- Email: [email protected]
A different approach to sociodemographic predictors of satisfaction with health care
PREDICTORS
APPROACH TO SOCIODEMOGRAPHIC OF SATISFACTION WITH HEALTH CARE JOHN G. Fox and DORIS M. STORMS
Program Planning Department. Department of International
Kaiser Permanente Health. The Johns
Health Plan, Inc.. Los Angeles. Hopkins University. Baltimore.
U.S.A. and U.S.A.
Abstract-The current literature reports greatly inconsistent relationships of sociodemographic variables to satisfaction with health care, so much so that attention has turned away from sociodemographic prediction of satisfaction. In this exploratory article, we propose that two intervening variables, orientation toward care and conditions of care, should produce consistency and refine the role of sociodemographic variables. When an individual’s conditions of care match his orientations toward care, satisfaction results. Lack of comparability of expectations and experience thus alter the sociodemographic satisfaction correlations between studies. The model’s predictions are supported with data from a community survey. If this model is further validated, it may redefine the importance of the current methodological search for dimensions of satisfaction.
The literature on satisfaction with health care presents contradictory findings about sociodemographic variables (SD variables). As summarized in the Appendix, SD variables (race, age, sex, income, etc.) can relate directly to satisfaction in one study, inversely in another, and be unrelated in a third. The situation has grown so chaotic that some writers dismiss SD variables as reliable predictors of satisfaction [I]. Instead the current literature concentrates on measuring types or scales of satisfaction C2-43. It is possible but unlikely that measurement techniques, while differing between studies, are so radically different as to explain the discrepancies. Focusing on the measurement of satisfaction might imply that narrow measures will yield stable predictors, that the inconsistent correlations are partly due to inconsistent measures of satisfaction. If there were one good set of measures, the literature implies, then different researchers might obtain stable results. We question that implication for two reasons. The first deals with health policy issues underlying measurement of satisfaction. The second, and the major focus of this paper, concerns the nature of the theoretical model linking SD variables to satisfaction. Before turning to the question of theoretical models of satisfaction, it is worthwhile noting an underlying health care policy issue. The major policy reason for measuring satisfaction is an assumed link between patient satisfaction and patient behavior. It would be important to know how satisfied people were if dissatisfaction led to poor compliance with treatment regimes, unproductive switching of providers, or inappropriate deferral of seeking treatment. The strength of this ahitude-behavior link is problematical. However, the implication of the stable-SD-results-from-better-measurement hypothesis is clear in this policy issue: if SD groups vary consistently in satisfaction. then perhaps they vary similarly in behavior, for GKI? reason. This social policy focus then leads to possible confusion over direction of causality: should we alter health services or the behaviors which result from (dissatisfaction with)
health services? If some SD group is dissatisfied with care, no matter what the setting and style of care (i.e. produces consistent SD correlations between studies in different settings), then whose fault is that? The implication of a poor-measurement hypothesis is also questionable for its inferences about the theoretical links between SD variables and satisfaction. Below we propose and test a theoretical model that predicts different SD effects between studies. If discrepant findings are predictable, then poor measurement is not the reason for their apparent inconsistency. Instead, the focus can shift from obtaining stability of results to understanding the conditions under which discrepant findings can be predicted. That in turn might have the practical consequence of helping determine which practice arrangements best satisfy particular SD subgroups. The following sections detail our model, first on an individual and later a group level. We will then test the model with data from a survey conducted in the Baltimore area.
A MODEL
OF INDIVIDUALS SATISFACTION WITH HEALTH CARE
Our model for indivuduals’ satisfaction is as follows. First, people differ in their orientations toward care, in what they want and expect from the health care encounter. This occurs because people differ in what they believe causes illness and in their sociallypatterned responses to illness. Second, care providers differ in their conditions of care, including theoretical approaches to care (i.e. metaphysics, chiropractic, allopathic. etc.), situation of care (i.e. location. speed, cost, etc.). and outcomes of care (i.e. cure. timing. etc.). Third, if orientations and conditions are congruent, people are satisfied, if not, they are dissatisfied. The proposed model is not new, but is the heart of much of the medical anthropology and sociology of
558
writers like Suchman
JOHN G. Fox and
[S. 63 and Geertsen detailed by Suchman
DORISM. STORMS
[7].* This
The second assumption is that health care providers differ in conditions of care. This can be stipulated. There are vast differences even with contemporary urban areas in providers’ approaches to care . patients and physicians may differ not only in their per(chiropractic, metaphysics like Christian Science, alloceptions and interpretations of symptoms and illness but pathy, naturopathy and other “holistic” medicines. also in the relative reliance they place upon the scientific or etc.). Different theoretical approaches to care may be formal approach to medical care.. . We propose that cermost striking in treatment of affective, emotional. or tain socio-cultural background factors will predispose the individual toward accepting or rejecting the approach of behaviora conditions. Writers like Szasz [25], for professional medicine and. hence, increase or decrease the example, see mental illness as a myth rather than a possibility of conflict between patient and physician [S]. biological/medical phenomenon. Even allopathic surThe model’s first assumption is that people differ in gical approaches may differ, as in the treatment of orientation toward care, largely because of *‘the breast cancer. Situations of care also differ. Within broader social and cultural systems in which they allopathy, for example, one may receive care from exist” [9]. These systems define both possible causes local free clinics, private office doctors. HMOs, uniof illness and permissible reactions to illness. As in- versity teaching hospitals, etc. Outcomes of care vary itially defined by Suchman, orientation toward medi- too, from cure to death. Our final assumption is that if a person’s orientacal care included “knowledge about disease (cognitive) . skepticism of medical care (affective). and tion matches the conditions of care he or she will be dependency in illness (behavioral)” and grossly could more satisfied than if there is no match. That is. there be reduced to two orientations, scientific (“an objec- must be congruence between the person’s orientation tive, formal, professional, inde~ndent approach to ill- and the provider’s approach to care, activities carried out in the provision of care, situation surrounding ness and medical care”) and popuiar (“a subjective, care, and the expected outcomes of care. For example. informal, lay, dependent health orientation”) [lo]. However, subsequent research [ 1l-133 indicated that a person may expect to be diagnosed as having strep Suchman’s definition was too narrowly conceived in throat, given a penicillin shot, told to stay home from terms of the groups he was analyzing. As most work, and begin to feel better in a couple of days. If this happens, the person is more likely to be satisfied recently redefined by Geertsen, et al:[14] orientations than if he or she is diagnosed as having a delusion, still include knowledge, dependency, and skepticism, but do not necessarily divide into ‘scientific’ versus told to pray, and does not feel any better. Similarly, a patient may visit several physicians in search of one ‘popular’ orientations except in particular groups. who will prescribe a desired psychoactive drug. Suchman [I5] operationally defined “knowledge The literature indicates that receiving the expected about disease” to be “measured by the correctness of diagnosis [26] or treatment [27,28] leads to satisfacresponses to thirteen questions dealing with etiology, symptoms, and prognosis of various diseases”. The tion, but less attention has been devoted to studying concept -.of “correctness” is culturally defined, and cer- the congruence of orientations and other conditions tainly not one we would expect to be shared by, say, a of care. For example, it is likely that a high social status teenager would feel degraded by a visit to a city physician, a Christian Scientist, and a chiropractor. venereal disease clinic, regardless of the medical or Therefore we substitute ‘beliefs’ for ‘knowledge’. Beliefs about care include theories of &he etiology of counselling skills of the clinic’s providers. In preventive care, persons’ orientations may lead to acceptillness and how illness should be treated. ance of providers’ advice on some matters but not Suchman [l&173 and others [18-231 have shown others. For example, a pediatrician’s advice may be how aspects of health care orientation vary between accepted about vitamin supplements, but not child social groups and cultures. For example, a person restraints in an auto or smoking by the mother. with an allopathic orientation may see water boiling To date, differences between orientations toward as appropriate to prevent dysentery, but a rural Perucare and actual conditions of care have been studied vian Indian may see it as inappropriate [24]. In the primarily for traditional societies, developing counallopathic model, boiling kills the germs that cause tries, and ethnic minorities. This emphasis on folk dysentery. In the humoral mode, boiling adds heat befiefs overshadows the wider applicability of the that is counte~roductive in treating a “hot” disease works of Suchman and Geertsen. The apptication of like dysentery. Equally dramatic differences of orientheir perspective. we believe, has great potential to tation exist within contemporary urban society, even illustrate more common urban-culture conditions like among users of ‘standard’ allopathic care, such as alcoholism, cardiovascular disease, cancer, drug over the appropriateness of abortion as a family planaddiction, auto accidents, dental disease, etc. ning method or whether alcoholism is a medical or moral condition. model was initially follows:
in 1965 as
THE GENERAL LEVEL OF SATISFACTION
* A very similar model has also been proposed in anaiysis of community satisfaction [81]. Community type preference, analogous to orientation toward health care, has been shown to correspond to satisfaction and, in combination with satisfaction, be highly predictive of intention to move. However community type preference is a unitdimensional variable, whereas we assume SD variables and associated orientations are multidimensional.
