Evidence map›Paper›PMID 41250017›Full record

ArticleBMC public health2025

Spatiotemporal trends and ecological determinants of human brucellosis among 31 provinces in mainland China, 2004-2021: a Bayesian spatiotemporal modeling study.

Weihao Li, Weiwei Meng, Liying Wang, Hanqi Ouyang, Guojing Yang

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Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Weihao LiKey Laboratory of Tropical Translational Medicine of Ministry of Education, School of Public Health, Hainan Medical University (Hainan Academy of Medical Sciences), Haikou, Hainan, 571199, China.
Weiwei MengHainan Provincial Public Health Clinical Center, Haikou, Hainan, 571129, China.
Liying WangKey Laboratory of Tropical Translational Medicine of Ministry of Education, School of Public Health, Hainan Medical University (Hainan Academy of Medical Sciences), Haikou, Hainan, 571199, China.
Hanqi OuyangKey Laboratory of Tropical Translational Medicine of Ministry of Education, School of Public Health, Hainan Medical University (Hainan Academy of Medical Sciences), Haikou, Hainan, 571199, China.
Guojing YangKey Laboratory of Tropical Translational Medicine of Ministry of Education, School of Public Health, Hainan Medical University (Hainan Academy of Medical Sciences), Haikou, Hainan, 571199, China. guojingyang@hotmail.com.

Funding

National Key Research and Development Program of People's Republic of China 2021YFC2300800 and 2021YFC2300804National Natural Science Foundation of China 82260655
6 · The paper itself

Abstract

backgroundBrucellosis shows pronounced spatiotemporal heterogeneity in China, with changing epidemiological patterns as traditionally non-endemic southern regions experience increasing incidence. This study analyzes the spatiotemporal distribution and influencing factors of brucellosis in China to inform differentiated prevention strategies.

methodsUsing human brucellosis data from 31 Chinese provinces (2004-2021), combined with meteorological, socioeconomic, and livestock husbandry indicators, we developed Bayesian spatiotemporal models to analyze provincial-level spatial effects, temporal effects, spatiotemporal interactions, and quantify the impact of multiple factors on brucellosis incidence risk at the provincial scale. Richardson classification was applied for hotspot analysis, and average annual percentage change (AAPC) evaluated incidence trends.

resultsChina reported 687,529 brucellosis cases (2004-2021). Spatial distribution showed a "high north, low south" pattern, with Inner Mongolia having the highest incidence (45.81/100,000) and Shanghai the lowest (0.01/100,000). Northern regions contained most hotspots (41.94%), while southern areas comprised most coldspots (51.61%). Temporal analysis revealed increasing risk (2004-2016), brief decline (2016-2018), and subsequent increase (post-2018). Spatiotemporal interaction effects indicated provincial-level risk pattern shifts from northern to central-southern regions, with Gansu, Hubei, Yunnan, and Hunan showing highest growth (AAPCs: 64.42%, 53.80%, 53.61%, 50.47%). Ecological regression identified six significant factors: mean temperature, sunshine duration, NDVI, population density, and medical institutions density negatively correlated with risk; dairy production positively associated with risk.

conclusionsAt the provincial level, brucellosis risk patterns in China show shifts from traditional northern high-incidence provinces to central-southern provinces. Environmental, socioeconomic, and livestock factors significantly influence disease risk. Prevention strategies should implement region-specific approaches while strengthening multi-departmental coordination.

Indexed as

BrucellosisAnimalsBayes TheoremChinaHumansIncidenceRisk FactorsSpatio-Temporal AnalysisBayesian spatiotemporal modelBrucellosisChinaRisk factors

Identifiers

PMID41250017
PMCPMC12625324

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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.