Cardiovascular Disease Knowledge/Morbidity--Socioeconomic Cohort Outcomes
|Study Design:||Observational Model: Natural History|
|Study Start Date:||September 1996|
|Estimated Study Completion Date:||August 1998|
Evidence shows a growing disparity in the prevalence of modifiable risk factors and incidence of cardiovascular disease between upper and lower socioeconomic status (SES) individuals. Trends in knowledge about risk factors and risk reduction strategies parallel these findings. Research determining the differential association between level of cardiovascular disease knowledge and subsequent clinical health status had not been conducted.
Analyses were stratified according to SES (via years of formal education), controlling for age, gender, and ethnicity (Latino/Anglo). Sociodemographic, physiologic, and knowledge measurements were available on each participant. Morbidity estimates and clinical health status indicators were available via primary and secondary discharge diagnostic codes from public-use hospital discharge databases collected on all California hospital admissions for the entire study period. The Stanford Five City Program data were merged with the hospital discharge data, matching on survey participant's social security number which was subsequently converted to a unique personal identifier. Baseline 1989/90 and 1991 through 1995 longitudinal outcomes were assessed.
There were three main aims, all of which had epidemiologic and cardiovascular disease health policy prevention implications: Aim 1: Characterize the distribution of hospitalized versus non-hospitalized SES sub-cohorts according to level of C.D. knowledge, physiologic risk factor prevalence, and clinical morbidity prevalence. Aim 2: Test the hypothesis that morbidity differences between hospitalized SES sub-cohorts would vary as a function of baseline level of cardiovascular disease knowledge and risk factor prevalence. Aim 3: Test the hypothesis that morbidity would rise among hospitalized lower SES sub-cohorts, resulting in widening health status disparities by the end of the study period. Parametric and nonparametric analytic methods were used, including analysis of variance and covariance, and various regression techniques.
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