Main findings
We found evidence of an association between nurse staffing levels in English general practices and non-elective hospital admissions for asthma, COPD and diabetes. The relationships seem to be associated with the skill mix (ratio of nurses to doctors) rather than the size of the clinical workforce relative to the number of patients.
Patients registered with practices employing more nurses were less likely to have a non-elective admission related to their asthma and COPD. For COPD, the significance of the association varied from one model to another. Where significant, (2/3 models) the association was negative with higher nurse staffing level associated with lower admissions for COPD. For diabetes, however, the association was positive and showed significance in two of the three models fitted. This suggests that although higher nurse staffing levels have been shown to be associated with better compliance with processes of care and better intermediate clinical outcomes resulting in achieving higher QOF scores [1], its association with non-elective hospital admissions is likely to be dependent on other factors including the specific disease, service configurations and patient related factors not included in our models.
Contradictory evidence exists in the literature regarding whether the attainment of higher QOF scores is associated with positive effect on morbidity, mortality, hospital referrals and non-elective admissions [14–17]. Although the QOF might accurately assess the process of care, it is questionable whether the same is possible for clinical outcomes, despite some intermediate outcomes being included in the framework. This is in part due to the lack of adjustment for case mix when QOF rewards are calculated. Evidence from Canada also suggests that the relationship between performance and the actual health gains is questionable [18]. There is also controversy around the appropriateness and evidence-base of the targets relating to the control of acetylated haemoglobin (HbA1c) levels in diabetic patients that needs to be achieved in general practice [19]. We previously found that higher nurse staffing levels were associated with attainment of higher QOF scores in four out of eight clinical domains and with performance on specific indicators of intermediate clinical outcomes within the QOF clinical domains [1]. Using hospital non-elective admissions as an indicator of the quality of care delivery in general practice offers an external measure that can be useful in validating QOF performance measures. It can also be taken as a proxy measure of clinical outcomes.
We found partial support for the relationship between staffing and quality that we observed previously when using this external measure. In this study, we found that, for asthma, intermediate nurse staffing levels in the 2nd and 3rd quintiles (3038-3901 and 3901-4824 patients per FTE practice nurse) had the lowest admission rates in the crude and the SAR models respectively. This might indicate that an optimal mix of GPs and nurses, that does not require the highest levels of nurse staffing, could be the best strategy for delivering care to asthma patients in general practice.
However, simple changes in staff-mix are not sufficient without consideration of the context in which people work and the organisational factors related to that [20]. Hence, the variation in the relationship between higher nurse staffing levels and admission rates across the three clinical areas may relate to variations in the activity of nurses and/or how services are locally organised in those areas. This further confirms our earlier conclusion that there is a need to investigate the configuration of services and deployment of nurses more specifically [1].
While higher levels of nurse staffing were associated with better QOF performance for diabetes, including levels of HbA1C, in our previous study[1] they were associated with higher levels of admissions here. Although we did not directly assess the association between QOF performance and admissions in this study, this finding does confirm that the relationship is not a straightforward one. There are known associations between several socioeconomic and patient related factors and developing diabetes and its complications that are independent of the access to and quality of care provided. The National Diabetes Audit revealed that "the most deprived in the UK are two and a half times more likely to have diabetes" and that "complications of diabetes such as heart disease, stroke and kidney damage are three and a half times higher in the lower socio-economic groups" [21]. Reid and colleagues also found that patient factors were the strongest predictor of the large variation in all admission rates between 120 London general practices, especially for emergency admissions [22]. We have attempted to control for the socioeconomic factors, including deprivation at the practice level, as well as patient related factors, in the SAR. However, there are other patient related factors, like complexity of the condition and patient adherence to medication that can contribute to the likelihood of a diabetes related admission, which we were unable to include in the model.
Strengths and limitations
This study includes data covering the vast majority of patients in English general practice. Although we excluded some practices, our study has examined evidence from the vast majority of English general practices providing care to 48 million patients. We have controlled for potential confounding variables but observational studies such as this cannot account for unmeasured factors. It may be that higher nurse staffing is associated with other unmeasured attributes of quality within the practice and if this is the case increasing nurse staffing will not bring benefits unless these factors are attended to. What remains unclear is whether there is a causal relationship at all.
We have used the rate of hospital non-elective admissions as an indicator of quality of primary care delivered to patients; however although rates of hospital non-elective admission for some chronic conditions are possible indicators of the quality of care, they should be interpreted in conjunction with measures of population composition and deprivation. Hospital non-elective admission rates are likely to be confounded by socioeconomic characteristics of the population, case-mix and secondary care characteristics (e.g. hospital policies) which are not under the control of the primary care providers. Lack of adjustment for this confounding can result in primary care trusts and general practitioners being penalised for patterns of service use that they cannot directly control [2]. This was echoed by Giuffrida et. al. [23] who argued that using admission rates for asthma, diabetes and epilepsy as an indicator of primary care quality can be misleading. In our analysis, we have not controlled for secondary care related factors that might impact on admissions.
The approaches used to model the data have their own advantages and disadvantages. The SAR model allows for some adjustment at the patient level albeit using a simple model whereas the admissions models are based on data collected solely on practices therefore only suffers from model misspecification at that level. There could be multiple reasons why some practices have more single admissions than others that go beyond the care they provide and there may be a stronger case for using subsequent rather than first admissions as a proxy measure of quality [14]. In our findings whereas the relationship between nurse staffing and diabetes non-elective admissions was significantly positive in the models for one or more admissions, the overall association was not statistically significant in the repeat admissions model although in all cases the relationships were broadly similar across all models. The lack of consistency in relationships seems to indicate that nonetheless there is residual confounding.
We did not explore the cost implications of the variations in staffing observed. The absolute numbers of admissions across these conditions is high and so the relatively small reductions in rates of admissions translate into large absolute numbers of people. However when considering the numbers of people admitted per practice it seems clear that, even if causality was assumed, employing nurses to reduce admissions may be an expensive solution and could only be justified if there was a wide benefit in quality associated with a sufficiently large group of patients or across several conditions. However, hospital admissions are expensive and so economic gains through prevented admissions could also be large.