Spatial Epidemiology and Obesity Risk

1. Spatial Epidemiology and Obesity Risk Phil HurvitzUrban Form LabCollege of Architecture & Urban PlanningUniversity of Washington Slide 1 (of 20)

2. Location and Health “…investigating how places are related to health will require learning to characterize places as well as we have learned to characterize the biology and behavior of people.” Diez Roux AV. Invited commentary: places, people, and health. Am J Epidemiol 2002;155(6):516-8 Slide 2 (of 20)

3. Overview • The spatial epidemiology approach, definitions, GIS • Factors in obesity risk • How GIS helps clarify spatial nature of risk factors • Current research in Urban Form Lab Slide 3 (of 20)

4. Overview • The spatial epidemiology approach, definitions, GIS • Factors in obesity risk • How GIS helps clarify spatial nature of risk factors • Current research in Urban Form Lab Slide 4 (of 20)

5. Spatial epidemiology definition “Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors.” Elliott P, Wartenberg D. Spatial epidemiology: current approaches and future challenges. Environ Health Perspect. 2004;112:998–1006 Slide 5 (of 20)

6. Spatial Epidemiology Approach • Epidemiology is concerned with the “who, what, and where” of disease, and scaling up from individual to population • Traditional epidemiology has frequently ignored spatial aspects of disease • Spatial epidemiology explicitly addresses spatial structures & functions that factor into disease

7. What is GIS? • A computer-based method for • Capture, • Storage, • Manipulation, • Analysis, and • Display of spatially referenced data Slide 6 (of 20)

8. What is GIS? • Any object or phenomenon that is or can be placed on a map can be stored, managed, and analyzed in a GIS. • Built environment features (streets, buildings, bus routes, restaurants, schools) • Households (address points, tax-lot polygons) • Individuals (points or travel lines/polygons) • Ground surface elevation or slope • Movement of objects through time and/or space • Demographics, socioeconomics • Disease occurrence Slide 7 (of 20)

9. GIS combines coordinate (map) and attribute (tabular/statistical) data Slide 8 (of 20)

10. Dr. John Snow I told myself I’d never do this The world’sfirst GIS geek? Slide 9 (of 20)

11. Importance of Snow’s work • Knowledge of spatial factors led to the conclusion that a local environmental effect was responsible for the cholera outbreak • A simple idea that led to breakthroughs in the identification and control of exposure to disease causing agents • Knowing where illness occurs is essential • Begs the question: Why is it taking so long to for spatial methods to be used in Public Health? Slide 10 (of 20)

12. The Big Picture • Spatial information science allows us to understand the spatial phenomena and processes related to disease • Knowledge of the inherently spatial patterns and processes of risk factors and the structure & function of environments will be critical in dealing with the obesity pandemic • Public Health experts need GIS in order to deal with current wicked problems Slide 11 (of 20)

13. Overview • The spatial epidemiology approach, definitions, GIS • Factors in obesity risk • How GIS helps clarify spatial nature of risk factors • Current research in Urban Form Lab Slide 12 (of 20)

14. Race SES Location/Exposure Factors in obesity risk Genetics Biology ↓ PhysicalActivity O ↑ Calories Slide 13 (of 20)

15. Medical/genetic/biological • John Brunzell, MD (2006.01.05) “Recidivism after weight loss” • Karen Foster-Schubert, MD (2006.01.12) “Ghrelin and the regulation of body weight and glucose homeostasis” • Scott Weigle, MD (2006.02.16) “Human diet studies on body weight regulation” • ECOR Symposium (2006.02.23)“Your brain on food”

16. Obesity trends

17. Genetics Biology Genetics Biology Factors in obesity risk Have genetics and biology changed substantially since 1985? Or is this effect personally, socially, culturally mediated? ↓ PhysicalActivity O ↑ Calories Slide 13 (of 20)

