Estimating the Lifetime Costs and Benefits of the Incredible Years Teacher Classroom Management Intervention Using Data From 30 Months Follow-Up of the Supporting Teachers and childRen in Schools Trial.

OBJECTIVES: The Incredible Years Teacher® Classroom Management (IY-TCM) intervention is associated with short-term improvements in mental health difficulties in young people. The aim was to estimate the long-term impact and cost-effectiveness of the IY-TCM intervention compared with no intervention. METHODS: An existing health economic model (LifeSim 1.0) was used to translate short-term changes in the Strength and Difficulties Questionnaire (SDQ), based on the Supporting Teachers and childRen in Schools cluster randomized controlled trial of the IY-TCM intervention in schools, into estimated medium- and long-term effects using multiple longitudinal data sets. LifeSim 1.0 was adapted to incorporate teacher-reported SDQ and account for individual heterogeneity. Cost-effectiveness analyses were conducted using the trial-based intervention cost with subgroup analyses on deprivation, conduct scores and parental depression in the simulated baseline population. RESULTS: Regression analyses show significant predictor variables for intervention effectiveness, including deprivation and baseline SDQ. LifeSim results indicate small gains in long-term outcomes, and cost-effective analyses estimated that the IY-TCM intervention could be cost-effective, but there was a large amount of uncertainty (net monetary benefit = £10, Estimated CI = -£134, £156). Benefits and certainty of cost-effectiveness were greater for some subgroups, such as those with high conduct scores at baseline (net monetary benefit = £206, Estimated CI = £26, £318). CONCLUSIONS: IY-TCM could be cost-effective, but there was a large amount of uncertainty around costs and benefits. Greater benefits for pupils with difficulties at baseline suggest that the intervention may be more cost-effective for schools in more deprived areas with high levels of conduct problems.

Are local public expenditure reductions associated with increases in inequality in emergency hospitalisation? Time-series analysis of English local authorities from 2010 to 2017.

BACKGROUND: Reductions in local government funding implemented in 2010 due to austerity policies have been associated with worsening socioeconomic inequalities in mortality. Less is known about the relationship of these reductions with healthcare inequalities; therefore, we investigated whether areas with greater reductions in local government funding had greater increases in socioeconomic inequalities in emergency admissions. METHODS: We examined inequalities between English local authority districts (LADs) using a fixed-effects linear regression to estimate the association between LAD expenditure reductions, their level of deprivation using the Index of Multiple Deprivation (IMD) and average rates of (all and avoidable) emergency admissions for the years 2010-2017. We also examined changes in inequalities in emergency admissions using the Absolute Gradient Index (AGI), which is the modelled gap between the most and least deprived neighbourhoods in an area. RESULTS: LADs within the most deprived IMD quintile had larger pounds per capita expenditure reductions, higher rates of all and avoidable emergency admissions, and greater between-neighbourhood inequalities in admissions. However, expenditure reductions were only associated with increasing average rates of all and avoidable emergency admissions and inequalities between neighbourhoods in local authorities in England's three least deprived IMD quintiles. For a LAD in the least deprived IMD quintile, a yearly reduction of £100 per capita in total expenditure was associated with a yearly increase of 47 (95% CI 22 to 73) avoidable admissions, 142 (95% CI 70 to 213) all-cause emergency admissions and a yearly increase in inequalities between neighbourhoods of 48 (95% CI 14 to 81) avoidable and 140 (95% CI 60 to 220) all-cause emergency admissions. In 2017, a LAD average population was ~170 000. CONCLUSION: Austerity policies implemented in 2010 impacted less deprived local authorities, where emergency admissions and inequalities between neighbourhoods increased, while in the most deprived areas, emergency admissions were unchanged, remaining high and persistent.

The Impact of Funding Inpatient Treatments for COVID-19 on Health Equity in the United States: A Distributional Cost-Effectiveness Analysis.