Before we elaborate on our group-level model for satisfaction and its consequences, a basic finding from the literature needs to be emphasized. Most people say they are satisfied in almost all studies, even those from less developed countries with substantial traditional medicines [29]. Although there may be methodological problems in determining the level of satisfaction [30], findings that even 92’6 of persons with
Approach to soctodemographic
predictors of satisfaction with health care
incomplete recoveries express satisfaction with the care they received [31] lead one to conclude people really are satisfied. We want to note this finding because it can block understanding of satisfaction with health care. Faced with a skewed dependent variable with little variance (everybody’s happy). researchers may focus on dimensions of satisfaction rather than explain the central fact of satisfaction. Our model posits that orientations must match conditions of care to produce satisfaction. As an analogy, assume there were two orientations toward vehicles, truckers’ and sports car drivers’, and two types of vehicles-trucks and sports cars. If both groups get the type vehicle they want. everybody is satisfied. Yet a search for a common dimension of ‘vehicular satisfaction’, say, handling or hauling power, may produce ambiguous results and obscure the real process leading to general satisfaction. In this example, the methodological search for commcm dimensions forces a simple model on a complex process having to do with both individual orientations and group interactions (supply and demand for vehicies by type). In addition, the overall level of satisfaction has important practical consequences for program planners and evaluators. Programs are evaluated on how well they fulfill overall needs.
SOCIODEMOGRAPHK VARIABLES AND MODEL-GROUP
THE
LEVEL PHENOMENA
We introduced a simple model of individuals’ satisfaction based on the work of Suchman and others. We now want to link this model to the wide variations in the literature of the effects of SD variables on satisfaction with health care. At this point, the model must be applied to group level phenomena and sampfe surveys. We think the model relates to both the consistency of results between studies and the strength of the results within any one study. Consistency between studies of SD variables’ effects should be a function of two factors: how widely shared orientations are and how universally studies reflect health care settings and populations. We would expect widely shared facets of orientations (like valuing economy, speed of recovery. and ease of access) to be more consistently a source of satisfaction or dissatisfaction. To the extent SD variables covary with more widely shared facets of orientation towards care,
559
different studies will be more likely to report consistent relationships to these SD variables. For example, everyone may value ease of access, but the elderly might value it more than the young in almost every group because of higher need or lower mobility. As another example, everyone may value economy, but the poor more so than the rich. If these were so, we would expect to see consistent age or income correlations to satisfaction with health care, so long as access or cost affected satisfaction. The other factor affecting consistency of SD variables’ effects between studies is the degree to which studies reflect particular health care settings and populations or al1 health care settings and popuiations. To the extent samples of providers and settings are limited, studies will be likely to report idiosyncratic relations, since certain settings may be matched to particuiar orientations no matter how common those orientations are in the population. To use the age sample again, a sample of teenaged pediatric patients’ satisfaction might show low age correlations due to the Iimited age span of the sample, even if there were some overall reiationship between age and satisfaction in the population. Beyond consistency of SD relationships, even general community sample surveys may vary in the stre~rh of reported SD relationships to satisfaction. This is best iltustrated in a four-cell design (see Figure 1). In cell A, when both orientations and conditions are relatively uniform, low correlations between SD variables and satisfaction should be found since everyone will be similarly satisfied. If people from all SD categories share similar orientations and all providers are similar to each other, any sample survey is likely to find low SD-satisfaction correlations. In cell B, when orientations are uniform (over SD variation) but conditions of care differ, then higher SD correlations should be found. We might expect SD correlations to reveal more powerful groups to be more satisfied, since the community might compete for the limited set of providers seen as desirable by all. In ceI1 C, if orientations differ between SD categories but care conditions are relatively uniform, we would expect high SD-satisfaction correlations. Here some groups will like the standard care and others will not. For example, if everyone has to use an HMO, some groups will be satisfied (those who now choose HMOs)
Correlations sheuld be when provtdersconditions of care are: Uniform and patients’ orientations toward care are :
Different
! WI
Cell A
High
LOW Uniform
Different
0
I
1 Ce” ‘High
1
‘*”
F0w
Fig. 1. The level of expected correlations between sociodemographic variables and satisfaction health care in community sample surveys.
with
JOHN G. Fox
560
and DORIS
more than others (those who now opt for fee-for-service coverage). Finally, in cell D, if orientations to care vary and conditions are also split, then correlations to SD variables should be low on a zero-order level. (Note that statistically controlling for care situation variables returns one to cell C and should increase SD-satisfaction correlations.) In cell D, we would expect low zero-order correlations for two reasons. First, if patients are assigned to care settings randomly, then a strong positive correlation to SD variables in one health care setting would be offset by an equally strong negative correlation at another setting. For example, Christian Scientists and medical students would tend to be dissatisfied with the other group’s providers. Alternatively, if patients sort themselves among care settings, low correlations to SD variables should result because all would seek the conditions which satisfy their orientation. If supply of health care providers is not fixed, we might even find a marketplace equilibrium with disparate orientations each matched to their preferred conditions and all people fairly satisfied. This last case parallels the sports car and truck analogy used earlier. Testing the model Given this framework, we would like to test whether data from a community sample survey conform to its predictions. The following sections present data from a community sample survey conducted in the Baltimore metropolitan area. In this section, we describe this survey in terms of our model and indicate the hypotheses to be tested. Baltimore is characterized by a fair degree of community and health care variability. As a community, it includes wealthy urban and suburban areas, inner city Black neighborhoods, several blue collar neighborhoods with distinct White ethnic backgrounds, and fairly rural areas close to the city itself. Health care facilities in Baltimore also differ and include university teaching hospitals, HMOs, private physician practices, and inner city clinics, as well as various alternative providers, like chiropractors and Christian Science practitioners. We assume gross orientations toward care are relatively more uniform. Baltimore residents’ health care orientations should be largely allopathic, valuing low cost, high access, and rapid recovery. If these assumptions are true, we should be in a situation similar to cell B and significant zero-order SD-satisfaction correlations should be found. Further, if we control statistically for the conditions of care (cost, access, etc.), we should be in a situation similar to cell A and SD-satisfaction correlations should generally cease to be significant. Finally, to the extent there are SD-correlated orientations which differ within all groups, we should be in a situation similar to cell C and those SD-satisfaction correlations should persist when conditions of care are controlled. The most likely SD candidates for such cross-cutting effect should be biological: age and sex differences (and correspondent health care need differences) exist in all groups. IMETHOD
During late 1974 and early 1975, 3398 residents of the Baltimore metropolitan area were sampled for
M. STORMS
telephone interviewing using equal column intervals in numerical telephone directories. After approximately two-thirds of the sample were telephoned. the remaining persons from under-represented geographic subareas were over-sampled to correct for unequal numbers of telephones per capita. In this way, the geographic distribution of respondents almost perfectly matched the Baltimore metropolitan area distribution. Completed interviews were obtained for 2582 respondents, a 7896 response rate. The major purpose of the survey was to study acceptance of physicians’ assistants and nurse practitioners, the results of which are reported elsewhere [32]. Since 22.4% of Baltimore area telephone numbers were unlisted, a series of comparisons was made of the sample’s fit to the local population. The obtained sample matched the Baltimore City County racial composition of 287: Black, and the sample’s income distribution closely matched national figures. The sample, however, significantly differed from the population on other SD variables. It fell 12.9”/, below local averages for percent males, 4.5”; below the population level for 18-20 year-olds, and 22.2% below the population level for persons with less fhan a high school education. The sample exceeded local averages for persons of ages over 65 years by 6.09; and those with a baccalaureate degree or higher education by 5.8%. The obtained sample was close to the local and national averages for access and utilization of health services. National norms [33] for proportion having a regular place of care and proportion requiring less than 15 minutes to get to care were close to those for the sample. Where local utilization averages exceed the national average, the sample matched the local averages. For instance, the proportion of the sample having seen a physician within the last 12 months matched the local rate [34] rather than the national proportion [35]. Thus, despite certain shortcomings of the sample which prevent accurate generalization to other populations, it is sufficient for analysis on our level, the community, and our problem, the strength of correlations rather than parameter estimates. Respondents who stated they had received medical care within the past 12 months were asked: If a score of ten represents the best possible medical care available and one represents a very poor quality of medical care, how would you rate the medical care you have received in the past year’?
