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Cited by 192Vibrio bacteria research studiesSalmonella and Campylobacter epidemiologyBacterial Infections and Vaccines

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Mapping the burden of cholera in sub-Saharan Africa and implications for control: an analysis of data across geographical scales

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Lessler, Justin, Moore, Sean M, Luquero, Francisco J, McKay, Heather S, Grais, Rebecca, Henkens, Myriam, Mengel, Martin, Dunoyer, Jessica, M'bangombe, Maurice, Lee, Elizabeth C, Djingarey, Mamoudou Harouna, Sudre, Bertrand, Bompangue, Didier, Fraser, Robert S M, Abubakar, Abdinasir, Perea, William, Legros, Dominique, Azman, Andrew S·2018·The Lancet
DOI 10.1016/s0140-6736(17)33050-7PMC PMC5946088
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Abstract

BACKGROUND: Cholera remains a persistent health problem in sub-Saharan Africa and worldwide. Cholera can be controlled through appropriate water and sanitation, or by oral cholera vaccination, which provides transient (∼3 years) protection, although vaccine supplies remain scarce. We aimed to map cholera burden in sub-Saharan Africa and assess how geographical targeting could lead to more efficient interventions. METHODS: We combined information on cholera incidence in sub-Saharan Africa (excluding Djibouti and Eritrea) from 2010 to 2016 from datasets from WHO, Médecins Sans Frontières, ProMED, ReliefWeb, ministries of health, and the scientific literature. We divided the study region into 20 km × 20 km grid cells and modelled annual cholera incidence in each grid cell assuming a Poisson process adjusted for covariates and spatially correlated random effects. We combined these findings with data on population distribution to estimate the number of people living in areas of high cholera incidence (>1 case per 1000 people per year). We further estimated the reduction in cholera incidence that could be achieved by targeting cholera prevention and control interventions at areas of high cholera incidence. FINDINGS: We included 279 datasets covering 2283 locations in our analyses. In sub-Saharan Africa (excluding Djibouti and Eritrea), a mean of 141 918 cholera cases (95% credible interval [CrI] 141 538-146 505) were reported per year. 4·0% (95% CrI 1·7-16·8) of districts, home to 87·2 million people (95% CrI 60·3 million to 118·9 million), have high cholera incidence. By focusing on the highest incidence districts first, effective targeted interventions could eliminate 50% of the region's cholera by covering 35·3 million people (95% CrI 26·3 million to 62·0 million), which is less than 4% of the total population. INTERPRETATION: Although cholera occurs throughout sub-Saharan Africa, its highest incidence is concentrated in a small proportion of the continent. Prioritising high-risk areas could substantially increase the efficiency of cholera control programmes. FUNDING: The Bill & Melinda Gates Foundation.

