Impact of Seasonality and Influenza Rates on Interventions to Reduce Hospital-Acquired <i>Clostridioides difficile</i> Rates
Bibliographic record
Abstract
Background: Hospital-acquired Clostridioides difficile infection (HA-CDI) rates are highly variable over time, posing problems for research assessing interventions that might improve rates. By understanding seasonality in HA-CDI rates and the impacts that other factors such as influenza admissions might have on these rates, we can account for them when establishing the relationship between interventions and infection rates. We assessed whether there were seasonal trends in HA-CDI and whether they could be accounted for by influenza rates. Methods: We assessed HA-CDI rates per 10,000 patient days, and the rate of hospitalized patients with influenza per 1,000 admissions in 4 acute-care facilities (n = 2,490 beds) in Calgary, Alberta, from January 2016 to December 2018. We used 4 statistical approaches in R (version 3.5.1 software): (1) autoregressive integrated moving average (ARIMA) to assess dependencies and trends in each of the monthly HA-CDI and influenza series; (2) cross correlation to assess dependencies between the HA-CDI and influenza series lagged over time; (3) Poisson harmonic regression models (with sine and cosine components) to assess the seasonality of the rates; and (4) Poisson regression to determine whether influenza rates accounted for seasonality in the HA-CDI rates. Results: Conventional ARIMA approaches did not detect seasonality in the HA-CDI rates, but we found strong seasonality in the influenza rates. A cross-correlation analysis revealed evidence of correlation between the series at a lag of zero (R = 0.41; 95% CI, 0.10–0.65) and provided an indication of a seasonal relationship between the series (Fig. 1). Poisson regression suggested that influenza rates predicted CDI rates (P < .01). Using harmonic regression, there was evidence of seasonality in HA-CDI rates (2 [2 df] = 6.62; P < .05) and influenza rates (2 [2 df] = 1,796.6; P < .001). In a Poisson model of HA-CDI rates with both the harmonic components and influenza admission rates, the harmonic components were no longer predictive of HA-CDI rates. Conclusions: Harmonic regression provided a sensitive means of identifying seasonality in HA-CDI rates, but the seasonality effect was accounted for by influenza admission rates. The relationship between HA-CDI and influenza rates is likely mediated by antibiotic prescriptions, which needs to be assessed. To improve precision and reduce bias, research on interventions to reduce HA-CDI rates should assess historic seasonality in HA-CDI rates and should account for influenza admissions. Funding: None Disclosures: 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".