IS GENERAL PRACTITIONER DECISION MAKING ASSOCIATED WITH PATIENT SOCIO-ECONOMIC STATUS?
A N T H O N Y SCOTT, I ALAN SHIELL 2 and M A D E L E I N E K I N G 2
*Health Economics Research Unit, Department of Public Health, University of Aberdeen, Aberdeen, Scotland and 2Centre for Health Economics Research and Evaluation, Department of Community
Medicine, University of Sydney, Sydney, Australia
Abstract--This paper presents a preliminary exploration into the relationship between decisions made by general practitioners (GPs) and the socio-economic status (SES) of patients. There is a large literature on the association between SES, health state and the use of health services, but relatively little has been published on the association between SES and decisions by clinicians once a patient is in the health system. The associations between GP decision making and the patient's SES, health status, gender and insurance status are examined using Iogit analysis. Three sets of binary choices are analysed: the decision to follow up; to prescribe; and to perform or to order a diagnostic test. Secondary data on consultations for a check up/examination were used to explore these relationships. The results suggest that SES is associated independently with the decision to test and the decision to prescribe but not with the decision to follow up. Patients of high SES are, ceteris paribus, more likely to be tested and less likely to receive a prescription compared with patients of low SES. Women are more likely to be tested and to receive a prescription than men. These findings have implications for the pursuit of equity as a goal of health services policy.
Key words--socio-eeonomic status, general practice, decision making, equity
INTRODUCTION
The decisions made by general practitioners (GPs) can have substantial effects on the use of health care resources. Decisions about referrals to specialists, diagnostic testing, follow up and prescribing all involve the use of resources which may have more beneficial uses.
Efficiency, however, is not the only concern. The way in which resources are distributed across sociooeconomic groups is also of interest. Differences in patients ' SES and the clinician's response to these differences may be factors influencing the widespread variations in practice patterns observed in most developed countries [1]. There is a substantial body of literature examining the influence of socio-economic status (SES) on health outcomes [2, 3] and the use of health services in aggregate [4, 5].
The influence of social factors on the process of the GP consultation has also been studied [6, 7]. Patients from lower social classes are likely to get shorter consultations and receive less information [8-10]. They are more likely to be passive recipients of health care, to invest responsibility for their physical health with the health care system and to express their illness in physical rather than emotional terms [11, 12]. Middle class patients are more active in seeking information from their doctors though they are no more likely to understand the explanation they receive [13, 14]. The relationships between social factors, health behaviour and the quality of medical
35
consultations are complex, however, and intra-class differences as well as inter-class differences also exist [15].
A number of studies have also considered the impact of social factors on decisions made by GPs. A negative association has been found between the social class of patients and both the likelihood of follow up visits and the ordering of a diagnostic test [16, 17]. Referral to specialist services is less likely for people with low educational status or, in the U.S.A., those without private health insurance [18, 19] though this may be more directly related to income than to SES.
Gender differences have also been observed although the direction is not consistent [7]. Clinicians are more likely to believe that women make excessive demands on their time and to attribute their complaints to psychosomatic rather than physical causes [20]. Other authors have, however, found that consultation style was not affected by the patient 's gender and that women reported higher levels of satisfaction with their GP [21].
Studies of the impact of gender on treatment decisions have yielded inconsistent results. In experimental studies, where the use of hypothetical scenarios removes the social context from the consultation, gender did not influence GP decision making [22]. In a small survey of actual treat- ment decisions, Armitage et ai. concluded that, " . . .workups were significantly more extensive for men than they were for women", though this
36 Anthony Scott et al.
finding did not apply to all of the diagnostic categories studied [23].
Other authors have found that women are more likely to be tested and to be followed up [16, 24]. In part, this is due to gender-specific health problems. After controlling for diagnosis, Hartley et al. found gender had no effect on test use [17]. However, in an extensive analysis of a national survey, Verbrugge and Steiner found that higher levels of medical care provided to women persisted even after medical factors were taken into account [25].
Socio-economic status and health status are likely to be closely correlated but three mechanisms by which SES may influence GP consultations and their outcomes, independent of health state, have been identified [14]. The first of these is social distance in which the social differences between classes influence the quality of doctor-patient communication. The second is health knowledge and beliefs, which focuses on class-based differences in knowledge and thence differences in health related behaviour [12, 26]. The third is professional control which suggests that it is the professional power of the doctor which is the main influence on the consultation, though this might be moderated by class stereotypes as seen by the doctor [27]. At an individual level, other hypotheses are concerned with the influence on the patient of time and money prices (proxied by occupation and income respect- ively) which are implicit in a particular course of action [28].
These hypotheses are not mutually exclusive and it is hard to distinguish the effect of each empirically [29]. They suggest how social and economic processes might influence GP decision making but provide little insight into the direction of any relationship between treatment decisions and SES. Specific hypotheses about the relationship between SES and GP decision making are hard to find in the research literature, hence the exploratory nature of the analysis reported here [7, 30].
The objective of this paper is to explore the association between SES and decision making in general practice, while controlling for the influence of general health status and the gender of patients. In multivariate analysis, if patient health status were all that influenced GP decision making, then one would expect the coefficients on the non-health variables to be statistically insignificant. If, after controlling for health status, socio-economic variables remained statistically significant, then this would suggest that factors other than health status are important.
Treatment choices are made by the GP in his or her role as agent for the patient. For any particular medical condition, there may be a number of treatment options open to the doctor. For the purposes of this paper, however, each decision is considered independently and thus the choice set facing the GP is assumed to be binary.
