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Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study

OBJECTIVE: The validity of risk-adjustment methods based on administrative data has been questioned because hospital referral regions with higher diagnosis frequencies report lower case-fatality rates, implying that diagnoses do not track the underlying health risk. The objective of this study is to...

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Autores principales: Li, Linyan, Chamoun, George F, Chamoun, Nassib G, Sessler, Daniel, Gopinath, Valérie, Saini, Vikas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479990/
https://www.ncbi.nlm.nih.gov/pubmed/34588267
http://dx.doi.org/10.1136/bmjopen-2021-054632
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author Li, Linyan
Chamoun, George F
Chamoun, Nassib G
Sessler, Daniel
Gopinath, Valérie
Saini, Vikas
author_facet Li, Linyan
Chamoun, George F
Chamoun, Nassib G
Sessler, Daniel
Gopinath, Valérie
Saini, Vikas
author_sort Li, Linyan
collection PubMed
description OBJECTIVE: The validity of risk-adjustment methods based on administrative data has been questioned because hospital referral regions with higher diagnosis frequencies report lower case-fatality rates, implying that diagnoses do not track the underlying health risk. The objective of this study is to test the hypothesis that regional variation of diagnostic frequency in inpatient records is not associated with different coding practices but a reflection of the underlying health risks. DESIGN: We applied two stratification methods to Medicare Analysis and Provider Review data from 2009 through 2014: (1) the number of chronic conditions; and, (2) quartiles of Risk Stratification Index (RSI)-defined risk. After sorting hospital referral regions into quintiles of diagnostic frequency, we examined all-cause mortality. SETTING: Medicare Analysis and Provider Review administrative database. PARTICIPANTS: 18 126 301 hospitalised Medicare fee-for-service beneficiaries aged 65 or older who had at least one hospital-based procedure between 2009 and 2014. EXPOSURE: Coding frequency and baseline regional population risk factors by hospital referral region. PRIMARY AND SECONDARY OUTCOME(S) AND MEASURE(S): One year all-cause mortality in patients having the same number of chronic conditions or within the same RSI score quartile across US health referral regions, grouped by diagnostic frequency. RESULTS: No consistent relationship between diagnostic frequency and mortality in the risk stratum defined by number of chronic conditions was detected. In the strata defined by RSI quartile, there was no decrease in mortality as a function of diagnostic frequency. Instead, adjusted mortality was positively correlated with socioeconomic risk factors. CONCLUSIONS: Using present-on-admission codes only, diagnostic frequency among inpatients with at least one hospital-based procedure appears to be consequent to differences in baseline population health status, rather than diagnostic coding practices. In this population, claims-based risk-adjustment using RSI appears to be useful for assessing hospital outcomes and performance.
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spelling pubmed-84799902021-10-08 Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study Li, Linyan Chamoun, George F Chamoun, Nassib G Sessler, Daniel Gopinath, Valérie Saini, Vikas BMJ Open Health Informatics OBJECTIVE: The validity of risk-adjustment methods based on administrative data has been questioned because hospital referral regions with higher diagnosis frequencies report lower case-fatality rates, implying that diagnoses do not track the underlying health risk. The objective of this study is to test the hypothesis that regional variation of diagnostic frequency in inpatient records is not associated with different coding practices but a reflection of the underlying health risks. DESIGN: We applied two stratification methods to Medicare Analysis and Provider Review data from 2009 through 2014: (1) the number of chronic conditions; and, (2) quartiles of Risk Stratification Index (RSI)-defined risk. After sorting hospital referral regions into quintiles of diagnostic frequency, we examined all-cause mortality. SETTING: Medicare Analysis and Provider Review administrative database. PARTICIPANTS: 18 126 301 hospitalised Medicare fee-for-service beneficiaries aged 65 or older who had at least one hospital-based procedure between 2009 and 2014. EXPOSURE: Coding frequency and baseline regional population risk factors by hospital referral region. PRIMARY AND SECONDARY OUTCOME(S) AND MEASURE(S): One year all-cause mortality in patients having the same number of chronic conditions or within the same RSI score quartile across US health referral regions, grouped by diagnostic frequency. RESULTS: No consistent relationship between diagnostic frequency and mortality in the risk stratum defined by number of chronic conditions was detected. In the strata defined by RSI quartile, there was no decrease in mortality as a function of diagnostic frequency. Instead, adjusted mortality was positively correlated with socioeconomic risk factors. CONCLUSIONS: Using present-on-admission codes only, diagnostic frequency among inpatients with at least one hospital-based procedure appears to be consequent to differences in baseline population health status, rather than diagnostic coding practices. In this population, claims-based risk-adjustment using RSI appears to be useful for assessing hospital outcomes and performance. BMJ Publishing Group 2021-09-28 /pmc/articles/PMC8479990/ /pubmed/34588267 http://dx.doi.org/10.1136/bmjopen-2021-054632 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Health Informatics
Li, Linyan
Chamoun, George F
Chamoun, Nassib G
Sessler, Daniel
Gopinath, Valérie
Saini, Vikas
Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study
title Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study
title_full Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study
title_fullStr Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study
title_full_unstemmed Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study
title_short Elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study
title_sort elucidating the association between regional variation in diagnostic frequency with risk-adjusted mortality through analysis of claims data of medicare inpatients: a cross-sectional study
topic Health Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479990/
https://www.ncbi.nlm.nih.gov/pubmed/34588267
http://dx.doi.org/10.1136/bmjopen-2021-054632
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