Abstract
OBJECTIVES
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.
METHODS
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.
RESULTS
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).
CONCLUSIONS
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.
Doerken, Sam