Government policy assumed that incentives for general practice through performance-related-pay would improve mortality rates and other outcomes. Its scheme for doing this appears not to have worked as intended, explains Evangelos Kontopantelis.
Primary care has enormous potential for improving population health outcomes – including mortality from common chronic conditions – through early intervention in the disease process and co-ordinated provision of care. In order to improve patient outcomes, policymakers worldwide have attempted to link remuneration for providers to quality of care through pay-for-performance programmes.
In the UK, a national primary care incentive scheme was introduced in 2004. The Quality and Outcomes Framework (QOF), one of largest pay-for-performance programmes in the world, links up to 25% of family practitioners’ income to performance on over 100 publicly reported quality indicators. Several indicators relate to organisation of care and patient experience, but the majority relate to management of common chronic diseases, including the leading causes of death in the UK: coronary heart disease, cancer, chronic obstructive pulmonary disease, hypertension, stroke and diabetes. The annual cost of the scheme is estimated at approximately £1bn.
Over time, there have been considerable improvements in the management of common chronic conditions, a worthwhile achievement by general practices. However, that does not necessarily translate into longer lives for patients. In our latest research, we therefore asked whether there was any association between practices’ performance on the QOF and mortality rates in the populations they serve.
In a complex data analysis, we failed to find a link between performance as assessed by the pay-for-performance incentive scheme and a reduction in mortality. We combined anonymised information from the Office for National Statistics and the Health and Social Care Information Centre and so were able to cover almost the entire population of England, including 99% of patients registered with a GP surgery, analysing data from 2007 to 2012. By far the greatest influence on mortality was poverty, followed by geographical location: mortality rates in urban areas are higher than mortality rates in rural areas. Both these factors are known to affect health, but the scale of their impact on mortality over time, particularly in the case of deprivation, was greater than expected.
But what do our findings mean? First, let us see what they do not mean: that primary care does not save lives; that the investment made in the QOF was squandered; that the effort made by thousands of practice staff in meeting QOF targets was wasted; nor that the QOF has had no impact on patient care and outcomes.
It might be the case that the indicators of the scheme need to be reconsidered and better aligned with existing evidence. For example, one of the QOF targets is that for patients with diabetes, glucose levels be controlled below 7.5%. Yet clinical trial findings indicate that intensive glucose control is associated with increased mortality, especially the risk of cardiovascular death in younger patients. Observational studies have generally demonstrated that both very low and very high levels of HbA1c (glycated haemoglobin, a measure of average blood sugar over time) in diabetic patients are linked to higher mortality rates. These relationships are also present with other biometric measurements, including blood pressure and total cholesterol levels.
These relationship patterns suggest that having target values of glucose levels are poor indicators for the quality of care, since they fail to capture the high risk associated with very low levels. Instead, target ranges might be more suitable. In addition, recording whether blood pressure or HbA1c control was met once every six or 15 months might not be a reliable indicator for the whole of the period. Day-to-day management is required for these biological parameters and variability as well as levels appears to be important.
Given that mortality rates fell substantially for several QOF conditions during the course of the programme – in particular, cerebrovascular disease and coronary heart disease – this suggests that improvements were due to factors outside primary care, or at least not incentivised under the QOF. For coronary heart disease, previous studies suggest that the main drivers behind mortality reductions have been population improvements in risk factors, mainly declining rates of smoking. Whilst the QOF included indicators relating to several risk factors, many of these incentivised processes (e.g. recording smoking status and offering cessation support) rather than improvements in outcomes (e.g. reducing smoking rates).
The relationships we investigated are complex and the research community may never fully manage to quantify the direct and indirect effects of the QOF, or agree on them. However, what we have found is that higher overall performance on QOF indicators is not associated with lower mortality rates for key incentivised conditions. The apparent lack of a large effect on mortality over the medium term may suggest that the QOF may not have been an optimal investment of health service resources. But there is a need for further investigation of how primary care practices can affect mortality rates and to what extent this is dependent on the quality of local secondary care services. We need to understand these relationships better. Patient-level analyses that investigate specific diseases, in isolation and in combination, may help us to understand this and improve policy and design effective interventions.
Our conclusion is that if incentive schemes continue to be used in primary care with the intention of improving population outcomes, indicators will need to be reconsidered and better aligned with evidence on which activities contribute to mortality reduction.
