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Turning up the volume to address biases in predicted healthcare-associated infections and enhance U.S. hospital rankings: A data-driven approach.
Armbrister, Avonne J; Finke, Ashley M; Long, Andrew M; Korvink, Michael; Gunn, Laura H.
Afiliación
  • Armbrister AJ; Department of Public Health Sciences, University of North Carolina (UNC) at Charlotte, Charlotte, NC.
  • Finke AM; Department of Public Health Sciences, University of North Carolina (UNC) at Charlotte, Charlotte, NC; School of Data Science, UNC Charlotte, Charlotte, NC.
  • Long AM; Department of Public Health Sciences, University of North Carolina (UNC) at Charlotte, Charlotte, NC.
  • Korvink M; Department of ITS Data Science, Premier, Inc., Charlotte, NC.
  • Gunn LH; Department of Public Health Sciences, University of North Carolina (UNC) at Charlotte, Charlotte, NC; School of Data Science, UNC Charlotte, Charlotte, NC; School of Public Health, Faculty of Medicine, Imperial College London, London, UK. Electronic address: laura.gunn@uncc.edu.
Am J Infect Control ; 50(2): 166-175, 2022 02.
Article en En | MEDLINE | ID: mdl-34425178
OBJECTIVES: To examine potential biases in standardized infection ratio (SIR) metrics due to static U.S. Centers for Disease Control and Prevention (CDC) parameters and non-linearity of infection outcomes with volume. Correspondingly, to enhance the CDC predictions by incorporating additional information from volume metrics and explore an alternative approach to more fairly rank hospitals to address the SIR=0 problem. METHODS: This population-based study uses publicly available 2019 healthcare-associated infections (HAI) data from 3096 acute care U.S. hospitals. HAI-specific Poisson generalized additive models illustrate the recalibration of CDC predictions, using volume-based spline functions to adjust for biases. Implied cumulative distribution functions (CDF) were derived, and HAI-facility-specific probabilities were calculated. Hospital rankings implied from these HAI-stratified probabilities were calculated. RESULTS: Calibration plots demonstrate existing biases associated with CDC infection over-predictions. Volume-based spline functions were significant for all HAIs (P<.0004). CDF-based rankings resulted in larger discrimination across hospitals based on strength of evidence, especially among SIR=0 facilities. National maps depict ranking differences by HAI and state. CONCLUSION: Adjustment of SIR biases, which differ by facility volume, is needed to produce more accurate and fairer hospital rankings.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infección Hospitalaria Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: Am J Infect Control Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infección Hospitalaria Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: Am J Infect Control Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos