Publications

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.

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.

Background There are substantial concerns about GP workload. The Quality and Outcomes Framework (QOF) has been perceived by both professionals and patients as bureaucratic, but the full impact of the QOF on GP workload is not well known. Aim To assess the impact of the QOF on GP consultation rates for patients with diabetes mellitus. Design and setting This study used interrupted time series of 13 248 745 general practice consultations for 37 065 patients with diabetes mellitus in England. Method Clinical Practice Research Datalink general practice data were used from 2000/2001 to 2014/2015, with introduction of the QOF (1 April 2004) as the intervention, and mean annual GP consultation rates as the primary outcome. Results Mean annual GP clinical consultation rates were 8.10 per patient in 2000/2001, 6.91 in 2004/2005, and 7.09 in 2014/2015. Introduction of the QOF was associated with an annual change in the trend of GP clinical consultation rates of 0.46 (95% confidence interval [CI] = 0.23 to 0.69, P = 0.001) consultations per patient, giving a post-QOF trend increasing by 0.018 consultations per year. Introduction of the QOF was associated with an immediate stepped increase of 'other' out-of-hours and non-clinical encounters, and trend change of 0.57 (95% CI = 0.34 to 0.81, P<0.001) per year, resulting in a post-QOF trend increasing by 0.27 other encounters per year. Conclusion Introduction of the QOF was associated with a modest increase in clinical GP consultation rates and substantial increase in other encounters for patients with diabetes independent of changes in diabetes prevalence. National prevalence of diabetes increased by 90.7% from 2004/2005 to 2014/2015, which, combined with this study's findings, means GPs would have provided nearly double the number of consultations for patients with diabetes over this timescale.

OBJECTIVE: Although U.K. and international guidelines recommend monotherapy, antipsychotic polypharmacy among patients with serious mental illness is common in clinical practice. However, empirical evidence on its effectiveness is scarce. Therefore, the authors estimated the effectiveness of antipsychotic polypharmacy relative to monotherapy in terms of health care utilization and mortality.

METHODS: Primary care data from Clinical Practice Research Datalink, hospital data from Hospital Episode Statistics, and mortality data from the Office of National Statistics were linked to compile a cohort of patients with serious mental illness in England from 2000 to 2014. The antipsychotic prescribing profile of 17,255 adults who had at least one antipsychotic drug record during the period of observation was constructed from primary care medication records. Survival analysis models were estimated to identify the effect of antipsychotic polypharmacy on the time to first occurrence of each of three outcomes: unplanned hospital admissions (all cause), emergency department (ED) visits, and mortality.

RESULTS: Relative to monotherapy, antipsychotic polypharmacy was not associated with increased risk of unplanned hospital admission (hazard ratio [HR]=1.14; 95% confidence interval [CI]=0.98-1.32), ED visit (HR=0.95; 95% CI=0.80-1.14), or death (HR=1.02; 95% CI=0.76-1.37). Relative to not receiving antipsychotic medication, monotherapy was associated with a reduced hazard of unplanned admissions to the hospital and ED visits, but it had no effect on mortality.

CONCLUSIONS: The study results support current guidelines for antipsychotic monotherapy in routine clinical practice. However, they also suggest that when clinicians have deemed antipsychotic polypharmacy necessary, health care utilization and mortality are not affected.

Background: Since the mid-1990s, excess mortality has increased markedly for adults aged 25–44 years in the north compared with the south of England. We examined the underlying causes of this excess mortality and the contribution of socioeconomic deprivation. Methods: Mortality data from the Office of National Statistics for adults aged 25–44 years were aggregated and compared between England's five most northern versus five most southern government office regions between Jan 1, 1981, and Dec 31, 2016. Poisson regression models, adjusted for age and sex, were used to quantify excess mortality in the north compared with the south by underlying cause of death (accidents, alcohol related, cardiovascular disease and diabetes, drug related, suicide, cancer, and other causes). The role of socioeconomic deprivation, as measured by the 2015 Index of Multiple Deprivation, in explaining the excess and regional variability was also explored. Findings: A mortality divide between the north and south appeared in the mid-1990s and rapidly expanded thereafter for deaths attributed to accidents, alcohol misuse, and drug misuse. In the 2014–16 period, the northern excess was incidence rate ratio (IRR) 1·47 (95% CI 1·39–1·54) for cardiovascular reasons, 2·09 (1·94–2·25) for alcohol misuse, and 1·60 (1·51–1·70) for drug misuse, across both men and women aged 25–44 years. National mortality rates for cardiovascular deaths declined over the study period but a longstanding gap between north and south persisted (from 33·3 [95% CI 31·8–34·8] in 1981 to 15·0 [14·0–15·9] in 2016 in the north vs from 23·5 [22·3–24·8] to 9·9 [9·2–10·5] in the south). Between 2014 and 2016, estimated excess numbers of death in the north versus the south for those aged 25–44 years were 1881 (95% CI 726–2627) for women and 3530 (2216–4511) for men. Socioeconomic deprivation explained up to two-thirds of the excess mortality in the north (IRR for northern effect reduced from 1·15 [95% CI 1·14–1·15; unadjusted] to 1·05 [1·04–1·05; adjusted for Index of Multiple Deprivation]). By 2016, in addition to the persistent north–south gap, mortality rates in London were lower than in all other regions, with IRRs ranging from IRR 1·13 (95% CI 1·12–1·15) for the East England to 1·22 (1·20–1·24) for the North East, even after adjusting for deprivation. Interpretation: Sharp relative rises in deaths from cardiovascular reasons, alcohol misuse and drug misuse in the north compared with the south seem to have created new health divisions between England's regions. This gap might be due to exacerbation of existing social and health inequalities that have been experienced for many years. These divisions might suggest increasing psychological distress, despair, and risk taking among young and middle-aged adults, particularly outside of London. Funding: Medical Research Council and Wellcome Trust.

Background: Introduced in 2004, the United Kingdom's (UK) Quality and Outcomes Framework (QOF) is the world's largest primary-care pay-for-performance programme. Given some evidence of the benefits and the substantial costs associated with the QOF, it remains unclear whether the programme is cost-effective. Therefore, we assessed the cost-effectiveness of continuing versus stopping the QOF. Methods: We developed a lifetime simulation model to estimate quality-adjusted life years (QALYs) and costs for a UK population cohort aged 40-74years (n=27,070,862) exposed to the QOF and for a counterfactual scenario without exposure. Based on a previous retrospective cross-country analysis using data from 1994 to 2010, we assumed the benefits of the QOF to be a change in age-adjusted mortality of -3.68 per 100,000 population (95% confidence interval -8.16 to 0.80). We used cost-effectiveness thresholds of £30,000/QALY, £20,000/QALY and £13,000/QALY to determine the optimal strategy in base-case and sensitivity analyses. Results: In the base-case analysis, continuing the QOF increased population-level QALYs and health-care costs yielding an incremental cost-effectiveness ratio (ICER) of £49,362/QALY. The ICER remained >£30,000/QALY in scenarios with and without non-fatal outcomes or increased drug costs, and under differing assumptions about the duration of QOF benefit following its hypothetical discontinuation. The ICER for continuing the programme fell below £30,000/QALY when QOF incentive payments were 36% lower (while preserving QOF mortality benefits), and in scenarios where the QOF resulted in substantial reductions in health-care spending or non-fatal cardiovascular disease events. Continuing the QOF was cost-effective in 18%, 3% and 0% of probabilistic sensitivity analysis iterations using thresholds of £30,000/QALY, £20,000/QALY and £13,000/QALY, respectively. Conclusions: Compared to stopping the QOF and returning all associated incentive payments to the National Health Service, continuing the QOF is not cost-effective. To improve population health efficiently, the UK should redesign the QOF or pursue alternative interventions.

