Shorter Treatment for Nonsevere Tuberculosis in African and Indian Children.

BACKGROUND: Two thirds of children with tuberculosis have nonsevere disease, which may be treatable with a shorter regimen than the current 6-month regimen. METHODS: We conducted an open-label, treatment-shortening, noninferiority trial involving children with nonsevere, symptomatic, presumably drug-susceptible, smear-negative tuberculosis in Uganda, Zambia, South Africa, and India. Children younger than 16 years of age were randomly assigned to 4 months (16 weeks) or 6 months (24 weeks) of standard first-line antituberculosis treatment with pediatric fixed-dose combinations as recommended by the World Health Organization. The primary efficacy outcome was unfavorable status (composite of treatment failure [extension, change, or restart of treatment or tuberculosis recurrence], loss to follow-up during treatment, or death) by 72 weeks, with the exclusion of participants who did not complete 4 months of treatment (modified intention-to-treat population). A noninferiority margin of 6 percentage points was used. The primary safety outcome was an adverse event of grade 3 or higher during treatment and up to 30 days after treatment. RESULTS: From July 2016 through July 2018, a total of 1204 children underwent randomization (602 in each group). The median age of the participants was 3.5 years (range, 2 months to 15 years), 52% were male, 11% had human immunodeficiency virus infection, and 14% had bacteriologically confirmed tuberculosis. Retention by 72 weeks was 95%, and adherence to the assigned treatment was 94%. A total of 16 participants (3%) in the 4-month group had a primary-outcome event, as compared with 18 (3%) in the 6-month group (adjusted difference, -0.4 percentage points; 95% confidence interval, -2.2 to 1.5). The noninferiority of 4 months of treatment was consistent across the intention-to-treat, per-protocol, and key secondary analyses, including when the analysis was restricted to the 958 participants (80%) independently adjudicated to have tuberculosis at baseline. A total of 95 participants (8%) had an adverse event of grade 3 or higher, including 15 adverse drug reactions (11 hepatic events, all but 2 of which occurred within the first 8 weeks, when the treatments were the same in the two groups). CONCLUSIONS: Four months of antituberculosis treatment was noninferior to 6 months of treatment in children with drug-susceptible, nonsevere, smear-negative tuberculosis. (Funded by the U.K. Medical Research Council and others; SHINE ISRCTN number, ISRCTN63579542.).

Distributional Cost-Effectiveness Analysis of Health Technologies: Data Requirements and Challenges.

Governments and health technology assessment agencies are putting greater focus on and efforts in understanding and addressing health inequities. Cost-effectiveness analyses are used to evaluate the costs and health gains of different interventions to inform the decision-making process on funding of new treatments. Distributional cost-effectiveness analysis (DCEA) is an extension of cost-effectiveness analysis that quantifies the equity impact of funding new treatments. Key challenges for the routine and consistent implementation of DCEA are the lack of clearly defined equity concerns from decision makers and endorsed measures to define equity subgroups and the availability of evidence that allows analysis of differences in data inputs associated with the equity characteristics of interest. In this article, we detail the data gaps and challenges to build robust DCEA analysis routinely in health technology assessment and suggest actions to overcome these hurdles.

Quality-Adjusted Life Expectancy Norms for the English Population.

OBJECTIVES: The National Institute for Health and Care Excellence in England has implemented severity-of-disease modifiers that give greater weight to health benefits accruing to patients who experience a larger shortfall in quality-adjusted life-years (QALYs) under current standard of care than healthy individuals. This requires an estimate of quality-adjusted life expectancy (QALE) of the general population based on age and sex. Previous QALE population norms are based on nearly 30-year-old assessments of health-related quality of life in the general population. This study provides updated QALE estimates for the English population based on age and sex. METHODS: 5-level version of EQ-5D data for 14 412 participants from the Health Survey for England (waves 2017 and 2018) were pooled, and health-related quality of life population norms were calculated. These norms were combined with official life tables from the Office for National Statistics for 2017 to 2019 using the Sullivan method to derive QALE estimates based on age and sex. Values were discounted using 0%, 1.5%, and 3.5% discount rates. RESULTS: QALE at birth is 68.24 QALYs for men and 68.21 QALYs for women. These values are significantly lower than previously published QALE population norms based on the older 3-level version of EQ-5D data. CONCLUSION: This study provides new QALE population norms for England that serve to establish absolute and relative QALY shortfalls for the purpose of health technology assessments.

Is primary health care worth it in the long run? Evidence from Brazil.

