Malaria remains a major public health problem, causing 435,000 deaths in 2017. The objective of this study was to estimate the prediction ability of vector species associated with the prediction power of environmental and socioeconomic factors for malaria risk. Logistic regression was used for malaria risk estimation. A Radial Basis Function model was applied for estimating the predictive ability of Anopheles species, environmental and socioeconomic factors. The lowest fever prevalence was found where Anopheles melas was dominant. Anopheles coluzzi and Anopheles gambiae were the dominant species where prevalence of malaria was high. Altitude, country and vector species were the best predictive factors. Anopheles arabiensis, An. coluzzi and An. gambiae were most common in urban areas. This study will improve the prediction of malaria risk in targeted areas. We have observed how important it is to adapt health policies according to the dominant malaria vector in a region.

Background: Malaria is a global public health problem with many cases each year (228 million cases in 2018 with 405,000 deaths). Most malaria cases occur in Africa. Methods: Data used for analysis are from Demographics and Health Surveys (DHS) 2017-2018 for Burkina Faso and DHS 2017 for Senegal. We added information from a synthesis of literature. Linear regression models were performed with an estimation of the mean number of persons using ITNs among groups (urban or rural areas, wealth level, highest education level in the household and age of household head) in each country. We evaluated the importance of co-factors in the relationship between the number of ITNs (insecticide-treated nets) in a household and the number of household members by calculating the R-squared. A criteria grid used for this synthesis of literature included eight important sub-groups: funding sources, entomological monitoring, use of ITNs, use of insecticide, malaria case management, health system organization, communication and surveillance. Results: Senegal and Burkina Faso have the same proportion (51%) of households in which all children under 5 sleep under ITNs. We found R-squared (R2=0.007 in Burkina Faso and R2=0.16 in Senegal) for the relationship between the number of ITNs in a household and household size. When wealth level, age of head of household, area of residence (rural or urban), highest education level in the household and number of bedrooms in the household were controlled for, we found R2=0.106 for Burkina Faso and R2=0.167 for Senegal. We found that Senegal’s national malaria program is decentralized with entomological monitoring in all districts, which is normal considering the intervention stage in the fight against malaria. In Burkina Faso, we found centralization of routine data.Conclusion: Our study synthesized the health policies applied in African countries which are at different stages of intervention in the fight against malaria and which have succeeded in maintaining low malaria prevalence (in Senegal) or in rapidly decreasing the prevalence of the disease (in Burkina Faso). Being close to elimination, Senegal required more active malaria surveillance than passive surveillance. Burkina Faso did not require a lot of active surveillance being not close to malaria elimination. These results merit a review in the context of each African country.

Aims The aim of our study was to provide a synthesis of successful policies applied in the fight against malaria in African countries at different stages of intervention; Burkina Faso and Senegal. Background Malaria is a global public health problem with many cases each year in the world (241 million cases with 247,000 deaths; 67% were under five children) in 2020. Most malaria cases occur in Sub-Saharan African countries (93%). Objective The objective of our study was to present policies implemented against malaria (with the best results) in these two African countries (Burkina Faso and Senegal) which are at different stages of intervention. These could serve as an example to others malaria endemics countries. To achieve our purpose, we used DHS survey data and information from a literature synthesis. Methods Data used for analysis are from Demographics and Health Surveys (DHS) 2017-2018 for Burkina Faso and DHS 2017 for Senegal. We added information from a synthesis of the literature. Linear regression models were performed with an estimation of the mean number of persons using insecticide-treated nets among groups (urban or rural areas, wealth level, highest education level in the household and age of household head) in each country. We evaluated the importance of co-factors in the relationship between the number of ITNs in a household and the number of household members by calculating the R-squared. A criteria grid used for this synthesis of literature included eight important sub-groups: funding sources, entomological monitoring, use of ITNs, use of insecticide, malaria case management, health system organization, communication and surveillance. Results Senegal and Burkina Faso have the same proportion (51%) of households in which all children under 5 sleep under ITNs. We found R-squared (R2=0.007 in Burkina Faso and R2=0.16 in Senegal) for the relationship between the number of ITNs in a household and household size. When wealth level, age of head of household, area of residence (rural or urban), education level in the household and number of bedrooms in the household were controlled for, we found R2=0.106 for Burkina Faso and R2=0.167 for Senegal. We found that Senegal’s National Malaria Program is decentralized with entomological monitoring in all districts, which is normal considering the intervention stage in the fight against malaria. In Burkina Faso, we found centralization of routine data. Conclusion Our study synthesized the health policies applied in African countries which are at different stages of intervention in the fight against malaria and which have succeeded in maintaining low malaria prevalence (in Senegal) or in rapidly decreasing the prevalence of the disease (in Burkina Faso). Being close to elimination, Senegal required more active malaria surveillance than passive surveillance. Burkina Faso did not require a lot of active surveillance being not close to malaria elimination. These results encourage a review in the context of each African country.

No abstract