In this national study of nearly 130
000 patients from 326 practices in primary care, we found associations between patients’ sociodemographic characteristics and their likelihood of referral. After adjustment for comorbidity (defined as the number of drug types prescribed), older patients were less likely to be referred for the three conditions examined: postmenopausal bleeding, hip pain, and dyspepsia. This gradient with age was particularly noticeable for postmenopausal bleeding. In addition, women were less likely than men to be referred for hip pain. We also found evidence of decreasing rates of referral with increasing deprivation for patients with hip pain and for those aged under 55 with dyspepsia, but not for patients with postmenopausal bleeding or those over 55 with dyspepsia.
Secondary analysis showed that the effects of age and sex were not explained by variations in overall referral rates between practices but were instead related to younger patients and men with hip pain being more likely to be referred than their older, female counterparts within the same practice. In contrast, inequalities relating to patients’ deprivation may in part be explained by general practitioners who work in more deprived areas being in general less likely to refer, rather than more socially disadvantaged patients being less frequently referred than their more affluent counterparts within the same practice.
Comparison with other studies
Variations in referral rates by age occurred regardless of the criteria for referral or the risk of cancer. These findings of lower referral rates with increasing age have also been shown for patients presenting with symptoms of ovarian cancer and it has been suggested that this may partly explain poor survival rates of older people with cancer in the United Kingdom.25
The variations may in part be explained by clinical uncertainty about the likely benefit or harm trade-offs of treatment in older patients or by patient preference. The involvement of patients in decisions about their care fulfils a fundamental tenet of high quality care, but it is important to unravel the origin of patient preferences. Possible explanations for variations in the choices that patients make include systematic differences in expectations for good health and in perceptions of the risk and benefits of defined interventions.26
Thus older people may be less willing to undergo procedures because of concerns about good outcomes or adverse consequences.27
Concepts of risk may be influenced by beliefs about what are considered “good innings” or “normal” ageing,28
and the benefits of treatment for older people.29
Such perceptions are likely to be defined, in part, as a result of interactions with doctors. For example, mortality and function after hip surgery are worse for older patients than for younger ones, but the absolute differences are small and on average older people can still expect an improvement in quality of life.30
The way in which this information is framed is likely to influence the decision made. With respect to sex inequalities, women may be reluctant to undergo hip surgery because prolonged rehabilitation might jeopardise the responsibility they have to care for others.31
Older women are also more likely to live alone and so may be concerned about being dependent on others in the postoperative period. Finally, qualitative research shows that older people tend to minimise their problems32
and may therefore be reluctant to seek help from specialists in secondary care.
The lack of information on severity of disease and patient preference meant that it was not possible to ascertain whether differences in referral rates reflected clinically appropriate joint decision making or inequity. The severity of disease is, however, unlikely to explain our findings because osteoarthritis of the hip is more severe and disabling in lower socioeconomic groups,33
postmenopausal bleeding secondary to endometrial cancer increases with age,34
and the incidence of stomach cancer increases with age and increasing deprivation.35
Therefore our results would be expected to be the converse of those observed (that is, referral would be more likely in more deprived and elderly people) if variations in referral were secondary to unmeasured severity of disease or risk of serious disease.
The conditions studied differed for both the presence of explicit referral criteria and the need to exclude a cancer diagnosis. Both criteria were present for postmenopausal bleeding and dyspepsia in patients aged over 55, and socioeconomic variations in referral rates were not observed. Neither criteria was present for hip pain or dyspepsia under age 55, and socioeconomic gradients in referral rates were shown. In common with other research36
we found that these gradients may in part be explained by a lower likelihood of referral by practices serving socially disadvantaged communities. The reasons for this are, however, unclear. One possibility is that the geographical distribution of specialist services may result in practices in socially disadvantaged areas having poor access to particular specialties. This is unlikely to explain our results because we examined common symptoms requiring access to the services of widely available gynaecologists, orthopaedic surgeons, and gastroenterologists and endoscopists. A second possibility is that practices serving socially disadvantaged communities tend to have higher workloads than those serving more advantaged areas and the patients often have multiple, chronic and complex problems with health and social care.37
As a result this may lead general practitioners to exhibit different referral practices. Thirdly, factors such as the type of contract agreed by general practitioners may be associated with both the deprivation of the population they serve and the likelihood of referral.
Finally, factors related to the general practitioner may partly explain our findings. However, no relation has yet been found between referral rates and the individual characteristics of general practitioners, such as age, years of experience, or membership of the Royal College of General Practitioners.7
Strengths and limitations of the study
We used multilevel modelling to assess whether the inequalities identified were associated with variations between or within practices. We were, however, unable to further examine clustering by individual general practitioner within a practice for two reasons. Firstly, because the health improvement network dataset does not include a field for general practitioners who refer. Although anonymised identifiers to distinguish between different people in a practice entering patient data are available, it may be other staff in the practice rather than the referring general practitioner who enter the referrals. Therefore we cannot reliably assign a referral to a particular general practitioner. Secondly, even if we could assign referrals to an individual general practitioner, it would not be possible to identify reliably an “individual general practitioner effect” for chronic conditions, where immediate referral is not indicated (in this case for hip pain and dyspepsia). This is because patients often see a different general practitioner at each visit, making it difficult to assign responsibility for the whole pathway, from initial presentation to referral, to a particular general practitioner.
