer EM, Yonemoto N, Yotebieng M, Naghavi M, Vos T, Hay SI, Murray CJL. Global, regional, and national burden of tuberculosis, 1990-2016: results from the Global Burden of Diseases, Injuries, and Risk Factors 2016 Study. Lancet Infect Dis 2018; 18:1329-1349. [PMID: 30507459 PMCID: PMC6250050 DOI: 10.1016/s1473-3099(18)30625-x] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2018] [Revised: 10/09/2018] [Accepted: 10/10/2018] [Indexed: 12/15/2022]
Abstract
BACKGROUND Although a preventable and treatable disease, tuberculosis causes more than a million deaths each year. As countries work towards achieving the Sustainable Development Goal (SDG) target to end the tuberculosis epidemic by 2030, robust assessments of the levels and trends of the burden of tuberculosis are crucial to inform policy and programme decision making. We assessed the levels and trends in the fatal and non-fatal burden of tuberculosis by drug resistance and HIV status for 195 countries and territories from 1990 to 2016. METHODS We analysed 15 943 site-years of vital registration data, 1710 site-years of verbal autopsy data, 764 site-years of sample-based vital registration data, and 361 site-years of mortality surveillance data to estimate mortality due to tuberculosis using the Cause of Death Ensemble model. We analysed all available data sources, including annual case notifications, prevalence surveys, population-based tuberculin surveys, and estimated tuberculosis cause-specific mortality to generate internally consistent estimates of incidence, prevalence, and mortality using DisMod-MR 2.1, a Bayesian meta-regression tool. We assessed how the burden of tuberculosis differed from the burden predicted by the Socio-demographic Index (SDI), a composite indicator of income per capita, average years of schooling, and total fertility rate. FINDINGS Globally in 2016, among HIV-negative individuals, the number of incident cases of tuberculosis was 9·02 million (95% uncertainty interval [UI] 8·05-10·16) and the number of tuberculosis deaths was 1·21 million (1·16-1·27). Among HIV-positive individuals, the number of incident cases was 1·40 million (1·01-1·89) and the number of tuberculosis deaths was 0·24 million (0·16-0·31). Globally, among HIV-negative individuals the age-standardised incidence of tuberculosis decreased annually at a slower rate (-1·3% [-1·5 to -1·2]) than mortality did (-4·5% [-5·0 to -4·1]) from 2006 to 2016. Among HIV-positive individuals during the same period, the rate of change in annualised age-standardised incidence was -4·0% (-4·5 to -3·7) and mortality was -8·9% (-9·5 to -8·4). Several regions had higher rates of age-standardised incidence and mortality than expected on the basis of their SDI levels in 2016. For drug-susceptible tuberculosis, the highest observed-to-expected ratios were in southern sub-Saharan Africa (13·7 for incidence and 14·9 for mortality), and the lowest ratios were in high-income North America (0·4 for incidence) and Oceania (0·3 for mortality). For multidrug-resistant tuberculosis, eastern Europe had the highest observed-to-expected ratios (67·3 for incidence and 73·0 for mortality), and high-income North America had the lowest ratios (0·4 for incidence and 0·5 for mortality). INTERPRETATION If current trends in tuberculosis incidence continue, few countries are likely to meet the SDG target to end the tuberculosis epidemic by 2030. Progress needs to be accelerated by improving the quality of and access to tuberculosis diagnosis and care, by developing new tools, scaling up interventions to prevent risk factors for tuberculosis, and integrating control programmes for tuberculosis and HIV. FUNDING Bill & Melinda Gates Foundation.
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Murray CJL, Callender CSKH, Kulikoff XR, Srinivasan V, Abate D, Abate KH, Abay SM, Abbasi N, Abbastabar H, Abdela J, Abdelalim A, Abdel-Rahman O, Abdi A, Abdoli N, Abdollahpour I, Abdulkader RS, Abebe HT, Abebe M, Abebe Z, Abebo TA, Abejie AN, Aboyans V, Abraha HN, Abreu DMX, Abrham AR, Abu-Raddad LJ, Abu-Rmeileh NME, Accrombessi MMK, Acharya P, Adamu AA, Adebayo OM, Adedeji IA, Adekanmbi V, Adetokunboh OO, Adhena BM, Adhikari TB, Adib MG, Adou AK, Adsuar JC, Afarideh M, Afshin A, Agarwal G, Agesa KM, Aghayan SA, Agrawal S, Ahmadi A, Ahmadi M, Ahmed MB, Ahmed S, Aichour AN, Aichour I, Aichour MTE, Akanda AS, Akbari ME, Akibu M, Akinyemi RO, Akinyemiju T, Akseer N, Alahdab F, Al-Aly Z, Alam K, Alebel A, Aleman AV, Alene KA, Al-Eyadhy A, Ali R, Alijanzadeh M, Alizadeh-Navaei R, Aljunid SM, Alkerwi A, Alla F, Allebeck P, Almasi A, Alonso J, Al-Raddadi RM, Alsharif U, Altirkawi K, Alvis-Guzman N, Amare AT, Ammar W, Anber NH, Andrei CL, Androudi S, Animut MD, Ansari H, Ansha MG, Antonio CAT, Appiah SCY, Aremu O, Areri HA, Arian N, Ärnlöv J, Artaman A, Aryal KK, Asayesh H, Asfaw ET, Asgedom SW, Assadi R, Atey TMM, Atique S, Atteraya MS, Ausloos M, Avokpaho EFGA, Awasthi A, Ayala Quintanilla BP, Ayele Y, Ayer R, Ayuk TB, Azzopardi PS, Babalola TK, Babazadeh A, Badali H, Badawi A, Bali AG, Banach M, Barker-Collo SL, Bärnighausen TW, Barrero LH, Basaleem H, Bassat Q, Basu A, Baune BT, Baynes HW, Beghi E, Behzadifar M, Behzadifar M, Bekele BB, Belachew AB, Belay AG, Belay E, Belay SA, Belay YA, Bell ML, Bello AK, Bennett DA, Bensenor IM, Bergeron G, Berhane A, Berman AE, Bernabe E, Bernstein RS, Bertolacci GJ, Beuran M, Bhattarai S, Bhaumik S, Bhutta ZA, Biadgo B, Bijani A, Bikbov B, Bililign N, Bin Sayeed MS, Birlik SM, Birungi C, Biswas T, Bizuneh H, Bleyer A, Basara BB, Bosetti C, Boufous S, Brady OJ, Bragazzi NL, Brainin M, Brazinova A, Breitborde NJK, Brenner H, Brewer JD, Briant PS, Britton G, Burstein R, Busse R, Butt ZA, Cahuana-Hurtado L, Campos-Nonato IR, Campuzano Rincon JC, Cano J, Car M, Cárdenas R, Carrero JJ, Carvalho F, Castañeda-Orjuela CA, Castillo Rivas J, Castro F, Catalá-López F, Çavlin A, Cerin E, Chalek J, Chang HY, Chang JC, Chattopadhyay A, Chaturvedi P, Chiang PPC, Chin KL, Chisumpa VH, Chitheer A, Choi JYJ, Chowdhury R, Christopher DJ, Cicuttini FM, Ciobanu LG, Cirillo M, Claro RM, Collado-Mateo D, Comfort H, Constantin MM, Conti S, Cooper C, Cooper LT, Cornaby L, Cortesi PA, Cortinovis M, Costa M, Cromwell E, Crowe CS, Cukelj P, Cunningham M, Daba AK, Dachew BA, Dandona L, Dandona R, Dargan PI, Daryani A, Das Gupta R, Das Neves J, Dasa TT, Dash AP, Weaver ND, Davitoiu DV, Davletov K, De Leo D, De Neve JW, Degefa MG, Degenhardt L, Degfie TT, Deiparine S, Demoz GT, Demtsu B, Denova-Gutiérrez E, Deribe K, Dervenis N, Des Jarlais DC, Dessie GA, Dharmaratne SD, Dhimal M, Dicker D, Ding EL, Dinsa GD, Djalalinia S, Do HP, Dokova K, Doku DT, Dolan KA, Doyle KE, Driscoll TR, Dubey M, Dubljanin E, Duken EE, Duraes AR, Ebrahimpour S, Edvardsson D, El Bcheraoui C, El-Khatib Z, Elyazar IR, Enayati A, Endries AY, Ermakov SP, Eshrati B, Eskandarieh S, Esmaeili R, Esteghamati A, Esteghamati S, Estep K, Fakhim H, Farag T, Faramarzi M, Fareed M, Farinha CSES, Faro A, Farvid MS, Farzadfar F, Farzaei MH, Fay KA, Fazeli MS, Feigin VL, Feigl AB, Feizy F, Fenny AP, Fentahun N, Fereshtehnejad SM, Fernandes E, Feyissa GT, Filip I, Finegold S, Fischer F, Flor LS, Foigt NA, Foreman KJ, Fornari C, Fürst T, Fukumoto T, Fuller JE, Fullman N, Gakidou E, Gallus S, Gamkrelidze A, Ganji M, Gankpe FG, Garcia GM, Garcia-Gordillo MÁ, Gebre AK, Gebre T, Gebregergs GB, Gebrehiwot TT, Gebremedhin AT, Gelano TF, Gelaw YA, Geleijnse JM, Genova-Maleras R, Gething P, Gezae KE, Ghadami MR, Ghadimi R, Ghadiri K, Ghasemi Falavarjani K, Ghasemi-Kasman M, Ghiasvand H, Ghimire M, Ghoshal AG, Gill PS, Gill TK, Giussani G, Gnedovskaya EV, Goli S, Gomez RS, Gómez-Dantés H, Gona PN, Goodridge A, Gopalani SV, Goulart AC, Goulart BNG, Grada A, Grosso G, Gugnani HCC, Guo J, Guo Y, Gupta PC, Gupta R, Gupta R, Gupta T, Haagsma JA, Hachinski V, Hafezi-Nejad N, Hagos TB, Hailegiyorgis TT, Hailu