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The decline in the working hours of general practitioners (GPs) is a key factor influencing access to healthcare in many countries. We investigate the effect of changes in hours worked by general practitioners (GPs) on waiting times in primary care using the MABEL longitudinal survey of Australian doctors. We estimate GP fixed effects models for waiting time and use family circumstances to instrument for GP’s hours worked. We find that a 10% reduction in hours worked increases average patient waiting time by 12%. Our findings highlight the importance of GPs’ labour supply at the intensive margin in determining the length of time patients must wait to see their doctor.

We examine whether family doctor firms in England respond to local competition by increasing their quality. We measure quality in terms of clinical performance and patient-reported satisfaction to capture its multi-dimensional nature. We use a panel covering 8 years for over 8000 English general practices, allowing us to control for unobserved local area effects. We measure competition by the number of rival doctors within a small distance. We find that increases in local competition are associated with increases in clinical quality and patient satisfaction, particularly for firms with lower quality. However, the magnitude of the effect is small.

Background: The Quality and Outcomes Framework (QOF) incentivises general practices in England to provide proactive care for people with serious mental illness (SMI) including schizophrenia, bipolar disorder and other psychoses. Better proactive primary care may reduce the risk of psychiatric admissions to hospital, but this has never been tested empirically. Methods: The QOF data set included 8234 general practices in England from 2006/2007 to 2010/2011. Rates of hospital admissions with primary diagnoses of SMI or bipolar disorder were estimated from national routine hospital data and aggregated to practice level. Poisson regression was used to analyse associations. Results: Practices with higher achievement on the annual review for SMI patients (MH9), or that performed better on either of the two lithium indicators for bipolar patients (MH4 or MH5), had more psychiatric admissions. An additional 1% in achievement rates for MH9 was associated with an average increase in the annual practice admission rate of 0.19% (95% CI 0.10% to 0.28%) or 0.007 patients (95% CI 0.003 to 0.01). Conclusions: The positive association was contrary to expectation, but there are several possible explanations: better quality primary care may identify unmet need for secondary care; higher QOF achievement may not prevent the need for secondary care; individuals may receive their QOF checks postdischarge rather than prior to admission; individuals with more severe SMI may be more likely to be registered with practices with better QOF performance; and QOF may be a poor measure of the quality of care for people with SMI.

The absolute income hypothesis—that holding other factors constant, the higher an individual's income the better is their health—is supported by a considerable body of evidence.1 2 3 However, according to the more recent relative income hypothesis, an individual's health is also affected by the distribution of income within society. Someone with a given income would have worse health if he or she lived in a society with greater inequality of income than in a society in which income is more equally distributed.4 Several recent papers examining the relation between population mortality and income inequality seem to support the relative income hypothesis.5 6 7 8 9 10 11 They suggest that greater inequality is associated with higher population mortality and that this relation persists even when account is taken of the average income of the population. However, some scepticism has been expressed about the relative income hypothesis.12 To quote one of the papers cited above, the “mechanisms underlying the association between income distribution and mortality are poorly understood.”7 There may be a very simple explanation for some, or all, of the reported associations between inequality of income and population health used to support the relative income hypothesis. They may be, at least partly, a statistical artefact caused by using population data rather than individual data. A positive correlation between population mortality and income inequality can arise at aggregate level even if inequality has no effect on the individual risk of mortality. Thus, we do not need the relative income hypothesis to explain the observed associations between population health and income inequality—the absolute income hypothesis will serve. The absolute income explanation can be illustrated with the help of the figure (the mathematical argument is presented in the Appendix 1). In this, the individual risk of mortality depends …

The relationship between the quality of health care and the extent of competition amongst providers has been the subject of intense policy interest and debate. As part of the ESHCRU programme we are undertaking a set of related investigations into this relationship in the hospital sector, in primary care (general practices) and in social care.

We analyse the determinants of annual net income and wages (net income/hours) of general practitioners (GPs) using data for 2,271 GPs in England recorded during Autumn 2008. The average GP had an annual net income of £97,500 and worked 43 hours per week. The mean wage was £51 per hour. Net income and wages depended on gender, experience, list size, partnership size, whether or not the GP worked in a dispensing practice, whether they were salaried or self-employed, whether they worked in a practice with a nationally or locally negotiated contract, and the characteristics of the local population (proportion from ethnic minorities, rurality, and income deprivation). The findings have implications for discrimination by GP gender and ethnicity, GP preferences for partnership size, incentives for competition for patients, compensating differentials for local population characteristics. They also shed light on the attractiveness to GPs in England of locally-negotiated (Personal Medical Services) versus nationally-negotiated (General Medical Services) contracts.

