Evidence map›Paper›PMID 41953175›Full record

ArticleChina CDC weekly2026

Knowledge, Attitudes, and Practices of Dog Owners Toward a Smart Health Education Pillbox for Controlling Echinococcosis - Western China, 2023-2024.

Shijie Yang, Chenqing Sun, Ning Xiao, Shizhu Li, Shuai Han, Liying Wang, Ying Wang, Jiangshan Zhao, Shanglin Wu, Xiao Ma and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in China CDC weekly, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Shijie YangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Chenqing SunNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Ning XiaoNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Shizhu LiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Shuai HanNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Liying WangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Ying WangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.
Jiangshan ZhaoXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, Urumqi City, Xinjiang Uygur Autonomous Region, China.
Shanglin WuNingxia Hui Autonomous Region Center for Disease Control and Prevention, Yinchuan City, Ningxia Hui Autonomous Region, China.
Xiao MaQinghai Institute for Endemic Disease Prevention and Control, Xining City, Qinghai Province, China.
Yu FengDepartment of Parasitic Diseases, Gansu Center for Disease Control and Prevention, Lanzhou City, Gansu Province, China.
Benfu LiYunnan Institute of Parasitic Diseases, Kunming City, Yunnan Province, China.
Tongmin WangCenter for Disease Control and Prevention of Xinjiang Production and Construction Corps, Urumqi City, Xinjiang Uygur Autonomous Region, China.
Kaisaier TuerxunjiangXinjiang Uygur Autonomous Region Center for Disease Control and Prevention, Urumqi City, Xinjiang Uygur Autonomous Region, China.
Xiaonong ZhouNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Health Commission Key Laboratory on Parasite and Vector Biology, WHO Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

What is already known about this topic?: Echinococcosis remains a critical public health challenge in western China. Conventional routine health education (RHE) strategies have consistently proven insufficient in achieving the sustained behavioral modifications necessary to reduce disease transmission and burden. What is added by this report?: This study provides the first large-scale experimental evidence that a Smart Health Education Pillbox (SHEP) significantly enhances knowledge, corrects misconceptions, and improves practice conversion efficiency regarding echinococcosis control among dog owners in endemic pastoral areas. These findings demonstrate the substantial value of precise, automated health education tools in controlling zoonotic diseases. What are the implications for public health practice?: The SHEP represents a scalable, precise health education tool that effectively bridges the knowledge-practice gap in resource-limited settings. Its demonstrated efficacy supports integration into national echinococcosis control programs as a cost-effective digital intervention that promotes sustainable behavior change and reduces zoonotic disease transmission.

Indexed as

cluster randomized trialdog ownersechinococcosisknowledge-attitude-practicessmart health education pillboxwestern China

Identifiers

PMID41953175
PMCPMC13054545

What OpenQuestion holds

Textmetadata
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.