Yan Kestens - Daily Mobility and Multiple Exposures: Collecting and Using Spatial Data in Health Research
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Seminar given with Basile Chaix at London School of Hygiene and Tropical Medicine on Mobility and Exposure assessment for Epidemiological Modelling. Organisation: Steven Cummins & Daniel Lewis.
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Yan Kestens - Daily Mobility and Multiple Exposures: Collecting and Using Spatial Data in Health Research
- 1. Daily Mobility and Multiple Exposures: Collecting and Using Spatial Data in Health Research Yan Kestens Montreal University, Social and Preventive Medicine Montreal Hospital University Research Center (CRCHUM) SPHERE Lab .org MAY 14th 2013
- 2. Context • Most health data contains limited spatial data • Yet society increasingly mobile, blurring of spatial and temporal limits • Increasing interest in lifecycle / cumulative aspect • What methods to add spatial to epidemiology?
- 3. Context • Most health data contains limited spatial data • Yet society increasingly mobile, blurring of spatial and temporal limits • Increasing interest in lifecycle / cumulative aspect • What methods to add spatial to epidemiology?
- 4. Context • Various methods to collect spatial data that can be used to to feed epidemiological models and estimate multiple exposures Mobility surveys Activity space questionnaires Wearable sensors Time use surveys
- 5. Context Mobility surveys Allowed to compute activity space exposure to multiple food sources Estimates of multiple exposures for health survey participants Association between activity space exposure and BMI
- 6. Context Activity space questionnaires Surveys regular destinations VERITAS – Map-based interactive online questionnaire RECORD Cohort Study, Paris, Adults BEANZ Study, Auckland, Adolescents & children ERA-AGE Healthy Aging, Montreal, Paris, Luxembourg
- 7. Context Wearable sensors Web server Acquisiti on server Outputs / Applications End users GISAlgorithms GSM towerSensors CAPTUR E PROCES SING USAGE
- 14. GPS – VERITAS spatial comparison • Sample of 89 RECORD Cohort Study participants from which we collected: – VERITAS activity locations – 7-day continuous GPS monitoring – GPS-prompted recall survey data: validation of activity locations, trips and transportation modes, nature of activities
- 15. VERITAS data Total Home Work Other weekly Other less than weekly Average 15,3 1,0 1,1 7,2 5,9 Median 14,0 1,0 1,0 7,0 5,0 Total of 1,314 self-reported activity locations for 89 participants HOME WORKPLACE OTHER DESTINATIONS WEEKLY LESS THAN WEEKLY Average number of reported locations
- 16. GPS data 0 10 20 30 40 50 60 70 80 90 100 Percentageofsurveyduration withGPSfixes Proportion of GPS survey duration with valid GPS data 5 Days & 07:07:15 3 Days & 10:25:25 6 Days & 04:45:20 5 Days & 07:07:15 Extraction of activity places from raw GPS data using kernel-based density algorithm SPHERELAB GPS ARCTOOLBOX
- 18. GPS data - GPS valid fix (raw) - GPS activity location (kernel density algorithm) - GPS confirmed location (prompted recall survey) VERITAS locations - Self-reported activity locations identified on an interactive online map (HOME, WORK, OTHER WEEKLY, LESS THAN WEEKLY) DISTANCE BETWEEN AND
- 19. 0 20 40 60 80 100 120 140 HOME WORK OTHER WEEKLY OTHER LESS THAN WEEKLY Distanceinmeters Shortest distance between VERITAS location and a GPS fix
- 20. 0 50 100 150 200 250 300 350 400 450 500 HOME WORK OTHER WEEKLY OTHER LESS THAN WEEKLY Distanceinmeters Shortest distance between VERITAS location and a GPS/algorithm detected activity location
- 21. 0 200 400 600 800 1000 1200 HOME WORK OTHER WEEKLY OTHER LESS THAN WEEKLY Distanceinmeters Median shortest distance between VERITAS location and GPS-prompted recall location
