Need, demand, supply in health care: working definitions, and their implications for defining access.

Effective policymaking in health care systems begins with a clear typology of the terminology - need, demand, supply and access to care - and their interrelationships. However, the terms are contested and their meaning is rarely stated explicitly. This paper offers working definitions of need, demand and supply. We draw on the international literature and use a Venn diagram to explain the terms. We then define access to care, reviewing alternative and competing definitions from the literature. We conclude by discussing potential applications of our conceptual framework to help to understand the interrelationships and trade-offs between need, demand, supply and access in health care.

Impact of prevention in primary care on costs in primary and secondary care for people with serious mental illness.

A largely unexplored part of the financial incentive for physicians to participate in preventive care is the degree to which they are the residual claimant from any resulting cost savings. We examine the impact of two preventive activities for people with serious mental illness (care plans and annual reviews of physical health) by English primary care practices on costs in these practices and in secondary care. Using panel two-part models to analyze patient-level data linked across primary and secondary care, we find that these preventive activities in the previous year are associated with cost reductions in the current quarter both in primary and secondary care. We estimate that there are large beneficial externalities for which the primary care physician is not the residual claimant: the cost savings in secondary care are 4.7 times larger than the cost savings in primary care. These activities are incentivized in the English National Health Service but the total financial incentives for primary care physicians to participate were considerably smaller than the total cost savings produced. This suggests that changes to the design of incentives to increase the marginal reward for conducting these preventive activities among patients with serious mental illness could have further increased welfare.

Labor supply and informal care responses to health shocks within couples: Evidence from the UK.

Shocks to health have been shown to reduce labor supply for the individual affected. Less is known about household self-insurance through a partner's response. Previous studies have presented inconclusive empirical evidence on the existence of a health-related Added Worker Effect, and results limited to labor and income responses. We use UK longitudinal data to investigate within households both the labor supply and informal care responses of an individual to the event of an acute health shock to their partner. Relying on the unanticipated timing of shocks, we combine Coarsened Exact Matching and Entropy Balancing algorithms with parametric analysis and exploit lagged outcomes to remove bias from observed confounders and time-invariant unobservables. We find no evidence of a health-related Added Worker Effect but a significant and sizable Informal Carer Effect. This holds irrespective of spousal labor market position or household financial status and ability to purchase formal care provision, suggesting that partners' substitute informal care provision for time devoted to leisure activities.

Exploring mental health disability gaps in the labour market: the UK experience during COVID-19.

People with long-term mental health problems that affect their daily activities are a growing proportion of the UK working population and they have a particularly low employment rate. We analyse gaps in labour market outcomes between mental health disabled and non-disabled people during the COVID-19 pandemic in the UK. We also decompose the outcome gaps in order to explore the relative importance of different factors in explaining these gaps. Our results suggest that the employment effects of the pandemic for mental health disabled people may have been temporary. However, they were more likely to be away from work and/or working reduced hours than people without a disability. Workers with mental health disability were over-represented in part-time work and in caring, leisure and other service occupations, which were disproportionately affected by COVID-19 and the economic response. This is important new evidence on the contribution of segmentation and segregation in explaining the labour market position of people with mental health disability. The longer term effects of the pandemic were still not apparent at the end of our analysis period (2021:Q3), but the concentration of disabled workers in cyclically sensitive sectors and part-time work means that they will always be particularly vulnerable to economic downturns.

Cuts to local government spending, multimorbidity and health-related quality of life: A longitudinal ecological study in England.