OBJECTIVES: We conducted a distributional cost-effectiveness analysis (DCEA) to evaluate how Medicare funding of inpatient COVID-19 treatments affected health equity in the United States. METHODS: A DCEA, based on an existing cost-effectiveness analysis model, was conducted from the perspective of a single US payer, Medicare. The US population was divided based on race and ethnicity (Hispanic, non-Hispanic black, and non-Hispanic white) and county-level social vulnerability index (5 quintile groups) into 15 equity-relevant subgroups. The baseline distribution of quality-adjusted life expectancy was estimated across the equity subgroups. Opportunity costs were estimated by converting total spend on COVID-19 inpatient treatments into health losses, expressed as quality-adjusted life-years (QALYs), using base-case assumptions of an opportunity cost threshold of $150 000 per QALY gained and an equal distribution of opportunity costs across equity-relevant subgroups. RESULTS: More socially vulnerable populations received larger per capita health benefits due to higher COVID-19 incidence and baseline in-hospital mortality. The total direct medical cost of inpatient COVID-19 interventions in the United States in 2020 was estimated at $25.83 billion with an estimated net benefit of 735 569 QALYs after adjusting for opportunity costs. Funding inpatient COVID-19 treatment reduced the population-level burden of health inequality by 0.234%. Conclusions remained robust across scenario and sensitivity analyses. CONCLUSIONS: To the best of our knowledge, this is the first DCEA to quantify the equity implications of funding COVID-19 treatments in the United States. Medicare funding of COVID-19 treatments in the United States could improve overall health while reducing existing health inequalities.

Guidance on the use of complex systems models for economic evaluations of public health interventions.

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

The inverse care law re-examined: a global perspective.

An inverse care law persists in almost all low-income and middle-income countries, whereby socially disadvantaged people receive less, and lower-quality, health care despite having greater need. By contrast, a disproportionate care law persists in high-income countries, whereby socially disadvantaged people receive more health care, but of worse quality and insufficient quantity to meet their additional needs. Both laws are caused not only by financial barriers and fragmented health insurance systems but also by social inequalities in care seeking and co-investment as well as the costs and benefits of health care. Investing in more integrated universal health coverage and stronger primary care, delivered in proportion to need, can improve population health and reduce health inequality. However, trade-offs sometimes exist between health policy objectives. Health-care technologies, policies, and resourcing should be subjected to distributional analysis of their equity impacts, to ensure the objective of reducing health inequalities is kept in sight.

Comparing healthcare quality: A common framework for both ordinal and cardinal data with an application to primary care variation in England.

The paper proposes a framework for comparing the quality of healthcare providers and assessing the variation in quality between them, which is directly applicable to both ordinal and cardinal quality data on a comparable basis. The resultant measures are sensitive to the full distribution of quality scores for each provider, not just the mean or the proportion meeting some binary quality threshold, thereby making full use of the multicategory response data increasingly available from patient experience surveys. The measures can also be standardized for factors such as age, sex, ethnicity, health and deprivation using a distribution regression model. We illustrate by measuring the quality of primary care services in England in 2019 using three different sources of publicly available, general practice-level information: multicategory response patient experience data, ordinal inspection ratings and cardinal clinical achievement scores. We find considerable variation at both local and regional levels using all three data sources. However, the correlation between the comparative quality indices calculated using the alternative data sources is weak, suggesting that they capture different aspects of general practice quality.

Quality adjusted life years based on health and consumption: A summary wellbeing measure for cross-sectoral economic evaluation.

We introduce a summary wellbeing measure for economic evaluation of cross-sectoral public policies with impacts on health and living standards. We show how to calculate period-specific and lifetime wellbeing using quality-adjusted life years based on widely available data on health-related quality of life and consumption and normative assumptions about three parameters-minimal consumption, standard consumption, and the elasticity of the marginal value of consumption. We also illustrate how these three parameters can be tailored to the decision-making context and varied in sensitivity analysis to provide information about the implications of alternative value judgments. As well as providing a general measure for cost-effectiveness analysis and cost-benefit analysis in terms of wellbeing, this approach also facilitates distributional analysis in terms of how many good years different population subgroups can expect to live under different policy scenarios.

