ArticleFrontiers in public health2026
Mobile signaling big data, healthcare-seeking flows, and multidimensional healthcare access in Shanghai: implications for healthcare supply-demand matching and medical security governance.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/purpose: Broad medical insurance coverage alone does not ensure that a multilevel medical security system works effectively. In a megacity, the key issue is whether residents can reach suitable care, keep choices across provider tiers, and use high-quality services within daily time-space constraints. Proximity-based measures often miss this problem because they do not capture cross-district healthcare-seeking, preferences for higher-level hospitals, or the concentration of quality resources in limited urban locations. Using Shanghai as a case, this study used Mobile signaling big data to identify spatial inequalities in healthcare access and to inform service-side improvement. Methods: This study used anonymized mobile signaling records from March 2019 to reconstruct a directed, weighted healthcare-seeking flow network linking demand units with 352 hospitals in Shanghai. This study then constructed the Multidimensional Healthcare Access Capability Index (MHACI) with entropy-weighted TOPSIS. The index integrated spatial accessibility, healthcare-seeking choice diversity, and connectivity to high-quality healthcare resources. Multiscale geographically weighted regression (MGWR) was used to estimate associations of MHACI with demographic structure, economic capacity, transport infrastructure, population density, and local healthcare capacity across space. Results: Healthcare-seeking flows in Shanghai were concentrated toward the urban core, with cross-district movement. MHACI showed a clear center-periphery gradient. Aging share was positively related to MHACI mainly in central and adjacent areas, whereas child share was linked to lower MHACI in core districts. Per capita GDP and local healthcare capacity had positive but localized effects. Population density shifted from a crowding burden in central areas to an agglomeration advantage in peripheral areas, while transport infrastructure mainly acted as a background condition. Conclusion: Unequal healthcare access in Shanghai reflects a mismatch between fixed healthcare provision and healthcare-seeking flows across urban space. Improving a multilevel medical security system requires not only formal coverage, but also stronger primary care, better connectivity across provider tiers, and place-specific governance in core, peri-urban, and outer suburban areas.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.