ArticleBMC public health2020
Crowdsourcing to promote hepatitis C testing and linkage-to-care in China: a randomized controlled trial protocol.
Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Development and validation of HBV surveillance models using big data and machine learning.Annals of medicine · 2024Article
- A randomized controlled trial enhancing viral hepatitis testing in primary care via digital crowdsourced intervention.NPJ digital medicine · 2022Article
- Digital Marketing: A Unique Multidisciplinary Approach towards the Elimination of Viral Hepatitis.Pathogens (Basel, Switzerland) · 2022Review
- Hepatitis C virus micro-elimination: Where do we stand?World journal of gastroenterology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 7 institutions in 4 countries.
Funding
Abstract
backgroundHepatitis C virus (HCV) is a growing public health problem with a large disease burden worldwide. In China many people living with HCV are unaware of their hepatitis status and not connected to care and treatment. Crowdsourcing is a technique that invites the public to create health promotion materials and has been found to increase HIV testing uptake, including in China. This trial aims to evaluate crowdsourcing as a strategy to improve HCV awareness, testing and linkage-to-care in China.
methodsA randomized controlled, two-armed trial (RCT) is being conducted in Shenzhen with 1006 participants recruited from primary care sectors of The University of Hong Kong-Shenzhen Hospital. Eligible participants are ≥30 years old; a resident in Shenzhen for at least one month after recruitment; no screening for HCV within the past 12 months and not known to have chronic HCV; and, having a WeChat social media account. Allocation is 1:1. Both groups will be administered a baseline and a follow-up survey (4-week post-enrollment). The intervention group will receive crowdsourcing materials to promote HCV testing once a week for two weeks and feedback will be collected thereafter, while the control group will receive no promotional materials. Feedback collected will be judged by a panel and selected to be implemented to improve the intervention continuously. Those identified positive for HCV antibodies will be referred to gastroenterologists for confirmation and treatment. The primary outcome will be confirmed HCV testing uptake, and secondary outcomes include HCV confirmatory testing and initiation of HCV treatment with follow-ups with specialist providers. Data will be collected on Survey Star DISCUSSION: This will be the first study to evaluate the impact of crowdsourcing to improve viral hepatitis testing and linkage-to-care in the health facilities. This RCT will contribute to the existing literature on interventions to improve viral hepatitis testing in primary care setting, and inform future strategies to improve HCV care training for primary care providers in China.
trial registrationChinese Clinical Trial Registry. ChiCTR1900025771. Registered September 7th, 2019, http://www.chictr.org.cn/showprojen.aspx?proj=42788.
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Registered trials
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.