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Related Experiment Video

Updated: May 17, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study.

Alan J Girling¹, Timothy P Hofer, Jianhua Wu

¹Department of Public Health, Epidemiology and Biostatistics, University of Birmingham, Birmingham, UK.

BMJ Quality & Safety

|October 17, 2012

Summary

Standardized mortality ratios (SMRs) may not accurately reflect preventable hospital deaths. Our model shows SMRs are highly sensitive to the actual proportion of preventable deaths, impacting hospital performance assessments.

Related Experiment Videos

Last Updated: May 17, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Healthcare quality and safety
Health services research
Medical statistics

Background:

Risk-adjustment schemes are crucial for monitoring hospital performance.
These schemes assume unexplained excess mortality indicates suboptimal care.
Standardized mortality ratios (SMRs) are commonly used metrics.

Purpose of the Study:

To develop a model estimating the proportion of SMR variation attributable to preventable mortality.
To assess the predictive value of SMRs for identifying hospitals with high preventable mortality rates.
To provide a 'reality check' for existing case-mix adjustment methodologies.

Main Methods:

Developed a statistical model to quantify the relationship between SMRs and preventable mortality.
Utilized literature values to populate the model and estimate predictive values.
Calculated the proportion of hospitals in the highest SMR percentile that also fall in the worst preventable mortality percentile.

Main Results:

The predictive value of SMRs is highly sensitive to the underlying proportion of preventable deaths.
If 6% of hospital deaths are preventable, SMRs predict preventable mortality with only 9% accuracy.
This predictive value can increase to 30% if 15% of deaths are preventable.

Conclusions:

Current risk-adjustment schemes using SMRs may misattribute preventable deaths.
The accuracy of SMRs in reflecting true hospital performance is contingent on the true rate of preventable mortality.
The developed model highlights the limitations of SMRs and suggests caution in their interpretation for quality assessment.