Myocardial Infarction Mortality in Rural and Urban Hospitals: Rethinking Measures of Quality of Care

Abstract

PURPOSE Patients with acute myocardial infarction have higher mortality rates in rural hospitals than in urban hospitals, suggesting substandard quality of care in the rural setting. We examined characteristics of patients experiencing myocardial infarction and used an instrumental variable technique to adjust for unmeasured confounding when comparing mortality rates for these hospitals.

METHODS We used the 2002 and 2003 Iowa State Inpatient Datasets, including 12,191 Iowa residents aged 18 years or older hospitalized with a principal diagnosis of acute myocardial infarction (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes 410.01– 410.91) in 116 Iowa hospitals classified as rural or urban. In-hospital mortality was the primary outcome measure. Age, sex, race, admission type, payer, and 2 comorbidity indices (Charlson Comorbidity Index and All Patient Refined Diagnosis-Related Groups) were determined to calculate risk-adjusted mortality. The distance from each patient’s home to the nearest urban Iowa hospital was used as an instrumental variable to compare risk-adjusted mortality controlled for unmeasured confounders.

RESULTS Unadjusted and risk-adjusted mortality rates using logistic regression models indicated significantly lower in-hospital mortality for patients with myocardial infarction admitted to urban hospitals than for their counterparts admitted to rural hospitals (unadjusted values, 6.4% vs 14%). The urban and rural groups differed significantly on characteristics studied, however. Analyses indicated that the traditional logistic regression models were possibly confounded by unmeasured patient factors, and when the same data were analyzed with the instrumental variable technique, mortality differences disappeared.

CONCLUSIONS In Iowa, mortality from myocardial infarction in rural hospitals is not higher than that in urban ones after controlling for unmeasured confounders. Current risk-adjustment models may not be sufficient when assessing hospitals that perform different functions within the health care system. Unmeasured confounding is a major concern when comparing heterogeneous and undifferentiated populations.