Evidence map›Paper›PMID 39491019›Full record

ArticleBMC public health2024

Spatio-temporal pattern and associate factors study on intestinal infectious diseases based on panel model in Zhejiang Province.

Lanfang Gu, Jian Cai, Yan Feng, Yancen Zhan, Zhixin Zhu, Nawen Liu, Xifei Guan, Xiuyang Li

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

8 authors.

Lanfang Gu *Department of Big Data in Health Science, Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Jian Cai *Institute for Communicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
Yan Feng *Institute for Communicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
Yancen ZhanDepartment of Big Data in Health Science, Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Zhixin ZhuDepartment of Big Data in Health Science, Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Nawen LiuDepartment of Big Data in Health Science, Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Xifei GuanDepartment of Big Data in Health Science, Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Xiuyang LiDepartment of Big Data in Health Science, Center for Clinical Big Data and Statistics, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China. lixiuyang@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntestinal infectious diseases (IIDs) can impact the growth and development of children and weaken adults. This study aimed to establish a spatial panel model to analyze the relationship between factors such as population, economy and health resources, and the incidence of common IIDs. The objective was to provide a scientific basis for the formulation diseases prevention measures.

methodsData on monthly reported cases of IIDs in each district and county of Zhejiang Province were collected from 2011 to 2021. The spatial distribution trend was plotted, and nine factors related to population, economy and health resources were selected for analysis. A spatial panel model was developed to identify statistically significant spatial patterns of influencing factors (P < 0.05).

resultsThe results revealed that each type of IIDs exhibited a certain level of clustering. Each IIDs had a significant radiation effect, HEV (b = 0.28, P < 0.05), bacillary dysentery (b = 0.38, P < 0.05), typhoid (b = 0.36, P < 0.05), other infectious diarrheas (OIDs) (b = 0.28, P < 0.05) and hand, foot and mouth disease (HFMD) (b = 0.39, P < 0.05), indicating that regions with high morbidity rates spread to neighboring areas. Among the population characteristics, density of population acted as a protective factor for bacillary dysentery (b=-1.81, P < 0.05), sex ratio acted as a protective factor for HFMD (b=-0.07, P < 0.05), and aging rate increased the risk of OIDs (b = 2.39, P < 0.05). Urbanization ratio posed a hazard factor for bacillary dysentery (b = 5.17, P < 0.05) and OIDs (b = 0.64, P < 0.05) while serving as a protective factor for typhoid (b=-1.61, P < 0.05) and HFMD (b=-0.39, P < 0.05). Per capita GDP was a risk factor for typhoid (b = 0.54, P < 0.05), but acted as a protective factor for OIDs (b=-0.45, P < 0.05) and HFMD (b=-0.27, P < 0.05). Additionally, the subsistence allowances ratio was a risk factor for HEV (b = 0.24, P < 0.05).

conclusionThe incidence of IIDs in Zhejiang Province exhibited a certain degree of clustering, with major hotspots identified in Hangzhou, Shaoxing, and Jinhua. It would be essential to consider the spillover effects from neighboring regions and implement targeted measures to enhance disease prevention based on regional development.

Indexed as

Spatio-Temporal AnalysisChinaCommunicable DiseasesHumansIncidenceIntestinal DiseasesRisk FactorsIntestinal infectious diseasesProtective factorsRisk factorsSpatio-temporal patternStandard deviation ellipse

Identifiers

PMID39491019
PMCPMC11533294

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.