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.
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.
Authors: Zeshan A Rajput; Samuel Mbugua; David Amadi; Viola Chepngeno; Jason J Saleem; Yaw Anokwa; Carl Hartung; Gaetano Borriello; Burke W Mamlin; Samson K Ndege; Martin C Were Journal: J Am Med Inform Assoc Date: 2012-02-24 Impact factor: 4.497
Authors: Christopher S Wood; Michael R Thomas; Jobie Budd; Tivani P Mashamba-Thompson; Kobus Herbst; Deenan Pillay; Rosanna W Peeling; Anne M Johnson; Rachel A McKendry; Molly M Stevens Journal: Nature Date: 2019-02-27 Impact factor: 49.962
Authors: Andrés Navarro; Luisa Rubiano; Juan David Arango; Carlos A Rojas; Neal Alexander; Nancy Gore Saravia; Eliah Aronoff-Spencer Journal: PLoS Negl Trop Dis Date: 2018-11-01
Authors: Alejandra Maria Del Castillo; Maria Del Mar Castro; Alexandra Cossio; Jonny Alejandro García Luna; Domiciano Rincón; Ruth Mabel Castillo; Miguel Darío Prieto; David Esteban Rebellón-Sánchez; Andrés Navarro; Neal Alexander Journal: Am J Trop Med Hyg Date: 2022-07-25 Impact factor: 3.707
Authors: Kay Polidano; Linda Parton; Suneth B Agampodi; Thilini C Agampodi; Binega H Haileselassie; Jayasundara M G Lalani; Clarice Mota; Helen P Price; Steffane Rodrigues; Getachew R Tafere; Leny A B Trad; Zenawi Zerihun; Lisa Dikomitis Journal: Front Public Health Date: 2022-02-15
Authors: Oscar Javier Oviedo Sarmiento; María Del Mar Castro; Yenifer Orobio Lerma; Leonardo Vargas Bernal; Andrés Navarro; Neal D E Alexander Journal: BMC Res Notes Date: 2021-05-31