Self-referral: Social and demographic determinants of consulting behaviour

Journalof Psychosomatic Printed in Great Britain.

CO22-3999183 $3.00 + .@I 0 1983 Pergamon Press Ltd.

Research, Vol. 21, No. 3, pp. 233-242, 1983.

SELF-REFERRAL: SOCIAL AND DEMOGRAPHIC DETERMINANTS OF CONSULTING BEHAVIOUR J. G. INGHAMand P. McC. MILLER (Received

29 July 1982; accepted in revisedform

18 December

1982)

Abstract-This report, which is one of a series, is concerned with socio-demographic correlates of self-referral, and the extent to which the observed associations can be explained by variations in symptom prevalence and severity. Social class, marital status, employment status, and distance from health centre, all show small but significant associations with self-referral. For social class the effect appears to be mediated by symptoms. People in social classes IV and V, women who are widowed, divorced or separated, and people who live near the health centre are more likely to visit their general practitioner.

INTRODUCTION EPIDEMIOLOGISTShave often pointed out that studies of clinical conditions in hospital patients are liable to distortion because of selection factors unrelated to the illnesses themselves. The selection process starts at the point of entry into primary care, and there is a widespread belief that illness is not the only factor that determines whether people consult their general practitioners. Many such factors have been suggested, social, demographic, and psychological [ 1, 21 but investigators have often failed to make allowance for variations in symptom severity and in other variable characteristics of illness, such as chronicity. For example, women seem to seek medical help more often than men but perhaps they have more frequent illness or more severe symptoms. If so, we need to know whether this is sufficient to explain their greater tendency to seek help. The same question arises whenever an association is demonstrated between consultations and any non-illness factor. The results to be reported are from a comparative study of consulters and nonconsulting controls in a Scottish Health Centre. In an earlier publication the groups were compared on different components of symptom severity and other aspects of illness [3] and it was shown that although symptoms often occur without a subsequent consultation, severity is a major factor in determining self-referral [4]. Nevertheless there is a substantial overlap in severity between consulters and non-consulting controls. Once this overlap had been verified, the next aim was to explore possible determinants of self-referral other than symptoms. The list of possibilities was a long one, so only social support and basic demographic questions were included, along with symptom assessments, in the core interview given to the whole sample. This provided the information to be reported here. Other data, about life stress and individual differences in personality and attitudes were obtained from sub-samples and will be described elsewhere.

DESIGN Reasons for choice of samples The patient’s decision to go to the doctor is probably a resultant of a large number of determining factors inter-related in a complex way. Some individuals will consult their doctors more readily and Medical Psychiatry,

Research Council Royal Edinburgh

Unit for Epidemiological Studies in Psychiatry, University Hospital, Morningside Park, Edinburgh EHlO 5HF. 233

Department

of

J. G.

234

INCHAM

and P. McC. MILLER

more frequently than others, irrespective of temporary variations in health status. Such consultation proneness can be investigated by assessing consultation rates over reasonably long periods, say at least one year, and looking at the correlates of these rates. It also seems likely, however, that there are other modifying factors that influence a consulting decision more immediately. For example, the nature of the illness and symptoms and also the exact social circumstances of the patient’s prior situation, as well as being involved directly in their own right, may interact with the long-term ‘consultation proneness’ factors and also with each other. In particular, illness and individual are likely to interact, as are illness and social determinant. The person who is most likely to seek help for illness A is not necessarily more likely to seek help for illness B. Similarly, the social factors that modify the likelihood of consultation for illness A are not necessarily the same as for illness B. In order to be able to investigate episodes, specific factors and their interactions, it was clearly necessary to make observations as soon as possible after the episode. The simplest way of achieving this was to sample people attending the health centre for new episodes of illness. By this method we could make observations while the patients were still suffering from the symptoms that brought them to the doctor and ask them about the circumstances preceding self-referral while these were still reasonably fresh in their minds. For the control sample it was necessary to select people who were not at that time affected by any illness or other influence that had caused a self-referral to the health centre. To do this we took only those individuals who had not consulted a doctor within the previous three months. For each consulter sampled another name was selected at random from those in the health centre register of the same sex and in the same age group. The casenotes were then examined to exclude those who had consulted within the previous three months in which case another random name was substituted. It occasionally happened that a control subject consulted after selection but before the interviewer arrived and in these instances also another random control was allocated. For sampling, the population was divided into three broad age groups 16-3536-55 and 56-75.

