How health inequalities accumulate and combine to affect treatment value: A distributional cost-effectiveness analysis of smoking cessation interventions

Reduction of health inequality is a goal in health policy, but commissioners lack information on how policies change health inequality. This study illustrates how decision models can be readily extended to produce information on health inequality impacts as well as for population health, using the example of smoking cessation therapies. We retrospectively adapt a model developed for public health guidance to undertake distributional cost effectiveness analysis. We identify and incorporate evidence on how inputs vary by area-level deprivation. Therapies are evaluated in terms of total population health, extent of inequality, and a summary measure of equally distributed equivalent health based on a societal value for inequality aversion. Last, we examine how accounting for social variation in different sets of parameters affects our results. All interventions increase population health and increase the slope index ofinequality. At estimated levels of health inequality aversion for England, our resultsindicate that the increases in inequality are compensated by the health gains. The inequality impacts are driven by higher benefits of quitting and higher intervention uptake amongst advantaged groups, despite the greater proportion of smokers in disadvantaged groups. Failure to account for differential effects between groups leadsto different conclusions about health inequality impact but does not alter conclusionsabout value for money. •We modify a smoking cessation decision model to include health inequality impacts.•The model includes differences in prevalence, uptake, mortality and morbidity.•All interventions improve population health and reduce health inequality.•Including uptake differences has the largest effect on inequality impacts.