Distributional Cost-Effectiveness Analysis: A Tutorial.
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ABSTRACT:
SUBMITTER: Asaria M
PROVIDER: S-EPMC4853814 | biostudies-literature | 2016 Jan
REPOSITORIES: biostudies-literature
Publications
Distributional Cost-Effectiveness Analysis: A Tutorial.
Asaria Miqdad M Griffin Susan S Cookson Richard R
Medical decision making : an international journal of the Society for Medical Decision Making 20150423 1
Distributional cost-effectiveness analysis (DCEA) is a framework for incorporating health inequality concerns into the economic evaluation of health sector interventions. In this tutorial, we describe the technical details of how to conduct DCEA, using an illustrative example comparing alternative ways of implementing the National Health Service (NHS) Bowel Cancer Screening Programme (BCSP). The 2 key stages in DCEA are 1) modeling social distributions of health associated with different interve ...[more]
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Project description:Public health decision makers value interventions for their effects on overall health and health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates health inequality concerns into economic evaluation by accounting for how parameters, such as effectiveness, differ across population groups. A good understanding of how and when accounting for socioeconomic differences between groups affects the assessment of intervention impacts on overall health and health inequality could inform decision makers where DCEA would add most value. We interrogated 2 DCEA models of smoking and alcohol policies using first national level and then local authority level information on various socioeconomic differences in health and intervention use. Through a series of scenario analyses, we explored the impact of altering these differences on the DCEA results. When all available evidence on socioeconomic differences was incorporated, provision of a smoking cessation service was estimated to increase overall health and increase health inequality, while the screening and brief intervention for alcohol misuse was estimated to increase overall health and reduce inequality. Ignoring all or some socioeconomic differences resulted in minimal change to the estimated impact on overall health in both models; however, there were larger effects on the estimated impact on health inequality. Across the models, there were no clear patterns in how the extent and direction of socioeconomic differences in the inputs translated into the estimated impact on health inequality. Modifying use or coverage of either intervention so that each population group matched the highest level improved the impacts to a greater degree than modifying intervention effectiveness. When local level socioeconomic differences were considered, the magnitude of the impacts was altered; in some cases, the direction of impact on inequality was also altered.
| S-EPMC7488816 | biostudies-literature
Project description:ObjectiveTo demonstrate how distributional cost-effectiveness analyses of childhood obesity interventions could be conducted and presented for decision makers.MethodsWe conducted modelled distributional cost-effectiveness analyses of three obesity interventions in children: an infant sleep intervention (POI-Sleep), a combined infant sleep, food, activity and breastfeeding intervention (POI-Combo) and a clinician-led treatment for primary school-aged children with overweight and obesity (High Five for Kids). For each intervention, costs and socioeconomic position (SEP)-specific effect sizes were applied to an Australian child cohort (n = 4898). Using a purpose-built microsimulation model we simulated SEP-specific body mass index (BMI) trajectories, healthcare costs and quality-adjusted life years (QALYs) from age 4 to 17 years for control and intervention cohorts. We examined the distribution of each health outcome across SEP and determined the net health benefit and equity impact accounting for opportunity costs and uncertainty due to individual-level heterogeneity. Finally, we conducted scenario analyses to test the effect of assumptions about health system marginal productivity, the distribution of opportunity costs and SEP-specific effect sizes. The results of the primary analyses, uncertainty analyses and scenario analyses were presented on an efficiency-equity impact plane.ResultsAccounting for uncertainty, POI-Sleep and High Five for Kids were found to be 'win-win' interventions, with a 67% and 100% probability, respectively, of generating a net health benefit and positive equity impact compared with control. POI-Combo was found to be a 'lose-lose' intervention, with a 91% probability of producing a net health loss and a negative equity impact compared with control. Scenario analyses indicated that SEP-specific effect sizes were highly influential on equity impact estimates for POI-Combo and High Five for Kids, while health system marginal productivity and opportunity cost distribution assumptions primarily influenced the net health benefit and equity impact of POI-Combo.ConclusionsThese analyses demonstrated that distributional cost-effectiveness analyses using a fit-for-purpose model are appropriate for differentiating and communicating the efficiency and equity impacts of childhood obesity interventions.
