Evidence map›Paper›PMID 35614385›Full record

ArticleBMC medical research methodology2022

Multi-dimensional vulnerability analysis on catastrophic health expenditure among middle-aged and older adults with chronic diseases in China.

Wenqing Miao, Xiyu Zhang, Baoguo Shi, Wanxin Tian, Bing Wu, Yongqiang Lai, Yuze Li, Zhipeng Huang, Qi Xia, Huiqi Yang and 8 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% of its field
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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

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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

18 authors at 5 institutions in 1 country.

Wenqing Miao *Center for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Xiyu Zhang *Center for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Baoguo ShiDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.
Wanxin Tian *Center for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Bing WuCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Yongqiang LaiCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Yuze Li *Department of Medicine, Jiamusi University, Jiamusi, 154007, Heilongjiang, China.
Zhipeng HuangCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Qi XiaCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Huiqi YangCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Fan DingSchool of Public Health and Management, Ningxia Medical University, Yinchuan, Ningxia, China.
Linghan ShanCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Ling XinCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Jingying MiaoCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Chenxi ZhangCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China.
Ye LiCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China. liye8459@163.com.
Xiaodong LiThe First Department of General Surgery, Heilongjiang Provincial Hospital, No. 82 Zhongshan Road, Xiangfang District, Harbin, 150036, Heilongjiang, China. jueze-2006@qq.com.
Qunhong WuCenter for Policy and Management Research, School of Health Management, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin, 150086, Heilongjiang, China. wuqunhong@163.com.
Harbin Medical University · CNHeilongjiang Provincial Hospital · CNJiamusi University · CNMinzu University of China · CNNingxia Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMiddle-aged and older adults are more likely to suffer from chronic diseases because of their particular health characteristics, which lead to a high incidence of catastrophic health expenditure (CHE). This study plans to analyse the different factors affecting CHE in middle-aged and older adults with chronic diseases, target the vulnerable characteristics, and suggest groups that medical insurance policies should pay more attention to.

methodsThe data used in this study came from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database. The method of calculating the CHE was adopted from the World Health Organization (WHO). The logistic regression was used to determine the family characteristics of chronic disease in middle-aged and older adults with a high probability of incurring CHE.

resultsThe incidence of CHE in middle-aged and older adults with chronic disease was highest in sub-poverty level families (26.20%) was lowest in wealthier level families (20.07%). Households with malignant tumours had the highest CHE incidence under any circumstances, especially if the householder had been using inpatient service in the past year. Among the comparison of CHE incidence in different types of medical insurance, the Urban and Rural Residents' Basic Medical Insurance (URRBMI) was the highest (27.46%). The incidence of CHE was 2.73 times (95% CI 2.30-3.24) and 2.16 times (95% CI 1.81-2.57) higher among people who had used inpatient services in the past year or outpatient services in the past month than those who had not used them.

conclusionsRelatively wealthy economic conditions cannot significantly reduce the financial burden of chronic diseases in middle-aged and older adults. For this particular group with multiple vulnerabilities, such as physical and social vulnerability, the high demand and utilization of health services are the main reasons for the high incidence of CHE. After achieving the goal of lowering the threshold of universal access to health services, the medical insurance system in the next stage should focus on multiple vulnerable groups and strengthen the financial protection for middle-aged and older adults with chronic diseases, especially for patients with malignant tumours.

Indexed as

Health ExpendituresNeoplasmsAgedAmbulatory CareChinaChronic DiseaseHumansInsurance, HealthLongitudinal StudiesMiddle AgedChronic diseasesMedical insuranceMiddle-aged and older adults

Identifiers

PMID35614385
PMCPMC9134696
OpenAlexW4281493676

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LicenceCC BY
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Registered trials

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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.