A pre-pandemic COVID-19 assessment of the costs of prevention and control interventions for healthcare associated infections in medical and surgical units in Québec
Bibliographic record
Abstract
Abstract Background Healthcare-associated infections (HCAIs) present a major public health problem that significantly affects patients, health care providers and the entire healthcare system. Infection prevention and control programs limit HCAIs and are an indispensable component of patient and healthcare worker safety. The clinical best practices (CBPs) of handwashing, screening, hygiene and sanitation of surfaces and equipment, and basic and additional precautions are keystones of infection prevention and control (IPC). Systematic reviews of IPC economic evaluations report the lack of rigorous empirical evidence demonstrating the cost-benefit of IPC program in general, and point to the lack of assessment of the value of investing in CBPs more specifically. Objective This study aims to assess overall costs associated with each of the four CBPs. Methods Across two Quebec hospitals, 48 healthcare workers were observed for two hours each shift, for two consecutive weeks. A modified time-driven activity-based costing framework method was used to capture all human resources (time) and materials required (e.g. masks, cloths, disinfectants) for each clinical best practice. Using a hospital perspective with a time horizon of one year, median costs per CBP per hour, as well as the cost per action, were calculated and reported in 2018 Canadian dollars. Sensitivity analyses were performed. Results A total of 1831 actions were recorded. The median cost of hand hygiene (N = 867) was 19.6 cents per action. For cleaning and disinfection of surfaces (N = 102), the cost was 21.4 cents per action, while cleaning of small equipment (N = 85) was 25.3 cents per action. Additional precautions median cost was $4.13 per action. The donning or removing or personal protective equipment (N = 720) cost was 75.9 cents per action. Finally, the total median costs for the five categories of clinical best practiced assessed were 27.2 cents per action. Conclusion The costs of clinical best practices were low, from 20 cents to $4.13 per action. This study provides evidence based arguments with which to support the allocation of resources to infection prevention and control practices that directly affect the safety of patients, healthcare workers and the public. Further research of costing clinical best care practices is warranted.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".