Practice size, caseload, deprivation and quality of care of patients with coronary heart disease, hypertension and stroke in primary care: national cross-sectional study

Data sources

The study was carried out using Quality and Outcomes Framework (QOF) data for England and Scotland collected from April 2004 to March 2005[8]. The data comprise 9,411 general practices with a population of 57,787,662 patients. Since data are anonymised the study was exempt from the need for ethical approval. Practices score points based on their levels of achievement against a range of evidence-based clinical indicators in 10 chronic disease areas including cardiovascular disease, which accounts for 47% of the total clinical points, as well as points in practice organization and management. Payments to practice are calculated from points achieved.

There are two types of data: data relating to QOF indicator or domain scores and disease prevalence information for each disease within the clinical domain of the QOF. The source of QOF data is a national IT system called the Quality Management and Analysis System (QMAS)[9]. This single national system ensures consistency in the calculation of quality achievement and prevalence. Clinical QOF data is extracted from individual general practice clinical systems and sent automatically to QMAS; organizational, access, patient experience and additional service indicators are entered by the practice directly into QMAS via a web-browser. Data from practices without QMAS-compliant computer systems were entered manually into QMAS (exceptionally few practices do not have QMAS-compliant computer systems). QOF information is collected at an aggregate level for each general practice and there is no patient-specific data within QMAS.

Linking deprivation measures to practices

Exclusions and exception reporting

Practices were excluded if they could not be matched via their postcode. We excluded practices in Scotland that were not fully part of the contract. In total, we excluded 441 (4.1%) practices with 2,264,884 (3.5%) patients, leaving 8,970 general practices with a total list size of 55,522,578 for analysis. From the Quality and Outcomes Framework (QOF) data, we excluded indicators reporting the presence of a disease register since all practices in QOF met this requirement. We used Stata version 9 for all analyses[16].

The Quality and Outcomes Framework allows exception reporting to avoid practices being penalized unfairly. For example, where patients do not attend for review, or if a medication cannot be prescribed due to a contraindication or side-effect. The criteria for exception reporting include: patients who are on maximum tolerated doses of medication whose levels remain sub-optimal; where a patient does not agree to investigation or treatment (informed dissent), and this has been recorded in their medical records; and where an investigative service or secondary care service is unavailable. Level of exception reporting was monitored for each individual practice and if it would be unusually high or low the data would need to be verified. The median rate of exception reporting after the first year was small <6% and we did not make any adjustment for this[8]. Primary care organizations are responsible for monitoring exception reporting in practices.

Analysis

We grouped practices into quintiles of list size; five groups based on the number of patients registered with the practice. We grouped practices into quintiles of prevalence, five groups based on the number of patients with cardiovascular disorders (coronary heart disease, hypertension and stroke) in each practice. Groupings of practices into three categories (tertiles): 1-deprived 2-intermediate and 3-affluent, based on the national rank of the geographic area in which the practice is located (i.e. practices in group 1 represent 33.3% of neighborhoods with lowest socio-economic status nationally). We calculated the percentage of practices who achieved each of the targets by caseload, practice size and deprivation.

Practice characteristics and cardiovascular disorders

Caseload, practice size and quality of care

For most of the indicators there was a high level of quality achievement regardless of caseload, except for selected indicators for early diagnostic investigations, where practices with a higher caseload of patients with cardiovascular had a higher level of achievement of indicators of quality (Table 3). Practices with lowest caseload achieved the score of 71.4% for referral of patients with newly diagnosed angina (diagnosed after 1/4/03) for exercise testing and/or specialist assessment (CHD 2) whereas the practices with highest caseload achieved 88.6%. Similarly, practices with low caseload achieved 73.3% score (the points achieved as a percentage of points available) for the percentage of patients with a diagnosis of coronary heart disease and left ventricular dysfunction which has been confirmed by an echocardiogram (LVD2), whereas the practices with highest caseload achieved 89.3%. Similarly, low caseload practices achieved 63% of referral for confirmation of newly diagnosed presumptive stroke by CT or MRI scan compared with 86% of practices with the highest caseloads (Table 4).

Deprivation and quality of care

Summary of main findings

Despite wide variations in practice size and deprivation levels, we found surprisingly little variation between general practices in the prevalence and management of cardiovascular disorders. Practices with a higher caseload had similar achievement of quality for many aspects of management, but practices with higher caseload achieved higher scores, and in larger practices and more affluent practices for some indicators for initial diagnosis and management including referrals for exercise testing and/or specialist assessment, an echocardiogram, CT or MRI scan. This may reflect better access to resources for initial management in larger practices than smaller ones, perhaps through local planning and commissioning of resources by primary care trusts for services where there is a higher caseload.