Reasons for a symptom-focused study The population of primary care consulters is clearly heterogeneous, and we wished to be able to subdivide the consulting sample according to illness. In deciding how this might be done, we were influenced by the fact that the study had to be patient-orientated. If the characteristics of the illness do modify the factors determining self-referral it must be the patient’s own conception of his condition that lies at the root of the matter. It is this rather than a medical diagnosis that should be the basis of the sub-division. It follows that the study must be symptom orientated. It is symptoms that patients are aware of, symptoms and their severity that determine whether the patient feels ill enough to seek medical aid. Our aim was to analyse the data at the level of single symptoms, and this necessitated a sample large enough to ensure that each symptom produced enough consulters. The symptoms had to be sufficiently prevalent at the primary care level for statistical analysis to be feasible. They were selected on the basis of previously published prevalence figures [5,6] and on a pilot study in Edinburgh [7].

METHODS

Sampling All samples were drawn from a population of patients registered in one health centre in a Scottish New Town. Consulters with new episodes of illness were sampled daily, at random, for one year. For each consulter a control was selected, also at random, from the same age/sex group in the list of registered patients, excluding any who had attended in the previous three months. Sampling methods and information about response rates and reasons for lapses have been described elsewhere in some detail 13).

Interviewers and genera/procedure The main interview, conducted in subjects’ homes, was complex and searching. Much depended upon the selection and training of interviewers. Thirteen people were selected initially for training, a11 married women with some previous interviewing experience, and ten completed the course. Training consisted of demonstration and practice, most of it under supervision, and continued until a senior member of the project team was satisfied that the interviewer had reached the required standard. Spot checks were done during the project and there was a continual monitoring of all schedules as they were handed in, with consultation as queries arose. Schedules were coded according to standard criteria.

Symptom information Presence or absence of seven target symptoms. The subjects troubles?’

followed

by a list of symptoms:

backache,

tiredness,

were asked, ‘Have you got any of these anxiety, headache, depression, irritability,

Self-referral:

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235

dizziness. They were asked to answer yes or no to each of these symptoms. If in doubt the interviewer was given several alternative ways of wording the question with the emphasis always upon whether the subject was troubled by the symptom. Chronicity. For each symptom the subject was asked to say whether it came on or got worse at any time during the previous twelve months and if so, when the change was first noticed. Any symptom with an onset within the previous twelve months was regarded as acute, others as chronic. A gradual worsening during the whole twelve months with no definite point at which the change was first noticed was also regarded as chronic. Symptom severity. The symptom severity scales have been described previously [3]. Each scale was presented in two formats; pair comparison, and visual analogue. Response inconsistencies. Each pair comparison scale had a built in consistency check and the number of inconsistencies was recorded. Distress scales. Using a visual analogue format the degree of unpleasantness associated with each symptom was rated by the subject. Concept of cause. Answers to the question, ‘What do you think is causing these symptoms?’ were coded as external physical, internal physical, psychological, or any combination of these. The subject’s own attribution was coded rather than the coder’s judgement of the most likely cause.

Socio-demographic information Sex and age. These were controlled

by matching

but were still included

in the analysis

for reasons

to be given later.

Social class. The classification

used was similar to the Registrar General’s. Women were classified to the occupation of the chief wage-earner in the household. Marital status. Subjects were classified as single, married and living with husbands, co-habiting, widowed, divorced or separated. Interviewers were asked to record a person as co-habiting only when the relationship was stable and had lasted for at least a year. Number in household. Children under 18 and adults were recorded separately. Confidant. Subjects were asked, ‘Suppose you have a personal problem or are worried about something, what would you do about it?’ and if necessary, ‘Is there anybody you could talk it over with?’ Probing questions were used to ascertain who the main confidant was. Three characteristics of the confiding relationship were rated: Quality: ‘Can you talk freely with -? Tell them everything?’ Availability: rated on a scale from l-5, from virtually unavailable to living with or nearby and available at any time. Reciprocity: ‘Do you think they tell you all their worries?’ Diffuse support. Questions were asked to ascertain how many acquaintances the subject had with whom regular conversations took place even if on a completely non-personal level. The four main areas covered were: Work: ‘Do you meet many people there or just a few? How many of these do you have a chat with from time to time?’ Relatives: ‘Are there any of them that you see or write to or ‘phone up at least once a fortnight?’ Neighbours: ‘How many of these do you have a chat with from time to time? Do you get on well with them?’ Visits: to clubs, organisations, pubs, bingo, friends, etc., probing questions to ascertain how many such groups were visited at least once a fortnight and how many people were met and talked to on such occasions. Distances from health centre. Addresses were recorded and coded according to district of residence. Employment status. Subjects who were going out to work at all, either full or part-time, were recorded as working and others as not working. according

