Neighborhood Socioeconomic Status
and Incidence of Coronary Heart Disease
in Women
Chloe E. Bird
Shih, RA, Eibner C, Griffin BA, Slaughter ME, Whitsel E, Margolis
K, Escarce JJ, Jewell A, Mouton C, Lurie N
AcademyHealth
June 30, 2009
Coronary Heart Disease (CHD) Is a Leading
Cause of Death in Women
• Prognosis is markedly worse for women
• Declines in CHD mortality are less pronounced
for women
•
Established risk factors
−
−
−
−
Low individual-level socioeconomic status (SES)
Poor health behaviors
Diabetes, hyperlipidemia, hypertension
Family history
• Could the socioeconmic status of one’s
neighborhood influence CHD risk?
2 6/29/2009
How Might Neighborhood Socioeconomic
Status (NSES) Affect Risk of CHD?
•
Increased exposure to stressors such as
violence and poverty
•
Reduced availability to outlets for nutritious food
and physical activity
•
Reduced access to quality health care services
•
Increased exposure to environmental pollutants
3 6/29/2009
Study Objective
To examine the relationship between NSES
and incident CHD events and deaths in
women in the Women’s Health Initiative
(WHI)
4 6/29/2009
Data
•
•
Geo-coded WHI Clinical Trial
−
N=68,132 women, age 50-79
−
Followed for up to 12 years
−
Data from 76 U.S. sites
−
Physician-adjudicated CHD outcomes
RAND Center for Population Health and Health
Disparities Data Core
−
NSES data at the census tract level
5 6/29/2009
Components of NSES Index
•
Median household income
•
Adults ≤ high school education (%)
•
Male unemployment (%)
•
Households with income below poverty (%)
•
Households receiving public assistance (%)
•
Households headed by a single female (%)
6 6/29/2009
CHD Outcomes
CHD event
6.9%
(4,688)
Myocardial Infarction (MI)
or CHD death
3.2%
(2,195)
CHD death
0.9%
(620)
7 6/29/2009
Methods
• Cox Proportional Hazard models
• Adjusted for baseline individual-level
− Socio-demographic characteristics
− Related comorbid conditions
− Health behaviors
− Family history of MI
• Accounted for geographic clustering
8 6/29/2009
Baseline Characteristics
• Race/ethnicity
− 82% non-Hispanic white
− 10% non-Hispanic black
− 4% Hispanic
• 61% Married
• 94% ≥ High school
• Household income
− 42%
≤ $34,999
− 22%
− 36%
$35,000 - $74,999
≥$75,000
9 6/29/2009
Lower NSES Is Independently Associated with
Increased Hazard of CHD Event or Death
Hazard Ratios
1.03
1.02
1.01
1.00
1.02
1.01
CHD event
1.01
MI/CHD death
CHD death
10 6/29/2009
Results In a Real World Context
Northwest DC
Southeast
DC
11 6/29/2009
CHD Hazard Ratios:
Southeast vs. Northwest DC
Hazard Ratios
2.00
1.80
1.60
1.50
1.40
1.23
1.27
1.20
1.00
CHD event
MI/CHD death
CHD death
0.80
12 6/29/2009
Limitations
• Limited generalizability of the WHI sample
• Longitudinal analysis not sufficient to infer
causality
• Healthier individuals may choose to live in
better neighborhoods
13 6/29/2009
Conclusions
•
Living in a lower NSES neighborhood is associated
with greater CHD risk among older women
•
Future research should examine mechanisms
through which neighborhood characteristics
influence health and risk of death
•
Social policies targeting neighborhood
characteristics may improve both individual and
population health
14 6/29/2009
Collaborators
Christine Eibner, co-PI Nicole Lurie, CPHHD Center
Director
Jose Escarce
Karen Margolis
Meena Fernandes
Regina Shih
Bonnie Ghosh-Dastidar
Mary Ellen Slaughter
Beth Ann Griffin
Eric Whitsel
Adria Jewell
Study funded by NHLBI R01HL084425
The authors declare no conflict of interest
15 6/29/2009
16 6/29/2009
Extra Slides
17 6/29/2009
Limitation: Selection
of Women into Neighborhoods
• Individuals may self-select into neighborhoods 
spurious correlation between neighborhood SES
and health outcomes
• Control for a comprehensive set of individual
characteristics
• Propensity score analysis
− Weighted women with different levels of NSES to be well
balanced on the covariates
• Analysis of movers
− Assume that CHD takes several years to develop
− If a recent move appears to influence CHD mortality, then
this is likely to be a selection effect
18 6/29/2009
Strengths
• Examined CHD in women
• Geographic and racial heterogeneity
• Longitudinal data with up to 12 years of followup
• Physician-adjudicated CHD outcomes
• Robust index of NSES beyond area-level
education and income
19 6/29/2009
Methods
• Cox Proportional Hazard model analyze time
until adverse CHD events
• Shared frailty models account for geographic
clustering of women at the census tract and
Metropolitan statistical area levels
hijk (t ) = ho (t )α j ρ k exp( xijk β )
• Neighborhood variables (x) measured at the
census tract level, from RAND’s CPHHD Data
Core
20 6/29/2009
Many Gaps in Existing Literature
• Traditionally, CHD has been understudied in women relative
to men
• Current literature on neighborhood status and CHD
suggests that neighborhood context may have a bigger effect
on women than men
• Prior studies on neighborhoods and health:
− Had limited geographic and racial heterogeneity
− Neglected the relative importance of residential instability
− Were cross-sectional in nature
− Lacked physician-adjudicated CHD outcomes
− Lacked a robust NSES index beyond education income six
neighborhood factors
21 6/29/2009
Limitations
• Limited generalizability of the WHI CT sample
• Individuals may self-select into neighborhoods
 spurious correlation between neighborhood
SES and health outcomes
• Inclusion of baseline comorbid conditions and
health behaviors  may be influenced by NSES
• Longitudinal data not sufficient to infer
causality
22 6/29/2009
Model Building
• Model 1 adjusts for:
− Individual-level sociodemographic controls
• Race/ethnicity, education, income, martial status, region,
study arm, and region of the U.S.
