Evidence map›Paper›PMID 31366334›Full record

ArticleBMC public health2019

Disparities in mental health care utilization among inpatients in various types of health institutions: a cross-sectional study based on EHR data in Shanghai, China.

Jianwei Shi, Lan Tang, Limei Jing, Jinsong Geng, Rui Liu, Li Luo, Ning Chen, Qian Liu, Xin Gong, Xiaojie Bo and 2 more

Abstract read
In one paragraph

Article in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 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

12 authors.

Jianwei ShiSchool of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
Lan TangPudong Weifang Community Health Center, Shanghai, 200120, China.
Limei JingShanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Jinsong GengDepartment of Medical Informatics, Evidence-based Medical Center, Medical School of Nantong University, Nantong, 226001, China.
Rui LiuShanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Li LuoSchool of Public Health, Fudan University, Shanghai, 200032, China.
Ning ChenSchool of Medicine, Tongji University, Shanghai, 200092, China.
Qian LiuSchool of Economics and Management, Tongji University, Shanghai, 200092, China.
Xin GongSchool of Medicine, Tongji University, Shanghai, 200092, China.
Xiaojie BoSchool of Medicine, Tongji University, Shanghai, 200092, China.
Yan YangSchool of Economics and Management, Tongji University, Shanghai, 200092, China.
Zhaoxin WangSchool of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China. supercell002@sina.com.

Funding

Natural Science Foundation of China 71603182Natural Science Foundation of China 71774116Shanghai Excellent Young Talents Project in Health System 2018YQ52Shanghai Health Policy Program 2016HP043Shanghai Health Policy Program 2018HP52
6 · The paper itself

Abstract

backgroundReform of the health care system in China has prompted concerns about the utilization of mental health services. This study aims to compare the utilization of mental health services among inpatients in various types of health institutions in Shanghai (community health care centres, secondary general hospitals, tertiary general hospitals, and specialty hospitals).

methodsBased on electronic health record (EHR) data, we extracted all of the mental hospitalization data from various types of public health institutions in Pudong New Area, Shanghai, China, from 2013 to 2016. The distribution of mentally ill inpatients and the possible factors contributing to the observed differences in these institutions were analysed.

resultsSpecialty psychiatric hospitals in Pudong New Area, Shanghai, admitted more inpatients and treated in patients with more severe disorders (49.73%). However, those who were male (OR = 0.545), were elderly (OR = 20.133), had inferior insurance (urban social insurance for citizens: OR = 4.013; paying themselves, OR = 29.489), had a longer length of stay (OR = 1.001) and had lower costs (OR = 0.910) were more likely to choose community health centres than specialty hospitals. Those who preferred the secondary and tertiary hospitals to the specialty ones were more likely to be in the male, elderly, married, shorter length of stay and higher-cost groups. Notably, compared to those with urban social insurance for workers, those who had urban social insurance for citizens (OR = 3.136) or paid out-of-pocket (OR = 9.822) were significantly clustered in the tertiary hospitals rather than the specialty hospitals.

conclusionsInpatients who were male, were older, had inferior insurance, had a longer length of stay and had lower costs preferred the elementary health services. However, the utilization of mental health care in high-tier institutions reflected defects, especially the fact that the current health insurance system does not adequately restrict patients' choices, and those who paid more tended to choose tertiary hospitals instead of professional specialty ones. We suggest that psychiatric services should be enhanced by instituting reforms, including public education, improved health insurance, a forceful referral system, and competency reinforcement for primary care physicians, to provide a more integrated mental health system.

Indexed as

Healthcare DisparitiesAdultAgedChinaCross-Sectional StudiesElectronic Health RecordsFemaleHealth FacilitiesHealth PolicyHospitalizationHumansMaleMental DisordersMental Health ServicesMiddle AgedChinaHealth policyInpatientMental disordersUtilization

Identifiers

PMID31366334
PMCPMC6668074

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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.