From Prevalence to Incidence: Validation of Healthcare-Associated Infection Incidence Estimations from Prevalence Data in a Serbian University Hospital

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Abstract
Introduction: Continuous prospective surveillance is considered the reference method for monitoring healthcare-associated infections (HAIs), but it requires substantial personnel and organizational resources. Point prevalence surveys provide a simpler alternative, although they primarily measure prevalence rather than incidence. The aim of this study was to evaluate the validity of the estimation of cumulative incidence of healthcare-associated infections (HAIs) from a point prevalence survey (PPS) across different hospital wards. Methods: A HAI PPS was conducted between 14 November and 2 December 2022, at the University Clinical Center Nis, Serbia, using the European Centre for Disease Prevention and Control (ECDC) protocol. Data from 774 patients were analyzed. Prevalence data from the Surgical Intensive Care Unit (SICU) and the Orthopedics and Traumatology department were converted into estimated cumulative incidence using the Rhame and Sudderth formula. These estimates were compared against the actual 2022 cumulative incidence obtained via continuous surveillance. Results: The overall HAI prevalence was 3.6% (28 HAIs among 27 patients; 95% CI: 2.5–5.2%). In the SICU, the formula-derived calculated incidence (5.3 per 100 admissions) demonstrated an excellent fit with the observed continuous surveillance incidence (5.54 per 100 admissions; ratio: 0.96), falling well within its 95% CI (4.24–7.22%). Conversely, in the Orthopedics department, the formula significantly overestimated the true incidence by a factor of 10.7 (2.6 vs. 0.24 per 100 admissions), lying far above the upper limit of the observed 95% CI (0.09–0.62%). Conclusions: Converting prevalence data into cumulative incidence estimates is an accurate tool for estimating HAI incidence in stable, high-risk environments like intensive care units. However, it should be applied with extreme caution in low-prevalence specialty wards, where small sample sizes and individual outliers can lead to profound misestimation.

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