Using Geospatial Technologies to Characterize Relationships Between Travel Behavior, Food Availability and Health

W. Jay Christian, MPHPhD CandidateDepartment of GeographyUniversity of Kentucky

NSF 1031430

Epidemic Obesity in the U.S.1990

1999

2009

Causes of Overweight/Obesity

• Genetics• Aging• Diseases• Drugs

• Lack of energy balance– More calories in (diet) than out (physical activity)

• Environment– Accessibility

• Healthful (or unhealthful) food• Facilities that support leisure-time physical activity and/or active

transportation

Measuring Overweight/Obesity

• Body Mass Index (BMI)

BMI = (mass(lb) x 703) (height(in))2

BMI = (mass(kg))(height(m))2

Underweight <18.5Normal 18.5-24.9Overweight 25.0-29.9Obese >30.0

Food Accessibility Research

• Existing research usually models the spatial accessibility of food based on residential proximity

Food Accessibility Research

• Existing research usually models the spatial accessibility of food based on residential proximity

Weight in relation to street network distance between child’s home and nearest fast food restaurant(Burdette & Whitaker 2004)

Manhattan block distance from census tract centroid to nearest supermarket associated with race(Zenk et al. 2005)

Food stores and restaurants per square mile in census tracts and block groups.(Wang et al. 2007)

“Out-of-home eating outlets” per 1000 residents in postal codes and relationship to “deprivation”(Macintyre et al. 2005)

DISTANCE DENSITY

Critiques

• No acknowledgement of individual mobility– Most people travel outside the census tract where they live on

a daily basis—Mean travel time to work is 25.1 mins! (US Census 2011)

– Retail food locations cluster along transportation corridors, near activity centers

• Modifiable areal unit problem (MAUP)– Scale and zoning effects influence statistical analysis

• Often no direct measurement of diet, food purchasing behaviors

Doctoral Dissertation Research

• Develop a model of accessibility based on activity space, rather than residential proximity

• Collect diet and food purchasing data in addition to height/weight

• Use ‘off the shelf’ GPS technology to capture activity space

• Use GIS to link activity spaces with retail food locations

Research Questions

1. How do residence-based and activity-based models of accessibility differ?

2. To what extent are diet and food purchasing associated with activity-based accessibility measures?

3. How are other individual characteristics related to activity-based accessibility?

Research Protocol

1. Participant recruitment (up to 200) via mailed flyers to every residence in Census Tract 5 of Lexington, KY

2. Telephone contact to affirm eligibility, schedule meeting for enrollment

3. Enrollment: consent, Questionnaire 1, GPS orientation, and Q2 scheduling

4. Three days of GPS logging

5. GPS debriefing and Questionnaire 2

Census Tract 5

UK

Downtown

AshlandPark

Bell Court

Mentelle

Kenwick

Participant Recruitment

• Six months of recruitment and data collection– May through October, 2011– Approximately 1400 households received over 3100 flyers via

click2mail.com

• 121 total participants– 102 (84.3%) with complete 3-day GPS tracks– 7 (5.8%) more with complete 2-day GPS tracks– 12 (9.9%) have GPS tracks with large gaps

• 18 participants in 7-day sub-study– Extra funds used to re-recruit – Enables comparison of 3-day and 7-day GPS tracks

Participant Recruitment

Residential density Participant density

Data Collection

• Questionnaire 1– Demographics – SES– Mobility – Godin physical activity

• GPS– Qstarz 1000XT– 3+ days of tracking– Logging at 3 second intervals

• Questionnaire 2– NHANES Dietary Screener– Food shopping habits– Retail food locations visited during GPS logging

• Retail food locations– Obtained from local health department– Coded using NAICS as a guide

Data Management & Processing

• GPS data review– Are there 3 full days of data?– Any major gaps that would alter activity space?– Any wildly inaccurate points?

– Approximately 10-30 minutes per participant– Qstarz software exports to GPX, KML, CSV, other formats for

later analysis in ArcGIS 10

• Questionnaire data entry– REDCap—secure, web-based data management software– Exports to Stata, other lesser formats for statistical analysis

Participant CharacteristicsTOTAL SAMPLE GOOD 3-DAY GPS

Gender Freq % Freq %Female 70 57.9 57 55.9Male 51 42.2 45 44.1Total 121 100.0 102 100.0

Income Freq % Freq %<$25K 21 17.4 18 17.7$25-50K 25 20.7 18 17.7$50-75K 28 23.1 25 24.5$75-100K 24 19.8 20 19.6$100K+ 21 17.4 19 18.6Refused 2 1.7 2 2.0Total 121 100.0 102 100.0

Education Freq % Freq %Less than Bachelor's 26 21.5 22 21.6Bachelor's degree 43 35.5 35 34.3Graduate degree 52 43.0 45 44.1Total 121 100.0 102 100.0

