Equitable access to health care: Methodological extensions to the analysis of physician utilization in Canada

HEALTH ECONOMICS, VOL. 2: 87-101 (1993)

ECONOMETRICS AND HEALTH ECONOMICS

EQUITABLE ACCESS TO HEALTH CARE: METHODOLOGICAL EXTENSIONS TO THE

ANALYSIS OF PHYSICIAN UTILIZATION IN CANADA

STEPHEN BIRCH JOHN EYLESIs3

K. BRUCE NEWBOLD3 Centre for Health Economics and Policy Analysis

Department of Clinical Epidemiology and Biostatistics Department of Geography, McMaster University, Hamilton, Canada.

SUMMARY

In this paper we analyse the distribution of family physician use in Canada to explore whether the stated goal of reasonable access to care has been achieved. We test hypotheses to see whether (a) variations in incidence and quantity of use are independent of need for care as proxied by self-assessed health status and (b) any observed relationship between variations in use and need is independent of other population characteristics.

Previous research has conceptual, statistical and data limitations which bring into question the validity of the findings. These limitations are addressed by using more appropriate data, a conditional model for service utilization and correction for self-selectivity of users in the statistical analysis.

Variations in need are identified as important and significant in explaining variations in both incidence and quantity of use with the estimated relationship between use and need being positive. Other population characteristics were found to be important and significant in explaining variations in use although household income is not among them. The relationship between use and need is associated with other variables including education, social support and region of residence.

These findings suggest that analyses of utilization based on simple multivariate techniques and aggregate data can produce a picture of utilization that conceals important, policy relevant relationships while revealing other relationships that are essentially artifacts of inappropriate aggregation in ways which provide a false sense of achievement.

KEY WORDS-Equity, physician services, Canada, utilization.

‘The primary objective of Canadian health policy is to protect, promote and restore the physical and mental well-being of residents of Canada and to facilitate reasonable access to health services without financial or other barriers’

Canada Health Act 1984

The Canada Health Act (CHA) consolidated existing Canadian legislation covering the provi-

sion and funding of hospital-based and physician services as well as providing fiscal penalties to provinces failing to abide by the legislation. Although the stated objective covers both ends (the protection, promotion and restoration of well-being) and means to those ends (the removal of barriers to facilitate access to services), no definitions are provided for the terms ‘reasonable’

Address for correspondence: Stephen Birch, Centre for Health Economics and Policy Analysis, McMaster University, 1200 Main Street West, Hamilton, Ontario, L8N 325, Canada.

1057-9230/93/02OO87- 15%12.50 0 1993 by John Wiley & Sons, Ltd.

Received 14 August 1992 Accepted 2 February 1993

88 S. BIRCH, J . EYLES AND K . B. NEWBOLD

and ‘access’, and hence no direction is given as to how scarce health-care resources are to be allocated in the absence of price mechanisms.

Aday and Andersen’ define access in terms of utilization of services by designated populations for whom they are intended. In the context of the CHA this implies that use of services is not to be determined by willingness and ability to pay, but should respond to the need for those services as indicated by the expected impact of service provision on protecting, promoting and restoring well- being. Indeed the Federal Government2 has stated more recently that the principle of reasonable access is to be applied to ‘...necessary hospital and physician services . . .’ (p. 19, emphasis added) indicating that services should be shared out (or rationed) on the basis of ex ante expectations of benefitting from service utilization (i.e., equal use for equal need).

In terms of judging whether a province provides reasonable access, however, the presence of charges to users of services has tended to be interpreted as an infringement of reasonable access. The lack of attention to other non-price factors that might be expected to influence the demand for and/or the supply of care appears to imply that service provision free at the point of delivery is a sufficient condition for ‘reasonable access’ to services. Indeed concerns of health policy makers at the Federal level have moved on to other issues as if the challenge of reasonable access has been met. But, under the systems of health-care funding which have historically predominated in Canada (e.g., fee-for-service, open-ended funding) there are few mechanisms in place to promote such resource allocation. Even where policies have been introduced to limit total expenditures on programmes (e.g., global budgets for hospitals and caps on physician billings), the design of the policies has paid little, if any, attention to the distribution of needs for health care within populations. Hence there is no a priori reason why the systems should be operating in a way consistent with the equitable and efficient provision of health-care (as defined or implied by the legislation).

