In 2017, a point-prevalence survey was conducted with 12,931 patients in 96 hospitals across Switzerland as part of the national strategy to prevent healthcare-associated infections (HAIs). We present novel statistical methods to assess incidence proportions of HAI and attributable length-of-stay (LOS) in point-prevalence surveys.

Follow-up data were collected for a subsample of patients and were used to impute follow-up data for all remaining patients. We used weights to correct length bias in logistic regression and multistate analyses. Methods were also tested in simulation studies.

The estimated incidence proportion of HAIs during hospital stay and not present at admission was 2.3% (95% confidence intervals [CI], 2.1–2.6), the most common type being lower respiratory tract infections (0.8%; 95% CI, 0.6–1.0). Incidence proportion was highest in patients with a rapidly fatal McCabe score (7.8%; 95% CI, 5.7–10.4). The attributable LOS for all HAI was 6.4 days (95% CI, 5.6–7.3) and highest for surgical site infections (7.1 days, 95% CI, 5.2–9.0). It was longest in the age group of 18–44 years (9.0 days; 95% CI, 5.4–12.6). Risk-factor analysis revealed that McCabe score had no effect on the discharge hazard after infection (hazard ratio [HR], 1.21; 95% CI, 0.89–1.63). Instead, it only influenced the infection hazard (HR, 1.84; 95% CI, 1.39–2.43) and the discharge hazard prior to infection (HR, 0.73; 95% CI, 0.66–0.82).

In point-prevalence surveys with limited follow-up data, imputation and weighting can be used to estimate incidence proportions and attributable LOS that would otherwise require complete follow-up data.

The pressures exerted by the coronavirus disease 2019 (COVID-19) pandemic pose an unprecedented demand on healthcare services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities.

We describe methods used by a university hospital to forecast case loads and time to peak incidence.

We developed a set of models to forecast incidence among the hospital catchment population and to describe the COVID-19 patient hospital-care pathway. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care-pathway model according to expert opinion (ie, the static model). Once sufficient local data were available, trends for the time-dependent effective reproduction number were fitted, and the care pathway was reparameterized using hazards for real patient admission, referrals, and discharge (ie, the dynamic model).

The static model, deployed before the epidemic, exaggerated the bed occupancy for general wards (116 forecasted vs 66 observed), ICUs (47 forecasted vs 34 observed), and predicted the peak too late: general ward forecast April 9 and observed April 8 and ICU forecast April 19 and observed April 8. After April 5, the dynamic model could be run daily, and its precision improved with increasing availability of empirical local data.

The models provided data-based guidance for the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when the population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.

To study the impact of duration of mechanical ventilation, hospitalization and multiple ventilation episodes on the development of pneumonia while accounting for extubation as a competing event.

A multicenter data base from a Spanish surveillance network was used to conduct a retrospective analysis of prospectively collected intensive care patients followed from admission to discharge.

Spanish intensive care units (ICUs).

Mechanically ventilated adult patients from 158 ICUs with 45,486 admissions, 48,705 ventilation episodes, and 314,196 ventilator days.

Competing-risk models were applied to account for extubation plus 48 hours as a competing event for acquiring ventilator-associated pneumonia (VAP).

Time in the ICU before mechanical ventilation was associated with an increased VAP hazard rate and with longer intubation time. This indirect prolongation of intubation increased the cumulative risk to eventually acquire VAP. For instance, comparing 3–4 versus 0 days, the adjusted VAP hazard ratio was 1.40 (95% confidence interval [CI], 1.19–1.64) and the adjusted extubation hazard ratio was 0.64 (95% CI, 0.61–0.68), which leads to an adjusted VAP subdistribution hazard ratio (sHR) of 2.13 (95% CI, 1.83–2.50). Similarly, due to prolonged intubation, multiple ventilation episodes increase the risk for VAP; the adjusted sHR is 1.52 (95% CI, 1.35–1.72) for the second episode compared to the first episode, and the adjusted sHR is 1.54 (95% CI, 1.03–2.30) for the third episode compared to the first episode. The Kaplan-Meier method produced an upward biased estimated cumulative risk for VAP.

A competing-risk analysis is necessary to receive unbiased risk estimates and to quantify the indirect effect of intubation time on the cumulative VAP risk. Our findings may guide physicians to improve medical decisions related to the harms and benefits of the duration of ventilation.

Competing risks are a necessary consideration when analyzing risk factors for nosocomial infections (NIs). In this article, we identify additional information that a competing risks analysis provides in a hospital setting. Furthermore, we improve on established methods for nested case-control designs to acquire this information.

Using data from 2 Spanish intensive care units and model simulations, we show how controls selected by time-dynamic sampling for NI can be weighted to perform risk-factor analysis for death or discharge without infection. This extension not only enables hazard rate analysis for the competing risk, it also enables prediction analysis for NI.

The estimates acquired from the extension were in good agreement with the results from the full (real and simulated) cohort dataset. The reduced dataset results averted any false interpretation common in a competing-risks setting.

Using additional information that is routinely collected in a hospital setting, a nested case-control design can be successfully adapted to avoid a competing risks bias. Furthermore, this adapted method can be used to reanalyze past nested case-control studies to enhance their findings.

Multistate and competing risks models have become an established and adequate tool with which to quantify determinants and consequences of nosocomial infections. In this tutorial article, we explain and demonstrate the basics of these models to a broader audience of professionals in health care, infection control, and hospital epidemiology.

Using a publicly available data set from a cohort study of intensive care unit patients, we show how hospital infection data can be displayed and explored graphically and how simple formulas are derived under some simplified assumptions for illustrating the basic ideas behind multistate models. Only a few simply accessible values (event counts and patient days) and a pocket calculator are needed to reveal basic insights into cumulative risk and clinical outcomes of nosocomial infection in terms of mortality and length of stay.

We show how to use these values to perform basic multistate analyses in own data or to correct biased estimates in published data, as these values are often reported. We also show relationships between multistate-based hazard ratios and odds ratios, which are derived from the popular logistic regression model.

No sophisticated statistical software is required to apply a basic multistate model and to avoid typical pitfalls such as time-dependent or competing-risks bias.

Infect Control Hosp Epidemiol 2017;38:953–959