Evidence map›Paper›PMID 41094577›Full record

ArticleInfectious diseases of poverty2025

Non-negligible impacts of urbanization on spatiotemporal variations of infectious disease: a case study of hemorrhagic fever with renal syndrome epidemics in China.

Qi Wei, Hongyan Ren, Liang Lu, Runhe Shi

Abstract read
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Article in Infectious diseases of poverty, 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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1 · What the graph read from it

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2 · The registry

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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

4 authors.

Qi WeiKey Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, 200241, China.
Hongyan RenState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. renhy@lreis.ac.cn.ORCID http://orcid.org/0000-0002-6948-5769
Liang LuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Runhe ShiKey Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, 200241, China. rhshi@geo.ecnu.edu.cn.

Funding

Fundamental Research Funds for the Central Universities East China Normal Universitythe National Key R&D Program of China 2023YFC2307502
6 · The paper itself

Abstract

backgroundHemorrhagic fever with renal syndrome (HFRS) poses a significant public health concern in China. However, the spatiotemporal patterns and underlying drivers of its transmission are not fully understood. This study aims to investigate spatiotemporal heterogeneity of HFRS incidence at the city level and explore its potential influencing factors.

methodsJoinpoint regression was utilized to analyze city-level HFRS incidence data (n = 314 cities, 2005-2021) collected from the National Infectious Disease Surveillance System. Furthermore, we employed the Geodetector method to identify the potential driving factors from a set of meteorological, vegetation, and urbanization variables.

resultsThe results from Joinpoint regression analysis revealed an overall declining trend in city-level HFRS incidence across China from 2005 to 2021. Of the cities analyzed, 126 showed an upward trend [the average annual percent change,(AAPC) > 0], 176 a downward trend (AAPC < 0), and 12 remained stable (AAPC = 0). Notably, upward-trend cities were predominantly concentrated in South China. Geodetector analysis indicated that selected climatic and vegetation factors accounted for 19-56% of the spatiotemporal heterogeneity in HFRS incidence, whereas urbanization factors explained only 3-5%. However, synergistic interactions between temperature and urbanization-related variables (i.e., land-use, economic, and demographic dimensions) significantly enhanced their explanatory power, particularly in upward-trend cities, where the combinations increased explanatory capacity by 124-184%.

conclusionIn summary, while climatic and vegetation factors remain the primary drivers of the spatiotemporal heterogeneity of HFRS epidemics in China, urbanization also exerts non-negligible influence on city-level incidence. This research offers valuable insights for public health authorities to strengthen their intervention capabilities against this disease.

Indexed as

EpidemicsHemorrhagic Fever with Renal SyndromeUrbanizationChinaCitiesHumansIncidenceSpatio-Temporal AnalysisChinaGeodector analysisHemorrhagic fever with renal syndromeJoinpoint regressionMeteorological factorMultidimensional urbanization

Identifiers

PMID41094577
PMCPMC12529830

What Socratic holds

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LicenceCC BY
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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 Socratic graph.