RESULTS

Age and sex Because the consulters and controls were matched for sex and roughly for age as well it was not possible to look at the effects of these variables by comparing the samples. However, we also had estimates of new episode 3-month consultation rates in the same population, in each of our age/sex groups. From these base rates we could calculate the percentage of people consulting amongst those who had each symptom in each age/sex group. The results of this exercise have been reported in

236

J. G. INGHAM and P. McC. MILLER

detail elsewhere [l and 21. In contrast to other studies there was a smaller proportion of consulters amongst the older age group in both sexes but this was almost certainly because we were restricting our definition of consulters to new episode attenders. There is little doubt that the preponderance of older people amongst primary care attenders as a whole is due primarily to those with chronic conditions. The figures confirmed other studies of sex differences. A higher proportion of women had consulted with at least one new episode of illness especially in the younger age group. The analysis showed that although the preponderance of women amongst people seeking help is to a iarge extent attributable to the fact that women have more symptoms of ill health and have them more severely than men, there is a further factor involved. They are more ready to go to the doctor when they feel ill. The present paper is mainly concerned with symptom variables and with sociodemographic factors. Symptom information Analysis at the single symptom level was reported in an earlier paper [3] which showed that symptom prevalence was sometimes surprisingly high amongst nonconsulting controls. It was clear, however, that controls with symptoms tended to have them less severely than did the consulters. The next stage in the analysis was to see whether consulting and control groups could be discriminated effectively, using only symptom information. Table I summarizes a hierarchical multiple regression analysis [8] in which a new set of symptom variables was added to the independent variables at each step. The dependent variable was consulter/control status. Sex, age and their interaction were used as independent variables at the first step, to reduce the sampling errors and to allow for their possible interactions with other variables. As shown in Table I most of the symptom variables contributed significantly to the discrimination but the original analogue ratings and paired statements were highly correlated and neither contributed significantly when the other had been partialled out. Either could have been retained but it was unnecessary and uneconomic to retain both. The paired statements were chosen because these scores were less likely to be contaminated by irrelevant response sets.

TABLE I.--LIVINGSTONRANDOMSAMPLE(N=I~I~)

Independent

factors

Sex, age and interaction Symptom severity (paired statements) Sex x severity Response inconsistency Distress Chronic symptoms Acute symptoms Concept of cause

*p < 0.001. Tp
*p < 0.05.

Number of component variables

R2

Increment R’

3 7 7 7 7 7 7 3

0.000 0.044 0.053 0.064 0.084 0.091 0.115 0.175

0.000 0.044’ 0.009* 0.011~ 0.020* 0.007 0.024’ 0.060’

in

Self-referral:

social and demographic

determinants

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behaviour

237

The order of entry of subsequent variables requires some explanation. A primary purpose of the research was to see to what extent self-referral could be explained by severity of symptom. Each symptom was conceived as a continuum representing levels of distress from complete absence, through various levels and frequencies of occurrence of normal distress up to the incapacitating symptoms typical of severe illness. Scores were obtained from all subjects, whether or not the symptom had been reported as present, so we were able to assess severity quite independently of the threshold at which the distress was considered by the subject to be a troublesome symptom. It was therefore logical to enter symptom severity into the regression equation first to discover how much of the dependent variance could be explained thereby, and only afterwards to assess the additional contribution made by the declaration of a troublesome symptom. Chronic symptom declaration was entered before acute symptoms on the grounds that the former may well have contributed to the causing of the latter. The index of response inconsistency was obtained from the response patterns of the symptom severity scales and it seemed appropriate to enter it immediately after these. The visual analogue distress ratings represented the additional component ‘unpleasantness’ that we would not have wished to include in the regression had it not contributed significantly after severity and symptom declaration. Interactions between age and sex and each of the symptom severity scores were also tested and one set (sex x severity) was retained as significant. Finally we wanted to know whether the decision to consult was influenced by the cause that the subject attributed to his own pattern of symptoms elicited by the question, ‘What do you think is causing these symptoms?’ This included any symptoms, not only those covered specifically by the schedule. The responses were coded (not by the interviewers but by independent coders) according to whether the attributed causes were external and physical, internal and physical, or psychological. The variable that was most highly associated with consultation status separated those who thought their symptoms had a physical cause that was primarily internal from the rest. Clearly the belief that symptoms indicate the presence of some internal physical abnormality is a significant factor in determining whether people decide to consult their doctors or not. Taking all the symptom information into account we were able to explain approximately 17% of the dependent variance. This analysis is clearly not unique because the sets of variables could have been entered in any order with varying outcomes. There are no hard and fast rules concerning order of entry. Theory, logic, research aims, available factual information and intuition ail play a part and other investigators may have structured our findings differently. The alternative would have been to use a strictly statistical criterion, as in stepwise analysis, but we believe that this would have been misleading.

