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

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Abstract

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, and in circulation about £8000. 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.

Introduction

One of the most fundamental yet unresolved issues in health policy is the extent to which additional health care expenditure yields patient benefits, in the form of improved health outcomes. The work of health technology agencies such as the English National Institute for Health and Clinical Excellence (NICE) has greatly improved our understanding at the micro-level of the costs and benefits of individual technologies. However, there remains a dearth of evidence at the macro-level on the benefits of increased health system expenditure.

The empirical problems of estimating the link between spending and health outcomes are manifest. If one relies on a time series of health outcome data for an individual health system it is difficult to disentangle the impact of expenditure from a wide range of other temporal influences on health, such as technological advances, epidemiological changes, and variations in broader economic circumstances. Similar methodological difficulties arise if one attempts a cross-sectional comparison of different health systems. In particular, when seeking to draw inferences from international comparisons, researchers have found it hard to adjust for all the potential external influences on health outcomes.

There is furthermore the possibility that indicators of health system inputs, such as expenditure, are endogenous, in the sense that they have to some extent been influenced by the levels of health outcome achieved in the past. And the difficulty of satisfactorily estimating the impact of health system inputs on outcomes is compounded by the great heterogeneity of health care, the multiple influences on outcomes, and the rather general nature of the outcome mortality measure traditionally used.

This paper takes advantage of a major new dataset developed in English health care, in the form of programme budgets, which enables us to address some of the difficulties associated with estimating the impact of health care expenditure on health outcomes. The data present expenditure on 23 broad programmes of care at the level of geographically defined local health authorities, known as Primary Care Trusts (PCTs), and embrace most items of publicly funded expenditure, including inpatient, outpatient and community care, and pharmaceutical prescriptions. They make it possible to examine the link between aggregate expenditure in a programme of care and the health outcomes achieved, notably in the form of disease-specific mortality rates.

The paper models the link between spending and outcomes in two of the largest programmes of health care: circulatory disease and cancer. We start with a brief review of previous empirical studies in this domain, which have rarely yielded conclusive results. The programme budgeting data are then described, and some descriptive statistics presented. We present a simple theoretical model of the budgetary problem faced by a PCT manager seeking to allocate limited funds between competing programmes of care. Well-specified econometric models are then developed that estimate (a) the budgetary expenditure choices and (b) the health outcomes achieved by PCTs in the two selected programmes of care. In contrast to many previous studies, the model results show a strong positive impact of expenditure on health outcomes. Finally, from the model results the paper is able to offer a quantitative estimate of the current cost of a life year saved in the two programmes of care. The important policy implications of these findings are discussed in Section 7.

Section snippets

Previous studies

In a comprehensive review, Nolte and McKee (2004) reported many studies that seek to estimate the impact of health care and other explanatory variables on some measure of health care outcome. Usually, this production function approach employs conventional regression analysis: for example, in an early cross-sectional study of 18 developed countries, Cochrane et al. (1978) used regression analysis to examine the statistical relationship between mortality rates and GNP and consumption of inputs

Programme budgeting in England

The English National Health Service (NHS) is the archetypal centrally planned and publicly funded health system. Its revenue derives almost entirely from national taxation, and access to care is generally free to the patient. Primary care is an important element of the system, and general practitioners act as gatekeepers to secondary care and pharmaceuticals. The system is organized geographically, with responsibility for the local administration of the NHS devolved to the Primary Care Trusts.

Theoretical model

We assume each PCT receives an annual financial lump sum budget yi from the national ministry, and that total expenditure cannot exceed this amount. The PCT must then decide how to allocate the expenditure across J programmes of care (j = 1, …, J; J = 23 in this case). For the jth programme of care there is a ‘health production function’ fj(·) that indicates the link between local spending xij on programme j and health outcomes in that programme, hij. Health outcomes might be measured in a variety

Model estimation

The theoretical model suggests the specification and estimation of a system of equations, with an expenditure and health outcome equation for each of the 23 programmes of care. In the absence of endogenous regressors the system would reduce to the estimation of seemingly related regressions. However, this approach makes infeasible data demands, requiring variables to identify expenditure, need, environmental factors and health outcomes in each of the 23 programmes of care. In the presence of

Empirical results

Eight of the 303 PCTs were excluded from the analysis because they were subject to boundary changes in the year studied, leading to incomplete data. The analysis therefore uses 295 observations throughout. We first present results for the cancer programme of care in Table 2. Columns under (1) present the OLS results for the two equations to be estimated using SMRs as the death measure and columns under (2) present comparable two-stage least squares results. Columns under (3) and (4) employ

Conclusions

This study shows that health care expenditure has a strong positive effect on outcomes in the two programmes of care investigated. Our estimates suggest that, relative to received wisdom, the marginal cost of a ‘life year’ saved is quite low, at approximately £8000 for circulatory disease and £13,100 for cancer.8

Acknowledgements

This study was funded by the Health Foundation under its Quest for Quality and Improved Performance (QQuIP) initiative. We are grateful to Hugh Gravelle, Andrew Jackson and Peter Brambleby for helpful comments and advice. The material has also benefited greatly from comments from Chris Murray (Institute for Health Metrics and Evaluation, University of Washington), Dean Jamison (University of California, San Francisco), participants at a seminar at the Harvard School of Public Health, and the

References (21)

  • H. Gravelle et al.

    International cross-section analysis of the determination of mortality

    Social Science Medicine

    (1987)
  • T.W. Anderson

    Introduction to Multivariate Statistical Analysis

    (1984)
  • A. Cochrane et al.

    Health service ‘input’ and mortality ‘output’ in developed countries

    Journal of Epidemiology and Community Health

    (1978)
  • J.G. Cragg et al.

    Testing identifiability and specification in instrumental variables models

    Econometric Theory

    (1993)
  • P. Cremieux et al.

    Health care spending as determinants of health outcomes

    Health Economics

    (1999)
  • Department of Health, 2005a. NHS Finance Manual. December 2005 edition. See...
  • Department of Health

    Unified Exposition Book: 2003/04, 2004/05 and 2005/06 PCT Revenue Resource Limits

    (2005)
  • Durbin, J., 1954. Errors in variables. Review of the International Statistical Institute. 22,...
  • Gill, P.S., Kai, J., Bhopal, R.S., Wild, S., 2007. Health care needs assessment: black and minority ethnic groups. In:...
There are more references available in the full text version of this article.

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