Evidence map›Paper›PMID 38596126›Full record

ArticleHeliyon2024

Prevalence and spatial distribution characteristics of human echinococcosis: A county-level modeling study in southern Xinjiang, China.

Yue Zhang, Jun Wu, Simayi Adili, Shuo Wang, Haiting Zhang, Guangzhong Shi, Jiangshan Zhao

Abstract 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 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

7 authors.

Yue ZhangDepartment of Public Health, Xinjiang Medical University, Urumqi, China.
Jun WuDepartment of Public Health, Xinjiang Medical University, Urumqi, China.
Simayi AdiliXinjiang Autonomous Regional Center for Disease Control and Prevention, Urumqi, 830002, China.
Shuo WangXinjiang Autonomous Regional Center for Disease Control and Prevention, Urumqi, 830002, China.
Haiting ZhangXinjiang Autonomous Regional Center for Disease Control and Prevention, Urumqi, 830002, China.
Guangzhong ShiXinjiang Autonomous Regional Center for Disease Control and Prevention, Urumqi, 830002, China.
Jiangshan ZhaoXinjiang Autonomous Regional Center for Disease Control and Prevention, Urumqi, 830002, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Human echinococcosis remains an important public health problem. The aim of this study was to analyze the prevalence and spatial distribution characteristics of human echinococcosis cases in southern Xinjiang, China from 2005 to 2021. Methods: Human echinococcosis cases were collected from the National Infectious Disease Reporting System. Joinpoint regression analysis was performed to explore the trends. Spatial autocorrelation, hot spot analysis, as well as spatial-temporal clustering analysis were conducted to confirm the distribution and risk factors. Results: A total of 4580 cases were reported in southern Xinjiang during 2005-2021, with a mean annual incidence of 2.56/100,000. Echinococcosis incidence showed an increasing trend from 2005 to 2017 (APC = 17.939, 95%CI: 13.985 to 22.029) and a decreasing trend from 2017 to 2021 (APC = -18.769, 95%CI: 28.157 to -8.154). Echinococcosis cases had a positive spatial autocorrelation in 2005-2021 (Moran's Conclusions: Our findings suggest that echinococcosis is still an important zoonotic parasitic disease in southern Xinjiang, yet it showed a certain degree of spatial clustering. It is crucial to implement comprehensive prevention and control measures to effectively combat the epidemic of echinococcosis.

Indexed as

EchinococcosisEpidemiologic characteristicsSouthern XinjiangSpatial clustering

Identifiers

PMID38596126
PMCPMC11002248

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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.