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.

Reducing health inequality is a major policy concern for low- and middle-income countries (LMICs) on the path to universal health coverage. However, health inequality impacts are rarely quantified in cost-effectiveness analyses of health programmes. Distributional cost-effectiveness analysis (DCEA) is a method developed to analyse the expected social distributions of costs and health benefits, and the potential trade-offs that may exist between maximising total health and reducing health inequality. This is the first paper to show how DCEA can be applied in LMICs. Using the introduction of rotavirus vaccination in Ethiopia as an illustrative example, we analyse a hypothetical re-designed vaccination programme, which invests additional resources into vaccine delivery in rural areas, and compare this with the standard programme currently implemented in Ethiopia. We show that the re-designed programme has an incremental cost-effectiveness ratio of US$69 per health-adjusted life year (HALY) compared with the standard programme. This is potentially cost-ineffective when compared with current estimates of health opportunity cost in Ethiopia. However, rural populations are typically less wealthy than urban populations and experience poorer lifetime health. Prioritising such populations can thus be seen as being equitable. We analyse the trade-off between cost-effectiveness and equity using the Atkinson inequality aversion parameter, ε, representing the decision maker's strength of concern for reducing health inequality. We find that the more equitable programme would be considered worthwhile by a decision maker whose inequality concern is greater than ε = 5.66, which at current levels of health inequality in Ethiopia implies that health gains are weighted at least 3.86 times more highly in the poorest compared with the richest wealth quintile group. We explore the sensitivity of this conclusion to a range of assumptions and cost-per-HALY threshold values, to illustrate how DCEA can inform the thinking of decision makers and stakeholders about health equity trade-offs.

BACKGROUND: It is not known whether equity-oriented primary care investment that seeks to scale up the delivery of effective care in disadvantaged communities can reduce health inequality within high-income settings that have pre-existing universal primary care systems. We provide some non-randomised controlled evidence by comparing health inequality trends between two similar jurisdictions-one of which implemented equity-oriented primary care investment in the mid-to-late 2000s as part of a cross-government strategy for reducing health inequality (England), and one which invested in primary care without any explicit equity objective (Ontario, Canada).

METHODS: We analysed whole-population data on 32,482 neighbourhoods (with mean population size of approximately 1,500 people) in England, and 18,961 neighbourhoods (with mean population size of approximately 700 people) in Ontario. We examined trends in mortality amenable to healthcare by decile groups of neighbourhood deprivation within each jurisdiction. We used linear models to estimate absolute and relative gaps in amenable mortality between most and least deprived groups, considering the gradient between these extremes, and evaluated difference-in-difference comparisons between the two jurisdictions.

RESULTS: Inequality trends were comparable in both jurisdictions from 2004-6 but diverged from 2007-11. Compared with Ontario, the absolute gap in amenable mortality in England fell between 2004-6 and 2007-11 by 19.8 per 100,000 population (95% CI: 4.8 to 34.9); and the relative gap in amenable mortality fell by 10 percentage points (95% CI: 1 to 19). The biggest divergence occurred in the most deprived decile group of neighbourhoods.

DISCUSSION: In comparison to Ontario, England succeeded in reducing absolute socioeconomic gaps in mortality amenable to healthcare from 2007 to 2011, and preventing them from growing in relative terms. Equity-oriented primary care reform in England in the mid-to-late 2000s may have helped to reduce socioeconomic inequality in health, though other explanations for this divergence are possible and further research is needed on the specific causal mechanisms.

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.

Long-range temporal choices are built into contemporary policy-making, with policy decisions having consequences that play out across generations. Decisions are made on behalf of the public who are assumed to give much greater weight to their welfare than to the welfare of future generations. The paper investigates this assumption. It briefly discusses evidence from sociological and economic studies before reporting the findings of a British survey of people's intergenerational time preferences based on a representative sample of nearly 10,000 respondents. Questions focused on two sets of policies: (i) health policies to save lives and (ii) environmental policies to protect against floods that would severely damage homes, businesses and other infrastructure. For both sets of policies, participants were offered a choice of three policy options, each bringing greater or lesser benefits to their, their children's and their grandchildren's generations. For both saving lives and protecting against floods, only a minority selected the policy that most benefited their generation; the majority selected policies bringing equal or greater benefits to future generations. Our study raises questions about a core assumption of standard economic evaluation, pointing instead to concern for future generations as a value that many people hold in common.

Objectives Reducing health inequalities is an explicit goal of England's health system. Our aim was to compare the performance of English local administrative areas in reducing socioeconomic inequality in emergency hospital admissions for ambulatory care sensitive chronic conditions. Methods We used local authority area as a stable proxy for health and long-term care administrative geography between 2004/5 and 2011/12. We linked inpatient hospital activity, deprivation, primary care, and population data to small area neighbourhoods (typical population 1500) within administrative areas (typical population 250,000). We measured absolute inequality gradients nationally and within each administrative area using neighbourhood-level linear models of the relationship between national deprivation and age-sex-adjusted emergency admission rates. We assessed local equity performance by comparing local inequality against national inequality to identify areas significantly more or less equal than expected; evaluated stability over time; and identified where equity performance was steadily improving or worsening. We then examined associations between change in socioeconomic inequalities and change in within-area deprivation (gentrification). Finally, we used administrative area-level random and fixed effects models to examine the contribution of primary care to inequalities in admissions. Results Data on 316 administrative areas were included in the analysis. Local inequalities were fairly stable between consecutive years, but 32 areas (10%) showed steadily improving or worsening equity. In the 21 improving areas, the gap between most and least deprived fell by 3.9 admissions per 1000 (six times the fall nationally) between 2004/5 and 2011/12, while in the 11 areas worsening, the gap widened by 2.4. There was no indication that measured improvements in local equity were an artefact of gentrification or that changes in primary care supply or quality contributed to changes in inequality. Conclusions Local equity performance in reducing inequality in emergency admissions varies both geographically and over time. Identifying this variation could provide insights into which local delivery strategies are most effective in reducing such inequalities.