METHODS
Background
The health care system in Australia is characterized by both private and public health insurance [31]. Medicare, financed through taxation, provides coverage both for primary care and for hospital services for public patients in public hospitals. GPs are paid by fee-for-service (FFS) according to the Medicare Fee Schedule but can charge above the schedule fee if they wish. GPs can send their bill either directly to Medicare for reimbursement (bulk-bill) or to the patient. I fGPs bulk-bill then patients face a zero money price and the GP receives 85% of the schedule fee as full payment. If the GP bills the patient, then the patient can claim back 85% of the schedule fee from Medicare. Thus, the patient faces a copayment equal to the difference between the Medicare subsidy and the fee charged by the GP. In 1990-91,67 % of GP services were bulk-billed [32].
Specialist services are also covered by Medicare in full if delivered in hospital and in part (75% of the scheduled fee) if delivered in the specialist's own rooms following referral by a GP. Specialists, however, may also bulk-bill. For the elderly and people claiming social security, there are several health care concession cards which cover part or all of the costs of pharmaceuticals and other health services.
Around 40% of the population are also covered by private health insurance. Private health insurance in Australia does not include coverage for GP visits, or for the gap between fees charged by GPs and the amount which can be claimed back from Medicare. Basic insurance allows public hospital patients a choice of doctor. Enhanced insurance covers part of the cost of private patients' use of private hospitals and also provides cover for many ancillary health care services. In this way, insurance can have an indirect influence on GP decision making if the GP is aware of their patient's insurance status.
Data
The data used in this analysis were taken from the 1989/90 National Health Survey (NHS) [33]. Approxi- mately 59,000 people were interviewed. Information collected included self-reported general health status, recent and long-term illness, health-related actions such as doctor consultations and the reasons for consultations, use of medications, episodes in hospital and aspects of lifestyle which might influence health, such as smoking, alcohol consumption and exercise. A range of socio-demographic information was also collected including employment, education, income and private health insurance.
Of those respondents who reported visiting a GP in the two weeks prior to interview, a 'condition-specific' subset of observations was selected for analysis. This reduced the influence of co-rnorbidities on the decisions taken by GPs and so made the decision- making process being analysed more homogeneous.
GP decision making and SES 37
Consultations that the patient reported were for a 'check up or examination' only, were chosen for analysis. Check-ups and examinations were chosen because these were the most common single reason for consulting a doctor (n = 567).
Dependent variables
Three decisions were analysed separately: the decision to prescribe; the decision to follow up; and the decision to order or to perform a diagnostic test. For prescribing, each respondent in the survey was asked "did the doctor give or prescribe for you any medicine, tablets or other medication?". Thirty seven percent (217/587) of respondents in the sample reported
that medication was prescribed. For follow up, the respondent was asked "did the doctor make an appointment for you to see him or her again?". Twenty five percent (146/587) answered yes to this question. These variables were coded as I if the answer was yes and 0 if the answer was no. For diagnostic testing, the respondent was asked "did the doctor take or arrange" an X-ray, a blood test, a urine test, any other test or none of these. This variable was coded as I if one or more of these tests were undertaken or arranged and 0 if none were undertaken or arranged. Thirty seven percent (215/587) of respon- dents had at least one diagnostic test undertaken or arranged.
Table 1. Explanatory variables for models where respondent was aged between 15 and 64 years (n = 287)
Explanatory variable
Proportion of respondents in each category of each variable for which a decision was made
Number in Coding Follow up Prescribe Test each category
Socio-economic status: Highest qualifications
Age first-left school
Occupat ion/employment status
Income
Health status: Self-assessed health status
Number of chronic conditions
Other: Gender
Whether respondent has private health insurance
Bachelor degree or higher 0.09 0.18 0.68 22 Trade/apprenticeship 0.19 0.06 0.42 3 I Certificate/diploma 0.11 0.28 0.58 74 Other 0.00 0.50 0.00 2 No post-school qualifications 0.19 0.28 0.49 158 < 15 years old 0.27 0.43 0.39 51 15-17 years old 0.14 0.23 0.54 199 _> 18 years old 0.11 0.14 0.59 37 Armed forces - - - - - - 0 Management/administration 0.09 0.23 0.45 22 Professionals 0.09 0.18 0.86 22 Para-professionals 0.13 0.19 0.69 16 Tradespersons 0.25 0.15 0.45 20 Clerks 0.12 0.22 0.48 50 Sales and personal service workers 0.05 0.20 0.55 20 Plant/machine operators + drivers 0.21 0 . I t 0.68 19 Labourers 0.19 0.23 0.58 26 Unemployed 0.07 0.21 0.36 14 Not in labour force 0.23 0.41 0.41 78 <$5000 0.17 0.17 0.49 35 $5000-$9999 0.20 0.38 0.33 66 $10,00(O514.999 0.13 0.35 0.61 23 $15,000-$19,999 0.24 0.26 0.53 34 $20.000-$24,999 0.16 0.25 0.55 51 $25,000-$29,999 0.14 0.19 0.57 21 $30,000-$34,999 0.06 0.28 0.67 18 $35,0(0)-$39,999 0.09 0.00 0.73 11 $40,000-$49,999 0.06 0.12 0.82 17 $50,000-$59,999 0.66 0.33 0.33 3 $60,000 or more 0.00 0.13 0.38 8
Numbers in bold indicate how variables were re-coded for the regression analysis. 'Unemployed' refers to those who have no job but are seeking employment whereas 'Not in labour force' refers to those who are neither employed nor unemployed, but are housewives or students.
