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The environments, mobility/transportation, and health

Basile Chaix (2013), Presentation at the Erasmus Medical Center, March 22 2013
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Basile Chaix

on 21 March 2013

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Transcript of The environments, mobility/transportation, and health

Basile Chaix, INSERM The environments,
mobility / transportation,
and health Erasmus Medical Centre
Rotterdam
March 22 2013 Introduction Exposure areas around activity places + estimated shortest paths between locations Challenges in the definition of mobility-based measures of exposure Exclusion of activity places
related to the outcome Both with destination surveys and GPS tracking, it is critical to collect information on the activities practiced at the different places for filtering of exposure measures. Selective daily mobility bias: Web server Acquisition server Outputs /
Applications End users GIS Algorithms GSM tower Sensors COMPONENT 1: Improvement of wearable device, real-time transmission COMPONENT 2: Data processing, GPS algorithms, Accelerometer algorithms COMPONENT 3: Web applications, prompted recall, counseling 4186 trips for 132 participants (med.: 30 / indiv.) Concluding remarks Important to account for daily mobility in health research HOME DESCRIPTIVE DATA ON TRIPS Regular mobility assessment VERITAS in RECORD: Participants are asked to
draw the delimitations of their perceived residential neighborhood Geocoding of activity places - residences
- workplaces
- supermarkets,
outdoor markets, bakeries,
butcher shops,
fruit and vegetable shops,
fish stores, cheese merchants,
specialized food stores
- tobacco shops,
- bank, post offices
- hairdressers
- transportation stations
- sport facilities
- entertainment facilities
- places for regular cultural,
community or spiritual activities
- places to which relatives are
taken and places where people
are visited Availability of Google Maps search functionalities to retrieve places and services Tooltips allow one to provide information on each service (visit frequency, etc.) - electronic surveys of regular destinations to assess chronic
environmental exposures
- GPS tracking to assess acute environmental exposures Standard deviational ellipse Exposure area: 0.5 mile around all GPS points Measurement in GPS-based "daily path areas" Selective daily mobility Complementary strategies to incorporate daily mobility in health research, including: Chaix & Kestens.
Am J Prev Med 2012 Chaix et Kestens.
Am J Prev Med 2012 - 4540 participants surveyed (March 21 2013)
- 65180 regular activity places geocoded Second wave of the RECORD Study (ongoing) Urban planning Public health "The loss of close collaboration between urban planning and public health professionals that characterized the post-World War II era has limited the design of effective interventions that might translate into improved health for urban populations" Mary Northridge, J Urban Health 2003;80:556-568. Land use planning Epidemiology Air pollution studies New Urbanism Urban health Neighborhood
and health studies Neighborhood and health (N&H) studies Early N&H studies Current N&H studies N'hood socio-economic status Multilevel analysis Built and service environment 1985 1990-1995 2002-2003 Social processes Incorporation of mobility 2010-2012 Social epidemiology Social epidemiology
Social sciences
Urban planning
Geography
Environ. psychology
Review on n'hoods and metabolic factors (131 articles) (Chaix, Annu Rev Pub Health 2009) 90% of studies focused on residential neighborhoods
6% focused on non-residential environments
4% accounted for both environments (Leal & Chaix, Obesity Reviews 2011) A static view of environmental exposures "Residential trap": a literature focused on the residential neighborhood Findings from geo-ethnography Boston, 10 families, use of 222 locations Matthews S. Am J Prev Med 2008;34:257-259. 6% in the residential census tract
21% in immediately adjacent census tracts
73% elsewhere across the city Innovative directions for research Spatial polygamy
-> collect extra-residential information
Activity space
Network of usual places (Matthews, Am J Prev Med 2008) (Schönfelder, Transport Policy 2003) (Flamm M, Kaufmann V) "Local trap": a literature focused on the local environment (Cummins, Int J Epidemiol 2007) Residential environment Neighborhood & health DYAD Neighborhood, mobility & health TRIAD At least two reasons to incorporate mobility in N&H research: Chaix & Kestens
Am J Prev Med 2012 Socioeconomic position Spatial behavior Transportation habits Low socioeconomic subgroups...: ... trapped in a low resource environment?
... travelling across low resource environments? Barriers in mobility and related spatial isolation as a source of social disadvantage Preston et al. J Transport Geography 2007;15(3):151-160. - Changes in the use of cycling between 1994 and 2008:
increase among high-white collar workers
decrease among blue-collar workers residences and workplaces in urban centers
