Evidence map›Paper›PMID 33849544›Full record

ArticleBMC health services research2021

Rural and urban differences in patient experience in China: a coarsened exact matching study from the perspective of residents.

Dantong Zhao, Zhongliang Zhou, Chi Shen, Rashed Nawaz, Dan Li, Yangling Ren, Yaxin Zhao, Dan Cao, Xiaohui Zhai

Abstract read
In one paragraph

Article in BMC health services research, 2021. 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
–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

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

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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.

Dantong ZhaoSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, Shaanxi, China.
Zhongliang ZhouSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, Shaanxi, China. zzliang1981@163.com.
Chi ShenSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, Shaanxi, China.
Rashed NawazSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, Shaanxi, China.
Dan LiSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, Shaanxi, China.
Yangling RenSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, Shaanxi, China.
Yaxin ZhaoSchool of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.
Dan CaoSchool of Public Policy and Administration, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, 710049, Shaanxi, China.
Xiaohui ZhaiSchool of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.

Funding

National Natural Science Foundation of China 71874137Shaanxi Province Postdoctoral Science Foundation 2017S024
6 · The paper itself

Abstract

backgroundPatient experience is a key measure widely used to evaluate quality of healthcare, yet there is little discussion about it in China using national survey data. This study aimed to explore rural and urban differences in patient experience in China.

methodsData regarding this study were drawn from Chinese General Social Survey (CGSS) 2015, with a sample size of 9604. Patient experience was measured by the evaluation on healthcare services. Coarsened exact matching (CEM) method was used to balance covariates between the rural and urban respondents. Three thousand three hundred seventy-two participants finally comprised the matched cohort, including 1592 rural residents and 1780 urban residents. Rural and urban differences in patient experience were tested by ordinary least-squares regression and ordered logistic regression.

resultsThe mean (SD) score of patient experience for rural and urban residents was 72.35(17.32) and 69.45(17.00), respectively. Urban residents reported worse patient experience than rural counterparts (Crude analysis: Coef. = - 2.897, 95%CI: - 4.434, - 1.361; OR = 0.706, 95%CI: 0.595, 0.838; Multivariate analysis: Coef. = - 3.040, 95%CI: - 4.473, - 1.607; OR = 0.675, 95%CI: 0.569, 0.801). Older (Coef. = 2.029, 95%CI: 0.338, 3.719) and healthier (Coef. = 2.287, 95%CI: 0.729, 3.845; OR = 1.217, 95%CI: 1.008, 1.469) rural residents living in western area (Coef. = 2.098, 95%CI: 0.464, 3.732; OR = 1.276, 95%CI: 1.044, 1.560) with higher social status (Coef. = 1.158, 95%CI: 0.756, 1.561; OR = 1.145, 95%CI: 1.090, 1.204), evaluation on adequacy (Coef. = 7.018, 95%CI: 5.045, 8.992; OR = 2.163, 95%CI: 1.719, 2.721), distribution (Coef. = 4.464, 95%CI: 2.471, 6.456; OR = 1.658, 95%CI: 1.312, 2.096) and accessibility (Coef. = 2.995, 95%CI: 0.963, 5.026; OR = 1.525, 95%CI: 1.217, 1.911) of healthcare resources had better patient experience. In addition, urban peers with lower education (OR = 0.763, 95%CI: 0.625, 0.931) and higher family economic status (Coef. = 2.990, 95%CI: 0.959, 5.021; OR = 1.371, 95%CI: 1.090,1.723) reported better patient experience.

conclusionsDifferences in patient experience for rural and urban residents were observed in this study. It is necessary to not only encourage residents to form a habit of seeking healthcare services in local primary healthcare institutions first and then go to large hospitals in urban areas when necessary, but also endeavor to reduce the disparity of healthcare resources between rural and urban areas by improving quality and capacity of rural healthcare institutions and primary healthcare system of China.

Indexed as

Health StatusRural PopulationChinaHumansPatient Outcome AssessmentSocioeconomic FactorsUrban PopulationChinaCoarsened exact matchingPatient experienceRural and urban differences

Identifiers

PMID33849544
PMCPMC8042990

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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.