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Figure 1 Mean annual cholera incidence (A) Mean annual cholera incidence per 100 000 people in sub-Saharan Africa between 2010 and 2016, and (B) locations with mean annual incidence of more than one per 1000 people, (C) more than one per 10 000 people, or (D) more than one per 100 000 people.
Figure 1 Mean annual cholera incidence (A) Mean annual cholera incidence per 100 000 people in sub-Saharan Africa between 2010 and 2016, and (B) locations with mean annual incidence of more than one per 1000 people, (C) more than one per 10 000 people, or (D) more than one per 100 000 people.CC BY · PubMed Central
Figure 2 Districts with mean annual cholera incidence above certain thresholds (A) Districts with mean annual cholera incidence of more than one case per 1000 people, (B) more than one case per 10 000 people, and (C) more than one case per 100 000 people. Districts with a mean of fewer than five cases annually are excluded. The colour scale represents the percentage of model iterations (ie, posterior draws) for which incidence exceeds the threshold, with darker shaded districts being over the threshold in a higher percentage of Markov chain Monte Carlo iterations.
Figure 2 Districts with mean annual cholera incidence above certain thresholds (A) Districts with mean annual cholera incidence of more than one case per 1000 people, (B) more than one case per 10 000 people, and (C) more than one case per 100 000 people. Districts with a mean of fewer than five cases annually are excluded. The colour scale represents the percentage of model iterations (ie, posterior draws) for which incidence exceeds the threshold, with darker shaded districts being over the threshold in a higher percentage of Markov chain Monte Carlo iterations.CC BY · PubMed Central
Figure 3 Annual cholera cases in sub-Saharan Africa averted as a function of the number of people targeted with an ideal intervention or mix of interventions The optimum grid cell targeting curve (blue) represents a strategy targeting all 20 km × 20 km grid cells in rank order by number of cases. The optimum district targeting curve (red) represents a strategy targeting all districts in rank order by number of cases regardless of country. The green curve represents a more realistic and practical strategy that targets all high-risk districts in each country at once, with countries ranked by the number of cases prevented. Lines are the mean values and shading shows the 95% credible intervals. Strategies targeting grid cells or districts by ranked incidence instead of number of cases are presented in the appendix.
Figure 3 Annual cholera cases in sub-Saharan Africa averted as a function of the number of people targeted with an ideal intervention or mix of interventions The optimum grid cell targeting curve (blue) represents a strategy targeting all 20 km × 20 km grid cells in rank order by number of cases. The optimum district targeting curve (red) represents a strategy targeting all districts in rank order by number of cases regardless of country. The green curve represents a more realistic and practical strategy that targets all high-risk districts in each country at once, with countries ranked by the number of cases prevented. Lines are the mean values and shading shows the 95% credible intervals. Strategies targeting grid cells or districts by ranked incidence instead of number of cases are presented in the appendix.CC BY · PubMed Central
Figure 4 Cholera incidence versus the coefficient of variation of the annual incidence Mean annual reported cholera incidence per 100 000 people versus the coefficient of variation of the annual incidence from 2000 to 2015 for 50 African countries based on reports to WHO (A),3 and from 2004 to 2014 for districts in Nigeria (B; states) and from 2000 to 2016 for the Democratic Republic of the Congo (C; zone de santé) using annual aggregated data for each country. Colouring of points and map areas corresponds to the position on the scatter plot, as shown in the bottom right inset in (A), to allow easier mapping between country maps and x–y plots. The size of the points correspond to the number of years that the country reported data to WHO. Countries and districts with a mean annual incidence of zero are dark green in the sub-maps, but are not plotted in the scatter plots. Black circles and crosses in (B) and (C) represent each country's position in (A). National-level data were available for both Djibouti and Eritrea; white areas on maps correspond to areas where data were not available.
Figure 4 Cholera incidence versus the coefficient of variation of the annual incidence Mean annual reported cholera incidence per 100 000 people versus the coefficient of variation of the annual incidence from 2000 to 2015 for 50 African countries based on reports to WHO (A),3 and from 2004 to 2014 for districts in Nigeria (B; states) and from 2000 to 2016 for the Democratic Republic of the Congo (C; zone de santé) using annual aggregated data for each country. Colouring of points and map areas corresponds to the position on the scatter plot, as shown in the bottom right inset in (A), to allow easier mapping between country maps and x–y plots. The size of the points correspond to the number of years that the country reported data to WHO. Countries and districts with a mean annual incidence of zero are dark green in the sub-maps, but are not plotted in the scatter plots. Black circles and crosses in (B) and (C) represent each country's position in (A). National-level data were available for both Djibouti and Eritrea; white areas on maps correspond to areas where data were not available.CC BY · PubMed Central

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Metadata

Authors
Lessler, J., Moore, S. M., Luquero, F. J., McKay, H. S., Grais, R., Henkens, M., Mengel, M., Dunoyer, J., M'bangombe, M., Lee, E. C., Djingarey, M. H., Sudre, B., Bompangue, D., Fraser, R. S. M., Abubakar, A., Perea, W., Legros, D., & Azman, A. S.
Affiliations
  • Johns Hopkins University (US)
  • University of Notre Dame (US)
  • Epicentre (FR)
  • Médecins Sans Frontières (BE)
  • Agence de Médecine Préventive (FR)
  • United Nations Children's Fund (US)
  • World Health Organization Regional Office for Africa (CG)
  • Ministry of Public Health (CD)
  • International Federation of Red Cross and Red Crescent Societies (CH)
  • World Health Organization Regional Office for the Eastern Mediterranean (EG)
  • World Health Organization (CH)
Corresponding author
Lessler, Justin
ORCID
Venue
The Lancet
Publisher
Elsevier BV
Published
2018-05
Volume
391
Issue
10133
Pages
1908-1915
ISSN
  • 0099-5355 (ISSN-L)
  • 0140-6736
DOI
10.1016/s0140-6736(17)33050-7
PMC
PMC5946088
OpenAlex
W2792363855
Cited by
192
References
17
Citations by year
2026: 12 · 2025: 24 · 2024: 33 · 2023: 27 · 2022: 22 · 2021: 21 · 2020: 22 · 2019: 20 · 2018: 11
FWCI
13.14
Type
journal article
Language
en
Open access
Licence
Locations
  • The Lancet (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion)
  • Medecins Sans Frontieres Field Research (Medecins Sans Frontieres) (submittedVersion)
  • Greater South Information System
Funders
Bill and Melinda Gates Foundation (OPP10892431, OPP1171700, OPP1127318)
Topics
  • Life Sciences › Immunology and Microbiology › Microbiology › Vibrio bacteria research studies
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Salmonella and Campylobacter epidemiology
  • Life Sciences › Immunology and Microbiology › Microbiology › Bacterial Infections and Vaccines
Keywords
cholera, cholera incidence, sub-Saharan Africa, targeted interventions, cholera control, water and sanitation, spatially correlated random effects
MeSH terms
Cholera, Demography, Humans, Markov Chains, Population Density, Sanitation, Vaccination, Incidence, Africa South of the Sahara, Mass Vaccination

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