In terms of the likely impact of the CHA on access to services, it is important to recognize that the Act ‘inherited’ a distribution of providers from a period when the price mechanism influ- enced accessibility to services. As a consequence, by ensuring payment of providers for insured services from the public purse, the Act tended to

reinforce existing service distributions or oppor- tunities to use service^.^ Although, in principle, providers are paid for providing services only where such services are needed, there is no mechanism to ensure or promote the sharing of services between competing needs on the basis of levels of need (i.e., equal access for equal need). Similarly, at the level of the individual, oppor- tunities to use services are affected by not only the price at the point of delivery, but the opportunity cost to the individual of going to, remaining at and returning from the point of delivery. In other words, the policies emerging from the CHA appear to have less to do with promoting equal access to services for equal need for those services and are more compatible with promoting equal access to services across populations heterogeneous in ability to pay for services. The purpose of this research is to consider whether reasonabe access, interpreted as service utilization in accordance with need for services, has been achieved in the context of family physician utilization among the Canadian population (we report elsewhere on corresponding analysis for other health care services ).

CORRELATES OF HEALTH-CARE UTILIZATION IN CANADA: EXISTING

RESEARCH

The Canadian literature on the utilization of health care across population groups in many ways reflects the philosophy and concerns of the legislation underlying the Medicare programme. Attention has tended to focus specifically on the relationships between price of care and utilization and income levels and utilization as opposed to a broad analysis of the determinants of utilization.6-”

Indeed little attention has been given to the role of needs for health care in explaining variations in the use of health-care services. But as Manga16 notes

‘If there is in fact a negative relationship between the need for medical care and income class . . . then a situ- ation in which there is no statistical difference in the utilization of medical care by income class may still be inequitable’ (p 640)

Indeed, if we choose to pursue equal access to services for equal need for services, this might

EQUITABLE ACCESS TO HEALTH CARE 89

require unequal access to services among income groups, i.e., discrimination in favour of poorer groups because of the greater incidence of treat- able conditions. I7

The importance of looking beyond use-income relationshi s is highlighted by an analysis of responses to the General Health Questionnaire. l9 The relative risk of having visited a physician in the last year is shown to be independent of income, which contrasts markedly with the observed positive relationship between income and probability of visiting a dentist, the cost of which is not generally covered by public- funding. But the relative risk of reporting experience of an activity-limiting health problem decreases with income, suggesting that the distribution of care may be determined, at least in part, by factors other than needs for care.

Considerable efforts have been made in the literature to model utilization behaviour in ways which recognize explicitly the influence of need. 1,20-24 Anderson21 proposed a model of utilization in which the explanatory variables were categorized into three groups; variables which predispose towards utilization (e.g., family com- position, social structure); those which enable utilization (e.g., income, insurance status); and those which generate the purpose of utilization, i.e., need factors. The same categorization of explanatory variables was used by Andersen and Newman” and Aday and Andersen’ but in the broader context of the existing health-care system. In particular, even if the factors in the Anderson model predicted utilization, the organization and structure of the health-care system may inhibit or prevent utilization occurring. Accessibility, in terms of whether persons in need of care receive it, represents a separate influence on utilization.

Although the Aday and Andersen model has formed the basis for much of the empirical literature on health-care utilization over the last 15 years, several authors have questioned the conceptual basis of the model. 25-29 Rundal126 argues that although the theory relating the predisposi- tion to use services to the actual use of services implies a conditional model, the models estimated have been additive. Similarly Arling2’ argues that

‘... the distinctions that have been drawn between need, enabling, and predisposing conditions have heuristic value but may obfuscate the causal relationships in the model. The hierarchical, additive approach of regressing health service use on these

broad categories of predictor variables tends to obscure the mediating effects of factors such as psychologic distress, ADL, impairment, social support and economic status’ (p. 370)

Some studies of health-care utilization in Cana- dian populations have introduced indicators of proxies for need as an explanatory variable. 7330-32

These studies have tended to focus on estimating the relative importance of need among the set of explanatory variables within the context of a simple additive model.

Several non-Canadian studies have gone beyond looking at the relative importance of different explanatory variables and considered the relationships between use and need more directly. 33-42 These studies point to significant variations in the relationship between use and need across population groups differing in some other explanatory variable (e.g., income). But none of these studies explicitly analyzed the interaction of need with these other explanatory variables. Moreover, both Collins and K l e i x ~ ~ ~ and O’Donnell and P r ~ p p e r ~ ~ show that analyzing variations in use across groups heterogeneous in needs produces biased estimates of use-need relationships if utilization occurs among those reporting no needs. Similarly the studies do not recognize the selectivity-bias inherent in studying samples of users of care (see below).

