Evidence map›Paper›PMID 40749207›Full record

ArticleJournal of medical Internet research2025

Disparities in Internet Medical Service Utilization Among Patients in Post-COVID-19 China: Cross-Sectional Study of Data From Provincial Field and National Online Surveys.

Zhenyu Sun, Xi Chen, Dongfu Qian

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 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

3 authors.

Zhenyu SunSchool of Health Policy & Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China, 86 15996273378.ORCID 0000-0003-3793-1178
Xi ChenSchool of Public Health, Yale University, New Haven, CT, United States.ORCID 0000-0002-2058-0351
Dongfu QianSchool of Health Policy & Management, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, China, 86 15996273378.ORCID 0000-0002-7704-1068

Funding

Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External ModulesP30AG021342 · NIA · YALE UNIVERSITY · PI Lauren Ferrante · 2002 to 2026
$37.9M
NIA NIH HHS P30 AG021342
6 · The paper itself

Abstract

Background: Internet medical services (IMS) expanded rapidly in China during the COVID-19 pandemic. Unfortunately, disparities in internet medical services utilization (IMSU) have marginalized disadvantaged groups of Chinese patients from digital health benefits. The extent and nature of these disparities remain poorly understood, with no research comprehensively addressing how unfavorable predictors, including demographic, socioeconomic, and health-related factors, shape IMSU status, patterns, and preferences in China after the COVID-19 pandemic. Objective: This study aims to gain a deeper understanding of the disparities and unfavorable predictors that limit IMSU among Chinese adult patients following the COVID-19 pandemic, providing key reference points for advancing equitable IMSU. Methods: This cross-sectional study used a triangular approach, combining data from a provincial field survey conducted in July 2023 and a national online survey conducted in March 2024. Participants were Chinese adult outpatients aged 18 years or older. Descriptive and comparative analyses were used to examine disparities in IMSU status, patterns, and preferences across different demographic, socioeconomic, and health status-related factors. Binary logistic regression models were applied to assess the associations between unfavorable predictors (constructed from selected demographic, socioeconomic, and health status-related factors) and IMSU status, patterns, and preferences. Results: Of the 2011 eligible participants in the Jiangsu provincial field survey, 787 (39.13%) reported using IMS at least once in the previous 12 months. Among the 1611 eligible participants in the national online survey, all reported accessing IMS during the same period; however, only 481 (29.86%) reported high-frequency use (defined as usage in the third quartile or above, ie, ≥6 times). Overall, participants with unfavorable predictors were less likely to engage in IMSU, deep IMS were used less frequently than shallow IMS, and participants with 6 or more cumulative unfavorable predictors had the lowest total of IMSU preference score (mean 48.98 and 57.37 in the provincial field and national online surveys, respectively). Based on combined data from the provincial field and national online surveys, significantly negative associations were observed between unfavorable predictors and IMSU status, patterns, and preferences. In particular, participants aged 60 years or above (odds ratio [OR] 0.40, 95% CI 0.25-0.63, P<.001) and those without everyday internet access (OR 0.54, 95% CI 0.41-0.71, P<.001) were the least likely to use IMS at a medium-to-high frequency. Participants without private health insurance (OR 0.59, 95% CI 0.44-0.79, P<.001) were the least likely to utilize deep IMS. Moreover, participants aged 60 years or above (OR 0.45, 95% CI 0.33-0.63, P<.001) and those with a high school education or less (OR 0.67, 95% CI 0.55-0.82, P<.001) were the least likely to prefer IMS to a moderate-to-strong degree. Conclusions: Widespread disparities in IMSU status, patterns, and preferences persisted among Chinese adult patients after the COVID-19 pandemic. More pro-disadvantaged patient policies may be warranted to narrow these disparities in IMSU, such as reimbursement for IMSU, to promote digital health equity in China.

Indexed as

COVID-19Healthcare DisparitiesInternetPatient Acceptance of Health CareTelemedicineAdolescentAdultAgedChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedPandemicsSARS-CoV-2adult patientsChinaCOVID-19 pandemicdisparitiesinternet medical services utilizationnational online surveypatternspreferencesprovincial field surveystatus

Identifiers

PMID40749207
PMCPMC12316444

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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.