18. Inner personalenvironment Personal/individualfactors = Social/communityenvironmentfactors Institutional/policyenvironmentfactors Builtenvironmentfactors Outerenvironments = + + Intervention – behaviorchange/modification Conceptual Framework for Social Ecologic Model • Social ecologic model considers impacts of environment (institutional, physical, social, etc.) on behavior. (Stokols, 1992; Sallis and Owen 1997) Person(behavior) Slide 14 (of 20)

19. Personal/Individual Factors • February 9- Dimitir Christakis, MD “Screen time and poor eating habits in children”

20. Personal/Individual Factors • “Eat less, exercise more.”

21. Personal/Individual Factors • Fast food availability and consumption are related, across age, gender, and socioeconomic classes, to • higher BMI (cross sectional) • weight gain (longitudinal) • insulin resistance (Hertzler, Webb et al. 1995; French, Harnack et al. 2000; French, Story et al. 2001; Paeratakul, Ferdinand et al. 2003; St-Onge, Keller et al. 2003; Chou, Grossman et al. 2004; Matheson, Killen et al. 2004, Pereira MA 2005)

22. Institutional/Policy Environment • Jan Norman, RD, CDE (2006.03.02) “Using environment and policy approaches to address obesity”

23. Social/Community/Built Environment • Lenna Liu, MD, MPH (2006.01.19)“On the front line: obesity in minority youth” • Deb Bowen, PhD (2006.01.26) “Community interventions for obesity”

24. Social/Community/Built Environment • The case of East Harlem (Dwyer 2005, Hunger and Obesity in East Harlem: Environmental Influences on Urban Food Access) • Disparity in disease by location • Higher rates of poverty • Lower education levels • Lower food security • Fewer food choices, more expensive “good” food • More fast/junk food options • Diabetes rates in East Harlem (14%) 7 times that of Upper East Side (2%) • Obesity prevalence highest in NYC

25. Social/Community/Built Environment • The case of New Orleans (Block, J. P. et al. 2004. "Fast food, race/ethnicity, and income - A geographic analysis." American Journal of Preventive Medicine 27(3): 211-217) • Predominantly African American neighborhoods had 2.4 fast food restaurants/mi2 vs 1.5/mi2 for predominantly Caucasian • Fast food restaurant density higher in lower income neighborhoods

26. Built Environment • “We shape our buildings; thereafter they shape us.” -Sir Winston Churchill • SES and built environment • Where can people afford to live? • Environmental justice • Spatial mismatch • Non-motorized transport and built environment • Auto ownership • Walkable & bikeable neighborhoods Slide 15 (of 20)

27. Built Environment/Demographics • People of color & lower income are by definition in lower SES condition • Less $$ means fewer choices • People of color & lower income have greater exposure to lower quality food sources • Fast food is cheaper, more energy dense, and nutritionally poor

28. The Big Picture • Intervention is important at all levels represented in the social ecologic model • Personal/individual • Institutional/Policy • Social/Community • Built Environment Slide 16 (of 20)

29. Overview • The spatial epidemiology approach, definitions, GIS • Factors in obesity risk • How GIS helps clarify spatial nature of risk factors • Current research in Urban Form Lab Slide 17 (of 20)

30. Recap: Why is GIS Important in Epidemiology? • Epidemiology and public health focus on population-wide effects • Population-wide effects can only be ascertained from individual-level measurements • GIS allows the measurement of individual characteristics within an explicitly spatial context Slide 18 (of 20)

31. Recap: Why is GIS Important in Epidemiology? • GIS allows the quantification of environment at a high level of detail (census → zip → parcel) • If location is an important factor in a public health issue, GIS should (must?) be incorporated as a data management and analysis tool Slide 18 (of 20)

32. Overview • The spatial epidemiology approach, definitions • GIS • Factors in obesity risk • How GIS helps clarify spatial nature of risk factors • Current research in Urban Form Lab Slide 19 (of 20)

33. Current health-related research in Urban Form Lab • Walkable-Bikeable Communities Project • Food Environments • Pedestrian Safety

