Nigel Rice

Socioeconomic determinants of rates of consultation in general practice based on fourth national morbidity survey of general practices.

Abstract Objective: To identify the socioeconomic determinants of consultation rates in general practice. Design: Analysis of data from the fourth national morbidity survey of general practices (MSGP4) including sociodemographic details of individual patients and small area statistics from the 1991 census. Multilevel modelling techniques were used to take account of both individual patient data and small area statistics to relate socioeconomic and health status factors directly to a measure of general practitioner workload. Results: Higher rates of consultations were found in patients who were classified as permanently sick, unemployed (especially those who became unemployed during the study year), living in rented accommodation, from the Indian subcontinent, living with a spouse or partner (women only), children living with two parents (girls only), and living in urban areas, especially those living relatively near the practice. When characteristics of individual patients are known and controlled for the role of “indices of deprivation” is considerably reduced. The effect of individual sociodemographic characteristics were shown to vary between different areas. Conclusions: Demographic and socioeconomic factors can act as powerful predictors of consultation patterns. Though it will always be necessary to retain some local planning discretion, the sets of coefficients estimated for individual level factors, area level characteristics, and for practice groupings may be sufficient to provide an indicative level of demand for general medical services. Although the problems in using socioeconomic data from individual patients would be substantial, these results are relevant to the development of a resource allocation formula for general practice. Key messages Characteristics of individual patients are much more powerful predictors of consulting patterns than the characteristics of the areas in which patients live The effects of individual socioeconomic factors themselves vary in different geographical areas Resource allocation methods based on area of residence (for example, Jarman score) will always be inferior to an approach that takes into account the characteristics of individual patients

Methods for assessing the cost-effectiveness of public health interventions: Key challenges and recommendations

RATIONALE Increasing attention is being given to the evaluation of public health interventions. Methods for the economic evaluation of clinical interventions are well established. In contrast, the economic evaluation of public health interventions raises additional methodological challenges. The paper identifies these challenges and provides suggestions for overcoming them. METHODS To identify the methodological challenges, five reviews that explored the economics of public health were consulted. From these, four main methodological challenges for the economic evaluation of public health interventions were identified. A review of empirical studies was conducted to explore how the methodological challenges had been approached in practice and an expert workshop convened to discuss how they could be tackled in the future. RESULTS The empirical review confirmed that the four methodological challenges were important. In all, 154 empirical studies were identified, covering areas as diverse as alcohol, drug use, obesity and physical activity, and smoking. However, the four methodological challenges were handled badly, or ignored in most of the studies reviewed. DISCUSSION The empirical review offered few insights into ways of addressing the methodological challenges. The expert workshop suggested a number of ways forward for overcoming the methodological challenges. CONCLUSION Although the existing empirical literature offers few insights on how to respond to these challenges, expert opinion suggests a number of ways forward. Much of what is suggested here has not yet been applied in practice, and there is an urgent need both for pilot studies and more methodological research.

MULTILEVEL MODELS AND HEALTH ECONOMICS

Multilevel analyses have become an accepted statistical technique in the field of education where over the past decade or so the methods have been developed to explore the relationships between pupil characteristics and the characteristics of the schools they attend. More recently, widespread use has extended to other social sciences and health research. However, to date, little use has been made of these techniques within the health economics literature. This paper presents an introductory account of multilevel models and describes some of the areas of health economics research that may benefit from their use.

Does health care spending improve health outcomes? Evidence from English programme budgeting data

Empirical evidence has hitherto been inconclusive about the strength of the link between health care spending and health outcomes. This paper uses programme budgeting data prepared by 295 English Primary Care Trusts to model the link for two specific programmes of care: cancer and circulatory diseases. A theoretical model is developed in which decision-makers must allocate a fixed budget across programmes of care so as to maximize social welfare, in the light of a health production function for each programme. This yields an expenditure equation and a health outcomes equation for each programme. These are estimated for the two programmes of care using instrumental variables methods. All the equations prove to be well specified. They suggest that the cost of a life year saved in cancer is about 13,100 pounds, and in circulation about 8000 pounds. These results challenge the widely held view that health care has little marginal impact on health. From a policy perspective, they can help set priorities by informing resource allocation across programmes of care. They can also help health technology agencies decide whether their cost-effectiveness thresholds for accepting new technologies are set at the right level.