Ronis and Harrison45 evaluate the use of interaction terms in studies of physician use. They show that the inclusion of interaction terms is unlikely to lead to large increases in explanatory power, particularly where there is substantial measurement error in predictor variables. Fur- thermore the use of interaction terms increases significance occurring by chance alone (i.e., inflates the alpha level), reduces the degrees of freedom leading to restrictions in the sensitivity of significance tests and can be confounded with non-linear effects. Nevertheless, these concerns should not discourage analysts from including interaction terms where a priori reasons exist for such effects since they can help in understanding relationships. In other words, interaction terms should be used sparingly and carefully and not without an underlying rationale. Where interactions between explanatory variables have been included in utilization models, significant effects were found. 29i46-49 In particular both Puffer48 and Winter49 found that specific interactions between need and socioeconomic status were sig-

90 S. BIRCH, J . EYLES AND K. B. NEWBOLD

nificant in explaining observed variations in use and improved the overall performance of the model. The purpose of this research is to explore the relationship between use and need in the context of family physician service delivery in Canada. In particular the analysis extends the existing work to consider factors which affect the use of health-care services in relation to needs.

METHODOLOGY

The primary research question to be addressed is whether the distribution of family physician service utilization among the Canadian population corresponds to the distribution of needs for health care. The first stage of the analysis considers the importance of variations in self-assessed health status (or indicators of need) in explaining the distribution of the incidence and quantity of service utilization. The results will indicate whether the findings of existing research are robust to the use of different data sets and alternative, more independent, indicators of need and correction for self-selectivity in the analysis of users of care.

The second stage of the analysis considers which factors are associated with the use of services in the presence of self-assessed needs for care. Exploration of these relationships will enable us to consider, among other things, the extent to which

services are being utilized as a substitute for other (i.e. non-health care) needs. For example, if within groups homogeneous in need, use is greater among groups reporting few social relationships this might indicate that family physician services are being substituted for inadequate social support facilities, the different provincial approaches to the implementation and funding of the provisions of the Canada Health Act are successful in allocating health-care resources in accordance with healthcare needs, the primary care services are able to meet the self-assessed needs of all income and education groups to the same degree.

The following null hypotheses are tested;

The probability and level of utilization of family physician services in a fixed time period are independent of self-assessed health status.

2. The relationship between level of utilization of family physician services and self-assessed health status is independent of income, education, province or region of residence and social support.

Rejection of the first hypothesis would indicate that the use of family physician services is related to population self-assessments of health-care needs. Rejection of the second hypothesis would indicate that although use might be related to need, the quantity of use given need is confounded by variables such as income, education, the province or region of residence, social support and satisfaction with health.

The hypotheses are tested using data from the 1985 General Social Survey (GSS).l9 This was a national survey of the non-institutionalized population age 15 years or older which collected data on individuals’ reported heaIth-care utilization, their self-assessed health status and well-being, various sociodemographic and socioeconomic variables and other bebavioural characteristics. Personal interviews were used to collect data for those persons in the sample age 65 or over at the time of the interview. For those persons in the sample age 15 or over but less than 65, telephone interviews were used. The sample was randomly selected and the total sample size was over 13,000 (approximately one per two thou- sand population). The overall response rate was 84.2 percent. The main exclusions from the sample were the Yukon and North West Terri- tories (both samples) and persons living on Indian Reserves (the elderly sample). The elderly sample was based on households previously surveyed for the Labour Force Survey. The non-elderly sample was allocated between provinces on the basis of the square root of the provincial populations.

It should be noted that the data on both use and need are based on self-reports and, like all such data, depend upon the recall and awareness of respondents. Although the validity of the data may be affected, the GSS provides information on needs independent of service use. Consequently it is one of the few data sources available for use in addressing our research questions.

The first hypothesis is tested using two analyt- ical approaches. Firstly, the incidence of use of family physician services is estimated using probit regression’’ in respect to the dichotomous nature of the dependent variable (0,l) and the assumption that the incidence of use is non-linear. When


the dependent variable can take on only two values, the assumptions of ordinary least squares regression (OLS) are violated. For example, one is no longer able to assume that the distribution of errors is normal. The probit model is defined by

Pu = F(G + PXi) where P is the probability of the event occurring, F( .) is the standard normal distribution function, Xi is an independent variable and (11 and /3 are estimated coefficients. Following Aday and Anderson, ’ explanatory variables are entered in blocks into an equation for use of services according to the categorization as ‘need’, ‘predisposing’ or ‘enabling’ in the utilization model (see Appendix). Because most of the variables included within the model are categorical, bivariate or multivariate representations are created. Variables are entered in an additive fashion and are forced into the equation based on a priori expectations concerning the relationship between use and the particular explanatory variable. Two exceptions to this are ‘activity level’ and ‘housing tenure’ which are allowed to enter the model if significant.

Coefficients are estimated using the maximum likelihood (ML) method of estimation, which provides consistent and unbiased estimates. As in OLS, the t-test determines whether a particular parameter differs from zero. Goodness-of-fit of the model is evaluated two ways. Firstly, the likelihood-ratio (LR) test (similar to an F-test in OLS) measures the overall significance of the model. The second measure, rho-squared, fulfills much the same role as R-squared in OLS with higher values indicating a greater leveI of explanatory power for the estimated equation. However, unlike R-squared, it is not a measure of the percentage variation explained by the model.

The second stage focuses attention on the subset of the sample recording some use of family physician services in the previous year and estimates an equation for quantity of use measured by the number of contacts with family physicians about one’s own health.

One approach to dealing with the analysis of data involving counts of events is to use a Poisson regression. Since counts must be non-negative integers, a discrete distribution such as the Poisson distribution provides a more appropriate basis for analysis than the normal distribution underlying OLS regression. 51

However, the dependent variable is a count of

independent events generated by a Poisson distribution. Hence where the occurrence of one event increases the probability of others, as might be the case in health-care utilization where the number of visits is not necessarily independent of supply factors, the use of Poisson regression is not appropriate. In situations where the independ- ence assumption is violated, the negative binomial model provides a close approximation to the Poisson model. ’* However use of the negative binomial mode1 does not allow for self-selectivity bias within the sample; if the process by which individuals ‘select’ themselves to be users is not fully known to the investigator, the estimate of the relationship, may be biased. Keeler et a/.” dealt with this problem by accounting for self- selectivity within the sampling frame of their study. Because the current study includes the secondary analysis of previously collected data, such an approach is not possible and correction for selectivity within the analysis is required, rendering the negative binomial model inappropriate. Instead, the problem is rectified using a two-stage estimation technique following H e ~ k m a n ’ ~ and Maddala. ” A correction factor, lambda, is estimated using probit analysis and then entered into a least-squares regression of number of contacts on need, predisposing and enabling variables. The significance of the lambda variable is an indicator of whether the correction was statistically important.

The second hypothesis is tested in two ways. Firstly, the sample is partitioned according to the level of need (as measured by the self-assessed general health question) and the two-stage estimation for quantity of use described above is repeated for each level of need. As mentioned above, analyzing the distribution of health-care utilization across need groups for all persons reporting some service use can produce biased estimates if utilization occurs among individuals reporting no health problems (i.e., it fails to allow for use unrelated to existing health problems, e.g., preventive services). Where explanatory variables are found to be significant in explaining variations in use within subsamples homogeneous in need this indicates that need is compromised in some way as the factor for allocating health care. To explore this relationship further, and in particular to use the variation in the need variable as part of the analysis, quantity of use is re-estimated for the full sample of users with the addition of interaction terms for need with each of the other

92 S. BIRCH, J. EYLES AND K . B. NEWBOLD

explanatory variables found to be significant in the within-need estimations.

RESULTS

Higher levels of self-assessed health are associated with lower probabilities of having contacted a family physician in the last year as compared to those reporting their health as poor. The associ-

Incidence of use of family physician ations are significant ( p < 0.05, two-tailed test) for self-assessments ‘excellent’ and ‘good’.

Table 1 records the results of the probit regression of uselno use on the explanatory variables.

Females are significantly more likely to have used a family physician than males and there is a

Table 1. Probit regression: incidence of family physician contacts

Explanatory variables B CI

General health

Sex

Age

Marital status

Region of residence

Employment status

Community contact

Smoking status

Drinking status

Household income

Education status

Constant

LR-test Rho-squared N Overall Vo correct

Excellent Good Fair

Male

15-19 20-24 25-44 45-64 65-74 Married Single Widow

Quebec Ontario Prairie British Columbia

Working Sick Looking

Frequent Infrequent

Daily Occasional

Daily Occasional

No ssgd (1) Ssgd (1) Some post (1)

- 0.684* - 0.500*

- 0.372* - 0.366*

- 0.235

-0.172 - 0.213* -0.152 - 0.086 - 0.055 -0.117 0.023

0.151* 0.082 0.279*

0.665* 0.175

- 0.098

- 0.031

- 0.412 - 0.403 - 0.041 - 0.048 - 0.066 -0.003

1.8E - 06 - 0.019 0.015 0.015 2.123* 332.6* 0.046

6805 77.6

(- 0.93, - 0.43) (- 0.75, - 0.25)