34. UFL Research: WBC Project • Walkable-Bikeable communities project • CDCs Funded • Develop instruments for quantifying walk and bike friendliness of urban environments • Integrated • Telephone survey on personal factors, e.g., physical activity • GIS-based objective measures of environment

35. WBC Analyst: Proximity and Buffer Measures calculates over 200 different built environment variables within user-specified distance of household Slide 19 (of 55)

36. UFL Research: WBC Project • Increased preference for walking associated with built environment features • Utilitarian destinations (e.g., bank, grocery, restaurant) • Presence of sidewalk • Higher residential density • Surface modeling predicts walkability across the sample frame

38. The Big Picture • Based on objective measures of the built environment, we can estimate the environmental effects on the propensity to walk • Predicts a preventive factor against obesity (physical activity) • Estimates are spatially explicit • Can simulate the effect of altering the built environment on walkability

39. UFL Research: Spatial Sampling • A novel method for defining a sample population for epidemiologic studies • Sample defined by spatially explicit built environment & sociodemographic criteria

40. UFL Research: Spatial Sampling Spatially-based population sampling is of benefit to inferential research using surveys. Our approach: • Ensures sufficient variation in and proper distribution of key variables in the sample(e.g., environmental characteristics such as residential density, proximity to activities, schools) • Ensures adequate occurrences of rare events in the sample(e.g., respondents belonging to racial minorities, those living close to public transit) • Controls for conditions of no interest(e.g., areas of low residential density) Slide 34 (of 55)

41. UFL Research: Spatial Sampling • Use the GIS to spatially stratify population of interest to construct a sample frame. Data can be taken from any GIS database, such as: • Tax-lot data: e.g., land use, assessed property values • Political data: e.g., urban growth boundary • Environmental data: e.g., slope • Census data: e.g., race • Randomly select individual residential units (a proxy for households) from the spatial sample frame. This limits the sample to a spatially and demographically specific population of interest. Slide 35 (of 55)

42. Spatial Sampling: A Demonstration of the Approach Example of criteria for delimiting a sample frame of a population “At Risk” of obesity: Households that reside: • Farther than 1 mile from a Neighborhood Center cluster of grocery stores and restaurants • In a residential unit in the bottom 1/3 of assessed property value • In a census block with greater racial diversity • Within the King County Urban Growth Boundary Slide 36 (of 55)

43. > 1 mi from [rest + gro] racially diverse combined sample frame(orange tax-lots) lower 1/3 property value GIS-Based Spatial Sampling: A Demonstration of the Approach Slide 37 (of 55)

44. The Big Picture • Use of spatial sampling can help epidemiologic studies by • Reducing sample size for the same statistical power • Lower the effective cost of data collection • Focus on specific subject characteristics • Provide output data that are spatially explicit (i.e., can be mapped and used with other spatial data in the GIS for further analysis)

45. UFL Research: Food Environments (Fast Foods) • Can we consider “Exposure” to fast food restaurants an epidemiologic risk factor? • Analysis of location of fast food restaurants • How do the densities and counts of these restaurants vary through space? • Are the differences in densities related to demographic variables? Slide 24 (of 55)

46. Fast Food Location Analysis: Where Are They? • Fast food restaurant addresses are available free online (Qwest – dexonline.com) • Online telephone directories have regular structure (server-side script generated html) that can be extracted with customized client-side scripts Slide 25 (of 55)

47. What is in the HTML Fast Food Location Analysis What the user sees Slide 26 (of 55)

48. Fast Food Location Analysis • Asset mapping: address geocoding places fast food restaurants in spatial framework common with other regional data sets Slide 27 (of 55)

49. Fast Food Location Analysis • Analysis of locations • Kernel interpolation method • Calculates density of fast food restaurants at all locations across study area Slide 28 (of 55)

50. Fast Food Location Analysis • Analysis of locations • Count of number of fast food restaurants within 1 mile for all locations Slide 29 (of 55)