A168 RATES OF <i>CLOSTRIDIOIDES DIFFICILE</i> INFECTIONS REQUIRING HOSPITALIZATION DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract Background Clostridium difficile infection (CDI) is the most common cause of nosocomial infectious diarrhea and is associated with a substantial burden of morbidity and mortality. Most cases of CDI are acquired through health care setting contacts, where appropriate infection prevention and control (IPC) measures such as contact isolation and proper hand hygiene have been demonstrated to prevent horizontal transmission. Aims We aimed to evaluate whether there was a reduction in CDI-related hospitalizations during the COVID-19 pandemic when an increased emphasis was placed on maintaining IPC measures. Methods We analyzed data from the National Inpatient Sample (NIS) between January 2018 and November 2020. The NIS is the largest publicly available administrative health database in the United States, capturing ampersand:003E7 million hospital admissions annually. All analyses were weighted for the complex survey design of the NIS. Temporal trends in monthly admissions for patients with CDI defined using International Classification of Disease 10th revision codes and excluding patients with established CDI carrier status were evaluated using joinpoint regression. Changes over time were expressed as monthly percent change (MPC). To establish whether temporal changes were related to a reduction in CDI or whether these were more generalized effects due to the pandemic, we compared these temporal trends to rates of hospitalizations for inflammatory bowel disease (IBD) and stroke. Results A total of 1,501,415 CDI admissions were identified. The mean age was 64.6 years and 54.2% of patient’s were female. 4.5% of admissions for CDI were associated with in-hospital mortality. Trends in hospitalization are summarized in Figure 1. Compared to the pre-pandemic period (January 2018-December 2019) CDI-related hospitalizations decreased by 9.1%/month [95% CI: -19.2%, +2.0%] (p=0.12) in early 2020 (January-March 2020). This reduction was not sustained: hospitalizations subsequently increased by +8.0% [95% CI: -4.0%, +21.6%] (p=0.19) in mid-2020 (April-June 2020) and were stable after July 2020 (MPC -0.9% [95% CI: -3.5%, +1.7%], p=0.48). Similar patterns were observed for both IBD and stroke-related hospitalizations. Total hospitalizations per month for CDI were similar by June 2020 as compared to the year preceding the pandemic. Conclusions Although an initial decrease in the rates of CDI hospitalizations were seen early in the pandemic (January-March 2020), this pattern was similarly seen for other chronic and acute conditions, and this has not been sustained long-term. Figure 1. Rates of CDI admissions (2018-2020) Funding Agencies None
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".