Socio-demographic

information

A similar analytical strategy was followed for the social and demographic data which comprised seven factors, in addition to age and sex, some of them represented as sets of two-valued dummy variables, twenty-one variables in all. A series of hypotheses were tested starting with the very general one that sociodemographic information taken as a whole, from the data in the core interview, is associated with the criterion. A multiple regression equation with age, sex and the

238

J. G. INGHAM and P. McC. MILLER

further seven sets of independent variables entered as predictors of the criterion, produced a multiple correlation of 0.18 (3.2% of the dependent variance explained as shown in Table II, column I). This is a small but highly significant correlation @ < 0.001) with N= 1416.* Interactions were not included in the equation and to have done so would have meant entering every possible combination of cross-products. Even excluding any but two-way interactions there would be 420 of these. Not only would this exceed the capacity of most computer packages but the likelihood of getting a significant overall multiple correlation would be greatly reduced. However, it did seem worthwhile to test the further general hypothesis that sex and age would modify the effects of the other 7 sets of variables. The 7 sets of interactions of sex and age with all other variables, 38 in all, were examined by entering the appropriate product terms into the equation in a single step after the combined effects of the 21 single variables had been partialled out. The extra dependent variance attributable to interactions (about 3% of the total) fell as expected far short of statistical significance, but, as we shall see, this does not mean that they can be discarded as of no further interest.

TABLE II.-LIVINGSTON RANDOM SAMPLE (N=1416). CONSULTER/CONTROLSTATUS

Independent

factors

Sex Age Sex x age Social class Marital status Sex x marital status5 Number in householdI/ Confidant Employment status Diffuse support Distance from health centre

Number of component variables

DEPENDENT

Increment in R’ for analyses: I II

I f 5 2 2 2 3 1 5 1

VARIABLE:

0.000 0.000 0.000 0.016; 0.005* 0.005*

0.033’

0.003~ 0.004~

i

‘p
as two separate

variables.

The next stage of the analysis required the setting up of more specific hypotheses, with the aim of discarding some of the information as irrelevant. A series of MR equations was computed, entering cumulatively, one additional set of independent variables for each new equation. The order in which the sets were entered (stated in Table II) was predetermined but a set was retained for subsequent runs only if it accounted for a statistically significant increment in dependent variance. For each set retained the next step was to see whether its interactions with sex and age contributed a significant increment. If so, they were retained as well. *Slight discrepancies values.

in sample sizes for different

analyses

are explained

by different

numbers

of missing

Self-referral:

