Using global positioning system technology and Google My Maps in follow-up studies—An experience from influenza surveillance study, Chennai, India. (February 2020)

• Record Type: Journal Article
• Authors: Mohan, Vinoth; Kumar M, Sasi; Kumar, C.P. Girish; Yuvaraj, Jayaraman; Krishnan, Anand; Amarchand, Ritvik; Prabu, Rajkumar
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 32(2020)
• Publication Date: 2020-02
• Subjects: GPS -- Google My Maps -- Cohort-studies -- Challenges -- Navigation; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2019.100321
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: Conducting long-term and large-scale cohort studies with study population spread over a vast area, involves several logistical difficulties during regular and repeated data collection. In the era of rapidly advancing information technology, it is possible to reduce the logistical burden and improve the quality and timely data collection in cohort studies. We describe a methodology that was developed with open source software tools to garner these benefits. Abstract: A multi-centric influenza surveillance conducted among 1500 elderly participants in Chennai, India, required weekly visits to the participants regularly for three years. Difficulties were faced in locating and navigating to households of the participants due to vast study area, adverse weather conditions and staff attrition, which affected data quality. To overcome these difficulties, we devised a new way of using the 'Global Position System' (GPS) and 'Google My Maps'. GPS coordinates of all participants' households were collected and merged with their demographic data using 'Microsoft excel'. Dataset was uploaded to 'Google My Maps' in appropriate layers. This map was used to locate and navigate to households of the participants and the average working hours in the field reduced by18% even in difficult circumstances. The average number of supervisory visits increased by 150%. This method will greatly facilitate the data collection in cohort based research studies.