the impact of the built environment on obesity and activity in an elderly population
DESCRIPTION
The Impact of the Built Environment on Obesity and Activity in an Elderly Population. Ethan M. Berke, MD, MPH Department of Community and Family Medicine Dartmouth Medical School. Acknowledgements. Anne Vernez-Moudon, Dr. es Sc University of Washington Department of Urban Design and Planning - PowerPoint PPT PresentationTRANSCRIPT
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The Impact of the Built Environment on Obesity and Activity in an Elderly Population
Ethan M. Berke, MD, MPH
Department of Community and Family Medicine
Dartmouth Medical School
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Acknowledgements
• Anne Vernez-Moudon, Dr. es Sc– University of Washington Department of Urban Design and
Planning
• Eric B. Larson, MD, MPH– Group Health Cooperative Center for Health Studies
• Thomas D. Koepsell, MD, MPH– University of Washington Department of Epidemiology
• Richard E. Hoskins, PhD, MPH– Washington State Department of Health
• Phil Hurvitz, MFR– University of Washington Department of Urban Design and
Planning
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Background
• Of population ≥ 65 y/o– > 40% overweight (BMI 25 - 29.9)– > 18% obese (BMI ≥ 30)
• Obesity in the elderly– Increased CV disease– Diabetes– Depression
• Physical activity provides many physical and psychological benefits
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Research Question
Are individuals 65 years of age and older living in areas of King County, WA, that are more walkable more active or less obese than elderly individuals living in areas that are less walkable?
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Choose data to answer the question
• Source of subject data
• Geographic data from publicly available sources
• Want data at individual-level– Avoid issues of ecologic fallacy and MAUP– Unique from census-based studies
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Methods - Patient Population
• Adult Changes in Thought (ACT) study– Group Health Cooperative study - 1994 - present– Prospective longitudinal design – ≥ 65 y/o– ~2500 subjects– Surveyed biennially– Information on BMI, self-reported walking– RxRisk, demographics, health conditions
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Walkable & Bikable Communities Project
• Custom extension in ArcView
• Uses public data:
•Tax parcel
•Streets, blocks, sidewalks, bus routes
•Land slope
•Proximity analysis
•Neighborhood clustershttp://gis.washington.edu/phurvitz/wbc/
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Walkable & Bikable Communities Project
• Surface model application– Create smooth layer of walkability
scores across study area
• Geocode subjects at parcel level
• Create buffers around each subject
• Compute walkability score for each person at each buffer size
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WBC Layers
2 0 2 4 Miles
N
Residential DensityResidential Dwelling Units Per Acre
Residential Dwelling Units Per Acre
1 DU/Acre
2 - 4 DU/Acre
5- 10 DU/Acre
11 - 100 DU/Acre
101+ DU/Acre
Residential Dwelling Units Per Acre
Adapted from UW Urban Form Lab
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WBC Layers
Block Size
Adapted from UW Urban Form Lab2 0 2 4 Miles
N
Block Size within the UGB
0.05 - 1 acre
1 - 5 acres
5 - 10 acres
10 - 20 acres
20 - 500 acres
500 - 47157.68 acres
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WBC Layers
Neighborhood Centers ofGrocery, Restaurant, Retail
Adapted from UW Urban Form Lab
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Neighborhood Characteristics
Environmental CharacteristicOdds of walking >150
min/week vs. not walking (airline measurement)
Shorter distance to closest grocery store (log-feet)
2.257**
Fewer grocery stores/markets within 1km buffer
1.499**
More grocery store/restaurant/retail clusters in 1km buffer
1.697**
Smaller size of closest office complex (log – sq feet)
1.284**
Longer distance to closest office / mixed-use complex (feet)
1.274**
Less number of educational parcels in 1km buffer (log-count)
1.553*
Smaller size of block where residence is located (acre)