38 Anthony Scott et al.
E x p l a n a t o r y v a r i a b l e s
Possible explanatory variables included two measures of general health status (self-assessed health status and the number of chronic conditions), four measures of SES (highest qualifications, age first-left school, occupation and income), whether the respondent had private health insurance and gender. Insurance status may also exert an indirect influence
on GP decision-making if the costs of specialist visits are covered by private health insurance. The decision
to refer has not been studied, but it is an element in the wider choice set facing the GP and may act as a substitute for the decisions analysed. Private health insurance status is also positively associated with
higher SES. The NHS data set contains informat ion on self-
assessed health status, SES variables and insurance
status for respondents aged 15 years or more. Data on occupation and employment status were elicited from respondents aged between 15 and 64 years
old.
Statistical analyses were conducted on two age-specific subsets o f observations for each decision- specific empirical model: respondents aged between 15 and 64 years old, and respondents aged 65 years old
and above. Thus, in total, six regression models were estimated (3 dependent variables and two age groups for each dependent variable).
Respondents were given the option of withholding information or answering 'not applicable' to various
questions. Where they did so, these observations were treated as missing values and subsequently deleted f rom the dataset.
One drawback of the coding system used by the ABS is that women who undertake domestic duties in their own homes were classified as 'not applicable/armed forces' in terms of their occupation and 'not in the labour force' (i.e. neither employed nor unemployed) in terms of employment status. Cross-tabulat ions
of gender by occupation and employment status confirmed that nearly all persons in both the 'no t applicable/armed forces' and ~not in the labour force' categories were women. Two further occupation
Table 2. Explanatory variables for models where respondent was aged 65 years or more (n = 182)
Explanatory variable
Proportion of respondents in each category of each variable for which a decision was made
Number in Coding Follow up Prescribe Test each category
Numbers in bold indicate how variables were re-coded for the regression analysis. 'Unemployed' refers to those who have no job but are seeking employment whereas 'Not in labour force' refers to those who are neither employed nor unemployed, but ant housewives or students.
GP decision making and SES 39
categories were therefore created. The first included those respondents who were classified as 'not in the labour force' for employment status and 'not applicable/armed forces' for occupation. This cat- egory thus includes those respondents who were housewives and a small number who were students (male and female). The second included those respondents who were classified as unemployed and 'not applicable/armed forces' for occupation. This takes account of those respondents who had no job but who were looking for one.
All variables were coded by the ABS as categorical. These codings are presented in Tables 1 and 2. The tables show the proportion of respondents in each category of each explanatory variable for which a decision was made and the total number of respondents in each category of each explanatory variable. To eliminate low cell counts variables were re-coded, but differently for each empirical model, reflecting different types of associations observed in the cross-tabulations. This seemed to be the most pragmatic way to code these variables for two reasons. First, the absence of any hypotheses on the nature of the relationship between explanatory variables and each dependent variable means that any recoding is
arbitrary. It would therefore be difficult to justify the superiority of one coding scheme compared to another on the basis of prior hypotheses. The main advantage of the coding scheme we have used is that it fully exploits the information from the cross-tabulations. Such information would be lost if other coding schemes were used. The second reason for coding the same variables differently in each dataset was that the presence of low cell counts differed between each dataset. The new codings are indicated in Tables 1 and 2 by bold type. Dummy variables were created on this basis using reference cell coding [34]. Categories representing highest .health status and highest SES were used as reference categories.
Statistical methods
As the dependent variable in each model was binary in nature (i.e. whether or not a certain decision was made), logistic regression techniques were appropriate. The probability that a decision occurs in the consultation, P(Yi = l) = n,, is as follows:
¢~+~x
lt~ = 1 + e ~+px (1)
Table 3. Results for the decision to follow up for respondents aged between 15 and 64 years (n = 287)
Explanatory variables Full model fl (SE) Reduced model fl (SE)
Highest qualifications: No qualifications' 0.335 (0.407)
Age first left school: < 15 years old b 0.508 (0.433)
Occupation:C Tradespersons 0.849 (0.741 ) Clerks, sales and personal service workers - 0 . 3 6 0 (0.680) Plant/machine operators, drivers and labourers 0.275 (0.670) Unemployed - 0.582 (I .255) Not in labour force 0.502 (0.710)
Income: d $20,000 to $29,999 0.311 (0.618) $15,000 to $19,999 0.717 (0.726) $10,000 to $14,999 0.023 (0.898) $9999 or less -- 0.125 (0.772)
Self-assessed health status: Fair or poor* 0.664 (0.429)
Number of chronic conditions f 0.030 (0.119)
Gender: Females~ 0.030 (0.396) Insurance slatlt$: No private health insurance h 0.704 (0.371)* Constant - 2.909 (0.620)***
Goodness-of-fit: Model ;e 2 (dO 23.18 (15d0* Percent y~ = I predicted correctly 2. ! 7% Percent y~ = 0 predicted correctly 100% LRI 0.09 LM2 (df) 325 (15dr)***
m
m
0.971 (0.366)***
0.694 (0.344)** - 2.270 (0,277)***
13.13 (2df)*** 0%
100% 0.05
292 (2dO***
*0.05 < P < 0.1; **0.01 < P < 0.05; ***P < 0.01. 'Relative to those with qualifications. bRelative to those who left aged 15 or more. "Relative to managers/administrators, professionals and para-professionals. dRelative to those with incomes of $30,000 or more. "Relative to those in excellent or good health. fFor every extra chronic condition a patient has the odds of a decision occurring changes by the value of the odds ratio
[where OR ffi exp(fl)]. ,Relative to males. hRelative to those with private health insurance.
• e~M 42 / I - -D
40 A n t h o n y Sco t t et al.
Equivalently,
In[ ~ ] = a +/ /X (2)
where ~ is a constant , / / is a vector of coefficients to be estimated and X is a vector of explanatory variables. Maximum likelihood estimation was used. Exponenti- ation of each estimated coefficient yields an odds ratio.
