Evidence map›Paper›PMID 40525033›Full record

ArticleCureus2025

Descriptive Epidemiology of COVID-19 in Hokkaido, Japan: Regional Burden and the Role of Railway-Driven Population Mobility.

Yuji Kaneko

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Yuji KanekoGeneral Medicine, HITO Medical Center, Ehime, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Understanding regional disparities in COVID-19 incidence and the factors influencing them is essential for effective public health responses. In Japan, particularly in Hokkaido, significant differences in case burden have been observed across municipalities. This study aimed to describe the epidemiological patterns of COVID-19 in Hokkaido by adjusting for age structure and to examine the association between railway-based population mobility and regional case burden. Method We categorized all municipalities in Hokkaido into five public health center jurisdictions: Sapporo, Otaru, Asahikawa, Hakodate, and Other Areas. COVID-19 incidence was assessed across the third to sixth waves of the pandemic (November 2020 to June 2022). To account for demographic differences, age-standardized incidence ratios (SIR) were calculated using national age-specific incidence rates and the indirect standardization method. Data on confirmed cases were obtained from government open datasets, and population mobility was measured using railway transport density from the Hokkaido Railway Company. Statistical analyses included chi-square tests and multiple regression analysis, including both full and simplified models to assess statistical associations, with the latter addressing multicollinearity among demographic variables. Results A total of 378,281 cases were reported during the study period, with the highest burden in Sapporo. SIR exceeded 1.0 in Sapporo, Otaru, and Asahikawa during multiple waves, indicating higher-than-expected incidence after age adjustment. Notably, Sapporo exhibited elevated SIR, particularly among older adults, while Otaru and Asahikawa showed higher rates among those aged 20-50 years. In regression analyses, railway transport density showed a statistically significant positive association with SIR in both univariable (p = 0.002) and simplified multivariable models that included the effective reproduction number (Rt). In the multivariable model, railway transport density remained positively associated (p < 0.001), while Rt showed a negative association (p = 0.032), with an adjusted R² of 0.524. These findings suggest that railway-based mobility and real-time transmission dynamics jointly influence regional disparities in COVID-19 burden. Conclusion This study highlights the significant impact of railway-based population mobility on COVID-19 transmission within Hokkaido, Japan. Age-standardized comparisons revealed regional variations in risk, likely influenced by urban proximity and transport infrastructure. These findings support the incorporation of geographic and mobility-related metrics into geographically tailored public health policies. Further research using larger datasets and multimodal transport indicators is recommended to validate and expand upon these insights.

Indexed as

age-standardized incidence ratescovid-19descriptive epidemiologypopulation mobilityrailway transportation

Identifiers

PMID40525033
PMCPMC12168740

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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.