Evidence map›Paper›PMID 35922775›Full record

ArticleBMC geriatrics2022

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

Chenxi Li, Shuyi Jin, Xingqi Cao, Ling Han, Ning Sun, Heather Allore, Emiel O Hoogendijk, Xin Xu, Qiushi Feng, Xiaoting Liu and 1 more

Abstract read
In one paragraph

Article in BMC geriatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Observational
  2. Observational
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Observational
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Chenxi Li *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 *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 CaoDepartment 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 HanDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Ning SunNingbo College of Health Sciences, Ningbo, Zhejiang, China.
Heather AlloreDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Emiel O HoogendijkDepartment of Epidemiology & Data Science, Amsterdam Public Health research institute, Amsterdam UMC - location VU University medical center, Amsterdam, the Netherlands.
Xin XuDepartment 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 FengDepartment of Sociology, National University of Singapore, Singapore, Singapore.
Xiaoting LiuSchool of Public Affairs, Zhejiang University, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China. xtliu@zju.edu.cn.
Zuyun LiuDepartment 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.

Funding

Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External ModulesP30AG021342 · NIA · YALE UNIVERSITY · PI Albert C Shaw · 2002 to 2026
$37.9M
Yale Alzheimer Disease Research CenterP30AG066508 · NIA · YALE UNIVERSITY · PI STEPHEN M STRITTMATTER, CHRISTOPHER H VAN DYCK · 2020 to 2026
$30.2M
China Health and Retirement Longitudinal StudyR01AG037031 · NIA · PEKING UNIVERSITY · PI STRAUSS, JOHN A, WANG, YAFENG · 2010 to 2024
$15.1M
Advancing Geriatrics Infrastructure & Network Growth - Admin Supplement: Designing and Testing an Informatics Tool to Aid Prescribers in Reducing Anticholinergic Burden in Older AdultsR24AG045050 · NIA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GURWITZ, JERRY H · 2014 to 2017
$3.6M
Biomarkers in the China Health and Retirement Longitudinal StudyR21AG033675 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CRIMMINS, EILEEN M, STRAUSS, JOHN A · 2009 to 2010
$369k
China Health and Retirement Longitudinal Study - PilotR21AG031372 · NIA · PEKING UNIVERSITY · PI ZHAO, YAOHUI · 2007 to 2008
$273k
NIA NIH HHS P30 AG021342NIA NIH HHS P30 AG066508NIA NIH HHS R01 AG037031NIA NIH HHS R21 AG031372NIA NIH HHS R21 AG033675NIA NIH HHS R24 AG045050
6 · The paper itself

Abstract

backgroundThe 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.

methodsWe 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.

resultsIn 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.

conclusionsThis 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.

Indexed as

Cognitive DysfunctionHealth ExpendituresAgedCatastrophic IllnessChinaHome EnvironmentHumansLongitudinal StudiesRetirementCatastrophic health expenditureChinese adultsCognitive impairmentLiving alone

Identifiers

PMID35922775
PMCPMC9351200

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.