(- 0.49,0.02) (- 0.45, - 0.30) (-0.63, -0.10) (-0.41,0.06)

(- 0.42, - 0.00) (- 0.36,0.05) (- 0.30,0.13) (- 0.22,o. 11) (- 0.30,0.07) (- 0.21,0.26) (- 0.24,0.04)

(- 0.07,0.23) (0.01,0.29)

(0.11,0.45)

(0.24,1.09) (- 0.12,0.06)

(- 0.01,0.36) (-1.40,0.57) (-1.39,O.S) (- 0.12,0.03) (-0.21,O.ll) (-0.19,0.06) (- 0.08,0.07)

(- 2.8E - 7,3.8E - 6) (- 0.12,0.08) (-0.09,0.12) (- 0.09,O. 12) (1.09,3.16)

* p < 0.05. ( 1 ) Ssgd = Secondary school graduation diploma. Some post = some post secondary school education but no degree.


general association of increasing probability of use with age group. Significantly greater probabilities of having contacted a family physician are also found for residents of Ontario and British Columbia, the two provinces with the highest physician to population ratios in the country,56 as compared to residents of the Atlantic provinces, and those unemployed and sick (as compared to those unemployed and not looking for work). None of the other explanatory variables is significant. In particular, there is no significant relationship between household income and the probability of having used a family physician supporting the findings of previous Canadian studies. 31

The overall explanatory power of the estimated equation is low as measured by the rho-square value but this is not unusual for cross-section studies of health care utilization. ” In addition, the low explanatory power of the present model can be attributed to the fact that over eighty per cent of the sample reported using a family physician during the last year. This imbalance in the sample adversely affects the ability of the model to predict uselnon-use. The cumulative rho- square increased from 0.013 for the need variables entered alone, to 0.015 following introduction of the enabling variables, to 0.046 when the predisposing variables were entered.

Quantity of use of family physicians Table 2 records the results of the two-stage esti-

mation for the number of contacts with a family physician in the last year. As with the incidence of use, increasing levels of self-assessed health are associated with significantly fewer contacts. Significantly more contacts are observed for increasing age groups, for females and for residents of Ontario and British Columbia. As was the case for incidence of use, the age group 20-24 is an exception in that although fewer contacts are estimated as compared to age group > 75, the difference is not significant. This was also observed by Broyles et al. 31 who suggested that it might be explained by the increased use of physicians for maternity and family planning services among females in this age group.

Significantly fewer contacts by those working and significantly more by those not working and sick are probably explained by selection effects that are not controlled for in the general health variable (i.e., the healthy worker effect). The onIy other significant association is the positive rela-

tionship between frequency of contacts with friends and relatives and the number of physician contacts. Although there is a considerable literature on the positive impact of social contacts and support on health status, ’ 8 - 5 9 this finding suggests that there is no evidence from the analysis of this data set that any such relationship, if it does exist, is reflected in lower levels of utilization of family physician services. On the contrary, the estimated relationship suggests that community contact might generate increases in levels of utilization for any given level of self-assessed health in addition to any positive impact that it might have on health status. This association was also found in a study of physician utilization by seniors in USA.29

As with the equation for incidence of use, neither household income, nor education nor marital status was found to be significantly associated with the quantity of use. The overall explanatory power of the estimated equation, as measured by the R-square value was 0.19 which is fairly high compared with other studies of utilization. 29 The correction for self-selectivity was found to be significant (lambda = 15.455 p < 0.05) with smoking, drinking and marital status becoming non-significant compared to a one stage multiple regression model.

Quantity of use within need levels Table 3 records the results of the two-stage esti-

mations performed separately on the subgroups with excellent, good, fair and poor health respect- ively. In each case the correction for selectivity was significant. Household income and marital status are not significant in explaining variations in use in any of the four equations. Education is not significant in the equations for good, fair and poor health but among those individuals reporting their health as excellent, significantly fewer contacts are reported by those with lower levels of education. Given that this association is observed only among those in excellent health this might be explained by a greater propensity of higher educated groups to use physicians for preventive purposes. Greater use by females is observed for all health levels except those with poor health for which sex is not significant. Those working are found to have significantly fewer contacts (good and fair health levels) and those not working and sick are found to have more contacts (fair health only) which are consistent with the heaIthy worker effect. Among those with good health, the greater