Background: Population health has stagnated or is declining in many high-income countries. We analysed whether nationally administered austerity cuts in England were associated with prevalence of multimorbidity (individuals with two or more long-term conditions) and health-related quality of life. Methods: We conducted an observational, longitudinal study on 147 local authorities in England. We examined associations of changes in spending over time (2009/10-2017/18), in total and by budget line, with (i) prevalence of multimorbidity, 2+ conditions (2011/12-2017/18), and (ii) health-related quality of life (EQ-5D-5L) score (2012/13-2016/17). We estimated linear, log-log regression models, incorporating local authority fixed-effects, time-varying demographic and socio-economic confounders, and time trends. Findings: All local authorities experienced real spending cuts, varying from 42% (Barking and Dagenham) to 0·3% (Sefton). A 1% cut in per capita total service expenditure was associated with a 0·10% (95% CI 0·03 to 0·16) increase in prevalence of multimorbidity. We found no association (0·003%; 95% CI -0·01 to 0·01) with health-related quality of life. By budget line, after controlling for other spending, a 1% cut in public health expenditure was associated with a 0·15% (95% CI 0·11 to 0·20) increase in prevalence of multimorbidity, and a 1% cut in adult social care expenditure was associated with a 0·01% (95% CI 0·002 to 0·02) decrease in average health-related quality of life. Interpretation: Fiscal austerity is associated with worse multimorbidity and health-related quality of life. Policymakers should consider the potential health consequences of local government expenditure cuts and knock-on effects for health systems. Funding: Medical Research Council.

Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions in England: A cohort study.

BACKGROUND: Patients with multimorbidities have the greatest healthcare needs and generate the highest expenditure in the health system. There is an increasing focus on identifying specific disease combinations for addressing poor outcomes. Existing research has identified a small number of prevalent "clusters" in the general population, but the limited number examined might oversimplify the problem and these may not be the ones associated with important outcomes. Combinations with the highest (potentially preventable) secondary care costs may reveal priority targets for intervention or prevention. We aimed to examine the potential of defining multimorbidity clusters for impacting secondary care costs. METHODS AND FINDINGS: We used national, Hospital Episode Statistics, data from all hospital admissions in England from 2017/2018 (cohort of over 8 million patients) and defined multimorbidity based on ICD-10 codes for 28 chronic conditions (we backfilled conditions from 2009/2010 to address potential undercoding). We identified the combinations of multimorbidity which contributed to the highest total current and previous 5-year costs of secondary care and costs of potentially preventable emergency hospital admissions in aggregate and per patient. We examined the distribution of costs across unique disease combinations to test the potential of the cluster approach for targeting interventions at high costs. We then estimated the overlap between the unique combinations to test potential of the cluster approach for targeting prevention of accumulated disease. We examined variability in the ranks and distributions across age (over/under 65) and deprivation (area level, deciles) subgroups and sensitivity to considering a smaller number of diseases. There were 8,440,133 unique patients in our sample, over 4 million (53.1%) were female, and over 3 million (37.7%) were aged over 65 years. No clear "high cost" combinations of multimorbidity emerged as possible targets for intervention. Over 2 million (31.6%) patients had 63,124 unique combinations of multimorbidity, each contributing a small fraction (maximum 3.2%) to current-year or 5-year secondary care costs. Highest total cost combinations tended to have fewer conditions (dyads/triads, most including hypertension) affecting a relatively large population. This contrasted with the combinations that generated the highest cost for individual patients, which were complex sets of many (6+) conditions affecting fewer persons. However, all combinations containing chronic kidney disease and hypertension, or diabetes and hypertension, made up a significant proportion of total secondary care costs, and all combinations containing chronic heart failure, chronic kidney disease, and hypertension had the highest proportion of preventable emergency admission costs, which might offer priority targets for prevention of disease accumulation. The results varied little between age and deprivation subgroups and sensitivity analyses. Key limitations include availability of data only from hospitals and reliance on hospital coding of health conditions. CONCLUSIONS: Our findings indicate that there are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care.

Does commuting mode choice impact health?

Governments around the world are encouraging people to switch away from sedentary modes of travel towards more active modes, including walking and cycling. The aim of these schemes is to improve population health and to reduce emissions. There is considerable evidence on the latter, but relatively little on the former. This paper investigates the impact of mode choice on physical and mental health. Using data from the UK Household Longitudinal Study, we exploit changes in mode of commute to identify health outcome responses. Individuals who change modes are matched with those whose mode remains constant. Overall we find that mode switches affect both physical and mental health. When switching from car to active travel we see an increase in physical health for women and in mental health for both genders. In contrast, both men and women who switch from active travel to car are shown to experience a significant reduction in their physical health and health satisfaction, and a decline in their mental health when they change from active to public transport.