A general-to-specific regression strategy was adopted. For each decision-specific data set, a model with all explanatory variables was estimated first. Variables were then excluded from the model on the basis of the likelihood ratio (LR) test. Thus, if the difference in the - 2 log likelihood between the reduced model and the full model was not significant at the 10% tevel of significance, then the variable was excluded. Categorical variables were excluded in blocks, e.g. if there was five dummy variables for occupation then all were excluded together and the LR test used to test the null hypothesis that the coefficients of this group of variables were equal to zero. Variables with the largest P-value (LR test) were excluded first.
The goodness-of-fit of the multiple logistic regression model was assessed in three ways. The first was the standard X 2 test which tested the null hypothesis that the vector of coefficients (8) were all equal to zero. The second used a classification table of observed responses and predicted probabilities. If the model correctly predicted a high percentage of actual positive responses where y, = I (where a prediction was correct if the predicted probability was > 0.5), and predicted a high percentage of actual negative
responses where y, ffi 0 (where a prediction was correct if the predicted probability was <0.5), then the model was deemed to be a good fit. The third was by calculating the percentage decrease in the log-likeli- hood ratio from the addition of the covariates. This is referred to as the likelihood ratio index (LRI) and lies between 0 and 1. At best these statistics give only a general impression of the adequacy of the model.
The specification of the multiple logistic regression model was tested using the Lagrange Multiplier (LM) approach [35]. The LM2 test statistic (distributed as X 2 with q degrees of freedom where q is the number of explanatory variables) was used to test the null hypothesis that the model was correctly specified and that there were no omitted variables [36, 37].
RESULTS
Decision to follow up
Respondents aged between 15and 64years old. Of the 287 consultations in this age group, 46 (16%) resulted in a follow up. The results from the full model suggest that only insurance status was significantly associated with the decision to follow up (Table 3). The reduced model, however, contains self-assessed health status as well as insurance status. Those respondents with no private health insurance were twice as likely to be followed up compared to respondents with private health insurance. Respondents in fair or poor health were 2.64 times as likely to be followed up relative to those in good or excellent health. None of the SES variables were significantly associated with the
Table 4. Results for the decision to follow up for respondents aged 65 years or more (n = 182)
Explanatory variables Full model p (SE) Reduced model ~ (SE)
Highest qualifications: No qualifications" -0 .058 (0.392)
Age first left school: < 15 years old b -0 .384 (0.334) Income: $9999 or less' -0 .001 (0.423)
Self-assessed health status: Poo# 0.457 (0.452)
Number of chronic conditions" 0.200 (0.090)**
Gender: Females f 0.123 (0.347)
Insurance s t a t u s : No private health insurance s 0,690 (0.357)* Constant - 1,283 (0.525)**
Goodness-of-fit: Model X 2 (dO 14.36 (7d0** Percent y~ = 1 predicted correctly 37% Percent yj = 0 predicted correctly 81% LRI 0.06 LM: (dO 182 (7dr)***
0.224(0.086)***
0.658 (0.333)** - 1.474 (0.393)***
1 i.59 (2dr)*** 39% 80% 0.05
182 (2dO***
*0.05 < P < 0.1; **0.01 < P < 0.05; ***P < 0.01. "Relative to those with qualifications. bRelative to those who left aged 15 or more. ~Relative to those with incomes of $10,000 or more. UR¢lative to those in cxo:llent, good or fair health. 'Fo r every extra chronic condition a patient has the odds of a decision occurring changes by thevalue
of the odds ratio. rRelative to males. ,Relative to those with private health insurance.
GP decision making and SES 41
decision to follow up in either the full or reduced model.
Respondents aged 65 years old and over. Compared to the 15-64 year old age group, a higher proportion of older respondents were followed up. Of the 182 consultations in this age group, 77 (42%) resulted in a follow up. Again, none of the SES variables was significantly associated with the decision to follow up (Table 4). The two variables that were significantly associated with the decision to follow up in both the full and reduced models were the number of chronic conditions and insurance status. For every chronic condition a respondent had, they were 1.25 times as likely to be followed up than someone with no chronic conditions. Independent of health status, those respondents with no private health insurance were nearly twice as likely (OR = 1.93) to be followed up than those with private health insurance.
Decision to prescribe
Respondents aged between 15 and 64 years old. Seventy three out of 287 consultations (25%) resulted in medication being prescribed. Nearly all variables in the full model that were significantly associated with
the decision to prescribe, remained in the reduced model (Table 5). Independent of health status, those respondents who left school before they were 15 years old were 2.27 times as likely to be prescribed medication compared to respondents who left school aged 15 years old or more. Respondents who reported being in fair or poor health were 2.37 times as likely to have medication prescribed relative to those in good or excellent health. For every chronic condition a patient had they were 1.5 times as likely to receive a prescription. Finally, females were more likely to receive a prescription (OR = 2.11) independent of their health status and SES.
Respondents aged 65years old or more. One hu'ndred and fifteen out of 182 respondents (63%) in this age group were prescribed medication, compared to only 25% of respondents in the 15--64 year old age group. In the reduced model, SES was significantly associated with the decision to prescribe (Table 6). Respondents with a gross annual income of < $15,000 were nearly 3 times as likely to be prescribed medication compared to those with a higher income. The number of chronic conditions the patient had also influenced the prescribing decision. For every chronic condition a patient had, they were 1.3 times as likely to be prescribed medication.