This paper assesses whether Brazilian primary health care is worth it in the long-run by estimating the accumulated costs and benefits of its flagship, the Family Health Strategy program (ESF). We employ an alternative strategy centered on years of exposure to the program to incorporate its dynamics. We also account for the program's heterogeneity with respect to the remuneration of ESF health teams and the intensity of coverage across Brazilian municipalities, measure by the number of people assisted by each ESF team, on average. To address heterogeneity in professional earnings, this paper employs, for the first time, a dataset containing the remuneration of professionals allocated to all ESF teams nationwide. The benefits are measured by the avoided deaths and hospitalizations due to causes sensitive to primary care. Results suggest that the net monetary benefit of the program is positive on average, with an optimum time of exposure of approximately 16 years. Significant heterogeneities in cost-benefit results were found since costs outweigh benefits in localities where the coverage is low intensive. On the other hand, the benefits outweigh the costs by 22.5% on average in municipalities with high intensive coverage.

Socioeconomic inequalities in HRQoL in England: an age-sex stratified analysis.

BACKGROUND: Socioeconomic status is a key predictor of lifetime health: poorer people can expect to live shorter lives with lower average health-related quality-of-life (HRQoL) than richer people. In this study, we aimed to improve understanding of the socioeconomic gradient in HRQoL by exploring how inequalities in different dimensions of HRQoL differ by age. METHODS: Data were derived from the Health Survey for England for 2017 and 2018 (14,412 participants). HRQoL was measured using the EQ-5D-5L instrument. We estimated mean EQ-5D utility scores and reported problems on five HRQoL dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) for ages 16 to 90+ and stratified by neighbourhood deprivation quintiles. Relative and absolute measures of inequality were assessed. RESULTS: Mean EQ-5D utility scores declined with age and followed a socioeconomic gradient, with the lowest scores in the most deprived areas. Gaps between the most and least deprived quintiles emerged around the age of 35, reached their greatest extent at age 60 to 64 (relative HRQoL of most deprived compared to least deprived quintile: females = 0.77 (95% CI: 0.68-0.85); males = 0.78 (95% CI: 0.69-0.87)) before closing again in older age groups. Gaps were apparent for all five EQ-5D dimensions but were greatest for mobility and self-care. CONCLUSION: There are stark socioeconomic inequalities in all dimensions of HRQoL in England. These inequalities start to develop from early adulthood and increase with age but reduce again around retirement age.

Methods to promote equity in health resource allocation in low- and middle-income countries: an overview.

Unfair differences in healthcare access, utilisation, quality or health outcomes exist between and within countries around the world. Improving health equity is a stated objective for many governments and international organizations. We provide an overview of the major tools that have been developed to measure, evaluate and promote health equity, along with the data required to operationalise them.Methods are organised into four key policy questions facing decision-makers: (i) what is the current level of inequity in health; (ii) does government health expenditure benefit the worst-off; (iii) can government health expenditure more effectively promote equity; and (iv) which interventions provide the best value for money in reducing inequity.Benefit incidence analysis can be used to estimate the distribution of current public health sector expenditure, with geographical resource allocation formulae and health system reform being the main government policy levers for improving equity. Techniques from the economic evaluation literature, such as extended and distributional cost-effectiveness analysis can be used to identify 'best buy' interventions from a health equity perspective. A range of inequality metrics, from gap measures and slope indices to concentration indices and regression analysis, can be applied to these approaches to evaluate changes in equity.Methods from the economics literature can provide policymakers with a toolkit for addressing multiple aspects of health equity, from outcomes to financial protection, and can be adapted to accommodate data commonly available in low- and middle-income settings.

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.

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.

Decomposition of Quality-Adjusted Life Expectancy Inequalities by Mortality and Health-Related Quality of Life Dimensions.

BACKGROUND: Quality-adjusted life expectancy (QALE) combines mortality risk and multidimensional health-related quality of life (HRQoL) information to measure healthy life expectancy in terms of quality-adjusted life years (QALYs). This paper estimates the relative importance of individual quality of life dimensions in explaining inequalities in QALE. METHODS: We combined EQ-5D-5L data from the Health Survey for England for 2017 and 2018 (N = 14,412) with full population mortality data from the Office for National Statistics to calculate QALE by age, sex and deprivation quintile. The effect of HRQoL dimensions on the socioeconomic gradient in QALE was decomposed using an iterative imputation approach, in which inequalities associated with socioeconomic status in each domain were removed by imputing the response distribution of the richest quintile for all participants. Sampling uncertainty in the HRQoL data was evaluated using bootstrapping. RESULTS: People in the least deprived fifth of neighbourhoods in England can expect to live 7.0 years longer and experience 11.1 more QALYs than those in the most deprived fifth. Inequalities in HRQoL accounted for 28.0% and 45.7% of QALE inequalities for males and females, respectively. Pain/discomfort, anxiety/depression and mobility were the most influential HRQoL domains. DISCUSSION: Our results identify the extent of inequalities associated with socioeconomic status in lifetime health and the relative importance of inequalities by mortality and HRQoL. The contributions of the individual dimensions of HRQoL towards lifetime inequalities vary substantially by sex. Our findings can help to identify the types of interventions most likely to alleviate health inequalities, which may be different for males and females.