Patterns of emergency admissions for ambulatory care sensitive conditions: a spatial cross-sectional analysis of observational data.

OBJECTIVES: To examine the spatial and temporal patterns of English general practices' emergency admissions for Ambulatory Care Sensitive Conditions (ACSCs). DESIGN: Observational study of practice level annual hospital emergency admissions data for ACSCs for all English practices from 2004-2017. PARTICIPANTS: All patients with an emergency admission to a National Health Service hospital in England who were registered with an English general practice. MAIN OUTCOME MEASURE: Practice level age and gender indirectly standardised ratios (ISARs) for emergency admissions for ACSC. RESULTS: In 2017, 41.8% of the total variation in ISARs across practices was between the 207 Clinical Commissioning Groups (CCGs) (the administrative unit for general practices) and 58.2% was across practices within CCGs. ACSC ISARs increased by 4.7% between 2004 and 2017, while those for conditions incentivised by the Quality and Outcomes Framework (QOF) fell by 20%. Practice ISARs are persistent: practices with high rates in 2004 also had high rates in 2017. Standardising by deprivation as well as age and gender reduced the coefficient of variation of practice ISARs in 2017 by 22%. CONCLUSIONS: There is persistent spatial pattern of emergency admissions for ACSC across England both within and across CCGs. We illustrate the reduction in ACSCs emergency admissions across the study period for conditions incentivised by the QOF but find that this was not accompanied by a reduction in variation in these admissions across practices. The observed spatial pattern persists when admission rates are standardised by deprivation. The persistence of spatial clusters of high emergency admissions for ACSCs within and across CCG boundaries suggests that policies to reduce potentially unwarranted variation should be targeted at practice level.

Trends in and drivers of healthcare expenditure in the English NHS: a retrospective analysis.

BACKGROUND: In England, rises in healthcare expenditure consistently outpace growth in both GDP and total public expenditure. To ensure the National Health Service (NHS) remains financially sustainable, relevant data on healthcare expenditure are needed to inform decisions about which services should be delivered, by whom and in which settings. METHODS: We analyse routine data on NHS expenditure in England over 9 years (2008/09 to 2016/17). To quantify the relative contribution of the different care settings to overall healthcare expenditure, we analyse trends in 14 healthcare settings under three broad categories: Hospital Based Care (HBC), Diagnostics and Therapeutics (D&T) and Community Care (CC). We exclude primary care and community mental health services settings due to a lack of consistent data. We employ a set of indices to aggregate diverse outputs and to disentangle growth in healthcare expenditure that is driven by activity from that due to cost pressures. We identify potential drivers of the observed trends from published studies. RESULTS: Over the 9-year study period, combined NHS expenditure on HBC, D&T and CC rose by 50.2%. Expenditure on HBC rose by 54.1%, corresponding to increases in both activity (29.2%) and cost (15.7%). Rises in expenditure in inpatient (38.5%), outpatient (57.2%), and A&E (59.5%) settings were driven predominately by higher activity. Emergency admissions rose for both short-stay (45.6%) and long-stay cases (26.2%). There was a switch away from inpatient elective care (which fell by 5.1%) and towards day case care (34.8% rise), likely reflecting financial incentives for same-day discharges. Growth in expenditure on D&T (155.2%) was driven by rises in the volume of high cost drugs (270.5%) and chemotherapy (110.2%). Community prescribing grew by 45.2%, with costs falling by 24.4%. Evidence on the relationship between new technologies and healthcare expenditure is mixed, but the fall in drug costs could reflect low generic prices, and the use of health technology assessment or commercial arrangements to inform pricing of new medicines. CONCLUSIONS: Aggregate trends in HCE mask enormous variation across healthcare settings. Understanding variation in activity and cost across settings is an important initial step towards ensuring the long-term sustainability of the NHS.