94 S. BIRCH, J. EYLES AND K. B. NEWBOLD

Table 2. Two-stage estimation: quantity of family physician contacts


General health

Sex

Age

Marital status

Region of residence

Employment status

Community contact

Smoking status

Drinking status

Household income Education status

Lambda R-squared F N

Excellent - 9.607* Good - 7.587* Fair - 4.398* Male - 2.973* 15-19 -4.411'

25-44 - 2.125* 45-64 - 1.926*

20-24 -1.471

65-74 -1.205 Married - 0.433 Single -0.714 Widow 0.486

Ontario 1.378* Prairies 1.195 British Columbia 2.215. Working -1.156* Sick 3.034* Looking 1.072 Frequent 8.965* Infrequent 8.956*

Quebec -1.159

Daily - 0.663 Occasional - 0.438 Daily - 0.274 Occasional 0.233

3.57E - 06

No ssgd - 0.061 Ssgd - 0.210 Some post - 0.043

15.455* 0. I9 44.367*

5505

(-1 1.66, - 7.56)

(- 6.21, - 2.58) (-3.96,-1.98) (- 6.99, -1.83)

(- 331,056) (- 3.89, - 0.36) (- 3.61, -0.24)

(- 2.90.0.49) (-1.92.1.05) (-2.45,1.02) (-1.48,2.45) (- 2.56,0.24)

(- 0.27,2.67)

(- 9.44, - 5.74)

(0.01,2.74)

(0.55,3.88)

(0.72.5.34)

(6.09,11.84) (6.08.1 1.83)

(- 2.03, - 0.28)

(- 0.63,2.77)

(-1.42,O.lO) (-2.01.1.13) (-1.72,1.17) (-0.74,1.21)

(-6E - 6,1.3E - 5) (-1.02,0.90) (-1.21,0.79) (-1.07,0.98) (1 0.93,19.98)

* p c 0.05.

numbers of physician contacts are associated with increasing age, increasing frequency of contact with friends and relatives and reduced frequency of smoking.

Of particular note is the absence of significant associations between use and any of the predisposing or enabling variables entered into the equation in the subgroup reporting poor health. This might be explained by the smaller sample size of

the poor health subgroup. However an alternative explanation is that the predisposing and enabling variables represent opportunity costs of service utilization. Although the opportunity costs therefore differ within populations homogeneous in need, for those in greater need, the value of the benefit of service utilization is so great (relative to other subgroups) that utilization is not affected. This raises the question of whether society should

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96 S. BIRCH, J. EYLES AND K. B. NEWBOLD

be concerned with differences in opportunity costs of service utilization if the socially-desirable outcome (i.e., equal use for equal need) is Household income was found to be non- obtained. significant in explaining variations in both the

Quantity of use with interaction effects

Table 4. Two-stage estimation: quantity of family physician contacts with need- income interactions -


General health

Sex

Age

Marital status

Region of residence

Employment status

Community contact

Smoking status

Drinking status

Household income

Education status

Need-income int.

Lambda

R-squared F N

Excellent Good Fair

Male

15-19 20-24 25-44 45-64 65-74

Married Single Widow

Quebec Ontario Prairie British Columbia

Working Sick Looking

Frequent Infrequent

Daily Occasional

Daily Occasional

No Ssgd Ssgd Some post Exc-income Good-income Fair-income

- 10.507* - 7.295*

- 3.207*

- 4.450*

-2.138* -1.934*

-2.313

-1.476

-1.239

- 0.430 - 0.749

0.471

-1.277 1.499 1.242 2.448*

-1.179* 3.173* 1.195

8.183* 8.166*

- 0.668 - 0.501

- 0.208 0.339

2.7E - 05

-0.100 - 0.204

0.026

2.5E - 06 - 2.7E - 05 - 9.2E - 05

17.140*

0.193 40.979*

5505

(-13.87, -7.15) (-10.27, -4.32) (- 5.38, 0.76)

(-4.43, -1.98)

(-7.33, -1.57) (- 3.72,0.77)

(- 4.09, - 0.18) (- 3.80, - 0.07)

(- 3.10.0.63)

(-2.07,1.21) (- 2.67,l. 17) (-1.69,2.63)

(- 2.84,0.28) (-0.02,3.01) (- 0.38,2.86)

(0.58,4.32)

(0.57,5.77)

(4.51,11.86) (4.48,11.85)

(-2.15,-0.20)

(- 0.69,3.08)

(-1.53,O. 16) (- 2.23,1.23)

(- 1.80,l. 39) (-0.75,1.42)

(-6.4E-5,1.2E-4)

(-1.16,0.96) (-1.30,0.90) (- 1 . 1 1 , 1 . 1 6)

