03432nas a2200349 4500000000100000008004100001260003700042653005700079653002400136100001400160700001800174700001600192700001800208700002000226700001400246700001300260700001700273700001400290700001300304700001300317700001700330700001400347700001500361700002200376700002000398245010200418856009900520300001300619490000700632520242900639022001403068 2021 d bPublic Library of Science (PLoS) 10 aPublic Health, Environmental and Occupational Health 10 aInfectious Diseases 1 aRubiano L 1 aAlexander NDE 1 aCastillo RM 1 aMartínez ÁJ 1 aGarcía Luna JA 1 aArango JD 1 aVargas L 1 aMadriñán P 1 aHurtado L 1 aOrobio Y 1 aRojas CA 1 adel Corral H 1 aNavarro A 1 aSaravia NG 1 aAronoff-Spencer E 1 avan Griensven J 00 aAdaptation and performance of a mobile application for early detection of cutaneous leishmaniasis uhttps://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0008989&type=printable ae0008989 0 v15 3 a
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.
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