Acute health shocks and labour market outcomes: Evidence from the post crash era.

We investigate the labour supply response to an acute health shock for individuals of all working ages, in the post crash era, combining coarsened exact matching and entropy balancing to preprocess data prior to undertaking parametric regression. Identification exploits uncertainty in the timing of an acute health shock, defined by the incidence of cancer, stroke, or heart attack, based on data from Understanding Society. The main finding implies a substantial increase in the baseline probability of labour market exit along with reduced hours and earnings. Younger workers display a stronger labour market attachment than older counterparts, conditional on a health shock. Impacts are stronger for women, older workers, and those who experience more severe limitations and impairments. This is shown to be robust to a broad range of approaches to estimation. Sensitivity tests based on pre-treatment outcomes and using future health shocks as a placebo treatment support our identification strategy.

Impact of family practice continuity of care on unplanned hospital use for people with serious mental illness.

OBJECTIVE: To investigate whether continuity of care in family practice reduces unplanned hospital use for people with serious mental illness (SMI). DATA SOURCES: Linked administrative data on family practice and hospital utilization by people with SMI in England, 2007-2014. STUDY DESIGN: This observational cohort study used discrete-time survival analysis to investigate the relationship between continuity of care in family practice and unplanned hospital use: emergency department (ED) presentations, and unplanned admissions for SMI and ambulatory care-sensitive conditions (ACSC). The analysis distinguishes between relational continuity and management/ informational continuity (as captured by care plans) and accounts for unobserved confounding by examining deviation from long-term averages. DATA COLLECTION/EXTRACTION METHODS: Individual-level family practice administrative data linked to hospital administrative data. PRINCIPAL FINDINGS: Higher relational continuity was associated with 8-11 percent lower risk of ED presentation and 23-27 percent lower risk of ACSC admissions. Care plans were associated with 29 percent lower risk of ED presentation, 39 percent lower risk of SMI admissions, and 32 percent lower risk of ACSC admissions. CONCLUSIONS: Family practice continuity of care can reduce unplanned hospital use for physical and mental health of people with SMI.

Association Between Antipsychotic Polypharmacy and Outcomes for People With Serious Mental Illness in England.

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.

Do care plans and annual reviews of physical health influence unplanned hospital utilisation for people with serious mental illness? Analysis of linked longitudinal primary and secondary healthcare records in England.

OBJECTIVE: To investigate whether two primary care activities that are framed as indicators of primary care quality (comprehensive care plans and annual reviews of physical health) influence unplanned utilisation of hospital services for people with serious mental illness (SMI). DESIGN, SETTING, PARTICIPANTS: Retrospective observational cohort study using linked primary care and hospital records (Hospital Episode Statistics) for 5158 patients diagnosed with SMI between April 2006 and March 2014, who attended 213 primary care practices in England that contribute to the Clinical Practice Research Datalink GOLD database. OUTCOMES AND ANALYSIS: Cox survival models were used to estimate the associations between two primary care quality indicators (care plans and annual reviews of physical health) and the hazards of three types of unplanned hospital utilisation: presentation to accident and emergency departments (A&E), admission for SMI and admission for ambulatory care sensitive conditions (ACSC). RESULTS: Risk of A&E presentation was 13% lower (HR 0.87, 95% CI 0.77 to 0.98) and risk of admission to hospital for ACSC was 23% lower (HR 0.77, 95% CI 0.60 to 0.99) for patients with a care plan documented in the previous year compared with those without a care plan. Risk of A&E presentation was 19% lower for those who had a care plan documented earlier but not updated in the previous year (HR: 0.81, 95% CI 0.67 to 0.97) compared with those without a care plan. Risks of hospital admission for SMI were not associated with care plans, and none of the outcomes were associated with annual reviews. CONCLUSIONS: Care plans documented in primary care for people with SMI are associated with reduced risk of A&E attendance and reduced risk of unplanned admission to hospital for physical health problems, but not with risk of admission for mental health problems. Annual reviews of physical health are not associated with risk of unplanned hospital utilisation.

