Self-rated health and standard risk factors for myocardial infarction : a cohort study

Objective: To investigate the relationship between self-rated health, adjusted for standard risk factors, and myocardial infarction. Design: Population-based prospective cohort study. Setting: Enrolment took place between 1990 and 2004 in Västerbotten County, Sweden Participants: Every year, persons in the total population, aged 40, 50 or 60 were invited. Participation rate was 60%. The cohort consisted of 75 386 men and women. After exclusion for stroke or myocardial infarction before, or within 12 months after enrolment or death within 12 months after enrolment, 72 530 persons remained for analysis. Mean follow-up time was 13.2 years. Outcome measures: Cox regression analysis was used to estimate HRs for the end point of first non-fatal or fatal myocardial infarction. HR were adjusted for age, sex, systolic blood pressure, total cholesterol, smoking, diabetes, body mass index, education, physical activity and self-rated health in the categories very good; pretty good; somewhat good; pretty poor or poor. Results: In the cohort, 2062 persons were diagnosed with fatal or non-fatal myocardial infarction. Poor selfrated health adjusted for sex and age was associated with the outcome with HR 2.03 (95% CI 1.45 to 2.84). All categories of self-rated health worse than very good were statistically significant and showed a dose–response relationship. In a multivariable analysis with standard risk factors (not including physical activity and education) HR was attenuated to 1.61 (95% CI 1.13 to 2.31) for poor self-rated health. All categories of self-rated health remained statistically significant. We found no interaction between self-rated health and standard risk factors except for poor self-rated health and diabetes. Conclusions: This study supports the use of self-rated health as a standard risk factor among others for myocardial infarction. It remains to demonstrate whether self-rated health adds predictive value for myocardial infarction in combined algorithms with standard risk factors