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Estimating extra length of stay due to healthcare-associated infections before and after implementation of a hospital-wide infection control program

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  • Habibollah Arefian
  • Stefan Hagel
  • Dagmar Fischer
  • André Scherag
  • Frank Martin Brunkhorst
  • Jens Maschmann
  • Michael Hartmann

Abstract

Introduction: Healthcare-associated infections (HAIs) are a major health concern and have substantial effects on morbidity and mortality and increase healthcare costs. We investigated the effect of a hospital-wide program for the prevention of HAIs on additional length of stay (LOS). Methods: We analyzed data from a prospective, single-center, quasi-experimental study with two surveillance periods before and after implementation of an infection prevention intervention program. HAI diagnosis was made according to surveillance definition criteria established by the US Centers for Disease Control and Prevention. A multistate model was used to estimate additional LOS for patients with HAI in both surveillance periods. Results: During the first and second periods, 1,568 and 2,336 HAIs were identified among 26,943 and 35,211 patients, respectively. For HAI patients exclusively treated in a general ward, additional LOS was 8.4 (95% confidence interval, CI: 6.8–10.0) days in the first period and 9.6 (95% CI: 8.3–11.0) days in the second period (p = 0.26). For HAI patients treated in both an intensive care unit (ICU) and a general ward, additional LOS was 8.1 (95% CI: 6.3–9.9) days in the first period to 7.3 (95% CI: 6.1–8.5) days in the second period (p = 0.47). Conclusions: Healthcare-associated infections prolong LOS. A hospital-wide infection control program did not alter the prolongation of LOS.

Suggested Citation

  • Habibollah Arefian & Stefan Hagel & Dagmar Fischer & André Scherag & Frank Martin Brunkhorst & Jens Maschmann & Michael Hartmann, 2019. "Estimating extra length of stay due to healthcare-associated infections before and after implementation of a hospital-wide infection control program," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-11, May.
    • Handle: RePEc:plo:pone00:0217159
    DOI: 10.1371/journal.pone.0217159
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    1. Arthur Allignol & Martin Schumacher & Jan Beyersmann, 2011. "Estimating summary functionals in multistate models with an application to hospital infection data," Computational Statistics, Springer, vol. 26(2), pages 181-197, June.
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