3.5. 1 Physical-need . . . . . 69 3.5.2 Summary of effect of need on health-care . . . 74 3.6 Chapter summary . . . 75
3. 1 Chapter overview
This chapter, organized around three main sections, surveys the empirical research on Andersen' s ( 1 968) model of health-care utilization. Each section corresponds to one of the three components of the model. Many of the studies reviewed in this chapter have been explicitly based on either the conceptual framework developed by Andersen and his associates or on revisions of that framework. Other studies have also been included that, whilst not based upon the model, have relevance for both its empirical status and the formulation of hypotheses in the present study. Altogether, the three sections cover a wide range of research whose common focus has been investigating the determinants of health-care. While each section will focus on its corresponding component, there will be some overlap with the other sections. For example, a study may have included an increased range of predisposing predictors, but the findings may also have implications for the other components of the model.
3.2 Introduction
Studies applying Andersen's ( 1 968) behavioural framework have organized their respective analyses around four major blocks of variables. The first three represent predictors of uilization of health services; these correspond to the need, enabling and predisposing components. The fourth block represents measures of health-care, the outcome variable of prime interest in testing Andersen's model.
The early research applying Andersen' s model can be characterized as having investigated the influence of relatively straightforward demographic and structural factors on health-care. Increasingly, the research has investigated the impact of more complex psychosocial factors, including social contact networks and ' patient' satisfaction with specific medical services. The purpose of including psychosocial variables has been twofold: first, to see whether other
variables besides those suggested in Andersen' s ( 1 968) behavioural framework increase the explained variance in health-care and, second, to clarify relationships among the various
predictors. In this regard, recent studies have examined regression-model-derived additive as
well as interactive effects of predictor variables on health-care (e.g., Wolinsky et al., 1 9 89). While additive effects relate to the presumed direct and independent impact of predictors on
health-care, interactive effects relate to regression coefficient differences across specified
populations. For example, an additive model would test the effect of income on a sample's
health-care, while an interactive model would compare the impact of income on use of health-
care by groups within that samplel.
3.3 Effect of predisposition on health-care
Predisposing variables have generated the most interest among researchers in recent years primarily because these variables represent social structural and demographic factors which
historically have been associated with differential access to goods and services. The central
issues raised in the more recent predisposing-focused research have concerned whether and how much patterns of health-care vary according to demographic group membership. Comparisons of health-care have been made across groups based on age, gender, ethnicity, education, employment status, marital status and social support. Attitudinal aspects of predisposition such as health beliefs and satisfaction with GP care have also been investigated.
The general findings on the effect of predisposition on health-care are presented first,
followed by studies which have focused in greater depth on selected aspects of predisposition or that have undertaken controlled multivariate analyses. In Andersen's ( 1 968) representative
lThe interaction-effects model incorporates tests of equality between coefficients to determine if there are significant differences in the impact of a variable on subgroups' use of health-care. It addresses questions about whether subgroups have similar or different patterns of health care use.
national study of 2,367 American families, family size and age of the oldest family member were strong predictors of GP and hospital care, although measures of perceived need were stronger predictors. Data were responses to household in-person interviews. Using a similar methodology, Andersen et al.'s ( 1 975) study of 3,800 Americans incorporated medically evaluated measures of need. While no predisposing variables predicted hospital admission, age was a strong predictor of length of hospitalization and of number of GP visits. The positive association between age and visits was largely due to greater levels of illness in the older age groups. While family size did not influence adults' GP contact, it influenced children's GP contact, with larger families having the least GP contact. Andersen et al. found that the larger-family reduced-GP-care association was more pronounced among low income families, older children and families living in inner city or rural areas. Hershey, Luft and Gianaris ( 1 975), whose data were responses to household interview surveys of 299 rural Californian families found that higher educational attainment was associated with a greater likelihood of annual medical checkups compared with others. After statistically controlling for health status, Hershey et al. found that women were no more frequent in their use of medical care than men.
In a study of health services utilization patterns in North and South America, England, Finland and Eastern Europe, involving over 14,000 respondents, Kohn
&
White, ( 1 976) found that women's rates of physician use were higher than men's. Age differences, though, were even larger than sex differences. However, while volume of visits increased with age, GP-visit frequency was not highest in the oldest age groups. In Wolinsky's ( 1 978) study of a representative sample of all-age Americans, younger malTied adults with smaller sized families were more likely than all others to use GP care2• Those who used dental care weremore likely than non-users to have high educational attainment and incomes. Stoller ( 1 982)3
found that almost half of the explained variance in an older sample's
GP
contact was.accounted for by predisposing variables. Education was the most important single predictor of
GP
contact in her study: more education was associated with a greater likelihood of having had past-yearGP
contact. This finding is consistent with the well-established relationship of education with patient compliance (Becker, 1 974).Compared with the younger 'old' adults (i.e.,
65
- 74 age group) older 'old' adults (i.e., 75years and older) in Wolinsky and 10hnson's ( 1 99 1 ) study had fewer bedrest days, but were
more likely to be placed in nursing home-care. Older women had fewer bedrest days, fewer hospital admissions, shorter hospital stays and fewer physician visits than older men. These findings are consistent with women's better overall health than men's in the middle- to older age groups. Health worries were positively related to use of several health services. Older adults who reported at least some control over their health were less likely to die within two years of the initial survey interview than those who reported no control.
