Background
Universal health care systems aim to provide health services based on need. Among many forms of the provision of universal health care systems around the world, the Canadian system is one of the most ambitious with public financing for all physician and hospital services deemed medically necessary with no payment at the point of service. Since the establishment of the Canada Health Act in 1984, which set criteria of public administration, comprehensiveness, universality, portability, and accessibility, supporters of the Canadian health care system have been a strong advocate for equal access for equal need. They have striven to remove financial or other barriers to access to physician and hospital services.
To what extent has the Canadian health care system met this goal of equitable access? In a comprehensive review of the Canadian literature on equity in health care in 1993, Birch and Abelson concluded that studies from 1970s and 1980s examining need and health care use showed an association between greater need and greater use but called for further research carefully examining the role of socioeconomic variables in a relationship between health care need and health care utilization [
1]. Looking at recent studies, the diagnosis is promising on the surface. As one would expect, given the strong association between socioeconomic status and health, most recent Canadian studies have shown that people in lower socioeconomic status use more health care services than their counterparts. In a series of ecological studies using administrative data and the census in Winnipeg, Manitoba, for example, Roos and his colleagues found that individuals from poorer neighbourhood had higher utilization rates of physician and hospital services than those from richer neighbourhood after adjusting for age and sex [
2‐
4]. At the individual level, using the Nova Scotia Nutrition Survey linked to administrative data Veugelers and Yip found that controlling for age and sex people with lower income were more likely to use general practitioner and hospital services than people with higher income, but there was no statistically significant relationship between income and specialist services [
5]. Separate studies using the same data set showed that lower income and education were associated with higher use of physician services after adjusting for age, sex, and region [
6] and higher use of physician and hospital services after adjusting for age, sex, death, and neighbourhood income and education [
7].
Studies adjusting only for age and sex, however, cannot tell whether greater use of health care services by people with lower socioeconomic status is reasonable given their need for health care. Only after controlling for additional need indicators, such as health status, can we assess equal access for equal need. Studies with such adjustment have shown mixed results. Katz, Hofer, and Manning from the 1990 Ontario Health Survey found that lower income was associated with greater use of physician services [
8]. However, Finkelstein, linking the Ontario portion of the 1995 National Population Health Survey (NPHS) to administrative data, and Dunlop, Coyte, and McIsaac in the 1994 NPHS found no statistically significant relationship between income and the use of general practitioner services [
9,
10]. In addition, Finkelstein and Dunlop and her colleagues found no relationship between income and the use of specialist services, although Dunlop and her colleagues found that women with less education used less specialist services [
9,
10].
Differences in results between studies may reflect a variety of factors including the data, statistical modeling strategies, and the need indicators used. Previous studies have varied considerably in the need indicators used beyond age and sex. For example, Katz and his colleagues adjusted for self-assessed health status alone [
8], while Dunlop and her colleagues included additional indicators such as self-reported chronic disease and chronic disease risk factors [
9]. Adjusting for differences in need by including a more comprehensive set of need indicators and allowing the effects of need indicators on health care utilization to vary by age seems logical, but this has not been done. For example, few studies [
9,
11] have adjusted for the presence of specific chronic health conditions. The type of chronic condition associated with ill health is likely to be an important need indicator since the capacity to benefit and the volume and type of services that may be effective varies by condition. Previous studies have also not allowed the effects of need indicators to vary by age.
Differences in study results may also reflect differences in modeling health services use (contact) versus volume of use (intensity). There are good reasons to expect that the processes determining use versus intensity of use may be different. Individual patients primarily make a decision of use or non-use, but health care providers play a major role in determining the volume of future contacts. Also, need indicators associated with having contact with the health system may be different from factors associated with the volume of contacts. To our knowledge, only a handful studies looking at health care utilization in Canada explicitly incorporated a two-stage process in analysis. They present mixed results. Some studies found no relationship between socioeconomic factors and health services use (contact) and volume of use after controlling for health care need. For example, using the 1985 General Social Survey, Birch, Eyles, and Newbold found that income and education had no effect on use or intensity of use of general practitioner services, except healthy people with lower education had greater intensity of use than healthy people with higher education [
12]. Using the same data, Newbold, Eyles, and Birch found that income had no association with the intensity of hospital use and education had no effect on either use or intensity of hospital use [
13]. Finkelstein in the Ontario study found that income and education had no effect on the use or intensity of use of specialist services, except education was associated with less contact with specialist services [
10]. Examining use or non-use of hospital services using the 1994 NPHS, Wilkins and Park reported no relationship with education but women in the 15–39 and 40–64 age groups with inadequate income used more hospital services than those women with adequate income [
11].
