Evidence map›Paper›PMID 36357825›Full record

ArticleBMC geriatrics2022

Inequity in the healthcare utilization among latent classes of elderly people with chronic diseases and decomposition analysis in China.

Jie Zhao, Chaoyang Yan, Dan Han, Yunyi Wu, Hui Liao, Ying Ma, Mei Zhang, Sangsang Li, Jing Wang

Open access · goldAbstract 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 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 19% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 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

9 authors at 1 institution in 1 country.

Jie ZhaoDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Chaoyang YanDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Dan HanDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Yunyi WuDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Hui LiaoDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Ying MaDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Mei ZhangDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Sangsang LiDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Jing WangDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China. jingwang@hust.edu.cn.
Huazhong University of Science and Technology · CN

Funding

Integrting Information About Aging SurveysR01AG030153 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Sara Adar, Alden L. Gross · 2007 to 2026
$41.7M
Mega Meta Data Set of the Health and Retirement Surveys Around the WorldRC2AG036619 · NIA · RAND CORPORATION · PI KAPTEYN, ARIE · 2009 to 2010
$2.0M
Archiving & Creating User Friendly Data: the Longitudinal Aging Survey in IndiaR03AG043052 · NIA · RAND CORPORATION · PI LEE, JINKOOK · 2012 to 2012
$95k
NIA NIH HHS R01 AG030153NIA NIH HHS R03 AG043052NIA NIH HHS RC2 AG036619
6 · The paper itself

Abstract

backgroundStudies have shown chronic disease-based healthcare utilization inequity is common. Hence, exploring this issue can help in establishing targeted measures and protecting the rights and interests of vulnerable groups. Against this background, the purpose of this study is to explore the latent classification of elderly patients with chronic disease and compare healthcare utilization inequity among latent classes.

methodsThis study used the data of 7243 elderly patient with chronic diseases collected from the China Health and Retirement Longitudinal Study in 2018. Latent class analysis was used to classify the patients with chronic diseases, and analysis of variance and [Formula: see text] tests were utilized to test the differences in characteristics among latent classes. Healthcare utilization inequity was measured based on the concentration index (CI), and the CI was decomposed to compare the horizontal index of healthcare utilization among the latent classes.

resultsThe patients with chronic diseases were divided into five latent classes, namely, the musculoskeletal system, hypertension, respiratory system, digestive system and cardiovascular system groups. Statistically significant differences in social demographic characteristics were observed among the five latent classes (P < 0.05). A pro-rich healthcare utilization inequity for all respondents was observed (outpatient CI = 0.080, inpatient CI = 0.135), and a similar phenomenon in latent classes was found except for the musculoskeletal system group in outpatient visits (CI = -0.037). The digestive system group had the worst equity (outpatient CI = 0.197, inpatient CI = 0.157) and the respiratory system group had the best (outpatient CI = 0.001, inpatient CI = 0.086). After balancing the influence of health need factors, healthcare utilization inequity was almost alleviated. Furthermore, for all respondents, the contribution of health need factors (65.227% for outpatient and 81.593% for inpatient) was larger than that of socioeconomic factors (-21.774% for outpatient and 23.707 for inpatient), and self-rated health status was the greatest contributor (57.167% for outpatient and 79.399% for inpatient). The characteristics were shown in latent classes.

conclusionsHealthcare utilization inequity still exists in elderly patients with chronic diseases, and the specific performances of inequity vary among latent classes. Moreover, self-rated health status plays an important role in healthcare utilization inequity. Providing financial support to low-income patients with certain chronic diseases, focusing on their physical and mental feelings and guiding them to evaluate their health status correctly could be essential for alleviating healthcare utilization inequity among elderly patients with chronic diseases.

Indexed as

Healthcare DisparitiesPatient Acceptance of Health CareAgedChinaChronic DiseaseHumansLongitudinal StudiesSocioeconomic FactorsConcentration indexHealthcare utilizationInequityLatent class analysis

Identifiers

PMID36357825
PMCPMC9650823
OpenAlexW4308695742

What OpenQuestion holds

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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.