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

Quantifying the Value of Reduced Health Disparities: Low-Dose Computed Tomography Lung Cancer Screening of High-Risk Individuals Within the United States

OBJECTIVE

This study aimed to measure the value of increasing lung cancer screening rates for high-risk individuals and its impact on health disparities.

METHODS

The model estimated changes in health economic outcomes if low-dose computed tomography screening increased from current to 100% compliance, following clinical guidelines. Current low-dose computed tomography screening rates were estimated by income, education, and race, using 2017-2019 Behavioral Risk Factor Surveillance System data. The model contained a decision tree module to segment the population by screening outcomes and a Markov chain module to estimate cancer progression over time. Model parameters included information on survival, quality of life, and costs related to cancer diagnosis, treatment, and adverse events. Distributional cost-effectiveness analysis estimated the net monetary value from reduced health disparities-measured using quality-adjusted life expectancy-across income, education, and race groups. Outcomes were assessed over 30 years.

RESULTS

Lung cancer screening eligibility using US Preventive Services Task Force guidelines was higher for individuals with income <$15 000 (47.2%) and without a high-school education (46.1%) than individuals with income >$50 000 (16.6%) and with a college degree (13.5%), respectively. Increasing lung cancer screening to 100% compliance was cost-effective ($64 654 per quality-adjusted life-year) and produced economic value by up to $560 per person ($182.1 billion for United States overall). Up to 32.2% of the value was due to reductions in health disparities.

CONCLUSIONS

Significant value in increasing lung cancer screening rates derived from reducing health disparities. Policy makers and clinicians may not be appropriately prioritizing cancer screening if value from reducing health disparities is unconsidered.

The health, economic and social burden of smoking in Argentina, and the impact of increasing tobacco taxes in a context of illicit trade

Tobacco tax increases, the most cost-effective measure in reducing consumption, remain underutilized in low and middle-income countries. This study estimates the health and economic burden of smoking in Argentina and forecasts the benefits of tobacco tax hikes, accounting for the potential effects of illicit trade. Using a probabilistic Markov microsimulation model, this study quantifies smoking-related deaths, health events, and societal costs. The model also estimates the health and economic benefits of different increases in the price of cigarettes through taxes. Annually, smoking causes 45,000 deaths and 221,000 health events in Argentina, costing USD 2782 million in direct medical expenses, USD 1470 million in labor productivity loss costs, and USD 1069 million in informal care costs-totaling 1.2% of the national gross domestic product. Even in a scenario that considers illicit trade of tobacco products, a 50% cigarette price increase through taxes could yield USD 8292 million in total economic benefits accumulated over a decade. Consequently, raising tobacco taxes could significantly reduce the health and economic burdens of smoking in Argentina while increasing fiscal revenue.

Distributional Cost-Effectiveness Analysis of Treatments for Non-Small Cell Lung Cancer: An Illustration of an Aggregate Analysis and its Key Drivers

BACKGROUND AND OBJECTIVE

Distributional cost-effectiveness analysis (DCEA) facilitates quantitative assessments of how health effects and costs are distributed among population subgroups, and of potential trade-offs between health maximisation and equity. Implementation of DCEA is currently explored by the National Institute for Health and Care Excellence (NICE) in England. Recent research conducted an aggregate DCEA on a selection of NICE appraisals; however, significant questions remain regarding the impact of the characteristics of the patient population (size, distribution by the equity measure of interest) and methodologic choices on DCEA outcomes. Cancer is the indication most appraised by NICE, and the relationship between lung cancer incidence and socioeconomic status is well established. We aimed to conduct an aggregate DCEA of two non-small cell lung cancer (NSCLC) treatments recommended by NICE, and identify key drivers of the analysis.

METHODS

Subgroups were defined according to socioeconomic deprivation. Data on health benefits, costs, and target populations were extracted from two NICE appraisals (atezolizumab versus docetaxel [second-line treatment following chemotherapy to represent a broad NSCLC population] and alectinib versus crizotinib [targeted first-line treatment to represent a rarer mutation-positive NSCLC population]). Data on disease incidence were derived from national statistics. Distributions of population health and health opportunity costs were taken from the literature. A societal welfare analysis was conducted to assess potential trade-offs between health maximisation and equity. Sensitivity analyses were conducted, varying a range of parameters.

RESULTS

At an opportunity cost threshold of £30,000 per quality-adjusted life-year (QALY), alectinib improved both health and equity, thereby increasing societal welfare. Second-line atezolizumab involved a trade-off between improving health equity and maximising health; it improved societal welfare at an opportunity cost threshold of £50,000/QALY. Increasing the value of the opportunity cost threshold improved the equity impact. The equity impact and societal welfare impact were small, driven by the size of the patient population and per-patient net health benefit. Other key drivers were the inequality aversion parameters and the distribution of patients by socioeconomic group; skewing the distribution to the most (least) deprived quintile improved (reduced) equity gains.

