BACKGROUND: Prevention of Clostridioides difficile infection (CDI) is a national priority and may be facilitated by deployment of the Targeted Assessment for Prevention (TAP) Strategy, a quality improvement framework providing a focused approach to infection prevention. This article describes the process and outcomes of TAP Strategy implementation for CDI prevention in a healthcare system. METHODS: Hospital A was identified based on CDI surveillance data indicating an excess burden of infections above the national goal; hospitals B and C participated as part of systemwide deployment. TAP facility assessments were administered to staff to identify infection control gaps and inform CDI prevention interventions. Retrospective analysis was performed using negative-binomial, interrupted time series (ITS) regression to assess overall effect of targeted CDI prevention efforts. Analysis included hospital-onset, laboratory-identified C. difficile event data for 18 months before and after implementation of the TAP facility assessments. RESULTS: The systemwide monthly CDI rate significantly decreased at the intervention (β2, -44%; P = .017), and the postintervention CDI rate trend showed a sustained decrease (β1 + β3; -12% per month; P = .008). At an individual hospital level, the CDI rate trend significantly decreased in the postintervention period at hospital A only (β1 + β3, -26% per month; P = .003). CONCLUSIONS: This project demonstrates TAP Strategy implementation in a healthcare system, yielding significant decrease in the laboratory-identified C. difficile rate trend in the postintervention period at the system level and in hospital A. This project highlights the potential benefit of directing prevention efforts to facilities with the highest burden of excess infections to more efficiently reduce CDI rates.
BACKGROUND: Prevention of Clostridioides difficile infection (CDI) is a national priority and may be facilitated by deployment of the Targeted Assessment for Prevention (TAP) Strategy, a quality improvement framework providing a focused approach to infection prevention. This article describes the process and outcomes of TAP Strategy implementation for CDI prevention in a healthcare system. METHODS: Hospital A was identified based on CDI surveillance data indicating an excess burden of infections above the national goal; hospitals B and C participated as part of systemwide deployment. TAP facility assessments were administered to staff to identify infection control gaps and inform CDI prevention interventions. Retrospective analysis was performed using negative-binomial, interrupted time series (ITS) regression to assess overall effect of targeted CDI prevention efforts. Analysis included hospital-onset, laboratory-identified C. difficile event data for 18 months before and after implementation of the TAP facility assessments. RESULTS: The systemwide monthly CDI rate significantly decreased at the intervention (β2, -44%; P = .017), and the postintervention CDI rate trend showed a sustained decrease (β1 + β3; -12% per month; P = .008). At an individual hospital level, the CDI rate trend significantly decreased in the postintervention period at hospital A only (β1 + β3, -26% per month; P = .003). CONCLUSIONS: This project demonstrates TAP Strategy implementation in a healthcare system, yielding significant decrease in the laboratory-identified C. difficile rate trend in the postintervention period at the system level and in hospital A. This project highlights the potential benefit of directing prevention efforts to facilities with the highest burden of excess infections to more efficiently reduce CDI rates.
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Keywords:
Clostridioides difficile Infection; HAI; National Healthcare Safety Network; TAP; TAP Report; Targeted Assessment for Prevention Strategy; cumulative attributable difference; healthcare-associated infections; hospital infection prevention and control; infection prevention interventions; negative-binomial interrupted time series regression; quality improvement; reduction; standardized infection ratio; surveillance data for action
Authors: Fernanda C Lessa; Yi Mu; Wendy M Bamberg; Zintars G Beldavs; Ghinwa K Dumyati; John R Dunn; Monica M Farley; Stacy M Holzbauer; James I Meek; Erin C Phipps; Lucy E Wilson; Lisa G Winston; Jessica A Cohen; Brandi M Limbago; Scott K Fridkin; Dale N Gerding; L Clifford McDonald Journal: N Engl J Med Date: 2015-02-26 Impact factor: 91.245
Authors: Clare Rock; Rebecca Perlmutter; David Blythe; Jacqueline Bork; Kimberly Claeys; Sara E Cosgrove; Kate Dzintars; Valeria Fabre; Anthony D Harris; Emily Heil; Yea-Jen Hsu; Sara Keller; Lisa L Maragakis; Aaron M Milstone; Daniel J Morgan; Prashila Dullabh; Petry S Ubri; Christina Rotondo; Richard Brooks; Surbhi Leekha Journal: BMJ Qual Saf Date: 2021-12-09 Impact factor: 7.035