BACKGROUND: Public sector austerity measures in many high-income countries mean that public health budgets are reducing year on year. To help inform the potential impact of these proposed disinvestments in public health, we set out to determine the return on investment (ROI) from a range of existing public health interventions.

METHODS: We conducted systematic searches on all relevant databases (including MEDLINE; EMBASE; CINAHL; AMED; PubMed, Cochrane and Scopus) to identify studies that calculated a ROI or cost-benefit ratio (CBR) for public health interventions in high-income countries.

RESULTS: We identified 2957 titles, and included 52 studies. The median ROI for public health interventions was 14.3 to 1, and median CBR was 8.3. The median ROI for all 29 local public health interventions was 4.1 to 1, and median CBR was 10.3. Even larger benefits were reported in 28 studies analysing nationwide public health interventions; the median ROI was 27.2, and median CBR was 17.5.

CONCLUSIONS: This systematic review suggests that local and national public health interventions are highly cost-saving. Cuts to public health budgets in high income countries therefore represent a false economy, and are likely to generate billions of pounds of additional costs to health services and the wider economy.

The use of financial incentives to improve quality in health care has become widespread. Yet evidence on the effectiveness of incentives suggests that they have generally had limited impact on the value of care and have not led to better patient outcomes. Lessons from social psychology and behavioral economics indicate that incentive programs in health care have not been effectively designed to achieve their intended impact. In the United States, Medicare's Hospital Readmission Reduction Program and Hospital Value- Based Purchasing Program, created under the Affordable Care Act (ACA), provide evidence on how variations in the design of incentive programs correspond with differences in effect. As financial incentives continue to be used as a tool to increase the value and quality of health care, improving the design of programs will be crucial to ensure their success. Expected final online publication date for the Annual Review of Public Health Volume 38 is March 20, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

BACKGROUND: Studies finding higher mortality rates for patients admitted to hospital at weekends rely on routine administrative data to adjust for risk of death, but these data may not adequately capture severity of illness. We examined how rates of patient arrival at accident and emergency (A&E) departments by ambulance-a marker of illness severity-were associated with in-hospital mortality by day and time of attendance.

METHODS: Retrospective observational study of 3 027 946 admissions to 140 non-specialist hospital trusts in England between April 2013 and February 2014. Patient admissions were linked with A&E records containing mode of arrival and date and time of attendance. We classified arrival times by day of the week and daytime (07:00 to 18:59) versus night (19:00 to 06:59 the following day). We examined the association with in-hospital mortality within 30 days using multivariate logistic regression.

RESULTS: Over the week, 20.9% of daytime arrivals were in the highest risk quintile compared with 18.5% for night arrivals. Daytime arrivals on Sundays contained the highest proportion of patients in the highest risk quintile at 21.6%. Proportions of admitted patients brought in by ambulance were substantially higher at night and higher on Saturday (61.1%) and Sunday (60.1%) daytimes compared with other daytimes in the week (57.0%). Without adjusting for arrival by ambulance, risk-adjusted mortality for patients arriving at night was higher than for daytime attendances on Wednesday (0.16 percentage points). Compared with Wednesday daytime, risk-adjusted mortality was also higher on Thursday night (0.15 percentage points) and increased throughout the weekend from Saturday daytime (0.16 percentage points) to Sunday night (0.26 percentage points). After adjusting for arrival by ambulance, the raised mortality only reached statistical significance for patients arriving at A&E on Sunday daytime (0.17 percentage points).

CONCLUSION: Using conventional risk-adjustment methods, there appears to be a higher risk of mortality following emergency admission to hospital at nights and at weekends. After accounting for mode of arrival at hospital, this pattern changes substantially, with no increased risk of mortality following admission at night or for any period of the weekend apart from Sunday daytime. This suggests that risk-adjustment based on inpatient administrative data does not adequately account for illness severity and that elevated mortality at weekends and at night reflects a higher proportion of more severely ill patients arriving by ambulance at these times.

OBJECTIVES: The UK's Quality and Outcomes Framework permits practices to exempt patients from financially-incentivised performance targets. To better understand the determinants and consequences of being exempted from the framework, we investigated the associations between exception reporting, patient characteristics and mortality. We also quantified the proportion of exempted patients that met quality targets for a tracer condition (diabetes).

DESIGN: Retrospective longitudinal study, using individual patient data from the Clinical Practice Research Datalink.

SETTING: 644 general practices, 2006/7 to 2011/12.

PARTICIPANTS: Patients registered with study practices for at least one year over the study period, with at least one condition of interest (2 460 341 in total).

MAIN OUTCOME MEASURES: Exception reporting rates by reason (clinical contraindication, patient dissent); all-cause mortality in year following exemption. Analyses with logistic and Cox proportional-hazards regressions, respectively.

RESULTS: The odds of being exempted increased with age, deprivation and multimorbidity. Men were more likely to be exempted but this was largely attributable to higher prevalence of conditions with high exemption rates. Modest associations remained, with women more likely to be exempted due to clinical contraindication (OR 0.90, 99% CI 0.88 to 0.92) and men more likely to be exempted due to informed dissent (OR 1.08, 99% CI 1.06 to 1.10). More deprived areas (both for practice location and patient residence) were non-linearly associated with higher exception rates, after controlling for comorbidities and other covariates, with stronger associations for clinical contraindication. Compared with patients with a single condition, odds ratios for patients with two, three, or four or more conditions were respectively 4.28 (99% CI 4.18 to 4.38), 16.32 (99% CI 15.82 to 16.83) and 68.69 (99% CI 66.12 to 71.37) for contraindication, and 2.68 (99% CI 2.63 to 2.74), 4.02 (99% CI 3.91 to 4.13) and 5.17 (99% CI 5.00 to 5.35) for informed dissent. Exempted patients had a higher adjusted risk of death in the following year than non-exempted patients, regardless of whether this exemption was for contraindication (hazard ratio 1.37, 99% CI 1.33 to 1.40) or for informed dissent (1.20, 99% CI 1.17 to 1.24). On average, quality standards were met for 48% of exempted patients in the diabetes domain, but there was wide variation across indicators (ranging from 8 to 80%).

CONCLUSIONS: Older, multimorbid and more deprived patients are more likely to be exempted from the scheme. Exception reported patients are more likely to die in the following year, whether they are exempted by the practice for a contraindication or by themselves through informed dissent. Further research is needed to understand the relationship between exception reporting and patient outcomes.

