Explaining variation in referral from primary to secondary care: cohort study
BMJ 2010; 341 doi: https://doi.org/10.1136/bmj.c6267 (Published 01 December 2010) Cite this as: BMJ 2010;341:c6267
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McBride et al.[1] hypothesized that socioeconomic inequalities in
referrals to secondary care are more likely to occur in the absence of
explicit guidance and potentially life-threatening conditions and found
their hypothesis to be borne out by the data they examined. In
particular, they found socioeconomic inequalities in referrals to
secondary care for hip pain, where there is little guidance as to whom to
refer, but not for referral for postmenopausal bleeding, where guidelines
strongly urge referral. In the case of dyspepsia, where referral is more
strongly urged for patients over 55 than patients under 55, the authors
found evidence of a socioeconomic gradient for patients under 55 but not
for patients over 55.
The authors' hypothesis seems entirely sound. But in analyzing the
statistical support for the hypothesis, the authors overlook the patterns
whereby, solely for reasons related to the shapes of the underlying
distributions of factors associated with likelihood of experiencing an
outcome, standard measures of differences between outcome rates tend to be
affected by the overall prevalence of an outcome. Stronger referral
recommendations generally lead to higher overall referral rates. In such
circumstances, the referenced distributional factors tend to cause
relative differences in referral rates to decrease but relative
differences in rates of failure to be referred to increase.[2-6] Absolute
differences between rates and odds ratios tend also to be affected by the
overall prevalence of an outcome, though in a more complicated way.
Roughly, where an outcome is uncommon (less than 50% for all groups being
compared) increases in the outcome tend to increase absolute differences
between rates; where an outcome is already common (more than 50% for all
groups being compared) further increases in the outcome tend to reduce
absolute differences between rates. Differences measured by odds ratios
tend to change in the opposite direction of absolute differences between
rates.[2,5,6] One can best determine the comparative size of difference
between outcome rates by deriving from each pair of rates the differences
between means of the hypothesized underlying distributions.[5,7]
In the case of a comparison of the socioeconomic inequalities in
referral for postmenopausal bleeding and hip pain, overall referral rates
are much higher for the former (61.4%) than for the latter (17.4). (I
leave person-years out of the matter.) Thus, the distributional factors
would tend to cause smaller relative differences between the rates of the
most and least deprived groups for postmenopausal bleeding than for hip
pain regardless of any meaningful difference in the size of the
inequalities. While the observed pattern of relative differences is
consistent with the distributionally-driven patterns, it is nevertheless
clear that, regardless of how one might appraise the comparative size of
the differences between rates of the most and least deprived groups, the
difference is much smaller for postmenopausal bleeding (61.6% versus
62.4%) than for hip pain (14.2% versus 19.6%). Still, it is worth noting
that the former pair of rates translates into a difference between means
of .021 standard deviations, while the latter pair of rates translates
into a difference between means of .215 standard deviations. The .021
figure indicates that between 50% and 51% of the most deprived group falls
below the mean for the least deprived group, while the .215 figure
indicates that about 58% of the most deprived group falls below the mean
of the least deprived group.
But whether, consistent with the authors' hypothesis, the inequality
in referrals for dyspepsia is larger for patients under 55 than patients
over 55 is not clear. The authors rely on relative differences between
rates of the most and least deprived groups within each age group. The
rate ratio is .76 for the younger group (in which the overall referral
rate is 11.7%) and a nonsignificant .91 for the older group (in which the
overall referral rate is 17.4%). But, putting statistical significance
issues aside, the smaller relative difference for the older group is to be
expected simply because referral is more common in that group. The
authors do not present referral rates for each deprivation group within
the two age groups. Thus, one cannot determine whether the relative
difference in failing to be referred or the absolute difference between
referral rates is larger in the older group, both of which patterns
commonly occur as a result of the shapes of the distributions. More
important, one cannot derive the difference between the underlying means.
Thus, from the data presented, it is not possible to know whether
socioeconomic inequality is in fact lower among older patients than among
younger patients.
These same issues exists in the appraisal of how pay-for-performance
(P4P) programs (which tend to increase overall procedure and appropriate
care rates) affect healthcare inequalities. See the Pay for Performance
sub-page of the Measuring Health Disparities page of jpscanlan.com,[8]
which discusses, inter alia, that reliance on absolute differences between
rates as a measure of healthcare inequalities has led to perceptions in
the United States that P4P will tend to increase healthcare inequalities
and perceptions in the United Kingdom that P4P will tend to reduce
healthcare inequalities. See also the Mortality and Survival page [9] of
jpscanlan.com and the Immunization Disparities sub-page of the Measuring
Health Disparities page of jpscanlan.com [10] regarding varying
perceptions of the comparative size of health and healthcare inequalities
depending on whether inequalities are measured in terms of the relative
difference in the favorable outcome or the relative difference in the
adverse outcome.
