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

Abstract

BackgroundDisease 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 findingsWe 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 significanceThis 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.

A Mobile App and Dashboard for Early Detection of Infectious Disease Outbreaks: Development Study

“…The ability to accurately capture and report data, such as symptoms and age, is important to create an optimal disease-specific intervention plan [ 21 , 26 , 27 ]. It is also crucial to identify and understand the unique characteristics of certain diseases; some diseases have a higher number of reported cases, but only a few patients have serious illnesses (eg, measles).…”

“…Underreporting has been associated with a number of factors, including clinicians’ lack of time and motivation and the use of complicated and inconsistent paper forms. The electronic system, which was consistent and user friendly, created an environment where clinicians could better engage in reporting and understand their notification obligations [ 21 , 25 , 29 ]. An interactive data visualization technique (eg, web dashboard) was also used to understand the unique characteristics of certain infectious diseases, thereby improving the accuracy and completeness of outbreak reporting [ 27 ].…”

“…The system was able to reduce the delay from 10 days to 1 day and had a higher completeness of data with over 10% improvements compared with traditional paper-based approaches. Similarly, Angues et al [ 21 ] used a mobile-based data collection platform and real-time cloud-based storage technology to reduce delayed recognition and intervention. Fahnrich et al [ 23 ] also developed a mobile-based system that can efficiently and promptly control Ebola in West Africa.…”

Aetiologies and therapies of nodding syndrome: a systematic review and meta-analysis

“…60 Histopathological investigations are ongoing, and recent unpublished investigations suggest that NS may be a unique neurodegenerative disorder comparable to a frontotemporal degenerative tauopathy. 88 An autoimmune reaction driven by molecular mimicry between OV and human LM-1 was recently proposed. 7 However, while Johnson and colleagues demonstrated an association between the presence of anti-LM-1 Ig (subtypes G and M) and NS, whether anti-LM-1 Ig contributes to, is a consequence of or is coincidental with the pathology of NS remains unclear.…”

COVID-19 and Teleneurology in Sub-Saharan Africa: Leveraging the Current Exigency

“…Most available telehealth programs deploy mobile health services to increase access to healthcare and health education ( 33 ). This model has enhanced monitoring of blood pressure control in stroke survivors ( 34 , 35 ), epilepsy diagnosis, care and follow up ( 36 ), movement disorders education ( 37 ), and the care of patients with Parkinson's disease ( 38 ). Its potential role in telerehabilitation has also been evaluated ( 39 ).…”