Catastrophic health expenditure among Chinese adults living alone with cognitive impairment: findings from the CHARLS.


Journal

BMC geriatrics
ISSN: 1471-2318
Titre abrégé: BMC Geriatr
Pays: England
ID NLM: 100968548

Informations de publication

Date de publication:
04 08 2022
Historique:
received: 26 04 2022
accepted: 26 07 2022
entrez: 3 8 2022
pubmed: 4 8 2022
medline: 6 8 2022
Statut: epublish

Résumé

The catastrophic health expenditure of older adults results in serious consequences; however, the issue of whether cognitive status and living situations contribute to such financial burdens is uncertain. Our aim was to compare the differences in catastrophic health expenditure between adults living alone with cognitive impairment and those adults living with others and with normal cognition. We identified 909 observations of participants living alone with cognitive impairment (cases) and 37,432 observations of participants living with others and with normal cognition (comparators) from the 2011/2012, 2013, 2015 and 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS). We used propensity score matching (1:2) to create matched cases and comparators in a covariate-adjusted logistic regression analysis. Catastrophic health expenditure was defined as an out-of-pocket cost for health care ≥40% of a household's capacity to pay. In comparison with participants living with others and with normal cognition, those adults living alone with cognitive impairment reported a higher percentage of catastrophic health expenditure (19.5% vs. 11.8%, respectively, P < 0.001). When controlling for age, sex, education, marital status, residence areas, alcohol consumption, smoking status and disease counts, we found that this subpopulation had significantly higher odds of having catastrophic health expenditure (odds ratio [OR] = 1.89, 95% confidence interval [CI]: 1.40, 2.56). Additional analyses confirmed the robustness of the results. This study demonstrated that adults living alone with cognitive impairment in the CHARLS experienced a high burden of catastrophic health expenditure. Health care policies on social health insurance and medical assistance should consider these vulnerable adults.

Sections du résumé

BACKGROUND
The catastrophic health expenditure of older adults results in serious consequences; however, the issue of whether cognitive status and living situations contribute to such financial burdens is uncertain. Our aim was to compare the differences in catastrophic health expenditure between adults living alone with cognitive impairment and those adults living with others and with normal cognition.
METHODS
We identified 909 observations of participants living alone with cognitive impairment (cases) and 37,432 observations of participants living with others and with normal cognition (comparators) from the 2011/2012, 2013, 2015 and 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS). We used propensity score matching (1:2) to create matched cases and comparators in a covariate-adjusted logistic regression analysis. Catastrophic health expenditure was defined as an out-of-pocket cost for health care ≥40% of a household's capacity to pay.
RESULTS
In comparison with participants living with others and with normal cognition, those adults living alone with cognitive impairment reported a higher percentage of catastrophic health expenditure (19.5% vs. 11.8%, respectively, P < 0.001). When controlling for age, sex, education, marital status, residence areas, alcohol consumption, smoking status and disease counts, we found that this subpopulation had significantly higher odds of having catastrophic health expenditure (odds ratio [OR] = 1.89, 95% confidence interval [CI]: 1.40, 2.56). Additional analyses confirmed the robustness of the results.
CONCLUSIONS
This study demonstrated that adults living alone with cognitive impairment in the CHARLS experienced a high burden of catastrophic health expenditure. Health care policies on social health insurance and medical assistance should consider these vulnerable adults.

Identifiants

pubmed: 35922775
doi: 10.1186/s12877-022-03341-8
pii: 10.1186/s12877-022-03341-8
pmc: PMC9351200
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

640

Subventions

Organisme : NIA NIH HHS
ID : R21 AG031372
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG021342
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG066508
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG033675
Pays : United States
Organisme : NIA NIH HHS
ID : R24 AG045050
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG037031
Pays : United States

Informations de copyright

© 2022. The Author(s).

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Auteurs

Chenxi Li (C)

Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China.

Shuyi Jin (S)

Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China.

Xingqi Cao (X)

Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China.

Ling Han (L)

Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

Ning Sun (N)

Ningbo College of Health Sciences, Ningbo, Zhejiang, China.

Heather Allore (H)

Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

Emiel O Hoogendijk (EO)

Department of Epidemiology & Data Science, Amsterdam Public Health research institute, Amsterdam UMC - location VU University medical center, Amsterdam, the Netherlands.

Xin Xu (X)

Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China.

Qiushi Feng (Q)

Department of Sociology, National University of Singapore, Singapore, Singapore.

Xiaoting Liu (X)

School of Public Affairs, Zhejiang University, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China. xtliu@zju.edu.cn.

Zuyun Liu (Z)

Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China. Zuyun.liu@outlook.com.

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