We used a well established method of assigning socioeconomic circumstances based on the Townsend deprivation score for the area of residence. This is an area based indicator of socioeconomic circumstances, which refers to an enumeration district covering a population of about 150 households. This method rests on the assumption that people conform to the socioeconomic profile of their residential area. We would not expect substantial variability in social deprivation in such a small cluster of households. Indeed, it has been shown that Townsend deprivation scores calculated at the enumeration district level are strongly correlated with a similar measure of deprivation calculated at the individual level and are similarly predictive of health.38
Nevertheless, with any measure of deprivation some patients will be misclassified. However, this misclassification is likely to be non-differential and, if anything, would attenuate and underestimate any effect of deprivation. In addition, our study included many elderly (and therefore retired) patients for whom individual measures of deprivation are less valid.39 40
In this situation our use of an area level measure of deprivation was most appropriate.
A disadvantage of using routine data is the non-standardised and incomplete coding of referral, which is likely to have led to some underestimation of the number of referrals. We aimed to avoid underestimation by including practices only from the point when their recording of referrals was consistent over time. Twenty seven practices were excluded as their referral rates became consistent only after the end of our study period. Had these practices had consistent referral data and therefore been included in the analysis, we might have seen overall slightly lower referral rates as the excluded practices serve more deprived communities (see web extra for characteristics of the practice); however, there is no reason why results of the difference in referral according to level of deprivation should be affected.
Our finding that 61% of women with postmenopausal bleeding were referred to secondary care compares favourably with the 41% found in a study using another UK primary care database.41
In addition, once referral data were being consistently recorded, all practices had referral rates that were within the ranges seen in a review,7
which examined referral rates per 1000 consultations from several studies. These findings suggest adequate completeness of recording of referral in the health improvement network.
To ensure that we did not include referrals unrelated to the study symptom, we applied a two week cut-off point for the recording of referrals. This may reduce the number of related referrals observed but there is no reason to believe that recording of referrals or administration of the database would be differentially affected by patients’ age, deprivation, or sex.
It is possible that we failed to include some eligible cases where general practitioners coded diagnoses rather than symptoms. To minimise this we included diagnostic codes where possible. Thus for hip pain we included codes for osteoarthritis of the hip, and for dyspepsia we included codes for reflux and oesophagitis. The effect of missing some cases that were coded as diagnoses would be to reduce our sample size. However, this should not affect the results of differences in referral by age, sex, or deprivation.
As an indicator of comorbidity we used simple counts of prescribed drugs. We acknowledge that these do not fully account for the effects of multiple morbidities, disability, or quality of life, all of which influence decisions about referral. However, the performance of several comorbidity scores (including diagnosis code based scores from the international classification of diseases, ninth revision) to control for confounding in epidemiological studies has been compared.21
This analysis found that the number of distinct drugs used was the best predictor of future visits to a doctor and of expenditure and was a good predictor of mortality and admissions to hospital. In contrast, diagnosis based scores were the best predictors of future morbidity and mortality. Our study focused on health service use rather than on patient health outcomes. We therefore decided that the score based on number of prescribed drugs was the most appropriate to use in this context.
We could not fully assess the impact of risk factors (smoking, body mass index, alcohol intake) on referral because of the paucity of data. In addition, general practitioners may recommend behaviour modification rather than referral in the presence of risk factors in dyspeptic patients, despite guidance from the National Institute for Health and Clinical Excellence that body mass index, smoking, and alcohol intake have little, if any, effect on dyspeptic symptoms.42
Smoking is a risk factor for complications after orthopaedic surgery but the evidence for the influence of obesity is contradictory.43 30
The presence of both these factors may therefore reduce the likelihood of referral. Although alcohol consumption does not seem to have an association with socioeconomic circumstances, the prevalence of smoking and obesity are higher in more disadvantaged groups44
; thus these factors may explain some of the deprivation gradient observed. The availability of higher quality data on smoking and body mass index would have allowed us to calculate more accurate estimates of social variations in referral.
Conclusions and policy implications
Socioeconomic inequalities in referral were more likely to occur in the absence of both explicit guidance and potentially life threatening conditions. They may in part be explained by a lower likelihood of referral by practices serving more deprived communities. Lower rates of referral by age occurred for all three symptoms studied, regardless of the presence of referral guidelines and the need to exclude a cancer diagnosis. Older patients were less likely to be referred than their younger counterparts within the same practice.
The inequalities in referral observed could lead to delays in treatment and poorer outcomes. If our results extend to other symptoms, this would be of concern for serious conditions, some of which commonly present in non-specific ways, such as cancers of lung, colorectum, and ovary. Differences may be explained by patient preference, comorbidity, characteristics of the general practitioner or service, or interactions between these factors. More research, including in-depth qualitative studies, is required to understand the complex determinants of inequalities in referral from primary care.
What is already known on this topic
- Inequalities in healthcare use exist
- Socially disadvantaged people, older people, and women are more likely to consult their general practitioner but less likely to receive secondary care
- It is unclear whether inequalities occur once patients are within the secondary sector or at the point of entry to specialist care
What this study adds
- The likelihood of referral to secondary care was associated with patient’s age, sex, and social deprivation
- Older patients were less likely to be referred for all symptoms, whereas deprivation was associated with lower referral for hip pain and for those aged under 55 with dyspepsia
- Inequalities in referral were more likely to occur in the absence of both explicit guidance and potentially life threatening conditions