Costs Attributable to Healthcare-Acquired Infection in Hospitalized Adults and a Comparison of Economic Methods
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Bibliographic record
Abstract
BACKGROUND: Hospitals will increasingly bear the costs for healthcare-acquired conditions such as infection. Our goals were to estimate the costs attributable to healthcare-acquired infection (HAI) and conduct a sensitivity analysis comparing analytic methods. METHODS: A random sample of high-risk adults hospitalized in the year 2000 was selected. Measurements included total and variable medical costs, length of stay (LOS), HAI site, APACHE III score, antimicrobial resistance, and mortality. Medical costs were measured from the hospital perspective. Analytic methods included ordinary least squares linear regression and median quantile regression, Winsorizing, propensity score case matching, attributable LOS multiplied by mean daily cost, semi-log transformation, and generalized linear modeling. Three-state proportional hazards modeling was also used for LOS estimation. Attributable mortality was estimated using logistic regression. RESULTS: Among 1253 patients, 159 (12.7%) developed HAI. Using different methods, attributable total costs ranged between $9310 to $21,013, variable costs were $1581 to $6824, LOS was 5.9 to 9.6 days, and attributable mortality was 6.1%. The semi-log transformation regression indicated that HAI doubles hospital cost. The totals for 159 patients were $1.48 to $3.34 million in medical cost and $5.27 million for premature death. Excess LOS totaled 844 to 1373 hospital days. CONCLUSIONS: Costs for HAI were considerable from hospital and societal perspectives. This suggests that HAI prevention expenditures would be balanced by savings in medical costs, lives saved and available hospital days that could be used by overcrowded hospitals to enhance available services. Our results obtained by applying different economic methods to a single detailed dataset may inform future cost analyses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it