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Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups

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 sur...

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Autores principales: Khagayi, Sammy, Desai, Meghna, Amek, Nyaguara, Were, Vincent, Onyango, Eric Donald, Odero, Christopher, Otieno, Kephas, Bigogo, Godfrey, Munga, Stephen, Odhiambo, Frank, Hamel, Mary J., Kariuki, Simon, Samuels, Aaron M., Slutsker, Laurence, Gimnig, John, Vounatsou, Penelope
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651924/
https://www.ncbi.nlm.nih.gov/pubmed/31337411
http://dx.doi.org/10.1186/s12936-019-2869-9
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author Khagayi, Sammy
Desai, Meghna
Amek, Nyaguara
Were, Vincent
Onyango, Eric Donald
Odero, Christopher
Otieno, Kephas
Bigogo, Godfrey
Munga, Stephen
Odhiambo, Frank
Hamel, Mary J.
Kariuki, Simon
Samuels, Aaron M.
Slutsker, Laurence
Gimnig, John
Vounatsou, Penelope
author_facet Khagayi, Sammy
Desai, Meghna
Amek, Nyaguara
Were, Vincent
Onyango, Eric Donald
Odero, Christopher
Otieno, Kephas
Bigogo, Godfrey
Munga, Stephen
Odhiambo, Frank
Hamel, Mary J.
Kariuki, Simon
Samuels, Aaron M.
Slutsker, Laurence
Gimnig, John
Vounatsou, Penelope
author_sort Khagayi, Sammy
collection PubMed
description 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 (RR(malaria) = 1.66; 95% Bayesian Credible Intervals: 1.07–2.54) and children 1–4 years (RR(malaria) = 2.29; 1.17–4.29). While clinical malaria was associated with both all-cause and malaria-specific mortality in combined ages (RR(all-cause) = 1.32; 1.01–1.74); (RR(malaria) = 2.50; 1.27–4.81), children 1–4 years (RR(all-cause) = 1.89; 1.00–3.51); (RR(malaria) = 3.37; 1.23–8.93) and in older children 5–14 years (RR(all-cause) = 3.94; 1.34–11.10); (RR(malaria) = 7.56; 1.20–39.54), no association was found among neonates, adults (15–59 years) and the elderly (60+ years). Distance to health facilities, socioeconomic status, elevation and survey year were important factors for all-cause and malaria-specific mortality. CONCLUSION: Malaria parasitaemia from cross-sectional surveys was associated with mortality across age groups over 4 to 5 year periods with clinical malaria more strongly associated with mortality than parasite prevalence. This effect was stronger in children 5–14 years compared to other age-groups. Further analyses of data from other HDSS sites or similar platforms would be useful in investigating the relationship between malaria and mortality across different endemicity levels. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12936-019-2869-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-66519242019-07-31 Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups Khagayi, Sammy Desai, Meghna Amek, Nyaguara Were, Vincent Onyango, Eric Donald Odero, Christopher Otieno, Kephas Bigogo, Godfrey Munga, Stephen Odhiambo, Frank Hamel, Mary J. Kariuki, Simon Samuels, Aaron M. Slutsker, Laurence Gimnig, John Vounatsou, Penelope Malar J Research 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 (RR(malaria) = 1.66; 95% Bayesian Credible Intervals: 1.07–2.54) and children 1–4 years (RR(malaria) = 2.29; 1.17–4.29). While clinical malaria was associated with both all-cause and malaria-specific mortality in combined ages (RR(all-cause) = 1.32; 1.01–1.74); (RR(malaria) = 2.50; 1.27–4.81), children 1–4 years (RR(all-cause) = 1.89; 1.00–3.51); (RR(malaria) = 3.37; 1.23–8.93) and in older children 5–14 years (RR(all-cause) = 3.94; 1.34–11.10); (RR(malaria) = 7.56; 1.20–39.54), no association was found among neonates, adults (15–59 years) and the elderly (60+ years). Distance to health facilities, socioeconomic status, elevation and survey year were important factors for all-cause and malaria-specific mortality. CONCLUSION: Malaria parasitaemia from cross-sectional surveys was associated with mortality across age groups over 4 to 5 year periods with clinical malaria more strongly associated with mortality than parasite prevalence. This effect was stronger in children 5–14 years compared to other age-groups. Further analyses of data from other HDSS sites or similar platforms would be useful in investigating the relationship between malaria and mortality across different endemicity levels. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12936-019-2869-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-23 /pmc/articles/PMC6651924/ /pubmed/31337411 http://dx.doi.org/10.1186/s12936-019-2869-9 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Khagayi, Sammy
Desai, Meghna
Amek, Nyaguara
Were, Vincent
Onyango, Eric Donald
Odero, Christopher
Otieno, Kephas
Bigogo, Godfrey
Munga, Stephen
Odhiambo, Frank
Hamel, Mary J.
Kariuki, Simon
Samuels, Aaron M.
Slutsker, Laurence
Gimnig, John
Vounatsou, Penelope
Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups
title 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_short 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
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651924/
https://www.ncbi.nlm.nih.gov/pubmed/31337411
http://dx.doi.org/10.1186/s12936-019-2869-9
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