(- 9.2E - 5,9.7E - 5 ) (-1.2E - 4,6.7E - 5 ) (-2.OE-4,1.3E-5)

(10.94,23.34)

* p < 0.05.


incidence and quantity of use in all previous stages of the analysis. This finding was reinforced by non-significant coefficients on interaction terms for household income and levels of general health in an estimated (two-stage) equation for number of contacts (Table 4). Separate estimations were performed including interaction terms for levels of general health and each of the three variables found to be significantly related to utilization in the within-need group analysis but which were not generally found to be significant in the analysis of the full sample, i.e., region of residence, education and community contact. Table 5 records the estimated coefficients of each of the interaction terms only. Although the size of

some of the coemcients on the non-interaction variables differed between the separate estimates, the general pattern of the findings and statistical significance of particular coefficients were common to each of the estimates. The results show significant coefficients for interactions of British Columbia and each health level, secondary school graduation and both excellent and good health, and some community contact with both good and fair health (Table 5 ) . Each of these interactions is compared to the reference level for each of the variables in the interaction term. In other words the significant negative coemcient on frequent community contact and good health implies that compared to individuals with no com-

Table 5. Estimated coefficients of interaction terms for quantity of family physician contacts

Need-region

Interaction B

Need-education Nossgd-excellent - 3.826

Somepost-excellent - 2.192 Nossgd-good - 2.304 Ssgd-good - 8.689* Somepost-good -2.945 Nossgd-fair - 0.240 Ssgd-fair - 5.062 Somepost-fair -1.571 Quebec-excellent -4.871 Ontario-excellent - 4.897 Prairie-excellent -4.612

Ssgd-excellent - 9.006*

BC-excellen t - 9.814. Quebec-good - 3.856 Ontario-good - 5.697 Prairie-good -4.180 BC-good -10.518* Quebec-fair - 2.442 Ontario-fair -4.812 Prairie-fair - 4.143 BC-fair - 9.409*

Need-community contact Freq.-excellent -5.130 1nfreq.-excellent - 6.721 Freq.-good -14.108* 1nfreq.-good -15.182*

1nfreq.-fair -13.597* N 5505

Freq.-fair -1.4E +01*

CI

(- 7.92,0.27) (-17.13, -0.89)

(-7.81,3.43) (-6.27.1.66)

(-16.75, - 0.63) (- 8.53,2.64) (-4.75.4.27)

(- 1 3.67,3.55) (- 7.72,4.57) (-11.91,2.17) (-1 1.13,1.34) (-1 1.79,2.56)

(-18.15, -1.47) (- 10.56,2.85) (-1 1.66,0.26) (-1 1.03,2.67)

(-18.61, -2.42) (- 9.68,4.80) (-1 1.36,1.73) (- 1 1.7 1,3.42)

(-18.12, -0.70) (- 23.79,13.53) (-25.33,ll.M)

(- 25.47, - 2.74) (- 26.64, - 3.73) (- 24.89, - 2.88) (- 24.66, - 2.53)

* p < 0.05.

98 S. BIRCH, J . EYLES AND K. B. NEWBOLD

munity contact and poor health, they record more physician contacts. In order to compare within need groups coefficients of interaction terms with the same need level must be compared. In particular, the interactions between good health and frequent community contact, and good health and infrequent community contacts are both significantly different to the coefficient for good health and no community contact. In order to test for the effect of frequent as opposed to infrequent social contacts the two estimated coefficients are compared using an F test. Following this general approach, within the good health category B.C. residents had significantly fewer physician contacts than Ontario residents, secondary school graduates had fewer physician contacts than non graduates from secondary school, but the number of contacts by those with frequent community contacts was not different to the number recorded by those with infrequent community contacts.

DISCUSSION

The purpose of this analysis was to test for the presence of systematic relationships between factors other than need for health care (as proxied by self-assessed health status) and the incidence and quantity of family physician utilization. Because we were not primarily concerned with predicting future utilization, the low levels of explanatory power, common for studies of this type, are not of major concern.

The main findings of our analysis are: 1.

2.

At the aggregate level the distribution of utilization of services is skewed towards those reporting greater levels of need. Those in greatest need (i.e., poor self-assessed health) represent 5 percent of the sample but use 32 percent of the reported family physician contacts. After controlling for other (predisposing and enabling) explanatory variables, the relationship between level of need and the incidence and quantity of family physician contacts remains positive and significant. Although this is compatible with the notion of vertical equity (unequal use for unequal need), significant relationships were also identified between utilization and other non-need variables. However household income was not one of those variables. At the disaggregate level (i.e., within need

groups) the quantity of family physician contacts was found to be independent of household income. Although this is compatible with the notion of horizontal equity (equal use for equal need), several non-need variables were found to be related systematically and significantly to utilization although the particular relationships differed across the need groups. These findings were supported by the estimated coefficients of interaction terms in the aggregate-level analysis.

