Distributional impact of the Malawian Essential Health Package

Abstract

<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>

Incorporating health inequality impact into economic evaluation in low- and middle-income countries: a systematic review

“…In the two studies which were not CEA [28,29], economic evaluations were conducted with health outcomes, but they did not report results on costs; both studies were included to extract the method used to estimate inequality impact. Mortality was most commonly included as main health outcome [15,17,24,26,[28][29][30][31][32]; generic metrics such as life years [26][27][28], disability-adjusted life years (DALYs) [18,25,28,[30][31][32], quality-adjusted life years (QALYs) [24] and health-adjusted life years (HALYs) [17], and other disease-specific outcomes, e.g. hepatitis B infections averted [24], cervical cancer cases averted [27] and obesity prevalence [28], were also included as health outcomes.…”

“…In general, there was good adherence to best-practice reporting standards, with 'satisfied' or 'partially satisfied' in almost all the 24 items. It should be noted that among the 10 CEA studies, one study did not report results of uncertainty analysis [26]; two studies conducted uncertainty analysis for the overall cost-effectiveness, rather than the impact on inequality [24,27]; two studies performed the uncertainty analysis to a range of assumptions [17,18]; four studies analyzed the uncertainty about the distribution of health benefits to support inequality impact assessment [15,[30][31][32]; only one studies carried out full uncertainty analysis (including probabilistic sensitivity analysis) for the inequality impact and provided the confidence level of the conclusion about whether the intervention increases/reduces health inequality [25].…”

“…Five studies (41.7%) assessed the impact in countries in Asia (China [24], India [15,30], Pakistan [31] and Laos [32]), five studies (41.7%) in Africa (Uganda [27], Ethiopia [17,26], Zambia [28], and Malawi [18]), one study (8.3%) in South America (Brazil [25]), and one study (8.3%) evaluated the results in 41 LMICs [29]. The studies were conducted in a wide variety of countries, nevertheless, concentrated in Asia and Africa.…”

“…Regarding the target populations and interventions or strategies assessed, more than half of the included studies (n = 7, 58.3%) focused on children or infants [17,24,26,[29][30][31][32], assessing the impact of vaccination programs, e.g. nationwide hepatitis B vaccination [24], rotavirus vaccination [30][31][32] and vaccines for a wide range of antigens [29], and children pneumonia treatment scale-up strategy [26]; three studies (25.0%) focused on general population or adult population, covering complex interventions such as excise tax on sugar sweetened beverages [28], population level essential health package (EHP) [18] and community level primary care system intervention [25]; one study (8.3%) focused on the impact of different cervical cancer screening strategies for women aged 30-49 years [27] and one study (8.3%) assessed the effect of public finance intervention for tuberculosis treatment [15].…”

“…More studies are applying the newly developed methods, ECEA or DCEA, in LMICs, such as in India [15], Ethiopia [17] and Malawi [18]. We sought to develop an overview of the extent to which inequality impact has been considered in economic evaluations of health interventions or policies in LMICs before and after these methods became more widely known.…”