BMJ No 7122 Volume 315
Papers Saturday 13 December 1997
Effect of doctors' ethnicity and country of qualification on prescribing patterns in single handed general practices: linkage of information collected by questionnaire and from routine data
Paramjit S Gill, Anthony Dowell, Conrad M Harris
Objectives: To test whether Asian general practitioners who qualified in the Indian subcontinent prescribe items more often, more expensive items, and fewer generic drugs than their British trained Asian and non-Asian counterparts.
Design: Linkage study using data collected by questionnaire and from routine sources.
Setting: General practices in England.
Subjects: 155 single handed general practitioners: 42 Asian doctors qualified in United Kingdom (group 1), 58 white doctors qualified in United Kingdom (group 2), and 55 Asian doctors qualified in Indian subcontinent (group 3).
Main outcome measures: Prescribing cost (cost per ASTRO-PU), prescribing frequency (number of items per ASTRO-PU), and generic prescribing (percentage of drugs prescribed that are generic).
Results: Doctors in group 1 were significantly younger than those in the other groups and had a higher proportion of patients who were from deprived wards. There was no difference between the groups in the proportion of female doctors and total list size. After adjustment for confounding factors, there were no significant differences between the three groups for prescribing cost (16.58 (95% confidence interval 6.39 to 26.77) for group 1, 17.31 (6.92 to 27.69) for group 2, 17.80 (7.22 to 28.38) for group 3, P=0.55); prescribing frequency (6.58 (4.60 to 8.40), 6.45 (4.70 to 8.30), 7.89 (6.16 to 9.64), P=0.34); and generic prescribing (44.44 (38.95 to 49.93), 47.41 (42.12 to 52.70), 44.04 (38.75 to 49.33), P=0.37).
Conclusions: Asian doctors qualified from the Indian subcontinent did not differ from British trained doctors in their prescribing practice. This study refutes the common belief that Asian doctors are high volume and high cost prescribers.
Introduction
The issuing of prescriptions by general practitioners is a complex activity that depends on the interplay of many factors, including variables associated with the doctor, the patient, doctor-patient interaction, and sociopsychological variables.(1) We know little about the effect of ethnic group and doctor's country of qualification on variations in prescribing patterns.
The evidence that Asian (from the Indian subcontinent) general practitioners prescribe more often and more expensively than non-Asian general practitioners seems to be anecdotal. Although there are studies reporting an association between a doctor's country of qualification and prescribing patterns, to date no study has reported an association between doctors' ethnicity and prescribing patterns. One of the earliest studies found that foreign trained doctors were more likely to be high cost prescribers than their British trained colleagues,(2) and two recent studies found that doctors who qualified outside the British Isles were more likely to issue a prescription.(3,4) However, none of these studies took account of variables such as the age and sex of the patients.
The aim of this study was to test the hypotheses that single handed general practitioners who are Asian (of Indian, Pakistani, Bangladeshi, or Sinhalese ethnic groups as defined in the 1991 census) and who qualified in the Indian subcontinent prescribe more items, more expensive items, and fewer generic drugs than their British trained Asian and non-Asian counterparts.
Subjects and methods
Subjects
| Sample frame We obtained the sample frame of doctors for the study in four stages (see figure) in order to identify the following three groups of general practitioners: Asian doctors who qualified in the United Kingdom, non-Asian doctors who qualified in the United Kingdom, and Asian doctors who qualified in the Indian subcontinent.
We chose single handed general practitioners for study because their prescribing and analysis cost (PACT) data most closely reflect their own prescribing.(5) In the first stage, the Department of Health categorised all single handed principals in England (2,701) by their country of qualification. Doctors who qualified outside the United Kingdom and the Indian subcontinent were excluded (242, 8.9%). During stage II, we obtained the doctors' surnames and initials, as well as the medical school of those who qualified in Britain, from the Department of Health. |  | |
| Sampling strategy for selection of single handed general practitioners |
We used the Medical Register(6) to obtain the doctors' forenames and to validate their school of qualification, as the Department of Health database does not differentiate between doctors whose primary degree was in the Indian subcontinent and those who had requalified by sitting one of the licentiate exams.(6)
Furthermore, the Department of Health database does not include ethnic group. We then categorised the general practitioners into Asian (98) and non-Asian (974) groups by their forenames and surnames.(7) This method was valid, as we found good agreement (kappa=0.95) with doctors' self determined ethnic group when we sent them a questionnaire (see below).
During stage III, six general practitioners were randomly selected from each group for the pilot study. The remaining 92 Asian doctors who qualified in the United Kingdom (group 1) were included in the main study to achieve sufficient sample size as we expected the response to our questionnaire to be low.(8) Further random samples of 92 general practitioners who were white and qualified in the United Kingdom (group 2) and 92 doctors who were Asian and qualified in the Indian subcontinent (group 3) were again supplied by the Department of Health (stage IV).