We recognize the critical issues surrounding the use of overall satisfaction measures [36]. There are good reasons for investigating satisfaction with different facets of care (which are outlined in the concluding section), but our purpose is to outline a general model of satisfaction in its simplest form. We want to investigate how the overall process of satisfaction involves SD variables. If this model is validated, then it would be appropriate to look at satisfaction differences within each respondent’s overall satisfaction level. RESULTS Of the 2582 respondents, 2061 (79.99b) had received health care in the past year. Of those 2061, 2021 (98.130) rated their satisfaction with the care they had
Approach to sociodemographIc
predictors of satisfaction with health care
Table I. Percent most satisfied (‘ten’) with medical care, by sociodemographic characteristics
0c,
Characteristics
Satisfaction with medical care N
P*
49.2 61.8
426 1419
0.005
64.5 63.0 67.4 62.0 56.8 58.0 43.4 33.3
31 92 347 292 597 362 297 0
0.005
56.9 59.3 50.0 60.3
483 1239 2 277
NS
32.0 48.1 49.7 61.6 65.7 74.2
200 397 294 349 367 399
Sex
Male Female Education l-3 years 4-6 years 7-9 years 10-l 1years H.S. graduate l-3 years college College graduate Not stated Race Black White Other Not stated Age 18-24 25-34 35-44 45-54 55-64 65+ Not stated Work status Employed School Retired Homemaker Nonemployed Family income $0-2000 $3-6000 $7-12,000 !&13-18,ooO 819,000+ Not stated
0.005 52.9 31.1 69.1 66.0 59.6
1026 53 265 577 99
0.01
63.5 69.0 58.1 54.0 52.8 67.9
85 145 320 291 235 695
0.005
* P = Significance level reached in ,$ analysis.
received. As is usual in satisfaction studies, the obtained scores were skewed toward the upper (satisfied) end of the scale. Of those who received care within the past year, 58.7% rated it with the highest possible rank and 84.7% rated it among the top three ranks (8-10). Since the scores are highly skewed toward the upper end, we will report percentage cross-tabulations with a dichotomy between the most satisfied respondents (?en”) and all other respon-
dents. Correlations are calculated over the whole range (one to ten). All the SD variables except race correspondent to satisfaction with health care, as shown in Tables 1 and 2. In Table 1, females are more satisfied than males; the less educated are more satisfied than the more educated; older persons are more satisfied than younger; retired people and homemakers are more satisfied than the other job categories;-and persons with lower family income are more satisfied than those with higher family income. The correlation matrix for SD variables in Table 2 indicates significant zero-order correlations between SD variables and satisfaction with care. Note that the sample size makes relatively small differences statistically significant. However, differences of 20-30”;, as in Table 1. are substantial. Tables 1 and 2 indicate, as hypothesized, SD variables have significant zero-order relationships to satisfaction with health care. In terms of our model; the significant correlations hypothesized for a Cell B pattern are found. Note, however, that the high correlations are also predicted by a Cell C pattern, The interpretation depends upon whether providers and orientations are assumed to be uniform. Health care conditions variables also correspond to satisfaction with medical care (Table 3). All access measures except cost-having a regular place of care, taking less time to get to care, having a personal physician rather than an institution as a place of care--are significantly related to satisfaction with care. Utilization of care-having a visit within the last 60 days, number of visits, and making preventive care visits---also is significantly related to satisfaction with care with the possible exception of those reporting three or more visits in the last 60 days. Self-reported health status-missing a day of activities within the last 60 days and having a chronic health condition-is less clearly related to satisfaction with health care. The chronic condition variable is correlated with age; so the significant correlation there may reflect an age effect. Thus far, we have shown zero-order SD-satisfaction correlations. The question now is whether, as hypothesized, controlling for health care conditions eliminates many of the SD variables’ correlations to medical care satisfaction. Table 4 represents data for satisfaction with health care among those receiving care in the past year. Two SD variables-age and sexremain significant after controlling for the health care variables (@ 0.236 and -0.072, respectively). Age is the most significant predictor of health care satisfac-
Table 2. Correlation matrix for sociodemographic Variable
Age Education Race (white) Family income Sex (male) Satisfaction with medical care
Age
Educ.
561
variables and satisfaction with health care
Race
Income
l.ooo 0.228 0.072
1.000 0.136
0.009
- 0.077
Sex
Satisfaction
1.ooo
- 0.386 0.032 - 0.246 -0.105 0.255
Correlations are Pearson’s r
l.OGQ 0.208 0.439 0.164 -0.118
1.000 -0.112
1.000
JOHN G.
562
Fox and DORISM. STORMS
Table 3. Percent most satisfied (‘ten’) with medical care, by health care conditions variables
Characteristics Regular place of care Yes No Not stated Normal place of care MD office Hospital O.P.D. Hospital emergency room Neighborhood clinic Other Not stated Care provider last 60 days Medical doctor Registered nurse Other Not stated Preventive care visits Yes No Not stated Time to get to care Less than 15 minutes 15-30 minutes 10-60 minutes 60+ minutes Not stated Cost of visit None 81-9 $10 $1 l-20 s21+ Not stated Number visits last 60 days 0
i 3 4+ Not stated Disability days last 60 days Yes No Not stated Chronic condition Yes No Not stated
Satisfaction with medical care 0P* N ,‘O 59.2 46.3 0
1897 123 0
61.4 53.2
1582 248
42.5 43.3 43.3 60.0
54 72 60 5
0.001
62.0 64.0 58.1 54.2
761 317 35 907
0.005
62.3 53.1 42.1
1264 738 19
0.005
60.1 56.7 57.6 42.1 17.6
944 868 191 19 17
0.005
61.1 62.6 54.9 56.9 57.7 66.0
301 447 586 473 111 103
NS
55.1 58.4 72.2 61.0 59.0 49.2
884 640 281 59 94 63
0.005
57.5 58.4 20.0
738 1300 5
NS
62.0 56.2 75.0
874 1143 4
0.02
0.005
correlations. Why do age and sex remain significant when other SD variables do not? Age and sex are probably the two strongest SD variables as predictors of health care utilization [37]. Because they strong11 correspond to utilization. they may create differing orientations toward health care even when other group (SD) factors are taken out. Thus, we conclude that these two variables fit our model to the extent that they create a cell C pattern, differing health care orientations in a care situation made uniform by statistically holding health care conditions variables constant. DISCUSSION
* P = Significance level reached in x2 analysis.
tion. The health care indexes (the variables described in the preceding paragraph combined in additive scales), however, outweigh all remaining SD variables, all of which (except race) were significant at the zeroorder level. The overall predictability of satisfaction with health care is low (R2 = 0.08) in keeping with the almost uniformly high level of satisfaction in the sample. The results in Table 4 indicate, as hypothesized, that some SD characteristics may cross-cut other SD groupings to create more widely-shared health care orientations, and thus more stable SD-satisfaction
The purpose of this exploratory paper is to propose a model for satisfaction with health care that provides a framework for the effects of SD variables. We propose that two sets of intervening variables--orientations toward care and conditions of care-modify the effects of SD variables on satisfaction, producing discrepancies between studies. The proposed model addresses two issues. First, it predicts the individual’s level of satisfaction by the congruence of conditions of care and orientations towards care. When they coincide, satisfaction results: if not, dissatisfaction results. Second, at an aggregate level, it addresses the consistency between and strength of results in studies of health care satisfaction. Studies should differ from each other in SDsatisfaction correlations to the degree their samples represent the population and to the degree the population represents uniform or variable orientations and/or conditions of care. The strengths of most SD-satisfaction zero-order correlations were expected to diminish when controlling for conditions of care. This was found to be the case except for age and sex. For age and sex, we argued that their over-reaching social and biological consequences for health (in terms of utilization rates, level of morbidity, nature of typical utilization, etc.) create a cross-cutting effect whereby their differing orientations persist within income, educational, racial. or other SD groups. The zero-order SD-satisfaction correlations for most SD variables may reflect correlated differences in conditions of care. whereas age and sex may correspond more strongly to differences in orientations toward care. It is possible, for example, that women or the elderly expect or desire different affective relationships with the health care provider than do men or the young, regardless of the provider’s situation of care (or that providers treat women and the elderly differently than their male or young patients). This article is exploratory. To the extent the model is validated, however, it affects the context of current research on satisfaction with health care. As we noted at the outset, current efforts focus more and more on defir,ing types of satisfaction-perceived competence of the providers, satisfaction with the cost of care, satisfaction with access to care, etc. For social scientists, the model makes such facets interesting in a comparative sense. One might ask, for example. whether people with different orientations also differ in the salience of different conditions of care (say, cost versus affective relationships with the provider). Also