Other studies found some relationship between socioeconomic factors and health services use (contact) or volume of use after need adjustment. For example, in a recent Organization for Economic Cooperation and Development (OECD) study van Doorslaer and his colleagues found that lower income was associated with less contact with general practitioners and specialists and less intensity of general practitioner and specialist services use [
14,
15]. Dunlop and her colleagues, who examined use versus non-use and high use versus less than high use, found that lower income was associated with less contact with specialists and lower education was associated with less contact with general practitioners [
9]. Newbold, Eyles, and Birch found that lower income was associated with less hospital admission [
13], while van Doorslaer and his colleagues found that lower income was associated with greater hospital admission and longer hospital stays [
14,
15].
Building on the previous research, this study investigated the unsolved yet crucial question of whether and where socioeconomic differences in the use of health care services occur in the Canadian health care system after adjusting for need for health care. We examined all services covered under the Canada Health Act: physician (both general practitioner and specialist) and hospital services. The primary contribution of this study is the use of improved data and methodology. We used the 2000/01 Canadian Community Health Survey (CCHS), which provides a large sample size (about 110,000) and a broader set of need indicators that have been used in previous studies. In addition to adding a wide range of need indicators, we allowed the effects of chronic conditions to vary by age. As analytical methods, we employed two-part ''hurdle'' models to examine use versus intensity of use.
Results
Table
2 presents health care utilization in the12 months prior to the survey among Canadians based on the 2000/01 CCHS. About 80% of the population had at least one visit to general practitioners, about 30% of the population had at least one visit to specialists, and about 9% of the population had at least one overnight stay at a hospital. Table
3 reports results of the two-part model estimations for general practitioner, specialist, and hospital services. In all models socioeconomic effects are need-adjusted.
Table 2
Health care utilization in 12 months
Mean visit (median) | 3.37 (2) | 0.84 (0) | 0.59 (0) |
% no use | 20.75 | 70.31 | 91.33 |
% at least 1 use | 79.25 | 29.69 | 8.67 |
Table 3
Two-part models of general practitioner, specialist, and hospital services
| Logistic | Zero-truncated negative binomial | Logistic | Zero-truncated negative binomial | Logistic | Zero-truncated negative binomial |
Variable | OR | IRR | OR | IRR | OR | IRR |
Age | *** | *** | *** | *** | *** | *** |
20–24 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
25–29 | 1.18 | 1.01 | 0.97 | 1.31 ** | 1.06 | 0.92 |
30–34 | 0.89 | 0.89 ** | 1.04 | 1.14 | 0.89 | 1.04 |
35–39 | 0.73 ** | 0.83 *** | 0.83 * | 1.06 | 0.72 ** | 1.17 |
