Evidence map›Paper›PMID 38660264›Full record

ArticleHeliyon2024

Risk effects of meteorological factors on human brucellosis in Jilin province, China, 2005-2019.

Shanjun Huang, Hao Wang, Zhuo Li, Zhaohan Wang, Tian Ma, Ruifang Song, Menghan Lu, Xin Han, Yiting Zhang, Yingtong Wang and 2 more

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
5.1field-weighted citation impact, top 5% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 8 citations in OpenAlex.

  1. Distribution and epidemiology of brucellosis in China.Veterinary research communications · 2026
    Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 2 countries.

Shanjun HuangDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Hao WangDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Zhuo LiDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Zhaohan WangDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Tian MaDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Ruifang SongDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Menghan LuDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Xin HanDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Yiting ZhangDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Yingtong WangDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Qing ZhenDepartment of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
Tiejun ShuiYunnan Center for Disease Control and Prevention, Kunming, Yunnan, PR China.
Jilin University · CNYunnan Center for Disease Control And Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The impact of climate on zoonotic infectious diseases (or can be referred to as climate-sensitive zoonotic diseases) is confirmed. Yet, research on the association between brucellosis and climate is limited. We aim to understand the impact of meteorological factors on the risk of brucellosis, especially in northeastern China. Methods: Monthly incidence data for brucellosis from 2005 to 2019 in Jilin province was obtained from the China Information System for Disease Control and Prevention (CDC). Monthly meteorological data (average temperature (°C), wind velocity (m/s), relative humidity (%), sunshine hours (h), air pressure (hPa), and rainfall (mm)) in Jilin province, China, from 2005 to 2019 were collected from the China Meteorological Information Center (http://data.cma.cn/). The Spearman's correlation was used to choose among the several meteorological variables. A distributed lag non-linear model (DLNM) was used to estimate the lag and non-linearity effect of meteorological factors on the risk of brucellosis. Results: A total of 24,921 cases of human brucellosis were reported in Jilin province from 2005 to 2019, with the peak epidemic period from April to June. Low temperature and low sunshine hours were protective factors for the brucellosis, where the minimum RR values were 0.50 (95 % CI = 0.31-0.82) for -13.7 °C with 1 month lag and 0.61 (95 % CI = 0.41-0.91) for 110.5h with 2 months lag, respectively. High temperature, high sunshine hours, and low wind velocity were risk factors for brucellosis. The maximum RR values were 2.91 (95 % CI = 1.43-5.92, lag = 1, 25.7 °C), 1.85 (95 % CI = 1.23-2.80, lag = 2, 332.6h), and 1.68 (95 % CI = 1.25-2.26, lag = 2, 1.4 m/s). The trends in the impact of extreme temperature and extreme sunshine hours on the transmission of brucellosis were generally consistent. Conclusion: High temperature, high sunshine hours, and low wind velocity are more conducive to the transmission of brucellosis with an obvious lag effect. The results will deepen the understanding of the relationship between climate and brucellosis and provide a reference for formulating relevant public health policies.

Indexed as

DLNMHuman brucellosisMeteorological factorSunshine hoursTemperatureWind velocity

Identifiers

PMID38660264
PMCPMC11040064
OpenAlexW4394804974

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.