GB, Haj-Mirzaian A, Haj-Mirzaian A, Hamadeh RR, Hamidi S, Handal AJ, Hankey GJ, Hao Y, Harb HL, Haririan H, Haro JM, Hasan M, Hassankhani H, Hassen HY, Havmoeller R, Hay SI, He Y, Hedayatizadeh-Omran A, Hegazy MI, Heibati B, Heidari B, Hendrie D, Henok A, Henry NJ, Herteliu C, Heydarpour F, Hibstu DT, Hole MK, Homaie Rad E, Hoogar P, Hosgood HD, Hosseini SM, Hosseini Chavoshi MM, Hosseinzadeh M, Hostiuc M, Hostiuc S, Hsairi M, Hsiao T, Hu G, Huang JJ, Iburg KM, Igumbor EU, Ikeda CT, Ilesanmi OS, Iqbal U, Irenso AA, Irvani SSN, Isehunwa OO, Islam SMS, Jahangiry L, Jahanmehr N, Jain SK, Jakovljevic M, Jalu MT, James SL, Jassal SK, Javanbakht M, Jayatilleke AU, Jeemon P, Jha RP, Jha V, Ji JS, Jonas JB, Jozwiak JJ, Jungari SB, Jürisson M, Kabir Z, Kadel R, Kahsay A, Kalani R, Kapil U, Karami M, Matin BK, Karch A, Karema C, Karimi SM, Kasaeian A, Kassa DH, Kassa GM, Kassa TD, Kassa ZY, Kassebaum NJ, Kastor A, Katikireddi SV, Kaul A, Kawakami N, Karyani AK, Kebede S, Keiyoro PN, Kemp GR, Kengne AP, Keren A, Kereselidze M, Khader YS, Khafaie MA, Khajavi A, Khalid N, Khalil IA, Khan EA, Khan MS, Khang YH, Khanna T, Khater MM, Khatony A, Khazaeipour Z, Khazaie H, Khoja AT, Khosravi A, Khosravi MH, Kibret GD, Kidanemariam ZT, Kiirithio DN, Kilgore PE, Kim D, Kim JY, Kim YE, Kim YJ, Kimokoti RW, Kinfu Y, Kinra S, Kisa A, Kivimäki M, Kochhar S, Kokubo Y, Kolola T, Kopec JA, Kosek MN, Kosen S, Koul PA, Koyanagi A, Krishan K, Krishnaswami S, Krohn KJ, Defo BK, Bicer BK, Kumar GA, Kumar M, Kumar P, Kumsa FA, Kutz MJ, Lad SD, Lafranconi A, Lal DK, Lalloo R, Lam H, Lami FH, Lang JJ, Lanksy S, Lansingh VC, Laryea DO, Lassi ZS, Latifi A, Laxmaiah A, Lazarus JV, Lee JB, Lee PH, Leigh J, Leshargie CT, Leta S, Levi M, Li S, Li X, Li Y, Liang J, Liang X, Liben ML, Lim LL, Limenih MA, Linn S, Liu S, Lorkowski S, Lotufo PA, Lozano R, Lunevicius R, Mabika CM, Macarayan ERK, Mackay MT, Madotto F, Mahmood TAE, Mahotra NB, Majdan M, Majdzadeh R, Majeed A, Malekzadeh R, Malik MA, Mamun AA, Manamo WA, Manda AL, Mangalam S, Mansournia MA, Mantovani LG, Mapoma CC, Marami D, Maravilla JC, Marcenes W, Marina S, Martins-Melo FR, März W, Marzan MB, Mashamba-Thompson TP, Masiye F, Mason-Jones AJ, Massenburg BB, Mathur MR, Maulik PK, Mazidi M, McGrath JJ, Mehata S, Mehendale SM, Mehndiratta MM, Mehrotra R, Mehrzadi S, Mehta KM, Mehta V, Mekonnen TC, Meles HG, Meles KG, Melese A, Melku M, Memiah PTN, Memish ZA, Mendoza W, Mengesha MM, Mengistu DT, Mengistu G, Mensah GA, Mereta ST, Meretoja A, Meretoja TJ, Mestrovic T, Mezgebe HB, Miangotar Y, Miazgowski B, Miazgowski T, Miller TR, Miller-Petrie MK, Mini GK, Mirabi P, Mirica A, Mirrakhimov EM, Misganaw AT, Moazen B, Mohammad KA, Mohammadi M, Mohammadifard N, Mohammadi-Khanaposhtani M, Mohammed MA, Mohammed S, Mokdad AH, Mola GD, Molokhia M, Monasta L, Montañez JC, Moradi G, Moradi M, Moradi-Lakeh M, Moradinazar M, Moraga P, Morgado-Da-Costa J, Mori R, Morrison SD, Mosapour A, Moschos MM, Mousavi SM, Muche AA, Muchie KF, Mueller UO, Mukhopadhyay S, Muller K, Murphy TB, Murthy GVS, Musa J, Musa KI, Mustafa G, Muthupandian S, Nachega JB, Nagel G, Naghavi M, Naheed A, Nahvijou A, Naik G, Naik P, Najafi F, Naldi L, Nangia V, Nansseu JR, Nascimento BR, Nawaz H, Ncama BP, Neamati N, Negoi I, Negoi RI, Neupane S, Newton CRJ, Ngalesoni FN, Ngunjiri JW, Nguyen G, Nguyen LH, Nguyen TH, Ningrum DNA, Nirayo YL, Nisar MI, Nixon MR, Nomura S, Noroozi M, Noubiap JJ, Nouri HR, Shiadeh MN, Nowroozi MR, Nyandwi A, Nyasulu PS, Odell CM, Ofori-Asenso R, Ogah OS, Ogbo FA, Oh IH, Okoro A, Oladimeji O, Olagunju AT, Olagunju TO, Olivares PR, Olusanya BO, Olusanya JO, Ong SK, Ortiz A, Osgood-Zimmerman A, Ota E, Otieno BA, Otstavnov SS, Owolabi MO, Oyekale AS, P A M, Pakhale S, Pakhare AP, Pana A, Panda BK, Panda-Jonas S, Pandey AR, Park EK, Parsian H, Patel S, Patil ST, Patle A, Patton GC, Paturi VR, Paudel D, Pedroso MM, Peprah EK, Pereira DM, Perico N, Pesudovs K, Petri WA, Petzold M, Pierce M, Pigott DM, Pillay JD, Pirsaheb M, Polanczyk GV, Postma MJ, Pourmalek F, Pourshams A, Poustchi H, Prakash S, Prasad N, Purcell CA, Purwar MB, Qorbani M, Quansah R, Radfar A, Rafay A, Rafiei A, Rahim F, Rahimi-Movaghar A, Rahimi-Movaghar V, Rahman M, Rahman MS, Rahman MHU, Rahman MA, Rahman SU, Rai RK, Rajati F, Rajsic S, Ram U, Ranabhat CL, Ranjan P, Rawaf DL, Rawaf S, Ray SE, Razo-García C, Reiner RC, Reis C, Remuzzi G, Renzaho AMN, Resnikoff S, Rezaei S, Rezaeian S, Rezai MS, Riahi SM, Rios-Blancas MJ, Roba KT, Roberts NLS, Roever L, Ronfani L, Roshandel G, Rostami A, Rubagotti E, Ruhago GM, Sabde YD, Sachdev PS, Saddik B, Saeedi Moghaddam S, Safari H, Safari Y, Safari-Faramani R, Safdarian M, Safi S, Safiri S, Sagar R, Sahebkar A, Sahraian MA, Sajadi HS, Salahshoor MR, Salam N, Salama JS, Salamati P, Saldanha RDF, Saleem Z, Salimi Y, Salimzadeh H, Salomon JA, Salvi SS, Salz I, Sambala EZ, Samy AM, Sanabria J, Sanchez-Niño MD, Santos IS, Santric Milicevic MM, Sao Jose BP, Sardana M, Sarker AR, Sarmiento-Suárez R, Saroshe S, Sarrafzadegan N, Sartorius B, Sarvi S, Sathian B, Satpathy M, Sawant AR, Sawhney M, Saxena S, Schaeffner E, Schelonka K, Schneider IJC, Schwebel DC, Schwendicke F, Seedat S, Sekerija M, Sepanlou SG, Serván-Mori E, Shabaninejad H, Shackelford KA, Shafieesabet A, Shaheen AA, Shaikh MA, Shakir RA, Shams-Beyranvand M, Shamsi M, Shamsizadeh M, Sharafi H, Sharafi K, Sharif M, Sharif-Alhoseini M, Sharma J, Sharma R, She J, Sheikh A, Shi P, Shibuya K, Shigematsu M, Shiri R, Shirkoohi R, Shiue I, Shokraneh F, Shukla SR, Si S, Siabani S, Sibai AM, Siddiqi TJ, Sigfusdottir ID, Sigurvinsdottir R, Silpakit N, Silva DAS, Silva JP, Silveira DGA, Singam NSV, Singh JA, Singh NP, Singh V, Sinha DN, Sliwa K, Soares Filho AM, Sobaih BH, Sobhani S, Soofi M, Soriano JB, Soyiri IN, Sreeramareddy CT, Starodubov VI, Steiner C, Stewart LG, Stokes MA, Strong M, Subart ML, Sufiyan MB, Sulo G, Sunguya BF, Sur PJ, Sutradhar I, Sykes BL, Sylaja PN, Sylte DO, Szoeke CEI, Tabarés-Seisdedos R, Tabb KM, Tadakamadla SK, Tandon N, Tassew AA, Tassew SG, Taveira N, Tawye NY, Tehrani-Banihashemi A, Tekalign TG, Tekle MG, Temsah MH, Terkawi AS, Teshale MY, Tessema B, Teweldemedhin M, Thakur JS, Thankappan KR, Thirunavukkarasu S, Thomas N, Thomson AJ, Tilahun B, To QG, Tonelli M, Topor-Madry R, Torre AE, Tortajada-Girbés M, Tovani-Palone MR, Toyoshima H, Tran BX, Tran KB, Tripathy SP, Truelsen TC, Truong NT, Tsadik AG, Tsegay A, Tsilimparis N, Tudor Car L, Ukwaja KN, Ullah I, Usman MS, Uthman OA, Uzun SB, Vaduganathan M, Vaezi A, Vaidya G, Valdez PR, Varavikova E, Varughese S, Vasankari TJ, Vasconcelos AMN, Venketasubramanian N, Villafaina S, Violante FS, Vladimirov SK, Vlassov V, Vollset SE, Vos T, Vosoughi K, Vujcic IS, Wagnew FS, Waheed Y, Walson JL, Wang Y, Wang YP, Weiderpass E, Weintraub RG, Weldegwergs KG, Werdecker A, Westerman R, Whiteford H, Widecka J, Widecka K, Wijeratne T, Winkler AS, Wiysonge CS, Wolfe CDA, Wu S, Wyper GMA, Xu G, Yamada T, Yano Y, Yaseri M, Yasin YJ, Ye P, Yentür GK, Yeshaneh A, Yimer EM, Yip P, Yisma E, Yonemoto N, Yoon SJ, Yotebieng M, Younis MZ, Yousefifard M, Yu C, Zadnik V, Zaidi Z, Zaman SB, Zamani M, Zare Z, Zeleke MM, Zenebe ZM, Zerfu TA, Zhang X, Zhao XJ, Zhou M, Zhu J, Zimsen SRM, Zodpey S, Zoeckler L, Lopez AD, Lim SS. 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. Lancet 2018; 392:1995-2051. [PMID: 30496106 PMCID: PMC6227915 DOI: 10.1016/s0140-6736(18)32278-5] [Citation(s) in RCA: 243] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 09/07/2018] [Accepted: 09/12/2018] [Indexed: 02/07/2023]
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-component 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 0·7% 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 (TFO30; 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. TFO30 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. FUNDING Bill & Melinda Gates Foundation.
