Neighborhoods and Health: What Do We Need to Know?

1. Neighborhoods and Health:What Do We Need to Know? Ichiro Kawachi Professor of Social Epidemiology Harvard School of Public Health

2. Services by Type and Neighborhood Western Consumer Union, 1994

4. Rockridge West Oakland

5. Rockridge Sources: www.transcolation.org, www.csua.berekeley.edu.

6. West Oakland(www.eirikjohnson.com/westoakland

7. Counterfactual Question • Suppose W. Oakland has double the mortality rate compared to Rockridge. • Does this mean your mortality risk would increase if you moved from Rockridge to W. Oakland? • What if W. Oakland has twice the rate of poverty compared to Rockridge (and being poor doubles your mortality risk). • But you yourself are not poor?

8. Compositional vs. Contextual • Compositional – the difference that people make to places • Contextual – the difference that places make to people

9. Multi-level Analysis:A Five-Slide Crash Course Individuals (level 1) nested within six neighborhoods (level 2). Good health Income Fixed intercept, Fixed slope Model – Ignoring Neighborhood Context

10. Random intercepts, Fixed slopes Good health Income Each neighborhood represented by a separate line at varying distances from average relationship indicated by thick line, i.e. intercepts allowed to vary.

11. Putting it into equations…Individual-level regression model yi = β0x0i + β1x1i + (ε0ix0i) Outcome: Good health score Fixed part of regression model: Intercept & slope, where x1 = income Random part of regression model: “Residual”

12. Neighborhood regression model(assuming health depends only on neighborhood context) Micro model (for health of ith individual in jth neighborhood): yij = β0jx0ij+ ε0ijx0ij Macro model (at neighborhood level, allowing intercept to vary): β0j = β0 + u0j Health in each of j neighhorhoods depends on fixed average β0, plus random difference allowed to vary for each neighborhood (uoj).

13. Putting individual and neighborhood models together… Random intercept model yij = β0x0ij + β1x1ij + (u0jx0ij + ε0ijx0ij)

14. Intra-class correlation (ICC) • Partitions variance in multi-level models attributable to individual vs. neighborhood levels. • ρ = (between-neighborhood variance) / (between-neighborhood variance) + (between-individual, within-neighborhood variance). • Typically ranges between 5-10% in neighborhood studies (depending on health outcome).

15. Example • Does obesity vary significantly across neighborhoods? • Chicago Community Adult Health Study (Morenoff et al. 2006). • Stratified, multi-stage probability sample of 3,105 adults living in 343 neighborhoods of Chicago. • In-home assessments of BMI. Source: Morenoff JD, Diez Roux AV, Osypuk T et al. “Residential environments and obesity”http://www.npc.umich.edu/news/events/healtheffects_agenda/

16. ICC for obesity Unadjusted ICC Adjusted ICC* *Adjusted for age, race, education, income, immigrant status (at level 1), and % white, % Hispanic, % residents with > 16 years of education (at level 2). Source: Morenoff et al. 2006

17. Oakes’ critique of multilevel approach • Social stratification sorts individuals into different neighborhoods. • By controlling for individual compositional effects of social class, the multilevel analyst runs the risk of making adjustments until “there is nothing for the neighborhood variables to explain.” • But if we don’t control for individual social class, we run the risk of residual confounding. • Even if we could find poor people living in affluent communities (or vice versa), “these people are exceptions to the rule and should not be given the same level of statistical credence as the majority”. Oakes JM. “The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology”. Soc Sci Med 2004; 58:1929-52.

18. Hurricane Katrina, August 2005

19. Flooding and Residential Segregation in New Orleans Flood level on September 2, 2005

20. Households Living in Poverty in New Orleans, 2000 Census

21. Poor Households in New Orleans with No Access to Cars - 2000 Census

22. Propensity Score • Conditional probability of being treated (T=1) given the individual’s covariates (Zi), which can be used to balance covariates across treatment groups to reduce bias. P(Zi) = Prob (T =1| Zi) • Logit or probit models

23. Advantages of Propensity Scores • Address dimensionality problem (groups can be balanced with a single scalar variable, i.e., probability of treatment assignment). • Address problems of off-support inference (via matching).

24. Hypothetical example of lack of overlap in propensity scores Source: JM Oakes & JS Kaufman, eds. Methods in Social Epidemiology. Jossey-Bass/Wiley: forthcoming.

25. Drawbacks of Propensity Score Matching • Can’t address unobserved characteristics. • Tends to limit investigations to binary treatment effects. • Missing data on propensity score predictors. • Unclear how to address propensity scores at level 2.

