How to Estimate Optimal Malaria Readiness Indicators at Health-District Level: Findings from the Burkina Faso Service Availability and Readiness Assessment (SARA) Data

Abstract

One of the major contributors of malaria-related deaths in Sub-Saharan African countries is the limited accessibility to quality care. In these countries, malaria control activities are implemented at the health-district level (operational entity of the national health system), while malaria readiness indicators are regionally representative. This study provides an approach for estimating health district-level malaria readiness indicators from survey data designed to provide regionally representative estimates. A binomial-hierarchical Bayesian spatial prediction method was applied to Burkina Faso Service Availability and Readiness Assessment (SARA) survey data to provide estimates of essential equipment availability and readiness for malaria care. Predicted values of each indicator were adjusted by the type of health facility, location, and population density. Then, a health district composite readiness profile was built via hierarchical ascendant classification. All surveyed health-facilities were mandated by the Ministry of Health to manage malaria cases. The spatial distribution of essential equipment and malaria readiness was heterogeneous. Around 62.9% of health districts had a high level of readiness to provide malaria care and prevention during pregnancy. Low-performance scores for managing malaria cases were found in big cities. Health districts with low coverage for both first-line antimalarial drugs and rapid diagnostic tests were Baskuy, Bogodogo, Boulmiougou, Nongr-Massoum, Sig-Nonghin, Dafra, and Do. We provide health district estimates and reveal gaps in basic equipment and malaria management resources in some districts that need to be filled. By providing local-scale estimates, this approach could be replicated for other types of indicators to inform decision makers and health program managers and to identify priority areas.

Keywords: Burkina Faso; SARA survey; binomial hierarchical Bayesian; geo-epidemiology; health district; malaria; service readiness; spatial analysis.

Figure 1

Geographical distribution of the availability of essential equipment at the health-district level: Posterior means of fitted values. (a) Adult weighing scale, (b) infant weighing scale, (c) stethoscope, (d) thermometer, (e) blood pressure apparatus, and (f) light source and (g) latex glove. Maps created by Toussaint Rouamba et al., 2019. Source of materials: The shapefile was obtained from the “Base Nationale de Découpage du territoire” of Burkina Faso (BNDT, 2006). The Service Availability and Readiness Assessment data for modelling were obtained from the Ministry of Health of Burkina Faso.

Figure 2

Geographical distribution of malaria readiness indicators regarding malaria diagnostic at the health-district level: Posterior medians of fitted values. (a) Availability of malaria rapid diagnostic test, (b) malaria diagnosis by clinical symptoms coupled with parasitological diagnosis, (c) malaria diagnosis by rapid diagnostic test, (d) malaria diagnosis by microscopy. Maps created by Toussaint Rouamba et al., 2019. Source of materials: The shapefile was obtained from the “Base Nationale de Découpage du territoire” of Burkina Faso (BNDT, 2006). The Service Availability and Readiness Assessment data for modelling were obtained from the Ministry of Health of Burkina Faso.

Figure 3

Geographical distribution of malaria readiness indicators regarding malaria treatment at the health-district level: Posterior medians of fitted values. (a) First-line antimalarial (artemisinin-based combination therapy), (b) artesunate rectal or injectable forms, (c) intermittent preventive treatment during pregnancy, (d) artemisinin-based combination therapy (ACT) out of stock. Maps created by Toussaint Rouamba et al., 2019. Source of materials: The shapefile was obtained from the “Base Nationale de Découpage du territoire” of Burkina Faso (BNDT, 2006). The Service Availability and Readiness Assessment data for modelling were obtained from the Ministry of Health of Burkina Faso.

Figure 4

Geographical distribution of malaria readiness indicators regarding staff training at the health-district level: Posterior medians of fitted values. (a) Staff trained on guidelines for malaria diagnosis and treatment, (b) staff trained on guidelines for intermittent preventive treatment (IPT) of malaria during pregnancy. Maps created by Toussaint Rouamba et al., 2019. Source of materials: The shapefile was obtained from the “Base Nationale de Découpage du territoire” of Burkina Faso (BNDT, 2006). The Service Availability and Readiness Assessment data for modelling were obtained from the Ministry of Health of Burkina Faso.

Figure 5

Geographical distribution of health district composite readiness profiles for malaria case management. The bold black lines represent the regional boundaries; the dashed black lines represent the health district boundaries. Maps created by Toussaint Rouamba et al., 2019. Source of materials: The shapefile was obtained from the “Base Nationale de Découpage du territoire” of Burkina Faso (BNDT, 2006). The Service Availability and Readiness Assessment data for modelling were obtained from the Ministry of Health of Burkina Faso.