BMI Category Freq % Freq %Underweight/Normal 71 58.7 58 56.9Overweight 33 27.3 29 28.4Obese 17 14.1 15 14.7Total 121 100.0 102 100.0

1020

3040

50B

ody

mas

s in

dex

0 2 4 6 8Limited-service trips per week

r=0.33p<0.001

Participant Characteristics

BMI Category NRed meat per week, mean P-value

Underweight/Normal (<25.0) 71 1.69 0.001Overweight (25.0-29.9) 33 3.25Obese (30.0+) 17 3.56

(test for trend)

RECODE of | strenuous | (STRENUOUS | EXERCISE | times per | RECODE of bmi week) | Under/Nor Overweigh | Total------------+----------------------+---------- 0 times/wk | 22 22 | 44 | 50.00 50.00 | 100.00 ------------+----------------------+----------1+ times/wk | 48 27 | 75 | 64.00 36.00 | 100.00 ------------+----------------------+---------- Total | 70 49 | 119 | 58.82 41.18 | 100.00

Pearson chi2(1) = 2.2440 Pr = 0.134

Activity Space Analysis

Insert GPS track map here

Activity space (¼ mile buffer GPS track) Supermarkets Limited-service Full-service

Statistical Analysis

1. How do residence-based and activity-based models of accessibility differ?

Supermarkets N Mean 95% CI P-valueCensus tract 16 1.19 0.84-1.54 <0.0001Activity space 16 5.13 3.85-6.40

Super_density N Mean 95% CI P-valueCensus tract 16 0.34 0.18-0.51 0.11Activity space 16 0.49 0.40-0.59

LimitedService N Mean 95% CI P-valueCensus tract 16 18.9 7.6-30.2 <0.0001Activity space 16 97.4 72.5-122.3

LS_density N Mean 95% CI P-valueCensus tract 16 7.04 1.47-12.62 0.51Activity space 16 8.85 7.19-10.50

Proportion_LS N Mean 95% CI P-valueCensus tract 16 0.46 0.39-0.54 0.01Activity space 16 0.56 0.53-0.60

Statistical Analysis

2. To what extent are diet and food purchasing associated with activity-based accessibility?

Diet, food purchasing Accessibility measuresSoda consumption SupermarketsRed meat consumption Supermarket densityLimited-service visits Limited-service restaurantsFruit consumption Limited-service densityVegetable consumption Limited-service proportionSupermarket visits Retail Food Environment Index (RFEI)

Statistical Analysis

3. How are individual characteristics related to activity-based accessibility?

Individual characteristics Accessibility measuresOverweight/obesity SupermarketsEducation Supermarket densityIncome Limited-service restaurantsGender Limited-service densityAge Limited-service proportionResidential history RFEIAutomobile use

Challenges

• Scheduling– Two interviews required, preferably on days immediately

before/after GPS logging– Graduate assistant available to help from mid-May till mid-August

• GPS accuracy– Weather, especially storms– Human factor

• Placement on person, in vehicles, in bags• Remembering to switch device to ‘Log’

– How to ‘fix’ gaps in GPS tracks

• Processing & Analysis– ArcGIS 10– GPS track buffering (crow-fly distances, street network)– Multilevel modeling necessary?

Future Directions

• Better dietary assessment– More complete food frequency questionnaire?– Photographs?

• Better physical activity measure– Accelerometer or pedometer– UP™ by Jawbone or fitbit™ ultra?

• Different contexts– Rural areas, Appalachia?– Transportation systems/mobilities

United States Census Bureau. 2011. Commuting in the United States: 2009. American Community Survey Reports, ACS-15. U.S. Census Bureau, Washington, DC.

Burdette HL, Whitaker RC. Neighborhood playgrounds, fast food restaurants, and crime: relationships to overweight in low-income preschool children. Preventive Medicine, 38: 57-63.

Macintyre S, McKay L, Cummins S, Burns C. 2005. Out-of-home food outlets and area deprivation: case study in Glasgow, UK. International Journal of Behavioral Nutrition and Physical Activity, 2: 16.

Wang MC, Kim S, Gonzalez A, MacLeod K, Winkleby M. 2007. Socioeconomic and food-related physical characteristics of the neighbourhood environment are associated with body mass index. J of Epidemiol Community Health, 61: 491-498.

Zenk SN, Schulz AJ, Israel BA, James SA, Bao S, Wilson ML. 2005. Neighborhood racial composition, neighborhood poverty, and the spatial accessibility of supermarkets in metropolitan Detroit. Am J Public Health, 95:660-667.

References

W. Jay Christian, MPH

PhD CandidateDepartment of Geographyjay.christian@uky.com

Staff EpidemiologistMarkey Cancer Control Programjchrist@kcr.uky.edu

Thank you.