Questionnaire
We sent a questionnaire to these 276 general practitioners that
asked for their demographic details. These included their ethnic group,
using the categories given in the 1991 census; medical school; year of
qualification; possession of the MRCGP diploma; length of time in
current post and in general practice; employment of assistants or
retainers; practice status (single handed, dispensing, training, or
fundholding); and total number of consultations during March 1994.
For inclusion in the study, respondents had to be single handed principals; had to have been in their current practice for more than a year (as the PACT data obtained were for a whole year); and had to be Asian and qualified in the United Kingdom (group 1), white and qualified in the United Kingdom (group 2), or Asian and qualified in the Indian subcontinent (group 3). These criteria resulted in the exclusion of 58 general practitioners: 35 from group 1, 13 from group 2, and 10 from group 3.
Sample size
We calculated that the necessary sample size was 40 doctors per
group (120 in total) in order to detect a difference of two items per
patient with a power of 80% and significance of 0.05.
Data collection
We used a linkage design to test the association between the
general practitioners' ethnicity and country of qualification and
their prescribing patterns. After getting consent, we obtained data
from four sources: the questionnaire sent to the single handed general
practitioners (see above), data from family health services
authorities, data from the 1991 census, and data from the Prescription
Pricing Authority.
Data from family health services authorities
These included:
 | List size, to calculate cost based and item based ASTRO-PUs (age, sex, and temporary resident originated prescribing units)(9) using a specific computer program written for this study by the FHS Computer Unit, Exeter. This process accounted for all patients registered with a particular general practitioner even if they were residing in other health authorities (further details from PSG) | |
| Number of patients giving rise to deprivation payments on 31 March 1994 | |
| Number of temporary residents on the doctor's list between 1 April 1993 and 31 March 1994. |
Data from 1991 census
We calculated the Townsend score(10) for each practice
by, firstly, assigning the post code of the practice to ward and,
secondly, retrieving the relevant variables from the files of the 1991
Census Local Base Statistics maintained at the Manchester Computing
Centre by the Census Dissemination Unit using the saspac91
computer package.(11) For practices with branch surgeries,
we used the mean Townsend index. In addition, we calculated the
proportion of Asian patients (residents of Indian, Pakistani,
Bangladeshi, and other ethnic groups from the Indian subcontinent) and
the social class (mainly non-manual versus manual or equal) of all
residents in each ward.(12)
Data from the Prescription Pricing Authority
These were the total number of items, net ingredient cost, and the
percentage of drugs that were generic for all categories of drugs
listed in the British National Fomulary that were
dispensed between 1 April 1993 and 31 March 1994.(13)
Prescribing outcome measures
We used the following prescribing variables as the outcome
measures:
| Cost per cost based ASTRO-PU(9) | |
| Number of items per item based ASTRO-PU(9) | |
| Percentage of all drugs prescribed that were generic formulations. |
Statistical analysis
We entered data from all four sources into a database and analysed
them using spss for Windows.(14) We compared the
groups using chi2 tests, analysis of variance, and
Kruskal-Wallis H tests, and we used Bonferroni correction to adjust for
multiple comparisons.
To adjust for potential confounding, we developed parsimonious models for each of the outcome variables using analysis of covariance,(15) and we examined assumptions underlying the model using standard residual plots and Levene's test for homogeneity of variances.(14)
All statistical tests were performed with and without outliers, and we found no difference. We identified and included the following variables in the model: doctors' age, sex, country and year of qualification, length of time in current practice and general practice, MRCGP diploma, and number of consultations a year; status of practice (dispensing, fundholding, or training); social class of patients; Townsend index; percentage of patients attracting deprivation payments; and percentage of patients belonging to an Asian ethnic group.
We chose the Townsend score as a proxy for morbidity because it has been shown to perform well when explaining variation in a range of health measures and it adheres closely to the concept of material deprivation.(16) We used the proportion of patients attracting deprivation payments as a proxy measure for deprivation: despite criticism of the Jarman score on methodological grounds, this was a pragmatic decision as general practitioners are paid for patients who reside in deprived wards.(17)
| Table 1 - Corrected percentage response to questionnaire by 218 single handed general practitioners according to ethnicity and country of qualification | |
|---|---|
| Group | % Response |
| 1 Asian, qualified in United Kingdom | 73.7 (42/57) |
| 2 White, qualified in United Kingdom | 73.4 (58/79) |
| 3 Asian, qualified in Indian subcontinent | 67.1 (55/82) |
| Total | 71.1 (155/218) |
Results
Characteristics of responders and non-responders
Table 1 shows the numbers of general practitioners who responded
to our questionnaire. Using two sample t tests, we found
no difference between the doctors who responded and the non-responders
in terms of age (mean difference -1.7 (95% confidence interval
-4.5 to 0.9)), Townsend score for the practice ward (mean difference
0.6 (-0.6 to 1.8)), and sex (chi2 test, df=1, P=0.87).
