Examining Prevalence and Risk Factors of Incontinence-Associated Dermatitis Using the International Pressure Ulcer Prevalence Survey
Bibliographic record
Abstract
PURPOSE: To evaluate prevalence and risk factors of incontinence-associated dermatitis (IAD). DESIGN: Retrospective analysis of 2016 International Pressure Ulcer Prevalence survey data. SUBJECTS AND SETTING: Adult patients who were in acute care, long-term acute care, long-term care, and rehabilitation facilities in the United States and Canada. METHODS: IAD prevalence was calculated among all patients surveyed, among the incontinent patients only, across multiple care settings, and by incontinence type. A logistic regression examined risk factors for IAD in the incontinent population. RESULTS: Nearly 1 in 5 incontinent patients had IAD documented. Incontinence-associated dermatitis prevalence in the entire patient population was 4.3% while incontinence prevalence was 18%. Of incontinent patients, prevalence of IAD ranged from 8.4% in long-term care facilities to 19% in acute care facilities. Facilities with higher rates of incontinence did not necessarily have higher prevalence of IAD. Incontinence-associated dermatitis prevalence by incontinence type ranged from 12% for patients with urinary incontinence to 26% for patients with fecal management systems. Regression results support the association of the following factors with an increased likelihood of IAD documented: all types of incontinence, fecal management systems, higher body weight, diminished mobility, additional linen layers, longer length of stay, and lower Braden Scale scores. CONCLUSIONS: Incontinence-associated dermatitis remains a concern in acute care settings. Risk factors associated with IAD were similar to risk factors previously reported for hospital-acquired pressure injuries, such as limited mobility, longer lengths of stay, and additional linen layers. By consistently documenting IAD as well as pressure injury prevalence, facilities may benchmark overall skin prevention models.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".