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Reiner RC, Graetz N, Casey DC, Troeger C, Garcia GM, Mosser JF, Deshpande A, Swartz SJ, Ray SE, Blacker BF, Rao PC, Osgood-Zimmerman A, Burstein R, Pigott DM, Davis IM, Letourneau ID, Earl L, Ross JM, Khalil IA, Farag TH, Brady OJ, Kraemer MUG, Smith DL, Bhatt S, Weiss DJ, Gething PW, Kassebaum NJ, Mokdad AH, Murray CJL, Hay SI. Variation in Childhood Diarrheal Morbidity and Mortality in Africa, 2000-2015. N Engl J Med 2018; 379:1128-1138. [PMID: 30231224 PMCID: PMC6078160 DOI: 10.1056/nejmoa1716766] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Diarrheal diseases are the third leading cause of disease and death in children younger than 5 years of age in Africa and were responsible for an estimated 30 million cases of severe diarrhea (95% credible interval, 27 million to 33 million) and 330,000 deaths (95% credible interval, 270,000 to 380,000) in 2015. The development of targeted approaches to address this burden has been hampered by a paucity of comprehensive, fine-scale estimates of diarrhea-related disease and death among and within countries. METHODS We produced annual estimates of the prevalence and incidence of diarrhea and diarrhea-related mortality with high geographic detail (5 km2) across Africa from 2000 through 2015. Estimates were created with the use of Bayesian geostatistical techniques and were calibrated to the results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016. RESULTS The results revealed geographic inequality with regard to diarrhea risk in Africa. Of the estimated 330,000 childhood deaths that were attributable to diarrhea in 2015, more than 50% occurred in 55 of the 782 first-level administrative subdivisions (e.g., states). In 2015, mortality rates among first-level administrative subdivisions in Nigeria differed by up to a factor of 6. The case fatality rates were highly varied at the national level across Africa, with the highest values observed in Benin, Lesotho, Mali, Nigeria, and Sierra Leone. CONCLUSIONS Our findings showed concentrated areas of diarrheal disease and diarrhea-related death in countries that had a consistently high burden as well as in countries that had considerable national-level reductions in diarrhea burden. (Funded by the Bill and Melinda Gates Foundation.).
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Affiliation(s)
- Robert C Reiner
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Nicholas Graetz
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Daniel C Casey
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Christopher Troeger
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Gregory M Garcia
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Jonathan F Mosser
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Aniruddha Deshpande
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Scott J Swartz
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Sarah E Ray
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Brigette F Blacker
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Puja C Rao
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Aaron Osgood-Zimmerman
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Roy Burstein
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - David M Pigott
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Ian M Davis
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Ian D Letourneau
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Lucas Earl
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Jennifer M Ross
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Ibrahim A Khalil
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Tamer H Farag
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Oliver J Brady
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Moritz U G Kraemer
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - David L Smith
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Samir Bhatt
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Daniel J Weiss
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Peter W Gething
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Nicholas J Kassebaum
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Ali H Mokdad
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Christopher J L Murray
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
| | - Simon I Hay
- From the Institute for Health Metrics and Evaluation (R.C.R., N.G., D.C.C., C.T., G.M.G., J.F.M., A.D., S.J.S., S.E.R., B.F.B., P.C.R., A.O.-Z., R.B., D.M.P., I.M.D., I.D.L., L.E., J.M.R., I.A.K., T.H.F., D.L.S., N.J.K., A.H.M., C.J.L.M., S.I.H.) and the Division of Allergy and Infectious Diseases, Department of Medicine (J.M.R.), University of Washington, and the Divisions of Pediatric Infectious Diseases (J.F.M.) and Pediatric Anesthesiology and Pain Medicine (N.J.K.), Seattle Children's Hospital - all in Seattle; the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine (O.J.B.), and the Department of Infectious Disease Epidemiology, Imperial College London (S.B.), London, and the Department of Zoology (M.U.G.K.) and the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery (S.B., D.J.W., P.W.G.), University of Oxford, Oxford - all in the United Kingdom; and the Computational Epidemiology Lab, Boston Children's Hospital, and Harvard Medical School - both in Boston (M.U.G.K.)
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8
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Graetz N, Friedman J, Osgood-Zimmerman A, Burstein R, Biehl MH, Shields C, Mosser JF, Casey DC, Deshpande A, Earl L, Reiner RC, Ray SE, Fullman N, Levine AJ, Stubbs RW, Mayala BK, Longbottom J, Browne AJ, Bhatt S, Weiss DJ, Gething PW, Mokdad AH, Lim SS, Murray CJL, Gakidou E, Hay SI. Mapping local variation in educational attainment across Africa. Nature 2018; 555:48-53. [PMID: 29493588 PMCID: PMC6346272 DOI: 10.1038/nature25761] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 01/17/2018] [Indexed: 11/09/2022]
Abstract
Educational attainment for women of reproductive age is linked to reduced child and maternal mortality, lower fertility and improved reproductive health. Comparable analyses of attainment exist only at the national level, potentially obscuring patterns in subnational inequality. Evidence suggests that wide disparities between urban and rural populations exist, raising questions about where the majority of progress towards the education targets of the Sustainable Development Goals is occurring in African countries. Here we explore within-country inequalities by predicting years of schooling across five by five kilometre grids, generating estimates of average educational attainment by age and sex at subnational levels. Despite marked progress in attainment from 2000 to 2015 across Africa, substantial differences persist between locations and sexes. These differences have widened in many countries, particularly across the Sahel. These high-resolution, comparable estimates improve the ability of decision-makers to plan the precisely targeted interventions that will be necessary to deliver progress during the era of the Sustainable Development Goals.
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Affiliation(s)
- Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Joseph Friedman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aaron Osgood-Zimmerman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Molly H Biehl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Chloe Shields
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Jonathan F Mosser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Sarah E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aubrey J Levine
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Rebecca W Stubbs
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Benjamin K Mayala
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Joshua Longbottom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Annie J Browne
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College London, London SW7 2AZ, UK
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Emmanuela Gakidou
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
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9
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Osgood-Zimmerman A, Millear AI, Stubbs RW, Shields C, Pickering BV, Earl L, Graetz N, Kinyoki DK, Ray SE, Bhatt S, Browne AJ, Burstein R, Cameron E, Casey DC, Deshpande A, Fullman N, Gething PW, Gibson HS, Henry NJ, Herrero M, Krause LK, Letourneau ID, Levine AJ, Liu PY, Longbottom J, Mayala BK, Mosser JF, Noor AM, Pigott DM, Piwoz EG, Rao P, Rawat R, Reiner RC, Smith DL, Weiss DJ, Wiens KE, Mokdad AH, Lim SS, Murray CJL, Kassebaum NJ, Hay SI. Mapping child growth failure in Africa between 2000 and 2015. Nature 2018; 555:41-47. [PMID: 29493591 PMCID: PMC6346257 DOI: 10.1038/nature25760] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 01/17/2018] [Indexed: 12/28/2022]
Abstract
Insufficient growth during childhood is associated with poor health outcomes and an increased risk of death. Between 2000 and 2015, nearly all African countries demonstrated improvements for children under 5 years old for stunting, wasting, and underweight, the core components of child growth failure. Here we show that striking subnational heterogeneity in levels and trends of child growth remains. If current rates of progress are sustained, many areas of Africa will meet the World Health Organization Global Targets 2025 to improve maternal, infant and young child nutrition, but high levels of growth failure will persist across the Sahel. At these rates, much, if not all of the continent will fail to meet the Sustainable Development Goal target—to end malnutrition by 2030. Geospatial estimates of child growth failure provide a baseline for measuring progress as well as a precision public health platform to target interventions to those populations with the greatest need, in order to reduce health disparities and accelerate progress. Geospatial estimates of child growth failure in Africa provide a baseline for measuring progress and a precision public health platform to target interventions to those populations with the greatest need.
