Comparing Smoking Cessation to Screening and Brief Intervention for Alcohol in Distributional Cost Effectiveness Analysis to Explore the Sensitivity of Results to Socioeconomic Inequalities Characterised in Model Inputs

Introduction A distribution of intervention impact across socioeconomic groups can be estimated from socioeconomic differences across a staircase from need (e.g. prevalence) up to intervention characteristics (e.g. effectiveness) using distributional cost effectiveness analysis (DCEA). The extent to which evidence on inequality at different steps of the staircase contributes to uncertainty in population level impact is not well understood. We used DCEAs in smoking cessation and alcohol interventions to explore how socioeconomic inequality in model inputs impacts upon final conclusions about health inequality and value for money. Methods Total population health and health inequality impacts were expressed using incremental net health benefit (NHB) and incremental ‘equally distributed equivalent’ (EDE) health, both measured in quality adjusted life years (QALYs). EDE reveals how the value of NHB is altered by inequality in its distribution. Scenario analyses explored: (i) the impact of ignoring socioeconomic differences in inputs; (ii) the value of eliminating differences; and (iii) how results differ between areas with different socioeconomic patterns. Results There was no discernible pattern relating the extent and direction of socioeconomic variation in model inputs to socioeconomic variation in model outputs. Differences in deprivation structure of the local population and other factors translate into different conclusions on the value for money of delivering the interventions locally compared to nationally. Discussion Our results suggest there is limited scope for informally assessing the importance of lack of knowledge about socioeconomic variation in model inputs. The influence of socioeconomic variation differs between disease areas and setting. Comparing smoking cessation to screening and brief intervention for alcohol in distributional cost effectiveness analysis 1 Executive Summary Background/Introduction Public health interventions can impact on total population health, and in doing so can alter the distribution of health within the population, which impacts on health inequalities. Inequality in health outcomes can be measured as an absolute difference between groups or individuals, as a relative difference, or in terms of the shortfall from some minimum standard. When health care decision makers are interested in improving health and reducing unfair health inequalities (i.e. reducing the perceived unfairness of the distribution of health across the population), they require tools that quantify changes in the distribution of health that result from the delivery of interventions. Where overall health improves but inequalities worsen, or vice versa, those tools should make explicit the trade-offs between them. Economic evaluations are routinely used to assess the impact of interventions on net health on average. This can be extended to characterise how net health benefits vary between groups in the population. For example, they may be used to determine the distribution of health outcomes across socioeconomic groups. When combined with information on the baseline distribution of health across these groups, impacts on absolute, relative and shortfall health inequality can be quantified. These extensions, known as distributional cost effectiveness analyses (DCEA), provide valuable information on the extent of any trade-offs between improvements in overall health versus reduction in measures of health inequality. Uncertainty in the inputs to economic evaluation, including in the extent that variables differ between groups, translates into uncertainty over the results. For some inputs there may be a complete absence of evidence on the extent of socioeconomic variation. This uncertainty implies the possibility of an incorrect conclusion about intervention impacts, including impacts on total health, health inequality, and the overall value for money of an intervention. Sensitivity analysis can quantify how the uncertainty in inputs, including uncertainty in the extent of differences between groups, impacts on the uncertainty in the conclusions about an intervention’s effect on overall health and health inequality. Using sensitivity analysis, we can explore how absence of evidence on socioeconomic differences in disease and intervention characteristics could impact on conclusions about value for money. The cost of uncertainty can be estimated in terms of the potential loss of health and/or greater avoidable health inequality from an incorrect decision. This can then be used to inform decisions about whether it would be worthwhile obtaining more information to reduce the uncertainty. The between group differences in inputs may vary between settings, for example the prevalence of smoking by socioeconomic status may differ between areas with different ethnic composition. Evaluating the impact on the population distribution of health from national implementation of an intervention may, therefore, not be informative for the change in the distribution of health that would be expected at a local level. Therefore, it is relevant to local decision makers to understand how local level variation will impact on overall health and changes in health inequality, compared to the national level estimates. Aims This project aims to explore how models that characterise socioeconomic variation in the value of model inputs can be analysed to inform four broad questions: (a) How influential is a lack of knowledge about socioeconomic differences on conclusions about the impact on overall health and health inequality? (b) How valuable would it be to eliminate socioeconomic differences related to the intervention being considered? 2 CHE Research Paper 184 (c) How generalisable are conclusions about impact on overall health and health inequality to contexts in which between group differences in the targeted populations differ? (d) To what extent do the answers to these three questions differ between two models in different disease areas, and what conclusions can we draw about the generalisability of their results to other interventions or disease areas? Methods Two case study models, previously developed to assess i) smoking cessation interventions and ii) brief interventions for alcohol misuse, were adapted to incorporate local level socioeconomic patterned data and to perform a range of scenario analyses. For the smoking cessation model, probabilistic sensitivity and value of information analysis was also performed. Both models consider costs (pound sterling, 2018 price year) under an NHS and personal social services perspective and measure health outcomes in quality adjusted life years (QALYs). Socioeconomic status is characterised by the Index of Multiple Deprivation (IMD). Outcomes considered are change in overall health, measured using incremental population net health benefit (i.e. health benefit of the intervention less health lost elsewhere as a result of resources not being available for other purposes) by assuming that £20,000 invested in other purposes produces one QALY. Change in health inequality is defined as the difference between the change in incremental health-related social welfare, measured using incremental equally distributed equivalent (EDE) health based on the Atkinson index, and change in incremental net health benefit. The Atkinson index describes the extent of welfare lost due to inequality in the population distribution of healthy life expectancy, based on UK population aversion to health inequality between rich and poor groups. The EDE is the amount of QALYs that, if equally distributed within the population, generate the same social welfare as the total QALYs in the distribution of health being evaluated. For both case study models, we: 1. Describe socioeconomic ‘gradients’ in the model inputs, i.e. how the input values vary across the different socioeconomic groups. We distinguish between those gradients that are modifiable, and those that are exogenous (non-modifiable) and relate to the setting in which the intervention will be employed. The extent of socioeconomic differences in model inputs are summarised using concentration indices. 2. Perform scenario analyses in which we explore: a. The impact of ignoring the socioeconomic differences in inputs on the model results, i.e. setting each input in all groups to the average; b. The potential value of eliminating the differences by ‘levelling up’ the model inputs for modifiable socioeconomic differences, i.e. setting the input to the ‘best’ value across groups; c. How the results differ when using data from local settings (Local Authorities) with different characteristics. The impact of each scenario is captured for pairwise comparisons by the change in incremental net benefit (iNHB), change in health-related social welfare (incremental EDE [iEDE] health) and change in inequality (iEDE-iNHB), compared to the base-case estimates. The pattern of socioeconomic differences in model inputs and the results of the scenario analyses are compared across the two case studies, to explore whether there are any patterns between the extent of inequality of model inputs and the estimated impact of the intervention on health inequality and other model outputs. The smoking cessation model is a cohort Markov model that assesses the cost effectiveness of two active interventions (Varenicline and 7.2mg e-cigarette) to quit smoking and ‘no intervention’ in a Comparing smoking cessation to screening and brief intervention for alcohol in distributional cost effectiveness analysis 3 cohort of adult smokers (18-75 years) over a lifetime horizon. The model includes three discrete and mutually exclusive health states: i) smokers, ii) former smokers and iii) death. Smokers and former smokers are at risk of 6 smoking related comorbidities, which are modelled as events with impact on costs and QALYs. Health-related quality of life (HRQoL) in each state is age and smoking status depen