− Neighborhood-level controls
• Length of residency, percent owner occupied housing, and
percent black or Hispanic
• Model 2 additionally adjusts for:
− Individual-level baseline health status
• BMI, waist hip ratio, self-reported history of diabetes,
hyperlipidemic medication use and/or self reported high
cholesterol, hypertension, smoking pack-years, alcohol
use, and hormone use
23 6/29/2009
*Sensitivity analyses allowing some covariates to be time-varying
CPH Model Results
CHD Death
Model 1
Model 2
CHD Death or MI
Model 1
Model 2
Any CHD Event
Model 1
Model 2
Census-tract-level
predictors
NSES
0.984
0.990
0.986
0.990
0.989
0.993
(0.968, 1.000) (0.974, 1.006) (0.977, 0.995) (0.981, 0.999) (0.982, 0.995) (0.986, 0.999)
Length of
residency
0.687
0.571
1.014
0.919
1.029
0.969
(0.221, 2.138) (0.182, 1.789) (0.548, 1.874) (0.495, 1.707) (0.662, 1.599) (0.621, 1.513)
% Owner
Occupied
Housing
0.909
0.918
0.931
0.907
0.975
0.928
(0.441, 1.872) (0.443, 1.902) (0.627, 1.384) (0.607, 1.354) (0.730, 1.302) (0.693, 1.244)
Model 1: Individual-level socio-demographic + neighborhood-level variables
Model 2: Model 1 + individual-level baseline health status
24 6/29/2009
Neighborhoods Influence CHD Directly, and
Indirectly Through Mediators
Neighborhood
Built
Environments
Baseline
Health
Status
Demographics
Individual
Characteristics
Neighborhood
Social
Environments
Per Capita
County
Medical Care
Resources
Community
Characteristics
Social Support
Health Habits
Follow-Up
Health Status
Incident
CHD
Outcomes
Psychological
Well-Being
Mediating
Factors
CHD
Outcomes
25 6/29/2009
WHI Data
Data Category
Examples
Individual Demographics
Age, race, education, income
Social Support
Social activities, emotional and
instrumental support
Quality of Life
Depression, personal outlook,
life satisfaction
Behavioral Risk Factors
Smoking, alcohol intake,
physical activity, diet
Health Status
Family health history,
biomarkers, chronic illness
Medical Care
Medications, use of medical care
CHD & Mortality Outcomes
MI, angina, revascularization
26 6/29/2009
Longitudinal Aspect of WHI Has the
Potential to Enhance Knowledge of
Neighborhoods and Health
• Majority of studies on neighborhoods and
health are cross sectional
• Entwisle (2007) found that—of 503 studies on
neighborhoods and health—only 2.4 percent
used longitudinal data
− Concluded that lack of longitudinal studies is a limitation of the
current literature
• Longitudinal data can be used to explore lagstructure of relationship between neighborhoods
and health
27 6/29/2009
Other Considerations
In additional models, we also examine:
• % of census tract residents who are Hispanic
• % of census tract residents who are Black
• Whether participants live in, or near a metropolitan area
− Two sets of analyses; one set limited to the MSA residents,
second set using measures of health care supply for nonmetropolitan areas
• Physician supply at the MSA or county level
• Relative contribution of residential stability
• Effect modification of NSES effects by residential stability
29 6/29/2009
Lower NSES Is Associated with Increased Risk
CHD death
CHD death/MI
Model 1 Model 2
Model 1 Model 2
0.984 0.990
0.977 0.990
CHD event
Model 1 Model 2
0.989 0.993
1.10
1.00
0.90
0.80
0.70
0.60
Model 1 Adjusts for individual-level socio-demographic + neighborhood-level variables
Model 2 Adjusts for Model 1 covariates + individual-level baseline health status
30 6/29/2009
Two Washington, D.C. Areas Illustrate Neighborhood
Effects on CHD Risk
CHD death
0.584
CHD death/MI
0.724
CHD event
0.753
1.10
1.00
0.90
0.80
0.70
0.60
0.50
0.40
• Compared to the same woman living in Northwest D.C. (top 25%), one
living in Southeast D.C. (bottom 25%) has
- 71% higher risk of CHD death (95% CI = 31%,123%)
- 38% higher risks of CHD death or MI (95% CI = 19%,61%)
- 32% higher risk of any CHD event (95% CI = 20%, 48%)
31 6/29/2009
Residential stability
• Residential instability may influence health
through:
− Limited access to health care
− Reduced social support
• High stability in low SES neighborhoods may
reflect blocked opportunities that affect health
Kirby & Kaneda, 2006; Rozanski et al., 1999; Chaix et al., 2006; 2007
32 6/29/2009
Results In a Real World Context
Northwest DC
Southeast
DC
33 6/29/2009