- 22. 0 20 40 60 80 100 120 140 Shortest distance between GPS activity location and VERITAS location by VERITAS category (median value; n=1,314)
- 24. t0 t1 t2 t3 t5 t6 t7 t8 t9 t10 t11 t12 t13 5 5 5 5 10 5 5 5 t14 t15 t16 20 5 5 Ellapsed time attributed to second of two consecutive GPS data fixes Sum of individual fix durations = total survey duration 1) Calculation of duration from GPS fixes x x xx 2) Computation of proportion of survey duration spent within… 100 m 250 m 500 m 1000 m …of a VERITAS self-reported location
- 25. 1000 250 500 100
- 26. 1000 250 500 100
- 27. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Within 100 m Within 250 m Within 500 m Within 1000m Proportion of total survey time spent within range of VERITAS locations
- 30. Convex hulls Standard deviation ellipse
- 33. GPS tracks GPS activity location
- 39. 0 20 40 60 80 100 120 140 160 180 200 VERITAS Convex Hull GPS Convex Hull VERITAS 1 STD ELLIPSE GPS 1STD ELLIPSE Areainkm2 Convex hull and 1 STD ellipse size 30 163 23 23
- 40. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Overlap Veritas convex hull Overlap GPS convex hull Overlap Veritas ellipse Overlap GPS ellipse Proportionofoverlap Spatial overlap (Convex hull, Standard deviation ellipse) 30 163 42 57 95 11 95
- 41. 95 0 2 4 6 8 10 12 14 16 18 20 Unweighted Time weighted Distanceinkm Distance between ellipse geographic centers 2.35 1.43
- 42. 1. How close are GPS data to VERITAS location? Quite close!
- 43. 2. How much time is spent around VERITAS locations? 1. How close are GPS data to VERITAS location? Quite close! A lot!
- 44. 2. How much time is spent around VERITAS locations? 3. What is the spatial correspondence between the two point distributions? 1. How close are GPS data to VERITAS location? Quite close! A lot! All depends!
- 45. CONCLUSIONS VERITAS an efficient tool to collect precise spatial information on regular destinations GPS provides objective measures of mobility, can prompt recall surveys Increasing use of embedded GPS sensors, health surveys and remote patient monitoring
- 47. CONCLUSIONS Web server Acquisition server Outputs / Applications End users GISAlgorithms GSM towerSensors
- 48. Thank you! SPHERE Lab .org Benoit Thierry from SPHERELAB Julie Méline from RECORD All the study participants!
- 49. References Chaix, B., Kestens, Y., Perchoux, C., Karusisi, N., Merlo, J., & Labadi, K. (2012). An interactive mapping tool to assess individual mobility patterns in neighborhood studies. Am J Prev Med, 43(4), 440-450. doi: 10.1016/j.amepre.2012.06.026 Chaix, B., Méline, J., Duncan, S., Merrien, C., Karusisi, N., Perchoux, C., Lewin, A., Laba di, K., Kestens, Y. (2013). GPS tracking in neighborhood and health studies: A step forward for environmental exposure assessment, a step backward for causal inference? Health & Place, 21(0), 46-51. Kestens, Y., Lebel, A., Chaix, B., Clary, C., Daniel, M., Pampalon, R., . . . SV, P. S. (2012). Association between activity space exposure to food establishments and individual risk of overweight. PLoS One, 7(8) Kestens, Y., Lebel, A., Daniel, M., Theriault, M., & Pampalon, R. (2010). Using experienced activity spaces to measure foodscape exposure. Health Place, 16(6), Thierry, B., Chaix, B., & Kestens, Y. (2013). Detecting activity locations from raw GPS data: a novel kernel-based algorithm. Int J Health Geogr, 12(1), 14. doi: 10.1186/1476- 072X-12-14 Perchoux, C., Chaix, B., Cummins, S., & Kestens, Y. (2013). Conceptualization and measurement of environmental exposure in epidemiology: Accounting for activity space related to daily mobility. Health Place, 21C, 86-93. doi: 10.1016/j.healthplace.2013.01.005