40–44 | 0.72 *** | 0.76 *** | 0.73 *** | 1.01 | 0.62 *** | 0.96 |
45–49 | 0.68 *** | 0.75 *** | 0.76 ** | 0.86 | 0.58 *** | 1.26 |
50–54 | 0.80 * | 0.76 *** | 0.82 * | 0.86 | 0.59 *** | 1.62 ** |
55–59 | 0.81 * | 0.72 *** | 0.77 ** | 0.78 ** | 0.68 ** | 1.67 ** |
60–64 | 0.79 * | 0.68 *** | 0.72 *** | 0.85 * | 0.71 ** | 1.45 * |
65–69 | 0.73 * | 0.56 *** | 0.45 *** | 0.60 *** | 1.02 | 2.02 ** |
70–74 | 0.88 | 0.53 *** | 0.43 *** | 0.51 *** | 0.92 | 2.33 *** |
75–79 | 0.81 | 0.58 *** | 0.43 *** | 0.52 *** | 1.07 | 2.29 *** |
80+ | 0.96 | 0.61 *** | 0.27 *** | 0.46 *** | 1.18 | 2.73 *** |
Sex | 0.35 *** | 0.74 *** | 0.44 *** | 1.00 | 0.67 ** | 1.20 |
Sex × age^^ | *** | *** | *** | * | *** | |
Minority | 1.26 *** | 1.14 *** | 0.95 | 0.73 *** | 0.76 *** | 0.90 |
Education
|
*
|
***
|
***
| | | |
Post-secondary graduate
|
1.00
|
1.00
|
1.00
|
1.00
|
1.00
|
1.00
|
Secondary graduate
|
0.99
|
1.04 **
|
0.91 ***
|
0.98
|
1.01
|
0.98
|
Less than secondary education
|
0.90 **
|
1.08 ***
|
0.74 ***
|
0.91 *
|
1.05
|
0.96
|
Currently in school
|
1.10
|
0.92
|
1.03
|
1.09
|
0.74
|
1.58
|
Missing
|
1.01
|
1.07
|
0.86
|
0.95
|
1.01
|
1.18
|
Home ownership | | *** | | *** | ** | * |
Own the dwelling | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Do not own the dwelling | 0.96 | 1.08 *** | 0.99 | 1.15 *** | 1.14 ** | 1.13 ** |
Missing | 0.70 | 1.02 | 0.82 | 0.82 | 1.37 | 1.00 |
Household income
|
***
|
**
|
***
| | |
*
|
Highest
|
1.00
|
1.00
|
1.00
|
1.00
|
1.00
|
1.00
|
Upper middle
|
0.93 *
|
1.06 **
|
0.87 ***
|
0.98
|
0.97
|
1.07
|
Middle
|
0.83 ***
|
1.06 **
|
0.79 ***
|
0.92
|
0.94
|
1.17 *
|
Lower middle
|
0.79 ***
|
1.11 ***
|
0.76 ***
|
0.90
|
0.93
|
1.25 **
|
Lowest
|
0.70 ***
|
1.12 **
|
0.67 ***
|
0.97
|
1.06
|
1.29 *
|
Missing
|
0.87 **
|
1.05
|
0.83 **
|
0.96
|
0.95
|
1.21 *
|
Self-help group participation^ | 1.30 ** | 1.18 *** | 1.36 *** | 1.28 *** | 1.34 *** | 1.41 *** |
Sense of belonging to community | ** | | | | | |
Yes | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
No | 0.88 *** | 0.97 * | 0.96 | 1.01 | 0.94 | 1.04 |
Missing | 0.64 ** | 0.91 | 0.91 | 0.80 | 1.00 | 0.86 |
Health Utilities Index | 0.95 | 0.93 *** | 0.91 ** | 0.91 * | 1.04 | 0.99 |
Self-perceived health | *** | *** | *** | *** | *** | *** |
Excellent | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Very good | 1.21 *** | 1.21 *** | 1.17 *** | 1.19 *** | 1.00 | 1.10 |
Good | 1.25 *** | 1.53 *** | 1.38 *** | 1.44 *** | 1.39 *** | 1.36 *** |
Fair or poor | 1.51 *** | 2.00 *** | 1.81 *** | 1.87 *** | 1.99 *** | 1.80 *** |
Missing | 0.82 | 1.54 * | 0.62 | 0.99 | 4.11 * | 1.36 |
Stress | *** | *** | | | | *** |
Not at all or not very stressed | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
A bit stressed | 1.07 * | 1.03 | 1.02 | 1.00 | 0.90 ** | 0.92 |
Quite a bit or extremely stressed | 1.16 *** | 1.10 *** | 1.05 | 1.06 | 0.94 | 0.92 *** |
Missing | 1.31 | 0.99 | 0.93 | 1.34 | 0.76 | 1.45 * |
Depression | *** | *** | *** | *** | *** | *** |
0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
1 | 0.96 | 1.17 ** | 1.10 | 1.05 | 1.19 | 0.93 |