1.192*
More dwelling units per acre of the parcel where the residence is located (log-dwelling units/acre)
1.959**
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WBC Surface Model
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Merging the Data
WBC walkability score - Probability of walking > 150 minutes per week vs. none
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Merging the Data
WBC walkability score - Probability of walking > 150 minutes per week vs. none
Patient data from ACT study-BMI-Activity data -Health information-Address for geo-coding-Demographics
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Merging the Data
Geographic Information System (GIS) analysis of patient data overlaid on geographic data
WBC walkability score - Probability of walking > 150 minutes per week vs. none
Patient data from ACT study-BMI-Activity data -Address for geo-coding-Demographics
Patient data from ACT study-BMI-Activity data -Health information-Address for geo-coding-Demographics
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Merging the Data
Patient data from ACT study-BMI-Activity data -Address for geo-coding-Demographics
Geographic Information System (GIS) analysis of patient data overlaid on geographic data
Statistical Analysis -Association of BMI, activity with walkability score
WBC walkability score - Probability of walking > 150 minutes per week vs. none
Patient data from ACT study-BMI-Activity data -Health information-Address for geo-coding-Demographics
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More walkable neighborhood Less walkable neighborhood
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Subject Characteristics
All Subjects (mean (SD) or
%)n=936
Women (mean (SD)
or %)n=601
Men (mean (SD) or
%) n=335
Age (years) 78.5 (6.1) 78.9 (6.1) 77.8 (6.0)
Gender (% Female) 64.2 64.2 35.8
CES-D score 5.8 (6.5) 6.4 (6.9) 4.7 (5.4)
RxRisk ($) 4142.1 (2307.9)3924.3
(1422.7)4532.6
(2223.3)
Income > $30000 (%) 49.3 37.6 69.1
More than 12 years education (%)
69.7 68.8 71.2
Smoking (%) 10.4 4.7 20.3
Arthritis (%) 3.7 4.1 3
BMI 27.0 (5.0) 27.0 (5.7) 27.1 (3.6)
Any walking for exercise (%) 48.4 46.1 50.9
Lived in same home at least 2 years (%)
79.1 77.9 81.2
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Results: Self-reported walkingAddress 2 years prior
Gender Buffer Radius (m)
Walkability Score (0 Š 100) - 75th percentile
Walkability Score (0 Š 100) - 25th percentile
Adjusted Odds Ratio (95% CI)
p-value
Different Men 100 47.90 30.65 9.14 (1.23-68.11) 0.03
500 47.71 31.65 6.64 (1.05-42.07) 0.05
1000 46.17 31.58 5.86 (1.01-34.17) 0.05
Women 100 47.90 30.65 1.63 (0.94-2.83) 0.08
500 47.71 31.65 1.73 (0.99-3.00) 0.05
1000 46.17 31.58 1.77 (1.03-3.04) 0.04
Same Men 100 47.90 30.65 0.88 (0.62-1.26) 0.49
500 47.71 31.65 0.87 (0.61-1.25) 0.46
1000 46.17 31.58 0.92 (0.62-1.36) 0.68
Women 100 47.90 30.65 1.33 (1.00-1.77) 0.05
500 47.71 31.65 1.34 (0.99-1.80) 0.06
1000 46.17 31.58 1.36 (0.99-1.87) 0.06
1 Analysis adjusted for CES-D score, income, education, arthritis, age, RxRisk score, living alone, and smoking
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Results: BMIGender Buffer
Radius (m)
Walkability Score (0 Š 100) - 75th percentile
Walkability Score (0 Š 100) - 25th percentile
Adjusted Odds Ratio (95% CI)
p-value
Men 100 47.90 30.65 0.79 (0.54-1.17) 0.24
500 47.71 31.65 0.81 (0.54-1.21) 0.29
1000 46.17 31.58 0.76 (0.49-1.18) 0.22
Women 100 47.90 30.65 0.99 (0.74-1.33) 0.97
500 47.71 31.65 1.03 (0.76-1.39) 0.86
1000 46.17 31.58 0.93 (0.67-1.30) 0.68
1
Analysis adjusted for CES-D score, income, education, arthritis, age, RxRisk score, living alone, and smoking
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Conclusion
• Neighborhood characteristics are associated with the frequency of walking for physical activity in older people.
• Whether this reduces obesity prevalence is less clear.
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GIS Study Features
• Novel use of individual-level neighborhood data
• Objective measures of neighborhood
• Merge geographic and medical databases
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Questions?