ABSTRACTMalaria risk factor assessment is a critical step in determining cost-effective intervention strategies and operational plans in a regional setting. We develop a multi-indicator multistep approach to model the malaria risks at the population level in western Kenya. We used a combination of cross-sectional seasonal malaria infection prevalence, vector density, and cohort surveillance of malaria incidence at the village level to classify villages into malaria risk groups through unsupervised classification. Generalized boosted multinomial logistics regression analysis was performed to determine village-level risk factors using environmental, biological, socioeconomic, and climatic features. Thirty-six villages in western Kenya were first classified into two to five operational groups based on different combinations of malaria risk indicators. Risk assessment indicated that altitude accounted for 45–65% of all importance value relative to all other factors; all other variable importance values were < 6% in all models. After adjusting by altitude, villages were classified into three groups within distinct geographic areas regardless of the combination of risk indicators. Risk analysis based on altitude-adjusted classification indicated that factors related to larval habitat abundance accounted for 63% of all importance value, followed by geographic features related to the ponding effect (17%), vegetation cover or greenness (15%), and the number of bed nets combined with February temperature (5%). These results suggest that altitude is the intrinsic factor in determining malaria transmission risk in western Kenya. Malaria vector larval habitat management, such as habitat reduction and larviciding, may be an important supplement to the current first-line vector control tools in the study area.
Comparing ownership and use of bed nets at two sites with differential malaria transmission in western Kenya