Table 5. Results for the decision to prescribe for respondents aged between 15 and 64 years (n = 287)
Explanatory variables Full model fl (SE) Reduced model fl (SE)
Highest qualifications: No qualifications' 0.543 (0.352) - -
Age first left school: < 15 years old b 0,808 (0.400)** 0.823 (0.358)**
Occupation:C Professionals, para-professionals, tradespersons - 0.618 (0.664) - - Clerks, sales and personal service workers - 0 . 6 7 5 (0.639) - - Plant/machine operators, drivers - 1.151 (0.960) - - Labourers - 0.694 (0.785) - - Unemployed - 0.166 (0.963) - - Not in labour force 0.276 (0.683) - -
*0.05 < P < 0.1; **0.01 < P < 0.05; ***P < 0.01. 'Relative to those with qualifications. bRelative to those who left aged 15 or more. CRelative to managers/administrators. dRelative to those with incomes of $15,000 or more. 'Relative to those in excellent or good health. tFor every extra chronic condition a patient has the odds of a decision occurring changes by the value of the odds ratio. .Relative to males. hRelative to those with private health insurance,
42 Anthony Scott et al.
Table 6. Results for the decision to prescribe for respondents aged 65 years or more (n = 182)
Explanatory variables Full model/~ (SE) Reduced model # (SE)
Highest qualifications: No qualifications' - 0.166 (0.419) - -
Age first left school: < 15 years old b 0.171 (0.350) - -
Income: c < $14,999 1.179 (0.551)** 1.000 (0.499)* *
Self-assessed health status: d Good 0.398 (0.482) - - Fair or poor 0.776 (0.523) - -
Number o f chronic conditions" 0.224 (0.096)** 0.263 (0.092)***
Goodness-of-fit: Model ~2 (dO 17.28 (8 dO** 13.73 (2 dO*** Percent y~ = I predicted correctly 89*/0 92% Percent y, = 0 predicted correctly 31% 21% LRI 0.07 0.06 LM2 (dt) 185 (8 dr)*** 182 (2 dO***
* = 0.05 ~ P < 0.1; **0.01 < P < 0.05; ***P < 0.01. "Relative to those with qualifications. bRelative to those who left aged 15 or more. 'Relative to those with incomes of $15,000 or more. ~Relative to those in excellent health. ~For every extra chronic condition a patient has the odds of a decision occurring changes by the value
of the odds ratio. fRelative to males. sRelative to those with private health insurance.
The decision to test
Respondents aged between 15 and 64 years old. For this age group, 149 out of 287 consultations (52%) resulted in a diagnostic test being undertaken or arranged. Income and gender were significantly associated with the decision to test in both the full and reduced models. Income had a significant and positive effect on this decision (Table 7). A diagnostic test was less likely to be ordered or undertaken for those respondents with annual incomes of < $10,000, compared to those with incomes of $30,000 or more. The same was true for respondents with incomes between $10,000 and $30,000. The health status of the patient was not significantly associated with the decision to order or undertake a diagnostic test. Also, women were 1.55 times as likely to have a test ordered or undertaken compared to men.
Respondents aged 65 years or more. Of the 182 consultations in this age group, 40 (22%) resulted in a diagnostic test being carried out or arranged (compared to 52% in the 15-64 year old age group). No variables influenced the decision to test in the full model (Table 8). The reduced model suggested that income, self-assessed health status and insurance status were weakly associated with the decision to order or perform a diagnostic test. Those with incomes < $10,000 were 2.44 times as likely to be tested compared to those with incomes of $10,000 or more. Respondents with no private health insurance were 0.5 times less likely to have a test ordered compared to those with private health insurance. Although the
t-ratios for the two health status variables were not significant, the LR test indicated that, together, these variables were significanly associated with the decision to test at the 10% significance level.
D I S C U S S I O N
The results of this study (summarized in Tables 9 and 10) suggest that patients aged between 15 and 64 years old and of high SES were more likely to have a diagnostic test ordered and were less likely to receive a prescription compared to patients oflow SES. These effects were independent of health status, which exhibited the expected negative relationship with each decision made by the GP. For the over 65 year old age group, patients of a high SES were less likely to be tested and prescribed medication. SES was not significantly associated with the decision to follow up. This provides evidence that patients' SES is associated independently with GP decision making in certain circumstances.
The results also provide evidence supporting the assertion [7] that women are more likely to be pre- scribed medication irrespective of their health status and SES. Thus the gender of patients may also influence GP decision making.
There are several factors to take into consideration when interpreting the results of this study. First, the data were self-reported. This has implications for the extent of recall bias. However, the Australian Bureau of Statistics uses trained interviewers, the question- naire was validated and pilot tested, the accuracy of
GP decision making and SES
Table 7. Results for the decision to test for respondents aged between 15 and 64 years (n = 287)
43
Explanatory variables Full Model fl(S.E.) Reduced Model fl (S.E.)
Highest qualifications: No qualifications' - 0.021 (0.559) - -
Age first left school: < 15 years old b - 0.354 (0.357) - -
Occupation :¢ Tradespersons, clerks, sales and personal service workers - 0.663 (0.398)* Plant /machine operators, drivers and labourers 0.297 (0.481) - - Unemployed - 0.507 (0.709) - - No t in labour force - 0 . 3 2 2 (0.474) - - Income: d $10.000--$29,999 - 0.334 (0.398) - 0.573 (0.342)* < $10,000 -- 1.012 (0.518)* - 1.360 (0.373)*** Self-assessed health status: Fair or poor" - 0 . 3 3 8 (0.366) - - Number of chronic conditions: f One - 0.038 (0.354) - - Two or more -0 .220 (0.341) - - Gender: Females~ 0.543 (0.295)* 0.437 (0.265)* Insurance status: No private health insurance ~ 0.082 (0.268) - - Constant 0.753 (0.539) 0.555 (0.293)* Goodness-q f-fit: Model ;(2 (dO 25.89 (13 dr)** 15.83 (3 dO*** Percent y~ = 1 predicted correctly 68% 55% Percent y, = 0 predicted correctly 59% 64% LRI 0.07 0.04 LM: (dr) 288 (13 dO*** 287 (3 dl)***
*0.05 _< P < 0.1; **0.01 < P < 0.05; ***P < 0.01. 'Relat ive to those with a bachelor degree or higher. bRelative to those who left aged 15 or more. ~Relative to managers /adminis t ra tors , professionals and para-professionals. dRelative to those with incomes of $30,000 or more. cRelative to those in excellent or good health. tRelative to those with no chronic conditions. JRelative to males. hRelative to those with private health insurance.
