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.