Neighborhoods and Health:What Do We Need to Know?

Ichiro Kawachi

Professor of Social Epidemiology

Harvard School of Public Health

Services by Type and Neighborhood

Type of Store Rockridge W. Oakland Elmhurst Fruitvale

Supermarket 4 1 2 2

Fast Food 0 2 3 7

Pharmacist 3 0 0 3

Banks 4 0 1 2

Check Cashier 0 2 2 6

Liquor 2 7 10 8

Western Consumer Union, 1994

Neighborhood Population % Ethnic

Median household

income ($)

Rockridge 17,333 78 white 46,512

W. Oakland 16,445 80 black 10,578

Elmhurst 36,312 76 black 25,597

Fruitvale 45,000 37 Latino

27 black

20 Asian

14 white

25,630

Rockridge

West Oakland

Rockridge

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

West Oakland(www.eirikjohnson.com/westoakland

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?

Compositional vs. Contextual

• Compositional – the difference that people make to places

• Contextual – the difference that places make to people

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

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.

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”

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).

Putting individual and neighborhood models together…

Random intercept model

yij = β0x0ij + β1x1ij + (u0jx0ij + ε0ijx0ij)

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).

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/

ICC for obesity

Full sample

Females Males Full sample

Females Males

10.06 17.78 7.42 6.32 8.98 5.78

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

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.

Hurricane Katrina, August 2005

Flood level on September 2, 2005

Flooding and Residential Segregation in New Orleans

Households Living in Poverty in New Orleans, 2000 Census

35

1112.7

0

5

10

15

20

25

30

35

40

Black White National

% poverty

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

58.5

34.1

0

10

20

30

40

50

60

70

Black White

% n

ot

ow

n c

ar

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

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).

Hypothetical example of lack of overlap in propensity scores

Est

imat

ed P

rob

abili

ty o

f E

xpo

sure

1.0

0

0.5

0.050 10050100

Number of Observed Subjects

Actually Exposed

Actually Unexposed

Source: JM Oakes & JS Kaufman, eds. Methods in Social Epidemiology. Jossey-Bass/Wiley: forthcoming.

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.

Challenges in Neighborhood Research

Challenge Solution

Disentangling composition from context.

Multilevel models

Elucidating mechanisms of neighborhood effects, i.e. moving beyond neighborhood poverty.

Systematic social observation

Unpacking neighborhood influences on obesity

• “Built environment”

• Local food environment

• Social environment – fear of crime

Playground Safety and Racial Composition of Boston Neighborhoods

Cradock et al. Am J Prev Med 2005;28(4):357-363.

Playground Safety in Boston

Neighborhood % Non-white residents

Mean playground safety score

Back Bay/Beacon Hill

15.2% 79.2

W. Roxbury 16.4 64.9

N. Dorchester 64.4 50.7

Roxbury 95.2 50.9

Mattapan 96.2 49.3

Cradock et al. Am J Prev Med 2005

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.

Illustration of connectivity

Source: Frank & Engelke 2005, figure 2, p. 199

Hypothesis

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

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

Neighborhood predictors of exercise*, by gender

Neighborhood variable from SSO

Females Males

% block faces with mixed commercial/residential use

-0.19 (0.31) 0.78 (0.27)**

Presence of recreational facilities

0.26 (0.09)** 0.06 (0.09)

*Linear coefficients for physical activity scale based on questionnaire

**p<.01

The local food environment

Concepts in Local Food Environment

• Access to supermarkets

• Exposure to fast food outlets

• Availability of healthy food options

Prevalence Ratios of Services by Neighborhood Wealth

Low Low-medium

Medium High-medium

High

Super-

Markets

1.0 2.8 2.6 3.6 3.3

Bars/

Taverns

1.0 0.6 0.7 0.4 0.3

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.

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)

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)

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)

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)

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

Challenges in Neighborhood Research

Challenge Solution

Separating composition from context

Multilevel models

Elucidating mechanisms Systematic social observation

Endogeneity and selection ?

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?

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.

Examples of Instruments

Effect of interest Instrument

Effect of education on mortality risk

Compulsory schooling laws in state of residence

Residential segregation and infant mortality

1. Public finance characteristics of MSA that increase benefits of segregation (e.g. # of municipal governments).

2. Local topography (# rivers that divide MSA into natural units).

Neighborhood poverty on health Relocation by FEMA after Hurricane Katrina

What instrument?

Proximity to fast food outlets

Risk of adult obesity

Preference for junk foods

Z

What instrument?

Proximity to fast food outlets

Risk of adult obesity

Preference for junk foods

Proximity to schools

Invalid instrument, I

Proximity to fast food outlets

Risk of adult obesity

Preference for junk foods

Proximity to schoolsDirect path from Z to y?

Invalid instrument, II

Proximity to fast food outlets

Risk of adult obesity

Preference for junk foods

Proximity to schools

Path from Z to common prior cause of x and y?

Invalid instrument, III

Proximity to fast food outlets

Risk of adult obesity

Preference for junk foods

Proximity to schoolsCommon prior cause of both Z and y?

Invalid instrument, III

Proximity to fast food outlets

Risk of adult obesity

Preference for junk foods

Proximity to schools

• Age structure of neighborhood

• Fertility rate (# kids)

Randomizing Neighborhood Exposures

• Natural experiments- Opening of new supermarket- Opening of new public space

- Implementation of new transport policy

• Randomized controlled trials- Cluster community trials- Residential mobility

Moving to OpportunityDemonstration Program

• Between 1994-97, 4248 families in Boston, Baltimore, Chicago, LA and New York were randomly assigned to: (1) housing voucher that could be used to move

to a low poverty (<10%) neighborhood; (2) housing voucher with no geographic restrictions; or (3) control group.

• In 2002, one adult (98% female) from each family were followed up by interview.

Jeffrey R. Kling, Jeffrey B. Liebman, Lawrence F. Katz. (http://www.ksg.harvard.edu/jeffreyliebman/MTOcomprehensivejune2005.pdf)

Obesity Outcomes in MTO

05

101520253035404550

Low poverty Traditional Control

% O

bes

ity

P = .04

P = .09