The White Paper Our Health, Our Care, Our Say noted concerns about geographical equity of access to GPs (Department of Health, 2006, page 63), listed the 30 PCTs with the lowest number of GPs per head of need adjusted population, and set out policy initiatives to attract additional providers of general practice services to these PCTs. We were asked to evaluate the impact of these policies on the bottom 30 PCTs and will report in Autumn 2010. In this report we consider a number of related measurement issues which are relevant for consideration of policy on equality of access to general practice.

Since 2003, 25% of UK general practitioners’ income has been determined by the quality of their care. The 65 clinical quality indicators in this scheme (the Quality and Outcomes Framework) are in the form of ratios, with financial reward increasing linearly with the ratio between a lower and upper threshold. The numerator is the number of patients for whom an indicator is achieved and the denominator is the number of patients the practices declare are suitable for the indicator. The number declared suitable is the number of patients with the relevant condition less the number exception reported by the practice for a specified range of reasons. Exception reporting is designed to avoid harmful treatment resulting from the application of quality targets to patients for whom they were not intended. However, exception reporting also gives GPs the opportunity to exclude patients who should in fact be treated in order to achieve higher financial rewards. This is inappropriate use of exception reporting or ‘gaming’. Practices can also increase income if they are below the upper threshold by reducing the number of patients declared with a condition (prevalence), or by increasing reported prevalence if they were above the upper threshold. This study examines the factors affecting delivered quality (the proportion of prevalent patients for indicators were achieved) and tests for gaming of exceptions and for prevalence reporting being responsive to financial incentives.

Since 2003, 25% of UK general practitioners’ income has been determined by the quality of their care. The 65 clinical quality indicators in this scheme (the Quality and Outcomes Framework) are in the form of ratios, with financial reward increasing linearly with the ratio between a lower and upper threshold. The numerator is the number of patients for whom an indicator is achieved and the denominator is the number of patients the practices declares are suitable for the indicator. The number declared suitable is the number of patients with the relevant condition less the number exception reported by the practice for a specified range of reasons. Exception reporting is designed to avoid harmful treatment resulting from the application of quality targets to patients for whom they were not intended. However, exception reporting also gives GPs the opportunity to exclude patients who should in fact be treated in order to achieve higher financial rewards. This is inappropriate use of exception reporting or ‘gaming’. Practices can also increase income if they are below the upper threshold by reducing the number of patients declared with a condition (prevalence), or by increasing reported prevalence if they were above the upper threshold. This study examines the factors affecting delivered quality (the proportion of prevalent patients for indicators were achieved) and tests for gaming of exceptions and for prevalence reporting being responsive to financial incentives.

Long waiting times for inpatient treatment in the UK National Health Service have long been a source of great popular and political concern, and therefore a target for policy initiatives. One such is the London Patient Choice Project, under which patients at risk of breaching inpatient waiting time targets were offered the choice of an alternative hospital with a guaranteed shorter wait. This paper uses a difference in difference econometric methodology to infer the impact of the choice project on ophthalmology waiting times. In line with our theoretical predictions, it finds that the project led to lower average waiting times in the London region and a convergence in waiting times amongst London hospitals.

We report an empirical analysis of the responses of the supply and demand for secondary care to waiting list size and waiting times. Whereas previous empirical analyses have used data aggregated to area level, our analysis is novel in that it focuses on the supply responses of a single hospital and the demand responses of the GP practices it serves, and distinguishes between outpatient visits, inpatient admissions, daycase treatment and emergency admissions. The results are plausible and in line with the theoretical model. For example: the demand from practices for outpatient visits is negatively affected by waiting times and distance to the hospital. Increases in waiting times and waiting lists lead to increases in supply; the supply of elective inpatient admissions is affected negatively by current emergency admissions and positively by lagged waiting list and waiting time. We use the empirical results to investigate the dynamic responses to one off policy measures to reduce waiting times and lists by increasing supply.

Objectives: Discounting of costs in health-related economic evaluation is generally regarded as uncontroversial, but there is disagreement about discounting health benefits. We sought to explore the current recommendations and practice in health economic evaluations with regard to discounting of costs and benefits. Methods: Recommendations for best practice on discounting for health effects as set out by government agencies, regulatory bodies, learned journals, and leading health economics texts were surveyed. A review of a sample of primary literature on health economic evaluations was undertaken to ascertain the actual current practice on discounting health effects and costs. Results: All of the official sources recommended a positive discount rate for both health effects and costs, and most recommended a specific rate (range, 1% to 8%). The most frequently specified rates were 3% and 5%. A total of 147 studies were reviewed; most of these used a discount rate for health of either 0% (n = 50) or 5% (n = 67). Over 90% of studies used the same discount rate for both health and cost. While 28% used a zero rate for both health and cost, in 64% a nonzero rate was used for both. Studies where the health measure was in natural clinical units (direct) were significantly more likely to have a zero discount rate. Conclusion: The finding that 28% of studies did not discount costs or benefits is surprising and concerning. A lower likelihood of discounting for benefits when they are in natural units may indicate confusion regarding the rationale for discounting health effects.