Evidence map›Paper›PMID 39984565›Full record

ArticleScientific reports2025

Analysis and prediction of the incidence temporal trends of echinococcosis in China from 2010 to 2021.

Jiajia Ai, Jingying Zheng, Chang Zhu, Yang Bai, Jianyang Shi, Kunlun Zhang, Jingtong He, Yameng Gao, Quzhen Gongsang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

9 authors.

Jiajia AiTibet Center for Disease Control and Prevention, Lhasa, 850000, China.
Jingying ZhengSchool of Public Health, University of Jilin, Changchun, 130021, China.
Chang ZhuSchool of Public Health, University of Jilin, Changchun, 130021, China.
Yang BaiSchool of Public Health, University of Jilin, Changchun, 130021, China.
Jianyang ShiSchool of Public Health, University of Jilin, Changchun, 130021, China.
Kunlun ZhangSchool of Public Health, University of Jilin, Changchun, 130021, China.
Jingtong HeSchool of Public Health, University of Jilin, Changchun, 130021, China.
Yameng GaoSchool of Public Health, University of Jilin, Changchun, 130021, China.
Quzhen GongsangTibet Center for Disease Control and Prevention, Lhasa, 850000, China. gongsang1212@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Echinococcosis poses a significant health burden in China, yet previous studies have mainly concentrated on its prevalence rather than incidence trends. This study analyzes echinococcosis incidence from 2010 to 2021 and projects future trends to enhance prevention and control strategies. The annual percentage change (APC) and average annual percentage change (AAPC) were calculated to determine temporal trends, while the time series was decomposed to assess seasonal patterns. During this period, China documented 53,141 new echinococcosis cases (excluding Hong Kong, Macao, and Taiwan), averaging 0.320 cases per 100,000 annually. The AAPC indicated a non-significant decline of -2.718%. The analysis identified two critical inflection points in 2014 and 2017, leading to three distinct trends: a non-significant decline from 2010 to 2014, a significant rise from 2014 to 2017, and a significant decrease from 2017 to 2021. Cases peaked in December, and predictions from the Seasonal Autoregressive Integrated Moving Average (SARIMA) model suggest a slight rise in incidence from September 2022 to August 2025, advising intensified efforts in preventive measures to prevent resurgence.

Indexed as

EchinococcosisChinaHumansIncidenceSeasonsEchinococcosisIncidenceJoinpoint regressionSARIMA modelSeasonal patterns

Identifiers

PMID39984565
PMCPMC11845582

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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.