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

• Published in: Malaria journal Vol. 18; no. 1; p. 247
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.07.2019; BioMed Central; BMC
• Subjects: Bayesian spatio-temporal; Disease transmission; Health and demographic surveillance system; Malaria; Mortality; Parasite prevalence; Patient outcomes; Plasmodium falciparum; Risk factors; Sentinel surveillance; Social class; Statistics; Kenya; Health and demographic surveillance system; Parasite prevalence; Malaria; Bayesian spatio-temporal; Mortality
• ISSN: 1475-2875; 1475-2875
• DOI: 10.1186/s12936-019-2869-9

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. 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. Using 5-year aggregated data, associations were established between parasite prevalence and malaria-specific mortality in the whole population (RR  = 1.66; 95% Bayesian Credible Intervals: 1.07-2.54) and children 1-4 years (RR  = 2.29; 1.17-4.29). While clinical malaria was associated with both all-cause and malaria-specific mortality in combined ages (RR  = 1.32; 1.01-1.74); (RR  = 2.50; 1.27-4.81), children 1-4 years (RR  = 1.89; 1.00-3.51); (RR  = 3.37; 1.23-8.93) and in older children 5-14 years (RR  = 3.94; 1.34-11.10); (RR  = 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. 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.