Equity and economic evaluation of system-level health interventions: A case study of Brazil's Family Health Program.

Distributional economic evaluation estimates the value for money of health interventions in terms of population health and health equity impacts. When applied to interventions delivered at the population and health system-level interventions (PSIs) instead of clinical interventions, additional practical and methodological challenges arise. Using the example of the Programme Saúde da Familia (PSF) in Brazil, a community-level primary care system intervention, we seek to illustrate these challenges and provide potential solutions. We use a distributional cost-effectiveness analysis (DCEA) approach to evaluate the impact of the PSF on population health and between-state health inequalities in Brazil. Data on baseline health status, disease prevalence and PSF effectiveness are extracted from the literature and incorporated into a Markov model to estimate the long-term impacts in terms of disability-adjusted life years. The inequality and average health impacts are analysed simultaneously using health-related social welfare functions. Uncertainty is computed using Monte Carlo simulation. The DCEA encountered several challenges in the context of PSIs. Non-randomized, quasi-experimental methods may not be powered to identify treatment effect heterogeneity estimates to inform a decision model. PSIs are more likely to be funded from multiple public sector budgets, complicating the calculation of health opportunity costs. We estimate a cost-per-disability-adjusted life years of funding the PSF of $2640. Net benefits were positive across the likely range of intervention cost. Social welfare analysis indicates that, compared to gains in average health, changes in health inequalities accounted for a small proportion of the total welfare improvement, even at high levels of social inequality aversion. Evidence on the population health and health equity impacts of PSIs can be incorporated into economic evaluation methods, although with additional complexity and assumptions. The case study results indicate that the PSF is likely to be cost-effective but that the inequality impacts are small and highly uncertain.

How Useful Are Early Economic Models? Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modelling".

Early economic modelling has long been recommended to aid research and development (R&D) decisions in medical innovation, although they are less frequently published and critically appraised. A review of 30 innovations by Grutters et al provides an opportunity to evaluate how early models are used in practice. The evidence of early models can be used to inform two types of decision: to continue development ("stop or go") or to alter future R&D activities. I argue that early models have limited use in stop or go decisions, as less resource and data undermine the reliability of the models' indicative estimates of cost-effectiveness. Whilst they are far more useful for informing future R&D directions, the best techniques available from statistical decision science, such as value of information analysis, are not regularly used. It is highly recommended that early models adopt these methods to best deal with uncertainty, quantify the potential value of further research, identify areas of study with the greatest potential benefit and generate recommendations on study design and sample size.

Estimating Social Variation in the Health Effects of Changes in Health Care Expenditure.

Background. A common aim of health expenditure is to reduce unfair inequalities in health. Although previous research has attempted to estimate the total health effects of changes in health expenditure, little is known about how changes affect different groups in the population. Methods. We propose a general framework for disaggregating the total health effects of changes in health expenditure by social groups. This can be performed indirectly when the estimate of the total health effect has first been disaggregated by a secondary factor (e.g., disease area) that can be linked to social characteristics. This is illustrated with an application to the English National Health Service. Evidence on the health effects of expenditure across 23 disease areas is combined with data on the distribution of disease-specific hospital utilization by age, sex, and area-level deprivation. Results. We find that the health effects from NHS expenditure changes are produced largely through disease areas in which individuals from more deprived areas account for a large share of health care utilization, namely, respiratory and neurologic disease and mental health. We estimate that 26% of the total health effect from a change in expenditure would accrue to the fifth of the population living in the most deprived areas, compared with 14% to the fifth living in the least deprived areas. Conclusions. Our approach can be useful for evaluating the health inequality impacts of changing health budgets or funding alternative health programs. However, it requires robust estimates of how health expenditure affects health outcomes. Our example analysis also relied on strong assumptions about the relationship between health care utilization and health effects across population groups.