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Affiliation(s)
- Aaron Osgood-Zimmerman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Anoushka I Millear
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Rebecca W Stubbs
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Chloe Shields
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Brandon V Pickering
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Damaris K Kinyoki
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Sarah E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College London, London SW7 2AZ, UK
| | - Annie J Browne
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Ewan Cameron
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aniruddha Deshpande
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Harry S Gibson
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Nathaniel J Henry
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Mario Herrero
- Commonwealth Scientific and Industrial Research Organisation, St Lucia, Queensland 4067, Australia
| | | | - Ian D Letourneau
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Aubrey J Levine
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Patrick Y Liu
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Joshua Longbottom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Benjamin K Mayala
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Jonathan F Mosser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Abdisalan M Noor
- Kenya Medical Research Institute-Wellcome Trust Collaborative Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford OX3 7FZ, USA
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Ellen G Piwoz
- Bill & Melinda Gates Foundation, Seattle, Washington 98109, USA
| | - Puja Rao
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Rahul Rawat
- Bill & Melinda Gates Foundation, Seattle, Washington 98109, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
| | - Kirsten E Wiens
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA
| | - Nicholas J Kassebaum
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA.,Department of Anesthesiology and Pain Medicine, Seattle Children's Hospital, Seattle, Washington 98105, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98121, USA.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7FZ, UK
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10
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Kyu HH, Maddison ER, Henry NJ, Mumford JE, Barber R, Shields C, Brown JC, Nguyen G, Carter A, Wolock TM, Wang H, Liu PY, Reitsma M, Ross JM, Abajobir AA, Abate KH, Abbas K, Abera M, Abera SF, Abera Hareri H, Ahmed M, Alene KA, Alvis-Guzman N, Amo-Adjei J, Andrews J, Ansari H, Antonio CA, Anwari P, Asayesh H, Atey TM, Atre S, Barac A, Beardsley J, Bedi N, Bensenor I, Beyene AS, Butt ZA, Cardona PJ, Christopher D, Dandona L, Dandona R, Deribe K, Deribew A, Ehrenkranz R, El Sayed Zaki M, Endries A, Feyissa TR, Fischer F, Gai R, Garcia-Basteiro AL, Gebrehiwot TT, Gesesew H, Getahun B, Gona P, Goodridge A, Gugnani H, Haghparast-Bidgoli H, Hailu GB, Hassen HY, Hilawe E, Horita N, Jacobsen KH, Jonas JB, Kasaeian A, Kedir MS, Kemmer L, Khader Y, Khan E, Khang YH, Khoja AT, Kim YJ, Koul P, Koyanagi A, Krohn KJ, Kumar GA, Kutz M, Lodha R, Magdy And El Razek H, Majdzadeh R, Manyazewal T, Memish Z, Mendoza W, Mezgebe HB, Mohammed S, Ogbo FA, Oh IH, Oren E, Osgood-Zimmerman A, Pereira D, Plass D, Pourmalek F, Qorbani M, Rafay A, Rahman M, Rai RK, Rao PC, Ray SE, Reiner R, Reinig N, Safiri S, Salomon JA, Sandar L, Sartorius B, Shamsizadeh M, Shey M, Shifti DM, Shore H, Singh J, Sreeramareddy CT, Swaminathan S, Swartz SJ, Tadese F, Tedla BA, Tegegne BS, Tessema B, Topor-Madry R, Ukwaja KN, Uthman OA, Vlassov V, Vollset SE, Wakayo T, Weldegebreal S, Westerman R, Workicho A, Yonemoto N, Yoon SJ, Yotebieng M, Naghavi M, Hay SI, Vos T, Murray CJL. The global burden of tuberculosis: results from the Global Burden of Disease Study 2015. Lancet Infect Dis 2018; 18:261-284. [PMID: 29223583 PMCID: PMC5831985 DOI: 10.1016/s1473-3099(17)30703-x] [Citation(s) in RCA: 202] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 09/30/2017] [Accepted: 10/02/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND An understanding of the trends in tuberculosis incidence, prevalence, and mortality is crucial to tracking of the success of tuberculosis control programmes and identification of remaining challenges. We assessed trends in the fatal and non-fatal burden of tuberculosis over the past 25 years for 195 countries and territories. METHODS We analysed 10 691 site-years of vital registration data, 768 site-years of verbal autopsy data, and 361 site-years of mortality surveillance data using the Cause of Death Ensemble model to estimate tuberculosis mortality rates. We analysed all available age-specific and sex-specific data sources, including annual case notifications, prevalence surveys, and estimated cause-specific mortality, to generate internally consistent estimates of incidence, prevalence, and mortality using DisMod-MR 2.1, a Bayesian meta-regression tool. We assessed how observed tuberculosis incidence, prevalence, and mortality differed from expected trends as predicted by the Socio-demographic Index (SDI), a composite indicator based on income per capita, average years of schooling, and total fertility rate. We also estimated tuberculosis mortality and disability-adjusted life-years attributable to the independent effects of risk factors including smoking, alcohol use, and diabetes. FINDINGS Globally, in 2015, the number of tuberculosis incident cases (including new and relapse cases) was 10·2 million (95% uncertainty interval 9·2 million to 11·5 million), the number of prevalent cases was 10·1 million (9·2 million to 11·1 million), and the number of deaths was 1·3 million (1·1 million to 1·6 million). Among individuals who were HIV negative, the number of incident cases was 8·8 million (8·0 million to 9·9 million), the number of prevalent cases was 8·9 million (8·1 million to 9·7 million), and the number of deaths was 1·1 million (0·9 million to 1·4 million). Annualised rates of change from 2005 to 2015 showed a faster decline in mortality (-4·1% [-5·0 to -3·4]) than in incidence (-1·6% [-1·9 to -1·2]) and prevalence (-0·7% [-1·0 to -0·5]) among HIV-negative individuals. The SDI was inversely associated with HIV-negative mortality rates but did not show a clear gradient for incidence and prevalence. Most of Asia, eastern Europe, and sub-Saharan Africa had higher rates of HIV-negative tuberculosis burden than expected given their SDI. Alcohol use accounted for 11·4% (9·3-13·0) of global tuberculosis deaths among HIV-negative individuals in 2015, diabetes accounted for 10·6% (6·8-14·8), and smoking accounted for 7·8% (3·8-12·0). INTERPRETATION Despite a concerted global effort to reduce the burden of tuberculosis, it still causes a large disease burden globally. Strengthening of health systems for early detection of tuberculosis and improvement of the quality of tuberculosis care, including prompt and accurate diagnosis, early initiation of treatment, and regular follow-up, are priorities. Countries with higher than expected tuberculosis rates for their level of sociodemographic development should investigate the reasons for lagging behind and take remedial action. Efforts to prevent smoking, alcohol use, and diabetes could also substantially reduce the burden of tuberculosis. FUNDING Bill & Melinda Gates Foundation.
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Deribe K, Cano J, Giorgi E, Pigott DM, Golding N, Pullan RL, Noor AM, Cromwell EA, Osgood-Zimmerman A, Enquselassie F, Hailu A, Murray CJL, Newport MJ, Brooker SJ, Hay SI, Davey G. Estimating the number of cases of podoconiosis in Ethiopia using geostatistical methods. Wellcome Open Res 2017. [PMID: 29152596 DOI: 10.12688/wellcomeopenres.12483.1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND In 2011, the World Health Organization recognized podoconiosis as one of the neglected tropical diseases. Nonetheless, the number of people with podoconiosis and the geographical distribution of the disease is poorly understood. Based on a nationwide mapping survey and geostatistical modelling, we predict the prevalence of podoconiosis and estimate the number of cases across Ethiopia. METHODS We used nationwide data collected in Ethiopia between 2008 and 2013. Data were available for 141,238 individuals from 1,442 villages in 775 districts from all nine regional states and two city administrations. We developed a geostatistical model of podoconiosis prevalence among adults (individuals aged 15 years or above), by combining environmental factors. The number of people with podoconiosis was then estimated using a gridded map of adult population density for 2015. RESULTS Podoconiosis is endemic in 345 districts in Ethiopia: 144 in Oromia, 128 in Southern Nations, Nationalities and People's [SNNP], 64 in Amhara, 4 in Benishangul Gumuz, 4 in Tigray and 1 in Somali Regional State. Nationally, our estimates suggest that 1,537,963 adults (95% confidence intervals, 290,923-4,577,031 adults) were living with podoconiosis in 2015. Three regions (SNNP, Oromia and Amhara) contributed 99% of the cases. The highest proportion of individuals with podoconiosis resided in the SNNP (39%), while 32% and 29% of people with podoconiosis resided in Oromia and Amhara Regional States, respectively. Tigray and Benishangul Gumuz Regional States bore lower burdens, and in the remaining regions, podoconiosis was almost non-existent. Discussion: The estimates of podoconiosis cases presented here based upon the combination of currently available epidemiological data and a robust modelling approach clearly show that podoconiosis is highly endemic in Ethiopia. Given the presence of low cost prevention, and morbidity management and disability prevention services, it is our collective responsibility to scale-up interventions rapidly.
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Affiliation(s)
- Kebede Deribe
- School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia.,Wellcome Trust Brighton and Sussex Centre for Global Health Research, Brighton and Sussex Medical School, Brighton, UK
| | - Jorge Cano
- London School of Hygiene & Tropical Medicine, London, UK
| | - Emanuele Giorgi
- London School of Hygiene & Tropical Medicine, London, UK.,Lancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancaster, UK
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nick Golding
- Spatial Ecology and Epidemiology Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,School of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | | | - Abdisalan M Noor
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.,Kenya Medical Research Institute-Wellcome Trust Collaborative Programme, Nairobi, Kenya
| | - Elizabeth A Cromwell
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | | | - Asrat Hailu
- Department of Microbiology, Immunology and Parasitology, Faculty of Medicine, Addis Ababa University, Addis Ababa, Ethiopia
| | | | - Melanie J Newport
- Wellcome Trust Brighton and Sussex Centre for Global Health Research, Brighton and Sussex Medical School, Brighton, UK
| | | | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Gail Davey
- Wellcome Trust Brighton and Sussex Centre for Global Health Research, Brighton and Sussex Medical School, Brighton, UK
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12
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Golding N, Burstein R, Longbottom J, Browne AJ, Fullman N, Osgood-Zimmerman A, Earl L, Bhatt S, Cameron E, Casey DC, Dwyer-Lindgren L, Farag TH, Flaxman AD, Fraser MS, Gething PW, Gibson HS, Graetz N, Krause LK, Kulikoff XR, Lim SS, Mappin B, Morozoff C, Reiner RC, Sligar A, Smith DL, Wang H, Weiss DJ, Murray CJL, Moyes CL, Hay SI. Mapping under-5 and neonatal mortality in Africa, 2000-15: a baseline analysis for the Sustainable Development Goals. Lancet 2017; 390:2171-2182. [PMID: 28958464 PMCID: PMC5687451 DOI: 10.1016/s0140-6736(17)31758-0] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 06/03/2017] [Accepted: 06/26/2017] [Indexed: 01/29/2023]
Abstract
BACKGROUND During the Millennium Development Goal (MDG) era, many countries in Africa achieved marked reductions in under-5 and neonatal mortality. Yet the pace of progress toward these goals substantially varied at the national level, demonstrating an essential need for tracking even more local trends in child mortality. With the adoption of the Sustainable Development Goals (SDGs) in 2015, which established ambitious targets for improving child survival by 2030, optimal intervention planning and targeting will require understanding of trends and rates of progress at a higher spatial resolution. In this study, we aimed to generate high-resolution estimates of under-5 and neonatal all-cause mortality across 46 countries in Africa. METHODS We assembled 235 geographically resolved household survey and census data sources on child deaths to produce estimates of under-5 and neonatal mortality at a resolution of 5 × 5 km grid cells across 46 African countries for 2000, 2005, 2010, and 2015. We used a Bayesian geostatistical analytical framework to generate these estimates, and implemented predictive validity tests. In addition to reporting 5 × 5 km estimates, we also aggregated results obtained from these estimates into three different levels-national, and subnational administrative levels 1 and 2-to provide the full range of geospatial resolution that local, national, and global decision makers might require. FINDINGS Amid improving child survival in Africa, there was substantial heterogeneity in absolute levels of under-5 and neonatal mortality in 2015, as well as the annualised rates of decline achieved from 2000 to 2015. Subnational areas in countries such as Botswana, Rwanda, and Ethiopia recorded some of the largest decreases in child mortality rates since 2000, positioning them well to achieve SDG targets by 2030 or earlier. Yet these places were the exception for Africa, since many areas, particularly in central and western Africa, must reduce under-5 mortality rates by at least 8·8% per year, between 2015 and 2030, to achieve the SDG 3.2 target for under-5 mortality by 2030. INTERPRETATION In the absence of unprecedented political commitment, financial support, and medical advances, the viability of SDG 3.2 achievement in Africa is precarious at best. By producing under-5 and neonatal mortality rates at multiple levels of geospatial resolution over time, this study provides key information for decision makers to target interventions at populations in the greatest need. In an era when precision public health increasingly has the potential to transform the design, implementation, and impact of health programmes, our 5 × 5 km estimates of child mortality in Africa provide a baseline against which local, national, and global stakeholders can map the pathways for ending preventable child deaths by 2030. FUNDING Bill & Melinda Gates Foundation.
