Med Care. 2024 Jun 5. doi: 10.1097/MLR.0000000000002019. Online ahead of print.
ABSTRACT
BACKGROUND: The Elixhauser Comorbidity Index (ECI) is widely used, but its performance in homeless populations has not been evaluated.
OBJECTIVES: Using a national sample of inpatients, this study compared homeless and nonhomeless inpatients on common clinical diagnoses and evaluated ECI performance in predicting mortality among homeless inpatients.
RESEARCH DESIGN: A retrospective study was conducted using 2019 National Inpatient Sample (NIS) data, the largest publicly available all-payer inpatient health care database in the United States.
SUBJECTS: Among 4,347,959 hospitalizations, 78,819 (weighted 1.8%) were identified as homeless.
MEASURES: The ECI consists of 38 medical conditions; homelessness was defined using the International Classification of Diseases Tenth Revision Clinical Modification (ICD-10-CM) diagnostic code, and clinical conditions were based on the Clinical Classifications Software Refined (CCSR) for ICD-10-CM.
RESULTS: Leading clinical diagnoses for homeless inpatients included schizophrenia and other psychotic disorders (13.3%), depressive disorders (9.4%), and alcohol-related disorders (7.2%); leading diagnoses for nonhomeless inpatients were septicemia (10.2%), heart failure (5.2%), and acute myocardial infarction (3.0%). Metastatic cancer and liver disease were the most common ECI diagnoses for both homeless and nonhomeless inpatients. ECI indicators and summary scores were predictive of in-hospital mortality for homeless and nonhomeless inpatients, with all models yielding concordance statistics above 0.80, with better performance found among homeless inpatients.
CONCLUSIONS: These findings underlie the high rates of behavioral health conditions among homeless inpatients and the strong performance of the ECI in predicting in-hospital mortality among homeless inpatients, supporting its continued use as a case-mix control method and predictor of hospital readmissions.
PMID:38838297 | DOI:10.1097/MLR.0000000000002019