2 | 1.33 *** | 1.30 *** | 1.31 *** | 1.44 *** | 1.34 *** | 1.32 *** |
3 | 1.92 *** | 1.47 *** | 1.56 *** | 1.74 *** | 1.73 *** | 1.34 ** |
4 | 1.91 *** | 1.78 *** | 1.61 *** | 2.03 *** | 2.67 *** | 1.98 *** |
Missing | 0.90 | 1.19 *** | 1.13 | 1.39 *** | 1.43 ** | 1.03 |
ADL | ** | *** | *** | *** | *** | * |
No limitation | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Sometimes | 0.98 | 1.08 *** | 1.11 ** | 1.13 ** | 0.94 | 0.93 |
Often | 1.19 ** | 1.17 *** | 1.35 *** | 1.20 *** | 1.19 ** | 1.07 |
IADL | *** | *** | *** | *** | *** | *** |
No | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Yes | 1.36 *** | 1.35 *** | 1.41 *** | 1.40 *** | 1.83 *** | 1.50 ** |
Missing | 1.76 | 1.09 | 1.42 | 1.35 | 1.52 | 1.22 |
Birth^ | 1.06 | 1.53 *** | 1.24 *** | 2.11 *** | 5.26 *** | 1.03 |
Injury^ | 2.30 *** | 1.29 *** | 1.43 *** | 1.11 ** | 1.75 *** | 1.09 |
Injury × age^^ | ** | *** | | | | |
Number of chronic conditions | *** | *** | *** | ** | ** | * |
0 | 1.00 | 1.00 | 1.00 | 1.00 ** | 1.00 | 1.00 |
1 | 1.52 *** | 1.32 *** | 1.67 *** | 1.17 ** | 1.41 *** | 1.20 ** |
2 | 2.05 *** | 1.57 *** | 2.29 *** | 1.20 ** | 1.48 *** | 1.27 ** |
3 | 2.44 *** | 1.73 *** | 2.67 *** | 1.26 *** | 1.56 *** | 1.33 ** |
4+ | 2.72 *** | 1.83 *** | 3.47 *** | 1.48 *** | 1.50 *** | 1.31 ** |
Missing | 1.04 | 1.74 *** | 2.16 *** | 1.20 | 1.41 * | 1.32 |
Food allergies^ | 0.86 * | 0.93 ** | 0.84 *** | 0.95 | 0.95 | 1.00 |
Other allergies^ | 0.91 * | 0.93 *** | 0.86 | 0.97 | 0.96 | 0.92 * |
Other allergies × age^^ | | | *** | | | |
Asthma^ | 1.10 | 1.04 * | 0.90 ** | 0.97 | 1.13 * | 0.97 |
Arthritis^ | 1.19 *** | 1.01 | 1.00 | 0.93 * | 0.98 | 0.85 *** |
High blood pressure^ | 1.94 *** | 1.20 *** | 0.85 *** | 0.91 | 1.12 * | 0.57 |
High blood pressure × age^^ | | | | | | ** |
Migraine^ | 0.87 | 1.08 ** | 0.89 ** | 0.94 | 0.99 | 0.84 ** |
Migraine × age^^ | *** | | | | | |
Urinary incontinence^ | 1.07 | 0.93 * | 1.13 * | 0.92 | 1.14 | 1.13 * |
Urinary incontinence × age^^ | | | | | | |
Diabetes^ | 1.35 ** | 1.20 | 1.02 | 1.08 | 8.71 *** | 1.10 |
Diabetes × age^^ | | ** | | | *** | |
Chronic bronchitis^ | 1.12 | 0.99 | 0.84 ** | 0.95 | 1.19 ** | 1.04 |
Heart disease^ | 1.37 *** | 1.13 *** | 1.60 *** | 0.93 | 1.91 *** | 0.20 ** |
Heart disease × age^^ | | | | | | ** |
Cancer^ | 0.26 | 1.20 *** | 3.53 *** | 1.64 | 2.46 | 0.45 |
Cancer × age^^ | *** | | | ** | *** | *** |
Stomach or intestinal ulcers^ | 1.17 | 1.10 ** | 1.77 * | 0.88 * | 1.15 | 0.92 |
Stomach or intestinal ulcers × age^^ | | | ** | | | |
Bowel disorders^ | 1.37 * | 1.10 | 1.49 *** | 1.05 | 1.34 *** | 3.55 *** |
Bowel disorders × age^^ | | | | | | *** |
Eye problem^ | 0.76 ** | 0.90 *** | 0.84 *** | 1.00 | 0.96 | 0.31 ** |
Eye problem × age^^ | | | | | | ** |
Thyroid condition^ | 1.63 *** | 1.06 * | 1.07 | 0.98 | 0.92 | 0.87 |
Epilepsy^ | 1.14 | 0.99 | 1.15 | 1.12 | 1.34 | 1.16 |
Epilepsy × age^^ | | | | | | *** |
Chronic fatigue syndrome^ | 0.93 | 1.05 | 0.94 | 0.98 | 0.84 * | 1.18 |
Chronic fatigue syndrome × age^^ | | | | | | ** |
