Evidence map›Paper›PMID 36597134›Full record

ArticleInternational journal for equity in health2023

Association of rurality, type of primary caregiver and place of death with end-of-life medical expenditures among the oldest-old population in China.

Zhong Li, Peiyin Hung, Kewei Shi, You Fu, Dongfu Qian

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Article in International journal for equity in health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Zhong LiSchool of Health Policy and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
Peiyin HungDepartment of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
Kewei ShiSurveillance and Health Equity Science, American Cancer Society, Atlanta, GA, USA.
You FuDepartment of Review and Investigation, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China. hawksfy@163.com.
Dongfu QianSchool of Health Policy and Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China. dqian@njmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnderstanding whether the type of primary caregiver and end-of-life (EOL) care location are associated with EOL medical expenditures is crucial to inform global debates on policies for efficient and effective EOL care. This study aims to assess trends in the type of primary caregiver and place of death stratified by rural‒urban status among the oldest-old population from 1998-2018 in China. A secondary objective is to determine the associations between rurality, the type of primary caregiver, place of death and EOL medical expenditures. 

methodsA total of 20,149 deaths of people aged 80 years or older were derived from the Chinese Longitudinal Health Longevity Survey (CLHLS). Cochran-Armitage tests and Cuzick's tests were used to test trends in the type of primary caregiver and place of death over time, respectively. Tobit models were used to estimate the marginal associations of rurality, type of primary caregiver, and place of death with EOL medical expenditures because CLHLS sets 100,000 Chinese yuan (approximately US$15,286) as the upper limit of the outcome variable. 

resultsOf the 20,149 oldest-old people, the median age at death was 97 years old, 12,490 (weighted, 58.6%, hereafter) were female, and 8,235 lived in urban areas. From 1998-2018, the prevalence of informal caregivers significantly increased from 94.3% to 96.2%, and home death significantly increased from 86.0% to 89.5%. The proportion of people receiving help from informal caregivers significantly increased in urban decedents (16.5%) but decreased in rural decedents (-4.0%), while home death rates significantly increased among both urban (15.3%) and rural (1.8%) decedents. In the adjusted models, rural decedents spent less than urban decedents did (marginal difference [95% CI]: $-229 [$-378, $-80]). Those who died in hospitals spent more than those who died at home ($798 [$518, $1077]). No difference in medical expenditures by type of primary caregiver was observed.

conclusionsOver the past two decades, the increases in informal caregiver utilization and home deaths were unequal, leading to substantially higher EOL medical expenditures among urban decedents and deceased individuals who died at hospitals than among their counterparts who lived in rural areas and died at home.

Indexed as

Health ExpendituresTerminal CareAged, 80 and overCaregiversDeathFemaleHumansLongitudinal StudiesMaleChinaEnd-of-life CareMedical ExpendituresPlace of DeathPrimary Caregivers

Identifiers

PMID36597134
PMCPMC9809123

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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.