Evidence map›Paper›PMID 42079844›Full record

ArticleGlobal health & medicine2026

Unlocking data elements potential for enhanced urban public health emergency governance: Configuration analysis based on 23 mega-cities in China.

Yinfeng Shi, Yajie Yu, Kunchang Li, Tingyue Shen

Abstract read
In one paragraph

Article in Global health & medicine, 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

4 authors.

Yinfeng ShiSocial Science Academic Press, Chinese Academy of Social Sciences, Beijing, China.
Yajie YuSchool of Accountancy, Shanghai Lixin University of Accounting and Finance, Shanghai, China.
Kunchang LiSchool of Information, Beijing Wuzi University, Beijing, China.
Tingyue ShenCenter for China Fiscal Development, Central University of Finance and Economics, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the core production element of the digital era, data's multiplier effect is key to risk prevention and the modernization of emergency governance. This article combines the practical application of data elements in public health emergency management, based on the technology-organization-environment (TOE) theoretical framework, takes 23 mega-cities in China as research cases, and uses the fuzzy-set qualitative comparative analysis (fsQCA) method to explore the impact of technology, organization, environment, and other conditions on the effectiveness of urban public health emergency governance. The results show a significant conditional correlation between the effectiveness of urban public health emergency governance and the conditions for applying data elements. Based on the characteristics of multiple concurrent paths, the driving paths can be classified into three categories: "technology-based", "organization-environment dual core", and "organization-technology as the mainstay + environment as the supplement". Local governments should combine the regional digital resource endowment, promote phased and differentiated application of data elements, strengthen interconnection of data-sharing platforms, coordinate construction of institutional mechanisms, accelerate multi-scenario application of data elements, strengthen two-way empowerment of technology-driven and organizational coordination, and effectively transform linkage advantages of multidimensional elements into governance effectiveness.

Indexed as

configuration analysisdata elementsemergency managementmega-citiespublic health

Identifiers

PMID42079844
PMCPMC13132665

What OpenQuestion holds

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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.