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The Impact of Digital Transformation on Public Health: A Multicountry Analysis

Mingchan Cheng et al.

Health and Social Care in the Community2026https://doi.org/10.1155/hsc/2320652article
AJG 2
Weight
0.50

What the paper says

This study examines the impact of digital transformation on public health using global panel data for 94 countries from 2003 to 2021, applying feasible generalized least squares (FGLS) estimation with country–time clustered standard errors. It also investigates the mechanisms through which digital transformation affects public health from the perspectives of human capital and health expenditure efficiency. The results demonstrate that digital transformation has a significant positive impact on public health, and the findings remain valid after robustness tests. Income‐level heterogeneity analyses suggest that digital transformation has the greatest positive impact on public health in low‐ and low‐middle‐income nations, while the effect is limited in upper‐middle‐income countries. Further mediation analyses indicate that digital transformation can indirectly improve public health by increasing human capital and health expenditure efficiency. This study extends the boundaries of research on the relationship between digital transformation and public health, offering insights into the broader possibilities for employing digital technology to improve public health.

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https://doi.org/https://doi.org/10.1155/hsc/2320652

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@article{mingchan2026,
  title        = {{The Impact of Digital Transformation on Public Health: A Multicountry Analysis}},
  author       = {Mingchan Cheng et al.},
  journal      = {Health and Social Care in the Community},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1155/hsc/2320652},
}

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The Impact of Digital Transformation on Public Health: A Multicountry Analysis

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.