Application of the case-mix index and length of stay for hospital waste management comparison
Introduction of a new adjusted metric
Bibliographic Data
| ID | 22067055 |
|---|---|
| Authors | Ádám Kaposi (University of Debrecen), Attila Nagy (0009-0009-1381-105X, University of Debrecen), Attila Csaba Nagy (0000-0002-0554-7350, University of Debrecen), Gabriella Gömöri (University of Debrecen), Dénes Kocsis (0000-0002-5797-9016, University of Debrecen, corresponding author) |
| Year | 2025 |
| Volume | 13 |
| Pages | 1623725-1623725 |
| Publication date | 2025-11-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2025.1623725 |
| PMID | 41312247 |
| OpenAlex | W4416128257 |
| Language | EN |
| References cited | 42 |
Introduction Hazardous healthcare waste (HHCW) presents escalating environmental and operational challenges, yet traditional indicators such as waste generation rate (kg/bed/day) fail to account for patient complexity or care intensity, leading to biased institutional comparisons. Despite various previous normalization attempts, no validated framework has yet integrated clinical and operational heterogeneity into a single benchmarking metric. This study introduces and validates the Complexity-Adjusted Waste Index (CAWI), a novel metric that integrates the Case-Mix Index (CMI) and Length of Stay (LOS) to normalize waste generation across hospitals with heterogeneous clinical profiles. Methods Using national data from 94 inpatient institutions in Hungary (2017–2021), CAWI was calculated and compared with conventional HHCW generation rates through Spearman correlation, Fisher’s Z -tests, and robust regression models. Results Results show that higher CMI correlates with increased HHCW ( r = 0.49, p < 0.001), while shorter LOS is associated with higher daily waste intensity ( r = −0.67, p < 0.001). CAWI demonstrated reduced statistical dispersion (SD = 0.15 vs. 0.27) and stronger correlations with key institutional variables, including number of ICU-patients ( r = 0.78 vs. 0.67) and number of inpatients ( r = 0.71 vs. 0.54), with significantly lower model error terms. Discussion By explicitly combining patient complexity and treatment intensity into a transferable normalization framework, CAWI advances current benchmarking approaches both theoretically and methodologically. The CAWI framework offers a statistically robust and scalable solution for complexity-sensitive benchmarking, enabling more accurate cross-institutional comparisons and supporting targeted waste reduction strategies aligned with circular economy principles
Benchmarking · Hospital waste · Scalability · Healthcare and Environmental Waste Management · Healthcare cost, quality, practices · Municipal Solid Waste Management
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Evaluation of inpatient services of tertiary comprehensive hospitals based on DRG payment
Identifying and Predicting Healthcare Waste Management Costs for an Optimal Sustainable Management System
Assessment of the health care waste generation rates and its management system in hospitals of Addis Ababa, Ethiopia, 2011
| Citation velocity | historical |
|---|---|
| Highly cited | No |