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Problematic Risk Adjustment in National Healthcare Safety Network Measures.

ABSTRACT: This article reviews the risk-adjustment models underpinning the National Healthcare Safety Network (NHSN) standardized infection ratios. After first describing the models, the authors focus on hospital intensive care unit (ICU) designation as a variable employed across the various risk models. The risk-adjusted frequency with which ICU services are reported in Medicare fee-for-service claims data was compared as a proxy for determining whether reporting of ICU days is similar across hospitals. Extreme variation was found in the reporting of ICU utilization among admissions for congestive heart failure, ranging from 25% in the lowest admission hospital quartile to 95% in the highest. The across-hospital variation in reported ICU utilization was found to be unrelated to patient severity. Given that such extreme variation appears in a designation of ICU versus non-ICU utilization, the NHSN risk-adjustment models' dependence on nursing unit designation should be a cause for concern.

SUBMITTER: Fuller RL

PROVIDER: S-EPMC7281913 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPredictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.MethodsA random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.ResultsBoth diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.ConclusionsPredicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.

Project description:OBJECTIVE To describe pathogen distribution and antimicrobial resistance patterns for healthcare-associated infections (HAIs) reported to the National Healthcare Safety Network (NHSN) from pediatric locations during 2011-2014. METHODS Device-associated infection data were analyzed for central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP), and surgical site infection (SSI). Pooled mean percentage resistance was calculated for a variety of pathogen-antimicrobial resistance pattern combinations and was stratified by location for device-associated infections (neonatal intensive care units [NICUs], pediatric intensive care units [PICUs], pediatric oncology and pediatric wards) and by surgery type for SSIs. RESULTS From 2011 to 2014, 1,003 hospitals reported 20,390 pediatric HAIs and 22,323 associated pathogens to the NHSN. Among all HAIs, the following pathogens accounted for more than 60% of those reported: Staphylococcus aureus (17%), coagulase-negative staphylococci (17%), Escherichia coli (11%), Klebsiella pneumoniae and/or oxytoca (9%), and Enterococcus faecalis (8%). Among device-associated infections, resistance was generally lower in NICUs than in other locations. For several pathogens, resistance was greater in pediatric wards than in PICUs. The proportion of organisms resistant to carbapenems was low overall but reached approximately 20% for Pseudomonas aeruginosa from CLABSIs and CAUTIs in some locations. Among SSIs, antimicrobial resistance patterns were similar across surgical procedure types for most pathogens. CONCLUSION This report is the first pediatric-specific description of antimicrobial resistance data reported to the NHSN. Reporting of pediatric-specific HAIs and antimicrobial resistance data will help identify priority targets for infection control and antimicrobial stewardship activities in facilities that provide care for children. Infect Control Hosp Epidemiol 2018;39:1-11.

Project description:ImportanceFew studies have examined the role of problematic anger in long-term adjustment of service members transitioning out of the military.ObjectiveTo determine the prevalence of problematic anger during the military-to-civilian transition period and the association of problematic anger with adjustment to civilian life.Design, setting, and participantsThis cohort study used 2 waves of survey data administered approximately 5 years apart (time 1 [T1; September 26, 2014, to August 25, 2016] and time 2 [T2; October 23, 2019, to August 31, 2021]) from the Millennium Cohort Study, a population-based military study. Participants were US active-duty service members within 24 months of separating from military service at T1. Statistical analysis was performed from September 2021 to May 2022.ExposuresProblematic anger was operationalized as scoring at least 12 points on the 5-item Dimensions of Anger Reactions scale at T1.Main outcomes and measuresBehavioral and functional health (depression, posttraumatic stress disorder, problem drinking, functional limitations), relationship health (relationship quality, coping with parental demands, social support), and economic health (major financial problems, financial insecurity, homelessness, employment status) were assessed at T2. Covariates, assessed at T1, included demographics, military characteristics, mental health, problem drinking, and physical health.ResultsOf the 3448 participants, 2625 (76.1%) were male, 217 (6.3%) were Hispanic, 293 (8.5%) were non-Hispanic Black, and 2690 (78.0%) were non-Hispanic White; the mean (SD) age was 40.1 (8.5) years; 826 (24.0%) met criteria for problematic anger. Prevalence of problematic anger was 15.9% (95% CI, 12.2%-19.7%) 24 months prior to military separation and 31.2% (95% CI, 26.2%-36.2%) 24 months following separation. After adjusting for covariates, problematic anger was associated with greater likelihood of behavioral and functional health outcomes (eg, posttraumatic stress disorder: adjusted odds ratio, 1.55, 95% CI, 1.23-1.96), relationship health difficulties (eg, low social support: aOR, 1.66; 95% CI, 1.23-2.24), and economic difficulties (eg, substantial financial insecurity: aOR, 1.64; 95% CI, 1.13-2.39) at T2.Conclusions and relevanceThis cohort study found an association between prevalence of problematic anger during the military-to-civilian transition and problematic anger with subsequent adjustment difficulties among US service members. These findings suggest the need to equip service members proactively with skills to identify and manage anger as a way to support them before and during this period of transition.

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Problematic Risk Adjustment in National Healthcare Safety Network Measures.

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