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Affiliation(s)
- Nick Golding
- School of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Joshua Longbottom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Annie J Browne
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Samir Bhatt
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Ewan Cameron
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Laura Dwyer-Lindgren
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Tamer H Farag
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Abraham D Flaxman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Maya S Fraser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Harry S Gibson
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Xie Rachel Kulikoff
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Bonnie Mappin
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Chloe Morozoff
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Amber Sligar
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Haidong Wang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Catherine L Moyes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Simon I Hay
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
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Vos T, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, Abdulkader RS, Abdulle AM, Abebo TA, Abera SF, Aboyans V, Abu-Raddad LJ, Ackerman IN, Adamu AA, Adetokunboh O, Afarideh M, Afshin A, Agarwal SK, Aggarwal R, Agrawal A, Agrawal S, Ahmadieh H, Ahmed MB, Aichour MTE, Aichour AN, Aichour I, Aiyar S, Akinyemi RO, Akseer N, Al Lami FH, Alahdab F, Al-Aly Z, Alam K, Alam N, Alam T, Alasfoor D, Alene KA, Ali R, Alizadeh-Navaei R, Alkerwi A, Alla F, Allebeck P, Allen C, Al-Maskari F, Al-Raddadi R, Alsharif U, Alsowaidi S, Altirkawi KA, Amare AT, Amini E, Ammar W, Amoako YA, Andersen HH, Antonio CAT, Anwari P, Ärnlöv J, Artaman A, Aryal KK, Asayesh H, Asgedom SW, Assadi R, Atey TM, Atnafu NT, Atre SR, Avila-Burgos L, Avokphako EFGA, Awasthi A, Bacha U, Badawi A, Balakrishnan K, Banerjee A, Bannick MS, Barac A, Barber RM, Barker-Collo SL, Bärnighausen T, Barquera S, Barregard L, Barrero LH, Basu S, Battista B, Battle KE, Baune BT, Bazargan-Hejazi S, Beardsley J, Bedi N, Beghi E, Béjot Y, Bekele BB, Bell ML, Bennett DA, Bensenor IM, Benson J, Berhane A, Berhe DF, Bernabé E, Betsu BD, Beuran M, Beyene AS, Bhala N, Bhansali A, Bhatt S, Bhutta ZA, Biadgilign S, Bicer BK, Bienhoff K, Bikbov B, Birungi C, Biryukov S, Bisanzio D, Bizuayehu HM, Boneya DJ, Boufous S, Bourne RRA, Brazinova A, Brugha TS, Buchbinder R, Bulto LNB, Bumgarner BR, Butt ZA, Cahuana-Hurtado L, Cameron E, Car M, Carabin H, Carapetis JR, Cárdenas R, Carpenter DO, Carrero JJ, Carter A, Carvalho F, Casey DC, Caso V, Castañeda-Orjuela CA, Castle CD, Catalá-López F, Chang HY, Chang JC, Charlson FJ, Chen H, Chibalabala M, Chibueze CE, Chisumpa VH, Chitheer AA, Christopher DJ, Ciobanu LG, Cirillo M, Colombara D, Cooper C, Cortesi PA, Criqui MH, Crump JA, Dadi AF, Dalal K, Dandona L, Dandona R, das Neves J, Davitoiu DV, de Courten B, De Leo DD, Defo BK, Degenhardt L, Deiparine S, Dellavalle RP, Deribe K, Des Jarlais DC, Dey S, Dharmaratne SD, Dhillon PK, Dicker D, Ding EL, Djalalinia S, Do HP, Dorsey ER, dos Santos KPB, Douwes-Schultz D, Doyle KE, Driscoll TR, Dubey M, Duncan BB, El-Khatib ZZ, Ellerstrand J, Enayati A, Endries AY, Ermakov SP, Erskine HE, Eshrati B, Eskandarieh S, Esteghamati A, Estep K, Fanuel FBB, Farinha CSES, Faro A, Farzadfar F, Fazeli MS, Feigin VL, Fereshtehnejad SM, Fernandes JC, Ferrari AJ, Feyissa TR, Filip I, Fischer F, Fitzmaurice C, Flaxman AD, Flor LS, Foigt N, Foreman KJ, Franklin RC, Fullman N, Fürst T, Furtado JM, Futran ND, Gakidou E, Ganji M, Garcia-Basteiro AL, Gebre T, Gebrehiwot TT, Geleto A, Gemechu BL, Gesesew HA, Gething PW, Ghajar A, Gibney KB, Gill PS, Gillum RF, Ginawi IAM, Giref AZ, Gishu MD, Giussani G, Godwin WW, Gold AL, Goldberg EM, Gona PN, Goodridge A, Gopalani SV, Goto A, Goulart AC, Griswold M, Gugnani HC, Gupta R, Gupta R, Gupta T, Gupta V, Hafezi-Nejad N, Hailu GB, Hailu AD, Hamadeh RR, Hamidi S, Handal AJ, Hankey GJ, Hanson SW, Hao Y, Harb HL, Hareri HA, Haro JM, Harvey J, Hassanvand MS, Havmoeller R, Hawley C, Hay SI, Hay RJ, Henry NJ, Heredia-Pi IB, Hernandez JM, Heydarpour P, Hoek HW, Hoffman HJ, Horita N, Hosgood HD, Hostiuc S, Hotez PJ, Hoy DG, Htet AS, Hu G, Huang H, Huynh C, Iburg KM, Igumbor EU, Ikeda C, Irvine CMS, Jacobsen KH, Jahanmehr N, Jakovljevic MB, Jassal SK, Javanbakht M, Jayaraman SP, Jeemon P, Jensen PN, Jha V, Jiang G, John D, Johnson SC, Johnson CO, Jonas JB, Jürisson M, Kabir Z, Kadel R, Kahsay A, Kamal R, Kan H, Karam NE, Karch A, Karema CK, Kasaeian A, Kassa GM, Kassaw NA, Kassebaum NJ, Kastor A, Katikireddi SV, Kaul A, Kawakami N, Keiyoro PN, Kengne AP, Keren A, Khader YS, Khalil IA, Khan EA, Khang YH, Khosravi A, Khubchandani J, Kiadaliri AA, Kieling C, Kim YJ, Kim D, Kim P, Kimokoti RW, Kinfu Y, Kisa A, Kissimova-Skarbek KA, Kivimaki M, Knudsen AK, Kokubo Y, Kolte D, Kopec JA, Kosen S, Koul PA, Koyanagi A, Kravchenko M, Krishnaswami S, Krohn KJ, Kumar GA, Kumar P, Kumar S, Kyu HH, Lal DK, Lalloo R, Lambert N, Lan Q, Larsson A, Lavados PM, Leasher JL, Lee PH, Lee JT, Leigh J, Leshargie CT, Leung J, Leung R, Levi M, Li Y, Li Y, Li Kappe D, Liang X, Liben ML, Lim SS, Linn S, Liu PY, Liu A, Liu S, Liu Y, Lodha R, Logroscino G, London SJ, Looker KJ, Lopez AD, Lorkowski S, Lotufo PA, Low N, Lozano R, Lucas TCD, Macarayan ERK, Magdy Abd El Razek H, Magdy Abd El Razek M, Mahdavi M, Majdan M, Majdzadeh R, Majeed A, Malekzadeh R, Malhotra R, Malta DC, Mamun AA, Manguerra H, Manhertz T, Mantilla A, Mantovani LG, Mapoma CC, Marczak LB, Martinez-Raga J, Martins-Melo FR, Martopullo I, März W, Mathur MR, Mazidi M, McAlinden C, McGaughey M, McGrath JJ, McKee M, McNellan C, Mehata S, Mehndiratta MM, Mekonnen TC, Memiah P, Memish ZA, Mendoza W, Mengistie MA, Mengistu DT, Mensah GA, Meretoja TJ, Meretoja A, Mezgebe HB, Micha R, Millear A, Miller TR, Mills EJ, Mirarefin M, Mirrakhimov EM, Misganaw A, Mishra SR, Mitchell PB, Mohammad KA, Mohammadi A, Mohammed KE, Mohammed S, Mohanty SK, Mokdad AH, Mollenkopf SK, Monasta L, Montico M, Moradi-Lakeh M, Moraga P, Mori R, Morozoff C, Morrison SD, Moses M, Mountjoy-Venning C, Mruts KB, Mueller UO, Muller K, Murdoch ME, Murthy GVS, Musa KI, Nachega JB, Nagel G, Naghavi M, Naheed A, Naidoo KS, Naldi L, Nangia V, Natarajan G, Negasa DE, Negoi RI, Negoi I, Newton CR, Ngunjiri JW, Nguyen TH, Nguyen QL, Nguyen CT, Nguyen G, Nguyen M, Nichols E, Ningrum DNA, Nolte S, Nong VM, Norrving B, Noubiap JJN, O'Donnell MJ, Ogbo FA, Oh IH, Okoro A, Oladimeji O, Olagunju