Abstract
BackgroundDetection 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.MethodsWe 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.ResultsThe 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.ConclusionsTranslating 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.
Highlights
Cutaneous leishmaniasis (CL) remains a significant public health concern in Colombia due to its high endemicity and prevalence in resource-limited and remote settings, where diagnosis, treatment and monitoring is challenging
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
Using participatory and human centered-design methods, we developed a mobile application for Android smartphones that supports community health workers to screen potential cases of CL in rural settings
Summary
Cutaneous leishmaniasis (CL) remains a significant public health concern in Colombia due to its high endemicity and prevalence in resource-limited and remote settings, where diagnosis, treatment and monitoring is challenging. In the Americas, Colombia reports the second highest incidence of CL, between 9.99 and 34.17 cases per 100,000 inhabitants over recent years [1] This reported incidence is based on passive surveillance of cases consulting health care institutions, leading to under-ascertainment, -diagnosis and -reporting, which hinder control and the provision of diagnostic and treatment resources in areas of transmission [2]. 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
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