Stroke^ | 2.17 | 0.54 | 0.80 * | 0.99 | 1.34 ** | 1.42 *** |
Stroke × age^^ | ** | ** | | | | |
Overweight | | *** | ** | *** | * | |
Underweight or acceptable weight | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Overweight | 1.06 * | 1.03 * | 0.99 | 1.00 | 1.02 | 1.03 |
Missing | 0.97 | 1.33 *** | 1.22 ** | 1.34 *** | 0.78 * | 0.90 |
Smoking | *** | | *** | | | |
Never smoked | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Former smoker | 1.04 | 1.02 | 1.14 *** | 1.01 | 1.09 | 1.05 |
Current smoker | 0.77 *** | 1.00 | 0.90 ** | 0.98 | 0.98 | 1.07 |
Missing | 1.03 | 0.88 | 0.91 | 0.93 | 1.24 | 0.65 |
Drinking | ** | *** | | *** | *** | *** |
Not regular drinker | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Regular drinker | 1.09 ** | 0.89 *** | 1.04 | 0.85 *** | 0.74 *** | 0.97 *** |
Missing | 1.43 * | 0.81 * | 0.97 | 1.37 | 1.33 | 0.91 |
Physical activity | ** | ** | | | *** | |
Active | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Moderate or inactive | 1.08 * | 1.06 *** | 1.04 | 1.05 | 1.18 *** | 0.99 |
Missing | 1.51 ** | 1.12 | 1.10 | 1.33 | 1.27 | 1.38 |
Fruit and vegetable consumption | ** | *** | *** | | * | |
5+ servings a day | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Less than 5 servings a day | 0.94 * | 0.92 *** | 0.88 *** | 0.97 | 0.91 ** | 0.97 |
Missing | 0.75 ** | 0.88 * | 0.78 * | 0.96 | 0.79 | 0.78 |
Alternative health care^ | 1.43 *** | 1.20 *** | 1.28 *** | 1.11 ** | 0.92 | 1.00 |
Province of residence | ** | ** | *** | ** | ** | ** |
ON | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
NF | 1.59 *** | 1.20 *** | 1.04 | 0.92 | 1.29 ** | 1.22 * |
PEI | 1.05 | 0.93 * | 0.88 * | 0.97 | 1.48 *** | 1.27 ** |
NS | 1.00 | 1.03 | 0.91 * | 0.80 *** | 1.02 | 1.06 |
NB | 1.23 *** | 0.90 *** | 1.02 | 0.71 *** | 1.40 *** | 1.34 *** |
QC | 0.66 *** | 0.74 *** | 1.50 *** | 0.80 *** | 1.34 *** | 0.96 |
MB | 0.84 *** | 0.99 | 0.90 * | 1.02 | 1.18 * | 1.08 |
SK | 1.12 * | 1.06 * | 0.79 *** | 0.81 ** | 1.38 *** | 1.00 |
AL | 0.92 * | 1.08 ** | 0.71 *** | 0.87 ** | 1.17 ** | 1.05 |
BC | 1.03 | 1.14 *** | 0.75 *** | 1.03 | 1.05 | 1.03 |
α
| | 0.95 | | 2.99 | | 1.64 |
n
| 110923 | 89151 | 111087 | 31819 | 111104 | 11291 |
Pseudo-R square | 0.12 | | 0.11 | | 0.15 | |
Log likelihood | -49891.01 | -38209980 | -60018.27 | -11517957 | -27813.50 | -5128804.50 |
Wald chi-square~ | 4847.42 *** | 13954.43 *** | 6072.91 *** | 2619.61 *** | 5297.16 *** | 2795.83 *** |
General practitioners
Education was not statistically significantly associated with the contact with general practitioners but was positively associated with the intensity of general practitioner services. Income was statistically significantly associated both with the contact with and the intensity of general practitioner services, and, strikingly, the direction of their graded associations was opposite in the logistic and zero-truncated negative binomial model. People with lower income were less likely to contact general practitioners than their counterparts, but, once they made the initial contact, they were likely to visit them more often.
