ja-14-186-1.pdf (346 KB)
Coverage estimates of insecticide-treated nets (ITNs) are often calculated at the national level, but are intended to be a proxy for coverage among the population at risk of malaria. The analysis uses data for surveyed households, linking survey enumeration areas (clusters) with levels of malaria endemicity and adjusting coverage estimates based on the population at risk. This analysis proposes an approach that is not dependent on being able to identify malaria risk in a location during the survey design (since survey samples are typically selected on the basis of census sampling frames that do not include information on malaria zones), but rather being able to assign risk zones after a survey has already been completed.
The analysis uses data from 20 recent nationally representative Demographic and Health Survey (DHS), Malaria Indicator Surveys (MIS), an AIDS Indicator Survey (AIS), and an Anemia and Malaria Prevalence Survey (AMP). The malaria endemicity classification was assigned from the Malaria Atlas Project (MAP) 2010 interpolated data layers, using the Geographic Positioning System (GPS) location of the survey clusters. National ITN coverage estimates were compared with coverage estimates in intermediate/high endemicity zones (i.e., the population at risk of malaria) to determine whether the difference between estimates was statistically different from zero (p-value <0.5).
Endemicity varies substantially in eight of the 20 studied countries. In these countries with heterogeneous transmission of malaria, stratification of households by endemicity zones shows that ITN coverage in intermediate/high endemicity zones is significantly higher than ITN coverage at the national level (Burundi, Kenya, Namibia, Rwanda, Tanzania, Senegal, Zambia, and Zimbabwe.). For example in Zimbabwe, the national ownership of ITNs is 28%, but ownership in the intermediate/high endemicity zone is 46%.
Incorporating this study's basic and easily reproducible approach into estimates of ITN coverage is applicable and even preferable in countries with areas at no/low risk of malaria and will help ensure that the highest-quality data are available to inform programmatic decisions in countries affected by malaria. The extension of this type of analysis to other malaria interventions can provide further valuable information to support evidence-based decision-making.