Health care expenditures, age, proximity to death and morbidity: Implications for an ageing population.

This paper uses Hospital Episode Statistics, English administrative data, to investigate the growth in admitted patient health care expenditures and the implications of an ageing population. We use two samples of around 40,000 individuals who (a) used inpatient health care in the financial year 2005/06 and died by the end of 2011/12 and (b) died in 2011/12 and had some hospital utilisation since 2005/06. We use a panel structure to follow individuals over seven years of this administrative data, containing estimates of inpatient health care expenditures (HCE), information regarding individuals' age, time-to-death (TTD), morbidities at the time of an admission, as well as the hospital provider, year and season of admission. We show that HCE is principally determined by proximity to death rather than age, and that proximity to death is itself a proxy for morbidity.

End-Of-Life Medical Spending In Last Twelve Months Of Life Is Lower Than Previously Reported.

Although end-of-life medical spending is often viewed as a major component of aggregate medical expenditure, accurate measures of this type of medical spending are scarce. We used detailed health care data for the period 2009-11 from Denmark, England, France, Germany, Japan, the Netherlands, Taiwan, the United States, and the Canadian province of Quebec to measure the composition and magnitude of medical spending in the three years before death. In all nine countries, medical spending at the end of life was high relative to spending at other ages. Spending during the last twelve months of life made up a modest share of aggregate spending, ranging from 8.5 percent in the United States to 11.2 percent in Taiwan, but spending in the last three calendar years of life reached 24.5 percent in Taiwan. This suggests that high aggregate medical spending is due not to last-ditch efforts to save lives but to spending on people with chronic conditions, which are associated with shorter life expectancies.

A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: an application to healthcare costs.

We conduct a quasi-Monte-Carlo comparison of the recent developments in parametric and semiparametric regression methods for healthcare costs, both against each other and against standard practice. The population of English National Health Service hospital in-patient episodes for the financial year 2007-2008 (summed for each patient) is randomly divided into two equally sized subpopulations to form an estimation set and a validation set. Evaluating out-of-sample using the validation set, a conditional density approximation estimator shows considerable promise in forecasting conditional means, performing best for accuracy of forecasting and among the best four for bias and goodness of fit. The best performing model for bias is linear regression with square-root-transformed dependent variables, whereas a generalized linear model with square-root link function and Poisson distribution performs best in terms of goodness of fit. Commonly used models utilizing a log-link are shown to perform badly relative to other models considered in our comparison.

Healthcare Cost Regressions: Going Beyond the Mean to Estimate the Full Distribution.

Understanding the data generating process behind healthcare costs remains a key empirical issue. Although much research to date has focused on the prediction of the conditional mean cost, this can potentially miss important features of the full distribution such as tail probabilities. We conduct a quasi-Monte Carlo experiment using the English National Health Service inpatient data to compare 14 approaches in modelling the distribution of healthcare costs: nine of which are parametric and have commonly been used to fit healthcare costs, and five others are designed specifically to construct a counterfactual distribution. Our results indicate that no one method is clearly dominant and that there is a trade-off between bias and precision of tail probability forecasts. We find that distributional methods demonstrate significant potential, particularly with larger sample sizes where the variability of predictions is reduced. Parametric distributions such as log-normal, generalised gamma and generalised beta of the second kind are found to estimate tail probabilities with high precision but with varying bias depending upon the cost threshold being considered.

Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold.