Summary of general findings on predisposing predictors on health-care. Age, sex, education
and family size have been repeatedly identified in the literature as significant contributors to health-care. A more detailed overview of studies that have concentrated on specific predisposing variables now follows.
3For a description of Stoller ( 1982) see Section 3 .5. 1
3.3.1 Sex
The literature has repeatedly shown that women are more frequent users of formal health-care than men, and in particular, of primary care services. This difference remains even after women's obstetrics-related medical care is excluded from comparisons (Verbrugge, 1 989). The literature on sex differences in health status reviewed in Verbrugge ( 1 989) documented women' s greater morbidity for both acute and most of the chronic but non-fatal illnesses, and men's greater morbidity for fatal chronic illnesses. Verbrugge ( 1 989) summarized the main findings as follows:
" For the great majority of population health indicators, women's rates exceed men ' s. The exceptions are higher males' rates for impairments, life-threatening chronic illnesses, and long-term major disability due to chronic conditions" (p. 283).
Five explanations for sex differences in health status have been advanced in the literature. The first relates the differences to intrinsic biological differences between males and females. B y this explanation, health tendencies, including patterns o f morbidity and mortality are primarily the product of biology. The second explanation involves acquired risks of illness and injury. Thus, differential exposure to the social milieu - lifestyle, health practices, and work and leisure activities - gives rise to health differences between the sexes. The third explanation emphasizes psychosocial aspects of illness behaviour. Men's and women' s different illness behaviour (i.e. , responses to illness symptoms) results in differential health outcomes (Mechanic, 1979). The fourth account of sex differences centres around health-reporting behaviour. This account proposes that sex differences in health status are magnified by men's and women's different interests in and propensities for talking about their health symptoms
(Shapiro, 1 984). The fifth explanation concerns the impact of prior patterns of health-care on current and future health. Thus, for example, regular (or, as the case may be, irregular) health-care has a cumulative effect on health.
The literature is in broad agreement about several sex differences in health; namely, that men are more vulnerable to biological risks and use fewer health services than women, and that women's illness symptomatology is heightened by their illness behaviour and health-reporting behaviour. The literature further shows that males and females are vulnerable to different sorts of acquired risks. For example, men engage in a greater level of smoking and alcohol consumption than women, whereas women engage in less strenuous activities, report greater levels of stress and role pressures than do men (Verbrugge, 1989). Research comparing the impact of men's and women's health status on use of health-care is now presented.
Verbrugge, 1989. Verbrugge compared sex differences in the impact of health status on morbidity and health behaviour. The aim of the comparison was to ascertain the extent to which the differences in health were associated with acquired risks, illness behaviour and health-reporting behaviour. Data were derived from a regional multistage probability sample of American metropolitan households. Face-to-face interviews were conducted with one randomly selected adult from each household, yielding a metropolitan sample of 302 men and 4 1 2 women. Of these, 243 men and 346 women also kept daily standardized personal health records to supplement the interview data.
Because analyses involved a large number of predictor and health outcome variables, only the main findings will be outlined. Bivariate analyses between predictors and health status variables confirmed women's poorer health on most indicators and their overall greater use
of health services. However, when health-risk factors such as smoking, job hazard, non- employment and stress were held constant, the difference narrowed substantially between mens' and womens' consumption of health services. In addition, although most of the reversals were non-significant and small, analyses revealed a consistent male health disadvantage. Health indicators that were now worse for men than women included number of chronic problems and chronic symptoms, heart trouble, restricted activity days and curative medical visits (i.e., visits in response to diagnosable medical need). For both men and women, physical illness symptoms were the strongest predictors of health-care. Other important (but lesser) predictors were older age, non-participation in the work-force and stress. A third level of important predictors were income, high valuation of health and health insurance. Verbrugge concluded that the evident male disadvantage in health - once predictors were statistically controlled - may be due to biological rather than social or environmental factors.
Kandrack et al., 1991. Kandrack et al. examined the contribution of socio-demographic variables to differences in men's and women's health status and health behaviours. Data were responses from structured face-to-face interviews with a regional American household sample of 293 women and 232 men. Health behaviours included beliefs about perceived health control and preventive health practices, the use of informal social networks for health concerns, use of cutback days and
GP
services4•No sex differences were found in either self-reported health or preventive health beliefs, but women reported a significantly greater number of social contacts than did men. Sex differences were found in the source of support: men were more likely to turn to their
spouses, while women were more likely to turn to their friends and children. Women reported significantly more cutback days (i.e., illness-related reduced activities) than men, but no differences were found in illness attitudes, physical-health or medication use. Differences were found, however, in health-care use. Compared with men, women had a greater number of cutback days, and consumed more medical services than did men. Sex, however, accounted for only 3% of the explained variance in past-year GP visits. Other socio-demographic variables explained a significant but relatively smaller amount of the differences between the sexes' health attitudes and health-care. Analyses were conducted to see whether there were any main or interaction effects of sex, marital and employment status on various health-related outcomes. Significant main effects were found for marital and employment status on the use of social networks. Married, employed adults had larger social networks than unmarried, unemployed adults. Marital status had no impact on the number of illness-disability days, but significant main effects of sex and employment were found for total disability days. In comparison with men, women had a higher number of cutback days.