Approach to sociodemographic
predictors of satisfaction with health care
563
Table 4. Satisfaction with health care regressed upon SD variables and health care conditions indices Independent
variable
Age Use Index Sex male Access index Health status index Race white Education Family income
Raw correlation 0.255 0.102 -0.112 0.078 0.011 0.009 -0.1 18 - 0.077
B 0.236 0.082 - 0.072 0.055 -0.042 0.010 - 0.008 - 0.003
Significance 0.001 0.001 0.003 0.01 NS NS NS NS
R = 0.286, RZ = 0.082
important is the way dimensions reveal interactions in the supply and demand for health care. If different groups value different things, do their orientations create a market for different providers? Can providers “sell” different facets of their conditions to different groups with different orientations? How do different groups learn their orientations? The issue of satisfaction dimensions thus integrates the study of satisfaction with the broader study of health care social systems, when viewed in the context of a more general model of satisfaction. The model also could make dimensions of satisfaction of greater use in a practical sense. Issues of use of oreventive care utilization, for example, may reflect multi-dimensional orientations toward care leading to acceptance of a given provider’s advice in some areas but not in others. As another example, the skepticism dimension of the orientation concept may be applicable to satisfaction with affective-behavioral care proeiders. In sum, seeking dimensions of satisfaction is a
worth-while goal, but one that reaches greater potential benefits when placed in a theoretical framework. REFERENCES
1 Wooley F. R.. Kane R. L.. Hughes C. C. and Wright D. D. The effects of doctor-patient communication on satisfaction and outcome of care. Sot. Sci. Med. 12, 123. 1978. 2. Locker D. and Dunt D. Theoretical and methodological issues in sociological studies of consumer satisfaction with medical care. Sot. Sci. Med. 12. 283. 1978. 3 Lebow J. L. Consumer assessments of ;he quality of medical care. Sot. Sci. Med. 12. 328, 1974. 4 Ware J. E.. Davies-Avery A. and Steward A. L. The measurement and meamng of patient satisfaction. HIth .\lud. Care Serl-. Rer. 1, I, 1978. 5 Suchman E. A. Soclomedical variations among ethnic groups. .4n1. J. qf‘Socio/. 70, 319. 1964. 6 Suchman E A. Social patterns of illness and medical care. J. H/~/I sot. Behar. 6, 2, 1965. 7 Geertsen R.. Klauber M. R.. Rindflesh M.. Kane R. L. and Gar) R. A reexamination of Suchman’s views on social factors in health care utilization. J. HIrh sot. Beh
13. Berkanovic E. and Reeder L. G. Ethnic, economic, and social psychological factors in the source of medical care. Sot. Probl. 21. 246. 1973. 14. Geertsen R. et al., &. cit. 15. Suchman E. A., Social patterns, op. cit., p. 3. 16. Ibid. 17. Suchman E. A., Sociomedical variations, op. cit. 18. Geertsen R. el al., op. cir. 19. Reeder L. and Berkanovic E., op. cit. 20. Berkanovic E. and Reeder L., op. cit. 21. Zola K. Culture and symptoms-an analysis of patients’ presenting complaints. Am. Sot. Retl. 31, 615, 1966. 22. Joseph A. Physician and patient: some aspects df inter-
personal relations between physicians and patient, with special regard to the relationship between white physicians and Indian patients. Appl. Anthrop. 1, 1, 1942. 23. Hautman M. A. Folk health and illness beliefs. Nurs. Pructir. 4, 23, 1979. 24. Wellin E. Water boiling in a Peruvian town. In Health, Culture and Community (Edited by Paul B. D.), pp. 71-103. Russel Sage Foundation, New York. 1955. 25. Szasz T. The Myth of Psychotherapy: Mental Healing us Religion, Rhetoric, and Repression. Doubleday, New York, 1978. 26. Mechanic D. Medical Sociology. Free Press, New York, 1968. 27. Korsch B. M., Gozzi E. K. and Francis V. Gaps in doctor-patient satisfaction. Pediatrics. 42, 855, 1968. 28. Francis V.. Korsch B. M. and Morris M. J. Gaps in doctor-patient communication: patients’ response to medical advice. N. Enal. J. Med. 280, 535, 1969. 29. Kuman S., Ramaiah T: J.. Kataria M., Gupta J. P. and Rao K. G. Repeat Evaluation of Community Health Volunteers Scheme-/979. National Institute of Health and Family Welfare, New Deli, 1979. 30. Locker D. and Dunt D., op. cit., p. 286. 31. Wooley F. R. et al., op. cit. 32. Storms D. and Fox J. The public’s view of physicians’ assistants and nurse practitioners: a survey of Baltimore urban residents. Med. Care 17, 526, 1979. 33. Aday L. and Andersen R. Development of Indices of Access to Medical Cure. Health Administration Press, Ann Arbor. 1975. 34. Bite T. W. Medical care for the disadvantaged: report on the survey of use of medical services in the Baltimore standard metropolitan statistical area, 1968-69. Final Report of HSM 110 69 203, NCHSRD, 1971. 35. Aday L. and Andersen R., op. cit. 36. Locker D. and Dunt D., op. cit. 37. Aday L. and Andersen R.,.op. cit. 38. Alpert J. J., Kosa J., Haggerty R. J., Robertson L. S. and Haegarty M. C. Attitudes and satisfaction of low income families receiving comprehensive pediatrics care. Am. J. publ. Hlrh 60, 499, 1970.
564
JOHN G. Fos
and DORIS M. STORMS
F. and Summers. C. F. Com39. Rojeck D.. Clemente. munity satisfaction: a study of contentment with local services. Rural Social. 40, 177, 1975. city people think of their 40. Koos E. L. Metropolis-what medical services. Am. J. pub/. Hkh 55, 1551, 1955. with patient evaluation 41. Linn L. S. Factors associated of health care. Milbank meml. Fund Q. 54, 531, 1975. satisfaction in a health mainten42. Pope C. R. Consumer ance organization. J. Hlrh sot. Behar. 19, 291, 1978. 43. Raphael W. Do we know what the patients think’?: a survey comparing the views of patients, staff, and committee members. Int. J. Nurs. Srud. 4, 209, 1967. E. A. Stages of illness and medical care. J. 44. Suchman Hfrh hum. Behar. 6, 114, 1965. D. Consumer satisfaction 45. Tessler R. and Mechanic with prepaid group practice: a comparative study. J. Hlrh sot. Behar. 16, 95, 1975. 46. Hulka B. S.. Kupper L. L., Cassel J. C. and Babineau R. A. Practice characteristics and quality of primary medical care: the doctor-patient relationship. Med. Cure 13, 808, 1975. 47. Kirscht J. P., Haefner D. P., Kegeles S. S. and Rosenstock I. M. A national study of health beliefs. J. Hlrh human. Behac. 7, 248, 1966. R. L.. Metzner C. A. and Worden C. Con48. Bashshur sumer satisfaction with group practice, the CHA case. Am. J. publ. Hlrh 57, 1991, 1967. 49. Andersen R., Kravits J. and Anderson 0. W. The public’s view of the crisis in medical care: an impetus for changing delivery systems? Econ. Busin. Bull. 24, 44, 1971. 50. Hulka B. S., Zyzanski S. J., Cassel J. C. and Thompson S. J. Satisfaction with medical care in a low income population. J. chron. Dis. 24, 661. 1971. 51. Linn L. S.. op. cir. 52. Pope C. R., op. cit. P., op. cit. 53. Tessler R. and Mechanic 54. Bashshur R. L., et al., op. cit. R. Patterns of 55. Gerst A., Rogson L. and Hetherington satisfaction with health plan coverage: a conceptual approach. Inquiry 6, 37, 1969. 56. Hulka B. S., Kupper L. L. er al., op. cir. 57. Gerst A. er al.. op. cir. 58. Hulka B. S.. Zyzanski S. J. er al.. op. cit. L. L. er al.. oi. cit. 59. Hulka B. S.. iupper 60. Hulka B. S.. Zyzanski S. J. et al., op. cir 61. Gerst A. er ul.. op. cir.
Apostle D. A. and Oder F. Factors that influence the public’s view of medical care. J. Am. med. .4.s.s.202, 592. 1967. 63 Korsch B. M. er al.. op. cir. 64 Linn L. S., op. cir. 65 Tessler R. and Mechanic D., op. cit. 66 Bashshur R. L. et al.. op. cit. R. W.. Hopkins C. E. and Roemer M. I. 67. Hetherington 62
Health
68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81.
Insurance
Plans.
Promise
and
Performance.
Wiley, New York. 1975. Gerst A. er al.. op. cit. Apostle D. A. and Oder F., op. cit. Tessler R. and Mechanic D., op. cir. Bashshur R. L. er al., op. cir. Hetherington R. W. et al., op. cit. Korsch B. M. er al.. op. cir. Pope C. R., op. cit. Raphael W., op. cir. Koos E. L., op. cit. Suchman E. A., Stages of, op. cir. Tessler R. and Mechanic D.. op. cit. Hulka B. S., Kupper L. L.. et al.. op. cit. Cartwright A. Human Relations und Hospirul Care. Routledge & Kegan Paul, London. 1964. Heaton T.. Fredrickson C.. Fuquitt G. V. and Ziches J. J. Residential preferences. community satisfaction. and the intention to move. Demography 16, 565. 1979. APPENDIX SOCIODEMOGRAPHIC VARIABLES EFFECTS ON SATISFACTION WITH CARE I& VARIOUS STUDIES
Age: Direct relationship found [38-431. No relationship found f44.451. Inverse relationshio found r46.471. Race: Blacks more satisfied [48]. Whites more satisfied [49, SO]. No relationship found [51. 521. Marital status: Singles more satisfied [53]. Marrieds more satisfied [54, 551. farnil: size: Larger families more satisfied [56, 571. Smaller fatmlies more satisfied [SS]. Education: Direct relationship found [59-621. No relationship found [63-661. Inverse relationship found [67]. Income: Direct relationshlp found [68-691. No relationship found [70]. Inverse relationship found [67]. Socioeconomic c/ass: Direct relationship found [73-751. No relationship found [7&78]. Inverse relatlonship found [79. SO].
