Evidence map›Paper›PMID 39920576›Full record

ArticleBMC primary care2025

Inpatient preferences among patients with multiple chronic conditions in China: A discrete choice experiment.

Yueqin Wang, Shiyin Wu, Yuehua Chen, Ling Xiao, Qingling Su, Xiaoyin Huang, Weikang Wang, Wanxin Li, Shanshan Du, Wenbin Liu and 1 more

Abstract read
In one paragraph

Article in BMC primary care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

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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

11 authors.

Yueqin Wang *Department of Social Medicine and Health Management, School of Health Management, Fujian Medical University, Fuzhou, China.
Shiyin Wu *Department of Social Medicine and Health Management, School of Health Management, Fujian Medical University, Fuzhou, China.
Yuehua ChenDepartment of Social Medicine and Health Management, School of Health Management, Fujian Medical University, Fuzhou, China.
Ling XiaoSchool of Public Health, Fujian Medical University, Fuzhou, China.
Qingling SuSchool of Public Health, Fujian Medical University, Fuzhou, China.
Xiaoyin HuangSchool of Public Health, Fujian Medical University, Fuzhou, China.
Weikang WangSchool of Public Health, Fujian Medical University, Fuzhou, China.
Wanxin LiSchool of Public Health, Fujian Medical University, Fuzhou, China.
Shanshan DuSchool of Public Health, Fujian Medical University, Fuzhou, China.
Wenbin Liu *Department of Social Medicine and Health Management, School of Health Management, Fujian Medical University, Fuzhou, China. wenbinliu126@126.com.ORCID 0000-0001-9369-9858
Weimin Ye *School of Public Health, Fujian Medical University, Fuzhou, China. ywm@mail.fjmu.edu.cn.

Funding

General Program of the Natural Science Foundation of Fujian Province 2021J01245Government of Fuqing city No. 2019B003High-level Talents Research Start-up Project of Fujian Medical University No. XRCZX2020037High-level Talents Research Start-up Project of Fujian Medical University No. XRCZX2021026
6 · The paper itself

Abstract

backgroundMultiple chronic conditions (MCC) have become a leading cause of low vitality and high mortality among the Chinese population. Although a series of policies has been implemented to promote patients' rational access to health care, patients still prefer a higher level of inpatient service, hampering the efficient utilization of resources in county hospitals, which are the first point of contact for inpatient care. Thus, this study aimed to identify the factors that affect MCC patients' inpatient preferences and the extent to which these factors influence their decisions, thereby guiding inpatient service utilization among MCC patients.

methodsFive attributes (institution scale, waiting time for hospital admission, presence of acquaintances, travel time from residence to hospital, and out-of-pocket expenses per visit) were identified to estimate inpatient choice for MCC patients through a discrete choice experiment. A partial factor analysis was performed to generate selection sets. Data were collected from MCC patients aged between 35 and 75 years, in Fuqing City, China. A mixed logit model was used to analyse MCC patients' preferences for each attribute. Willingness to pay was estimated by regression coefficients, and interaction terms were included in the model to estimate the heterogeneity of inpatient preferences among MCC patients.

resultsA total of 504 valid questionnaires were included in the analysis. The most important attribute of patients when choosing inpatient care is out-of-pocket expenses per visit, followed by travel time from residence to hospital, waiting time for hospital admission, institution scale, and presence of acquaintances. In addition, patients were willing to pay ¥1253, ¥434, and ¥323 for shorter times from the residence to the hospital, larger institutional scale, and beds available on the day, respectively. The findings of the interaction analysis indicated that age and gender also influence MCC patients' inpatient preferences.

conclusionThis study provides evidence of the inpatient preferences of MCC patients. Increasing inpatient insurance reimbursement rates, bolstering the leading role of district and county hospitals in the area, and strengthening information systems will empower district and county hospitals to effectively serve as the first point of contact for inpatient care.

Indexed as

Choice BehaviorInpatientsMultiple Chronic ConditionsPatient PreferenceAdultAgedChinaChronic DiseaseFemaleHealth ExpendituresHospitalizationHumansMaleMiddle AgedSurveys and QuestionnairesTravelDiscrete choice experiment (DCE)Inpatient preferencesMultiple chronic conditions (MCC)

Identifiers

PMID39920576
PMCPMC11806789

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LicenceCC BY-NC-ND
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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.