References:
1. McBride D, Hardoon S, Walters K, et al.. Explaining variation in
referral from primary to secondary care: Cohort study. BMJ
2010;341:c6267.
2. Scanlan JP. Can we actually measure health disparities? Chance
2006:19(2):47-51:
http://www.jpscanlan.com/images/Can_We_Actually_Measure_Health_Dispariti...
3. Scanlan JP. Race and mortality. Society 2000;37(2):19-35:
http://www.jpscanlan.com/images/Race_and_Mortality.pdf
4. Scanlan JP. Measurement Problems in the National Healthcare
Disparities Report, presented at American Public Health Association 135th
Annual Meeting & Exposition, Washington, DC, Nov. 3-7, 2007;
http://www.jpscanlan.com/images/ORAL_ANNOTATED.pdf (oral);
http://www.jpscanlan.com/images/Addendum.pdf (addendum);
http://www.jpscanlan.com/images/APHA_2007_Presentation.ppt (PowerPoint
Presentation)
5. Scanlan JP. Measuring Health Inequalities by an Approach
Unaffected by the Overall Prevalence of the Outcomes at Issue, presented
at the Royal Statistical Society Conference 2009, Edinburgh, Scotland,
Sept. 7-11, 2009:
http://www.jpscanlan.com/images/Scanlan_RSS_2009_Presentation.ppt
6. Scanlan's Rule page of jpscanlan.com:
http://jpscanlan.com/scanlansrule.html
7. Solutions sub-page of Measuring Health Disparities page of
jpscanlan.com: http://www.jpscanlan.com/measuringhealthdisp/solutions.html
8. http://www.jpscanlan.com/measuringhealthdisp/payforperformance.
9. http://jpscanlan.com/mortalityandsurvival2.html
10. http://jpscanlan.com/scanlansrule/immunizationdisparities.html
Competing interests: No competing interests
McBride and colleagues had analyzed a widely used general practice database (the health improvement network) from the United Kingdom and suggested that socioeconomic inequalities play an important role in referral[1]. Under the United Kingdom's National Health Service (NHS) scheme, patients' preference might be substantially affected by the accessibility of medical service such as long waiting time and registered primary doctors[2]. Patients' preference might be underestimated under such regulated medical system.
In a free market medical system, Taiwan for example, the national health insurance operated in a single payer scheme, covered more than 99% of inhabitants and offered free choice of health providers or practice methods[3]. A total of 1.9 million visiting records among 13 medical centers in Taiwan in 2000 were analyzed using sequential association rules. The most frequent visiting pattern in temporal sequence had been plotted. The preliminary results showed that the referral patterns varied by disease. For benign neoplasms, most patients were not referred to another medical center (left upper part) compared to affective disorder (right part), patient were referred (least likely by doctors) to every medical centers within geographical boundary. On the contrary, for malignancies (left lower part), patients would be referred (most probably by themselves for second opinions) to few targeted medical centers in spite of geographical boundaries.
If the access to medical services had been eased, patients' preference plays a dominant role in their referral patterns. The self-referral patterns in a free market system, whether good or bad, raises a question: has the patients' preference being ignored by managed medical systems?
1. McBride D, Hardoon S, Walters K, Gilmour S, Raine R. Explaining variation in referral from primary to secondary care: cohort study. BMJ;341.
2. Chen LC, Schafheutle EI, Noyce PR. The impact of nonreferral outpatient co-payment on medical care utilization and expenditures in Taiwan. Research in Social & Administrative Pharmacy 2009;5(3):211-24.
3. Wen CP, Tsai SP, Chung WSI. A 10-year experience with universal health insurance in Taiwan: Measuring changes in health and health disparity. Annals of Internal Medicine 2008;148(4):258-67.
Referral patterns among medical centers in a free market medical system by diseases (Taiwan, 2000).
Blue circles denote 13 medical centers arranged approximately by geographic locations.
Curves with arrows denote frequent (2%, approximates 38,000 patients) referral patterns between medical centers.
Competing interests: No competing interests
Dear Sir.
McBride and colleagues conclude that one of the reasons for
inequalities in primary to secondary care referrals associated with
socioeconomic circumstances, was the absence of explicit guidance (1).
Referral to a speciality service is a crucial point in a patient's
management. In his accompanying editorial Jiwa suggests that the decision
is complex, reflecting the needs and expectations of individual patients
and their families, the knowledge and experience of the individual
practitioner, and the range, type and level of services(2). It is not
surprising that research into the cause of variations in primary to
secondary care referrals, and potential solutions, remains inconclusive.
