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

TY - JOUR

T1 - How health inequalities accumulate and combine to affect treatment value

T2 - a distributional cost-effectiveness analysis of smoking cessation interventions

AU - Love-Koh, James

AU - Pennington, Becky

AU - Owen, Lesley

AU - Taylor, Matthew

AU - Griffin, Susan

N1 - © 2020 Published by Elsevier Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

PY - 2020/11

Y1 - 2020/11

N2 - IntroductionReduction 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.MethodsWe 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.ResultsAll interventions increase population health and reduce the slope index of inequality. At estimated levels of health inequality aversion for England, our results indicate that the increases in inequality account for between 8%-10% of the overall social value of the interventions. DiscussionThe inequality impacts are driven by the greater proportion of smokers in disadvantaged groups, despite higher benefits of quitting and higher intervention uptake amongst advantaged groups. Failure to account for differential effects between groups leads to changes about the magnitude of the health inequality impact but does not alter conclusions about value for money.

AB - IntroductionReduction 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.MethodsWe 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.ResultsAll interventions increase population health and reduce the slope index of inequality. At estimated levels of health inequality aversion for England, our results indicate that the increases in inequality account for between 8%-10% of the overall social value of the interventions. DiscussionThe inequality impacts are driven by the greater proportion of smokers in disadvantaged groups, despite higher benefits of quitting and higher intervention uptake amongst advantaged groups. Failure to account for differential effects between groups leads to changes about the magnitude of the health inequality impact but does not alter conclusions about value for money.

U2 - 10.1016/j.socscimed.2020.113339

DO - 10.1016/j.socscimed.2020.113339

M3 - Article

SN - 0277-9536

VL - 265

JO - Social Science & Medicine

JF - Social Science & Medicine

M1 - 113339

ER -