Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya

Abstract

Background: Infectious diseases often demonstrate heterogeneity of transmission among host populations. This heterogeneity reduces the efficacy of control strategies, but also implies that focusing control strategies on "hotspots" of transmission could be highly effective.

Methods and findings: In order to identify hotspots of malaria transmission, we analysed longitudinal data on febrile malaria episodes, asymptomatic parasitaemia, and antibody titres over 12 y from 256 homesteads in three study areas in Kilifi District on the Kenyan coast. We examined heterogeneity by homestead, and identified groups of homesteads that formed hotspots using a spatial scan statistic. Two types of statistically significant hotspots were detected; stable hotspots of asymptomatic parasitaemia and unstable hotspots of febrile malaria. The stable hotspots were associated with higher average AMA-1 antibody titres than the unstable clusters (optical density [OD] = 1.24, 95% confidence interval [CI] 1.02-1.47 versus OD = 1.1, 95% CI 0.88-1.33) and lower mean ages of febrile malaria episodes (5.8 y, 95% CI 5.6-6.0 versus 5.91 y, 95% CI 5.7-6.1). A falling gradient of febrile malaria incidence was identified in the penumbrae of both hotspots. Hotspots were associated with AMA-1 titres, but not seroconversion rates. In order to target control measures, homesteads at risk of febrile malaria could be predicted by identifying the 20% of homesteads that experienced an episode of febrile malaria during one month in the dry season. That 20% subsequently experienced 65% of all febrile malaria episodes during the following year. A definition based on remote sensing data was 81% sensitive and 63% specific for the stable hotspots of asymptomatic malaria.

Conclusions: Hotspots of asymptomatic parasitaemia are stable over time, but hotspots of febrile malaria are unstable. This finding may be because immunity offsets the high rate of febrile malaria that might otherwise result in stable hotspots, whereas unstable hotspots necessarily affect a population with less prior exposure to malaria.

Figure 1. The distribution of homesteads monitored in the three cohorts is shown within Kilifi District.

Figure 2. The incidence of febrile malaria for each homestead is plotted against distance from the perimeter of the cluster for (A) clusters of febrile malaria and (B) clusters of asymptomatic parasitaemia.

Negative distances indicate a location inside the cluster, and positive distances indicate a location outside. Loess curves are shown in red (using a bandwidth of 0.8).

Figure 3. The distribution of homesteads in clusters of febrile malaria episodes (red), against homesteads outside the clusters (green) by year of monitoring for the three different cohorts.

Figure 4. The distribution of homesteads in clusters of febrile malaria episodes (red), against homesteads outside the clusters (green) on a pooled analysis by season for the three different cohorts.

Figure 5. The distribution of homesteads in clusters of asymptomatic parasitaemia (red), against homesteads outside the clusters (green) by year of monitoring for the three different cohorts.