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

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish (US)
Article numbere0006588
JournalPLoS Neglected Tropical Diseases
Volume12
Issue number6
DOIs
StatePublished - Jun 1 2018

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Nodding Syndrome
Telemedicine
Uganda
Cell Phones
Absence Epilepsy
Geographic Information Systems
Wounds and Injuries
Feasibility Studies
Medical Students
Sleep
Software
Public Health
Head

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Infectious Diseases

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A real-time medical cartography of epidemic disease (Nodding syndrome) using village-based lay mHealth reporters. / Valdes Angues, Raquel; Suits, Austen; Palmer, Valerie; Okot, Caesar; Okot, Robert A.; Atonywalo, Concy; Gazda, Suzanne K.; Kitara, David L.; Lantum, Moka; Spencer, Peter.

In: PLoS Neglected Tropical Diseases, Vol. 12, No. 6, e0006588, 01.06.2018.

Research output: Contribution to journalArticle

Valdes Angues, R, Suits, A, Palmer, V, Okot, C, Okot, RA, Atonywalo, C, Gazda, SK, Kitara, DL, Lantum, M & Spencer, P 2018, 'A real-time medical cartography of epidemic disease (Nodding syndrome) using village-based lay mHealth reporters', PLoS Neglected Tropical Diseases, vol. 12, no. 6, e0006588. https://doi.org/10.1371/journal.pntd.0006588
Valdes Angues, Raquel ; Suits, Austen ; Palmer, Valerie ; Okot, Caesar ; Okot, Robert A. ; Atonywalo, Concy ; Gazda, Suzanne K. ; Kitara, David L. ; Lantum, Moka ; Spencer, Peter. / A real-time medical cartography of epidemic disease (Nodding syndrome) using village-based lay mHealth reporters. In: PLoS Neglected Tropical Diseases. 2018 ; Vol. 12, No. 6.
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