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Depression Treatment Status of Economically Disadvantaged African American Older Adults

Background: It is known that depression remains largely untreated in underserved communities. Hence, it is desirable to gain more knowledge on the prevalence and correlates of untreated depression among African-American (AA) older adults in economically disadvantaged areas. This knowledge may have t...

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Autores principales: Cobb, Sharon, Bazargan, Mohsen, Sandoval, Jessica Castro, Wisseh, Cheryl, Evans, Meghan C., Assari, Shervin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7139636/
https://www.ncbi.nlm.nih.gov/pubmed/32156089
http://dx.doi.org/10.3390/brainsci10030154
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author Cobb, Sharon
Bazargan, Mohsen
Sandoval, Jessica Castro
Wisseh, Cheryl
Evans, Meghan C.
Assari, Shervin
author_facet Cobb, Sharon
Bazargan, Mohsen
Sandoval, Jessica Castro
Wisseh, Cheryl
Evans, Meghan C.
Assari, Shervin
author_sort Cobb, Sharon
collection PubMed
description Background: It is known that depression remains largely untreated in underserved communities. Hence, it is desirable to gain more knowledge on the prevalence and correlates of untreated depression among African-American (AA) older adults in economically disadvantaged areas. This knowledge may have the public health benefit of improving detection of AA older adults with depression who are at high risk of not receiving treatment, thereby reducing this health disparity. Objective: To study health and social correlates of untreated depression among AA older adults in economically disadvantaged areas. Methods: Between 2015 and 2018, this cross-sectional survey was conducted in South Los Angeles. Overall, 740 AA older adults who were 55+ years old entered this study. Independent variables were age, gender, living arrangement, insurance type, educational attainment, financial strain, chronic medical conditions, and pain intensity. Untreated depression was the dependent variable. Logistic and polynomial regression models were used to analyze these data. Results: According to the polynomial regression model, factors such as number of chronic medical conditions and pain intensity were higher in individuals with depression, regardless of treatment status. As our binary logistic regression showed, age, education, and number of providers were predictive of receiving treatment for depression. Conclusion: Age, educational attainment, number of providers (as a proxy of access to and use of care) may be useful to detect AA older adults with depression who are at high risk of not receiving treatment. Future research may focus on decomposition of the role of individual-level characteristics and health system-level characteristics that operate as barriers and facilitators to AA older adults receiving treatment for depression.
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spelling pubmed-71396362020-04-10 Depression Treatment Status of Economically Disadvantaged African American Older Adults Cobb, Sharon Bazargan, Mohsen Sandoval, Jessica Castro Wisseh, Cheryl Evans, Meghan C. Assari, Shervin Brain Sci Article Background: It is known that depression remains largely untreated in underserved communities. Hence, it is desirable to gain more knowledge on the prevalence and correlates of untreated depression among African-American (AA) older adults in economically disadvantaged areas. This knowledge may have the public health benefit of improving detection of AA older adults with depression who are at high risk of not receiving treatment, thereby reducing this health disparity. Objective: To study health and social correlates of untreated depression among AA older adults in economically disadvantaged areas. Methods: Between 2015 and 2018, this cross-sectional survey was conducted in South Los Angeles. Overall, 740 AA older adults who were 55+ years old entered this study. Independent variables were age, gender, living arrangement, insurance type, educational attainment, financial strain, chronic medical conditions, and pain intensity. Untreated depression was the dependent variable. Logistic and polynomial regression models were used to analyze these data. Results: According to the polynomial regression model, factors such as number of chronic medical conditions and pain intensity were higher in individuals with depression, regardless of treatment status. As our binary logistic regression showed, age, education, and number of providers were predictive of receiving treatment for depression. Conclusion: Age, educational attainment, number of providers (as a proxy of access to and use of care) may be useful to detect AA older adults with depression who are at high risk of not receiving treatment. Future research may focus on decomposition of the role of individual-level characteristics and health system-level characteristics that operate as barriers and facilitators to AA older adults receiving treatment for depression. MDPI 2020-03-07 /pmc/articles/PMC7139636/ /pubmed/32156089 http://dx.doi.org/10.3390/brainsci10030154 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cobb, Sharon
Bazargan, Mohsen
Sandoval, Jessica Castro
Wisseh, Cheryl
Evans, Meghan C.
Assari, Shervin
Depression Treatment Status of Economically Disadvantaged African American Older Adults
title Depression Treatment Status of Economically Disadvantaged African American Older Adults
title_full Depression Treatment Status of Economically Disadvantaged African American Older Adults
title_fullStr Depression Treatment Status of Economically Disadvantaged African American Older Adults
title_full_unstemmed Depression Treatment Status of Economically Disadvantaged African American Older Adults
title_short Depression Treatment Status of Economically Disadvantaged African American Older Adults
title_sort depression treatment status of economically disadvantaged african american older adults
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7139636/
https://www.ncbi.nlm.nih.gov/pubmed/32156089
http://dx.doi.org/10.3390/brainsci10030154
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