Modelling interrupted time series to evaluate prevention and control of infection in healthcare

Abstract

The most common methods for evaluating interventions to reduce the rate of new Staphylococcus aureus (MRSA) infections in hospitals use segmented regression or interrupted time-series analysis. We describe approaches to evaluating interventions introduced in different healthcare units at different times. We compare fitting a segmented Poisson regression in each hospital unit with pooling the individual estimates by inverse variance. An extension of this approach to accommodate potential heterogeneity allows estimates to be calculated from a single statistical model: a 'stacked' model. It can be used to ascertain whether transmission rates before the intervention have the same slope in all units, whether the immediate impact of the intervention is the same in all units, and whether transmission rates have the same slope after the intervention. The methods are illustrated by analyses of data from a study at a Veterans Affairs hospital. Both approaches yielded consistent results. Where feasible, a model adjusting for the unit effect should be fitted, or if there is heterogeneity, an analysis incorporating a random effect for units may be appropriate.

Figures

Fig. 1.

Observed incident MRSA cases per 1000 patient-days. (a) Unit A, (b) unit B, and (c) area C.

Fig. 2.

Representation of model 1: within an individual unit. β0 = Starting baseline MRSA rate; β1 = slope of line prior to t0; β2 = drop at t0; β1 + β3 = slope after t0.

Fig. 3.

Representation of stacked data in model 2. ln(λ) = β0 + β1T + β2I + β3T* + γ1U2 + γ2U3; β1 = slope of line prior to intervention for units A, B, and area C; β2 = average drop at the intervention (drop occurs at T = 25 for unit A, T = 49 for unit B, T = 70 for area C; β1 + β3 = slope after intervention for units A, B, and area C. If β3 = 0, then the slopes are the same (β1) both before and after the intervention for all three units.

Fig. 4.

Representation of a model with a post-intervention threshold. ln(λ) = β0 + β1T + β2I +β3T* + β4T†.