Most of other variables, including chronic conditions such as high blood pressure, migraine, diabetes, heart disease, cancer, eye problem, and stroke were statistically significant predictors both of the contact with and the intensity of general practitioner services. However, the sense of belonging to community, smoking, arthritis, and thyroid condition were associated only with contact with general practitioners, while home ownership, the Health Utilities Index, having given birth in the past 5 years, overweight, food allergies, other allergies, and stomach or intestinal ulcers were only associated with the intensity of the use. The effects of migraine, diabetes, cancer, and stroke varied by age. Neither education nor income interacted with sex.
Specialists
People with lower education and lower household income were less likely to contact specialists than their counterparts, but education and income had no statistically significant relationship with the intensity of specialist services.
Most of other variables were statistically significantly associated with either the contact or intensity of specialist services use. The sense of belonging to community, stress, physical activity, and some chronic conditions (arthritis, urinary incontinence, diabetes, thyroid condition, epilepsy, chronic fatigue syndrome, and stroke) were not associated with specialist services use or intensity of use. Having a lower Health Utilities Index, having smoked, and having 5 or more servings of fruits and vegetables a day were positively associated with the contact with specialists but not the intensity of use, while being a minority, owing the dwelling, and being a regular drinker were negatively associated with the intensity of specialist use but not with the contact with specialists. Many chronic conditions, such as food allergies, other allergies, asthma, high blood pressure, migraine, chronic bronchitis, heart disease, stomach or intestinal ulcers, bowel disorders, and eye problem, were only associated with the contact with specialist services. The effects of other allergies, cancer, and stomach or intestinal ulcers varied by age. The interaction term between sex and education was statistically significant in the model of the intensity of specialist services. In a separate analysis stratified by sex, we found that among men education was overall statistically significant (p = 0.004), but this was due to the category of "currently in school" (the incidence rate ratio: 1.88, p = 0.006).
Hospital
Socioeconomic status had no statistically significant association with hospital use or non-use (hospital admission) and the intensity of use (hospital stay). All variables but the sense of belonging to community, Health Utilities Index, overweight, smoking, fruit and vegetable consumption, the use of alternative health care, and some chronic conditions (food allergies, other allergies, asthma, urinary incontinence, stomach or intestinal ulcers, eye problems and thyroid condition) were statistically significantly associated both or either of hospital admission or stay. Being female, non-visible minority, having given birth in the past 5 years, being moderately active or inactive, and having injury, diabetes, and chronic bronchitis were positively associated with hospital admission, while they had no association with hospital stay. On the other hand, stress, arthritis, high blood pressure, migraine, epilepsy, and chronic fatigue syndrome was a predictor of hospital stay though it was not for hospital adminission. Effects of many chronic conditions on hospital use varied by age, including high blood pressure, diabetes, heart disease, cancer, bowel disorders, epilepsy, and chronic fatigue syndrome. Effects of education and income did not vary by sex.
Discussion
This study examined whether and where socioeconomic differences in need-adjusted use of general practitioner, specialist, and hospital services occur in Canada using two-part models. One of the attractive features of this approach is explicit recognition of differences in processes affecting use versus the intensity of use of different types of services. For example, the role of patients versus providers in determining utilization varies between use and intensity of use of different types of health services use. The type and importance of need indicators are also likely to vary between use and intensity of use of different types of health services. For example, ambulatory conditions such as allergies and arthritis will be more important drivers of general practitioner use, while heart disease and cancer will be more important drivers of need for specialist and hospital services. Moreover, some types of conditions (e.g., diabetes) may require more follow-up than others (e.g., allergies), and thus will be more strongly associated with intensity of use.
