ArticleBMC research notes2019
Community-led data collection using Open Data Kit for surveillance of animal African trypanosomiasis in Shimba hills, Kenya.
Article in BMC research notes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- Scaling trachoma surveillance in endemic areas using machine learning.BMC infectious diseases · 2026Article
- Pathogenic Significance of Trypanosomatids: Progress in Drug Resistance, Control Strategies, and Artificial Intelligence.Interdisciplinary perspectives on infectious diseases · 2026Review
- A review of reported stakeholder engagement in early-stage translational research.Journal of clinical and translational science · 2025Article
- The evolution of Kenya's animal health surveillance system and its potential for efficient detection of zoonoses.Frontiers in veterinary science · 2024Article
- Assessing the Feasibility of Retrospective and Prospective Clinical Audit in Farm Animal Veterinary Practice.Veterinary sciences · 2021Article
- Detecting bracoviral orthologs distribution in five tsetse fly species and the housefly genomes.BMC research notes · 2020Article
- Prevalence of trypanosomes associated with drug resistance in Shimba Hills, Kwale County, Kenya.BMC research notes · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveIn Sub-Saharan Africa, there is an increase in trypanosome non-susceptibility to multiple trypanocides, but limited information on judicious trypanocide use is accessible to smallholder farmers and agricultural stakeholders in disease endemic regions, resulting in widespread multi-drug resistance. Huge economic expenses and the laborious nature of extensive field studies have hindered collection of the requisite large-scale prospective datasets required to inform disease management. We examined the efficacy of community-led data collection strategies using smartphones by smallholder farmers to acquire robust datasets from the trypanosomiasis endemic Shimba hills region in Kenya. We used Open Data Kit, an open-source smartphone application development software, to create a data collection App.
resultsOur study provides proof of concept for the viability of using smartphone Apps to remotely collect reliable large-scale information from smallholder farmers and veterinary health care givers in resource poor settings. We show that these datasets can be reliably collated remotely, analysed, and the findings can inform policies that improve farming practices and economic wellbeing while restricting widespread multi-drug resistance. Moreover, this strategy can be used to monitor and manage other infectious diseases in other rural, resource poor settings.
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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.