regular use of public transport - High white-collar workers (2008): - Blue collar workers (2008): lack of access to public transport from residences / workplaces
higher dependence on cars National Survey on Transport and Travel Social disparities in transportation mode use likely result in socio-spatial disparities in physical activity Contextual expology = a subdicipline interested in contextual exposure
assessment = identification
of the places
and times of
exposure Environmental exposure assessment in the RECORD Study Chronic environmental exposures Acute environmental exposures Electronic survey of regular mobility Global Positioning System tracking Chaix et al. RECORD Study.
Int J Epidemiol 2012 a succession of 27 screens with standard questions and interactive maps (the where
and when of
exposure) Measuring exposure to contexts with regular mobility data Zenk, Health Place 2011;17:1150-1161. A high density of fast-foods was associated with:
a higher intake of saturated fat
a lower intake of whole grains Transportation habits: contributing to environmental effects on health? Neighborhood determinants of walking for transport, RECORD Study Bassett et al.
J Phys Act Health. 2008 Hamer et al. Prev Med 2008;46(1):9-13 Transportation and health
Ecological data Transportation and health as an emerging field Transportation and health:
Individual longitudinal data Free bus pass for people >60y in England in 2006 Associated with an increased use of public transport
Older participants using public transport had a decreased risk of becoming obese between 2004 and 2008 (JECH 2012;66:176-180) Light rail transit system (LRT) installed in North Carolina Nearby residents using the LRT had an average -1.18 reduction in BMI and an 81% reduced odds of becoming obese between 2006 and 2008 Use of motorized transportation over 1997-2006 (China) Use of motorized transportation for >5y was related to a 1.2 kg greater weight gain Transportation and health
Reviews and meta-analyses Walking or biking to work was associated with a decreased cardiovascular risk, especially among women (hypertension, stroke, myocardial infarction, coronary heart disease mortality) Wanner et al. Am J Prev Med 2012;42(5):493-502 Of 30 studies, 25 reported associations for all, most, or some variables in the expected direction: more active transport (walking, cycling) associated with lower body weight Studies of mobility based on GPS tracking Public health / Nutrition studies GPS tracking and accelerometry
... but lack of information on activity places and transportation modes Limitations of current literature Transportation studies GPS tracking and mobility survey (often over one day)
... but without reliable information on physical activity and health Integrate the Public health / Nutrition approach and the Transportation approach to GPS studies RECORD GPS Study ...as an alliance between... Transportation authorities/operators Public health Ministry of transportation
CERTU
STIF
RATP
IdF Regional Council INPES
IReSP
ARS of Ile-de-France STUDY OUTPUTS Important to strengthen the partnership between Public health and Urban planning among: - researchers & research administrators
- decision makers
- field workers - identification of population with specific needs
- active/motorized transportation and its health effects
- improved exposure assessment Regular activity space Confounding due to factors that determine both places of daily mobility and health or health behavior Adjusted for age, sex, marital status, education, employment status, occupation, income, dwelling property 1. Timetable with
activity places
and trips
2. Corrected
geographic file
of the trajectory ...integrating:
cleaned GPS data (activity places, trips)
corrections and additions from the survey
attribute data (activity type, etc.) FINAL OUTPUTS 3. Aggregation of accelerometry according to
the 7-day timetable Day (departure) Exposure area: 0.5 mile around all GPS points Measurement in GPS-based "daily path areas" Selective daily mobility A high density of fast-foods was associated with:
a higher intake of saturated fat
a lower intake of fruits/veggies
a lower intake of whole grains
... when measured in the daily
path area
... but not when measured in the
residential neighborhood Chaix, Health Place 2013;21C:46-51. Annual km traveled by motor vehicles (over 6.4y, Spain) Among those with unchanged exposure, those in the highest category of distance traveled had a greater risk of becoming overweight/obese (HR = 1.4) (Am J Prev Med 2013;44:254-259) (Am J Prev Med 2010;39:105-112) (Am J Prev Med 2012;43:1-10) Rissel et al. Int J Environ Res Public Health 2012;9:2454-78 The use of public transports was associated with a 8 to 33 mn longer time of walking per day. RECORD GPS Study March 21 2013: recruitment
of 210 participants Algorithm for the automatic processing of GPS data - Cleaning of GPS data (based on signal
quality, etc.)
- Interpolation of missing data
- Detection of activity places
- Segmentation of the trajectory into
activity places and trips between them
- Detection of transportation modes

