Realtime Mapping of Epidemic Disease with Magpi
From Magpi users at the Oregon Health and Science University comes this field report of using Magpi to longitudinally conduct surveillance of a childhood neurological disease in a low-resource setting (rural Uganda), using minimally-trained lay health workers, with detection of previously unknown and new cases in real time. A great demonstration of using mobile data collection for disease mapping and surveillance at low cost (and with no need for programmers).
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, a childhood epileptic encephalopathy in East Africa) using software-programmed smartphones operated by minimally trained lay mHealth reporters.
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 MagpiR software (https:// home.magpi.com/). Data submitted for analysis in 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. A medical diagnostician physically examined a sample of households reporting existing and newly reported NS cases, the large majority of which had longstanding but unregistered NS.
mHealth-based surveillance not only provided a real-time map of the health status of children with established Nodding Syndrome but also revealed previously unknown children with head nodding. While logistical hurdles had to be overcome, the study demonstrates the feasibility of using lay reporters to build a current and continuously updatable medical geography of the rural populations in which they reside. Additionally, the lay mHealth reporters were incented to seek future local healthcare opportunities. Wide application of this type of real-time health surveillance could result in the early detection and control of disease in remote populations.
• Minimally trained lay mhealth reporters can reliably collect and transmit health data.
• Multiple electronic health data inputs are reliably integrated using Magpi software.
• Longitudinal real-time population health surveillance is challenging but feasible.
• Village-based mHealth surveillance detects unknown cases and new cases in real time.
Full Title of Paper: A real-time medical cartography of epidemic disease (Nodding syndrome) using village-based lay mHealth reporters.
Authors: Raquel Valdes Angues (1), Austen Suits (2), Valerie S Palmer (1), Caesar Okot (3), Robert A Okot (3), Concy Atonywalo (4), David L Kitara (5), Suzanne K Gazda (3), Moka Lantum (5), Peter S Spencer (1)*.
Affiliations: (1) Department of Neurology, Oregon Health & Science University (OHSU), Portland, Oregon, USA; (2) University of Washington, Seattle, Washington, USA; (3) Hope for Humans, Gulu, Uganda; (4) Awere Health Center III, Pader, Uganda; (5) School of Medicine, Gulu University, Gulu, Uganda, and (6) MicroClinic Technologies, Ltd, Kenya.
Citation: Valdes Angues et al., PLoS Negl Trop Dis. A real-time medical cartography of epidemic disease (Nodding syndrome) using village-based lay mHealth reporters. Jun 15;12(6):e0006588. doi: 10.1371/journal.pntd.0006588. eCollection 2018 Jun. https://www.ncbi.nlm.nih.gov/pubmed/?term=valdes+spencer+nodding+lay
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