OBJECTIVE: To develop and validate a mortality risk algorithm for obese black and white men and women to elucidate risk factors prognostic of short-term mortality among obese persons. METHODS: Prospective cohort study. Reasons for geographic and racial differences in stroke (REGARDS) study, is a cohort of black and white men and women aged ≥45 years. Obese (≥30 kg m(-2) ) participants in REGARDS (n = 11 288) were randomly assigned to the derivation data set or an independent validation set. RESULTS: During the mean follow-up period of 4.9 years, 8.9% (n = 504) in the derivation cohort and 8.7% (n = 492) in the validation cohort died. The best-fitting model based on data from the derivation cohort included demographic (age, sex), coronary heart disease (CHD) conditions (diabetes, systolic blood pressure, history of CHD), health behaviors (smoking, physical activity, alcohol use), and socioeconomic variables (income, use of physician services). The C-statistic when the model was applied to the validation cohort was 0.80. Observed and predicted rates of mortality were similar across deciles of mortality risk by race. CONCLUSIONS: A risk algorithm was established and validated to predict mortality among black and white obese subjects based on CHD risk factors, behavioral risk factors, and socioeconomic status.
OBJECTIVE: To develop and validate a mortality risk algorithm for obese black and white men and women to elucidate risk factors prognostic of short-term mortality among obesepersons. METHODS: Prospective cohort study. Reasons for geographic and racial differences in stroke (REGARDS) study, is a cohort of black and white men and women aged ≥45 years. Obese (≥30 kg m(-2) ) participants in REGARDS (n = 11 288) were randomly assigned to the derivation data set or an independent validation set. RESULTS: During the mean follow-up period of 4.9 years, 8.9% (n = 504) in the derivation cohort and 8.7% (n = 492) in the validation cohort died. The best-fitting model based on data from the derivation cohort included demographic (age, sex), coronary heart disease (CHD) conditions (diabetes, systolic blood pressure, history of CHD), health behaviors (smoking, physical activity, alcohol use), and socioeconomic variables (income, use of physician services). The C-statistic when the model was applied to the validation cohort was 0.80. Observed and predicted rates of mortality were similar across deciles of mortality risk by race. CONCLUSIONS: A risk algorithm was established and validated to predict mortality among black and white obese subjects based on CHD risk factors, behavioral risk factors, and socioeconomic status.
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