Comparison of multistate model, survival regression, and matched case–control methods for estimating excess length of stay due to healthcare-associated infections

Abstract

A recent systematic review recommended time-varying methods for minimizing bias when estimating the excess length of stay (LOS) for healthcare-associated infections (HAIs); however, little evidence exists concerning which time-varying method is best used for HAI incidence studies. To undertake a retrospective analysis of data from a one-year prospective incidence study of HAIs, in one teaching hospital and one general hospital in NHS Scotland. Three time-varying methods - multistate model, multivariable adjusted survival regression, and matched case-control approach - were applied to the data to estimate excess LOS and compared. The unadjusted excess LOS estimated from the multistate model was 7.8 (95% confidence interval: 5.7-9.9) days, being shorter than the excess LOS estimated from survival regression adjusting for the admission characteristics (9.9; 8.4-11.7) days, and the adjusted estimates from matched case-control approach (10; 8.5-11.5) days. All estimates from the time-varying methods were much lower than the crude time-fixed estimates of 27 days. Studies examining LOS associated with HAI should consider a design which addresses time-dependent bias as a minimum. If there is an imbalance in patient characteristics between the HAI and non-HAI groups, then adjustment for patient characteristics is also important, where survival regression with time-dependent covariates is likely to provide the most flexible approach. Matched design is more likely to result in data loss, whereas a multistate model is limited by the challenge in adjusting for covariates. These findings have important implications for future cost-effectiveness studies of infection prevention and control programmes.