The US hospital standardised mortality ratio: Retrospective database study of Massachusetts hospitals

Abstract

Summary Objectives To present a case-mix adjustment model that can be used to calculate Massachusetts hospital standardised mortality ratios and can be further adapted for other state-wide data-sets. Design We used binary logistic regression models to predict the probability of death and to calculate the hospital standardised mortality ratios. Independent variables were patient sociodemographic characteristics (such as age, gender) and healthcare details (such as admission source). Statistical performance was evaluated using c statistics, Brier score and the Hosmer–Lemeshow test. Setting Massachusetts hospitals providing care to patients over financial years 2005/6 to 2007/8. Patients 1,073,122 patients admitted to Massachusetts hospitals corresponding to 36 hospital standardised mortality ratio diagnosis groups that account for 80% of in-hospital deaths nationally. Main outcome measures Adjusted in-hospital mortality rates and hospital standardised mortality ratios. Results The significant factors determining in-hospital mortality included age, admission type, primary diagnosis, the Charlson index and do-not-resuscitate status. The Massachusetts hospital standardised mortality ratios for acute (non-specialist) hospitals ranged from 60.3 (95% confidence limits 52.7–68.6) to 130.3 (116.1–145.8). The reference standard hospital standardised mortality ratio is 100 with the values below and above 100 suggesting either random or special cause variation. The model was characterised by excellent discrimination (c statistic 0.87), high accuracy (Brier statistics 0.03) and close agreement between predicted and observed mortality rates. Conclusions We have developed a case-mix model to give insight into mortality rates for patients served by hospitals in Massachusetts. Our analysis indicates that this technique would be applicable and relevant to Massachusetts hospital care as well as to other US hospitals.