Capitation and Risk Adjustment in Health Care Financing: An International Progress Report

In every system of health care, capitation payments have become the accepted tool used by health care purchasers in much of the developed world to determine prospective budgets. The policy prescription of capitation is perceived to address both equity objectives (of great importance in publicly funded systems of health care) and efficiency objectives (the dominant concern in competitive insurance markets). An examination of the current state of the art in 20 countries outside the United States in which health care capitation has been implemented confirms that capitation has assumed central importance within diverse systems of health care. In practice, however, the setting of capitation payments has been heavily constrained to date by poor data availability and unsatisfactory analytic methodology.

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 NICEs decision-making has changed over time. We model NICEs 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.

Ethics and geographical equity in health care

Important variations in access to health care and health outcomes are associated with geography, giving rise to profound ethical concerns. This paper discusses the consequences of such concerns for the allocation of health care finance to geographical regions. Specifically, it examines the ethical drivers underlying capitation systems, which have become the principal method of allocating health care finance to regions in most countries. Although most capitation systems are based on empirical models of health care expenditure, there is much debate about which needs factors to include in (or exclude from) such models. This concern with legitimate and illegitimate drivers of health care expenditure reflects the ethical concerns underlying the geographical distribution of health care finance.

Capitation funding in the public sector

A fundamental requirement of government at all levels—national and local—is to distribute the limited funds that it wishes to spend on particular public services between geographical areas or institutions, which are effectively competitors for such funds. Increasing use is now being made of capitation methods for such purposes, in which a standard estimate of expected expenditure is attached to a citizen with given characteristics. Statistical methods are playing an important role in determining such capitations, but they give rise to profound methodological problems. This paper examines the rationale for capitation and discusses the associated methodological issues. It illustrates the issues raised with two examples taken from the UK public sector: in personal social services and hospital care. Severe limitations of the data mean that small area data are used as the unit of observation, giving rise to considerable complexity in the model to be estimated. As a result, a range of methodologies including two-stage least squares and multilevel modelling methods are deployed. The paper concludes with a suggestion for an approach which would represent an improvement on current capitation methods, but which would require data on individuals rather than on small areas.

A person based formula for allocating commissioning funds to general practices in England: development of a statistical model.

Objectives To develop a formula for allocating resources for commissioning hospital care to all general practices in England based on the health needs of the people registered in each practice Design Multivariate prospective statistical models were developed in which routinely collected electronic information from 2005-6 and 2006-7 on individuals and the areas in which they lived was used to predict their costs of hospital care in the next year, 2007-8. Data on individuals included all diagnoses recorded at any inpatient admission. Models were developed on a random sample of 5 million people and validated on a second random sample of 5 million people and a third sample of 5 million people drawn from a random sample of practices. Setting All general practices in England as of 1 April 2007. All NHS inpatient admissions and outpatient attendances for individuals registered with a general practice on that date. Subjects All individuals registered with a general practice in England at 1 April 2007. Main outcome measures Power of the statistical models to predict the costs of the individual patient or each practice’s registered population for 2007-8 tested with a range of metrics (R2 reported here). Comparisons of predicted costs in 2007-8 with actual costs incurred in the same year were calculated by individual and by practice. Results Models including person level information (age, sex, and ICD-10 codes diagnostic recorded) and a range of area level information (such as socioeconomic deprivation and supply of health facilities) were most predictive of costs. After accounting for person level variables, area level variables added little explanatory power. The best models for resource allocation could predict upwards of 77% of the variation in costs at practice level, and about 12% at the person level. With these models, the predicted costs of about a third of practices would exceed or undershoot the actual costs by 10% or more. Smaller practices were more likely to be in these groups. Conclusions A model was developed that performed well by international standards, and could be used for allocations to practices for commissioning. The best formulas, however, could predict only about 12% of the variation in next year’s costs of most inpatient and outpatient NHS care for each individual. Person-based diagnostic data significantly added to the predictive power of the models.

Contractual conditions, working conditions and their impact on health and well-being.

Given changes in the labour market in past decades, it is of interest to evaluate whether and how contractual and working conditions affect health and psychological well-being in society today. We consider the effects of contractual and working conditions on self-assessed health and psychological well-being using twelve waves (1991/1992–2002/2003) of the British Household Panel Survey. For self-assessed health, the dependent variable is categorical, and we estimate non-linear dynamic panel ordered probit models, while for psychological well-being, we estimate a dynamic linear specification. The results show that both contractual and working conditions have an influence on health and psychological well-being and that the impact is different for men and women.