TO, Olagunju AT, Olsen HE, Olusanya BO, Olusanya JO, Ong K, Opio JN, Oren E, Ortiz A, Osgood-Zimmerman A, Osman M, Owolabi MO, PA M, Pacella RE, Pana A, Panda BK, Papachristou C, Park EK, Parry CD, Parsaeian M, Patten SB, Patton GC, Paulson K, Pearce N, Pereira DM, Perico N, Pesudovs K, Peterson CB, Petzold M, Phillips MR, Pigott DM, Pillay JD, Pinho C, Plass D, Pletcher MA, Popova S, Poulton RG, Pourmalek F, Prabhakaran D, Prasad NM, Prasad N, Purcell C, Qorbani M, Quansah R, Quintanilla BPA, Rabiee RHS, Radfar A, Rafay A, Rahimi K, Rahimi-Movaghar A, Rahimi-Movaghar V, Rahman MHU, Rahman M, Rai RK, Rajsic S, Ram U, Ranabhat CL, Rankin Z, Rao PC, Rao PV, Rawaf S, Ray SE, Reiner RC, Reinig N, Reitsma MB, Remuzzi G, Renzaho AMN, Resnikoff S, Rezaei S, Ribeiro AL, Ronfani L, Roshandel G, Roth GA, Roy A, Rubagotti E, Ruhago GM, Saadat S, Sadat N, Safdarian M, Safi S, Safiri S, Sagar R, Sahathevan R, Salama J, Saleem HOB, Salomon JA, Salvi SS, Samy AM, Sanabria JR, Santomauro D, Santos IS, Santos JV, Santric Milicevic MM, Sartorius B, Satpathy M, Sawhney M, Saxena S, Schmidt MI, Schneider IJC, Schöttker B, Schwebel DC, Schwendicke F, Seedat S, Sepanlou SG, Servan-Mori EE, Setegn T, Shackelford KA, Shaheen A, Shaikh MA, Shamsipour M, Shariful Islam SM, Sharma J, Sharma R, She J, Shi P, Shields C, Shifa GT, Shigematsu M, Shinohara Y, Shiri R, Shirkoohi R, Shirude S, Shishani K, Shrime MG, Sibai AM, Sigfusdottir ID, Silva DAS, Silva JP, Silveira DGA, Singh JA, Singh NP, Sinha DN, Skiadaresi E, Skirbekk V, Slepak EL, Sligar A, Smith DL, Smith M, Sobaih BHA, Sobngwi E, Sorensen RJD, Sousa TCM, Sposato LA, Sreeramareddy CT, Srinivasan V, Stanaway JD, Stathopoulou V, Steel N, Stein MB, Stein DJ, Steiner TJ, Steiner C, Steinke S, Stokes MA, Stovner LJ, Strub B, Subart M, Sufiyan MB, Sunguya BF, Sur PJ, Swaminathan S, Sykes BL, Sylte DO, Tabarés-Seisdedos R, Taffere GR, Takala JS, Tandon N, Tavakkoli M, Taveira N, Taylor HR, Tehrani-Banihashemi A, Tekelab T, Terkawi AS, Tesfaye DJ, Tesssema B, Thamsuwan O, Thomas KE, Thrift AG, Tiruye TY, Tobe-Gai R, Tollanes MC, Tonelli M, Topor-Madry R, Tortajada M, Touvier M, Tran BX, Tripathi S, Troeger C, Truelsen T, Tsoi D, Tuem KB, Tuzcu EM, Tyrovolas S, Ukwaja KN, Undurraga EA, Uneke CJ, Updike R, Uthman OA, Uzochukwu BSC, van Boven JFM, Varughese S, Vasankari T, Venkatesh S, Venketasubramanian N, Vidavalur R, Violante FS, Vladimirov SK, Vlassov VV, Vollset SE, Wadilo F, Wakayo T, Wang YP, Weaver M, Weichenthal S, Weiderpass E, Weintraub RG, Werdecker A, Westerman R, Whiteford HA, Wijeratne T, Wiysonge CS, Wolfe CDA, Woodbrook R, Woolf AD, Workicho A, Xavier D, Xu G, Yadgir S, Yaghoubi M, Yakob B, Yan LL, Yano Y, Ye P, Yimam HH, Yip P, Yonemoto N, Yoon SJ, Yotebieng M, Younis MZ, Zaidi Z, Zaki MES, Zegeye EA, Zenebe ZM, Zhang X, Zhou M, Zipkin B, Zodpey S, Zuhlke LJ, Murray CJL. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017; 390:1211-1259. [PMID: 28919117 PMCID: PMC5605509 DOI: 10.1016/s0140-6736(17)32154-2] [Citation(s) in RCA: 4400] [Impact Index Per Article: 628.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 07/22/2017] [Accepted: 07/26/2017] [Indexed: 02/05/2023]
Abstract
BACKGROUND As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016. METHODS We estimated prevalence and incidence for 328 diseases and injuries and 2982 sequelae, their non-fatal consequences. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between incidence, prevalence, remission, and cause of death rates for each condition. For some causes, we used alternative modelling strategies if incidence or prevalence needed to be derived from other data. YLDs were estimated as the product of prevalence and a disability weight for all mutually exclusive sequelae, corrected for comorbidity and aggregated to cause level. We updated the Socio-demographic Index (SDI), a summary indicator of income per capita, years of schooling, and total fertility rate. GBD 2016 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). FINDINGS Globally, low back pain, migraine, age-related and other hearing loss, iron-deficiency anaemia, and major depressive disorder were the five leading causes of YLDs in 2016, contributing 57·6 million (95% uncertainty interval [UI] 40·8-75·9 million [7·2%, 6·0-8·3]), 45·1 million (29·0-62·8 million [5·6%, 4·0-7·2]), 36·3 million (25·3-50·9 million [4·5%, 3·8-5·3]), 34·7 million (23·0-49·6 million [4·3%, 3·5-5·2]), and 34·1 million (23·5-46·0 million [4·2%, 3·2-5·3]) of total YLDs, respectively. Age-standardised rates of YLDs for all causes combined decreased between 1990 and 2016 by 2·7% (95% UI 2·3-3·1). Despite mostly stagnant age-standardised rates, the absolute number of YLDs from non-communicable diseases has been growing rapidly across all SDI quintiles, partly because of population growth, but also the ageing of populations. The largest absolute increases in total numbers of YLDs globally were between the ages of 40 and 69 years. Age-standardised YLD rates for all conditions combined were 10·4% (95% UI 9·0-11·8) higher in women than in men. Iron-deficiency anaemia, migraine, Alzheimer's disease and other dementias, major depressive disorder, anxiety, and all musculoskeletal disorders apart from gout were the main conditions contributing to higher YLD rates in women. Men had higher age-standardised rates of substance use disorders, diabetes, cardiovascular diseases, cancers, and all injuries apart from sexual violence. Globally, we noted much less geographical variation in disability than has been documented for premature mortality. In 2016, there was a less than two times difference in age-standardised YLD rates for all causes between the location with the lowest rate (China, 9201 YLDs per 100 000, 95% UI 6862-11943) and highest rate (Yemen, 14 774 YLDs per 100 000, 11 018-19 228). INTERPRETATION The decrease in death rates since 1990 for most causes has not been matched by a similar decline in age-standardised YLD rates. For many large causes, YLD rates have either been stagnant or have increased for some causes, such as diabetes. As populations are ageing, and the prevalence of disabling disease generally increases steeply with age, health systems will face increasing demand for services that are generally costlier than the interventions that have led to declines in mortality in childhood or for the major causes of mortality in adults. Up-to-date information about the trends of disease and how this varies between countries is essential to plan for an adequate health-system response. FUNDING Bill & Melinda Gates Foundation, and the National Institute on Aging and the National Institute of Mental Health of the National Institutes of Health.