data-transcription was checked and details about GP consultations were limited to the previous two weeks [33]. The degree of any bias should therefore be limited.
Secondly, each of the models exhibited poor goodness-of-fit. The poor to moderate predictive power and the relatively small decreases in the log-likelihood ratios of the models may be related to the significant LM_, statistic. This indicates that each model is misspecified which could be due to the omission of relevant explanatory variables. Models examining the discrete choice behaviour of individuals using cross-sectional data are often poorly specified because of the inherent randomness of behaviour or because of factors which influence decisions but which cannot be observed by analysts [37].
A more notable omission was information on the characteristics of the doctor and the remuneration system which are both known to influence decisions made by GPs [38, 39]. Cramer showed that the omission of any relevant explanatory variables can cause the estimated coefficients in logistic regression (and the odds ratios derived from them) to be biased towards zero [40]. Thus, if the regression coefficients in the models estimated here are smaller than they should be, then their statistical significance will have been underestimated. Although this reinforces the con- clusions regarding variables that are statistically
significant, the conclusions that other variables have no significant effect may be incorrect. It is plausible, therefore, that SES may exhibit a stronger association than we have demonstrated here.
As well as omitted variables, there is likely to be an interaction between GP characteristics (including the SES of origin) and the SES of patients because of GP choice of where to practice and, to a lesser extent, patient choice of GP. The omission of doctor variables may therefore be important. Other interactive effects could not be considered because of problems with sub-group sample sizes. There was some suggestion of a positive association between self-reported health status and the decision to test (Table 8). This may reflect confounding by SES but may also indicate a class difference in the propensity to attend for health screening checks (positive health action).
Finally, the possibility that the data were "forced to confess under duress" cannot be entirely ruled out [41]. The exploratory nature of the analysis, made necessary because of the relative lack of prior hypotheses about the relationship between SES and GP decision making, has been acknowledged. The results are plausible and consistent with what has been found elsewhere but may depend critically on the way variables of interest have been classified. Limitations on sample size forced us to dichotomize some variables and, as a result, a relationship may be suggested where
44 A n t h o n y Scott et al.
Table 8. Results for the decision to test for respondents aged 65 years o r m o r e ( n = 182)
Explanatory variables Full model ,8 (SE) Reduced model ~ (SE)
Highest qualifications: No qualifications" -0 .389 (0.456) - -
Age first left school: < 15 years old b -0.417 (0.391) - -
Income: < $9999 ~ 0.902 (0.569) 0.895 (0.543)*
Self-assessed health status: d Fair - 0.764 (0.524) - 0.717 (0.504) Poor 0.589 (0.504) 0.746 (0.484)
Number of chronic conditions: Two or more' 0.580 (0.557) - -
Gender: Females f 0.288 (0.42 I) - -
Insurance status: No private health insurance* -0.488 (0.416) -0.680 (0.400)* Constant - 1.797 (0.700)** - 1.565 (0.507)***
Goodness-of-fit: Model X 2 (df) 14.03 (8 dO* 10.63 (4 dO** Percent y~ = 1 predicted correctly 0% 5% Percent y~ = 0 predicted correctly 100% 99% LRI 0.07 0.06 LM2 (dr) 208 (8 dO 195 (4 dO
*0.05 < P < 0.1; **0.01 < P < 0.05; ***P < 0.0l. ,Relative to those with post school qualifications. bRelative to those who left aged 15 or more. cRelative to those with incomes of $10,000 or more. dRelative to those in excellent and good health. ,Relative to those with 0 or 1 chronic condition. 'Relative to males. ~Relative to those with private health insurance.
none exists in fact. In defence, the basis of reclassifying the variables was cell count, internal consistency and the associations observed in cross-tabulations, rather than the anticipated relationship with decision making.
If there is a relationship between social status and GP decision making, then one might expect it to be continuous or 'dose-responsive'. A true dichotomous relationship, say between income and decision making, may indicate a 'threshold effect' in which GPs are price sensitive for people on very low incomes but not responsive for patients with incomes above the threshold.
An income effect (working through differences in the real price that people face for GP services) may explain some of the findings reported here. People on low incomes and elderly people are likely to hold a health care concession card which entitles the holder to reduced or zero priced prescriptions. This may encourage the GP to prescribe where he or she might not otherwise. This implies, however, that the GP is
aware of the patient's entitlement and is more likely to prescribe because the patient faces a lower price. There is little evidence on the effect of private patient costs on GP decision making though Janssen has found that time costs and out of pocket expenses incurred by the patient did not influence the GP's decision to follow up [28].
