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54 results

Page 1 of 3

  • The impact of healthcare associated infections on mortality and length of stay in Singapore—A time-varying analysis

  • Yiying Cai, Jamie Jay-May Lo, Indumathi Venkatachalam, Andrea L. Kwa, Paul A. Tambyah, Li Yang Hsu, Adrian Barnett, Kalisvar Marimuthu, Nicholas Graves
  • Journal: Infection Control & Hospital Epidemiology / Volume 41 / Issue 11 / November 2020
  • Published online by Cambridge University Press: 15 July 2020, pp. 1315-1320
  • Print publication: November 2020
  • Objective:

    Methods that include the time-varying nature of healthcare-associated infections (HAIs) avoid biases when estimating increased risk of death and excess length of stay. We determined the excess mortality risk and length of stay associated with HAIs among inpatients in Singapore using a multistate model that accommodates the timing of key events.

    Design:

    Analysis of existing prospective cohort study data.

    Setting:

    Seven public acute-care hospitals in Singapore.

    Patients:

    Inpatients reviewed in a HAI point-prevalence survey (PPS) conducted between June 2015 and February 2016.

    Methods:

    We modeled each patient’s admission over time using 4 states: susceptible with no HAI, infected, died, and discharged alive. We estimated the excess mortality risk and length of stay associated with HAIs, with adjustment for the baseline characteristics between the groups for mortality risk.

    Results:

    We included 4,428 patients, of whom 469 had ≥1 HAI. Using a multistate model, the expected excess length of stay due to any HAI was 1.68 days (95% confidence interval [CI], 1.15–2.21 days). Surgical site infections were associated with the longest excess length of stay of 4.68 days (95% CI, 2.60–6.76 days). After adjusting for baseline differences, HAIs were associated with increased hazards of in-hospital mortality (adjusted hazard ratio [aHR], 1.32; 95% CI, 1.09–1.65) and decreased hazards in being discharged (aHR, 0.75; 95% CI, 0.67–0.84).

    Conclusions:

    HAIs are associated with increased length of hospital stay and mortality in hospitalized patients. Avoiding nosocomial infections can improve patient outcomes and free valuable bed days.

  • Analysis of multisite intervention studies using generalized linear mixed models

  • Nicole M. White, Adrian G. Barnett
  • Journal: Infection Control & Hospital Epidemiology / Volume 40 / Issue 8 / August 2019
  • Published online by Cambridge University Press: 21 June 2019, pp. 910-917
  • Print publication: August 2019

  • Multisite intervention studies have become increasingly common in infection control, for example, looking for a change in hospital infection rates after a regional policy change. The design of these studies can take various forms, from pre–post observational studies to randomized trials, in which sites are randomly assigned to the intervention or in which the intervention is sequentially introduced to different sites over time. Data collected under these settings are clustered by hospital and/or ward, consist of repeated measurements and, in some cases, exhibit temporal and/or seasonal patterns. Failure to account for these features in data analysis could well result in biased estimates of intervention effectiveness and impact on the generalizability of model results.

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