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Wang H, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, Abera SF, Abraha HN, Abu-Raddad LJ, Abu-Rmeileh NME, Adedeji IA, Adedoyin RA, Adetifa IMO, Adetokunboh O, Afshin A, Aggarwal R, Agrawal A, Agrawal S, Ahmad Kiadaliri A, Ahmed MB, Aichour MTE, Aichour AN, Aichour I, Aiyar S, Akanda AS, Akinyemiju TF, Akseer N, Al Lami FH, Alabed S, Alahdab F, Al-Aly Z, Alam K, Alam N, Alasfoor D, Aldridge RW, Alene KA, Al-Eyadhy A, Alhabib S, Ali R, Alizadeh-Navaei R, Aljunid SM, Alkaabi JM, Alkerwi A, Alla F, Allam SD, Allebeck P, Al-Raddadi R, Alsharif U, Altirkawi KA, Alvis-Guzman N, Amare AT, Ameh EA, Amini E, Ammar W, Amoako YA, Anber N, Andrei CL, Androudi S, Ansari H, Ansha MG, Antonio CAT, Anwari P, Ärnlöv J, Arora M, Artaman A, Aryal KK, Asayesh H, Asgedom SW, Asghar RJ, Assadi R, Assaye AM, Atey TM, Atre SR, Avila-Burgos L, Avokpaho EFGA, Awasthi A, Babalola TK, Bacha U, Badawi A, Balakrishnan K, Balalla S, Barac A, Barber RM, Barboza MA, Barker-Collo SL, Bärnighausen T, Barquera S, Barregard L, Barrero LH, Baune BT, Bazargan-Hejazi S, Bedi N, Beghi E, Béjot Y, Bekele BB, Bell ML, Bello AK, Bennett DA, Bennett JR, Bensenor IM, Benson J, Berhane A, Berhe DF, Bernabé E, Beuran M, Beyene AS, Bhala N, Bhansali A, Bhaumik S, Bhutta ZA, Bicer BK, Bidgoli HH, Bikbov B, Birungi C, Biryukov S, Bisanzio D, Bizuayehu HM, Bjerregaard P, Blosser CD, Boneya DJ, Boufous S, Bourne RRA, Brazinova A, Breitborde NJK, Brenner H, Brugha TS, Bukhman G, Bulto LNB, Bumgarner BR, Burch M, Butt ZA, Cahill LE, Cahuana-Hurtado L, Campos-Nonato IR, Car J, Car M, Cárdenas R, Carpenter DO, Carrero JJ, Carter A, Castañeda-Orjuela CA, Castro FF, Castro RE, Catalá-López F, Chen H, Chiang PPC, Chibalabala M, Chisumpa VH, Chitheer AA, Choi JYJ, Christensen H, Christopher DJ, Ciobanu LG, Cirillo M, Cohen AJ, Colquhoun SM, Coresh J, Criqui MH, Cromwell EA, Crump JA, Dandona L, Dandona R, Dargan PI, das Neves J, Davey G, Davitoiu DV, Davletov K, de Courten B, De Leo D, Degenhardt L, Deiparine S, Dellavalle RP, Deribe K, Deribew A, Des Jarlais DC, Dey S, Dharmaratne SD, Dherani MK, Diaz-Torné C, Ding EL, Dixit P, Djalalinia S, Do HP, Doku DT, Donnelly CA, dos Santos KPB, Douwes-Schultz D, Driscoll TR, Duan L, Dubey M, Duncan BB, Dwivedi LK, Ebrahimi H, El Bcheraoui C, Ellingsen CL, Enayati A, Endries AY, Ermakov SP, Eshetie S, Eshrati B, Eskandarieh S, Esteghamati A, Estep K, Fanuel FBB, Faro A, Farvid MS, Farzadfar F, Feigin VL, Fereshtehnejad SM, Fernandes JG, Fernandes JC, Feyissa TR, Filip I, Fischer F, Foigt N, Foreman KJ, Frank T, Franklin RC, Fraser M, Friedman J, Frostad JJ, Fullman N, Fürst T, Furtado JM, Futran ND, Gakidou E, Gambashidze K, Gamkrelidze A, Gankpé FG, Garcia-Basteiro AL, Gebregergs GB, Gebrehiwot TT, Gebrekidan KG, Gebremichael MW, Gelaye AA, Geleijnse JM, Gemechu BL, Gemechu KS, Genova-Maleras R, Gesesew HA, Gething PW, Gibney KB, Gill PS, Gillum RF, Giref AZ, Girma BW, Giussani G, Goenka S, Gomez B, Gona PN, Gopalani SV, Goulart AC, Graetz N, Gugnani HC, Gupta PC, Gupta R, Gupta R, Gupta T, Gupta V, Haagsma JA, Hafezi-Nejad N, Hakuzimana A, Halasa YA, Hamadeh RR, Hambisa MT, Hamidi S, Hammami M, Hancock J, Handal AJ, Hankey GJ, Hao Y, Harb HL, Hareri HA, Harikrishnan S, Haro JM, Hassanvand MS, Havmoeller R, Hay RJ, Hay SI, He F, Heredia-Pi IB, Herteliu C, Hilawe EH, Hoek HW, Horita N, Hosgood HD, Hostiuc S, Hotez PJ, Hoy DG, Hsairi M, Htet AS, Hu G, Huang JJ, Huang H, Iburg KM, Igumbor EU, Ileanu BV, Inoue M, Irenso AA, Irvine CMS, Islam SMS, Islam N, Jacobsen KH, Jaenisch T, Jahanmehr N, Jakovljevic MB, Javanbakht M, Jayatilleke AU, Jeemon P, Jensen PN, Jha V, Jin Y, John D, John O, Johnson SC, Jonas JB, Jürisson M, Kabir Z, Kadel R, Kahsay A, Kalkonde Y, Kamal R, Kan H, Karch A, Karema CK, Karimi SM, Karthikeyan G, Kasaeian A, Kassaw NA, Kassebaum NJ, Kastor A, Katikireddi SV, Kaul A, Kawakami N, Kazanjan K, Keiyoro PN, Kelbore SG, Kemp AH, Kengne AP, Keren A, Kereselidze M, Kesavachandran CN, Ketema EB, Khader YS, Khalil IA, Khan EA, Khan G, Khang YH, Khera S, Khoja ATA, Khosravi MH, Kibret GD, Kieling C, Kim YJ, Kim CI, Kim D, Kim P, Kim S, Kimokoti RW, Kinfu Y, Kishawi S, Kissoon N, Kivimaki M, Knudsen AK, Kokubo Y, Kopec JA, Kosen S, Koul PA, Koyanagi A, Kravchenko M, Krohn KJ, Kuate Defo B, Kuipers EJ, Kulikoff XR, Kulkarni VS, Kumar GA, Kumar P, Kumsa FA, Kutz M, Lachat C, Lagat AK, Lager ACJ, Lal DK, Lalloo R, Lambert N, Lan Q, Lansingh VC, Larson HJ, Larsson A, Laryea DO, Lavados PM, Laxmaiah A, Lee PH, Leigh J, Leung J, Leung R, Levi M, Li Y, Liao Y, Liben ML, Lim SS, Linn S, Lipshultz SE, Liu S, Lodha R, Logroscino G, Lorch SA, Lorkowski S, Lotufo PA, Lozano R, Lunevicius R, Lyons RA, Ma S, Macarayan ER, Machado IE, Mackay MT, Magdy Abd El Razek M, Magis-Rodriguez C, Mahdavi M, Majdan M, Majdzadeh R, Majeed A, Malekzadeh R, Malhotra R, Malta DC, Mantovani LG, Manyazewal T, Mapoma CC, Marczak LB, Marks GB, Martin EA, Martinez-Raga J, Martins-Melo FR, Massano J, Maulik PK, Mayosi BM, Mazidi M, McAlinden C, McGarvey ST, McGrath JJ, McKee M, Mehata S, Mehndiratta MM, Mehta KM, Meier T, Mekonnen TC, Meles KG, Memiah P, Memish ZA, Mendoza W, Mengesha MM, Mengistie MA, Mengistu DT, Menon GR, Menota BG, Mensah GA, Meretoja TJ, Meretoja A, Mezgebe HB, Micha R, Mikesell J, Miller TR, Mills EJ, Minnig S, Mirarefin M, Mirrakhimov EM, Misganaw A, Mishra SR, Mohammad KA, Mohammadi A, Mohammed KE, Mohammed S, Mohan MBV, Mohanty SK, Mokdad AH, Mollenkopf SK, Molokhia M, Monasta L, Montañez Hernandez JC, Montico M, Mooney MD, Moore AR, Moradi-Lakeh M, Moraga P, Morawska L, Mori R, Morrison SD, Mruts KB, Mueller UO, Mullany E, Muller K, Murthy GVS, Murthy S, Musa KI, Nachega JB, Nagata C, Nagel G, Naghavi M, Naidoo KS, Nanda L, Nangia V, Nascimento BR, Natarajan G, Negoi I, Nguyen CT, Nguyen QL, Nguyen TH, Nguyen G, Ningrum DNA, Nisar MI, Nomura M, Nong VM, Norheim OF, Norrving B, Noubiap JJN, Nyakarahuka L, O'Donnell MJ, Obermeyer CM, Ogbo FA, Oh IH, Okoro A, Oladimeji O, Olagunju AT, Olusanya BO, Olusanya JO, Oren E, Ortiz A, Osgood-Zimmerman A, Ota E, Owolabi MO, Oyekale AS, PA M, Pacella RE, Pakhale S, Pana A, Panda BK, Panda-Jonas S, Park EK, Parsaeian M, Patel T, Patten SB, Patton GC, Paudel D, Pereira DM, Perez-Padilla R, Perez-Ruiz F, Perico N, Pervaiz A, Pesudovs K, Peterson CB, Petri WA, Petzold M, Phillips MR, Piel FB, Pigott DM, Pishgar F, Plass D, Polinder S, Popova S, Postma MJ, Poulton RG, Pourmalek F, Prasad N, Purwar M, Qorbani M, Quintanilla BPA, Rabiee RHS, Radfar A, Rafay A, Rahimi-Movaghar A, Rahimi-Movaghar V, Rahman MHU, Rahman SU, Rahman M, Rai RK, Rajsic S, Ram U, Rana SM, Ranabhat CL, Rao PV, Rawaf S, Ray SE, Rego MAS, Rehm J, Reiner RC, Remuzzi G, Renzaho AMN, Resnikoff S, Rezaei S, Rezai MS, Ribeiro AL, Rivas JC, Rokni MB, Ronfani L, Roshandel G, Roth GA, Rothenbacher D, Roy A, Rubagotti E, Ruhago GM, Saadat S, Sabde YD, Sachdev PS, Sadat N, Safdarian M, Safi S, Safiri S, Sagar R, Sahathevan R, Sahebkar A, Sahraian MA, Salama J, Salamati P, Salomon JA, Salvi SS, Samy AM, Sanabria JR, Sanchez-Niño MD, Santos IS, Santric Milicevic MM, Sarmiento-Suarez R, Sartorius B, Satpathy M, Sawhney M, Saxena S, Saylan MI, Schmidt MI, Schneider IJC, Schulhofer-Wohl S, Schutte AE, Schwebel DC, Schwendicke F, Seedat S, Seid AM, Sepanlou SG, Servan-Mori EE, Shackelford KA, Shaheen A, Shahraz S, Shaikh MA, Shamsipour M, Shamsizadeh M, Sharma J, Sharma R, She J, Shen J, Shetty BP, Shi P, Shibuya K, Shifa GT, Shigematsu M, Shiri R, Shiue I, Shrime MG, Sigfusdottir ID, Silberberg DH, Silpakit N, Silva DAS, Silva JP, Silveira DGA, Sindi S, Singh JA, Singh PK, Singh A, Singh V, Sinha DN, Skarbek KAK, Skiadaresi E, Sligar A, Smith DL, Sobaih BHA, Sobngwi E, Soneji S, Soriano JB, Sreeramareddy CT, Srinivasan V, Stathopoulou V, Steel N, Stein DJ, Steiner C, Stöckl H, Stokes MA, Strong M, Sufiyan MB, Suliankatchi RA, Sunguya BF, Sur PJ, Swaminathan S, Sykes BL, Szoeke CEI, Tabarés-Seisdedos R, Tadakamadla SK, Tadese F, Tandon N, Tanne D, Tarajia M, Tavakkoli M, Taveira N, Tehrani-Banihashemi A, Tekelab T, Tekle DY, Temsah MH, Terkawi AS, Tesema CL, Tesssema B, Theis A, Thomas N, Thompson AH, Thomson AJ, Thrift AG, Tiruye TY, Tobe-Gai R, Tonelli M, Topor-Madry R, Topouzis F, Tortajada M, Tran BX, Truelsen T, Trujillo U, Tsilimparis N, Tuem KB, Tuzcu EM, Tyrovolas S, Ukwaja KN, Undurraga EA, Uthman OA, Uzochukwu BSC, van Boven JFM, Varakin YY, Varughese S, Vasankari T, Vasconcelos AMN, Velasquez IM, Venketasubramanian N, Vidavalur R, Violante FS, Vishnu A, Vladimirov SK, Vlassov VV, Vollset SE, Vos T, Waid JL, Wakayo T, Wang YP, Weichenthal S, Weiderpass E, Weintraub RG, Werdecker A, Wesana J, Wijeratne T, Wilkinson JD, Wiysonge CS, Woldeyes BG, Wolfe CDA, Workicho A, Workie SB, Xavier D, Xu G, Yaghoubi M, Yakob B, Yalew AZ, Yan LL, Yano Y, Yaseri M, Ye P, Yimam HH, Yip P, Yirsaw BD, Yonemoto N, Yoon SJ, Yotebieng M, Younis MZ, Zaidi Z, Zaki MES, Zeeb H, Zenebe ZM, Zerfu TA, Zhang AL, Zhang X, Zodpey S, Zuhlke LJ, Lopez AD, Murray CJL. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017; 390:1084-1150. [PMID: 28919115 PMCID: PMC5605514 DOI: 10.1016/s0140-6736(17)31833-0] [Citation(s) in RCA: 488] [Impact Index Per Article: 69.