ABSTRACT
Background
Risk-adjustment models are widely used methodological approaches within the healthcare industry to measure hospital performance and quality of care. However, the Centers for Medicare and Medicaid Services (CMS) do not fully adjust for socioeconomic status (SES) in acute myocardial infarction (AMI) models. A review and evidence synthesis was conducted to identify associations of SES factors with hospital readmission and mortality in AMI patients.
Methods
Multiple electronic databases were queried to identify studies assessing risk for AMI-related mortality or hospital readmissions and SES factors. Identified studies were screened by title and abstract. Full-text reviews followed for articles meeting the inclusion criteria, including quality assessments. Data were extracted from all included studies, and evidence synthesis was performed to identify associations between SES factors and outcome variables.
Results
Ten studies were included in the review. One study showed that Black patients had higher AMI-related readmission rates compared to White patients (mean difference 4.3% [SD 1.4%], p < 0.001). Another study showed that income inequality was associated with increased risk of AMI-related readmissions (RR 1.18 [95% CI], 1.13–1.23). One study found that unemployed individuals experienced significantly greater rates of AMI-related mortality than those working full-time (HR 2.08, 1.51–2.87). According to another study, lack of health insurance was associated with worse rates for in-hospital AMI-related mortality (OR 1.77, 1.72–1.82). Based on one study, AMI-related mortality was higher in those with <8 years of education compared to those with >16 years (17.5% vs. 3.5%, p < 0.0001). Five of six studies found a significant association between ZIP code/neighborhood/location and AMI-related readmission or mortality.
Conclusion
Race, ZIP code/neighborhood/location, insurance status, income/poverty, and education comprise SES factors found to be associated with AMI-related mortality and/or readmission outcomes. Including these SES factors in future updates of CMS’s risk-adjusted models has the potential to provide more appropriate compensation mechanisms to hospitals.
Acknowledgments
None stated.
Transparency statements
Disclosure of financial/other conflicts of interest
The authors have no relevant conflicts of interest to disclose. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Declaration of funding
No funding was received for the production of this manuscript.