Compared with all general practitioners in England, the responders were
older, were more likely to be male, practised in more deprived areas,
and had larger list sizes (table
2).
| Table 2 - Characteristics of single handed general practitioners who responded to questionnaire and those who did not respond, and of all general practitioner principals in England. (Values are mean (SD) unless stated otherwise) | ||||
|---|---|---|---|---|
| Responders (n=155) | Non-responders (n=63) | All principals in England* | ||
| Single handed | All | |||
| Age (years) | 47.3 (7.9) | 49.02 (9.6) | 50.2 (7.9) | 43.79 (89.8) |
| Sex ratio (male: female) | 6.4:1 | 6.9:1 | 5.9:1 | 2.8:1 |
| Practice ward: | ||||
| Townsend index | 2.9 (4.1) | 2.3 (4.1) | 0 | |
| List size | 2261 (673) | 2133 (893)† | 1915 | |
| *Data from Department of Health for 1 April 1993.
†Data from Prescription Pricing Authority for December 1993. | ||||
Characteristics of responders by group
Table 3 shows that Asian doctors who qualified in the United
Kingdom (group 1) were significantly younger than doctors in the other
groups and had a higher proportion of patients who were from deprived
wards. There was no difference between the three groups in the
proportion of female doctors and total list
size.
Table 3 - Characteristics of 155 single handed general
practitioners who responded to questionnaire by ethnicity and country
of qualification
| Group 1 (n=42)* |
Group 2
(n=58)† |
Group 3 (n=55)‡ |
Difference | (P value) Mean (SD) age
(years) | 42.2
(7.1) | 47.1 (8.4) | 51.3
(5.6) | <0.0001** | Sex ratio (male:
female) | 5:1 | 8.7:1 | 5.9:1 | 0.64p
| Mean (SD) list size | 2274 (574) | 2336
(687) | 2174 (728) | 0.44** | Median % of
patients attracting deprivation
payments | 0.34 | 0 | 1.10 | 0.02**** | *Asian, qualified in United Kingdom.
| †White, qualified in United Kingdom. ‡Asian, qualified in Indian subcontinent. **Analysis of variance. *** chi2 test, df=2. ****Kruskal-Wallis H test, chi2=7.41, df=2. | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Prescribing practice
Table 4 shows the mean prescribing variables for the three groups
before and after adjustment for confounding factors. There were no
significant differences between the groups for total cost (cost per
ASTRO-PU), frequency of prescribing (number of items per ASTRO-PU), and
rate of prescribing of generic
drugs.
| Table 4 - Prescribing variables of 155 single handed general practitioners who responded to questionnaire by ethnicity and country of qualification. (Values are means (95% CI) unless stated otherwise) | |||||
|---|---|---|---|---|---|
| Difference | |||||
| Variable | Group 1 (n=42)* | Group 2
(n=58)† | Group 3 (n=55)‡ | Adjusted R2 | P value |
| Prescribing cost (cost per ASTRO-PU) | |||||
| Unadjusted | 16.61 (15.29 to 17.93) | 17.11 (16.05 to 18.16) | 17.64 (16.52 to 18.73) | 0.46 | |
| Adjusted** | 16.58 (6.39 to 26.77) | 17.31 (6.92 to 27.69) | 17.80
(7.22 to 28.38) | 0.33 | 0.55 |
| Prescribing frequency (No of items per ASTRO-PU) | |||||
| Unadjusted | 6.61 (5.98 to 7.25) | 6.37
(5.90 to 6.84) | 7.96 (7.27 to 8.65) | 0.0003 | |
| Adjusted** | 6.58 (4.60 to 8.40) | 6.45
(4.70 to 8.30) | 7.89 (6.16 to 9.64) | 0.37 | 0.34 |
| Generic prescribing (% of drugs that were generic) | |||||
| Unadjusted | 46.26 (42.73 to 49.78) | 48.47
(45.07 to 51.87) | 43.49 (40.97 to 46.02) | 0.07 | |
| Adjusted** | 44.44 (38.95 to 49.93) | 47.41
(42.12 to 52.70) | 44.04 (38.75 to 49.33) | 0.23 | 0.37 |
| *Asian, qualified in United Kingdom.
