In:
PLOS Neglected Tropical Diseases, Public Library of Science (PLoS), Vol. 15, No. 2 ( 2021-2-11), p. e0008989-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1935-2735
DOI:
10.1371/journal.pntd.0008989
DOI:
10.1371/journal.pntd.0008989.g001
DOI:
10.1371/journal.pntd.0008989.g002
DOI:
10.1371/journal.pntd.0008989.g003
DOI:
10.1371/journal.pntd.0008989.t001
DOI:
10.1371/journal.pntd.0008989.t002
DOI:
10.1371/journal.pntd.0008989.t003
DOI:
10.1371/journal.pntd.0008989.t004
DOI:
10.1371/journal.pntd.0008989.s001
DOI:
10.1371/journal.pntd.0008989.s002
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2429704-5
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