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Adaptation and performance of a mobile application for early detection of cutaneous leishmaniasis.
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- Additional Information
- Source:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101291488 Publication Model: eCollection Cited Medium: Internet ISSN: 1935-2735 (Electronic) Linking ISSN: 19352727 NLM ISO Abbreviation: PLoS Negl Trop Dis Subsets: MEDLINE
- Publication Information:
Original Publication: San Francisco, CA : Public Library of Science
- Subject Terms:
- Abstract:
Background: Detection and management of neglected tropical diseases such as cutaneous leishmaniasis present unmet challenges stemming from their prevalence in remote, rural, resource constrained areas having limited access to health services. These challenges are frequently compounded by armed conflict or illicit extractive industries. The use of mobile health technologies has shown promise in such settings, yet data on outcomes in the field remain scarce.
Methods: We adapted a validated prediction rule for the presumptive diagnosis of CL to create a mobile application for use by community health volunteers. We used human-centered design practices and agile development for app iteration. We tested the application in three rural areas where cutaneous leishmaniasis is endemic and an urban setting where patients seek medical attention in the municipality of Tumaco, Colombia. The application was assessed for usability, sensitivity and inter-rater reliability (kappa) when used by community health volunteers (CHV), health workers and a general practitioner, study physician.
Results: The application was readily used and understood. Among 122 screened cases with cutaneous ulcers, sensitivity to detect parasitologically proven CL was >95%. The proportion of participants with parasitologically confirmed CL was high (88%), precluding evaluation of specificity, and driving a high level of crude agreement between the app and parasitological diagnosis. The chance-adjusted agreement (kappa) varied across the components of the risk score. Time to diagnosis was reduced significantly, from 8 to 4 weeks on average when CHV conducted active case detection using the application, compared to passive case detection by health facility-based personnel.
Conclusions: Translating a validated prediction rule to a mHealth technology has shown the potential to improve the capacity of community health workers and healthcare personnel to provide opportune care, and access to health services for underserved populations. These findings support the use of mHealth tools for NTD research and healthcare.
Competing Interests: The authors have declared that no competing interests exist.
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- Grant Information:
D43 TW006589 United States TW FIC NIH HHS; MR/R010161/1 United Kingdom MRC_ Medical Research Council; R21 TW009907 United States TW FIC NIH HHS
- Publication Date:
Date Created: 20210211 Date Completed: 20210621 Latest Revision: 20220323
- Publication Date:
20240104
- Accession Number:
PMC7904137
- Accession Number:
10.1371/journal.pntd.0008989
- Accession Number:
33571192
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