ArticleFrontiers in public health2025
Application of the case-mix index and length of stay for hospital waste management comparison: introduction of a new adjusted metric.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Financing Regimes and Case-Mix Complexity in Psychiatric Hospitals Beyond the Pandemic Shock-Insights from a Regional European Healthcare System.Healthcare (Basel, Switzerland) · 2026Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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 Results: Results show that higher CMI correlates with increased HHCW ( 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.
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