Framingham study data and “established wisdom” about cigarette smoking and coronary heart disease

Objective: We investigated the association between childhood socioeconomic status (SES) and coronary heart disease (CHD) in older Americans. Method: We used Health and Retirement Study data from 1992 to 2012 to examine a nationally representative sample of Americans aged ≥50 years ( N = 30,623). We modeled CHD as a function of childhood and adult SES using maternal and paternal educational level as a proxy for childhood SES. Results: Respondents reporting low childhood SES were significantly more likely to have CHD than respondents reporting high childhood SES. Respondents reporting both low childhood and adult SES were 2.34 times more likely to have CHD than respondents reporting both high childhood and adult SES. People with low childhood SES and high adult SES were 1.60 times more likely than people with high childhood SES and high adult SES to report CHD in the fully adjusted model. High childhood SES and low adult SES increased the likelihood of CHD by 13%, compared with high SES both as a child and adult. Conclusion: Childhood SES is significantly associated with increased risk of CHD in later life among older adult Americans.

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Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings

European Journal of Cardiovascular Prevention & Rehabilitation ◽

10.1097/01.hjr.0000173109.14228.71 ◽

2005 ◽

Vol 12 (5) ◽

pp. 459-464 ◽

Cited By ~ 4

Author(s):

Guizhou Hu ◽

Martin M. Root

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Prediction Model ◽

Empirical Model ◽

Complex Disease ◽

Prediction Models ◽

Longitudinal Research ◽

Study Data ◽

Individual Risk ◽

Data Set

Background No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique. Design The construction and validation of statistical models using available data sets. Methods and results Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power ( x2 = 43.8, P < 0.00001). Conclusions Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.

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