Findings in relation to other studies

Two principal hypotheses have been used to explain volume-outcome relationships: 1) Physicians (and hospitals) develop more effective skills if they treat more patients ("practice makes perfect"); or 2) physicians (and hospitals) achieving better outcomes receive more referrals and thus accrue larger volumes ("selective referral")[1]. While the first hypothesis may particularly apply to our findings (e.g. more accurate case ascertainment), the latter is less likely to apply to NHS primary care than secondary care and since UK general practitioners usually provide all round general medical services rather than specialist chronic disease management and patients' ability to choose their GP is largely determined by whether they live within practice catchment areas. We cannot test either hypothesis with our current data. Moreover, we believe that other explanations may be more plausible in primary care volume-outcome relationships; principally, how the delivery of care is organized and shared between the members of the primary care team. This is supported by one study reporting that organizational domains in the new contract were associated with lower achievement in smaller practices compared with larger ones, whereas scores for clinical care were similar[17]. Chronic disease management can be complex and require input from many different elements of the primary care team. Many of the processes of care assessed in our study could be effectively performed by nurses, for example checking of blood cholesterol. Such sharing of care may be more easily achievable in larger practices, which can generally employ a wider range of staff. Indeed, it is known that many practices employed additional nurses and other supporting staff when the new contract was introduced.

A recent study suggests that the "wealth-health gradient" in cardiovascular mortality may be partially ameliorated by more rigorous management of known risk factors among less affluent people[18]. Our findings that prevalence of cardiovascular disorders was similar in deprived and affluent practices is unexpected and, in this light, our finding that some aspects of the management of cardiovascular diseases were worse in practices in deprived areas is of concern and consistent with one other study reporting that socially deprived areas experience a lower quality of care overall[19]. However, most aspects of risk management showed relatively little association with practice area based deprivation. This suggests that people living in neighborhoods with low socio-economic status are now receiving cardiovascular disease management in primary care of similar quality to people living in more affluent neighborhoods.

Strengths and limitations

This is the largest population-based study to date examining volume-outcome association for cardiovascular disorders in primary care. The structure of primary care in the United Kingdom offers some unique opportunities for examining the association between volume and outcome. Almost the entire population is registered with a general practitioner, who is responsible for providing primary care services and arranging referrals for specialist care. This means that general practitioners have well-defined denominator populations, which in turn allows the calculation of accurate disease prevalence and treatment rates.

One limitation of the study is that patients with cardiovascular disorders but not coded on the computer record would not have been identified. However, general practices are now paid by the results they achieve and also on prevalence. Quality and Outcomes Framework data also have some other limitations. There is no information on gender, age, ethnicity and severity of disease or co morbidity or individual patients' socio-economic status (this was assessed indirectly through practice area based deprivation scores and is not necessarily a good marker of deprivation for individual registered patients) and thus no adjustment for these is possible. However, even if there were systematic difference in patient characteristics and case-mix, practices still had the option to 'exception report' patients, thus enabling the practices to still achieve maximum scores. When patients are 'exception reported' they are excluded from that score. Initial reports suggest that only a small minority of practices achieved high scores by exception reporting[8]. There is also a risk of manipulation or gaming (for example, recording a patient's blood pressure as being lower than it actually is) which will be difficult to detect. Although this may occur in some areas, practices are subject to an annual detailed inspection and the penalties for making fraudulent claims are severe[7].

There was also limited information about practice characteristics. We defined practices with large list sizes as large practices but did not have information about the number of GPs employed by a practice. Hence the caseload of a condition per GP may be similar or lower to that in smaller practices even if the caseload per practice is high. We were unable to account for this since we did not have information about the number of GPs employed per practice. Other possible explanations for the differences we found in quality achievement for referring patients with newly diagnosed cardiovascular disorders to early diagnostic facilities, such as local waiting lists or urban-rural differences in access that we felt went beyond the scope of this study.

Although the indicators of the quality of care are 'evidence-based', they are process measures or intermediate outcomes. We have no data on outcomes such as mortality or quality of life. Our findings are also limited to the management of cardiovascular disorders and we cannot say whether similarly weak volume-quality relationships would occur in the management of other diseases in primary care. Finally, our study is unable to explain the mechanism through which volume may influence quality in primary care.

Although numerous studies have examined the volume-outcome relationship in secondary care,[20] very few previous studies have examined this relationship in primary care. Hippisley-Cox and colleagues compared a number of areas of practice activity in small and larger practices in the Trent region of England[21]. They found no strong evidence that small practices offered poorer quality of care than larger practices. Another study in London also found no great associations between practice size and quality of care[22].