Comorbidities and ethnic health disparities in the UK biobank.

Autor:	Teagle WL; National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland, USA., Norris ET; National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland, USA.; Applied Bioinformatics Laboratory, Atlanta, Georgia, USA.; School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.; PanAmerican Bioinformatics Institute, Valle del Cauca, Cali, Colombia., Rishishwar L; National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland, USA.; Applied Bioinformatics Laboratory, Atlanta, Georgia, USA.; School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.; PanAmerican Bioinformatics Institute, Valle del Cauca, Cali, Colombia., Nagar SD; School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.; PanAmerican Bioinformatics Institute, Valle del Cauca, Cali, Colombia., Jordan IK; Applied Bioinformatics Laboratory, Atlanta, Georgia, USA.; School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.; PanAmerican Bioinformatics Institute, Valle del Cauca, Cali, Colombia., Mariño-Ramírez L; National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland, USA.; PanAmerican Bioinformatics Institute, Valle del Cauca, Cali, Colombia.
Jazyk:	angličtina
Zdroj:	JAMIA open [JAMIA Open] 2022 Jul 11; Vol. 5 (3), pp. ooac057. Date of Electronic Publication: 2022 Jul 11 (Print Publication: 2022).
DOI:	10.1093/jamiaopen/ooac057
Abstrakt:	Objective: The goal of this study was to investigate the relationship between comorbidities and ethnic health disparities in a diverse, cosmopolitan population. Materials and Methods: We used the UK Biobank (UKB), a large progressive cohort study of the UK population. Study participants self-identified with 1 of 5 ethnic groups and participant comorbidities were characterized using the 31 disease categories captured by the Elixhauser Comorbidity Index. Ethnic disparities in comorbidities were quantified as the extent to which disease prevalence within categories varies across ethnic groups and the extent to which pairs of comorbidities co-occur within ethnic groups. Disease-risk factor comorbidity pairs were identified where one comorbidity is known to be a risk factor for a co-occurring comorbidity. Results: The Asian ethnic group shows the greatest average number of comorbidities, followed by the Black and then White groups. The Chinese group shows the lowest average number of comorbidities. Comorbidity prevalence varies significantly among the ethnic groups for almost all disease categories, with diabetes and hypertension showing the largest differences across groups. Diabetes and hypertension both show ethnic-specific comorbidities that may contribute to the observed disease prevalence disparities. Discussion: These results underscore the extent to which comorbidities vary among ethnic groups and reveal group-specific disease comorbidities that may underlie ethnic health disparities. Conclusion: The study of comorbidity distributions across ethnic groups can be used to inform targeted group-specific interventions to reduce ethnic health disparities. (Published by Oxford University Press on behalf of the American Medical Informatics Association 2022.)
Databáze:	MEDLINE
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