Attributing Responsibility: Hospitals Account for 20% of Variance in Acute Myocardial Infarction Patient Mortality

Understanding the large heterogeneity in hospital readmissions and mortality for acute myocardial infarction

This study aims to investigate the variation in two acute myocardial infarction (AMI) outcomes across public hospitals in Portugal. In-hospital mortality and 30-day unplanned readmissions were studied using two distinct AMI cohorts of adults discharged from all acute care public hospital centers in Portugal from 2012-2015. Hierarchical generalized linear models were used to assess the association between patient and hospital characteristics and hospital variability in the two outcomes. Our findings indicate that hospitals are not performing homogeneously-the risk of adverse events tends to be consistently larger in some hospitals and consistently lower in other hospitals. While patient characteristics accounted for a larger share of the explained between-hospital variance, hospital characteristics explain an additional 8% and 10% of hospital heterogeneity in the mortality and the readmission cohorts respectively. Admissions to hospitals with low AMI caseloads or located in Alentejo/Algarve and Lisbon had a higher risk of mortality. Discharges from larger-sized hospitals were associated with increased risk of readmissions. Future health policies should incorporate these findings in order to incentivize more consistent health care outcomes across hospitals. Further investigation addressing geographical disparities, hospital caseload and practices is needed to direct actions of improvement to specific hospitals.

Impact of model misspecification in shared frailty survival models

Survival models incorporating random effects to account for unmeasured heterogeneity are being increasingly used in biostatistical and applied research. Specifically, unmeasured covariates whose lack of inclusion in the model would lead to biased, inefficient results are commonly modeled by including a subject-specific (or cluster-specific) frailty term that follows a given distribution (eg, gamma or lognormal). Despite that, in the context of parametric frailty models, little is known about the impact of misspecifying the baseline hazard or the frailty distribution or both. Therefore, our aim is to quantify the impact of such misspecification in a wide variety of clinically plausible scenarios via Monte Carlo simulation, using open-source software readily available to applied researchers. We generate clustered survival data assuming various baseline hazard functions, including mixture distributions with turning points, and assess the impact of sample size, variance of the frailty, baseline hazard function, and frailty distribution. Models compared include standard parametric distributions and more flexible spline-based approaches; we also included semiparametric Cox models. The resulting bias can be clinically relevant. In conclusion, we highlight the importance of fitting models that are flexible enough and the importance of assessing model fit. We illustrate our conclusions with two applications using data on diabetic retinopathy and bladder cancer. Our results show the importance of assessing model fit with respect to the baseline hazard function and the distribution of the frailty: misspecifying the former leads to biased relative and absolute risk estimates, whereas misspecifying the latter affects absolute risk estimates and measures of heterogeneity.

Negative Association of Hospital Efficiency Under Increasing Geographic Elevation on Acute Myocardial Infarction In-Patient Mortality

Although variation in-patient outcomes based on hospitals' geographic location has been studied, altitude of hospitals above sea level may also affect patient outcomes. Possibly, because of negative physical and psychological effects of altitude on hospital employees, hospital efficiency may decline at higher altitudes. Greater focus on hospital efficiency, despite decreasing efficiency at higher altitudes, could increase demands on hospital employees and further deteriorate patient outcomes. Using data envelopment analysis on a sample of 840 hospital-year observations representing 95,504 patients with acute myocardial infarction (AMI) in the United States, and controlling for patient, hospital, and county characteristics and controlling for hospital, state, and year fixed effects, we find support for the negative association between hospital altitude and efficiency; for 1 percentage point increase in efficiency and every 1,000 feet increase in altitude above the sea level, the mortality of patients with AMI increases by 0.66 percentage points. The findings have implications for hospital performance at increasing geographic elevation and introduces to the literature the notion of "health economics of elevation," to suggest that elevation of a hospital may be an important criterion for consideration for policy makers and insurance firms.