A number of OECD countries have introduced waiting time prioritisation policies which give explicit priority to severely ill patients with high marginal disutility of waiting. There is however little empirical evidence on how patients are actually prioritised. We exploit a unique opportunity to investigate this issue using a large national dataset with accurate measures of severity on nearly 400,000 patients. We link data from a national patient-reported outcome measures survey to administrative data on all patients waiting for a publicly funded hip and knee replacement in England during the years 2009-14. We find that patients suffering the most severe pain and immobility have shorter waits than those suffering the least, by about 24% for hip replacement and 11% for knee replacement, and that the association is approximately linear. These differentials are more closely associated with pain than immobility, and are larger in hospitals with longer average waiting times. These result suggests that doctors prioritise patients according to severity even when no formal prioritisation policy is in place and average waiting times are short.

BACKGROUND: There are substantial socioeconomic inequalities in both life expectancy and healthcare use in England. In this study, we describe how these two sets of inequalities interact by estimating the social gradient in hospital costs across the life course.

METHODS: Hospital episode statistics, population and index of multiple deprivation data were combined at lower-layer super output area level to estimate inpatient hospital costs for 2011/2012 by age, sex and deprivation quintile. Survival curves were estimated for each of the deprivation groups and used to estimate expected annual costs and cumulative lifetime costs.

RESULTS: A steep social gradient was observed in overall inpatient hospital admissions, with rates ranging from 31 298/100 000 population in the most affluent fifth of areas to 43 385 in the most deprived fifth. This gradient was steeper for emergency than for elective admissions. The total cost associated with this inequality in 2011/2012 was £4.8 billion. A social gradient was also observed in the modelled lifetime costs where the lower life expectancy was not sufficient to outweigh the higher average costs in the more deprived populations. Lifetime costs for women were 14% greater than for men, due to higher costs in the reproductive years and greater life expectancy.

CONCLUSIONS: Socioeconomic inequalities result in increased morbidity and decreased life expectancy. Interventions to reduce inequality and improve health in more deprived neighbourhoods have the potential to save money for health systems not only within years but across peoples' entire lifetimes, despite increased costs due to longer life expectancies.

BACKGROUND: There are substantial socioeconomic inequalities in both life expectancy and healthcare use in England. In this study, we describe how these two sets of inequalities interact by estimating the social gradient in hospital costs across the life course.

METHODS: Hospital episode statistics, population and index of multiple deprivation data were combined at lower-layer super output area level to estimate inpatient hospital costs for 2011/2012 by age, sex and deprivation quintile. Survival curves were estimated for each of the deprivation groups and used to estimate expected annual costs and cumulative lifetime costs.

RESULTS: A steep social gradient was observed in overall inpatient hospital admissions, with rates ranging from 31 298/100 000 population in the most affluent fifth of areas to 43 385 in the most deprived fifth. This gradient was steeper for emergency than for elective admissions. The total cost associated with this inequality in 2011/2012 was £4.8 billion. A social gradient was also observed in the modelled lifetime costs where the lower life expectancy was not sufficient to outweigh the higher average costs in the more deprived populations. Lifetime costs for women were 14% greater than for men, due to higher costs in the reproductive years and greater life expectancy.

CONCLUSIONS: Socioeconomic inequalities result in increased morbidity and decreased life expectancy. Interventions to reduce inequality and improve health in more deprived neighbourhoods have the potential to save money for health systems not only within years but across peoples' entire lifetimes, despite increased costs due to longer life expectancies.

BACKGROUND: The use of Electronic Health Records databases for medical research has become mainstream. In the UK, increasing use of Primary Care Databases is largely driven by almost complete computerisation and uniform standards within the National Health Service. Electronic Health Records research often begins with the development of a list of clinical codes with which to identify cases with a specific condition. We present a methodology and accompanying Stata and R commands (pcdsearch/Rpcdsearch) to help researchers in this task. We present severe mental illness as an example.

METHODS: We used the Clinical Practice Research Datalink, a UK Primary Care Database in which clinical information is largely organised using Read codes, a hierarchical clinical coding system. Pcdsearch is used to identify potentially relevant clinical codes and/or product codes from word-stubs and code-stubs suggested by clinicians. The returned code-lists are reviewed and codes relevant to the condition of interest are selected. The final code-list is then used to identify patients.

RESULTS: We identified 270 Read codes linked to SMI and used them to identify cases in the database. We observed that our approach identified cases that would have been missed with a simpler approach using SMI registers defined within the UK Quality and Outcomes Framework.

CONCLUSION: We described a framework for researchers of Electronic Health Records databases, for identifying patients with a particular condition or matching certain clinical criteria. The method is invariant to coding system or database and can be used with SNOMED CT, ICD or other medical classification code-lists.

Objective Primary percutaneous coronary intervention (PPCI) for ST-elevation myocardial infarction (STEMI) is insufficiently implemented in many countries. We investigated patient and hospital characteristics associated with PPCI utilisation. Methods Whole country registry data (MINAP, Myocardial Ischaemia National Audit Project) comprising PPCI-capable National Health Service trusts in England (84 hospital trusts; 92 350 hospitalisations; 90 489 patients), 2003-2013. Multilevel Poisson regression modelled the relationship between incidence rate ratios (IRR) of PPCI and patient and trust-level factors. Results Overall, standardised rates of PPCI increased from 0.01% to 86.3% (2003-2013). While, on average, there was a yearly increase in PPCI utilisation of 30% (adjusted IRR 1.30, 95% CI 1.23 to 1.36), it varied substantially between trusts. PPCI rates were lower for patients with previous myocardial infarction (0.95, 0.93 to 0.98), heart failure (0.86, 0.81 to 0.92), angina (0.96, 0.94 to 0.98), diabetes (0.97, 0.95 to 0.99), chronic renal failure (0.89, 0.85 to 0.90), cerebrovascular disease (0.96, 0.93 to 0.99), age >80 years (0.87, 0.85 to 0.90), and travel distances >30 km (0.95, 0.93 to 0.98). PPCI rates were higher for patients with previous percutaneous coronary intervention (1.09, 1.05 to 1.12) and among trusts with >5 interventional cardiologists (1.30, 1.25 to 1.34), more visiting interventional cardiologists (1-5: 1.31, 1.26 to 1.36; ≥6: 1.42, 1.35 to 1.49), and a 24 h, 7-days-a-week PPCI service (2.69, 2.58 to 2.81). Half of the unexplained variation in PPCI rates was due to between-trust differences. Conclusions Following an 8 year implementation phase, PPCI utilisation rates stabilised at 85%. However, older and sicker patients were less likely to receive PPCI and there remained between-trust variation in PPCI rates not attributable to differences in staffing levels. Compliance with clinical pathways for STEMI is needed to ensure more equitable quality of care.