BACKGROUND: Cost-effectiveness analysis involves the comparison of the incremental cost-effectiveness ratio of a new technology, which is more costly than existing alternatives, with the cost-effectiveness threshold. This indicates whether or not the health expected to be gained from its use exceeds the health expected to be lost elsewhere as other health-care activities are displaced. The threshold therefore represents the additional cost that has to be imposed on the system to forgo 1 quality-adjusted life-year (QALY) of health through displacement. There are no empirical estimates of the cost-effectiveness threshold used by the National Institute for Health and Care Excellence. OBJECTIVES: (1) To provide a conceptual framework to define the cost-effectiveness threshold and to provide the basis for its empirical estimation. (2) Using programme budgeting data for the English NHS, to estimate the relationship between changes in overall NHS expenditure and changes in mortality. (3) To extend this mortality measure of the health effects of a change in expenditure to life-years and to QALYs by estimating the quality-of-life (QoL) associated with effects on years of life and the additional direct impact on QoL itself. (4) To present the best estimate of the cost-effectiveness threshold for policy purposes. METHODS: Earlier econometric analysis estimated the relationship between differences in primary care trust (PCT) spending, across programme budget categories (PBCs), and associated disease-specific mortality. This research is extended in several ways including estimating the impact of marginal increases or decreases in overall NHS expenditure on spending in each of the 23 PBCs. Further stages of work link the econometrics to broader health effects in terms of QALYs. RESULTS: The most relevant 'central' threshold is estimated to be £12,936 per QALY (2008 expenditure, 2008-10 mortality). Uncertainty analysis indicates that the probability that the threshold is < £20,000 per QALY is 0.89 and the probability that it is < £30,000 per QALY is 0.97. Additional 'structural' uncertainty suggests, on balance, that the central or best estimate is, if anything, likely to be an overestimate. The health effects of changes in expenditure are greater when PCTs are under more financial pressure and are more likely to be disinvesting than investing. This indicates that the central estimate of the threshold is likely to be an overestimate for all technologies which impose net costs on the NHS and the appropriate threshold to apply should be lower for technologies which have a greater impact on NHS costs. LIMITATIONS: The central estimate is based on identifying a preferred analysis at each stage based on the analysis that made the best use of available information, whether or not the assumptions required appeared more reasonable than the other alternatives available, and which provided a more complete picture of the likely health effects of a change in expenditure. However, the limitation of currently available data means that there is substantial uncertainty associated with the estimate of the overall threshold. CONCLUSIONS: The methods go some way to providing an empirical estimate of the scale of opportunity costs the NHS faces when considering whether or not the health benefits associated with new technologies are greater than the health that is likely to be lost elsewhere in the NHS. Priorities for future research include estimating the threshold for subsequent waves of expenditure and outcome data, for example by utilising expenditure and outcomes available at the level of Clinical Commissioning Groups as well as additional data collected on QoL and updated estimates of incidence (by age and gender) and duration of disease. Nonetheless, the study also starts to make the other NHS patients, who ultimately bear the opportunity costs of such decisions, less abstract and more 'known' in social decisions. FUNDING: The National Institute for Health Research-Medical Research Council Methodology Research Programme.

The Influence of Cost-Effectiveness and Other Factors on Nice Decisions.

The National Institute for Health and Care Excellence (NICE) emphasises that cost-effectiveness is not the only consideration in health technology appraisal and is increasingly explicit about other factors considered relevant but not the weight attached to each. The objective of this study is to investigate the influence of cost-effectiveness and other factors on NICE decisions and whether NICE's decision-making has changed over time. We model NICE's decisions as binary choices for or against a health care technology in a specific patient group. Independent variables comprised of the following: clinical and economic evidence; characteristics of patients, disease or treatment; and contextual factors potentially affecting decision-making. Data on all NICE decisions published by December 2011 were obtained from HTAinSite [www.htainsite.com]. Cost-effectiveness alone correctly predicted 82% of decisions; few other variables were significant and alternative model specifications had similar performance. There was no evidence that the threshold has changed significantly over time. The model with highest prediction accuracy suggested that technologies costing £40 000 per quality-adjusted life-year (QALY) have a 50% chance of NICE rejection (75% at £52 000/QALY; 25% at £27 000/QALY). Past NICE decisions appear to have been based on a higher threshold than £20 000-£30 000/QALY. However, this may reflect consideration of other factors that cannot be easily quantified. © 2014 The Authors. Health Economics published by John Wiley & Sons Ltd.