CONCLUSION

Using two illustrative examples and varying model parameters to simulate alternative decision problems, this study suggests that key drivers of an aggregate DCEA are the opportunity cost threshold, the characteristics of the patient population, and the level of inequality aversion. These drivers raise important questions in terms of the implications for decision making. Further research is warranted to examine the value of the opportunity cost threshold, capture the public's views on unfair differences in health, and estimate robust distributional weights incorporating the public's preferences. Finally, guidance from health technology assessment organisations, such as NICE, is needed regarding methods for DCEA construction and how they would interpret and incorporate those results in their decision making.

Incorporating Concern for Health Equity Into Resource Allocation Decisions: Development of a Tool and Population-Based Valuation for Uganda

OBJECTIVES

Health economic analyses that simultaneously address the concerns of increasing population health and reducing health inequalities require information on public preferences for using healthcare resources to reduce health inequalities and how this is valued relative to improving total population health. Previous research has quantified this preference in the form of an inequality aversion parameter in a specified social welfare function. This study aimed to elicit general population's views on health inequality and to estimate an inequality aversion parameter in Uganda.

METHODS

Adult respondents from the general population were recruited and interviewed using survey adapted from an existing questionnaire, including trade-off questions between 2 hypothetical healthcare programs. Data on participants' demographic and socioeconomic characteristics and health-related quality of life measured by 5-level version of EQ-5D were collected.

RESULTS

A nationally representative sample of 165 participants were included, with mean age of 37.1 years and mean 5-level version of EQ-5D at 0.836. Most respondents indicated willingness to trade-off some total population health to reduce health inequality. Translating the preferences into an Atkinson inequality aversion parameter (14.70) implies that health gain to the poorest 20% of people should be given approximately 6 times the weight of health gains to the richest 20%.

CONCLUSIONS

Our study suggests it is feasible to adapt questionnaires of this type for a Ugandan population and this approach could be used to measure public aversion to health inequality in other settings. The elicited inequality aversion parameter can be used to support the assessment of health inequality impact in economic evaluation in Uganda.

Incorporating Equity Concerns in Cost-Effectiveness Analyses: A Systematic Literature Review

OBJECTIVE

The aim of this study was to review analytical methods that enable the incorporation of equity concerns within economic evaluation.

METHODS

A systematic search of PubMed, Embase, and EconLit was undertaken from database inception to February 2021. The search was designed to identify methodological approaches currently employed to evaluate health-related equity impacts in economic evaluation studies of healthcare interventions. Studies were eligible if they described or elaborated on a formal quantitative method used to integrate equity concerns within economic evaluation studies. Cost-utility, cost-effectiveness, cost-benefit, cost-minimisation, and cost-consequence analyses, as well as health technology appraisals, budget impact analyses, and any relevant literature reviews were included. For each of the identified methods, we provided summaries of the scope of equity considerations covered, the methods employed and their key attributes, data requirements, outcomes, and strengths and weaknesses. A traffic light assessment of the practical suitability of each method was undertaken, alongside a worked example applying the different methods to evaluate the same decision problem. Finally, the review summarises the typical trade-offs arising in cost-effectiveness analyses and discusses the extent to which the evaluation methods are able to capture these.

RESULTS

In total, 68 studies were included in the review. Methods could broadly be grouped into equity-based weighting (EBW) methods, extended cost-effectiveness analysis (ECEA), distributional cost-effectiveness analysis (DCEA), multi-criteria decision analysis (MCDA), and mathematical programming (MP). EBW and MP methods enable equity consideration through adjustment to incremental cost-effectiveness ratios, whereas equity considerations are represented through financial risk protection (FRP) outcomes in ECEA, social welfare functions (SWFs) in DCEA, and scoring/ranking systems in MCDA. The review identified potential concerns for EBW methods and MCDA with respect to data availability and for EBW methods and MP with respect to explicitly measuring changes in inequality. The only potential concern for ECEA related to the use of FRP metrics, which may not be relevant for all healthcare systems. In contrast, DCEA presented no significant concerns but relies on the use of SWFs, which may be unfamiliar to some audiences and requires societal preference elicitation. Consideration of typical cost-effectiveness and equity-related trade-offs highlighted the flexibility of most methods with respect to their ability to capture such trade-offs. Notable exceptions were trade-offs between quality of life and length of life, for which we found DCEA and ECEA unsuitable, and the assessment of lost opportunity costs, for which we found only DCEA and MP to be suitable. The worked example demonstrated that each method is designed with fundamentally different analytical objectives in mind.

CONCLUSIONS

The review emphasises that some approaches are better suited to particular decision problems than others, that methods are subject to different practical requirements, and that significantly different conclusions can be observed depending on the choice of method and the assumptions made. Further, to fully operationalise these frameworks, there remains a need to develop consensus over the motivation for equity assessment, which should necessarily be informed with stakeholder involvement. Future research of this topic should be a priority, particularly within the context of equity evaluation in healthcare policy decisions.