Transcript Neighborhoods and Health - University of Pennsylvania

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
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
Neighborhood
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
Unadjusted ICC
Adjusted ICC*
Full
sample
Females
Males
Full
sample
Females
Males
10.06
17.78
7.42
6.32
8.98
5.78
*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
Flooding and Residential Segregation in New Orleans
Flood level on September 2, 2005
Households Living in Poverty in
New Orleans, 2000 Census
40
35
35
30
25
% poverty 20
15
11
12.7
10
5
0
Black
White
National
Poor Households in New Orleans with
No Access to Cars - 2000 Census
70
% not own car
60
58.5
50
40
34.1
30
20
10
0
Black
White
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
Estimated Probability of Exposure
1.0
Actually Exposed
0.5
Actually Unexposed
0.0
100
50
0
50
100
Number of Observed Subjects
Source: JM Oakes & JS Kaufman, eds. Methods in Social Epidemiology. JosseyBass/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
% block faces with
-0.19 (0.31)
mixed
commercial/residential
use
Presence of
0.26 (0.09)**
recreational facilities
Males
0.78 (0.27)**
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
Neighborhood Wealth
Low
LowMedium
medium
HighHigh
medium
SuperMarkets
1.0
2.8
2.6
3.6
3.3
Bars/
Taverns
1.0
0.6
0.7
0.4
0.3
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 2year 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.
2.
Neighborhood poverty on health
Public finance characteristics
of MSA that increase benefits
of segregation (e.g. # of
municipal governments).
Local topography (# rivers that
divide MSA into natural units).
Relocation by FEMA after Hurricane
Katrina
What instrument?
Z
Proximity to fast
food outlets
Preference for
junk foods
Risk of adult
obesity
What instrument?
Proximity to schools
Proximity to fast
food outlets
Preference for
junk foods
Risk of adult
obesity
Invalid instrument, I
Proximity to schools
Direct path from Z to y?
Proximity to fast
food outlets
Preference for
junk foods
Risk of adult
obesity
Invalid instrument, II
Proximity to schools
Proximity to fast
food outlets
Preference for
junk foods
Path from Z to common
prior cause of x and y?
Risk of adult
obesity
Invalid instrument, III
Common prior cause
of both Z and y?
Proximity to schools
Proximity to fast
food outlets
Preference for
junk foods
Risk of adult
obesity
Invalid instrument, III
• Age structure of
neighborhood
Proximity to schools
• Fertility rate (# kids)
Proximity to fast
food outlets
Preference for
junk foods
Risk of adult
obesity
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 Opportunity
Demonstration 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/MTOcompreh
ensivejune2005.pdf)
Obesity Outcomes in MTO
% Obesity
P = .04
P = .09
50
45
40
35
30
25
20
15
10
5
0
Low poverty
Traditional
Control