Expanding the statistical toolbox: Analytic approaches for cohort studies with healthcare-associated infectious outcomes

Rebecca A. Pierce, Justin Lessler, Aaron M. Milstone

Research output: Contribution to journalReview article

Abstract

Purpose of review Healthcare-associated infections (HAIs) are a leading cause of adverse patient outcomes. Further elucidation of the etiology of these infections and the pathogens that cause them has been a primary goal of research in infection control and healthcare epidemiology. Longitudinal studies, in particular, afford a range of statistical methods to better understand the process of pathogen acquisition or HAI development. This review intends to convey the scope of available statistical methodology. Recent findings Despite the range of methods available, logistic regression remains the dominant statistical approach in use. Poisson regression, survival methods, and mechanistic (mathematical) models remain underutilized. Recent studies that use these approaches are looking beyond associations to answer questions about the timing, duration, and mechanism of infectious risk. Summary Logistic regression remains an important approach to the study of HAIs, but in the context of cohort studies, it is most appropriate for short observation periods, during which mechanism is not of primary interest. Additional statistical methodologies are available to build upon risk factor analysis to better inform the process of risk and infection in the hospital setting.

Original languageEnglish (US)
Pages (from-to)384-391
Number of pages8
JournalCurrent opinion in infectious diseases
Volume28
Issue number4
DOIs
StatePublished - Aug 25 2015

    Fingerprint

Keywords

  • Poisson regression
  • healthcare-associated infection
  • logistic regression
  • mathematical models
  • survival analysis

ASJC Scopus subject areas

  • Microbiology (medical)
  • Infectious Diseases

Cite this