Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study
Abstract 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, an...
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ftdoajarticles:oai:doaj.org/article:2cd19d67418147099fee9e382af7e522 2023-05-15T15:16:44+02:00 Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study Sayantee Jana Sze Hang Fu Hellen Gelband Patrick Brown Prabhat Jha 2022-03-01T00:00:00Z https://doi.org/10.1186/s12936-022-04112-x https://doaj.org/article/2cd19d67418147099fee9e382af7e522 EN eng BMC https://doi.org/10.1186/s12936-022-04112-x https://doaj.org/toc/1475-2875 doi:10.1186/s12936-022-04112-x 1475-2875 https://doaj.org/article/2cd19d67418147099fee9e382af7e522 Malaria Journal, Vol 21, Iss 1, Pp 1-10 (2022) Spatio-temporal modelling India Million Death Study Malaria mortality Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2022 ftdoajarticles https://doi.org/10.1186/s12936-022-04112-x 2022-12-31T07:12:21Z Abstract 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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 21 1 |
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Directory of Open Access Journals: DOAJ Articles |
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Spatio-temporal modelling India Million Death Study Malaria mortality Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Spatio-temporal modelling India Million Death Study Malaria mortality Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Sayantee Jana Sze Hang Fu Hellen Gelband Patrick Brown Prabhat Jha Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study |
topic_facet |
Spatio-temporal modelling India Million Death Study Malaria mortality Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract 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. |
format |
Article in Journal/Newspaper |
author |
Sayantee Jana Sze Hang Fu Hellen Gelband Patrick Brown Prabhat Jha |
author_facet |
Sayantee Jana Sze Hang Fu Hellen Gelband Patrick Brown Prabhat Jha |
author_sort |
Sayantee Jana |
title |
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_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_sort |
spatio-temporal modelling of malaria mortality in india from 2004 to 2013 from the million death study |
publisher |
BMC |
publishDate |
2022 |
url |
https://doi.org/10.1186/s12936-022-04112-x https://doaj.org/article/2cd19d67418147099fee9e382af7e522 |
geographic |
Arctic |
geographic_facet |
Arctic |
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Arctic |
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Arctic |
op_source |
Malaria Journal, Vol 21, Iss 1, Pp 1-10 (2022) |
op_relation |
https://doi.org/10.1186/s12936-022-04112-x https://doaj.org/toc/1475-2875 doi:10.1186/s12936-022-04112-x 1475-2875 https://doaj.org/article/2cd19d67418147099fee9e382af7e522 |
op_doi |
https://doi.org/10.1186/s12936-022-04112-x |
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Malaria Journal |
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21 |
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1 |
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1766347037116203008 |