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Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey.
J Glob Health. 2020 Jun; 10(1):010602.JG

Abstract

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.

Authors+Show Affiliations

Institute for International Programs, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA. KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India.KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India.KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India.KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India.KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India. INDEPTH Network, 40 Mensah Wood Street, East Legon, Accra, Ghana.Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.KEM Hospital Research Centre, Sardar Moodliar Road, Rasta Peth, Pune, India. INDEPTH Network, 40 Mensah Wood Street, East Legon, Accra, Ghana.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32426124

Citation

Marsh, Andrew, et al. "Validating a GPS-based Approach to Detect Health Facility Visits Against Maternal Response to Prompted Recall Survey." Journal of Global Health, vol. 10, no. 1, 2020, p. 010602.
Marsh A, Hirve S, Lele P, et al. Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey. J Glob Health. 2020;10(1):010602.
Marsh, A., Hirve, S., Lele, P., Chavan, U., Bhattacharjee, T., Nair, H., Campbell, H., & Juvekar, S. (2020). Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey. Journal of Global Health, 10(1), 010602. https://doi.org/10.7189/jogh.10.010602
Marsh A, et al. Validating a GPS-based Approach to Detect Health Facility Visits Against Maternal Response to Prompted Recall Survey. J Glob Health. 2020;10(1):010602. PubMed PMID: 32426124.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey. AU - Marsh,Andrew, AU - Hirve,Siddhivinayak, AU - Lele,Pallavi, AU - Chavan,Uddhavi, AU - Bhattacharjee,Tathagata, AU - Nair,Harish, AU - Campbell,Harry, AU - Juvekar,Sanjay, PY - 2020/5/20/entrez PY - 2020/5/20/pubmed PY - 2020/6/5/medline SP - 010602 EP - 010602 JF - Journal of global health JO - J Glob Health VL - 10 IS - 1 N2 - 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. SN - 2047-2986 UR - https://www.unboundmedicine.com/medline/citation/32426124/Validating_a_GPS-based_approach_to_detect_health_facility_visits_against_maternal_response_to_prompted_recall_survey L2 - https://doi.org/10.7189/jogh.10.010602 DB - PRIME DP - Unbound Medicine ER -