Development of a checklist to guide equity considerations in health technology assessment.

OBJECTIVES: Health technology assessment (HTA) can impact health inequities by informing healthcare priority-setting decisions. This paper presents a novel checklist to guide HTA practitioners looking to include equity considerations in their work: the equity checklist for HTA (ECHTA). The list is pragmatically organized according to the generic HTA phases and can be consulted at each step. METHODS: A first set of items was based on the framework for equity in HTA developed by Culyer and Bombard. After rewording and reorganizing according to five HTA phases, they were complemented by elements emerging from a literature search. Consultations with method experts, decision makers, and stakeholders further refined the items. Further feedback was sought during a presentation of the tool at an international HTA conference. Lastly, the checklist was piloted through all five stages of an HTA. RESULTS: ECHTA proposes elements to be considered at each one of the five HTA phases: Scoping, Evaluation, Recommendations and Conclusions, Knowledge Translation and Implementation, and Reassessment. More than a simple checklist, the tool provides details and examples that guide the evaluators through an analysis in each phase. A pilot test is also presented, which demonstrates the ECHTA's usability and added value. CONCLUSIONS: ECHTA provides guidance for HTA evaluators wishing to ensure that their conclusions do not contribute to inequalities in health. Several points to build upon the current checklist will be addressed by a working group of experts, and further feedback is welcome from evaluators who have used the tool.

Universal or targeted cardiovascular screening? Modelling study using a sector-specific distributional cost effectiveness analysis.

Distributional cost effectiveness analysis is a new method that can help to redesign prevention programmes by explicitly modelling the distribution of health opportunity costs as well as the distribution of health benefits. Previously we modelled cardiovascular disease (CVD) screening audit data from Liverpool, UK to see if the city could redesign its cardiovascular screening programme to enhance its cost effectiveness and equity. Building on this previous analysis, we explicitly examined the distribution of health opportunity costs and we looked at new redesign options co-designed with stakeholders. We simulated four plausible scenarios: a) no CVD screening, b) 'current' basic universal CVD screening as currently implemented, c) enhanced universal CVD screening with 'increased' population-wide delivery, and d) 'universal plus targeted' with top-up delivery to the most deprived fifth. We also compared assumptions around whether displaced health spend would come from programmes that might benefit the poor more and how much health these programmes would generate. The main outcomes were net health benefit and change in the slope index of inequality (SII) in QALYs per 100,000 person years. 'Universal plus targeted' dominated 'increased' and 'current' and also reduced health inequality by -0.65 QALYs per 100,000 person years. Results are highly sensitive to assumptions about opportunity costs and, in particular, whether funding comes from health care or local government budgets. By analysing who loses as well as who gains from expenditure decisions, distributional cost effectiveness analysis can help decision makers to redesign prevention programmes in ways that improve health and reduce health inequality.

Aggregate Distributional Cost-Effectiveness Analysis of Health Technologies.

BACKGROUND: Health inequalities can be partially addressed through the range of treatments funded by health systems. Nevertheless, although health technology assessment agencies assess the overall balance of health benefits and costs, no quantitative assessment of health inequality impact is consistently undertaken. OBJECTIVES: To assess the inequality impact of technologies recommended under the NICE single technology appraisal process from 2012 to 2014 using an aggregate distributional cost-effectiveness framework. METHODS: Data on health benefits, costs, and patient populations were extracted from the NICE website. Benefits for each technology were distributed to social groups using the observed socioeconomic distribution of hospital utilization for the targeted disease. Inequality measures and estimates of cost-effectiveness were compared using the health inequality impact plane and combined using social welfare indices. RESULTS: Twenty-seven interventions were evaluated. Fourteen interventions were estimated to increase population health and reduce health inequality, 8 to reduce population health and increase health inequality, and 5 to increase health and increase health inequality. Among the latter 5, social welfare analysis, using inequality aversion parameters reflecting high concern for inequality, indicated that the health gain outweighs the negative health inequality impact. CONCLUSIONS: The methods proposed offer a way of estimating the health inequality impacts of new health technologies. The methods do not allow for differences in technology-specific utilization and health benefits, but require less resources and data than conducting full distributional cost-effectiveness analysis. They can provide useful quantitative information to help policy makers consider how far new technologies are likely to reduce or increase health inequalities.