These results may also reflect differences in the behavioural characteristics of individual patients and doctors which are common to people in particular socio-economic groups. Patients have expectations (for example about the number of tests to which they should be subjected in periodic health checks) which are often higher than clinical recommendations [42]. In addition, doctors are known to be influenced by such expectations in the belief that patients regard the extent of diagnostic testing as an indicator of the quality of medical care [43]. Whether or not there are class differences in expectations has not been examined but, in general, people from lower socio-economic backgrounds are likely to be more diffident about
Table 9. The influence of socio-economic status on decision making for those aged between 15 and 64 years
Explanatory variable Follow up Prescribe Diagnostic test
High SES NA - + High health status - - NA No private health insurance + NA NA Females NA + +
NA, Not included in the reduced model. - , Decision less likely to occur. + , Decision more likely to occur.
GP decision making and SES
Table 10. The influence of SES on decision making for those aged 65 years or more
Explanatory variables Follow up Prescribe Diagnostic test
High SES NA - - High health status - - NS No private health insurance + NA - Females NA NA NA
NS, not significantly associated with the decision in the reduced model (i.e. Wald ~2 p value > 0.1). NA, not included in the reduced models. - , Decision less likely to occur. + , Decision more likely to occur.
45
expressing their preferences and therefore may appear less interested even though this is not the case [44--46].
Strategies orientated towards educating physicians in communicat ion skills have had some success [47, 48]. Strategies aimed at developing the consumer skills of the low income patient to encourage him or her to take a more active, inquisitive role need further development [12].
Alternatively, if the results of this study are indicative of a more pervasive influence of SES, then it is doubtful whether any response aimed at the individual GP or patient will be effective in reducing these differences in clinical practice. Social differences in health behaviour may remain even after controlling for known individual differences [49].
The results of this study also confirm the literature which suggests that women are more likely to be tested and to receive prescriptions than are men [7]. This finding is independent of self-reported health status though gender-specific reasons for attending the GP for a check-up cannot be ruled out.
The same factors which influence the relationship between GP decision making and SES may also be responsible for the findings with respect to gender. Women tend to occupy less prestigious and less well rewarded social roles and are more predominant in low-paid or part-time work [50, 51]. However, the increased propensity to prescribe and to test holds for women even after controlling for SES. It is likely therefore that the effect shown here is the result of gender per se though whether our findings reflect an appropriate response to a gender-specific diagnosis or sexual stereotyping is unclear.
These results have implications for the achievement of equity, however this is defined [52-55], as an objective of health services policy. In terms of access and utilization, health services may be equitable at the point of first contact but not thereafter. In terms of final health state, the effect on health outcomes of the differences in decisions made by GPs for people of different social backgrounds has not been assessed. What is evident from this study, however, is that any analysis o f the efficiency of G P provision cannot ignore the social context in which the consultations take place.
Acknowledgements--This research was supported in part by a grant from the General Practice Evaluation Programme of the Commonwealth Department of Health, Housing, Local Government, and Community Services. Support is also
acknowledged from the New South Wales Department of Health who provide core funding for CHERE. The authors would like to thank Jane Hall, Ross Lazarus, Stephen .Leeder, Gavin Mooney and David Newell and two anonymous referees for comments on earlier drafts. Any errors or omissions are the responsibility of the authors.
REFERENCES
1. Andersen T. F. and Mooney G. (Ed.) The Challenges of Medical Practice Variations. Macmillan, London, 1990.
2. Smith G. D. and Egger M. Socio-economic differences in mortality in Britain and the United States. Am. J. publ. HIth 82, 1079, 1992.
3. Smith G. D., Bartley M. and Btane D. The Black report on socio-economic inequalities in health ten years on. Br. Med. J. 310, 373, 1990.
4. Wagstaff A., van Doorslaer E. and Paci P. On the measurement of horizontal inequity in the delivery of health care. J. hlth Econ. 10, 169, 1991.
5. O'Donnell O. and Propper C. Equity and the distribution on UK National Health Service Resources. J. hlth Econ. 10, 1, 1991.
6. Eisenberg J. M. Socioiogic influences on decision making by clinicians. Ann. intern. Med. 90, 957, 1979.
7. Clark J. A., Potter D. A. and McKinlay J. B. Bringing social structure back into clinical decision making. Soc. Sci. Med. 32, 853, 1991.
8. Cartwright A. and O'Brien M. Social class variations in health care and in the nature of general practice consultations. In The Sociology of the National Health Sen, ice (Edited by Stacey M.), Sociology Review Monograph No. 22. Keele University, Keele, 1976.
9. Bochner S. Doctor, patients and their cultures. In Doctor-Patient Communication (Edited by Pendleton D. and Hasler J.). Academic Press, London, 1983.
I0. Bain D. J. G. Doctor-patient communication in general practice. Med. Educ. 10, 124, 1976.
11. Barsky A. J. and Klerman G. L. Overveiw: hyper- chondriasis, bodily complaints and somatic styles. Am. J. Psychiatr. 140, 273, 1983.
12. Cockerham W. C., Luescher G., Kunz G. and Spaeth J. L. Social stratification and self-management of health. J. Hlth soc. Behav. 27, i, 1986.
13. Pendleton D. and Bochner S. The communication of medical information in general practice consultations as a function of a patients social class. Soc. Sci. Med. 14A, 669, 1980.
14. Boulton M., Tuckett D., Olson C. and Williams A. Social class and the general practice consultation. Sociol. Hlth Illness 8, 325, 1986.
15. Pill R. and Stott N. Concepts of illness causation and responsibility. Some preliminary data from a sample of working class mothers. Soc. Sci. Med. 16, 43, 1982.
16. Wilensky G. R. and Rossiter L. F. The relative importance of physician induced demand in the demand for medical care. Milbank Memorial Fund Q. 61, 252, 1983.
46 Anthony Scott et al.
17. Hanley R. M., Charlton J. R., Harris C. M. et al. Influence of patient characteristics on test ordering in general practice. Br. Med. J. 289, 735, 1984.
