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Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study
BACKGROUND: India has a substantial burden of malaria, concentrated in specific areas and population groups. Spatio-temporal modelling of deaths due to malaria in India is a critical tool for identifying high-risk groups for effective resource allocation and disease control policy-making, and subseq...
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/PMC8932160/ https://www.ncbi.nlm.nih.gov/pubmed/35300715 http://dx.doi.org/10.1186/s12936-022-04112-x |
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author | Jana, Sayantee Fu, Sze Hang Gelband, Hellen Brown, Patrick Jha, Prabhat |
author_facet | Jana, Sayantee Fu, Sze Hang Gelband, Hellen Brown, Patrick Jha, Prabhat |
author_sort | Jana, Sayantee |
collection | PubMed |
description | BACKGROUND: India has a substantial burden of malaria, concentrated in specific areas and population groups. Spatio-temporal modelling of deaths due to malaria in India is a critical tool for identifying high-risk groups for effective resource allocation and disease control policy-making, and subsequently for the country’s progress towards United Nations 2030 Sustainable Development Goals. METHODS: In this study, a spatio-temporal model with the objective of understanding the spatial distribution of malaria mortality rates and the rate of temporal decline, across the country, has been constructed. A spatio-temporal “random slope” model was used, with malaria risk depending on a spatial relative risk surface and a linear time effect with a spatially-varying coefficient. The models were adjusted for urban/rural status (residence of the deceased) and Normalized Difference Vegetation Index (NDVI), using 2004–13 data from the Million Death Study (MDS) (the most recent data available), with nationwide geographic coverage. Previous studies based on MDS had focused only on aggregated analyses. RESULTS: The rural population had twice the risk of death due to malaria compared to the urban population. Malaria mortality in some of the highest-risk regions, namely the states of Odisha and Jharkhand, are declining faster than other areas; however, the rate of decline was not uniformly correlated with the level of risk. The overall decline was faster after 2010. CONCLUSION: The results suggest a need for increased attention in high-risk rural populations, which already face challenges like inadequate infrastructure, inaccessibility to health care facilities, awareness, and education around malaria mortality and prevalence. It also points to the urgent need to restart the MDS to document changes since 2013, to develop appropriate malaria control measures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-022-04112-x. |
format | Online Article Text |
id | pubmed-8932160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89321602022-03-23 Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study Jana, Sayantee Fu, Sze Hang Gelband, Hellen Brown, Patrick Jha, Prabhat Malar J Research BACKGROUND: India has a substantial burden of malaria, concentrated in specific areas and population groups. Spatio-temporal modelling of deaths due to malaria in India is a critical tool for identifying high-risk groups for effective resource allocation and disease control policy-making, and subsequently for the country’s progress towards United Nations 2030 Sustainable Development Goals. METHODS: In this study, a spatio-temporal model with the objective of understanding the spatial distribution of malaria mortality rates and the rate of temporal decline, across the country, has been constructed. A spatio-temporal “random slope” model was used, with malaria risk depending on a spatial relative risk surface and a linear time effect with a spatially-varying coefficient. The models were adjusted for urban/rural status (residence of the deceased) and Normalized Difference Vegetation Index (NDVI), using 2004–13 data from the Million Death Study (MDS) (the most recent data available), with nationwide geographic coverage. Previous studies based on MDS had focused only on aggregated analyses. RESULTS: The rural population had twice the risk of death due to malaria compared to the urban population. Malaria mortality in some of the highest-risk regions, namely the states of Odisha and Jharkhand, are declining faster than other areas; however, the rate of decline was not uniformly correlated with the level of risk. The overall decline was faster after 2010. CONCLUSION: The results suggest a need for increased attention in high-risk rural populations, which already face challenges like inadequate infrastructure, inaccessibility to health care facilities, awareness, and education around malaria mortality and prevalence. It also points to the urgent need to restart the MDS to document changes since 2013, to develop appropriate malaria control measures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-022-04112-x. BioMed Central 2022-03-17 /pmc/articles/PMC8932160/ /pubmed/35300715 http://dx.doi.org/10.1186/s12936-022-04112-x 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 Jana, Sayantee Fu, Sze Hang Gelband, Hellen Brown, Patrick Jha, Prabhat Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study |
title | Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study |
title_full | Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study |
title_fullStr | Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study |
title_full_unstemmed | Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study |
title_short | Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study |
title_sort | spatio-temporal modelling of malaria mortality in india from 2004 to 2013 from the million death study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8932160/ https://www.ncbi.nlm.nih.gov/pubmed/35300715 http://dx.doi.org/10.1186/s12936-022-04112-x |
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