Challenges of DHS and MIS to capture the entire pattern of malaria parasite risk and intervention effects in countries with different ecological zones: the case of Cameroon

Abstract

BackgroundIn 2011, the demographic and health survey (DHS) in Cameroon was combined with the multiple indicator cluster survey. Malaria parasitological data were collected, but the survey period did not overlap with the high malaria transmission season. A malaria indicator survey (MIS) was also conducted during the same year, within the malaria peak transmission season. This study compares estimates of the geographical distribution of malaria parasite risk and of the effects of interventions obtained from the DHS and MIS survey data.MethodsBayesian geostatistical models were applied on DHS and MIS data to obtain georeferenced estimates of the malaria parasite prevalence and to assess the effects of interventions. Climatic predictors were retrieved from satellite sources. Geostatistical variable selection was used to identify the most important climatic predictors and indicators of malaria interventions.ResultsThe overall observed malaria parasite risk among children was 33 and 30% in the DHS and MIS data, respectively. Both datasets identified the Normalized Difference Vegetation Index and the altitude as important predictors of the geographical distribution of the disease. However, MIS selected additional climatic factors as important disease predictors. The magnitude of the estimated malaria parasite risk at national level was similar in both surveys. Nevertheless, DHS estimates lower risk in the North and Coastal areas. MIS did not find any important intervention effects, although DHS revealed that the proportion of population with an insecticide-treated nets access in their household was statistically important. An important negative relationship between malaria parasitaemia and socioeconomic factors, such as the level of mother’s education, place of residence and the household welfare were captured by both surveys.ConclusionTiming of the malaria survey influences estimates of the geographical distribution of disease risk, especially in settings with seasonal transmission. In countries with different ecological zones and thus different seasonal patterns, a single survey may not be able to identify all high risk areas. A continuous MIS or a combination of MIS, health information system data and data from sentinel sites may be able to capture the disease risk distribution in space across different seasons.Electronic supplementary materialThe online version of this article (10.1186/s12936-018-2284-7) contains supplementary material, which is available to authorized users.

A combination of metabolic resistance and high frequency of the 1014F kdr mutation is driving pyrethroid resistance in Anopheles coluzzii population from Guinea savanna of Cameroon

“…In Cameroon malaria is endemic, with some variation in the transmission intensity in specific areas, such as highland, Sahel and Guinea savanna [5–7], with Plasmodium falciparum as the most common species [8–11]. Malaria transmission in the north of Cameroon is characterized by seasonal patterns linked to rainy season, which covers the period from August to October [6]. In northern Cameroon, the main malaria vectors belong to the An.…”

Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon.

“…Thus, malaria cases were confirmed based on both rapid diagnostic tests and on laboratory analysis. A clinical case was defined as a malaria-attributable febrile episode (body temperature in excess of 37.5°C ), accompanied by headaches, nausea, excess sweating and/or fatigue censored by a 30-day window (6,27). Since the households are the variables to be analyzed, a malaria year as described in this study, is the average number of people per year who show clinical symptoms of Plasmodium falciparum malaria within the cells whose centroid falls within a radius of 10 km (for rural points) or 2 km (for urban points) (28).…”

“…Massoda (27), compared malaria survey programs in different ecological zones in Cameroon and recommended on the needs of intervention programs during high transmission rainy seasons.…”

Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon

“…Massoda (25), compared malaria survey programs in different ecological zones in Cameroon and recommended on the needs of intervention programs during high transmission rainy seasons.…”