Less affluent area of residence and lesser-insured status predict an increased risk of death or myocardial infarction after angiographic diagnosis of coronary disease.
OBJECTIVE
Low socioeconomic status (SES) predicts coronary artery disease (CAD) onset, but its value among patients with CAD is uncertain. Geographic measures (e.g., residential neighborhood) may predict risk, but this requires further evaluation.


METHODS
A cohort of 3410 patients with significant, angiographically-defined CAD (> or =1 lesion of > or =70% stenosis) joined a registry during the period between 1993 and 2000 and was followed for 6.7 years (median 3.7 years). A geographic SES measure-residential economic status (RES)-and insurance type were examined for association with mortality or myocardial infarction (MI).


RESULTS
In Cox regression adjusting for 17 covariates, lower RES quartile was associated with increased death/MI (p-trend<0.001), death (p-trend=0.001), and MI (p-trend=0.07). First RES quartile (vs. fourth) predicted death/MI (hazard ratio [HR]=1.32, 95% confidence interval [CI]=1.07-1.62, p=0.01) and death (HR=1.46, CI=1.12-1.91, p=0.006), but not MI (HR=1.18, p=0.31). Compared with private insurance, self-pay (HR=1.88, p=0.053), charity care (HR=1.71, p<0.001), and Medicaid (HR=1.43, p=0.24), but not Medicare (HR=0.95, p=0.68), were associated with death/MI.


CONCLUSIONS
Both geographic (RES) and economic (insurance) measures of SES independently predicted risk of death/MI in a large population with angiographically-defined CAD. This suggests that SES remains a significant predictor of health outcomes after CAD has developed, and that geographic measures of SES deserve further evaluation.
Horne
Benjamin D
BD
Cardiovascular Department, LDS Hospital, Salt Lake City, Utah 84143, USA.
Muhlestein
Joseph B
JB
Lappé
Donald L
DL
Renlund
Dale G
DG
Bair
Tami L
TL
Bunch
T Jared
TJ
Anderson
Jeffrey L
JL
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002608:Charities / Q000706:statistics & numerical data; D017023:Coronary Angiography; D003327:Coronary Disease / Q000000981:diagnostic imaging / Q000191:economics; D005260:Female; D005382:Financing, Personal / Q000706:statistics & numerical data; D006801:Humans; D019458:Insurance Coverage / Q000706:statistics & numerical data*; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data; D008875:Middle Aged; D009203:Myocardial Infarction / Q000191:economics* / Q000401:mortality*; D011204:Poverty Areas; D016016:Proportional Hazards Models; D012042:Registries; D012111:Residence Characteristics; D018570:Risk Assessment / Q000191:economics*; D012307:Risk Factors; D012923:Social Class; D016019:Survival Analysis; D014589:Utah / Q000453:epidemiology