†White, qualified in United Kingdom. ‡Asian, qualified in Indian subcontinent. **Adjusted for appropriate variables mentioned in the text. | |||||
Discussion
Doctors' ethnicity and country of qualification
Our study shows that, for all the drugs issued, the Asian doctors
who had qualified in the Indian subcontinent did not issue
significantly more items, more expensive items, or fewer generic drugs
than Asian and white doctors who had qualified in the United Kingdom.
This refutes the myth of Asian doctors being high cost prescribers.
It is hardly surprising that, for those Asian doctors who qualified from British medical schools, their ethnicity had no effect on their prescribing. The medical school of qualification, and hence country, would be more likely to affect prescribing through secondary socialisation, which occurs over five to six years.(2)(18) Socialisation through medical school, specialty, and practising in a group practice changes prescribing behaviour.(19)
The models we used in this study explained only a small part of the variation in prescribing patterns that we found (23-37%): a large part was due to other, unmeasured factors. This is in line with a study that used routine statistics on general practices in one family health services authority: the authors found that their model explained only 42% of prescribing costs.(20) It must be emphasised that, as we did not study appropriateness, the differing levels of prescribing by general practitioners might have been appropriate for their patients.
This is the first study which specifically addresses the effect of doctors' ethnicity and country of qualification on prescribing patterns. Other studies have found that doctors who were foreign trained were high cost and high volume prescribers.(2-4) These studies did not, however, account for other confounding factors, particularly patient demography.
Limitations of study
Chance
We calculated the power of this study from the number of issued
items as the outcome measure. After the study, we found that power
calculation for all other outcome measures, such as cost and rate of
generic prescribing, was above 90%. This indicates that chance is an
unlikely explanation for these results.
Bias
The results could have arisen from inaccuracies in list
sizes-that is, more patients listed on a health authority register
than were actually registered with the general practice.(21)
Such list inflation could reduce cost of prescribing per patient in
inner city areas so that comparison with other practices is
invalid.(5) However, we attempted to account for all
patients by using a unique program to extract data from health
authority computers (FHS Computer Unit, Exeter). Furthermore, the
general practice contract, which needs accurate denominators for
payment for achieving certain targets, has probably reduced
inaccuracies of health authority registers. The increasing use of
computers within general practice(22) may also have
contributed to increased accuracy of practice registers of patients'
age and sex, from which patient information is passed onto the
responsible health authority.
Using data from the 1991 census, we estimated the proportion of Asian residents and social class in each ward to minimise bias, but the census data contain errors because of underenumeration.(23) Also, because of the selective way in which patients choose practices, errors are present in census derived variables for general practices.(24) However, these derived variables are currently the best measures that are available of the socioeconomic characteristics of practice populations.(25)
Confounding
Confounding variables might be an alternative explanation for the
results. We identified a number of factors from the literature, which,
where feasible, we measured and included in the analysis. These
adjusted models still explained little of the variation in the
prescribing dependent variables (17-32%), with the rest due to other
factors. This should be interpreted with caution as it was not the
objective of this study to determine which factors explain variation in
prescribing outcomes.
Validity and generalisability
The excellent response rate (71%) from the general practitioners
is worthy of comment-single handed general practitioners are generally
less likely to respond to questionnaires.(8) All
non-responders were accounted for and did not differ in terms of age,
sex, or practice ward deprivation from the other single handed general
practitioners. As single handed practitioners are different from those
that are in partnerships,(26) these results cannot be
generalised to other general practitioners.
Conclusions
This study refutes beliefs that Asian doctors are high volume and
high cost prescribers. We hope that this lays another myth to rest. It
is important to bear in mind that the issuing of a prescription does
not occur in a vacuum: it involves a complex interplay of factors, many
of which have not been measured in this study. We do not yet know how
the prescribing patterns of different groups of doctors vary with
different groups of patients in different settings.
|
We thank our colleagues who gave invaluable help, especially
Glen Scrivener, David Lloyd, Dave Clucas, and Sue Bogle. We also thank
all the staff at the family health services authorities, Alan Scott of
the FHS Computer Unit, and Steve Parker of the NHS Executive for help
with data extraction. We are grateful to all the general practitioners
who took time to participate in this study.
Funding: Partly funded by the Scientific Foundation Board of
the Royal College of General Practitioners.
(Accepted 20 November 1997)
Conflict of interest: None.
Centre for Research in Primary
Care,
University of Leeds,
Leeds LS2 9JT
Paramjit S
Gill, research tutor
Anthony
Dowell, director
Academic Unit of General
Practice, University of Leeds
Conrad M
Harris, professor of general
practice
Correspondence to: Dr P S Gill,
Department of
General Practice,
University of Birmingham,
Edgbaston,
Birmingham
B15 2TT
email: P.S.Gill@bham.ac.uk
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