Development and validation of a rapid assessment tool for malaria prevention

Abstract

Background

Insecticide-treated bed nets (ITN) have been shown to be efficacious in reducing malaria morbidity and mortality in many regions. Unfortunately in some areas, malaria has persisted despite the scale up of ITNs. Recent reports indicate that human behaviour and mosquito behaviour are potential threats to the efficacy of ITNs. However, these concerns are likely highly heterogeneous even at very small scales. This study aimed at developing, testing and validating a rapid assessment tool to collect actionable information at local levels for a quick evaluation of potential barriers to malaria prevention. Methods

The study was conducted at the Webuye Health and Demographic Surveillance Site in Bungoma East Sub-County, Kenya. Based on the findings from the case–control study, 12 primary surveillance components that encompass the major impediments to successful prevention were identified and used to develop a rapid assessment tool. Twenty community health volunteers were trained to identify patients with laboratory-confirmed malaria in six peripheral health facilities located within six sub locations and subsequently followed them up to their homes to conduct a rapid assessment. Sampling and analysis of the results of the survey are based on Lot Quality Assurance. Results

The tool was able to detect local heterogeneity in bed net coverage, bed net use and larval site abundance in the six health facility catchment areas. Nearly all the catchment areas met the action threshold for incomplete household coverage (i.e. not all household members not using a net the previous night) except the peri-urban area. Although the threshold for nets not in good condition was set very high (≥50%), only two catchment areas failed to meet the action threshold. On the indicator for “Net not used every day last week”, half of the areas failed, while for net ownership, only two areas met the action threshold. Conclusion

The rapid assessment tool was able to detect marked heterogeneity in key indicators for malaria prevention between patients attending health facilities, and can distinguish between priority areas for intervention. There is need to validate it for use in other contexts.