Bayesian spatio-temporal modeling of mortality in relation to malaria incidence in Western Kenya

Introduction
The effect of malaria exposure on mortality using health facility incidence data as a measure of transmission has not been well investigated. Health and demographic surveillance systems (HDSS) routinely capture data on mortality, interventions and other household related indicators, offering a unique platform for estimating and monitoring the incidence-mortality relationship in space and time.
Methods
Mortality data from the HDSS located in Western Kenya collected from 2007 to 2012 and linked to health facility incidence data were analysed using Bayesian spatio-temporal survival models to investigate the relation between mortality (all-cause/malaria-specific) and malaria incidence across all age groups. The analysis adjusted for insecticide-treated net (ITN) ownership, socio-economic status (SES), distance to health facilities and altitude. The estimates obtained were used to quantify excess mortality due to malaria exposure.
Results
Our models identified a strong positive relationship between slide positivity rate (SPR) and all-cause mortality in young children 1–4 years (HR = 4.29; 95% CI: 2.78–13.29) and all ages combined (HR = 1.55; 1.04–2.80). SPR had a strong positive association with malaria-specific mortality in young children (HR = 9.48; 5.11–37.94), however, in older children (5–14 years), it was associated with a reduction in malaria specific mortality (HR = 0.02; 0.003–0.33).
Conclusion
SPR as a measure of transmission captures well the association between malaria transmission intensity and all-cause/malaria mortality. This offers a quick and efficient way to monitor malaria burden. Excess mortality estimates indicate that small changes in malaria incidence substantially reduce overall and malaria specific mortality.