E-learning and health inequality aversion: A questionnaire experiment.

In principle, questionnaire data on public views about hypothetical trade-offs between improving total health and reducing health inequality can provide useful normative health inequality aversion parameter benchmarks for policymakers faced with real trade-offs of this kind. However, trade-off questions can be hard to understand, and one standard type of question finds that a high proportion of respondents-sometimes a majority-appear to give exclusive priority to reducing health inequality. We developed and tested two e-learning interventions designed to help respondents understand this question more completely. The interventions were a video animation, exposing respondents to rival points of view, and a spreadsheet-based questionnaire that provided feedback on implied trade-offs. We found large effects of both interventions in reducing the proportion of respondents giving exclusive priority to reducing health inequality, though the median responses still implied a high degree of health inequality aversion and-unlike the video-the spreadsheet-based intervention introduced a substantial new minority of non-egalitarian responses. E-learning may introduce as well as avoid biases but merits further research and may be useful in other questionnaire studies involving trade-offs between conflicting values.

Using Cost-Effectiveness Analysis to Address Health Equity Concerns.

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.

Eliciting the Level of Health Inequality Aversion in England.

Health inequality aversion parameters can be used to represent alternative value judgements about policy concern for reducing health inequality versus improving total health. In this study, we use data from an online survey of the general public in England (n = 244) to elicit health inequality aversion parameters for both Atkinson and Kolm social welfare functions. We find median inequality aversion parameters of 10.95 for Atkinson and 0.15 for Kolm. These values suggest substantial concern for health inequality among the English general public which, at current levels of quality adjusted life expectancy, implies weighting health gains to the poorest fifth of people in society six to seven times as highly as health gains to the richest fifth. Copyright © 2016 John Wiley & Sons, Ltd.

The Social Distribution of Health: Estimating Quality-Adjusted Life Expectancy in England.

OBJECTIVE: To model the social distribution of quality-adjusted life expectancy (QALE) in England by combining survey data on health-related quality of life with administrative data on mortality. METHODS: Health Survey for England data sets for 2010, 2011, and 2012 were pooled (n = 35,062) and used to model health-related quality of life as a function of sex, age, and socioeconomic status (SES). Office for National Statistics mortality rates were used to construct life tables for age-sex-SES groups. These quality-of-life and length-of-life estimates were then combined to predict QALE as a function of these characteristics. Missing data were imputed, and Monte-Carlo simulation was used to estimate standard errors. Sensitivity analysis was conducted to explore alternative regression models and measures of SES. RESULTS: Socioeconomic inequality in QALE at birth was estimated at 11.87 quality-adjusted life-years (QALYs), with a sex difference of 1 QALY. When the socioeconomic-sex subgroups are ranked by QALE, a differential of 10.97 QALYs is found between the most and least healthy quintile groups. This differential can be broken down into a life expectancy difference of 7.28 years and a quality-of-life adjustment of 3.69 years. CONCLUSIONS: The methods proposed in this article refine simple binary quality-adjustment measures such as the widely used disability-free life expectancy, providing a more accurate picture of overall health inequality in society than has hitherto been available. The predictions also lend themselves well to the task of evaluating the health inequality impact of interventions in the context of cost-effectiveness analysis.

Distributional cost-effectiveness analysis of health care programmes--a methodological case study of the UK Bowel Cancer Screening Programme.

