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

Raquel Valdes Angues, Austen Suits, Valerie S Palmer, Caesar Okot, Robert A Okot, Concy Atonywalo, Suzanne K Gazda, David L Kitara, Moka Lantum and Peter S Spencer

PLOS Neglected Tropical Diseases, 2018, vol. 12, issue 6, 1-20

Abstract: 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. Author summary: Absence of health monitoring of rural populations in low-income countries allows diseases to emerge and spread for months before their detection by public health authorities. We tested the feasibility of using smartphones operated by lay villagers to report health information in real time from the populations in which they reside. Eight young lay adults from remote rural regions of northern Uganda were trained to administer questions and transmit answers using pre-programmed mobile phones. Weekly, over a 3-month period, each lay reporter monitored an average of 40 children suffering from an epileptic disorder known as Nodding syndrome (NS). For each child, episodes of head nodding, convulsions, injuries, deaths and availability of anti-seizure medication were reported weekly and the data instantaneously assembled by customized software for analysis in Uganda and the USA. This system not only provided a real-time map of the health status of children with established NS but also discovered children previously unknown to have head nodding. While logistical hurdles had to be overcome, the study demonstrates the feasibility of using lay workers operating software-equipped mobile smartphones to build a current and continuously updatable medical geography of the rural populations in which they reside. Wide application of such systems could result in the early detection and control of disease.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pntd00:0006588

DOI: 10.1371/journal.pntd.0006588

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