National healthcare-associated infections surveillance programs: A scoping review
Bibliographic record
Abstract
Background: National surveillance of healthcare-associated infections (HAIs) is necessary to identify areas of concern, monitor trends, and provide benchmark rates enabling comparison between hospitals.Benchmark rates require representative and large sample sizes often based on pooling of surveillance data.We performed a scoping review to understand the organization of national HAI surveillance programs globally.Methods: The search strategy included a literature review, Google search and personal communications with HAI surveillance program managers.Thirty-five countries were targeted from four regions (North America, Europe, United Kingdom and Oceania).The following information was retrieved: name of surveillance program, survey types (prevalence or incidence), frequency of reports, mode of participation (mandatory or voluntary), and infections under surveillance.Results: Two hundred and twenty articles of 6,688 identified were selected.The four countries with most publications were the US (48.2%), Germany (14.1%), Spain (6.8%) and Italy (5.9%).These articles identified HAI surveillance programs in 28 of 35 countries (80.0%), operating on a voluntary basis and monitoring HAI incidence rates.Most HAIs monitored surgical site infections in hip (n=20, 71.4%) and knee (n=19, 67.9%) and Clostridoides difficile infections (n=17, 60.7%). Conclusion:Most countries analyzed have HAI surveillance programs, with characteristics varying by country.Patient-level data reporting with numerators and denominators is available for almost every surveillance program, allowing for reporting of incidence rates and more refined benchmarks, specific to a given healthcare category thus offering data that can be used to measure, monitor, and improve the incidence of HAIs.
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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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".