the impact of the built environment on obesity and activity in an elderly population

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

• 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

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

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?

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

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

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/

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

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

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

WBC Layers

Neighborhood Centers ofGrocery, Restaurant, Retail

Adapted from UW Urban Form Lab

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**

WBC Surface Model

Merging the Data

WBC walkability score - Probability of walking > 150 minutes per week vs. none

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

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

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

More walkable neighborhood Less walkable neighborhood

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

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

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

Conclusion

• Neighborhood characteristics are associated with the frequency of walking for physical activity in older people.

• Whether this reduces obesity prevalence is less clear.

GIS Study Features

• Novel use of individual-level neighborhood data

• Objective measures of neighborhood

• Merge geographic and medical databases

Questions?