Role of Socioeconomic Status Measures in Long-term Mortality Risk Prediction After Myocardial Infarction

Bibliographic Data

ID9103697
AuthorsNoa Molshatzki (Tel Aviv University, corresponding author), Yaacov Drory (Arthur M. Sackler Gallery), Vicki Myers (0000-0001-5866-3948, corresponding author), Uri Goldbourt (corresponding author), Yael Benyamini (0000-0001-5110-8212, Tel Aviv University), David M Steinberg (Exact Sciences (United States)), Yariv Gerber (0000-0003-4287-8754, corresponding author)
Year2011
Volume49
Issue7
Pages673-678
Publication date2011-07-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0b013e318222a508
PMID21666512
OpenAlexW2327150763
LanguageEN
Citations received3
References cited27

BACKGROUND: The relationship of risk factors to outcomes has traditionally been assessed by measures of association such as odds ratio or hazard ratio and their statistical significance from an adjusted model. However, a strong, highly significant association does not guarantee a gain in stratification capacity. Using recently developed model performance indices, we evaluated the incremental discriminatory power of individual and neighborhood socioeconomic status (SES) measures after myocardial infarction (MI). METHODS: Consecutive patients aged ≤65 years (N=1178) discharged from 8 hospitals in central Israel after incident MI in 1992 to 1993 were followed-up through 2005. A basic model (demographic variables, traditional cardiovascular risk factors, and disease severity indicators) was compared with an extended model including SES measures (education, income, employment, living with a steady partner, and neighborhood SES) in terms of Harrell c statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). RESULTS: During the 13-year follow-up, 326 (28%) patients died. Cox proportional hazards models showed that all SES measures were significantly and independently associated with mortality. Furthermore, compared with the basic model, the extended model yielded substantial gains (all P<0.001) in c statistic (0.723 to 0.757), NRI (15.2%), IDI (5.9%), and relative IDI (32%). Improvement was observed both for sensitivity (classification of events) and specificity (classification of nonevents). CONCLUSIONS: This study illustrates the additional insights that can be gained from considering the IDI and NRI measures of model performance and suggests that, among community patients with incident MI, incorporating SES measures into a clinical-based model substantially improves long-term mortality risk prediction

Environmental health · Intensive care medicine · Myocardial infarction · Population · Socioeconomic status · Term (time) · Acute Myocardial Infarction Research · Chronic Disease Management Strategies · Emergency Medicine · Health disparities and outcomes · Internal Medicine · Medicine

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