Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey

Bibliographic Data

ID19551355
AuthorsAndrew Marsh (0000-0001-8845-3562, Johns Hopkins University, corresponding author), Siddhivinayak Hirve (0000-0002-9651-7789, Dr. D. Y. Patil Medical College, Hospital and Research Centre), Pallavi Lele (Dr. D. Y. Patil Medical College, Hospital and Research Centre), Uddhavi Chavan (Dr. D. Y. Patil Medical College, Hospital and Research Centre), Tathagata Bhattacharjee (0000-0001-9437-0894, Dr. D. Y. Patil Medical College, Hospital and Research Centre), Harish Nair (0000-0002-9432-9100, University of Edinburgh), Harry Campbell (0000-0002-6169-6262, University of Edinburgh), Sanjay Juvekar (0000-0002-7209-6924, Indepth Network)
Year2020
Volume10
Issue1
Pages010602-010602
Publication date2020-06-01
Open AccessYes
TypeARTICLE
VenueJournal of Global Health (JOURNAL)
Journal identifiersISSN: 2047-2978 • E-ISSN: 2047-2986
PublisherInternational Society of Global Health (PUBLISHER • GB)
DOI10.7189/jogh.10.010602
PMID32426124
OpenAlexW3021640345
LanguageEN
References cited32

INTRODUCTION: Common approaches to measure health behaviors rely on participant responses and are subject to bias. Technology-based alternatives, particularly using GPS, address these biases while opening new channels for research. This study describes the development and implementation of a GPS-based approach to detect health facility visits in rural Pune district, India. METHODS: Participants were mothers of under-five year old children within the Vadu Demographic Surveillance area. Participants received GPS-enabled smartphones pre-installed with a location-aware application to continuously record and transmit participant location data to a central server. Data were analyzed to identify health facility visits according to a parameter-based approach, optimal thresholds of which were calibrated through a simulation exercise. Lists of GPS-detected health facility visits were generated at each of six follow-up home visits and reviewed with participants through prompted recall survey, confirming visits which were correctly identified. Detected visits were analyzed using logistic regression to explore factors associated with the identification of false positive GPS-detected visits. RESULTS: We enrolled 200 participants and completed 1098 follow-up visits over the six-month study period. Prompted recall surveys were completed for 694 follow-up visits with one or more GPS-detected health facility visits. While the approach performed well during calibration (positive predictive value (PPV) 78%), performance was poor when applied to participant data. Only 440 of 22 251 detected visits were confirmed (PPV 2%). False positives increased as participants spent more time in areas of high health facility density (odds ratio (OR) = 2.29, 95% confidence interval (CI) = 1.62-3.25). Visits detected at facilities other than hospitals and clinics were also more likely to be false positives (OR = 2.78, 95% CI = 1.65-4.67) as were visits detected to facilities nearby participant homes, with the likelihood decreasing as distance increased (OR = 0.89, 95% CI = 0.82-0.97). Visit duration was not associated with confirmation status. CONCLUSIONS: The optimal parameter combination for health facility visits simulated by field workers substantially overestimated health visits from participant GPS data. This study provides useful insights into the challenges in detecting health facility visits where providers are numerous, highly clustered within urban centers and located near residential areas of the population which they serve

Confidence interval · Data collection · Environmental health · False positive paradox · Family medicine · Global Positioning System · Logistic regression · Medical emergency · Odds · Odds ratio · Recall · Statistics · Computer Science · Data-Driven Disease Surveillance · Medicine · Mobile Health and mHealth Applications · Psychology · Survey Methodology and Nonresponse

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