Effective coverage and systems effectiveness for malaria case management in sub-Saharan African countries

Abstract

Scale-up of malaria preventive and control interventions over the last decade resulted in substantial declines in mortality and morbidity from the disease in sub-Saharan Africa and many other parts of the world. Sustaining these gains will depend on the health system performance. Treatment provides individual benefits by curing infection and preventing progression to severe disease as well as community-level benefits by reducing the infectious reservoir and averting emergence and spread of drug resistance. However many patients with malaria do not access care, providers do not comply with treatment guidelines, and hence, patients do not necessarily receive the correct regimen. Even when the correct regimen is administered some patients will not adhere and others will be treated with counterfeit or substandard medication leading to treatment failures and spread of drug resistance. We apply systems effectiveness concepts that explicitly consider implications of health system factors such as treatment seeking, provider compliance, adherence, and quality of medication to estimate treatment outcomes for malaria case management. We compile data for these indicators to derive estimates of effective coverage for 43 high-burden Sub-Saharan African countries. Parameters are populated from the Demographic and Health Surveys and other published sources. We assess the relative importance of these factors on the level of effective coverage and consider variation in these health systems indicators across countries. Our findings suggest that effective coverage for malaria case management ranges from 8% to 72% in the region. Different factors account for health system inefficiencies in different countries. Significant losses in effectiveness of treatment are estimated in all countries. The patterns of inter-country variation suggest that these are system failures that are amenable to change. Identifying the reasons for the poor health system performance and intervening to tackle them become key priority areas for malaria control and elimination policies in the region.

Fig 1. Decision Tree Model for Case Management of Uncomplicated Malaria.

Fig 2. Effective Coverage and Malaria Service Performance Indicators by Country (%).

Labeled on the axis are: E Effective coverage; A Access to any care provider; Pf Access to a formal care provider; Dact Compliance with the first-line antimalarial treatment; H Adherence; T Cure rate. Labels for country ISO3 codes are listed in the S1 Table.

Fig 3. Effective Coverage (E) and Access to Any Provider (A) by Country (%).

Dashed lines starting with a 45 degree angle and steeper illustrate the ratio of fever episodes treated effectively (Effective Coverage) over episodes for which treatment was sought (Access). Labels for country ISO3 codes are listed in the S1 Table.

Fig 4. Impact of Service Parameters on Level of Effective Coverage: Nigeria.

Labeled on the x-axis are: A Access to any care provider; Pf Access to a formal care provider; Da Compliance with the first-line antimalarial treatment; H Adherence; T Cure rate. Effective coverage. E, evaluated by varying each of the service indicators from 0 to 100% while holding all other inputs at base value. Values of effective coverage at lowest and highest values of inputs are labeled on the y-axis to illustrate the sensitivity. Baseline values of inputs and effective coverage are shown in dash lines.

Fig 5. Access, First-Line Antimalarial Treatment, and Effective Coverage of Malaria Case Management by Country.

A. Access to any provider; D _a Compliance with the first-line antimalarial treatment; C. Effective coverage. Maps were generated by authors using spmap command[73] in Stata 13 SE; Country coordinates were sourced from Global Administrative Areas (http://gadm.org).