Fairness in primary care procurement. Measures of under-doctoredness: Sensitivity analysis and trends

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. Robustness of PCT rankings GP provision per head of need adjusted population is measured as GP_perhead=(GPs/need weights × raw population) x 100,000 There are reasonable alternative definitions of “GPs”, needs, and population. We examined how sensitive rankings of PCTs and the set of 30 worst provided PCTs were to these alternative definitions. • Using the White Paper measure of GPs (whole time equivalents, excluding registrars and retainers, as at March 2005) we found that the set of worst provided PCTs is not very sensitive to alternative needs and raw population measures. For 12 alternative need and population adjustments, only 3 of the White Paper PCTs are not in the 30 PCTs which appear most often in the bottom 30 over the 12 alternatives (Table 5). • But GPs make up 30% of the staff in general practice and the mix of GPs, practice nurses, and other practice staff varies considerably across PCTs (Figure 6). Hence rankings of PCTs and the set of worst provided PCTs are much more sensitive to the definition of general practice staff (Tables 2, 9, 10; Figure 5). • When the date at which GPs were measured was changed from March 2005 to September 2005, 23 of the White Paper’s bottom 30 PCTs were still in the bottom 30 (Table 2) and measures of provision for different need weights and raw population were very highly correlated (Table 7). • Measures of provision calculated using the White Paper definitions were also highly correlated between consecutive years (Table 12) Robustness of measures of overall inequality in distribution It is also of policy interest to know how the overall level of geographical inequality in GP distribution across all PCTs is changing over time. Overall inequality may be affected both by policies targeted at the worst provided PCTs and by more general policies, for example by increases in the overall supply of GPs. We used the Gini coefficient as the measure of overall inequality of distribution of GPs per need adjusted population across the 303 PCTs. We examined the effect of alternative definitions of GPs, need, population on the Gini coefficient. We found (Table 20): • the Gini is insensitive to the definition of “GPs” • the Gini is greatly affected by the choice of need adjustment and population measure. Using the White Paper definition of GPs, the Gini is greatest when the need adjustment is the Standardised Mortality Ratio and the population is measured by GP lists. It is smallest when the need adjustment is by consultation rate and the White Paper population measure is used. We also examined trends in the Gini • recent (2002-2005) trends are similar across alternative measures of GP provision per need adjusted population • they all suggest a very small trend increase in inequality (Table 21, Figure 8) • data from 1974-2005 suggest, allowing for breaks in the series caused by changing definitions and NHS administrative geography, that inequality has not fallen since the mid 1980s and may have increased slightly (Figure 9) Conclusion Our main conclusion is that whilst the set of worst provided PCTs varies, sometimes substantially, with the choice of GP supply measure, need adjustment, and population base, the set of 30 identified by the White Paper contains a core of around 10 PCTs which are amongst the worst provided on most possible alternative definitions. The White Paper set also contains a larger fringe group which are in the bottom 30 on some definitions, particularly when the White Paper definition of GPs is used, but which also often fall outside the worst provided bottom 30. There is no obviously right set of definitions of GPs, need adjustments, and populations which can be implemented with available data. Judgements are required and those underlying the White Paper seem not unreasonable. However, we suggest that consideration be given to broadening the definition of the general practice staff from GPs to include practice nurses and possibly non-clinical staff as well.