Estimated Effects of Different Alcohol Taxation and Price Policies on Health Inequalities: A Mathematical Modelling Study

Meier, Petra; Holmes, John; Angus, Colin; Ally, Abdallah; Meng, Yang; Brennan, Alan

doi:10.1371/journal.pmed.1001963

Cited by 109 publications

(105 citation statements)

References 32 publications

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“…At the request of the UK health minister, for example, Holmes et al [35] used CEA to model the social class distribution of the impacts of minimum alcohol pricing in the United Kingdom. They examined the effects on alcohol consumption, spending, and alcohol-related health harm and found that health benefits are substantially concentrated on heavy drinkers in routine and manual worker households.…”

Section: Equity Impact Analysismentioning

confidence: 99%

Using Cost-Effectiveness Analysis to Address Health Equity Concerns

et al. 2017

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This articles serves as a guide to using cost-effectiveness analysis (CEA) to address health equity concerns. We first introduce the "equity impact plane," a tool for considering trade-offs between improving total health—the objective underpinning conventional CEA—and equity objectives, such as reducing social inequality in health or prioritizing the severely ill. Improving total health may clash with reducing social inequality in health, for example, when effective delivery of services to disadvantaged communities requires additional costs. Who gains and who loses from a cost-increasing health program depends on differences among people in terms of health risks, uptake, quality, adherence, capacity to benefit, and—crucially—who bears the opportunity costs of diverting scarce resources from other uses. We describe two main ways of using CEA to address health equity concerns: 1) equity impact analysis, which quantifies the distribution of costs and effects by equity-relevant variables, such as socioeconomic status, location, ethnicity, sex, and severity of illness; and 2) equity trade-off analysis, which quantifies trade-offs between improving total health and other equity objectives. One way to analyze equity trade-offs is to count the cost of fairer but less cost-effective options in terms of health forgone. Another method is to explore how much concern for equity is required to choose fairer but less cost-effective options using equity weights or parameters. We hope this article will help the health technology assessment community navigate the practical options now available for conducting equity-informative CEA that gives policymakers a better understanding of equity impacts and trade-offs.

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Section: Equity Impact Analysismentioning

confidence: 99%

Using Cost-Effectiveness Analysis to Address Health Equity Concerns

et al. 2017

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“…Price-based policies may be particularly effective at reducing the growing health inequalities in areas with greater levels of deprivation, such as the North of England and urban settings [27,38]. We have demonstrated that Bayesian structural time-series models can support effective and timely planning for allocation of public health resources and the prioritization of interventions to reduce alcoholrelated harm.…”

Section: Potential Implicationsmentioning

confidence: 99%

Forecasting the 2021 local burden of population alcohol‐related harms using Bayesian structural time–series

et al. 2019

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Background and aims Harmful alcohol use places a significant burden on health services. Sophisticated nowcasting and forecasting methods could support service planning, but their use in public health has been limited. We aimed to use a novel analysis framework, combined with routine public health data, to improve now‐ and forecasting of alcohol‐related harms. Design We used Bayesian structural time–series models to forecast alcohol‐related hospital admissions for 2020/21 (from 2015 to 2016). Setting England. Participants We developed separate models for each English lower‐tier local authority. Measurements Our primary outcome was alcohol‐related hospital admissions. Model covariates were population size and age‐structure. Findings Nowcasting validation indicated adequate accuracy, with 5‐year nowcasts underestimating admissions by 2.2% nationally and 3.3% locally, on average. Forecasts indicated a 3.3% increase in national admissions in 2020/21, corresponding to a 0.2% reduction in the crude rate of new admissions, due to population size changes. Locally, the largest increases were forecast in urban, industrial and coastal areas and the largest decreases in university towns and ethnically diverse areas. Conclusions In 2020/21, alcohol‐related hospital admissions are expected to increase in urban and coastal areas and decrease in areas associated with inward migration of younger people, including university towns and areas with greater ethnic diversity. Bayesian structural time–series models enable investigation of the future impacts of alcohol‐related harms in population subgroups and could improve service planning and the evaluation of natural experiments on the impact of interventions to reduce the societal impacts of alcohol.

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“…Those opposed to the introduction of a minimum unit price have argued that it is regressive and poorly targeted, disproportionately affecting moderate drinkers on low incomes (Gornall, 2014). Evidence emerging from the Sheffield Alcohol Policy Model refutes this assertion, suggesting that a minimum unit price would have greater effects on heavy drinkers than on moderate drinkers (Purshouse et al, 2010;Holmes et al, 2014;Meier et al, 2016). While these mathematical modelling studies are informative, we also need to consider individual level data from people in relation to their income, amount of alcohol they consume and the price they pay for alcohol to identify who will be most affected by the introduction of a MUP (Crawford et al, 2012).…”

Section: Introductionmentioning

confidence: 99%