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 05/21/2017] [Accepted: 06/07/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUND Detailed assessments of mortality patterns, particularly age-specific mortality, represent a crucial input that enables health systems to target interventions to specific populations. Understanding how all-cause mortality has changed with respect to development status can identify exemplars for best practice. To accomplish this, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated age-specific and sex-specific all-cause mortality between 1970 and 2016 for 195 countries and territories and at the subnational level for the five countries with a population greater than 200 million in 2016. METHODS We have evaluated how well civil registration systems captured deaths using a set of demographic methods called death distribution methods for adults and from consideration of survey and census data for children younger than 5 years. We generated an overall assessment of completeness of registration of deaths by dividing registered deaths in each location-year by our estimate of all-age deaths generated from our overall estimation process. For 163 locations, including subnational units in countries with a population greater than 200 million with complete vital registration (VR) systems, our estimates were largely driven by the observed data, with corrections for small fluctuations in numbers and estimation for recent years where there were lags in data reporting (lags were variable by location, generally between 1 year and 6 years). For other locations, we took advantage of different data sources available to measure under-5 mortality rates (U5MR) using complete birth histories, summary birth histories, and incomplete VR with adjustments; we measured adult mortality rate (the probability of death in individuals aged 15-60 years) using adjusted incomplete VR, sibling histories, and household death recall. We used the U5MR and adult mortality rate, together with crude death rate due to HIV in the GBD model life table system, to estimate age-specific and sex-specific death rates for each location-year. Using various international databases, we identified fatal discontinuities, which we defined as increases in the death rate of more than one death per million, resulting from conflict and terrorism, natural disasters, major transport or technological accidents, and a subset of epidemic infectious diseases; these were added to estimates in the relevant years. In 47 countries with an identified peak adult prevalence for HIV/AIDS of more than 0·5% and where VR systems were less than 65% complete, we informed our estimates of age-sex-specific mortality using the Estimation and Projection Package (EPP)-Spectrum model fitted to national HIV/AIDS prevalence surveys and antenatal clinic serosurveillance systems. We estimated stillbirths, early neonatal, late neonatal, and childhood mortality using both survey and VR data in spatiotemporal Gaussian process regression models. We estimated abridged life tables for all location-years using age-specific death rates. We grouped locations into development quintiles based on the Socio-demographic Index (SDI) and analysed mortality trends by quintile. Using spline regression, we estimated the expected mortality rate for each age-sex group as a function of SDI. We identified countries with higher life expectancy than expected by comparing observed life expectancy to anticipated life expectancy on the basis of development status alone. FINDINGS Completeness in the registration of deaths increased from 28% in 1970 to a peak of 45% in 2013; completeness was lower after 2013 because of lags in reporting. Total deaths in children younger than 5 years decreased from 1970 to 2016, and slower decreases occurred at ages 5-24 years. By contrast, numbers of adult deaths increased in each 5-year age bracket above the age of 25 years. The distribution of annualised rates of change in age-specific mortality rate differed over the period 2000 to 2016 compared with earlier decades: increasing annualised rates of change were less frequent, although rising annualised rates of change still occurred in some locations, particularly for adolescent and younger adult age groups. Rates of stillbirths and under-5 mortality both decreased globally from 1970. Evidence for global convergence of death rates was mixed; although the absolute difference between age-standardised death rates narrowed between countries at the lowest and highest levels of SDI, the ratio of these death rates-a measure of relative inequality-increased slightly. There was a strong shift between 1970 and 2016 toward higher life expectancy, most noticeably at higher levels of SDI. Among countries with populations greater than 1 million in 2016, life expectancy at birth was highest for women in Japan, at 86·9 years (95% UI 86·7-87·2), and for men in Singapore, at 81·3 years (78·8-83·7) in 2016. Male life expectancy was generally lower than female life expectancy between 1970 and 2016, and the gap between male and female life expectancy increased with progression to higher levels of SDI. Some countries with exceptional health performance in 1990 in terms of the difference in observed to expected life expectancy at birth had slower progress on the same measure in 2016. INTERPRETATION Globally, mortality rates have decreased across all age groups over the past five decades, with the largest improvements occurring among children younger than 5 years. However, at the national level, considerable heterogeneity remains in terms of both level and rate of changes in age-specific mortality; increases in mortality for certain age groups occurred in some locations. We found evidence that the absolute gap between countries in age-specific death rates has declined, although the relative gap for some age-sex groups increased. Countries that now lead in terms of having higher observed life expectancy than that expected on the basis of development alone, or locations that have either increased this advantage or rapidly decreased the deficit from expected levels, could provide insight into the means to accelerate progress in nations where progress has stalled. FUNDING Bill & Melinda Gates Foundation, and the National Institute on Aging and the National Institute of Mental Health of the National Institutes of Health.
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Deribe K, Cano J, Giorgi E, Pigott DM, Golding N, Pullan RL, Noor AM, Cromwell EA, Osgood-Zimmerman A, Enquselassie F, Hailu A, Murray CJL, Newport MJ, Brooker SJ, Hay SI, Davey G. Estimating the number of cases of podoconiosis in Ethiopia using geostatistical methods. Wellcome Open Res 2017; 2:78. [PMID: 29152596 PMCID: PMC5668927 DOI: 10.12688/wellcomeopenres.12483.2] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/07/2017] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND In 2011, the World Health Organization recognized podoconiosis as one of the neglected tropical diseases. Nonetheless, the number of people with podoconiosis and the geographical distribution of the disease is poorly understood. Based on a nationwide mapping survey and geostatistical modelling, we predict the prevalence of podoconiosis and estimate the number of cases across Ethiopia. METHODS We used nationwide data collected in Ethiopia between 2008 and 2013. Data were available for 141,238 individuals from 1,442 villages in 775 districts from all nine regional states and two city administrations. We developed a geostatistical model of podoconiosis prevalence among adults (individuals aged 15 years or above), by combining environmental factors. The number of people with podoconiosis was then estimated using a gridded map of adult population density for 2015. RESULTS Podoconiosis is endemic in 345 districts in Ethiopia: 144 in Oromia, 128 in Southern Nations, Nationalities and People's [SNNP], 64 in Amhara, 4 in Benishangul Gumuz, 4 in Tigray and 1 in Somali Regional State. Nationally, our estimates suggest that 1,537,963 adults (95% confidence intervals, 290,923-4,577,031 adults) were living with podoconiosis in 2015. Three regions (SNNP, Oromia and Amhara) contributed 99% of the cases. The highest proportion of individuals with podoconiosis resided in the SNNP (39%), while 32% and 29% of people with podoconiosis resided in Oromia and Amhara Regional States, respectively. Tigray and Benishangul Gumuz Regional States bore lower burdens, and in the remaining regions, podoconiosis was almost non-existent. Discussion: The estimates of podoconiosis cases presented here based upon the combination of currently available epidemiological data and a robust modelling approach clearly show that podoconiosis is highly endemic in Ethiopia. Given the presence of low cost prevention, and morbidity management and disability prevention services, it is our collective responsibility to scale-up interventions rapidly.
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Affiliation(s)
- Kebede Deribe
- School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia.,Wellcome Trust Brighton and Sussex Centre for Global Health Research, Brighton and Sussex Medical School, Brighton, UK
| | - Jorge Cano
- London School of Hygiene & Tropical Medicine, London, UK
| | - Emanuele Giorgi
- London School of Hygiene & Tropical Medicine, London, UK.,Lancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancaster, UK
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Nick Golding
- Spatial Ecology and Epidemiology Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,School of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | | | - Abdisalan M Noor
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.,Kenya Medical Research Institute-Wellcome Trust Collaborative Programme, Nairobi, Kenya
| | - Elizabeth A Cromwell
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | | | - Asrat Hailu
- Department of Microbiology, Immunology and Parasitology, Faculty of Medicine, Addis Ababa University, Addis Ababa, Ethiopia
| | | | - Melanie J Newport
- Wellcome Trust Brighton and Sussex Centre for Global Health Research, Brighton and Sussex Medical School, Brighton, UK
| | | | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Gail Davey
- Wellcome Trust Brighton and Sussex Centre for Global Health Research, Brighton and Sussex Medical School, Brighton, UK
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Burstein R, Golding N, Osgood-Zimmerman A, Longbottom J, Dwyer-Lindgren L, Browne A, Earl L, Morozoff C, Lim S, Wang H, Flaxman A, Weiss D, Bhatt S, Farag T, Krause L, Dowell S, Gething P, Murray C, Moyes C, Hay S. High Spatial Resolution Mapping of Changing Inequalities in Child
Mortality Across Africa between 2000 and 2015. Ann Glob Health 2017. [DOI: 10.1016/j.aogh.2017.03.135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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