Case-mix and variation in specialist referrals in general practice

Method

We used data from the General Practice Research Database (GPRD) on 1 323 611 patients registered in 211 general practices across England and Wales in 1997.12 The database records include demographic information, disease incidence and medical, surgical and psychiatric referrals for each patient. Several studies have assessed the validity of these data.13^,14

General practices contributing data to the GPRD follow guidelines for the recording of administrative, diagnostic, and referral data. Diagnoses were recorded using OXMIS or Read codes, and these were later converted to ICD-9-CM codes. Each patient was assigned to an ACG group, based on their combination of diagnoses, age, and sex.9 The ACGs were ranked according to their morbidity burden in the US and then grouped into six mutually exclusive categories, each containing roughly the same number of ACGs.4 We refer to these categories of ACGs as morbidity groups, ranging from group 1 (healthiest) to group 6 (least healthy). Age was divided into four groups representing children, young adults, older adults and the elderly (0–15, 16–34, 35–64 and ≥65 years respectively).

Patients were excluded from analysis if they were registered with a practice for less than 180 days. Practices were excluded if less than 2% of their registered patients were referred or if their deprivation code was missing. Since only a small percentage of patients were referred more than once, we combined patients with one or more referrals in a single category and treated referral as a binary outcome.

Statistical methods

Multilevel logistic regression models with random intercepts were used, and these take into account the clustered nature of the data (patients within practices).15 Four models with different sets of predictors were considered:

• Model 1: no predictors;

• Model 2: age and sex;

• Model 3: morbidity;

• Model 4: age, sex and morbidity.

These models allow us to split the variation in referrals into two of its components: variation between practices and variation between patients within practices. The percentage of total variation in referrals explained by each of the models was estimated using an R² measure developed for multilevel models by Snijders and Bosker.16 We made graphical comparisons of the agreement between the observed referrals and those predicted by the models for each practice. Receiver Operating Characteristic (ROC) areas were calculated to assess how well the models were able to discriminate between those patients who were referred and those who were not.17 A value of 1 for the ROC area indicates that the associated model discriminates perfectly between patients with high and low referrals, while an area of 0.5 indicates that the model discriminates no better than chance. MlwiN v1.10 software was used for multilevel modelling;18 Stata 8.0 was used for all other analyses.

Summary of main findings

Even after excluding practices with very low referrals (less than 2% of patients referred annually), there was still a wide range in patients referred by practice (2.4–24.4%). Patient morbidity explains considerably more of the total variability in referrals than patient age and sex. We found that about 5% of the total variation in referrals was occurring at the practice level, and this supports research suggesting that the amount of variability between practices may be less than implied by previous studies based on aggregate information.19 However, it may not be straightforward to understand the significance of the size of variability with respect to decisions on policy-related issues.19 Furthermore, the findings suggest that there is considerable variation in referrals between patients within practices; and that morbidity explains substantially more of this (around 24% more) than age and sex. There are often many factors other than the diagnostic label that influence whether the patient is referred or not, and this could explain why a large proportion of the variability remains unexplained.1 We also found that morbidity was a better predictor of practice referrals than age and sex. Thus, morbidity holds promise as a targeting tool to predict which patient groups are most likely to require referrals.

Strengths and limitations of this study

This was a large primary care-based study that used data from practices contributing to the GPRD, which has been extensively validated and shown to be of high quality.12^,13 The GPRD practices are reasonably representative of the age–sex profile of the UK population, and their referral rates are similar to national averages.19 However, there is some under-representation of inner-city practices and the average size of the practices is greater than the national average.19 The database provided us with individual level data, thus avoiding the ‘ecological fallacy’ (that is, associations at the practice level may not hold at the individual level), and we incorporated the clustering of patients in practices in our statistical models. This was the first large-scale study in the UK to control for diagnostic-based morbidity groupings specifically designed for use in primary care when examining variation in specialist referrals in general practice. However, the ACG system was derived for use in the US, and so may need some further adaptation to maximise its utility in the UK.11 Finally, the system depends on diagnostic codes recorded by GPs during consultations and hence, differences in the way that GPs record similar conditions on their practice computers could introduce bias.

Comparison with existing literature

Many previous studies have reported wide variations in general practice referral rates. Studies that have adjusted for age and sex have typically found that the observed variation decreased by less than 10%, in line with our own findings.19 This could be improved upon by adjusting for other factors, such as socioeconomic deprivation or practice characteristics, but most of the variation in referrals still remains unaccounted for.1 The potential of a morbidity measure based on each patient's medical history to explain general practice referrals has not previously been investigated in the UK. Studies from the US suggest that it is better than age and sex in explaining variation in referrals, again in line with our own findings.6

Implications for general practice

Variation in general practice referrals is an important issue for clinicians, managers, patients and politicians. Age and sex seem to explain little of this. The Johns Hopkins ACG Case-Mix System explains substantially more variation, although most remains unexplained. More research is needed to assess the cost-effectiveness of implementing and administrating such a system.

The unexplained variation seems to occur largely within practices, so that the GP decision of whether to refer a patient varies, even for patients with a similar age, sex and morbidity group. This may be partly due to patient factors that cannot be easily measured, such as the perceived patient pressure or their level of confidence in their GP.21^,22 The GP's perception of medical need, experience in treating the condition for which the patient was referred and the local provision of specialist services may also be important.21^,22 Understanding other reasons for variation in referrals could help to ensure more efficient use of NHS resources.

The findings suggest that any investigation of how practices are using specialist services should be interpreted cautiously even after adjustment for age and sex. In contrast, measures such as diagnostic-based morbidity are likely to give more useful information, but caution should be exercised even after adjusting for these, as much of the variability in use of services is likely to remain unexplained. Similar caveats are likely to apply to other areas of primary care, such as prescribing costs, mortality rates and the quality measures in the new GP contract. The association of these measures with case-mix should be explored to ensure that practices that seem to have a high intensity of resource use, that have high death rates, or that do not achieve quality targets are not inappropriately scrutinised or penalised for this.