APPROACH TO SOCIODEMOGRAPHIC OF SATISFACTION WITH HEALTH CARE JOHN G. Fox and DORIS M. STORMS
Program Planning Department. Department of International
Kaiser Permanente Health. The Johns
Health Plan, Inc.. Los Angeles. Hopkins University. Baltimore.
U.S.A. and U.S.A.
Abstract-The current literature reports greatly inconsistent relationships of sociodemographic variables to satisfaction with health care, so much so that attention has turned away from sociodemographic prediction of satisfaction. In this exploratory article, we propose that two intervening variables, orientation toward care and conditions of care, should produce consistency and refine the role of sociodemographic variables. When an individual’s conditions of care match his orientations toward care, satisfaction results. Lack of comparability of expectations and experience thus alter the sociodemographic satisfaction correlations between studies. The model’s predictions are supported with data from a community survey. If this model is further validated, it may redefine the importance of the current methodological search for dimensions of satisfaction.
The literature on satisfaction with health care presents contradictory findings about sociodemographic variables (SD variables). As summarized in the Appendix, SD variables (race, age, sex, income, etc.) can relate directly to satisfaction in one study, inversely in another, and be unrelated in a third. The situation has grown so chaotic that some writers dismiss SD variables as reliable predictors of satisfaction [I]. Instead the current literature concentrates on measuring types or scales of satisfaction C2-43. It is possible but unlikely that measurement techniques, while differing between studies, are so radically different as to explain the discrepancies. Focusing on the measurement of satisfaction might imply that narrow measures will yield stable predictors, that the inconsistent correlations are partly due to inconsistent measures of satisfaction. If there were one good set of measures, the literature implies, then different researchers might obtain stable results. We question that implication for two reasons. The first deals with health policy issues underlying measurement of satisfaction. The second, and the major focus of this paper, concerns the nature of the theoretical model linking SD variables to satisfaction. Before turning to the question of theoretical models of satisfaction, it is worthwhile noting an underlying health care policy issue. The major policy reason for measuring satisfaction is an assumed link between patient satisfaction and patient behavior. It would be important to know how satisfied people were if dissatisfaction led to poor compliance with treatment regimes, unproductive switching of providers, or inappropriate deferral of seeking treatment. The strength of this ahitude-behavior link is problematical. However, the implication of the stable-SD-results-from-better-measurement hypothesis is clear in this policy issue: if SD groups vary consistently in satisfaction. then perhaps they vary similarly in behavior, for GKI? reason. This social policy focus then leads to possible confusion over direction of causality: should we alter health services or the behaviors which result from (dissatisfaction with)
health services? If some SD group is dissatisfied with care, no matter what the setting and style of care (i.e. produces consistent SD correlations between studies in different settings), then whose fault is that? The implication of a poor-measurement hypothesis is also questionable for its inferences about the theoretical links between SD variables and satisfaction. Below we propose and test a theoretical model that predicts different SD effects between studies. If discrepant findings are predictable, then poor measurement is not the reason for their apparent inconsistency. Instead, the focus can shift from obtaining stability of results to understanding the conditions under which discrepant findings can be predicted. That in turn might have the practical consequence of helping determine which practice arrangements best satisfy particular SD subgroups. The following sections detail our model, first on an individual and later a group level. We will then test the model with data from a survey conducted in the Baltimore area.
A MODEL
OF INDIVIDUALS SATISFACTION WITH HEALTH CARE
Our model for indivuduals’ satisfaction is as follows. First, people differ in their orientations toward care, in what they want and expect from the health care encounter. This occurs because people differ in what they believe causes illness and in their sociallypatterned responses to illness. Second, care providers differ in their conditions of care, including theoretical approaches to care (i.e. metaphysics, chiropractic, allopathic. etc.), situation of care (i.e. location. speed, cost, etc.). and outcomes of care (i.e. cure. timing. etc.). Third, if orientations and conditions are congruent, people are satisfied, if not, they are dissatisfied. The proposed model is not new, but is the heart of much of the medical anthropology and sociology of
558
writers like Suchman
JOHN G. Fox and
[S. 63 and Geertsen detailed by Suchman
DORISM. STORMS
[7].* This
The second assumption is that health care providers differ in conditions of care. This can be stipulated. There are vast differences even with contemporary urban areas in providers’ approaches to care . patients and physicians may differ not only in their per(chiropractic, metaphysics like Christian Science, alloceptions and interpretations of symptoms and illness but pathy, naturopathy and other “holistic” medicines. also in the relative reliance they place upon the scientific or etc.). Different theoretical approaches to care may be formal approach to medical care.. . We propose that cermost striking in treatment of affective, emotional. or tain socio-cultural background factors will predispose the individual toward accepting or rejecting the approach of behaviora conditions. Writers like Szasz [25], for professional medicine and. hence, increase or decrease the example, see mental illness as a myth rather than a possibility of conflict between patient and physician [S]. biological/medical phenomenon. Even allopathic surThe model’s first assumption is that people differ in gical approaches may differ, as in the treatment of orientation toward care, largely because of *‘the breast cancer. Situations of care also differ. Within broader social and cultural systems in which they allopathy, for example, one may receive care from exist” [9]. These systems define both possible causes local free clinics, private office doctors. HMOs, uniof illness and permissible reactions to illness. As in- versity teaching hospitals, etc. Outcomes of care vary itially defined by Suchman, orientation toward medi- too, from cure to death. Our final assumption is that if a person’s orientacal care included “knowledge about disease (cognitive) . skepticism of medical care (affective). and tion matches the conditions of care he or she will be dependency in illness (behavioral)” and grossly could more satisfied than if there is no match. That is. there be reduced to two orientations, scientific (“an objec- must be congruence between the person’s orientation tive, formal, professional, inde~ndent approach to ill- and the provider’s approach to care, activities carried out in the provision of care, situation surrounding ness and medical care”) and popuiar (“a subjective, care, and the expected outcomes of care. For example. informal, lay, dependent health orientation”) [lo]. However, subsequent research [ 1l-133 indicated that a person may expect to be diagnosed as having strep Suchman’s definition was too narrowly conceived in throat, given a penicillin shot, told to stay home from terms of the groups he was analyzing. As most work, and begin to feel better in a couple of days. If this happens, the person is more likely to be satisfied recently redefined by Geertsen, et al:[14] orientations than if he or she is diagnosed as having a delusion, still include knowledge, dependency, and skepticism, but do not necessarily divide into ‘scientific’ versus told to pray, and does not feel any better. Similarly, a patient may visit several physicians in search of one ‘popular’ orientations except in particular groups. who will prescribe a desired psychoactive drug. Suchman [I5] operationally defined “knowledge The literature indicates that receiving the expected about disease” to be “measured by the correctness of diagnosis [26] or treatment [27,28] leads to satisfacresponses to thirteen questions dealing with etiology, symptoms, and prognosis of various diseases”. The tion, but less attention has been devoted to studying concept -.of “correctness” is culturally defined, and cer- the congruence of orientations and other conditions tainly not one we would expect to be shared by, say, a of care. For example, it is likely that a high social status teenager would feel degraded by a visit to a city physician, a Christian Scientist, and a chiropractor. venereal disease clinic, regardless of the medical or Therefore we substitute ‘beliefs’ for ‘knowledge’. Beliefs about care include theories of &he etiology of counselling skills of the clinic’s providers. In preventive care, persons’ orientations may lead to acceptillness and how illness should be treated. ance of providers’ advice on some matters but not Suchman [l&173 and others [18-231 have shown others. For example, a pediatrician’s advice may be how aspects of health care orientation vary between accepted about vitamin supplements, but not child social groups and cultures. For example, a person restraints in an auto or smoking by the mother. with an allopathic orientation may see water boiling To date, differences between orientations toward as appropriate to prevent dysentery, but a rural Perucare and actual conditions of care have been studied vian Indian may see it as inappropriate [24]. In the primarily for traditional societies, developing counallopathic model, boiling kills the germs that cause tries, and ethnic minorities. This emphasis on folk dysentery. In the humoral mode, boiling adds heat befiefs overshadows the wider applicability of the that is counte~roductive in treating a “hot” disease works of Suchman and Geertsen. The apptication of like dysentery. Equally dramatic differences of orientheir perspective. we believe, has great potential to tation exist within contemporary urban society, even illustrate more common urban-culture conditions like among users of ‘standard’ allopathic care, such as alcoholism, cardiovascular disease, cancer, drug over the appropriateness of abortion as a family planaddiction, auto accidents, dental disease, etc. ning method or whether alcoholism is a medical or moral condition. model was initially follows:
in 1965 as
THE GENERAL LEVEL OF SATISFACTION
* A very similar model has also been proposed in anaiysis of community satisfaction [81]. Community type preference, analogous to orientation toward health care, has been shown to correspond to satisfaction and, in combination with satisfaction, be highly predictive of intention to move. However community type preference is a unitdimensional variable, whereas we assume SD variables and associated orientations are multidimensional.