In 2001 NICE developed advice on the appropriate referral to
specialist services in response to concerns that attempts to reduce
waiting list times may affect the quality of care (3). This advice was not
subsequently updated but further referral advice was to be incorporated
into new clinical guidelines. In a recent Quality, Innovation, Prevention
and Productivity (QIPP) workshop (4) it was proposed that it would be
useful for NICE to revisit this issue. The Institute has now collated all
its referral guidance into a searchable database
http://www.nice.org.uk/usingguidance/referraladvice/index.jsp.
Uncertainity remains over the impact of guidance on reducing
variation in referral rates from primary to secondary care; the extent of
its impact being dependent upon both the specific features of the
guidance, and the local cause of variation (5). An important and recurrent
theme in the literature, is a need to stimulate better joint working and
dialogue between primary and secondary care. Referral guidelines should
not, as has been cautioned, reduce the willingness of GPs to tolerate
uncertainty and increase referrals to secondary care(6). Accordingly, NICE
referral guidance should be used to encourage local health communities to
discuss referral issues and develop local referral protocols.
Implementing NICE guidance can provide a way for GP commissioners to
ensure that patients receive treatment that is proven to be both
clinically and cost effective, including when it is appropriate to refer a
patient from primary to secondary care. Following NICE guidance can also
free up resources and capacity that can then be channelled into other
services.
1 McBride D, Hardoon S, Walters K, Gilmour S, Raine R. Explaining
variation in referral from primary to secondary care: cohort study.
BMJ2010;341:c6267.
2 Jiwa M. Referral from primary to secondary care BMJ 2010; 341:c6175
3 National Institute for Health and Clinical Excellence Referral
Advice: A guide to appropriate referral from general to specialist
services. NICE (2001)
http://www.nice.org.uk/media/94D/BE/Referraladvice.pdf
4http://www.nice.org.uk/aboutnice/whatwedo/niceandthenhs/QualityProductiv...
5 Fertig A, Roland MO, King H, Moore AT, Understanding variation in
general practitioner referral rates: are inappropriate referrals
important, and would guidelines help to reduce rates. British Medical
Journal 1993: 307; 1467-70.
6 O'Donnell CA (2000) Variation in GP referral rates: what can we
learn from the literature? Fam Pract. 17: 462-71
Competing interests: No competing interests
Patients,their social networks, their diseases, their General
Practitioners (GPs), the treatment guidelines, and the local resources
available are all important determinants of the variations in referral
from primary care. McBride et al make the incorrect statement that 'no
relation has yet been found between referral rates and the individual
characteristics of general practitioners'. The variation in the
performance of individual doctors in the referral process is difficult to
isolate because doctors work with different expectations with different
populations in very different practices.
Research has shown that in
daytime routine referrals (ref 1) female doctors refer more patients, and
in out of hours (OOH) emergency referrals (ref 2) both female and more
risk averse GPs refer more patients to hospital than other GPs. GPs who
care for the same population OOH show a greater than four fold variation
between the highest and lowest quartiles of referring GPs. Although low
referring GPs may also be a concern the high referring GPs are typically
cautious, believe it's better to refer if in doubt, and express anxiety
about the consequences of a decision not to refer both for the patient and
themselves. We may not understand the root cause of different referral
behaviour, but understanding the effect of individual GPs and targeting
interventions that improve their performance may reduce inappropriate
variation in referral.
References
1 Why do Physicians vary so widely in their referral rates? Franks P,
Williams G, Zwanziger J, Mooney C, Sorbero M. J Gen Intern Med 2000;15:163
-168
2 Risk taking in general practice: GP out-of-hours referrals to
hospital- a cross sectional study. Rossdale M, Kemple T, Payne S, Calnan
M, Greenwood R, Ingram J. British Journal of General Practice 2009;59:24-
28
Competing interests: No competing interests
Re: Explaining variation in referral from primary to secondary care: cohort study
Three color codes can be given by peripheral health worker on the health card of a pregnant woman: One code for immediate emergency care and the other for level of care required (primary, secondary or tertiary care) by the subject and third code would signify the severity of disease.
Severity of disease e.g. severe anaemia (< 7 gm% of hemoglobin ) will be coded red, mild to moderate (7-11 gm%) as orange and normal (>11 gm%) as green in the column provided in the card.
For immediate emergency care e.g. in severe anaemia close to term should be immediately referred to higher center where blood transfusion can take place and should be marked as red in the boxes provided for time of care and type of skilled care (third code). Whereas, the mild to moderate can be managed at the place close to the subject e.g. subcentre (green).
Similarly the color code can be developed for all the obstetric complications. Not only it will be alerting the patient as well as health worker to the gravity of the situation (at a glance), but it will also increase the health care utilization by giving a visual coding and mobilizing the patient and their families.
Competing interests: No competing interests