A real-time medical cartography of epidemic disease (Nodding syndrome) using village-based lay mHealth reporters.

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Raquel Valdes Angues, Austen Suits, Valerie S Palmer, Caesar Okot, Robert A Okot, Concy Atonywalo, Suzanne K Gazda, David L Kitara, Moka Lantum, Peter S Spencer
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2018
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic
• Online Access: https://doi.org/10.1371/journal.pntd.0006588; https://doaj.org/article/3c02dfe4a3d44ccaad568fe1bfa7f61f

BACKGROUND:Disease surveillance in rural regions of many countries is poor, such that prolonged delays (months) may intervene between appearance of disease and its recognition by public health authorities. For infectious disorders, delayed recognition and intervention enables uncontrolled disease spread. We tested the feasibility in northern Uganda of developing real-time, village-based health surveillance of an epidemic of Nodding syndrome (NS) using software-programmed smartphones operated by minimally trained lay mHealth reporters. METHODOLOGY AND PRINCIPAL FINDINGS:We used a customized data collection platform (Magpi) that uses mobile phones and real-time cloud-based storage with global positioning system coordinates and time stamping. Pilot studies on sleep behavior of U.S. and Ugandan medical students identified and resolved Magpi-programmed cell phone issues. Thereafter, we deployed Magpi in combination with a lay-operator network of eight mHealth reporters to develop a real-time electronic map of child health, injury and illness relating to NS in rural northern Uganda. Surveillance data were collected for three consecutive months from 10 villages heavily affected by NS. Overall, a total of 240 NS-affected households and an average of 326 children with NS, representing 30 households and approximately 40 NS children per mHealth reporter, were monitored every week by the lay mHealth team. Data submitted for analysis in the USA and Uganda remotely pinpointed the household location and number of NS deaths, injuries, newly reported cases of head nodding (n = 22), and the presence or absence of anti-seizure medication. CONCLUSIONS AND SIGNIFICANCE:This study demonstrates the feasibility of using lay mHealth workers to develop a real-time cartography of epidemic disease in remote rural villages that can facilitate and steer clinical, educational and research interventions in a timely manner.