This paper presents an application of a new methodological framework for undertaking distributional cost-effectiveness analysis to combine the objectives of maximising health and minimising unfair variation in health when evaluating population health interventions. The National Health Service bowel cancer screening programme introduced in 2006 is expected to improve population health on average and to worsen population health inequalities associated with deprivation and ethnicity--a classic case of 'intervention-generated inequality'. We demonstrate the distributional cost-effectiveness analysis framework by examining two redesign options for the bowel cancer screening programme: (i) the introduction of an enhanced targeted reminder aimed at increasing screening uptake in deprived and ethnically diverse neighbourhoods and (ii) the introduction of a basic universal reminder aimed at increasing screening uptake across the whole population. Our analysis indicates that the universal reminder is the strategy that maximises population health, while the targeted reminder is the screening strategy that minimises unfair variation in health. The framework is used to demonstrate how these two objectives can be traded off against each other, and how alternative social value judgements influence the assessment of which strategy is best, including judgements about which dimensions of health variation are considered unfair and judgements about societal levels of inequality aversion.

Justice and the NICE approach.

When thinking about population level healthcare priority setting decisions, such as those made by the National Institute for Health and Care Excellence, good medical ethics requires attention to three main principles of health justice: (1) cost-effectiveness, an aspect of beneficence, (2) non-discrimination, and (3) priority to the worse off in terms of both current severity of illness and lifetime health. Applying these principles requires consideration of the identified patients who benefit from decisions and the unidentified patients who bear the opportunity costs.

The appraisal of public health interventions: the use of theory.

BACKGROUND: Public health decision-making is hampered by inappropriate adherence to underpowered randomized controlled trials (RCTs) which give inconclusive results and lead to decision-makers being loath to recommend interventions with strong theoretical and observational support. METHODS: We outline situations in which robust decisions about health interventions can be made without trial evidence. We present a new approach in which theory, causal models and past observations are given proper regard in the decision-making process. RESULTS: Using our approach, we provide examples where the use of causal theories and observations in areas, such as salt reduction, smoking cessation and gardening to improve mental health, is sufficient for deciding that such interventions are effective for improving health without needing the support of underpowered RCTs. Particularly where RCT evidence is inconclusive, our approach may provide similar aggregate health outcomes for society for vastly lower cost. CONCLUSIONS: When knowledge and theoretical understanding are unable sufficiently to reduce doubt about the direction of effect from an intervention, decisions should be made using evidence-based medicine approaches. There are, however, many cases where the combination of robust theory, causal understanding and observation are able to provide sufficient evidence of the direction of effect from an intervention that current practice should be altered.

Comparing the performance of the Charlson/Deyo and Elixhauser comorbidity measures across five European countries and three conditions.

BACKGROUND: The Charlson and Elixhauser comorbidity measures are commonly used methods to account for patient comorbidities in hospital-level comparisons of clinical quality using administrative data. Both have been validated in North America, but there is less evidence of their performance in Europe and in pooled cross-country data, which are features of the European Collaboration for Healthcare Optimization (ECHO) project. This study compares the performance of the Charlson/Deyo and Elixhauser comorbidity measures in predicting in-hospital mortality using data from five European countries in three inpatient groups. METHODS: Administrative data is used from five countries in 2008-2009 for three indicators commonly used in hospital quality comparisons: mortality rates following acute myocardial infarction, coronary artery bypass graft surgery and stroke. Logistic regression models are constructed to predict mortality controlling for age, gender and the relevant comorbidity measure. Model discrimination is evaluated using c-statistics. Model calibration is evaluated using calibration slopes. Overall goodness-of-fit is evaluated using Nagelkerke's R(2) and the Akaike information criterion. All models are validated internally by using bootstrapping and externally by using the 2009 model parameters to predict mortality in 2008. RESULTS: The Elixhauser measure has better overall predictive ability in terms of discrimination and goodness-of-fit than the Charlson/Deyo measure or the age-sex only model. There is no clear difference in model calibration. These findings are robust to the choice of country, to pooling all five countries and to internal and external validation. CONCLUSIONS: The Elixhauser list contains more comorbidities, which may enable it to achieve better discrimination than the Charlson measure. Both measures achieve similar calibration, so for the purpose of ECHO we judged the Elixhauser measure to be preferable.