social and demographic

determinants

of consulting

behaviour

239

For example, the Social Class set was entered, after sex and age, to test the hypothesis that social class accounts to some degree for the discrimination between consulters and controls. This hypothesis was confirmed at the 0.1% level of confidence, accounting for 1.6% of the discriminant variance and social class was therefore retained for further analysis. Although the direct effects of sex and age had been eliminated by matching, there was every reason to expect them to be important determinants or modifiers of consulting behaviour. For each retained set the interaction of each variable in the set with sex and age was computed. For social class, neither contributed significantly to dependent variance accounted for and they were discarded from subsequent runs. The sequence was repeated for each of the remaining sets of variables. Whenever a set was found to contribute significantly, its interactions with age and sex were tested in a further step. The factors contributing a significant increment to the dependent variance explained in the final step of this analysis are those shown in column II of Table II. Order of en try AS in Table I, it is clear that the final analysis is not unique because the sets could have been entered in any order with varying outcomes. In deciding order of entry, the three main considerations were: (a) level of generality of the concept represented by the factor, (b) the degree of confidence with which the hypothesis was entertained apriori and (c) the importance that we attached to it in the research. In other situations causal priority may be an important consideration but here it played only a minor role. The increment in variance explained, entered alongside each factor in column II, is an index of the extent to which the criterion can be explained by that factor, independently of all those entered before it. What it does not show is whether other factors lower down the list might have done the same job, had they been entered earlier. An examination of the computer output from each run provides a statement of how much each variable would contribute were it to be entered into the equation at the next step. There was, for example, some indication that size of household, in particular the number of adults, would have been included had it been entered before marital status. The regression analyses may be somewhat open to question when based upon binary variables and, although it seemed unlikely that we were being seriously misled, it was clearly desirable to seek confirmation using other methods. For this reason, and also to demonstrate the findings in a simpler and more direct fashion, contingency tables were formed. Table III reveals very clearly the source of the effect of marital status. Widowed, divorced or separated women, perhaps because they are living alone, are at greater risk of being patients than either single women, or married women living with their husbands. There is no evidence of this for men, confirming the interaction shown in Table II. Table IV indicates that social classes IV and V show the greatest likelihood of self-referral and there is a steady increase from professional and middle class, through skilled to unskilled working class. Table V simply confirms that the association between distance and consulting status is in the expected direction.

240

J. G.

INGHAM

and P. McC. MILLER

TABLE III.-LIVINGSTON

RANDOMSAMPLE(N= 1416)

Male

Single Patients Controls Total

Female Widowed divorced separated

Married/ co-habiting

40 44 84

295 289 584

Total

Single

342 343 685

39 39 78

I 10 17

Married co-habiting 286 309 595

x’ = 0.78 p = 0.68

Patients Controls Total

41 17 58

Total 366 365 731

x1 = 10.82 p=o.O04

TABLE IV.-LIVINGSTON RANDOM SAMPLE (IV= 1405). TABLE-CONSULTATION X SOCIALCLASS

I and II

Widowed divorced separated

CONTINGENCY

Social class (Livingston coding) III III (Non-manual) (Manual)

80 115 195

141 162 303

IV and V

260 276 536

223 148 371

x2 = 23.64. p=o.Oo.

TABLE

V.-LIVINGSTON

RANDOM

Distance Less than i/2 mile Patients Controls Total

650 621 1271

SAMPLE

(N=

1416)

from health centre More than l/2-1 mile 1mile 24 26 50

34 61 95

x2 = 8.42. p=o.o15

Socio-demographic factors and symptoms The reduced number of sets of socio-demographic variables could now be used in a further analysis, to see whether their link with consultation status could be explained by symptom severity. Table VI shows the results of the analysis before and after partialling out the symptom information. The dependent variance explained by social class is greatly reduced after the influence of symptom variables has been removed. Social class does indeed owe much of its association with consultation status to the fact that individuals in social classes IV and V tend to have more symptoms and this would be expected to increase their likelihood of consultation. For other social variables however symptoms seem to be irrelevant. The increment in variance explained by distance from health centre does decrease slightly and becomes non-significant but the change is slight and should probably not be taken too seriously.

Self-referral: TABLE VI.-COMPLETE

Independent

social and demographic

determinants

of consulting

behaviour

241

RANDOM SAMPLE. DEPENDENT VARIABLE: CONSULTER~CONTROLSTATUS (N=

factors

Sex, age and interaction Symptom variables6 Social class Marital status Sex x Marital status Employment status Distance from health centre

Number of component variables 3 4s 5 2 2 1 1

1411)

