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A method for small-area estimation of population mortality in settings affected by crises
BACKGROUND: Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and confli...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751462/ https://www.ncbi.nlm.nih.gov/pubmed/35016675 http://dx.doi.org/10.1186/s12963-022-00283-6 |
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author | Checchi, Francesco Testa, Adrienne Gimma, Amy Koum-Besson, Emilie Warsame, Abdihamid |
author_facet | Checchi, Francesco Testa, Adrienne Gimma, Amy Koum-Besson, Emilie Warsame, Abdihamid |
author_sort | Checchi, Francesco |
collection | PubMed |
description | BACKGROUND: Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution. METHODS: We describe here a ‘small-area estimation’ method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method’s implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts. RESULTS: Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates. CONCLUSIONS: The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12963-022-00283-6. |
format | Online Article Text |
id | pubmed-8751462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87514622022-01-11 A method for small-area estimation of population mortality in settings affected by crises Checchi, Francesco Testa, Adrienne Gimma, Amy Koum-Besson, Emilie Warsame, Abdihamid Popul Health Metr Research BACKGROUND: Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution. METHODS: We describe here a ‘small-area estimation’ method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method’s implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts. RESULTS: Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates. CONCLUSIONS: The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12963-022-00283-6. BioMed Central 2022-01-11 /pmc/articles/PMC8751462/ /pubmed/35016675 http://dx.doi.org/10.1186/s12963-022-00283-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Checchi, Francesco Testa, Adrienne Gimma, Amy Koum-Besson, Emilie Warsame, Abdihamid A method for small-area estimation of population mortality in settings affected by crises |
title | A method for small-area estimation of population mortality in settings affected by crises |
title_full | A method for small-area estimation of population mortality in settings affected by crises |
title_fullStr | A method for small-area estimation of population mortality in settings affected by crises |
title_full_unstemmed | A method for small-area estimation of population mortality in settings affected by crises |
title_short | A method for small-area estimation of population mortality in settings affected by crises |
title_sort | method for small-area estimation of population mortality in settings affected by crises |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751462/ https://www.ncbi.nlm.nih.gov/pubmed/35016675 http://dx.doi.org/10.1186/s12963-022-00283-6 |
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