Infections caused by Clostridioides difficile represent an important cause of healthcare-associated diarrhea, with impact on hospital morbidity and mortality. Surveillance is essential to monitor incidence, declare outbreaks, and assess epidemiological changes associated with critical events such as the Covid-19 pandemic.

To evaluate the monthly temporal trend of C. difficile positivity and the impact of the Covid-19 pandemic in a tertiary public teaching hospital between 2013 and 2024.

A retrospective ecological study based on laboratory tests for detection of C. difficile antigen/toxin performed between January 2013 and December 2024. Data were organized into monthly time series, with calculation of the proportion of positive tests per month. Trends were assessed using simple linear regression and the Mann-Kendall test. Potential interruption of the series was analyzed using an interrupted time series (ITS) model, with March 2020 as the inflection point. A Joinpoint model was also fitted to detect trend breaks.

A total of 5,413 laboratory tests for detection of C. difficile antigen/toxin were included, of which 951 were positive (17.6%). Monthly positivity progressively ranged from 0% to 65%. Linear regression identified a significant increasing trend over the period (β = 0.0014; p < 0.001; R² = 0.51). The Mann-Kendall test confirmed an ascending monotonic trend (tau = 0.5377; p < 0.001). The ITS model indicated an increased trend after the onset of the pandemic (β before = 0.0014; β after = 0.0044; p < 0.001), although the immediate change was not statistically significant. The Joinpoint model identified an inflection point in November 2022, with a marked change in the slope of the curve (β₁ = 0.0014 before; increase of +0.0083 after the point).

The increase in Clostridioides difficile infections in the hospital environment occurred progressively and significantly, with an increase and accentuation of the trend after the pandemic. The role of the healthcare-associated infection control committee became essential to identify clinical suspicion of infections in the context of a potential change in the profile of hospitalized patients after the natural event of the Covid-19 pandemic.