18. Fylkesnes K., Johnsen R. and Forde O. H. The Tromso study: factors affecting patient-initiated and provider- initiated use of health care services. Sociol. Hlth Illness 14, 275, 1992.
19. Penchansky R. and Fox D. Frequency of referral and patient characteristics in group practice. Med. Care 8, 368, 1970.
20. Bernstein B. and Kane R. Physician's attitudes toward female patients. Med. Care 19, 600, 1981.
21. Buller M. K. and Buller D. B. Physicians' communi- cation style and patient satisfaction. J. Hlth soc. Behav. 28, 375, 1987.
22. McCranie E. W., Horowitz A. J. and Martin R. M. Alleged sex-role stereotyping in the assessment of women's physical complaints: a study of general practitioners. Sac. Sci. Med. 12, i l 1, 1978.
23. Armitage K. J., Schneiderman L. J. and Bass R. A. Response of physicians to medical complaints in men and women. J. Am. Med. Assoc. 241, 2186, 1979.
24. Kristiansen I. S. and Hjortdahl P. The general practitioner and laboratory utilisation: why does it vary? Family Practice 9, 22, 1991.
25. Verbrugge L. M. and Steiner R. P. Physician treat- ment of men and women patients. Med. Care 19, 609, 1981.
26. Rundell T. G. and Wheeler J. The effect ofincome on use of preventive care: an evaluation of an alternative explanation. J. Hlth soc. Behav. 20, 397, 1979.
27. Friedson E. Profession o f Medicine: A Stud), of Applied Knowledge. University of Chicago Press, Chicago, 1970.
28. Janssen R. Time prices and the demand for GP services. Soc. Sci. Med. 34, 725, 1992.
29. Waitzkin H. Information giving in medical care. J. Hlth soc. Behav. 26, 81, 1985.
30. Scott A., King M. and Shiell A. Factors Influencing Decision Making in General Practice: the Feasibility of Analysing Secondary Data. Discussion Paper 16. Centre for Health Economics Research and Evaluation, Sydney, 1993.
31. Altman S. and Jackson T. Health care in Australia: lessons from down under. Hlth Affairs 10, 129, 1991.
32. Health Insurance Commission. Annual Report 1990--91. Commonwealth of Australia, Canberra, 1991.
33. Castles I. 1989-90 National Health Survey Users' Guide. Australian Bureau of Statistics Catalogue No. 4363.0, Commonwealth of Australia, Canberra, 1991.
34. Hosmer D. W. and Lemeshow S. Applied Logistic Regression. John Wiley and Sons, New York, 1989.
35. Godfrey L. G. Misspecification Tests in Econometrics. Econometric Society Monographs No.16, Cambridge University Press, London, 1988,
36. Davidson R. and MacKinnon J. G. Convenient specification tests for logit and probit models. J. Econometrics 25, 241, 1984.
37, Pudney S. Modelling Individual Choice. The Econo- metrics of Corners, Kinks and Holes. Basil Blackwell, Oxford, 1989,
38. Scott A. and Hall J. Evaluating the effects of GP remuneration: problems and prospects. Hlth Policy 31, 183, 1995.
39. Kristiansen I. S. and Mooney G. Remuneration of GP services: time for more explicit objectives? A review of the systems in five industrialised countries. Hlth Policy 24, 203, 1993.
40. Cramer J. S. The Logit Model: An Introduction for Economists. Edward Arnold, New York, 1991.
41. Mills J. L. Data torturing. N. Engl. J. Med. 329, 1196, 1993.
42. Romm F. J. Periodic health examination: effect of costs on patient expectations. S. Med. J. 78, 1330, 1985.
43. Hartley R. M., Epstein A. M., Harris C. M. and McNeil B. J. Differences in ambulatory test ordering in England and America. Role of doctors' beliefs and attitudes. Am. J. Med. 82, 513, 1987.
44. Cartwright A. and Anderson R. General Practice Revisited. Tavistock, London, 1981.
45. Biaxter M. and Paterson E. Consulting behaviour in a group of young families. J. R. Coll. Gen. Practitioners 32, 657, 1982.
46. Waitzkin H. Doctor-patient communication: clinical implications of social scientific research. J. Am. Med. Assoc. 17, 2441, 1984.
47. Maguire P., Roe P. and Goldberg D. The value of feedback in teaching interview skills to medical students. Psychol. Med. 8, 695, 1978
48. Maguire P., Fairbairn S. and Fletcher C. Consultation skills of young doctors. Br. Med. J. 292, 1573, 1986
49. Kenkal D. S. Relationship between schooling and health behaviour remains after differences in knowledge are controlled for. J. Political Econ. 99, 287, 1991.
50. Bird C. E. and Fremont A. M. Gender, time use and health. J. Hlth soc. Behav. 32, 114, 1991.
51. Ross C. E. and Bird C. E. Sex stratification and health lifestyle: consequences for men's and women's perceiv~ health. J. Hlth soc. Behav. 35, 161, t994.
52. Mooney G., Hall J., Donaldson C. and Gerard K. Utilisation as a measure of equity: weighing heat? J. hlth Econ. 10, 477, 1991.
53. Culyer A. J., van Doorslaer E. and Wagstaff A. Comment: utilisation as a measure of equity. J. hlth Econ. II , 93, 1992.
54. Mooney G., Hall J., Donaldson C. and Gerard K. Reweighing heat: response to Culyer, van Doorslaer and Wagstaff. J. hlth Econ. 11, 199, 1992.
55. Culyer A. J., van Doorslaer E. and Wagstaff A. Access, utilisation and equity: a further comment. J, hlth Econ. 11,207, 1992.
