AUTHOR=Kaposi Adam , Nagy Attila , Gomori Gabriella , Kocsis Denes 

TITLE=Application of the case-mix index and length of stay for hospital waste management comparison: introduction of a new adjusted metric

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1623725

DOI=10.3389/fpubh.2025.1623725

ISSN=2296-2565

ABSTRACT=IntroductionHazardous 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.MethodsUsing 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.ResultsResults 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.DiscussionBy 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.