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

INTRODUCTION: Reduction of health inequality is a goal in health policy, but commissioners lack information on how policies change health inequality. This study illustrates how decision models can be readily extended to produce information on health inequality impacts as well as for population health, using the example of smoking cessation therapies. METHODS: We retrospectively adapt a model developed for public health guidance to undertake distributional cost effectiveness analysis. We identify and incorporate evidence on how inputs vary by area-level deprivation. Therapies are evaluated in terms of total population health, extent of inequality, and a summary measure of equally distributed equivalent health based on a societal value for inequality aversion. Last, we examine how accounting for social variation in different sets of parameters affects our results. RESULTS: All interventions increase population health and increase the slope index ofinequality. At estimated levels of health inequality aversion for England, our resultsindicate that the increases in inequality are compensated by the health gains. DISCUSSION: The inequality impacts are driven by higher benefits of quitting and higher intervention uptake amongst advantaged groups, despite the greater proportion of smokers in disadvantaged groups. Failure to account for differential effects between groups leadsto different conclusions about health inequality impact but does not alter conclusionsabout value for money.

Evaluation of Intervention Impact on Health Inequality for Resource Allocation.

INTRODUCTION: We describe a simplified distributional cost-effectiveness analysis based on aggregate data to estimate the health inequality impact of public health interventions. METHODS: We extracted data on costs, health outcomes expressed as quality-adjusted life years (QALYs), and target populations for interventions within National Institute for Health and Care Excellence (NICE) public health guidance published up to October 2016. Evidence on variation by age, gender, and index of multiple deprivation informed socioeconomic distributions of incremental QALYs, health opportunity costs, and the baseline distribution of health. Total population QALYs, summary measures of inequality, and a health equity impact plane show results by intervention and by guideline. A value for inequality aversion from a general population survey in England let us combine impacts on health inequality and total health into a single measure of intervention value. RESULTS: Our estimates suggest that of 134 interventions considered by NICE, 70 (52%) reduce inequality and increase health, 21 (16%) involve a tradeoff between improving health and improving health inequality, and 43 (32%) reduce health and increase health inequality. Fully implemented, the potential impact of all recommendations was 23,336,181 additional QALYs for the population of England and Wales and a reduction of the gap in quality-adjusted life expectancy between the healthiest and least healthy from 13.78 to 13.34 QALYs. The combined value of the additional health and reduction in inequality was 28,723,776 QALYs. DISCUSSION: Our analysis takes account of the fact that existing public health spending likely benefits the most disadvantaged. This simple method applied separately to economic evaluation produces evidence of intervention impacts on the distribution of health that is vital in determining value for money when health inequality reduction is a policy goal.

Correction to: The Future of Precision Medicine: Potential Impacts for Health Technology Assessment.

The article The Future of Precision Medicine: Potential Impacts for Health Technology Assessment written by James Love­Koh, Alison Peel Juan, Carlos Rejon­Parrilla, KateAnastasia Chalkidou, Hannah Wood, Matthew Taylor was originally published electronically on the publisher's internet portal (currently Springer Link) on [13th July, 2018] with incorrect spelling of the co-author "Juan Carlos Rejon-Parilla". The correct spelling is "Juan Carlos Rejon-Parrilla".

The Future of Precision Medicine: Potential Impacts for Health Technology Assessment.

OBJECTIVE: Precision medicine allows healthcare interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information, or treatment response. We analysed what developments are expected in precision medicine over the next decade and considered the implications for health technology assessment (HTA) agencies. METHODS: We performed a pragmatic literature search to account for the large size and wide scope of the precision medicine literature. We refined and enriched these results with a series of expert interviews up to 1 h in length, including representatives from HTA agencies, research councils and researchers designed to cover a wide spectrum of precision medicine applications and research. RESULTS: We identified 31 relevant papers and interviewed 13 experts. We found that three types of precision medicine are expected to emerge in clinical practice: complex algorithms, digital health applications and 'omics'-based tests. These are expected to impact upon each stage of the HTA process, from scoping and modelling through to decision-making and review. The complex and uncertain treatment pathways associated with patient stratification and fast-paced technological innovation are central to these effects. DISCUSSION: Innovation in precision medicine promises substantial benefits but will change the way in which some health services are delivered and evaluated. The shelf life of guidance may decrease, structural uncertainty may increase and new equity considerations will emerge. As biomarker discovery accelerates and artificial intelligence-based technologies emerge, refinements to the methods and processes of evidence assessments will help to adapt and maintain the objective of investing in healthcare that is value for money.