EVIDENCE-BASED PUBLIC HEALTH POLICY AND PRACTICE

The relative impact of 13 chronic conditions across three different outcomes

Anthony V Perruccio, J Denise Power, Elizabeth M Badley

Department of Public Health Sciences, University of Toronto, and Division of Health Care and Outcomes Research, Toronto Western Research Institute, Toronto, Canada

Correspondence to:
Anthony V Perruccio, Toronto Western Research Institute, 399 Bathurst St., MP10-316, Toronto, ON, Canada, M5T 2S8; perrucci{at}uhnres.utoronto.ca

Study objective: Previous estimates of individual and population attributable risks for adverse outcomes due to chronic conditions have considered only a limited number of conditions and outcomes, with some studies using inappropriate formulae or methods of estimation. This study re-examines the magnitude of individual and population attributable risks for a wide range of conditions and various health outcomes.

Design: Log-Poisson regression was used to calculate prevalence ratios as an indicator of individual risk and population-associated fractions of 13 chronic conditions, examining activity limitations, self-rated health and physician visits. The effect of multimorbidity on prevalence ratios was examined.

Setting: Canada, 2000–01.

Participants: Nationally representative sample of Canadians aged 12+ years (n _ 130 880).

Main results: At the individual level, fibromyalgia/chronic fatigue syndrome and cancer, and to a lesser extent stroke and heart disease, were associated with an increased risk of both activity limitations and a self-rated health status of fair or poor; high blood pressure was associated with four or more physician visits in the previous 12 months. In contrast, population attributable fractions were substantial for arthritis/rheumatism, heart disease, back problems and high blood pressure across all outcomes. Adjustment for multimorbidity resulted in a marked decreases in prevalence ratios.

Conclusions: Differences in the ranking of individual risks and population attributable fractions for different diseases and outcomes are substantial. This needs to be taken into account when setting priorities, as interventions may need to be targeted to different conditions depending on which aspects of health are being considered, and whether the focus is on individuals, such as in clinical care, or improving the health of the population.

Keywords: chronic disease; health survey; population; cross-sectional analysis; health priorities

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