Background Provision of universal coverage is essential for achieving equity in healthcare, but inequalities still exist in universal healthcare systems. Between 2004/2005 and 2011/2012, the National Health Service (NHS) in England, which has provided universal coverage since 1948, made sustained efforts to reduce health inequalities by strengthening primary care. We provide the first comprehensive assessment of trends in socioeconomic inequalities of primary care access, quality and outcomes during this period. Methods Whole-population small area longitudinal study based on 32 482 neighbourhoods of approximately 1500 people in England from 2004/2005 to 2011/2012. We measured slope indices of inequality in four indicators: (1) patients per family doctor, (2) primary care quality, (3) preventable emergency hospital admissions and (4) mortality from conditions considered amenable to healthcare. Results Between 2004/2005 and 2011/2012, there were larger absolute improvements on all indicators in more-deprived neighbourhoods. The modelled gap between the most-deprived and least-deprived neighbourhoods in England decreased by: 193 patients per family doctor (95% CI 173 to 213), 3.29 percentage points of primary care quality (3.13 to 3.45), 0.42 preventable hospitalisations per 1000 people (0.29 to 0.55) and 0.23 amenable deaths per 1000 people (0.15 to 0.31). By 2011/2012, inequalities in primary care supply and quality were almost eliminated, but socioeconomic inequality was still associated with 158 396 preventable hospitalisations and 37 983 deaths amenable to healthcare. Conclusions Between 2004/2005 and 2011/2012, the NHS succeeded in substantially reducing socioeconomic inequalities in primary care access and quality, but made only modest reductions in healthcare outcome inequalities.

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 interventions, and 2) evaluating social distributions of health with respect to the dual objectives of improving total population health and reducing unfair health inequality. As well as describing the technical methods used, we also identify the data requirements and the social value judgments that have to be made. Finally, we demonstrate the use of sensitivity analyses to explore the impacts of alternative modeling assumptions and social value judgments.

OBJECTIVE: To measure changes in socioeconomic inequality in the distribution of family physicians (general practitioners (GPs)) relative to need in England from 2004/2005 to 2013/2014.

DESIGN: Whole-population small area longitudinal data linkage study.

SETTING: England from 2004/2005 to 2013/2014.

PARTICIPANTS: 32 482 lower layer super output areas (neighbourhoods of 1500 people on average).

MAIN OUTCOME MEASURES: Slope index of inequality (SII) between the most and least deprived small areas in annual full-time equivalent GPs (FTE GPs) per 100 000 need adjusted population.

RESULTS: In 2004/2005, inequality in primary care supply as measured by the SII in FTE GPs was 4.2 (95% CI 3.1 to 5.3) GPs per 100 000. By 2013/2014, this SII had fallen to -0.7 (95% CI -2.5 to 1.1) GPs per 100 000. The number of FTE GPs per 100 000 serving the most deprived fifth of small areas increased over this period from 54.0 to 60.5, while increasing from 57.2 to 59.9 in the least deprived fifth, so that by the end of the study period there were more GPs per 100 000 need adjusted population in the most deprived areas than in the least deprived. The increase in GP supply in the most deprived fifth of neighbourhoods was larger in areas that received targeted investment for establishing new practices under the 'Equitable Access to Primary Medical Care'.

CONCLUSIONS: There was a substantial reduction in socioeconomic inequality in family physician supply associated with national policy. This policy may not have completely eliminated socioeconomic inequality in family physician supply since existing need adjustment formulae do not fully capture the additional burden of multimorbidity in deprived neighbourhoods. The small area approach introduced in this study can be used routinely to monitor socioeconomic inequality of access to primary care and to indicate workforce shortages in particular neighbourhoods. http://creativecommons.org/licenses/by/4.0.

OBJECTIVES: Little is known about service utilisation by patients with severe mental illness (SMI) in UK primary care. We examined their consultation rate patterns and whether they were impacted by the introduction of the Quality and Outcomes Framework (QOF), in 2004.

DESIGN: Retrospective cohort study using individual patient data collected from 2000 to 2012.

SETTING: 627 general practices contributing to the Clinical Practice Research Datalink, a large UK primary care database.

PARTICIPANTS: SMI cases (346,551) matched to 5 individuals without SMI (1,732,755) on age, gender and general practice.

OUTCOME MEASURES: Consultation rates were calculated for both groups, across 3 types: face-to-face (primary outcome), telephone and other (not only consultations but including administrative tasks). Poisson regression analyses were used to identify predictors of consultation rates and calculate adjusted consultation rates. Interrupted time-series analysis was used to quantify the effect of the QOF.

RESULTS: Over the study period, face-to-face consultations in primary care remained relatively stable in the matched control group (between 4.5 and 4.9 per annum) but increased for people with SMI (8.8-10.9). Women and older patients consulted more frequently in the SMI and the matched control groups, across all 3 consultation types. Following the introduction of the QOF, there was an increase in the annual trend of face-to-face consultation for people with SMI (average increase of 0.19 consultations per patient per year, 95% CI 0.02 to 0.36), which was not observed for the control group (estimates across groups statistically different, p=0.022).

CONCLUSIONS: The introduction of the QOF was associated with increases in the frequency of monitoring and in the average number of reported comorbidities for patients with SMI. This suggests that the QOF scheme successfully incentivised practices to improve their monitoring of the mental and physical health of this group of patients.

OBJECTIVES: Little is known about the prevalence of comorbidity rates in people with severe mental illness (SMI) in UK primary care. We calculated the prevalence of SMI by UK country, English region and deprivation quintile, antipsychotic and antidepressant medication prescription rates for people with SMI, and prevalence rates of common comorbidities in people with SMI compared with people without SMI.

DESIGN: Retrospective cohort study from 2000 to 2012.

SETTING: 627 general practices contributing to the Clinical Practice Research Datalink, a UK primary care database.

PARTICIPANTS: Each identified case (346,551) was matched for age, sex and general practice with 5 randomly selected control cases (1,732,755) with no diagnosis of SMI in each yearly time point.

OUTCOME MEASURES: Prevalence rates were calculated for 16 conditions.

RESULTS: SMI rates were highest in Scotland and in more deprived areas. Rates increased in England, Wales and Northern Ireland over time, with the largest increase in Northern Ireland (0.48% in 2000/2001 to 0.69% in 2011/2012). Annual prevalence rates of all conditions were higher in people with SMI compared with those without SMI. The discrepancy between the prevalence of those with and without SMI increased over time for most conditions. A greater increase in the mean number of additional conditions was observed in the SMI population over the study period (0.6 in 2000/2001 to 1.0 in 2011/2012) compared with those without SMI (0.5 in 2000/2001 to 0.6 in 2011/2012). For both groups, most conditions were more prevalent in more deprived areas, whereas for the SMI group conditions such as hypothyroidism, chronic kidney disease and cancer were more prevalent in more affluent areas.

CONCLUSIONS: Our findings highlight the health inequalities faced by people with SMI. The provision of appropriate timely health prevention, promotion and monitoring activities to reduce these health inequalities are needed, especially in deprived areas.

