Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups
Abstract Background Parasite prevalence has been used widely as a measure of malaria transmission, especially in malaria endemic areas. However, its contribution and relationship to malaria mortality across different age groups has not been well investigated. Previous studies in a health and demogra...
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ftdoajarticles:oai:doaj.org/article:2a359f4e1bce47deb11b5063751d3ca4 2023-05-15T15:14:49+02:00 Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups Sammy Khagayi Meghna Desai Nyaguara Amek Vincent Were Eric Donald Onyango Christopher Odero Kephas Otieno Godfrey Bigogo Stephen Munga Frank Odhiambo Mary J. Hamel Simon Kariuki Aaron M. Samuels Laurence Slutsker John Gimnig Penelope Vounatsou 2019-07-01T00:00:00Z https://doi.org/10.1186/s12936-019-2869-9 https://doaj.org/article/2a359f4e1bce47deb11b5063751d3ca4 EN eng BMC http://link.springer.com/article/10.1186/s12936-019-2869-9 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-019-2869-9 1475-2875 https://doaj.org/article/2a359f4e1bce47deb11b5063751d3ca4 Malaria Journal, Vol 18, Iss 1, Pp 1-12 (2019) Malaria Mortality Parasite prevalence Bayesian spatio-temporal Health and demographic surveillance system Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2019 ftdoajarticles https://doi.org/10.1186/s12936-019-2869-9 2022-12-31T07:19:13Z Abstract Background Parasite prevalence has been used widely as a measure of malaria transmission, especially in malaria endemic areas. However, its contribution and relationship to malaria mortality across different age groups has not been well investigated. Previous studies in a health and demographic surveillance systems (HDSS) platform in western Kenya quantified the contribution of incidence and entomological inoculation rates (EIR) to mortality. The study assessed the relationship between outcomes of malaria parasitaemia surveys and mortality across age groups. Methods Parasitological data from annual cross-sectional surveys from the Kisumu HDSS between 2007 and 2015 were used to determine malaria parasite prevalence (PP) and clinical malaria (parasites plus reported fever within 24 h or temperature above 37.5 °C). Household surveys and verbal autopsy (VA) were used to obtain data on all-cause and malaria-specific mortality. Bayesian negative binomial geo-statistical regression models were used to investigate the association of PP/clinical malaria with mortality across different age groups. Estimates based on yearly data were compared with those from aggregated data over 4 to 5-year periods, which is the typical period that mortality data are available from national demographic and health surveys. Results Using 5-year aggregated data, associations were established between parasite prevalence and malaria-specific mortality in the whole population (RRmalaria = 1.66; 95% Bayesian Credible Intervals: 1.07–2.54) and children 1–4 years (RRmalaria = 2.29; 1.17–4.29). While clinical malaria was associated with both all-cause and malaria-specific mortality in combined ages (RRall-cause = 1.32; 1.01–1.74); (RRmalaria = 2.50; 1.27–4.81), children 1–4 years (RRall-cause = 1.89; 1.00–3.51); (RRmalaria = 3.37; 1.23–8.93) and in older children 5–14 years (RRall-cause = 3.94; 1.34–11.10); (RRmalaria = 7.56; 1.20–39.54), no association was found among neonates, adults (15–59 years) and the elderly (60+ years). Distance to ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 18 1 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Malaria Mortality Parasite prevalence Bayesian spatio-temporal Health and demographic surveillance system Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
spellingShingle |
Malaria Mortality Parasite prevalence Bayesian spatio-temporal Health and demographic surveillance system Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Sammy Khagayi Meghna Desai Nyaguara Amek Vincent Were Eric Donald Onyango Christopher Odero Kephas Otieno Godfrey Bigogo Stephen Munga Frank Odhiambo Mary J. Hamel Simon Kariuki Aaron M. Samuels Laurence Slutsker John Gimnig Penelope Vounatsou Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups |
topic_facet |
Malaria Mortality Parasite prevalence Bayesian spatio-temporal Health and demographic surveillance system Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background Parasite prevalence has been used widely as a measure of malaria transmission, especially in malaria endemic areas. However, its contribution and relationship to malaria mortality across different age groups has not been well investigated. Previous studies in a health and demographic surveillance systems (HDSS) platform in western Kenya quantified the contribution of incidence and entomological inoculation rates (EIR) to mortality. The study assessed the relationship between outcomes of malaria parasitaemia surveys and mortality across age groups. Methods Parasitological data from annual cross-sectional surveys from the Kisumu HDSS between 2007 and 2015 were used to determine malaria parasite prevalence (PP) and clinical malaria (parasites plus reported fever within 24 h or temperature above 37.5 °C). Household surveys and verbal autopsy (VA) were used to obtain data on all-cause and malaria-specific mortality. Bayesian negative binomial geo-statistical regression models were used to investigate the association of PP/clinical malaria with mortality across different age groups. Estimates based on yearly data were compared with those from aggregated data over 4 to 5-year periods, which is the typical period that mortality data are available from national demographic and health surveys. Results Using 5-year aggregated data, associations were established between parasite prevalence and malaria-specific mortality in the whole population (RRmalaria = 1.66; 95% Bayesian Credible Intervals: 1.07–2.54) and children 1–4 years (RRmalaria = 2.29; 1.17–4.29). While clinical malaria was associated with both all-cause and malaria-specific mortality in combined ages (RRall-cause = 1.32; 1.01–1.74); (RRmalaria = 2.50; 1.27–4.81), children 1–4 years (RRall-cause = 1.89; 1.00–3.51); (RRmalaria = 3.37; 1.23–8.93) and in older children 5–14 years (RRall-cause = 3.94; 1.34–11.10); (RRmalaria = 7.56; 1.20–39.54), no association was found among neonates, adults (15–59 years) and the elderly (60+ years). Distance to ... |
format |
Article in Journal/Newspaper |
author |
Sammy Khagayi Meghna Desai Nyaguara Amek Vincent Were Eric Donald Onyango Christopher Odero Kephas Otieno Godfrey Bigogo Stephen Munga Frank Odhiambo Mary J. Hamel Simon Kariuki Aaron M. Samuels Laurence Slutsker John Gimnig Penelope Vounatsou |
author_facet |
Sammy Khagayi Meghna Desai Nyaguara Amek Vincent Were Eric Donald Onyango Christopher Odero Kephas Otieno Godfrey Bigogo Stephen Munga Frank Odhiambo Mary J. Hamel Simon Kariuki Aaron M. Samuels Laurence Slutsker John Gimnig Penelope Vounatsou |
author_sort |
Sammy Khagayi |
title |
Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups |
title_short |
Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups |
title_full |
Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups |
title_fullStr |
Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups |
title_full_unstemmed |
Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups |
title_sort |
modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups |
publisher |
BMC |
publishDate |
2019 |
url |
https://doi.org/10.1186/s12936-019-2869-9 https://doaj.org/article/2a359f4e1bce47deb11b5063751d3ca4 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 18, Iss 1, Pp 1-12 (2019) |
op_relation |
http://link.springer.com/article/10.1186/s12936-019-2869-9 https://doaj.org/toc/1475-2875 doi:10.1186/s12936-019-2869-9 1475-2875 https://doaj.org/article/2a359f4e1bce47deb11b5063751d3ca4 |
op_doi |
https://doi.org/10.1186/s12936-019-2869-9 |
container_title |
Malaria Journal |
container_volume |
18 |
container_issue |
1 |
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1766345227642077184 |