Developing a summary hospital mortality index: retrospective analysis in English hospitals over five years

Abstract

Objectives: To develop a transparent and reproducible measure for hospitals that can indicate when deaths in hospital or within 30 days of discharge are high relative to other hospitals, given the characteristics of the patients in that hospital, and to investigate those factors that have the greatest effect in changing the rank of a hospital, whether interactions exist between those factors, and the stability of the measure over time.

Design: Retrospective cross sectional study of admissions to English hospitals.

Setting: Hospital episode statistics for England from 1 April 2005 to 30 September 2010, with linked mortality data from the Office for National Statistics.

Participants: 36.5 million completed hospital admissions in 146 general and 72 specialist trusts.

Main outcome measures: Deaths within hospital or within 30 days of discharge from hospital.

Results: The predictors that were used in the final model comprised admission diagnosis, age, sex, type of admission, and comorbidity. The percentage of people admitted who died in hospital or within 30 days of discharge was 4.2% for males and 4.5% for females. Emergency admissions comprised 75% of all admissions and 5.5% died, in contrast to 0.8% who died after an elective admission. The percentage who died with a Charlson comorbidity score of 0 was 2% in contrast with 15% who died with a score greater than 5. Given these variables, the relative standardised mortality rates of the hospitals were not noticeably changed by adjusting for the area level deprivation and number of previous emergency visits to hospital. There was little evidence that including interaction terms changed the relative values by any great amount. Using these predictors the summary hospital mortality index (SHMI) was derived. For 2007/8 the model had a C statistic of 0.911 and accounted for 81% of the variability of between hospital mortality. A random effects funnel plot was used to identify outlying hospitals. The outliers from the SHMI over the period 2005-10 have previously been identified using other mortality indicators.

Conclusion: The SHMI is a relatively simple tool that can be used in conjunction with other information to identify hospitals that may need further investigation.

Fig 1 Diffsum plots showing summary hospital mortality index final model versus age and sex; final model versus final model plus deprivation score, final model versus final model plus number of emergency admissions in past 12 months, and final model versus final model plus age×comorbidity interaction. Dotted lines show a 5% change in expected values

Fig 2 Funnel plots showing expected number of deaths and summary hospital mortality index (SHMI) for years 2005/6 to 2009/10. A random effects model with a 10% level of trimming was used to calculate 95% and 99.9% control lines