Evidence map›Paper›PMID 42814989›Full record

ArticleJournal of medical Internet research2026

Digital and Out-of-Facility Care-Seeking Pathways After Acute Respiratory Infection Symptoms in China: Multicity Population-Based Cross-Sectional Study.

Ting Zhang, Jinzhao Cui, Chao Zhang, Xiaochen Zhang, Yongtao Chi, Dazhu Huo, Shengjie Lai, Jianxing Yu, Yu Yang, Zhongjie Li and 1 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ting Zhang *School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0002-9569-9357
Jinzhao Cui *School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0009-0007-5095-3722
Chao Zhang *School of Public Health, Shandong Second Medical University, Weifang, Shandong, China.ORCID https://orcid.org/0009-0002-2836-4108
Xiaochen ZhangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0009-0001-3982-7342
Yongtao ChiSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0009-0009-7645-0490
Dazhu HuoSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0009-0001-9933-6705
Shengjie LaiSchool of Geography and Environmental Science, University of Southampton, Southampton, England, United Kingdom.ORCID https://orcid.org/0000-0001-9781-8148
Jianxing YuSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0002-9696-2460
Yu YangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0009-0004-6271-4735
Zhongjie LiSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0002-0356-0463
Weizhong YangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0002-6599-825X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital technologies increasingly shape responses to acute respiratory infection (ARI) symptoms through information seeking, consultation, medication purchase, monitoring, and access to diagnostic products or services. However, how digital health engagement overlaps with in-person facility attendance and whether digital and other out-of-facility behaviors provide complementary signals for episodes that are less visible in routine facility-based surveillance remain unclear.

objectiveThis study estimates the distribution of ARI episodes by in-person facility attendance and digital health engagement, describes digital and nondigital out-of-facility actions and the reported ordering of these actions, and examines subgroup differences.

methodsWe conducted a population-based cross-sectional survey in 11 Chinese cities using multistage stratified random sampling. Participants reported ARI symptoms within the preceding 14 days and subsequent health-related actions. The primary digital composite included online health information seeking, AI-assisted consultation, telemedicine, online medication purchase, wearable device-based self-monitoring, digitally accessed self-administered rapid antigen testing, and at-home pathogen specimen collection. Participants were cross-classified by in-person facility attendance and digital health engagement. Weighted descriptive proportions were estimated using poststratification weights, with 95% CIs calculated using Wilson score intervals based on the Kish effective sample size. Behavioral sequences were descriptively reconstructed from retrospectively reported timing of actions using 1 representative action per reported day, with adjacent-transition CIs estimated by participant-level bootstrap resampling. Modified Poisson regression with individual-level robust variance estimation was used to estimate adjusted associations with in-person facility attendance, expressed as adjusted prevalence ratios (aPRs).

resultsAmong 247,530 participants, 68,650 reported ARI symptoms. Among symptomatic participants, 19.18% (95% CI 18.87%-19.48%) reported in-person facility attendance, and 18.94% (95% CI 18.64%-19.24%) reported at least one digital health behavior. Digital health engagement without in-person facility attendance comprised 11.20% (95% CI 10.95%-11.44%) of ARI episodes, while 7.74% (95% CI 7.54%-7.95%) involved both dimensions. Digital health engagement was positively associated with in-person facility attendance during the same episode (aPR 2.12, 95% CI 2.05-2.20). Compared with participants reporting 2 or fewer symptoms, the aPRs were 3.31 (95% CI 3.16-3.46) for 3 symptoms and 4.69 (95% CI 4.51-4.88) for 4 or more symptoms. Among participants with nonempty sequences, the most common sequence beginning with an out-of-facility action was offline medication purchase followed by no subsequent reported action (6058/26,494, 22.87%). Digital health engagement was lowest among adults aged 60 years or older.

conclusionsMost reported ARI episodes did not involve in-person facility attendance and may be underrepresented in routine facility-based surveillance. Digital health behaviors identified potential complementary signals for some episodes, while common pathways involving offline medication purchase suggest that pharmacy-derived data may provide additional information. These data sources require direct validation, and lower digital health engagement among older adults indicates that digital signals may not represent all population groups equally.

Indexed as

Patient Acceptance of Health CareRespiratory Tract InfectionsAcute DiseaseAdolescentAdultAgedChinaCross-Sectional StudiesDigital HealthFemaleHumansMaleMiddle AgedTelemedicineYoung Adultacute respiratory infectioncross-sectional studydigital health engagementin-person facility attendancetelemedicine

Identifiers

PMID42814989
PMCPMC13669710

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.