- Empirical tests to validate and
calibrate the algorithm After the second wave of the RECORD Study
GPS and accelerometer at the belt for 7 days
Participants report information on their activity places in a diary
Device sent back by mail Mobility Web Mapping application
(for the RECORD GPS Study) Survey of activities, visit frequencies, and transportation modes over 7 days
Delete fictive activity places
Report unobserved or unidentified activity places
Identification of activity places also in VERITAS
Aim: use of the application in near-real time with over-the-air data transmission 32% of the overall energy expenditure over 7 days related to transport PRELIMINARY DESCRIPTIVE DATA Compared to trips including a travel by car, trips including a travel with public transport imply: (Freedson VM3, 2011) 1586 additionals footsteps
a 88.8 kcal higher energy expenditure
...or respectively
216 additional steps for each 10 mn of trip
11.1 kcal for each 10 mn of trip (n = 60) Time (departure) Distance (calculated) Mode (when unique) Week 75%
Week-end 25% < 500 m 23%
500 m–1000 m 16%
1000 m–2500 m 19%
2500 m–5000 m 13%
5000 m–10000 m 12%
> 10000 m 17% Morning (6h-10h) 17%
Day (10h-17h) 53%
Evening (17h-22h) 27%
Night (22h-6h) 3% Walk 46%
Bike 2%
Car 42%
Metro 6%
Bus 2%
Train 2% Proportion of trips by walking according to the calculated distance (n = 132, 4186 trips) DESCRIPTIVE DATA ON TRIPS Outcome variable: use of the train in the trip Analyses at the trip level (n = 4186) Summary of findings of RECORD wave 1 The RECORD Cohort Study - Wave 1 : 2007 - 2008
N = 7290 participants
- Wave 2 : 2011 - 2013
N = 8600 (additional inclusions)
- 30-79 year old in 2007 - 2008
- 111 municipalities of the Paris
region and 10 districts of Paris Chaix et al. RECORD Study. Int J Epidemiol 2012 - Wave 1 and wave 2: - Medical, clinical, biological checkup
- Socioeconomic questionnaire
- Behavioral questionnaire
- Psychological questionnaire
- Questionnaire on the neighborhood - Wave 2 : Chaix et al. Epidemiology, 2011. 22(1): p. 18-26. Neighborhood influences on participation in the study.
Correction of neighborhood effects on type 2 diabetes from bias. Leal & Chaix. Epidemiology, 2011. 22(5): p. 694-703. Associations between neighborhood socioeeconomic status and body mass index or waist circumference. Separability of these associations from those with individual socioeconomic status? Leal & Chaix. Am J Epidemiol, 2012. 175(11): p. 1152-1162. Physical and service environments as predictors of body mass index and waist circumference. Application of other matching techniques to assess whether the associations are separable. Chaix et al. PLoS One, 2012. 7(3): p. e32908. Within-supermarket correlation in body mass index and waist circumference. Shopping in a hard-discount, especially among low educated individuals, is associated with excess weight and fat. Summary of findings of RECORD wave 1 Chaix et al. Hypertension, 2010. 55(3): p. 769-775. Association between neighborhood socioeconomic status and blood pressure. Mediating role of excess weight and fat. Van Hulst & Chaix. J Hypertens, 2012. 30(7): p. 1336-46. A typology of neighborhoods was associated with blood pressure, even after adjustment for NSES and risk factors. Different patterns reported for systolic & diastolic blood pressure. Chaix et al. Soc Sci Med, 2011. 73(10): p. 1543-1550. Association between neighborhood socioeconomic status and resting heart rate. Mediating role of physical activity and excess weight and fat. Karusisi & Chaix. Prev Med, 2012. 55(1): p. 50-55. The presence/quality of green/open spaces was associated with jogging and with jogging inside rather than outside one's neighborhood. Additional supportive effect of social cohesion. VERITAS electronic survey of regular destinations Chaix et al. Health Place. 2013;21C:46-51.
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