Before we elaborate on our group-level model for satisfaction and its consequences, a basic finding from the literature needs to be emphasized. Most people say they are satisfied in almost all studies, even those from less developed countries with substantial traditional medicines [29]. Although there may be methodological problems in determining the level of satisfaction [30], findings that even 92’6 of persons with
Approach to soctodemographic
predictors of satisfaction with health care
incomplete recoveries express satisfaction with the care they received [31] lead one to conclude people really are satisfied. We want to note this finding because it can block understanding of satisfaction with health care. Faced with a skewed dependent variable with little variance (everybody’s happy). researchers may focus on dimensions of satisfaction rather than explain the central fact of satisfaction. Our model posits that orientations must match conditions of care to produce satisfaction. As an analogy, assume there were two orientations toward vehicles, truckers’ and sports car drivers’, and two types of vehicles-trucks and sports cars. If both groups get the type vehicle they want. everybody is satisfied. Yet a search for a common dimension of ‘vehicular satisfaction’, say, handling or hauling power, may produce ambiguous results and obscure the real process leading to general satisfaction. In this example, the methodological search for commcm dimensions forces a simple model on a complex process having to do with both individual orientations and group interactions (supply and demand for vehicies by type). In addition, the overall level of satisfaction has important practical consequences for program planners and evaluators. Programs are evaluated on how well they fulfill overall needs.
SOCIODEMOGRAPHK VARIABLES AND MODEL-GROUP
THE
LEVEL PHENOMENA
We introduced a simple model of individuals’ satisfaction based on the work of Suchman and others. We now want to link this model to the wide variations in the literature of the effects of SD variables on satisfaction with health care. At this point, the model must be applied to group level phenomena and sampfe surveys. We think the model relates to both the consistency of results between studies and the strength of the results within any one study. Consistency between studies of SD variables’ effects should be a function of two factors: how widely shared orientations are and how universally studies reflect health care settings and populations. We would expect widely shared facets of orientations (like valuing economy, speed of recovery. and ease of access) to be more consistently a source of satisfaction or dissatisfaction. To the extent SD variables covary with more widely shared facets of orientation towards care,
559
different studies will be more likely to report consistent relationships to these SD variables. For example, everyone may value ease of access, but the elderly might value it more than the young in almost every group because of higher need or lower mobility. As another example, everyone may value economy, but the poor more so than the rich. If these were so, we would expect to see consistent age or income correlations to satisfaction with health care, so long as access or cost affected satisfaction. The other factor affecting consistency of SD variables’ effects between studies is the degree to which studies reflect particular health care settings and populations or al1 health care settings and popuiations. To the extent samples of providers and settings are limited, studies will be likely to report idiosyncratic relations, since certain settings may be matched to particuiar orientations no matter how common those orientations are in the population. To use the age sample again, a sample of teenaged pediatric patients’ satisfaction might show low age correlations due to the Iimited age span of the sample, even if there were some overall reiationship between age and satisfaction in the population. Beyond consistency of SD relationships, even general community sample surveys may vary in the stre~rh of reported SD relationships to satisfaction. This is best iltustrated in a four-cell design (see Figure 1). In cell A, when both orientations and conditions are relatively uniform, low correlations between SD variables and satisfaction should be found since everyone will be similarly satisfied. If people from all SD categories share similar orientations and all providers are similar to each other, any sample survey is likely to find low SD-satisfaction correlations. In cell B, when orientations are uniform (over SD variation) but conditions of care differ, then higher SD correlations should be found. We might expect SD correlations to reveal more powerful groups to be more satisfied, since the community might compete for the limited set of providers seen as desirable by all. In ceI1 C, if orientations differ between SD categories but care conditions are relatively uniform, we would expect high SD-satisfaction correlations. Here some groups will like the standard care and others will not. For example, if everyone has to use an HMO, some groups will be satisfied (those who now choose HMOs)
Correlations sheuld be when provtdersconditions of care are: Uniform and patients’ orientations toward care are :
Different
! WI
Cell A
High
LOW Uniform
Different
0
I
1 Ce” ‘High
1
‘*”
F0w
Fig. 1. The level of expected correlations between sociodemographic variables and satisfaction health care in community sample surveys.
with
JOHN G. Fox
560
and DORIS
more than others (those who now opt for fee-for-service coverage). Finally, in cell D, if orientations to care vary and conditions are also split, then correlations to SD variables should be low on a zero-order level. (Note that statistically controlling for care situation variables returns one to cell C and should increase SD-satisfaction correlations.) In cell D, we would expect low zero-order correlations for two reasons. First, if patients are assigned to care settings randomly, then a strong positive correlation to SD variables in one health care setting would be offset by an equally strong negative correlation at another setting. For example, Christian Scientists and medical students would tend to be dissatisfied with the other group’s providers. Alternatively, if patients sort themselves among care settings, low correlations to SD variables should result because all would seek the conditions which satisfy their orientation. If supply of health care providers is not fixed, we might even find a marketplace equilibrium with disparate orientations each matched to their preferred conditions and all people fairly satisfied. This last case parallels the sports car and truck analogy used earlier. Testing the model Given this framework, we would like to test whether data from a community sample survey conform to its predictions. The following sections present data from a community sample survey conducted in the Baltimore metropolitan area. In this section, we describe this survey in terms of our model and indicate the hypotheses to be tested. Baltimore is characterized by a fair degree of community and health care variability. As a community, it includes wealthy urban and suburban areas, inner city Black neighborhoods, several blue collar neighborhoods with distinct White ethnic backgrounds, and fairly rural areas close to the city itself. Health care facilities in Baltimore also differ and include university teaching hospitals, HMOs, private physician practices, and inner city clinics, as well as various alternative providers, like chiropractors and Christian Science practitioners. We assume gross orientations toward care are relatively more uniform. Baltimore residents’ health care orientations should be largely allopathic, valuing low cost, high access, and rapid recovery. If these assumptions are true, we should be in a situation similar to cell B and significant zero-order SD-satisfaction correlations should be found. Further, if we control statistically for the conditions of care (cost, access, etc.), we should be in a situation similar to cell A and SD-satisfaction correlations should generally cease to be significant. Finally, to the extent there are SD-correlated orientations which differ within all groups, we should be in a situation similar to cell C and those SD-satisfaction correlations should persist when conditions of care are controlled. The most likely SD candidates for such cross-cutting effect should be biological: age and sex differences (and correspondent health care need differences) exist in all groups. IMETHOD
During late 1974 and early 1975, 3398 residents of the Baltimore metropolitan area were sampled for
M. STORMS
telephone interviewing using equal column intervals in numerical telephone directories. After approximately two-thirds of the sample were telephoned. the remaining persons from under-represented geographic subareas were over-sampled to correct for unequal numbers of telephones per capita. In this way, the geographic distribution of respondents almost perfectly matched the Baltimore metropolitan area distribution. Completed interviews were obtained for 2582 respondents, a 7896 response rate. The major purpose of the survey was to study acceptance of physicians’ assistants and nurse practitioners, the results of which are reported elsewhere [32]. Since 22.4% of Baltimore area telephone numbers were unlisted, a series of comparisons was made of the sample’s fit to the local population. The obtained sample matched the Baltimore City County racial composition of 287: Black, and the sample’s income distribution closely matched national figures. The sample, however, significantly differed from the population on other SD variables. It fell 12.9”/, below local averages for percent males, 4.5”; below the population level for 18-20 year-olds, and 22.2% below the population level for persons with less fhan a high school education. The sample exceeded local averages for persons of ages over 65 years by 6.09; and those with a baccalaureate degree or higher education by 5.8%. The obtained sample was close to the local and national averages for access and utilization of health services. National norms [33] for proportion having a regular place of care and proportion requiring less than 15 minutes to get to care were close to those for the sample. Where local utilization averages exceed the national average, the sample matched the local averages. For instance, the proportion of the sample having seen a physician within the last 12 months matched the local rate [34] rather than the national proportion [35]. Thus, despite certain shortcomings of the sample which prevent accurate generalization to other populations, it is sufficient for analysis on our level, the community, and our problem, the strength of correlations rather than parameter estimates. Respondents who stated they had received medical care within the past 12 months were asked: If a score of ten represents the best possible medical care available and one represents a very poor quality of medical care, how would you rate the medical care you have received in the past year’?