PURPOSE: The purpose of this study was to examine the association between the prevalence of both diabetes-concordant and diabetes-discordant conditions and the quality of diabetes care at the family practice level in England. We hypothesized that the prevalence of concordant (or discordant) conditions would be associated with better (or worse) quality of diabetes care.

METHODS: We conducted a cross-sectional study using practice-level data (7,884 practices). We estimated the practice-level prevalence of diabetes and 15 other chronic conditions, which were classified as diabetes concordant (ie, with the same pathophysiologic risk profile and therefore more likely to be part of the same management plan) or diabetes discordant (ie, not directly related in either their pathogenesis or management). We measured quality of diabetes care with diabetes-specific indicators (8 processes and 3 intermediate outcomes of care). We used linear regression models to quantify the effect of the prevalence of the conditions on aggregate achievement rate for quality of diabetes care.

RESULTS: Consistent with the proposed model, the prevalence rates of 4 of 7 concordant conditions (obesity, chronic kidney disease, atrial fibrillation, heart failure) were positively associated with quality of diabetes care. Similarly, negative associations were observed as predicted for 2 of the 8 discordant conditions (epilepsy, mental health). Observations for other concordant and discordant conditions did not match predictions in the hypothesized model.

CONCLUSIONS: The quality of diabetes care provided in English family practices is associated with the prevalence of other major chronic conditions at the practice level. The nature and direction of the observed associations cannot be fully explained by the concordant-discordant model.

Objective Chile, a South American country recently defined as a high-income nation, carried out a major healthcare system reform from 2005 onwards that aimed at reducing socioeconomic inequality in health. This study aimed to estimate income-related inequality in self-reported health status (SRHS) in 2000 and 2013, before and after the reform, for the entire adult Chilean population. Methods Using data on equivalized household income and adult SRHS from the 2000 and 2013 CASEN surveys (independent samples of 101 046 and 172 330 adult participants, respectively) we estimated Erreygers concentration indices (CIs) for above average SRHS for both years. We also decomposed the contribution of both "legitimate" standardizing variables (age and sex) and "illegitimate" variables (income, education, occupation, ethnicity, urban/rural, marital status, number of people living in the household, and healthcare entitlement). Results There was a significant concentration of above average SRHS favoring richer people in Chile in both years, which was less pronounced in 2013 than 2000 (Erreygers corrected CI 0.165 [Standard Error, SE 0.007] in 2000 and 0.047 [SE 0.008] in 2013). To help interpret the magnitude of this decline, adults in the richest fifth of households were 33% more likely than those in the poorest fifth to report above-average health in 2000, falling to 11% in 2013. In 2013, the contribution of illegitimate factors to income-related inequality in SRHS remained higher than the contribution of legitimate factors. Conclusions Income-related inequality in SRHS in Chile has fallen after the equity-based healthcare reform. Further research is needed to ascertain how far this fall in health inequality can be attributed to the 2005 healthcare reform as opposed to economic growth and other determinants of health that changed during the period.

The English National Health Service is moving towards providing comprehensive 7-day hospital services in response to higher death rates for emergency weekend admissions. Using Hospital Episode Statistics between 1st April 2010 and 31st March 2011 linked to all-cause mortality within 30 days of admission, we estimate the number of excess deaths and the loss in quality-adjusted life years associated with emergency weekend admissions. The crude 30-day mortality rate was 3.70% for weekday admissions and 4.05% for weekend admissions. The excess weekend death rate equates to 4355 (risk adjusted 5353) additional deaths each year. The health gain of avoiding these deaths would be 29 727-36 539 quality-adjusted life years per year. The estimated cost of implementing 7-day services is £1.07-£1.43 bn, which exceeds by £339-£831 m the maximum spend based on the National Institute for Health and Care Excellence threshold of £595 m-£731 m. There is as yet no clear evidence that 7-day services will reduce weekend deaths or can be achieved without increasing weekday deaths. The planned cost of implementing 7-day services greatly exceeds the maximum amount that the National Health Service should spend on eradicating the weekend effect based on current evidence. Policy makers and service providers should focus on identifying specific service extensions for which cost-effectiveness can be demonstrated. Copyright © 2015 John Wiley & Sons, Ltd.

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.

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.

BACKGROUND: Socioeconomic deprivation is associated with inequalities in health care and outcomes. Despite concerns that the Quality and Outcomes Framework pay-for-performance scheme in the UK would exacerbate inequalities in primary care delivery, gaps closed over time. Local schemes were promoted as a means of improving clinical engagement by addressing local health priorities. We evaluated equity in achievement of target indicators and practice income for one local scheme.

METHODS: We undertook a longitudinal survey over four years of routinely recorded clinical data for all 83 primary care practices. Sixteen indicators were developed that covered five local clinical and public health priorities: weight management; alcohol consumption; learning disabilities; osteoporosis; and chlamydia screening. Clinical indicators were logit transformed from a percentage achievement scale and modelled allowing for clustering of repeated measures within practices. This enabled our study of target achievements over time with respect to deprivation. Practice income was also explored.

RESULTS: Higher practice deprivation was associated with poorer performance for five indicators: alcohol use registration (OR 0.97; 95 % confidence interval 0.96,0.99); recorded chlamydia test result (OR 0.97; 0.94,0.99); osteoporosis registration (OR 0.98; 0.97,0.99); registration of repeat prednisolone prescription (OR 0.98; 0.96,0.99); and prednisolone registration with record of dual energy X-ray absorptiometry (DEXA) scan/referral (OR 0.92; 0.86,0.97); practices in deprived areas performed better for one indicator (registration of osteoporotic fragility fracture (OR 1.26; 1.04,1.51). The deprivation-achievement gap widened for one indicator (registered females aged 65-74 with a fracture referred for a DEXA scan; OR 0.97; 0.95,0.99). Two other indicators indicated a similar trend over two years before being withdrawn (registration of fragility fracture and over-75 s with a fragility fracture assessed and treated for osteoporosis risk). For one indicator the deprivation-achievement gap reduced over time (repeat prednisolone prescription (OR 1.01; 1.01,1.01). Larger practices and those serving more affluent areas earned more income per patient than smaller practices and those serving more deprived areas (t = -3.99; p =0.0001).

CONCLUSIONS: Any gaps in achievement between practices were modest but mostly sustained or widened over the duration of the scheme. Given that financial rewards may not reflect the amount of work undertaken by practices serving more deprived patients, future pay-for-performance schemes also need to address fairness of rewards in relation to workload.

OBJECTIVES: To quantify the relationship between a national primary care pay-for-performance programme, the UK's Quality and Outcomes Framework (QOF), and all-cause and cause-specific premature mortality linked closely with conditions included in the framework.

DESIGN: Longitudinal spatial study, at the level of the "lower layer super output area" (LSOA).

SETTING: 32482 LSOAs (neighbourhoods of 1500 people on average), covering the whole population of England (approximately 53.5 million), from 2007 to 2012.

