The Government of Malawi has signed contracts called service level agreements (SLAs) with mission health facilities in order to exempt their catchment populations from paying user fees. Government in turn reimburses the facilities for the services that they provide. SLAs started in 2006 with 28 out of 165 mission health facilities and increased to 74 in 2015. Most SLAs cover only maternal, neonatal and in some cases child health services due to limited resources. This study evaluated the effect of user fee exemption on the utilization of maternal health services. The difference-in-differences approach was combined with propensity score matching to evaluate the causal effect of user fee exemption. The gradual uptake of the policy provided a natural experiment with treated and control health facilities. A second control group, patients seeking non-maternal health care at CHAM health facilities with SLAs, was used to check the robustness of the results obtained using the primary control group. Health facility level panel data for 142 mission health facilities from 2003 to 2010 were used. User fee exemption led to a 15% (P <  0.01) increase in the mean proportion of women who made at least one antenatal care (ANC) visit during pregnancy, a 12% (P < 0.05) increase in average ANC visits and an 11% (P < 0.05) increase in the mean proportion of pregnant women who delivered at the facilities. No effects were found for the proportion of pregnant women who made the first ANC visit in the first trimester and the proportion of women who made postpartum care visits. We conclude that user fee exemption is an important policy for increasing maternal health care utilization. For certain maternal services, however, other determinants may be more important.

Malawi, like many low-income and middle-income countries, has used health benefits packages (HBPs) to allocate scarce resources to key healthcare interventions. With no widely accepted method for their development, HBPs often promise more than can be delivered, given available resources. An analytical framework is developed to guide the design of HBPs that can identify the potential value of including and implementing different interventions. It provides a basis for informing meaningful discussions between governments, donors and other stakeholders around the trade-offs implicit in package design. Metrics of value, founded on an understanding of the health opportunity costs of the choices faced, are used to quantify the scale of the potential net health impact (net disability adjusted life years averted) or the amount of additional healthcare resources that would be required to deliver similar net health impacts with existing interventions (the financial value to the healthcare system). The framework can be applied to answer key questions around, for example: the appropriate scale of the HBP; which interventions represent ‘best buys’ and should be prioritised; where investments in scaling up interventions and health system strengthening should be made; whether the package should be expanded; costs of the conditionalities of donor funding and how objectives beyond improving population health can be considered. This is illustrated using data from Malawi. The framework was successfully applied to inform the HBP in Malawi, as a core component of the country’s Health Sector Strategic Plan II 2017–2022.

BackgroundTeenage pregnancies and childbearing are important health concerns in low-and middle-income countries (LMICs) including Malawi. Addressing these challenges requires, among other things, an understanding of the socioeconomic determinants of and contributors to the inequalities relating to these outcomes. This study investigated the trends of the inequalities and decomposed the underlying key socioeconomic factors which accounted for the inequalities in teenage pregnancy and childbearing in Malawi.MethodsThe study used the 2004, 2010 and 2015–16 series of nationally representative Malawi Demographic Health Survey covering 12,719 women. We used concentration curves to examine the existence of inequalities, and then quantified the extent of inequalities in teenage pregnancies and childbearing using the Erreygers concentration index. Finally, we decomposed concentration index to find out the contribution of the determinants to socioeconomic inequality in teenage pregnancy and childbearing.ResultsThe teenage pregnancy and childbearing rate averaged 29% (p<0.01) between 2004 and 2015–16. Trends showed a “u-shape” in teenage pregnancy and childbearing rates, albeit a small one (34.1%; p<0.01) in 2004: (25.6%; p<0.01) in 2010, and (29%; p<0.01) in 2016. The calculated concentration indices -0.207 (p<0.01) in 2004, -0.133 (p<0.01) in 2010, and -0.217 (p<0.01) in 2015–16 indicated that inequality in teenage pregnancy and childbearing worsened to the disadvantage of the poor in the country. Additionally, the decomposition exercise suggested that the primary drivers to inequality in teenage pregnancy and child bearing were, early sexual debut (15.5%), being married (50%), and wealth status (13.8%).ConclusionThe findings suggest that there is a need for sustained investment in the education of young women concerning the disadvantages of early sexual debut and early marriages, and in addressing the wealth inequalities in order to reduce the incidences of teenage pregnancies and childbearing.

Abstract In low- and middle-income countries (LMICs), making the best use of scarce resources is essential to achieving universal health coverage. The design of health benefits packages creates the opportunity to select interventions on the basis of explicit objectives. Distributional cost-effectiveness analysis (DCEA) provides a framework to evaluate interventions based on two objectives: increasing population health and reducing health inequality. We conduct aggregate DCEA of potential health benefits package interventions to demonstrate the feasibility of this approach in LMICs, using the case of the Malawian health benefits package. We use publicly available survey and census data common to LMICs and describe what challenges we encountered and how we addressed them. We estimate that diseases targeted by the health benefits package are most prevalent in the poorest population quintile and least prevalent in the richest quintile. The survey data we use indicate socioeconomic patterns in intervention uptake that diminish the population health gain and inequality reduction from the package. We find that a similar set of interventions would be prioritized when impact on health inequality is incorporated alongside impact on overall population health. However, conclusions about the impact of individual interventions on health inequalities are sensitive to assumptions regarding the health opportunity cost, the utilization of interventions, the distribution of diseases across population groups and the level of aversion to inequality. Our results suggest that efforts to improve access to the Essential Health Package could be targeted to specific interventions to improve the health of the poorest fastest but that identifying these interventions is uncertain. This exploratory work has shown the potential for applying the DCEA framework to inform health benefits package design within the LMIC setting and to provide insight into the equity impact of a health benefits package.

BackgroundThe district resource allocation formula in Malawi was recently reviewed to include stunting as a proxy measure of socioeconomic status. In many countries where the concept of need has been incorporated in resource allocation, composite indicators of socioeconomic status have been used. In the Malawi case, it is important to ascertain whether there are differences between using single variable or composite indicators of socioeconomic status in allocations made to districts, holding all other factors in the resource allocation formula constant.MethodsPrincipal components analysis was used to calculate asset indices for all districts from variables that capture living standards using data from the Malawi Multiple Indicator Cluster Survey 2006. These were normalized and used to weight district populations. District proportions of national population weighted by both the simple and composite indicators were then calculated for all districts and compared. District allocations were also calculated using the two approaches and compared.ResultsThe two types of indicators are highly correlated, with a spearman rank correlation coefficient of 0.97 at the 1% level of significance. For 21 out of the 26 districts included in the study, proportions of national population weighted by the simple indicator are higher by an average of 0.6 percentage points. For the remaining 5 districts, district proportions of national population weighted by the composite indicator are higher by an average of 2 percentage points. Though the average percentage point differences are low and the actual allocations using both approaches highly correlated (ρ of 0.96), differences in actual allocations exceed 10% for 8 districts and have an average of 4.2% for the remaining 17. For 21 districts allocations based on the single variable indicator are higher.ConclusionsVariations in district allocations made using either the simple or composite indicators of socioeconomic status are not statistically different to recommend one over the other. However, the single variable indicator is favourable for its ease of computation.