| S-EPMC10232580 | biostudies-literature
Project description:BackgroundCost-effectiveness is a tool to maximize health benefits and to improve efficiency in healthcare. However, efficient outcomes are not always the most equitable ones. Distributional cost-effectiveness analysis (DCEA) offers a framework for incorporating equity concerns into cost-effectiveness analysis.ObjectiveThis systematic review aims to outline the challenges and limitations in applying DCEA in healthcare settings.MethodsWe searched Medline, Scopus, BASE, APA Psych, and JSTOR databases. We also included Google Scholar. We searched for English-language peer-reviewed academic publications, while books, editorials and commentary papers were excluded. Titles and abstract screening, full-text screening, reference list reviews, and data extraction were performed by the main researcher. Another researcher checked every paper for eligibility. Details, such as study population, disease area, intervention and comparators, costs and health effects, cost-effectiveness findings, equity analysis and effects, and modelling technique, were extracted. Thematic analysis was applied, focusing on challenges, obstacles, and gaps in DCEA.ResultsIn total, 615 references were identified, of which 18 studies met the inclusion criteria. Most of these studies were published after 2017. DCEA studies were mainly conducted in Europe and Africa and used quality health-adjusted measurements. In the included studies, absolute inequality indices were used more frequently than relative inequality indices. Every stage of the DCEA presented challenges and/or limitations.ConclusionThis review provides an overview of the literature on the DCEA in healthcare as well as the challenges and limitations related to the different steps needed to conduct the analysis. In particular, we found problems with data availability, the relative unfamiliarity of this analysis among policymakers, and challenges in estimating differences among socioeconomic groups.
| S-EPMC9819735 | biostudies-literature
Project description:This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modeling approach. Alongside the tutorial, we provide easy-to-use functions in the statistics package R. We argue that this multi-state modeling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision-analytic model, which also has the option to use a state-arrival extended approach. In the state-arrival extended multi-state model, a covariate that represents patients' history is included, allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis, including deterministic and probabilistic sensitivity analyses. Finally, we show how to create 2 common methods of visualizing the results-namely, cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate to accommodate parametric multi-state modeling that facilitates extrapolation of survival curves.
| S-EPMC5424858 | biostudies-literature
Project description:Cost-effectiveness analyses (CEA) of randomised controlled trials are a key source of information for health care decision makers. Missing data are, however, a common issue that can seriously undermine their validity. A major concern is that the chance of data being missing may be directly linked to the unobserved value itself [missing not at random (MNAR)]. For example, patients with poorer health may be less likely to complete quality-of-life questionnaires. However, the extent to which this occurs cannot be ascertained from the data at hand. Guidelines recommend conducting sensitivity analyses to assess the robustness of conclusions to plausible MNAR assumptions, but this is rarely done in practice, possibly because of a lack of practical guidance. This tutorial aims to address this by presenting an accessible framework and practical guidance for conducting sensitivity analysis for MNAR data in trial-based CEA. We review some of the methods for conducting sensitivity analysis, but focus on one particularly accessible approach, where the data are multiply-imputed and then modified to reflect plausible MNAR scenarios. We illustrate the implementation of this approach on a weight-loss trial, providing the software code. We then explore further issues around its use in practice.
| S-EPMC6021473 | biostudies-literature
Project description:BackgroundTo assess the potential impact of a tax-induced cigarette price increase on financial and health outcomes by different socioeconomic groups.MethodsIn a modeled condition using pooled cross-section data from Household Income and Expenditure Survey (2002-2017) and Iran 2019 population data, a methodology of an extended cost effectiveness analysis (ECEA) was applied to model the impact on cigarette consumption of hypothetically increased cigarette tax. The methodology was employed to evaluate: [1] health benefits (premature deaths averted); [2] health expenditures regarding smoking-related disease treatment averted; [3] additional tax revenues raised; [4] change in household expenditures on cigarettes; and [5] financial risk protection among male Iranian smokers in a time span of 60 years following a one-time increase in cigarette price of 75%. The Stata version 15.1 (StataCorp., College Station, TX, USA) was used to perform the relevant analysis and estimate regression models.ResultsA 75% increase in cigarettes price through taxation would reduce the number of smokers by more than half a million, 11% of them in the poorest quintile; save about 1.9 million years of life (11% of which would be gained in the lowest quintile compared to 20% in the highest one); eliminate a total of US$196.4 million of health expenditures (9% of which would benefit the bottom quintile). Such a policy could raise the additional annual tax revenues by roughly US$ 1 billion, where the top two quintiles bear around 46% of the total tax burden. We estimated that the tax increase would avert an estimated 56,287 cases of catastrophic expenditure that wholly concentrated among the bottom two expenditure quintiles.ConclusionIncreasing cigarette tax can provide health and financial benefits, and would be pro-poor in terms of health gains, Out-of-Pocket (OOP) savings, and financial risk protection against smoking-related diseases.