We recognize the critical issues surrounding the use of overall satisfaction measures [36]. There are good reasons for investigating satisfaction with different facets of care (which are outlined in the concluding section), but our purpose is to outline a general model of satisfaction in its simplest form. We want to investigate how the overall process of satisfaction involves SD variables. If this model is validated, then it would be appropriate to look at satisfaction differences within each respondent’s overall satisfaction level. RESULTS Of the 2582 respondents, 2061 (79.99b) had received health care in the past year. Of those 2061, 2021 (98.130) rated their satisfaction with the care they had
Approach to sociodemographIc
predictors of satisfaction with health care
Table I. Percent most satisfied (‘ten’) with medical care, by sociodemographic characteristics
0c,
Characteristics
Satisfaction with medical care N
P*
49.2 61.8
426 1419
0.005
64.5 63.0 67.4 62.0 56.8 58.0 43.4 33.3
31 92 347 292 597 362 297 0
0.005
56.9 59.3 50.0 60.3
483 1239 2 277
NS
32.0 48.1 49.7 61.6 65.7 74.2
200 397 294 349 367 399
Sex
Male Female Education l-3 years 4-6 years 7-9 years 10-l 1years H.S. graduate l-3 years college College graduate Not stated Race Black White Other Not stated Age 18-24 25-34 35-44 45-54 55-64 65+ Not stated Work status Employed School Retired Homemaker Nonemployed Family income $0-2000 $3-6000 $7-12,000 !&13-18,ooO 819,000+ Not stated
0.005 52.9 31.1 69.1 66.0 59.6
1026 53 265 577 99
0.01
63.5 69.0 58.1 54.0 52.8 67.9
85 145 320 291 235 695
0.005
* P = Significance level reached in ,$ analysis.
received. As is usual in satisfaction studies, the obtained scores were skewed toward the upper (satisfied) end of the scale. Of those who received care within the past year, 58.7% rated it with the highest possible rank and 84.7% rated it among the top three ranks (8-10). Since the scores are highly skewed toward the upper end, we will report percentage cross-tabulations with a dichotomy between the most satisfied respondents (?en”) and all other respon-
dents. Correlations are calculated over the whole range (one to ten). All the SD variables except race correspondent to satisfaction with health care, as shown in Tables 1 and 2. In Table 1, females are more satisfied than males; the less educated are more satisfied than the more educated; older persons are more satisfied than younger; retired people and homemakers are more satisfied than the other job categories;-and persons with lower family income are more satisfied than those with higher family income. The correlation matrix for SD variables in Table 2 indicates significant zero-order correlations between SD variables and satisfaction with care. Note that the sample size makes relatively small differences statistically significant. However, differences of 20-30”;, as in Table 1. are substantial. Tables 1 and 2 indicate, as hypothesized, SD variables have significant zero-order relationships to satisfaction with health care. In terms of our model; the significant correlations hypothesized for a Cell B pattern are found. Note, however, that the high correlations are also predicted by a Cell C pattern, The interpretation depends upon whether providers and orientations are assumed to be uniform. Health care conditions variables also correspond to satisfaction with medical care (Table 3). All access measures except cost-having a regular place of care, taking less time to get to care, having a personal physician rather than an institution as a place of care--are significantly related to satisfaction with care. Utilization of care-having a visit within the last 60 days, number of visits, and making preventive care visits---also is significantly related to satisfaction with care with the possible exception of those reporting three or more visits in the last 60 days. Self-reported health status-missing a day of activities within the last 60 days and having a chronic health condition-is less clearly related to satisfaction with health care. The chronic condition variable is correlated with age; so the significant correlation there may reflect an age effect. Thus far, we have shown zero-order SD-satisfaction correlations. The question now is whether, as hypothesized, controlling for health care conditions eliminates many of the SD variables’ correlations to medical care satisfaction. Table 4 represents data for satisfaction with health care among those receiving care in the past year. Two SD variables-age and sexremain significant after controlling for the health care variables (@ 0.236 and -0.072, respectively). Age is the most significant predictor of health care satisfac-
Table 2. Correlation matrix for sociodemographic Variable
Age Education Race (white) Family income Sex (male) Satisfaction with medical care
Age
Educ.
561
variables and satisfaction with health care
Race
Income
l.ooo 0.228 0.072
1.000 0.136
0.009
- 0.077
Sex
Satisfaction
1.ooo
- 0.386 0.032 - 0.246 -0.105 0.255
Correlations are Pearson’s r
l.OGQ 0.208 0.439 0.164 -0.118
1.000 -0.112
1.000
JOHN G.
562
Fox and DORISM. STORMS
Table 3. Percent most satisfied (‘ten’) with medical care, by health care conditions variables
Characteristics Regular place of care Yes No Not stated Normal place of care MD office Hospital O.P.D. Hospital emergency room Neighborhood clinic Other Not stated Care provider last 60 days Medical doctor Registered nurse Other Not stated Preventive care visits Yes No Not stated Time to get to care Less than 15 minutes 15-30 minutes 10-60 minutes 60+ minutes Not stated Cost of visit None 81-9 $10 $1 l-20 s21+ Not stated Number visits last 60 days 0
i 3 4+ Not stated Disability days last 60 days Yes No Not stated Chronic condition Yes No Not stated
Satisfaction with medical care 0P* N ,‘O 59.2 46.3 0
1897 123 0
61.4 53.2
1582 248
42.5 43.3 43.3 60.0
54 72 60 5
0.001
62.0 64.0 58.1 54.2
761 317 35 907
0.005
62.3 53.1 42.1
1264 738 19
0.005
60.1 56.7 57.6 42.1 17.6
944 868 191 19 17
0.005
61.1 62.6 54.9 56.9 57.7 66.0
301 447 586 473 111 103
NS
55.1 58.4 72.2 61.0 59.0 49.2
884 640 281 59 94 63
0.005
57.5 58.4 20.0
738 1300 5
NS
62.0 56.2 75.0
874 1143 4
0.02
0.005
correlations. Why do age and sex remain significant when other SD variables do not? Age and sex are probably the two strongest SD variables as predictors of health care utilization [37]. Because they strong11 correspond to utilization. they may create differing orientations toward health care even when other group (SD) factors are taken out. Thus, we conclude that these two variables fit our model to the extent that they create a cell C pattern, differing health care orientations in a care situation made uniform by statistically holding health care conditions variables constant. DISCUSSION
* P = Significance level reached in x2 analysis.
tion. The health care indexes (the variables described in the preceding paragraph combined in additive scales), however, outweigh all remaining SD variables, all of which (except race) were significant at the zeroorder level. The overall predictability of satisfaction with health care is low (R2 = 0.08) in keeping with the almost uniformly high level of satisfaction in the sample. The results in Table 4 indicate, as hypothesized, that some SD characteristics may cross-cut other SD groupings to create more widely-shared health care orientations, and thus more stable SD-satisfaction
The purpose of this exploratory paper is to propose a model for satisfaction with health care that provides a framework for the effects of SD variables. We propose that two sets of intervening variables--orientations toward care and conditions of care-modify the effects of SD variables on satisfaction, producing discrepancies between studies. The proposed model addresses two issues. First, it predicts the individual’s level of satisfaction by the congruence of conditions of care and orientations towards care. When they coincide, satisfaction results: if not, dissatisfaction results. Second, at an aggregate level, it addresses the consistency between and strength of results in studies of health care satisfaction. Studies should differ from each other in SDsatisfaction correlations to the degree their samples represent the population and to the degree the population represents uniform or variable orientations and/or conditions of care. The strengths of most SD-satisfaction zero-order correlations were expected to diminish when controlling for conditions of care. This was found to be the case except for age and sex. For age and sex, we argued that their over-reaching social and biological consequences for health (in terms of utilization rates, level of morbidity, nature of typical utilization, etc.) create a cross-cutting effect whereby their differing orientations persist within income, educational, racial. or other SD groups. The zero-order SD-satisfaction correlations for most SD variables may reflect correlated differences in conditions of care. whereas age and sex may correspond more strongly to differences in orientations toward care. It is possible, for example, that women or the elderly expect or desire different affective relationships with the health care provider than do men or the young, regardless of the provider’s situation of care (or that providers treat women and the elderly differently than their male or young patients). This article is exploratory. To the extent the model is validated, however, it affects the context of current research on satisfaction with health care. As we noted at the outset, current efforts focus more and more on defir,ing types of satisfaction-perceived competence of the providers, satisfaction with the cost of care, satisfaction with access to care, etc. For social scientists, the model makes such facets interesting in a comparative sense. One might ask, for example. whether people with different orientations also differ in the salience of different conditions of care (say, cost versus affective relationships with the provider). Also