PARTICIPANTS: 8647 English general practices participating in the QOF for at least one year of the study period, including over 99% of patients registered with primary care.

INTERVENTION: National pay-for-performance programme incentivising performance on over 100 quality-of-care indicators.

MAIN OUTCOME MEASURES: All-cause and cause-specific mortality rates for six chronic conditions: diabetes, heart failure, hypertension, ischaemic heart disease, stroke, and chronic kidney disease. We used multiple linear regressions to investigate the relationship between spatially estimated recorded quality of care and mortality.

RESULTS: All-cause and cause-specific mortality rates declined over the study period. Higher mortality was associated with greater area deprivation, urban location, and higher proportion of a non-white population. In general, there was no significant relationship between practice performance on quality indicators included in the QOF and all-cause or cause-specific mortality rates in the practice locality.

CONCLUSIONS: Higher reported achievement of activities incentivised under a major, nationwide pay-for-performance programme did not seem to result in reduced incidence of premature death in the population.

AIMS/HYPOTHESIS: We aimed to describe the shape of observed relationships between risk factor levels and clinically important outcomes in type 2 diabetes after adjusting for multiple confounders.

METHODS: We used retrospective longitudinal data on 246,544 adults with type 2 diabetes from 600 practices in the Clinical Practice Research Datalink, 2006-2012. Proportional hazards regression models quantified the risks of mortality and microvascular or macrovascular events associated with four modifiable biological variables (HbA1c, systolic BP, diastolic BP and total cholesterol), while controlling for important patient and practice covariates.

RESULTS: U-shaped relationships were observed between all-cause mortality and levels of the four biometric risk factors. Lowest risks were associated with HbA1c 7.25-7.75% (56-61 mmol/mol), total cholesterol 3.5-4.5 mmol/l, systolic BP 135-145 mmHg and diastolic BP 82.5-87.5 mmHg. Coronary and stroke mortality related to the four risk factors in a positive, curvilinear way, with the exception of systolic BP, which related to deaths in a U-shape. Macrovascular events showed a positive and curvilinear relationship with HbA1c but a U-shaped relationship with total cholesterol and systolic BP. Microvascular events related to the four risk factors in a curvilinear way: positive for HbA1c and systolic BP but negative for cholesterol and diastolic BP.

CONCLUSIONS/INTERPRETATION: We identified several relationships that support a call for major changes to clinical practice. Most importantly, our results support trial data indicating that normalisation of glucose and BP can lead to poorer outcomes. This makes a strong case for target ranges for these risk factors rather than target levels.

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.

Financial and reputational incentives are increasingly common components in strategies to performance manage the medical profession. Judging the impacts of incentives is challenging, however, and the science of framework design remains in its infancy. Oliver's taxonomy therefore offers a useful and timely guide to the approaches that are most likely to be successful (and unsuccessful) in the field of health care. The use of incentives to date has focused on process measures and a narrow range of outcomes, a pragmatic approach that has produced some substantial quality gains within the constraints of existing health care systems. Improvement of specific technical aspects of quality may, however, have been achieved at the expense of trust, cooperation and benevolence. Deficits in these indispensible virtues will undermine any attempt to performance manage the medical and allied professions.

BACKGROUND: Cross-country comparisons of socioeconomic equity in health care typically use sample survey data on general services such as physician visits. This study uses comprehensive administrative data on a specific service: hip replacement.

METHODS: We analyse 651 652 publicly funded hip replacements, excluding fractures and accidents, in adults over 35 in Denmark, England, Portugal and Spain from 2002 to 2009. Sub-national administrative areas are split into socioeconomic quintile groups comprising approximately one-fifth of the national population. Area-level Poisson regression with Huber-White standard errors is used to calculate age-sex standardised hip replacement rates by quintile group, together with gaps and ratios between richest and poorest groups (Q5 and Q1) and the middle group (Q3).

RESULTS: We find pro-rich-area inequality in England (2009 Q5/Q1 ratio 1.35 [CI 1.25-1.45]) and Spain (2009 Q5/Q1 ratio 1.43 [CI 1.17-1.70]), pro-poor-area inequality in Portugal (2009 Q5/Q1 ratio 0.67 [CI 0.50-0.83]) and no significant inequality in Denmark. Pro-rich-area inequality increased over time in England and Spain but not significantly. Within-country differences between socioeconomic quintile groups are smaller than between-country differences in general population averages: hip replacement rates are substantially lower in Portugal and Spain (8.6 and 7.4 per 10 000 in 2009) than England and Denmark (20.2 and 27.8 per 10 000 in 2009).

CONCLUSION: Despite limitations regarding individual-level inequality and area heterogeneity, analysis of area-level data on publicly funded hospital activity can provide useful cross-country comparisons and longitudinal monitoring of socioeconomic inequality in specific health services. Although this kind of analysis cannot provide definitive answers, it can raise important questions for decision makers.

OBJECTIVE: To assess the feasibility, strengths and weaknesses of using administrative data to compare hospital performance across countries, using mortality after coronary artery bypass graft (CABG) surgery as an illustrative example.

METHODS: Country specific and pooled models using individual-level data and logistic regression methods assess individual hospital performance using funnel plots accounting for multiple testing. Outcomes are adjusted for age, sex, comorbidities and indicators of patient severity. Data includes patients from all publicly funded hospitals delivering CABG surgery in England and Spain. Inpatient hospital-level standardized mortality rates within 30 days of CABG surgery are calculated for 83 999 CABG patients between 2007 and 2009.

RESULTS: Unadjusted national mortality rates are 5% in Spain and 2.3% in England. Country-specific models identified similar patterns of excess mortality 'alerts' and 'alarms' in hospitals in Spain or England. Pooling data from both countries identifies larger numbers of alerts and alarms in Spanish hospitals, and risk-adjustment increased the already large national mortality difference. This was reduced but not eliminated by accounting for lower volume in Spanish hospitals.

CONCLUSION: Cross-national comparisons potentially add value by providing international performance benchmarks. Hospital-level analysis across countries can illuminate differences in hospital performance, which might not be identified using country-specific data or incomplete registry data, and can test hypotheses that may explain national differences. Difficulties of making data comparable between countries, however, compound the usual within-country measurement problems.

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.

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.

OBJECTIVES: To conduct a fully independent, external validation of a research study based on one electronic health record database using a different database sampling from the same population.

DESIGN: Retrospective cohort analysis of β-blocker therapy and all-cause mortality in patients with cancer.

SETTING: Two UK national primary care databases (PCDs): the Clinical Practice Research Datalink (CPRD) and Doctors' Independent Network (DIN).

PARTICIPANTS: CPRD data for 11,302 patients with cancer compared with published results from DIN for 3462 patients; study period January 1997 to December 2006.

