METHODS

Study design and health measures

This study used data on participants of the Nova Scotia Nutrition Survey, linked with information on subsequent use of health services and vital statistics.^5,9,27 The survey was held in 1990 and consisted of 2198 non-institutionalised residents of the Canadian province of Nova Scotia, aged 18–75 years, from a two stage cluster sample stratified by age, gender, and region. Survey information of 82 participants (3.8%) could not be linked with provincial health care databases and vital statistics and thus were not included in the analyses. The remaining 2116 survey participants were categorised into socioeconomic strata on the basis of their household income (less than $20 000; $20 000 to $39 999; and $40 000 or more). As the question on income was elective, this information was not available for 300 participants (13.6%). Analyses were therefore restricted to the remaining 1816 participants. Information on health care use included the number of family physician services, specialist services, and days in hospital during the eight years afer the interview. During these eight years, 79 (4.4 %) participants migrated out of Nova Scotia while 149 (8.2%) died; their observations, until the date of departure or death, were included in the analyses. The database linkages used in this study were approved by the Health Sciences Human Research Ethics Board of Dalhousie University, Halifax, Nova Scotia, Canada.

Analytical approaches

We compared characteristics and health outcomes of survey participants that were included and excluded from the present analyses using χ², median, and t tests.

We evaluated whether public health care reduces socioeconomic disparities in health in three different ways. Firstly, we described health care use (family physician services, specialist services, and days in hospital) for each of the socioeconomic strata during the eight years of follow up. As the distributions of health services use are highly skewed, we calculated the median number of health services in each stratum. In addition, we dichotomised health services use into low and heavy categories, using the median as a cut off, and calculated age and gender adjusted odds ratios of low compared with heavy health services use across socioeconomic strata using logistic regression.

Secondly, we evaluated the interrelations between health services use, socioeconomic background, and mortality during the eight years of study. We quantified socioeconomic differences in mortality in terms of age adjusted and gender adjusted odds ratios using logistic regression. When these odds ratios are further adjusted for health services use, the new odds ratios will be closer to unity if health services are used comparatively more by people of lower socioeconomic background and with comparatively poor health. In contrast, the adjustment for health services use will result in diverging odds ratios if people of lower socioeconomic background and with poor health use comparatively fewer health services. We present age and gender adjusted odds ratios for mortality by income category and odds ratios further adjusted for use of family physician and specialist services and days in hospital.

In a third analytical approach, we investigated health services use in the last years of life assuming that health in these years is poor regardless of socioeconomic background. For those who died during follow up, we described health services use (family physician services, specialist services, and days in hospital) during the three years preceding death for each of the socioeconomic strata. Here we also dealt with skewed distributions by using median values and calculating odds ratios that characterised the socioeconomic variation in low (below median) and heavy (above median) health services use.

We followed published guidelines²⁸ for the longitudinal analysis of surveys with stratified sampling, which recommend adjusting for all variables used in defining sample weights (age, sex, and region), without incorporating these weights. As region did not substantially affect our estimates of interest, we retained only age and sex as covariates. Data were analysed using S-PLUS 2000 software.

RESULTS

Table 1 presents the characteristics and health outcomes of the 1816 participants with complete information in comparison with the 82 (3.8%) that were excluded because of missing linkable identifiers, and with the 300 (13.6%) that were excluded because of missing information on household income. The 82 for whom data linkage could not be established were substantially younger and from a higher income and better educated background. This clearly represents a young prosperous subgroup including students whose high mobility hampered linkages. The 300 with missing information on income were more likely to be female, non-smokers, and less educated, but did not substantially differ from included participants with respect to health services use.

Figure 1 provides an initial visualisation of the distribution of health services use by income group. Participants who reported a household income of less than $20 000 constituted 31% of the study population and used disproportionately more health services; of all services delivered to this study population, the low income group used 43% of the family physician services, 38% of the specialist services, and 50% of the days in hospital. Participants with a household income of more than $40 000 constituted 29% of the study population and used 21% of the family physician services, 26% of the specialist services, and 13% of the days in hospital. This figure should be interpreted with caution, as differences are not controlled for the effects of age and gender. Table 2 presents the age and gender adjusted odds ratios for health services use by income group. Participants with an income of more than $40 000 were about half as likely (odds ratio =0.51) to be heavy users of family physician services than those with an income of less than $20 000. These differences were statistically significant. Variation in specialist services use across income groups was substantially less; all income groups had comparatively even distributions of heavy versus low users. With respect to hospital use, figures similar to those for family physician use were obtained; participants with higher income had fewer days in hospital than those in the lower income groups. These differences were also statistically significant.

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Figure 1

Distribution of study participants and health services use by income.

Table 3 shows the differences in mortality by income group. The odds ratios adjusted for age and gender indicate lower mortality with increasing income (odds ratios of 0.79 and 0.56 for middle and high income groups respectively). These differences were attenuated by accounting for level of family physician services used (odds ratios of 0.90 and 0.68). Although the differences are not statistically significant, the attenuation suggests that family physician services were used more heavily by individuals with lower income and poorer health. Based on the premise that health services benefit health, these figures also indicate that family physician services reduced income disparities in mortality. Adjusting for specialist services use (odds ratios of 0.70 and 0.33) augmented the variation in mortality, indicating that specialist care did not contribute to a reduction in socioeconomic disparities. Accounting for days in hospital had an attenuating effect on income differences in mortality (odds ratios of 0.82 and 0.66) similar to the effect of family physician services.