Another unique aspect of this study is the use of a broad set of need indicators, and the incorporation of interactions between particular need indicators with age. Overall, previous studies are likely to have under-adjusted for need indicators. Many previous studies have adjusted for only general measures of health status, and studies vary considerably in the types and range of need indicators used. The problem with using general measures of health status alone is that they do not recognize that different health problems may produce the same sense of health, but different need for types and intensity of health services. The capacity to benefit from care and the intensity and complexity of treatment options vary widely by type of health problem. The effects of need indicators are also likely to vary by age. For example, complications and severity of diabetes tend to increase with age, thus increasing the need for health services.
Our approach unveiled possible socioeconomic inequities at the entry to health care services. After adjustment for need, lower income was associated with less contact with general practitioners, but among those who had contact, lower income and education were associated with greater intensity of use of general practitioners. Both lower income and education were associated with less contact with specialists, but there was no statistically significant relationship between these socioeconomic variables and intensity of specialist use among the users. Neither income nor education was statistically significantly associated with use and non-use and intensity of hospital use. To obtain an overall picture of where socioeconomic differences in need-adjusted use of health services exist, it is important to explore who are the primary decision-makers regarding the contact with and the intensity of each of these three types of health services. A patient largely initiates contact with a general practitioner, while general practitioners play a major decision-making role in subsequent visits and referrals to specialists. Referrals to specialists, however, are not entirely determined by general practitioners. Effectiveness of patients' negotiation with general practitioners and geographic variation of substitution between general practitioners and specialists would also be important factors for the contact with specialists. The patient's role in the decision of hospital use, on the other hand, is likely substantially limited. Specialists play a major decision-making role in the decision to admit patients to hospital, as well as in the duration of hospital stays.
Taken together, our results show that disaggregating socioeconomic variations in health care use by type of service and contact versus intensity uncovers complex variations. Complexity in patterns of health services use by socioeconomic status is evident in the results of other studies as well. For example, Roos and Mustard found that excess use of hospital services for residents of lower income neighbourhoods in Winnipeg, Manitoba reflected higher admission for medical, not surgical reasons, and higher admissions for conditions that are avoidable or which can be managed in an ambulatory care setting [
4]. Other Canadian studies have shown that residents of lower income neighbourhoods in Ontario [
23] and Quebec [
24] have lower rates of cardiac catherization and revasculariszation following admission for an acute myocardial infarction. Yet, premature mortality due to cardiovascular disease is higher among lower socioeconomic groups [
25,
26].
What we can conclude is that socioeconomic status is associated with how and when patients contact the health care system, but further study is needed to disaggregate the reasons for our findings. For example, our results for general practitioner services suggest that lower socioeconomic status may be associated with contacting the system later in the stages of disease severity or symptom severity; however, if this is the case, one might expect to see higher use of specialist or hospital services associated with lower SES. This was not the case. Future work needs to examine socioeconomic variation in the use of different types of specialist and hospital services (e.g. medical versus surgical admissions) to explicate the reasons for the effects we observe.
To further explain where socioeconomic differences in need-adjusted use of health services occur, future work needs to further explore socioeconomic differences in referral patterns, and the reasons for those differences. Dunlop and her colleagues controlled for the probability of using general practitioner services six times or more for the past year in their analysis of use and non-use of specialist services and frequent (6+) and less frequent use of specialist services [
9]. This approach may help to adjust differences in specialist use for differences in the possibility of referral, but more detailed understanding of the process is needed. For example, we need to know if socioeconomic differences in referral to specialists reflect general practitioner's decisions, effectiveness of a patient's negotiation with the general practitioner to see specialist, or substitution between general practitioners and specialists reflecting geographic differences in access.