PRIMARY AND SECONDARY OUTCOME MEASURES: All-cause mortality: overall; by treatment subgroup (β-blockers only, β-blockers plus other blood pressure lowering medicines (BPLM), other BPLMs only); and by cancer site.

RESULTS: Using CPRD, β-blocker use was not associated with mortality (HR=1.03, 95% CI 0.93 to 1.14, vs patients prescribed other BPLMs only), but DIN β-blocker users had significantly higher mortality (HR=1.18, 95% CI 1.04 to 1.33). However, these HRs were not statistically different (p=0.063), but did differ for patients on β-blockers alone (CPRD=0.94, 95% CI 0.82 to 1.07; DIN=1.37, 95% CI 1.16 to 1.61; p<0.001). Results for individual cancer sites differed by study, but only significantly for prostate and pancreas cancers. Results were robust under sensitivity analyses, but we could not be certain that mortality was identically defined in both databases.

CONCLUSIONS: We found a complex pattern of similarities and differences between databases. Overall treatment effect estimates were not statistically different, adding to a growing body of evidence that different UK PCDs produce comparable effect estimates. However, individually the two studies lead to different conclusions regarding the safety of β-blockers and some subgroup effects differed significantly. Single studies using even internally well-validated databases do not guarantee generalisable results, especially for subgroups, and confirmatory studies using at least one other independent data source are strongly recommended.

BACKGROUND: A range of policy initiatives have addressed inequalities in healthcare and health outcomes. Local pay-for-performance schemes for primary care have been advocated as means of enhancing clinical ownership of the quality agenda and better targeting local need compared with national schemes such as the UK Quality and Outcomes Framework (QOF). We investigated whether professionals' experience of a local scheme in one English National Health Service (NHS) former primary care trust (PCT) differed from that of the national QOF in relation to the goal of reducing inequalities.

METHODS: We conducted retrospective semi-structured interviews with primary care professionals implementing the scheme and those involved in its development. We purposively sampled practices with varying levels of population socio-economic deprivation and achievement. Interviews explored perceptions of the scheme and indicators, likely mechanisms of influence on practice, perceived benefits and harms, and how future schemes could be improved. We used a framework approach to analysis.

RESULTS: Thirty-eight professionals from 16 general practices and six professionals involved in developing local indicators participated. Our findings cover four themes: ownership, credibility of the indicators, influences on behaviour, and exacerbated tensions. We found little evidence that the scheme engendered any distinctive sense of ownership or experiences different from the national scheme. Although the indicators and their evidence base were seldom actively questioned, doubts were expressed about their focus on health promotion given that eventual benefits relied upon patient action and availability of local resources. Whilst practices serving more affluent populations reported status and patient benefit as motivators for participating in the scheme, those serving more deprived populations highlighted financial reward. The scheme exacerbated tensions between patient and professional consultation agendas, general practitioners benefitting directly from incentives and nurses who did much of the work, and practices serving more and less affluent populations which faced different challenges in achieving targets.

CONCLUSIONS: The contentious nature of pay-for-performance was not necessarily reduced by local adaptation. Those developing future schemes should consider differential rewards and supportive resources for practices serving more deprived populations, and employing a wider range of levers to promote professional understanding and ownership of indicators.

This Guidance for Priority Setting in Health Care (GPS-Health), initiated by the World Health Organization, offers a comprehensive map of equity criteria that are relevant to health care priority setting and should be considered in addition to cost-effectiveness analysis. The guidance, in the form of a checklist, is especially targeted at decision makers who set priorities at national and sub-national levels, and those who interpret findings from cost-effectiveness analysis. It is also targeted at researchers conducting cost-effectiveness analysis to improve reporting of their results in the light of these other criteria. The guidance was develop through a series of expert consultation meetings and involved three steps: i) methods and normative concepts were identified through a systematic review; ii) the review findings were critically assessed in the expert consultation meetings which resulted in a draft checklist of normative criteria; iii) the checklist was validated though an extensive hearing process with input from a range of relevant stakeholders. The GPS-Health incorporates criteria related to the disease an intervention targets (severity of disease, capacity to benefit, and past health loss); characteristics of social groups an intervention targets (socioeconomic status, area of living, gender; race, ethnicity, religion and sexual orientation); and non-health consequences of an intervention (financial protection, economic productivity, and care for others).

In 2009 the UK National Institute for Health and Clinical Excellence (NICE) announced that its health technology appraisal committees would henceforth give special additional weight to health gains from life-extending end-of-life treatments. This was a response to mounting concern from NICE's stakeholders that effective new drugs for end-stage cancer often fail NICE's standard test of cost effectiveness. This change of policy may be justifiable on procedural grounds as the result of a democratic political process responding to stakeholder concerns. However, according to the "accountability for reasonableness" framework proposed by the philosopher Norman Daniels and endorsed by NICE, there also needs to be transparency about the substantive ethical grounds for public health care resource allocation decisions. In that spirit, I analyze eleven potentially relevant justifications for the NICE "end-of-life premium," drawn from the economics and philosophy literature: (1) rule of rescue, (2) fair chances, (3) ex post willingness to pay, (4) caring externality, (5) financial protection, (6) symbolic value, (7) diminishing marginal value of future life years, (8) concentration of benefits, (9) dread, (10) time to set your affairs in order, and (11) severity of illness. I conclude that none of them yields a coherent ethical justification for the NICE end-of-life premium.

Waiting times for elective surgery, like hip replacement, are often referred to as an equitable rationing mechanism in publicly-funded healthcare systems because access to care is not based on socioeconomic status. Previous work has established that that this may not be the case and there is evidence of inequality in NHS waiting times favouring patients living in the least deprived neighbourhoods in England. We advance the literature by explaining variations of inequalities in waiting times in England in four different ways. First, we ask whether inequalities are driven by education rather than income. Our analysis shows that education and income deprivation have distinct effects on waiting time. Patients in the first quintile with least deprivation in education wait 9% less than patients in the second quintile and 14% less than patients in the third-to-fifth quintile. Patients in the fourth and fifth most income-deprived quintile wait about 7% longer than patients in the least deprived quintile. Second, we investigate whether inequalities arise "across" hospitals or "within" the hospital. The analysis provides evidence that most inequalities occur within hospitals rather than across hospitals. Moreover, failure to control for hospital fixed effects results in underestimation of the income gradient. Third, we explore whether inequalities arise across the entire waiting time distribution. Inequalities between better educated patients and other patients occur over large part of the waiting time distribution. Moreover we find that the education gradient becomes smaller for very long waiting. Fourth, we investigate whether the gradient may reflect the fact that patients with higher socioeconomic status have a different severity as proxied through a range of types and the number of diagnoses (in addition to age and gender) compared to those with lower socioeconomic status. We find no evidence that differences in severity explain the social gradient in waiting times. (C) 2012 Elsevier Ltd. All rights reserved.