Table 4 presents results of analyses similar to table 2 but only for the 149 participants who died during follow up. As would be expected, the level of health services use was much higher in the last years of life than in the general population (comparing median values presented in table 4 versus table 2). There was some variation in level of family physician services and hospital days used by income group, although not statistically significant. In contrast, specialist services use in the last years of life was significantly higher in the highest income group.

DISCUSSION

Lower socioeconomic groups experience poorer health status and have higher health care needs. This study shows that people with lower socioeconomic background and with poor health used comparatively more family physician and hospital services and in such a way as to ameliorate the socioeconomic differences in mortality. In contrast, specialist services were comparatively less used by people with lower socioeconomic background and with poor health; this underuse bears the potential to widen the socioeconomic gap in health.

Ecological comparisons of neighbourhoods within the Canadian cities of Winnipeg and Toronto demonstrated greater use of health services in lower income neighbourhoods relative to more affluent neighbourhoods.^29,30 Similar trends were observed among people participating in the Canadian National Population Health Survey³¹ and in this study. Clearly, the poorer health of lower socioeconomic groups drives their increased use of health services, and universal coverage allows this to happen. To investigate whether this increased use of health services in lower socioeconomic groups is sufficient to meet their greater needs, researchers have quantified health services use while adjusting for socioeconomic differences in health care needs. In this respect, they have used self rated health and self reported health problems as proxies for health care need. Using such proxies, Dunlop et al³² found that people of lower socioeconomic background consulted their family physician more frequently even if their higher needs for health services were controlled for. Specialist services, in contrast, were more frequently reported in higher income groups.³² With respect to hospital care, Newbold et al³³ found no statistical differences among socioeconomic groups when differences in need for health services were controlled for. These findings are in agreement with our observations of health services use in the last three years of life, during which we assumed that need for health services was high regardless of a person’s socioeconomic background. The studies by Dunlop et al and Newbold et al used self reported health services use based on national cross sectional surveys, whereas we conducted longitudinal analysis based on eight years of administrative health services data within the province of Nova Scotia. Also, the referenced studies considered need for health services use that was proxied by self rated health and self reported health problems, whereas we considered mortality during the eight years of follow up as a health outcome. The differing designs and populations of the above studies strengthen the mutual concern that specialist services do not equitably reach lower socioeconomic groups despite the existence of universal coverage. Furthermore, this study adds to the above findings by demonstrating that differential use of specialist services bears the potential to widen the socioeconomic gap in health, whereas family physician and hospital services are demonstrated to ameliorate the socioeconomic differences in mortality.

The increased use of family physician and hospital services in lower socioeconomic groups seems to correspond to their higher need resulting from their poorer health. This mechanism may contribute to the attenuation of the socioeconomic gradient in health. An example; hypertension is more prevalent in lower socioeconomic groups (A M Yip, et al, XVI IEA World Congress of Epidemiology, Montreal, QC, 18–22 August 2002). Family physician practice to control hypertension will reduce the cardiovascular risk burden that will particularly benefit the health of lower socioeconomic groups, thus contributing to the amelioration of the socioeconomic gradient in health. This study also showed that specialist services were comparatively less used in lower socioeconomic groups, while their need is again expected to be higher. This lower use of specialist services may be a result of differential referral to specialist services, as reported previously.^7,29,32,34

The high location rate (75%) and response rate (79%) of the Nova Scotia Nutrition Survey should be considered as an additional strength of this study. However, the exclusion of 3.8% of participants without linkable identifiers and 13.6% without income information should be acknowledged as a limitation affecting the generalisability of the present results. Despite the exclusion of these participants, the sample size of 1816 participants seemed effective to reveal statistically significant associations between income groups with respect to health services (table 2). However, the sample size was insufficient to reveal statistically significant income differences in mortality and the extent to which income differences change when considering health services (table 3). This limitation urges confirmation of these findings by research in countries that provide universal coverage to their residents. Also, contrasting findings in countries with and without universal coverage are indicative of their effectiveness in socioeconomic disparities in health.

In conclusion, the expenditures for health care are enormous and likely to increase further in light of the aging population in most developed countries. The effectiveness of these expenditures in reducing socioeconomic health disparities has received comparatively little attention. As health care coverage is universal and hence the assessment of effectiveness is hampered by the absence of a comparison group, our understanding to date comes primarily from comparisons of the situation before and after the introduction of universal coverage and from comparisons of countries with and without universal coverage. As these comparisons all have limitations, the importance of new approaches to demonstrate effectiveness, such as this study, become apparent. These findings add new knowledge in that universal coverage of family physician and hospital services ameliorates the socioeconomic mortality differential. Specialist services, however, seem to be underused by lower socioeconomic groups and may contribute to widening this socioeconomic gap. Further research into the mechanisms and types of specialist services involved is important to facilitate health policy and targeted interventions aimed at further reduction of socioeconomic disparities in health.