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The Hospital Compare Mortality Model and the Volume–Outcome Relationship
Author(s) -
Silber Jeffrey H.,
Rosenbaum Paul R.,
Brachet Tanguy J.,
Ross Richard N.,
Bressler Laura J.,
EvenShoshan Orit,
Lorch Scott A.,
Volpp Kevin G.
Publication year - 2010
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/j.1475-6773.2010.01130.x
Subject(s) - medicine , logistic regression , random effects model , emergency medicine , acute care , mortality rate , myocardial infarction , hospital care , health care , meta analysis , economics , economic growth
Objective. We ask whether Medicare's Hospital Compare random effects model correctly assesses acute myocardial infarction (AMI) hospital mortality rates when there is a volume–outcome relationship. Data Sources/Study Setting. Medicare claims on 208,157 AMI patients admitted in 3,629 acute care hospitals throughout the United States. Study Design. We compared average‐adjusted mortality using logistic regression with average adjusted mortality based on the Hospital Compare random effects model. We then fit random effects models with the same patient variables as in Medicare's Hospital Compare mortality model but also included terms for hospital Medicare AMI volume and another model that additionally included other hospital characteristics. Principal Findings. Hospital Compare's average adjusted mortality significantly underestimates average observed death rates in small volume hospitals. Placing hospital volume in the Hospital Compare model significantly improved predictions. Conclusions. The Hospital Compare random effects model underestimates the typically poorer performance of low‐volume hospitals. Placing hospital volume in the Hospital Compare model, and possibly other important hospital characteristics, appears indicated when using a random effects model to predict outcomes. Care must be taken to insure the proper method of reporting such models, especially if hospital characteristics are included in the random effects model.