| S-EPMC8364107 | biostudies-literature
Project description:BackgroundSeveral studies have shown the beneficial effects of tobacco fiscal policy, but distributional effects have been less examined, especially at the subnational level. The objective of this study is to analyse the distributional effects of a one-peso tobacco tax increase (roughly equivalent to tripling the current excise tax) on health, poverty, and financial outcomes at the subnational level in Mexico.MethodsWe employ an extended cost-effectiveness analysis that estimates life-years gained, smoking attributable deaths averted, treatment costs averted, number of persons avoiding poverty and catastrophic health expenditures, and additional tax revenues by income group across five regions.ResultsWith the one-peso tax increase (or 44% price increase), about 1.5 million smokers would quit smoking across the five regions, resulting in nearly 630 thousand premature deaths averted and 12.6 million life years gained. The bottom income quintile would gain three times more life years gains than the top quintile (ratio 3:1), and the largest gain for the most deprived would occur in the South (ratio 19:1), the region with the highest poverty incidence. Costs averted and additional tax revenues would reach 44.6 and 16.2 billion pesos, respectively. Moreover, 251 thousand individuals would avoid falling into poverty, including 53.2 in the lowest income quintile, and 563.9 thousand would avoid catastrophic health expenditures. Overall, the bottom income group would obtain 26% of the life years gained and 24% of the cost averted, while only paying 3% of the additional tax revenue.ConclusionsThe most significant gains from a substantial cigarette price increase would be for the poorest 20%, especially in the South, the most impoverished region of Mexico. Therefore, tobacco taxes are an opportunity for governments to advance in equity and towards the achievement of sustainable development goals on non-communicable diseases.
| S-EPMC8772114 | biostudies-literature
Project description:Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, and conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs.
| S-EPMC9742144 | biostudies-literature
Project description:In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation.
| S-EPMC9844995 | biostudies-literature
Project description:ObjectiveTo provide up-to-date and comprehensive US data tables to estimate future net resource use, including nonlabor market production, and examine distributional impacts of including nonhealth and future costs in cost-effectiveness results.MethodsUsing a published US cancer prevention simulation model, the paper evaluated the lifetime cost effectiveness of implementing a 10% excise tax on processed meats across age- and sex-specific population subgroups. The model examined multiple scenarios accounting for cancer-related healthcare expenditure (HCE) only, cancer-related and unrelated background HCE, adding productivity benefits (i.e., patient time, cancer-related productivity loss, and background labor and nonlabor market production), and with nonhealth consumption costs, adjusted for household economies of scale. Additional analyses include using population-average versus age-sex-specific estimates for quantifying production and consumption value, as well as comparing direct model estimation versus postcorrections with Meltzer's approximation for incorporating future resource use.ResultsAccounting for nonhealth and future costs impacted cost-effectiveness results across population subgroups, often leading to changes in "cost-saving" determination. Including nonlabor market production had a noticeable impact on estimating future resource use and reduced the bias toward undervaluing productivity among females and older populations. The use of age-sex-specific estimates resulted in less favorable cost-effectiveness results compared with population-average estimates. Meltzer's approximation provided reasonable corrections among the middle-aged population for re-engineering cost-effectiveness ratios from a healthcare sector to a societal perspective.ConclusionWith updated US data tables, this paper can help researchers conduct a comprehensive value assessment to reflect net resource use (health and nonhealth resource use minus production value) from a societal perspective.
| S-EPMC10189225 | biostudies-literature