Approach to sociodemographic
predictors of satisfaction with health care
563
Table 4. Satisfaction with health care regressed upon SD variables and health care conditions indices Independent
variable
Age Use Index Sex male Access index Health status index Race white Education Family income
Raw correlation 0.255 0.102 -0.112 0.078 0.011 0.009 -0.1 18 - 0.077
B 0.236 0.082 - 0.072 0.055 -0.042 0.010 - 0.008 - 0.003
Significance 0.001 0.001 0.003 0.01 NS NS NS NS
R = 0.286, RZ = 0.082
important is the way dimensions reveal interactions in the supply and demand for health care. If different groups value different things, do their orientations create a market for different providers? Can providers “sell” different facets of their conditions to different groups with different orientations? How do different groups learn their orientations? The issue of satisfaction dimensions thus integrates the study of satisfaction with the broader study of health care social systems, when viewed in the context of a more general model of satisfaction. The model also could make dimensions of satisfaction of greater use in a practical sense. Issues of use of oreventive care utilization, for example, may reflect multi-dimensional orientations toward care leading to acceptance of a given provider’s advice in some areas but not in others. As another example, the skepticism dimension of the orientation concept may be applicable to satisfaction with affective-behavioral care proeiders. In sum, seeking dimensions of satisfaction is a
worth-while goal, but one that reaches greater potential benefits when placed in a theoretical framework. REFERENCES
1 Wooley F. R.. Kane R. L.. Hughes C. C. and Wright D. D. The effects of doctor-patient communication on satisfaction and outcome of care. Sot. Sci. Med. 12, 123. 1978. 2. Locker D. and Dunt D. Theoretical and methodological issues in sociological studies of consumer satisfaction with medical care. Sot. Sci. Med. 12. 283. 1978. 3 Lebow J. L. Consumer assessments of ;he quality of medical care. Sot. Sci. Med. 12. 328, 1974. 4 Ware J. E.. Davies-Avery A. and Steward A. L. The measurement and meamng of patient satisfaction. HIth .\lud. Care Serl-. Rer. 1, I, 1978. 5 Suchman E. A. Soclomedical variations among ethnic groups. .4n1. J. qf‘Socio/. 70, 319. 1964. 6 Suchman E A. Social patterns of illness and medical care. J. H/~/I sot. Behar. 6, 2, 1965. 7 Geertsen R.. Klauber M. R.. Rindflesh M.. Kane R. L. and Gar) R. A reexamination of Suchman’s views on social factors in health care utilization. J. HIrh sot. Beh
13. Berkanovic E. and Reeder L. G. Ethnic, economic, and social psychological factors in the source of medical care. Sot. Probl. 21. 246. 1973. 14. Geertsen R. et al., &. cit. 15. Suchman E. A., Social patterns, op. cit., p. 3. 16. Ibid. 17. Suchman E. A., Sociomedical variations, op. cit. 18. Geertsen R. el al., op. cir. 19. Reeder L. and Berkanovic E., op. cit. 20. Berkanovic E. and Reeder L., op. cit. 21. Zola K. Culture and symptoms-an analysis of patients’ presenting complaints. Am. Sot. Retl. 31, 615, 1966. 22. Joseph A. Physician and patient: some aspects df inter-
personal relations between physicians and patient, with special regard to the relationship between white physicians and Indian patients. Appl. Anthrop. 1, 1, 1942. 23. Hautman M. A. Folk health and illness beliefs. Nurs. Pructir. 4, 23, 1979. 24. Wellin E. Water boiling in a Peruvian town. In Health, Culture and Community (Edited by Paul B. D.), pp. 71-103. Russel Sage Foundation, New York. 1955. 25. Szasz T. The Myth of Psychotherapy: Mental Healing us Religion, Rhetoric, and Repression. Doubleday, New York, 1978. 26. Mechanic D. Medical Sociology. Free Press, New York, 1968. 27. Korsch B. M., Gozzi E. K. and Francis V. Gaps in doctor-patient satisfaction. Pediatrics. 42, 855, 1968. 28. Francis V.. Korsch B. M. and Morris M. J. Gaps in doctor-patient communication: patients’ response to medical advice. N. Enal. J. Med. 280, 535, 1969. 29. Kuman S., Ramaiah T: J.. Kataria M., Gupta J. P. and Rao K. G. Repeat Evaluation of Community Health Volunteers Scheme-/979. National Institute of Health and Family Welfare, New Deli, 1979. 30. Locker D. and Dunt D., op. cit., p. 286. 31. Wooley F. R. et al., op. cit. 32. Storms D. and Fox J. The public’s view of physicians’ assistants and nurse practitioners: a survey of Baltimore urban residents. Med. Care 17, 526, 1979. 33. Aday L. and Andersen R. Development of Indices of Access to Medical Cure. Health Administration Press, Ann Arbor. 1975. 34. Bite T. W. Medical care for the disadvantaged: report on the survey of use of medical services in the Baltimore standard metropolitan statistical area, 1968-69. Final Report of HSM 110 69 203, NCHSRD, 1971. 35. Aday L. and Andersen R., op. cit. 36. Locker D. and Dunt D., op. cit. 37. Aday L. and Andersen R.,.op. cit. 38. Alpert J. J., Kosa J., Haggerty R. J., Robertson L. S. and Haegarty M. C. Attitudes and satisfaction of low income families receiving comprehensive pediatrics care. Am. J. publ. Hlrh 60, 499, 1970.
564
JOHN G. Fos
and DORIS M. STORMS
F. and Summers. C. F. Com39. Rojeck D.. Clemente. munity satisfaction: a study of contentment with local services. Rural Social. 40, 177, 1975. city people think of their 40. Koos E. L. Metropolis-what medical services. Am. J. pub/. Hkh 55, 1551, 1955. with patient evaluation 41. Linn L. S. Factors associated of health care. Milbank meml. Fund Q. 54, 531, 1975. satisfaction in a health mainten42. Pope C. R. Consumer ance organization. J. Hlrh sot. Behar. 19, 291, 1978. 43. Raphael W. Do we know what the patients think’?: a survey comparing the views of patients, staff, and committee members. Int. J. Nurs. Srud. 4, 209, 1967. E. A. Stages of illness and medical care. J. 44. Suchman Hfrh hum. Behar. 6, 114, 1965. D. Consumer satisfaction 45. Tessler R. and Mechanic with prepaid group practice: a comparative study. J. Hlrh sot. Behar. 16, 95, 1975. 46. Hulka B. S.. Kupper L. L., Cassel J. C. and Babineau R. A. Practice characteristics and quality of primary medical care: the doctor-patient relationship. Med. Cure 13, 808, 1975. 47. Kirscht J. P., Haefner D. P., Kegeles S. S. and Rosenstock I. M. A national study of health beliefs. J. Hlrh human. Behac. 7, 248, 1966. R. L.. Metzner C. A. and Worden C. Con48. Bashshur sumer satisfaction with group practice, the CHA case. Am. J. publ. Hlrh 57, 1991, 1967. 49. Andersen R., Kravits J. and Anderson 0. W. The public’s view of the crisis in medical care: an impetus for changing delivery systems? Econ. Busin. Bull. 24, 44, 1971. 50. Hulka B. S., Zyzanski S. J., Cassel J. C. and Thompson S. J. Satisfaction with medical care in a low income population. J. chron. Dis. 24, 661. 1971. 51. Linn L. S.. op. cir. 52. Pope C. R., op. cit. P., op. cit. 53. Tessler R. and Mechanic 54. Bashshur R. L., et al., op. cit. R. Patterns of 55. Gerst A., Rogson L. and Hetherington satisfaction with health plan coverage: a conceptual approach. Inquiry 6, 37, 1969. 56. Hulka B. S., Kupper L. L. er al., op. cir. 57. Gerst A. er al.. op. cir. 58. Hulka B. S.. Zyzanski S. J. er al.. op. cit. L. L. er al.. oi. cit. 59. Hulka B. S.. iupper 60. Hulka B. S.. Zyzanski S. J. et al., op. cir 61. Gerst A. er ul.. op. cir.
Apostle D. A. and Oder F. Factors that influence the public’s view of medical care. J. Am. med. .4.s.s.202, 592. 1967. 63 Korsch B. M. er al.. op. cir. 64 Linn L. S., op. cir. 65 Tessler R. and Mechanic D., op. cit. 66 Bashshur R. L. et al.. op. cit. R. W.. Hopkins C. E. and Roemer M. I. 67. Hetherington 62
Health
68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81.
Insurance
Plans.
Promise
and
Performance.
Wiley, New York. 1975. Gerst A. er al.. op. cit. Apostle D. A. and Oder F., op. cit. Tessler R. and Mechanic D., op. cir. Bashshur R. L. er al., op. cir. Hetherington R. W. et al., op. cit. Korsch B. M. er al.. op. cir. Pope C. R., op. cit. Raphael W., op. cir. Koos E. L., op. cit. Suchman E. A., Stages of, op. cir. Tessler R. and Mechanic D.. op. cit. Hulka B. S., Kupper L. L.. et al.. op. cit. Cartwright A. Human Relations und Hospirul Care. Routledge & Kegan Paul, London. 1964. Heaton T.. Fredrickson C.. Fuquitt G. V. and Ziches J. J. Residential preferences. community satisfaction. and the intention to move. Demography 16, 565. 1979. APPENDIX SOCIODEMOGRAPHIC VARIABLES EFFECTS ON SATISFACTION WITH CARE I& VARIOUS STUDIES
Age: Direct relationship found [38-431. No relationship found f44.451. Inverse relationshio found r46.471. Race: Blacks more satisfied [48]. Whites more satisfied [49, SO]. No relationship found [51. 521. Marital status: Singles more satisfied [53]. Marrieds more satisfied [54, 551. farnil: size: Larger families more satisfied [56, 571. Smaller fatmlies more satisfied [SS]. Education: Direct relationship found [59-621. No relationship found [63-661. Inverse relationship found [67]. Income: Direct relationshlp found [68-691. No relationship found [70]. Inverse relationship found [67]. Socioeconomic c/ass: Direct relationship found [73-751. No relationship found [7&78]. Inverse relatlonship found [79. SO].