Our results confirm findings of some previous studies. Our study replicates the positive association between income and the contact with general practitioners found by van Doorslaer and his colleagues [
14,
15]. Our finding of the positive association between socioeconomic status and the contact with specialists also confirms findings by three other studies [
9,
10,
14,
15]. Like Finkelstein [
10], we found no statistically significant relationship between socioeconomic status and the intensity of specialist use. As to the contact with hospitals, our study confirms Wilkins and Park for no relationship with education [
11].
However, our other results are not consistent with previous findings [
9,
12,
14,
15]. Discrepancies between the study by van Doorslaer et al. and our study are particularly worth investigating given that their study used the same data and the same type of statistical modeling approach: a two-part "hurdle" model (logistic and zero-truncated negative binomial regression models). van Doorslaer and his colleagues found negative associations between income and hospital admission and stay while we found no such relationships. A primary difference between these studies is the extent of need adjustment. While van Doorslaer and his colleagues only used self-perceived health and activity status (i.e., impact of health problems on home, work or school, and other activities) as need indicators, we used more comprehensive adjustments for need (see Table
1).
To what extent can differences in need adjustment explain differences in the study results between van Doorslaer et al. and us? Did the study by van Doorslaer et al. under-adjust for need indicators, or did our study over-adjust? The answers ultimately depend on how need for health services should be defined, and whether it is measured in a way that is valid and reliable. To date, the choice and mix of need indicators has in large part been governed by data availability. Data availability is becoming less of concern with data such as the CCHS, and future work must establish a conceptual framework for the choice and mix of need indicators. Such a framework must be sensitive to the measurement construct of each need indicator and, at the same time, easy to interpret for full policy potential. Using the CCHS, which offers a wide selection of need indicators, we guided our selection and mix of need indicators based on the behavioural model by Andersen and Newman [
17] and used a large selection of need indicators to capture the multidimensional concept of need for health care. For example, we believed that the Health Utilities Index captures general health status of the respondent in the standardized manner while self-perceived health allows the respondent's own evaluation of the health status. It also made sense to us to include a count of chronic conditions as well as binary variables indicating the presence of each chronic condition as a count of chronic conditions can be considered as a proxy for severity or the existence of comorbidities while different chronic conditions present different types and quantities of health care need. Yet our selection of need indicators will benefit from an in-depth conceptual analysis of the choice and mix of need indicators.
This study has important limitations. First, many of the need indicators employed in the analysis are, in part, determined by health care utilization [
18,
27]. As a consequence some bias in the effects of need indicators and the adjustment for need can be expected. For example, self-report of chronic conditions generally follows from diagnosis by a health professional. Those with less utilization are thus less likely to report conditions. Evidence suggests that even general measures of health status may be affected by health services utilization. However, models not adjusting for chronic conditions, which are most likely to be plagued by endogeneity, did not alter the primary study findings. Second, health care utilization in this study is measured by self-report, thereby subject to recall bias [
28‐
30]. Ideally, as Finkelstein [
10] and a series of Nova Scotian studies [
5‐
7] showed, self-reported health care utilization need to be validated or replaced by administrative data. Third, this study used cross-sectional data, which are not ideal for analysis of health care utilization. In this study, we looked at the need indicators and socioeconomic status of the respondents in the survey year and estimated their use of health care services in the previous year. To estimate contemporaneous relationships between need indicators, socioeconomic status, and health care utilization, longitudinal data are the best. Fourth, the two-part model assumes that the first visit to a physician or stay at a hospital leads to the subsequent visits [
19]. This assumption may be violated in our study, as people may have had multiple health problems, each of which requires separate visits to physicians. The CCHS only provides information on the number of visits to physicians, and there is no way one can know relations of multiple visits. Finally, the standard errors in our analysis did not fully account for the design effect. Replication methods such as the bootstrap or the jackknife would be preferred [
31]. The public use version of the CCHS, used for this study, did not contain the necessary information to permit such procedures. However, bootstrapped standard errors are very unlikely to alter our results. The study employed a large sample size, so effects are estimated with high precision. Even if the standard errors for the socioeconomic variables of interest were 3–4 times their size, the study findings would be unaltered.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
YA and GK contributed equally to conception, design, analysis, interpretation of data, and writing the manuscript. Both authors read and approved the final manuscript.