Excluding symptom variables R* Increment

Including symptom variables Increment R*

0.000

0.000 0.175 0.181 0.187 0.191 0.195 0.196

0.016 0.021 0.026 0.029 0.033

0.016’ 0.005* 0.005* 0.003* 0.004g

*p
inconsistencies,

0.175’ 0.006 0.006t 0.004* 0.004* 0.002

Distress

DISCUSSION

With or without symptom variables the contribution of social factors to the explanation of consultation variance, whilst statistically significant, is slight. The interactions with sex have revealed interesting and useful observations (Table III) and response inconsistencies have been shown to have a small but significant association with consultation. The pair comparison scales were designed to detect these inconsistencies often produced by irrelevant response sets and consulters tended to produce more of them than controls. A possible explanation is that their responses were influenced occasionally by a tendency to justify their visits to the doctor. Consulters are also more likely to believe that their symptoms have internal physical causes. Taking all of these symptom and socio-demographic variables into the equation it became possible to explain 20% of the variance and to clarify the extent to which social factors are mediated through their influence upon symptom variables. Members of the unskilled working class are more likely to consult than other social classes but this can be largely accounted for by their symptoms. Others have found increased medical referral to be associated with lower social class in England [9] though the reverse seems to be the case in the U.S.A. where low income is probably a deterrent against the use of expensive medical services [lo]. It is interesting that as in the present study, after correcting for symptoms, Hannay [ll], also found no evidence that social class influenced self-referral. A similarly raised likelihood of consultation is to be found amongst widowed, separated and divorced women, but in this instance, the link seems to exist independently of symptom variables. It may be the need for social support and reassurance that is the crucial factor for this group. Distance from the health centre appears to be linked to a small degree with consulting tendency (Table V), those living furthest away being least likely to consult, though there is no clear evidence for the effect when symptom severity is allowed for. This is perhaps understandable when we realise that many of the more distant subjects had registered when they lived nearer and then moved away into newer and pleasanter areas with better amenities. It is not altogether surprising if they have fewer and less severe symptoms. Often they had moved away from rented

J. G. INGHAMand P. McC. MILLER

242 accommodation and

those

into owner-occupation

living

in newer

study the mean medical

houses

referral

and Hannay had

fewer

number not

decrease

with

symptom

‘iceberg’

distance.

This

more

severe

symptoms been

severe

is a curious symptoms

were

other

distance

(i.e.

not.

all new

episode

findings indicated

may

not hold

that

consulters,

status corrected or depression symptoms

variables to

and

that

sufferers with

certain

at any rate

centre,

the

did increase

those

is far from

to make

for

with from

less

severe

but there

children,

for

Indeed are

have

women

influential

patients This

statements

controls

the initial

and

analyses

for

symptoms.

was unimportant

or depression.

general

non-consulting

presenting

severity

influential

than anxiety

with

sub-samples.

social

for symptom

other

of distance

the health

to imply

distance

we are attempting

according

but quite

from

to the doctor)

it seems

by

be a deterrent,

compared

up for

different

classified

home

[ 12 and 131.

that

consulters

in his

gradings

not referred

because

the role

age

be stressed

occupiers

Although

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the patient’s

discouraged

that it may

and men over retirement It should

of

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Clearly

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of medical

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that owner

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[l l] found

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already

groups

example

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about

that

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a group in another

of

marital anxiety of

acute paper.

REFERENCES with mild symptoms in general practice. Social Psychiatry I. INGHAMJG, MILLER P McC. Consulting 1982(a); 17: 77-88. 2. INGHAM JG, MILLER P McC. In Clinical Psychology and Medicine (Edited by MAIN C), Chapt. 8. New York: Plenum Press, 1982(b). 3. INGHAM JG, MILLER P McC. Symptom prevalence and severity in a general practice population. JEpidemiol Commun Hith 1979; 33: 191-198. 4. INGHAM JG. In What is a Case? (Edited by WING, BENNINGTON, ROBINS), Chapt. 3. London: Grant McIntyre, 1981. 5. LOGAN WPD, CUSHION AA. Morbidity Statistics from General Practice, 1 (General). H.M.S. Office, London, 1958. disorder and its declaration in contrasting areas 6. INGHAMJG, RAWNSLEYK, HUGHESD. Psychiatric of South Wales. PsycholMed 1972; 2: 281-292. of illness declaration. J Psychosom Res 1976; 20: 7. INGHAM JG, MILLER P McC. The determinants 309-316. Analyses for the Behavioural 8. COHEN J, COHEN P. Applied Multiple Regression/Correlation Sciences. Hillsdale, New Jersey: Lawrence Erlbaum Associates, 1975. 9. MARSHGN, MCNAY RA. Factors affecting workload in general practice. Br Med J 1974; 1: 319-321. indication and use of physician services. Medical Cure 1972; 10: 10. BICE TW et al. Socio-economic 261-271. 11. HANNAY DR. The Symptom Iceberg. London: Routledge & Kegan Paul, 1979. 12. PARKIN D. Distance as an influence on demand in general practice. J Epidemiol Commun Hlth 1979; 33: 96-99. 13. MORRELL DC, GAGE HG, ROBINSON NA. Patterns of demand in general practice. J R Coll Gen Practitioners 1970; 19: 331-342.