Association of regional racial/cultural context and socioeconomic status with arthritis in the population: A multilevel analysis

Authors


Abstract

Objective

To examine the extent to which differences in individual- and regional-level socioeconomic status and racial/cultural origin account for geographic variations in the prevalence of self-reported arthritis, and to determine whether regional characteristics modify the effect of individual characteristics associated with reporting arthritis.

Methods

Analyses were based on the 2000–2001 Canadian Community Health Survey (>15 years, n = 127,513). Arthritis was self-reported as a long-term condition diagnosed by a health professional. A 2-level logistic regression model was used to identify predictors of reporting arthritis. Individual-level variables included age, sex, income, education, immigration status, racial/cultural origin, smoking, physical activity, and body mass index. Regional-level variables included the proportion of low-income families, low education, unemployment, recent immigrants, Aboriginals, and Asians.

Results

At the individual level, age, sex, low income, low education, Aboriginal origin, current smoking, and overweight/obesity were positively associated with reporting arthritis; recent immigration and Asian origin were negatively associated with reporting arthritis. At the regional level, percentages of low-income families and the Aboriginal population were independently associated with reporting arthritis. Regional income and racial/cultural origin moderated the effects of individual income and racial/cultural origin; low-income individuals residing in regions with a higher proportion of low-income families reported arthritis more than low-income individuals living in better-income regions.

Conclusion

Both individual and regional factors were found to contribute to variations in the prevalence of arthritis, although significant unexplained variation remained. Further research is required to better understand the mechanisms that underlie these regional effects and to identify other contributing factors to the remaining variation.

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