Methods

Data source
Analytical techniques
Limitations

Data source

Estimates are based on data from the 2005 Canadian Community Health Survey (CCHS), cycle 3.1.  The CCHS covers the household population aged 12 or older in all provinces and territories, except members of the regular Forces and residents of institutions, Indian reserves, Canadian Forces bases, and some remote areas.  Data for cycle 3.1 were collected between January and December 2005 from a sample of 132,947 persons.  The response rate was 79%.  More information about the CCHS is available in a published report.⁴

This analysis focuses on two age groups:  18 to 64 (92,362 respondents) and 65 or older (28,197 respondents).  Together, these 120,559 respondents represented a household population of 25 million people aged 18 or older.  The two age groups were analyzed separately, because the factors related to their physician consultations tend to differ.

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Analytical techniques

Rates of consultation with general practitioners (GPs) and specialists were estimated based on CCHS data weighted to represent the population of the provinces and territories in 2005.  Cross-tabulations were produced to show the prevalence of physician consultations by need  (number of chronic conditions, self-perceived general health, self-perceived mental health), predisposing characteristics (sex, age group, racial or cultural group), and enabling characteristics (language, education, household income, urban or rural residence, having a regular doctor) based on the Andersen model^2,3 and availability in the CCHS (see Definitions).   Unadjusted odds ratios were estimated for each need factor in relation to a GP consultation, four or more GP consultations, and a specialist consultation.  Adjusted logistic regression models were used to assess the odds of consultations when the effects of need, predisposing characteristics and enabling characteristics were controlled simultaneously. 

To account for the sample design of the CCHS, the bootstrap technique was used to calculate confidence intervals and coefficients of variation and for testing the statistical significance of differences between the estimates.  A significance level of p < 0.05 was applied in all cases.^5-7

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Limitations

This analysis could not include the full range of factors in the Andersen model.  For example, attitudinal/belief variables about health and illness are among the model’s predisposing factors, but questions to elicit such information were not asked by the CCHS.  Similarly, the survey did not collect information about community-related variables such as health care facilities and number of doctors, which figure among the model’s enabling factors.

Although the Andersen model (and this analysis) restricts “need” factors to chronic conditions and fair or poor self-perceived health, Canadians use medical services for preventive as well as illness care.  As a result, the observed association between need and physician consultations is likely weaker than it would have been if need had included a broader range of factors, such as annual check-ups, gynecological care and screening.

The data were collected from household residents.  Although relatively few people live in institutions, their characteristics may differ in ways that would have affected the outcomes if they had been included in the survey.  And even in the household population, those who participated in the survey may have been healthier and more likely than non-respondents to engage in health-promoting behaviour such as consulting physicians.

The CCHS excludes homeless people and residents of isolated northern communities and Indian reservations.  These exclusions preclude consideration of the health care received by some groups who are at high risk of illness, who may have low household income, and for whom access to physicians may be limited.

The data from the CCHS are self-reported.  A potential for bias exists if some socio-demographic groups differ in their willingness to report their health status or their use of health care services.⁶