Particular findings worthy of further attention are:

i)

ii)

iii)

the level of community contact appears to be positively associated with the quantity of use, although the extent of the difference appears to be associated with the level of need. the level of education appears to be positively associated with use, particularly among those with lower levels of need. noticeable and, in some cases, significant regional patterns of utilization are observed. The supply of services, or service providers, can at best explain only part of this regional pattern.

Although we are unable to analyze the efficiency of utilization in terms of the appro- priateness of services received (i.e., necessary versus non-necessary services) this does not detract from our findings. For example, if the additional services received by the better educated are non-necessary, they would not be expected to have a positive impact on the health of the less educated even if access were to be more ‘reasonable’. Notwithstanding this, they still represent an opportunity cost to the population in terms of forgone (needed) services to the population, irrespective of their level of education.

Our findings concerning the importance of need and lack of importance of income in explaining variations in utilization are qualitatively the same as produced in previous studies. 31,32 However the methodological developments introduced into this analysis have been shown to be significant, in a statistical sense, which suggests that the quantitative estimates of relationships produced in previous studies may be biased estimates of the true quantitative relationships.

In addition, our analysis has gone on to explore whether the observed reIationship between use


and need is independent of the level of other socioeconomic and sociodemographic variables. Our findings suggest that analyses of utilization using simple multivariate techniques and aggregate data can produce a picture of utilization that conceals important and policy-relevant relationships while revealing other relationships that are essentially artifacts of inappropriate aggregation in ways which provide a false sense of achievement in terms of allocating resources in line with needs for care. In particular, populations of users of care represent neither a random selection of the total population, nor a homogeneous group of service users. Our analysis of subgroups of users classified according to their level of need, and our use of interactions between need and other variables to explain variations in utilization, indicate that users of care are a mixture of essentially heterogeneous groups. Within these groups, utilization appears to vary in ways indicative of the existence of barriers to ‘reasonable access’ to services, but ways which differ between the groups.

Policy-makers might take comfort in the apparent success of health-care policy in so far as household income is not associated in any systematic or significant way with utilization. However the identification of other systematic and significant relationships involving non-need factors points to the possible inadequacies of both Federal and Provincial policies (i.e., federal trans- fers conditional upon the absence of user charges and provincial policies to prohibit such charges for insured services) as methods of achieving the broader goal of ‘reasonable access’.

We conclude, therefore, that issues of equity and efficiency of health-care delivery can only be addressed in ways which are policy-informing by considering variations in utilization among subgroups defined according to, and hence homogeneous in, the policy variable of interest, in this case need. While the findings produced in this analysis are limited by issues of sample size, they point towards non-income related barriers to reasonable access such as education, social support and region of residence.

ACKNOWLEDGEMENTS

Funding for this research was provided by a grant from the National Health and Research Development Program of Health and Welfare Canada. Stephen Birch

is funded by a research scholar award under the same program and the Rose Levy Rosenstadt award of the Faculty of Health Sciences, McMaster University. We would like to thank Jerry Hurley, Bernie O’Brien, Greg Stoddart and Sue Elliott for the help and assistance at various stages of the research and Lori Houghton for her skills and patience in deciphering our hand written manuscripts.

APPENDIX: MEAN AND STANDARD DEVIATIONS OF VARIABLES SELECTED

FOR ANALYSIS

Name Reference’ Mean S.D.

Use FP contacts (# times)

General health Need

Predisposing Sex Age Marital status

Region Employment Activity level Community contact Smoking Drinking

Enabling Household income Education

Housing tenure

Poor

Female 2 75 Widowed/ Divorced Atlantic Unemployed Active Not at all Not at all Not a1 all

- University degree Rented

2.79 4.07

1.88 0.77

0.52 0.50 3.39 1.09

1.46 0.82 2.93 1.06 2.09 1 .40 1.96 0.74 1.40 0.49 2.32 0.92 2.07 0.50

32406.0 19084.0

2.37 1.23 0.67 0.47

‘Where variable is categorized for the purposes of the analysis it is represented by a single a-series of dummy variables.

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Documents

Equitable access to health care: Methodological extensions to the analysis of physician utilization in Canada