Introduction

The publication of the white paper The New NHS, Modern, Dependable1 proposing the creation of primary care groups with responsibilities to meet the healthcare needs of their populations within an annual budget, together with the government's commitment to provide healthcare services on an equitable basis,2 has highlighted the need to define practice budgets on a rational basis and to link expenditure to population healthcare needs. For hospital and community health services expenditure, mechanisms already exist for allocating monies from central government to health authorities, and thence to general practice,3 on the basis of population need. For prescribing expenditure, allocations have only recently moved towards a weighted capitation system to allocate monies to health authorities, and in 1996-7, for the first time, a proportion of the prescribing budget was based on a needs weighting. After appropriate adjustments for the age, sex, and temporary resident characteristics of practices using what are termed age, sex, and temporary resident originating prescribing units (ASTRO-PUs)4, a weighting for the proportion of people in the 1991 census declaring themselves as unable to work owing to permanent sickness or disability was applied to calculate health authority allocations.5

The methodology for devolving health authority prescribing budgets to individual general practices on the basis of population need is much less advanced. Primary care prescribing budgets have largely been based on previous years' spending, with adjustments for an uplift plus growth factor for practices whose budget share, adjusted for the demographics of practice lists and other (unspecified) need factors, was below the local average.6 Little regard has been given to differences in population need.

We report on the results of a study commissioned by the NHS Executive to examine the determinants of NHS prescribing expenditures at practice level by relating costs to population needs, with the explicit purpose of developing a needs based capitation formula capable of allocating annually about £4.5 billion of NHS revenues to health authorities and primary care groups. At the time this study was commissioned the composition of primary care groups was unknown and in their absence general practice, which represents the lowest unit of analysis possible using current data, was used as the focus of the work. Target allocations to primary care groups and health authorities can be seen as aggregates of allocations for individual practices for which they are responsible. The results of this study have informed target allocations for prescribing budgets for the year 1999-2000. Full details of the study can be found in Rice et al.7

Methods

The demand for health care is a complex process, but in order to proceed the following were assumed to be of relevance for prescribing. Demand was measured as expressed demand for prescriptions using utilisation in the form of total practice net ingredient costs for 1997-8. We consider two types of determinants of this demand to be important: the health needs of registered list populations and the supply characteristics of general practices. It is assumed that underlying socioeconomic and demographic characteristics of populations give rise to healthcare needs, in terms of morbidity. This in turn gives rise to the demand for healthcare services including prescriptions. It is also assumed that other socioeconomic characteristics, such as social needs and expectations, independently influence demand over and above those operating through health needs.

The adopted style of general practice can be assumed to have a significant impact on the costs of prescribing. For example, more innovative and better informed practices actively encouraging cost effective prescribing may be cheaper per capita for a given level of need. As well as influencing utilisation, supply may itself be influenced by past use and needs, creating, over time, a feedback loop between supply and utilisation. This renders the use of conventional statistical methods, such as ordinary least squares, inappropriate; instead methods akin to two stage least square, which explicitly aim to take account of the potential simultaneous determination of utilisation and supply, are required. Therefore, an important feature of the work presented here is the attempt to separate out the independent effects of needs and supply on utilisation.3

Results

Table 1 presents the model selected adopting the above procedures. Health authority effects and the list inflation variable are not shown. Four needs variables were selected: percentage of adults in households permanently sick (psick), percentage of dependants in no carer households (pnocare), percentage of working age population who are students (pstudents), and percentage of births on practice lists (pbirths). Table 2 provides full definitions of these needs variables together with descriptive statistics. Positive coefficients indicate that higher percentages of these variables were associated with greater cost per ASTRO(97)-PU; the converse was true for negative coefficients. The needs and supply variables together explained 41% of variation in cost per ASTRO(97)-PU. A separate regression of net ingredient cost per capita on ASTRO(97)-PUs and supply resulted in an R2 of 0.35. Inspection of standardised residuals against normal scores showed no serious signs of departure from normality, and the reset test indicated no evidence to reject the null hypothesis of adequate model specification, F(3, 8392)=0.24; P=0.86.

The four variables selected are intuitively plausible as needs drivers of prescribing expenditure, and exhibited the expected signs of association with costs. Permanent sickness played a dominant role in the modelling, and although there are some doubts over its interpretation (self reported morbidity which limits activity, rather than an objective measure of morbidity), it was found to be a stronger predictor than standardised mortality or illness ratios or self reported limiting long term illness. It is also in line with the current formula used to allocate prescribing monies to health authorities.5 The percentage of dependants with no carers is likely to be reflective of wider socioeconomic circumstances, whereas the inclusion of the percentage of births on practice lists is likely to capture both an effect of women of childbearing age and the increased demands of young children. The percentage of students is likely to reflect several factors including those associated with young mobile healthy populations and a lack of permanent residence.

It should be emphasised that for allocation purposes only the coefficients attached to the needs (and constant) variables are of relevance. Supply variables were included in the modelling procedure to condition upon to ensure that we were able to control for any correlation that may have existed between needs and supply. However, it is only the needs coefficients that determine the gradient upon which actual target allocations are intended to be based. In no way is it intended that, for example, dispensing and non-dispensing practices should be treated differently when deriving needs based allocations; it is only the needs composition of their respective lists that are of importance.

Discussion

We derived a robust needs based capitation formula capable of setting target budgets for health authority and primary care group prescribing allocations. The resulting model contains four intuitively plausible needs drivers, has good statistical specification, and is capable of explaining up to 62% (35%+0.65% (41%)) of variation in prescribing expenditure at practice level. The formula has been implemented by the NHS Executive to set target allocations for health authorities and primary care groups for 1999-2000.14

The possibility for further refinements to the model seems limited using current data sources. In future, enhancements to the model would be gained through the use of income related data and data on nursing home patients should these become readily available. Income related data may take several forms, but the inclusion of data provided through the low income scheme and income support is likely to prove most valuable. It was not possible to include data on nursing home residents in this study owing to a lack of comprehensive and reliable data, but in recognition of the need for local flexibility health authorities will be allowed to make adjustments to target shares for primary care groups to reflect the extra costs of prescribing to nursing home residents.14

Further advances in understanding the needs based mechanisms of prescribing may best be achieved through moving to data measured at the individual patient level. Although, for the foreseeable future, it seems unlikely that such data will be collected on a routine basis, much could be gained from a survey of individual patients and their practices. This may form the basis of a future research agenda not only in the area of prescribing but also to inform resource allocation methodology in other areas of the NHS budget.