County Differences in Liver Mortality in the United States : Impact of Sociodemographics, Disease Risk Factors, and Access to Care
Copyright © 2021 AGA Institute. Published by Elsevier Inc. All rights reserved..
BACKGROUND AND AIMS: Data have demonstrated state-wide variability in mortality rates from liver disease (cirrhosis + hepatocellular carcinoma), but data are lacking at the local level (eg, county) to identify factors associated with variability in liver disease-related mortality and hotspots of liver disease mortality.
METHODS: We used Centers for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research data from 2009 to 2018 to calculate county-level, age-adjusted liver disease-related death rates. We fit multivariable linear regression models to adjust for county-level covariates related to demographics (ie, race and ethnicity), medical comorbidities (eg, obesity), access to care (eg, uninsured rate), and geographic (eg, distance to closest liver transplant center) variables. We used optimized hotspot analysis to identify clusters of liver disease mortality hotspots based on the final multivariable models.
RESULTS: In multivariable models, 61% of the variability in among-county mortality was explained by county-level race/ethnicity, poverty, uninsured rates, distance to the closest transplant center, and local rates of obesity, diabetes, and alcohol use. Despite adjustment, significant within-state variability in county-level mortality rates was found. Of counties in the top fifth percentile (ie, highest mortality) of fully adjusted mortality, 60% were located in 3 states: Oklahoma, Texas, and New Mexico. Adjusted mortality rates were highly spatially correlated, representing 5 clusters: South Florida; Appalachia and the eastern part of the Midwest; Texas and Oklahoma; New Mexico, Arizona, California, and southern Oregon; and parts of Washington and Montana.
CONCLUSIONS: Our data demonstrate significant intrastate differences in liver disease-related mortality, with more than 60% of the variability explained by patient demographics, clinical risk factors for liver disease, and access to specialty liver care.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:160 |
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| Enthalten in: |
Gastroenterology - 160(2021), 4 vom: 05. März, Seite 1140-1150.e1 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Goldberg, David [VerfasserIn] |
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| Links: |
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| Themen: |
Chronic Liver Disease |
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| Anmerkungen: |
Date Completed 05.08.2021 Date Revised 04.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1053/j.gastro.2020.11.016 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM317872788 |
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| 500 | |a Date Revised 04.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 AGA Institute. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a BACKGROUND AND AIMS: Data have demonstrated state-wide variability in mortality rates from liver disease (cirrhosis + hepatocellular carcinoma), but data are lacking at the local level (eg, county) to identify factors associated with variability in liver disease-related mortality and hotspots of liver disease mortality | ||
| 520 | |a METHODS: We used Centers for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research data from 2009 to 2018 to calculate county-level, age-adjusted liver disease-related death rates. We fit multivariable linear regression models to adjust for county-level covariates related to demographics (ie, race and ethnicity), medical comorbidities (eg, obesity), access to care (eg, uninsured rate), and geographic (eg, distance to closest liver transplant center) variables. We used optimized hotspot analysis to identify clusters of liver disease mortality hotspots based on the final multivariable models | ||
| 520 | |a RESULTS: In multivariable models, 61% of the variability in among-county mortality was explained by county-level race/ethnicity, poverty, uninsured rates, distance to the closest transplant center, and local rates of obesity, diabetes, and alcohol use. Despite adjustment, significant within-state variability in county-level mortality rates was found. Of counties in the top fifth percentile (ie, highest mortality) of fully adjusted mortality, 60% were located in 3 states: Oklahoma, Texas, and New Mexico. Adjusted mortality rates were highly spatially correlated, representing 5 clusters: South Florida; Appalachia and the eastern part of the Midwest; Texas and Oklahoma; New Mexico, Arizona, California, and southern Oregon; and parts of Washington and Montana | ||
| 520 | |a CONCLUSIONS: Our data demonstrate significant intrastate differences in liver disease-related mortality, with more than 60% of the variability explained by patient demographics, clinical risk factors for liver disease, and access to specialty liver care | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Chronic Liver Disease | |
| 650 | 4 | |a Cirrhosis | |
| 650 | 4 | |a Hepatocellular Carcinoma | |
| 650 | 4 | |a Liver Mortality | |
| 700 | 1 | |a Ross-Driscoll, Katherine |e verfasserin |4 aut | |
| 700 | 1 | |a Lynch, Raymond |e verfasserin |4 aut | |
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