26. Challenges in Neighborhood Research

27. Unpacking neighborhood influences on obesity • “Built environment” • Local food environment • Social environment – fear of crime

28. Playground Safety and Racial Composition of Boston Neighborhoods Cradock et al. Am J Prev Med 2005;28(4):357-363.

29. Playground Safety in Boston Cradock et al. Am J Prev Med 2005

30. Concepts in “Built Environment” “Walkability” • Proximity – How close travel destinations are in space? a) density – concentration of people & dwellings. b) mixture of use – industrial, commercial, residential. • Connectivity – Number and directness of travel routes. Source: Frank LD & Engelke P. “Multiple impacts of the built environment on public health: Walkable places and the exposure to air pollution.” Int Regional Sci Rev 2005;28:193-216.

31. Illustration of connectivity Source: Frank & Engelke 2005, figure 2, p. 199

32. Hypothesis • Low physical activity (and higher obesity) associated with • Low population density • Fewer travel destinations • Single use zoning • Low connectivity

33. Assessment of Built Environment through Systematic Social Observation (SSO) • Chicago Community Adult Health Study (Morenoff et al. 2006). • Trained observers sent out to 1672 street blocks in which survey respondents resided. • Assessment of block faces: • Proportion of block faces that have mixed commercial & residential land use • Presence of grocery stores • Presence of recreational facilities Source: Morenoff et al, 2006

34. Neighborhood predictors of exercise*, by gender *Linear coefficients for physical activity scale based on questionnaire **p<.01

35. The local food environment

36. Concepts in Local Food Environment • Access to supermarkets • Exposure to fast food outlets • Availability of healthy food options

37. Prevalence Ratios of Services by Neighborhood Wealth Neighborhood Wealth Morland et al. “Neighborhood characteristics associated with the location of food stores and food service places.” Am J Prev Med. 2002 Jan;22(1):23-9.

38. Is the density of fast food outlets higher in low income neighborhoods? YES • Block et al, New Orleans (Am J Prev Med 2004;27:221-17) • Reidpath et al, Melbourne, Australia (Health & Place 2002;8:141-5) NO • Macintyre et al, Glasgow, Scotland (Int J Behav Nutr & Phys Act 2005;2:16). • Austin et al, Chicago (AJPH 2005;95(9):1575)

39. Are healthful foods less available in low income neighborhoods? YES • Wechsler et al. -- Low fat milk availability in 251 bodegas of Washington Heights, NYC. (AJPH 1995;85:1690-2) • Sloane et al. – Fresh fruits/veg, low fat dairy availability in South LA. (J Gen Intern Med 2003;18: 568-75) • Lewis et al. – Menus in 659 restaurants of South LA. (AJPH 2005;95:668-73) • Horowitz et al. – Diabetes-healthy foods in East Harlem vs. Upper East Side. (AJPH 2004;94:1549-54)

40. Does Access = Utilization? YES • Morland et al. – Access to supermarkets associated with healthier diets among African-Americans. (AJPH 2002;92:1761-7) • Rose & Richards – Easy access to supermarkets associated with more fruit intake in 1996-97 National Food Stamp Program Survey. (Public Health Nutr 2004;7:1081-8) • Laraia et al. – Proximity to supermarkets associated with better diet quality during pregnancy in Pregnancy, Infection & Nutrition (PIN) cohort. (Prev Med 2004;39:869-75)

41. Does Access = Utilization? NO • Cheadle et al. – Change in availability of low fat/high fiber products in grocery stores not associated with 2-year change in diet. (Prev Med 1993;22:361-72) • Cummins et al. - Opening of new supermarket in deprived area of Glasgow not associated with subsequent healthier eating habits. (JECH 2005;59:1035-40) • Burdette & Whitaker – Proximity to fast food restaurants not associated with obesity among 7,020 low-income children in Cincinnati, OH. (Prev Med 2004;38:57-63)

42. What we need to know • Moving beyond density measures to measuring consumer nutrition environments - Prices - In-store advertising and product placement - Shelf space • Stronger links to actual behavior and utilization • Stronger study designs - Natural experiments - Interventions & evaluations

43. Challenges in Neighborhood Research

44. Endogeneity in Identifying Neighborhood Effects on Health • People choose where they live - Physically active people move to places where there are parks and recreational facilities. • Services choose where to locate - Junk food outlets move in where there is demand. • How can we overcome this bias?

45. Methods to Deal with Endogeneity • Collect additional data on unobserved variables. • Instrumental variables -- Manipulate X in a way that has no effect on Y (other than through induced changes in values of Y). • Randomize X.

46. Examples of Instruments

47. What instrument? Z Proximity to fast food outlets Risk of adult obesity Preference for junk foods

48. What instrument? Proximity to schools Proximity to fast food outlets Risk of adult obesity Preference for junk foods

49. Invalid instrument, I Proximity to schools Direct path from Z to y? Proximity to fast food outlets Risk of adult obesity Preference for junk foods

50. Invalid instrument, II Proximity to schools Proximity to fast food outlets Risk of adult obesity Preference for junk foods Path from Z to common prior cause of x and y?