To summarise Kandrack et al.'s ( 1 99 1 ) findings: although no important sex differences in physical-need status were found, women had a greater number of cutback days and consumed more GP services than did men.
Bemard, Hayward, Rosevear and McMahon, 1993. Bernard et al. monitored admissions to
a metropolitan hospital in the midwestern United States to determine the extent of sex differences in length of hospital stays, use of intensive care and ancillary services expenditure (e.g., radiology, nuclear medicine, respiratory supplies). Data comprised medical records of 1 9,387 patients admitted and discharged over a three year period. Significant sex differences were found: On average women were hospitalized almost one quarter of a day longer than
men, and women were also less likely to be placed in intensive care. Costs of ancillary care
were greater for men primarily because of their greater use of intensive care. Women's less frequent use of intensive care could not be attributed to their having different diagnoses because comparisons were based on similarity of diagnosis. However, Bernard et al. suggested that differences in illness severity may have accounted for men's more frequent use of intensive care. While the data did not permit an exploration of this possibility, research suggests that men's chronic illnesses, on average, are of greater severity than women's (e.g., Verbrugge, 1 989). Women's slightly longer hospital stays were attributed to their being less likely than men to receive care from their families when they returned home. Bernard et al. explained the findings in terms of women's fixed role obligations in which they are more likely to give than to receive home-care. The finding that married men had the shortest hospital stays of all groups concurs with the fixed-role-obligation explanation for women spending, on average, one-quarter of a day longer in hospital. Findings by Bernard et al. ( 1 993) and Verbrugge (1 989) parallel American survey research data which has shown that women comprise 60% of GP-visits and men, 60% of hospital admissions (The American National Center for Health Statistics, 1 992).
Summary: Sex. Two main trends have emerged in the research. First, women utilize a greater range of primary medical care services even after obstetrics care has been removed from the comparisons. Some research has shown that, once important health predictors are held constant, sex differences in health become smaller, and some patterns reverse to show that men pay more curative GP visits than do women. Other research suggests that health-care differences remain even when physical-need status is held constant. Second, findings show that men use more hospital-care resources than women.
3.3.2 Ethnicity
Questions regarding whether and how much ethnic differences exist in the use of health-care have been explored in several studies. Andersen ( 1 968) found no ethnic differences in the health-care patterns of black and white Americans. However, in a later study, Andersen et al. ( 1 975) found that while black Americans reported fewer and less severe illness symptoms than white Americans, their GP visits were prompted by more serious illnesses. The more indepth research on ethnicity and health-care is now considered.
Wolinsky et al., 1989. These researchers evaluated the health status and health-care of older adults from five ethnic groupS5. Data were pooled from several annual Health Interview Surveys to provide sufficiently large samples and a disproportionate sampling method was used in order to obtain approximately 1 ,000 cases "representative of each subpopulation" (p.
4 1 9). The sampling method yielded the following sample composition: 877 Puerto-Ricans;
1 003 Cubans; 1 ,026 Mexican-Americans; 1 , 1 28 black Americans and 93,49 1 white
Americans6. Wolinsky et al. found numerous ethnic differences across different types of GP
and hospital care. White Americans' physician use was less dependent on medical need in comparison with the other ethnic sub-populations. Among the minorities, need explained 2.6 to 3.5 times more of the variance in physician contact than it did for white Americans. The overall model worked best for the non-white ethnic groups' GP care, suggesting a relatively
greater predictability of GP utilization among minority older than white older. For example, the model accounted for 32% of the variance in Puerto-Ricans' and 1 5% in white Americans' GP care. On the other hand, the model worked best in explaining white Americans' hospital care; their need and enabling variables accounted for substantially more of the explained
5 Although groups were designated by their country of origin, they were American citizens.
6Wolinsky explained that "because our focus is on the comparison of parameter estimates across ethnic subpopulations, the data are not differentially weighted" (p.420).
variance than that of the other ethnic groups. Findings supported Andersen's ( 1 968) proposition that hospital care was largely a need-based service for all ethnic groups except Cubans whose hospital care was more dependent on predisposition.
Several interactive effects (i.e., between-sub-population differences in regression coefficient sizes) of ethnicity on health-care were found. Firstly, the only ethnic group whose GP visits significantly increased with age were black Americans, indicating a greater severity of illnesses among black older than among the other subgroups. Secondly, Puerto-Ricans and Mexican-Americans were more likely than the other three subgroups to visit the doctor in