This is a presentation I gave at Texas A&M University in November 2012. It is a talk that summarizes this publication: http://rd.springer.com/article/10.1007/s10708-011-9445-3

1. An Application of Bayesian Spatial
Statistical Methods to the Study of
Poverty Segregation and Infant Mortality
Rates in the United States
P. JOHNELLE SPARKS, PHD
DEPARTMENT OF DEMOGRAPHY
THE UNIVERSITY OF TEXAS AT SAN ANTONIO
COREY S. SPARKS, PHD
DEPARTMENT OF DEMOGRAPHY
THE UNIVERSITY OF TEXAS AT SAN ANTONIO
JOSEPH CAMPBELL, PHD
USAA
MODELING SECTION

2. Outline
 Introduction
 Infant Mortality in the US
 Risk factors at the individual level
 Risk factors at the population level
 Linking Residential Segregation
 Methods and Data
 Results
 Further Issues to discuss

3. Introduction
 The United States has an Infant Mortality Rate (IMR)
higher than one would expect, given the stature of our
economy and access to advanced medical resources
 Numerous studies have examined this phenomena from
both individual and population-level perspectives
 Disparities exist by race, ethnicity, birth weight, environmental
conditions
 Here, we explore the connection between residential
segregation and infant mortality from a population
perspective

4. 20
120
0
80
180
40
60
100
140
160
Iceland
Sweden
Ireland
Portugal
Channel Islands
Korea South
Taiwan
New Zealand
Lithuania
In the world
Slovakia
Qatar
US: Ranked48th
Bahrain
Chile
Costa Rica
Montenegro
31st Among OECD Countries
Romania
Barbados
Dominica
Thailand
St. Vincent & the Grenadines
Syria
Samoa
Honduras
Jordan
Philippines
Kazakhstan
Kosovof
Bhutan
Botswana
Solomon Islands
Kiribati
Infant Mortality Rate, 2008
Eritrea
Cambodia
Ghana
Pakistan
Mauritania
Sudan
Equatorial Guinea
Iraq
Zambia
Guinea
Sierra Leone
IMR

5. Infant Deaths by Cause, 2008
Congressional Research Service R41378

6. Infant Mortality in the US
 Contributing Factors:
 Inconsistent recording of births
 Are we just bad at counting?
 We use the WHO standard, like other nations
 Unfavorable rates of LBW and short gestational age births
 US rate of LBW and short for gestational age births is double that
of some European nations
 Our IMR would be 39 if this was adjusted!
 These risk factors also varies widely by race/ethnicity of mother
 Racial and ethnic disparities

7. US IMR by Race/Ethnicity of Mother, 2007
NCHS Data Brief #74, Sept 2011

8. Infant Mortality in the US
 In summary, the US has several conditions that puts
infants and mothers in an unfavorable position
 Most of these are related to socioeconomic inequality
within the population
 One potentially important factor, related to
inequality is residential location
 More to the point, the spatial arrangement of racial/ethnic
minorities and low-income individuals relates to that of the
majority group

9. Residential Segregation
 Racial and socioeconomic residential segregation
generally exposes minority groups to negative
structural, social, economic, material, and individual
level resources.
 More specifically, poor housing quality, environmental
contaminants, lower educational and employment
opportunities, access restrictions to social services, limited access
to healthy and fresh food options, high crime rates, low
investment in infrastructure, and poor access to medical services
 Some authors go on to say that segregation leads to higher levels
of economic inequality among racial/ethnic groups
 Minorities are isolated from employment opportunities and social
services available in more white areas

10. Residential Segregation
 Less work has focused on the explicit role of poverty
segregation on infant health
 Outside of using the poverty rate
 This is often because poverty rates and minority
concentration are highly correlated
 However it may be the relative differences in
economic resources for minority residents that
impact infant mortality chances
 Persons living in areas with better than expected economic
circumstances, also noted as positive income
incongruity, may benefit from certain forms of residential
segregation

11. Residential Segregation
 Regardless of whether segregation is racial or
economic, it is multidimensional
 It is not clear how certain dimensions of residential
segregation are protective or harmful against infant mortality
risks, particularly when measuring both racial and poverty
residential segregation in a spatial context.
 Some authors have found that residential isolation
led to poorer infant health outcomes, while racial
clustering led to better outcomes
 In terms of poverty segregation, we might expect that
areas with higher interaction (integration) might
lead to lower infant mortality rates

12. Spatial is Special
 When considering health outcomes, policy makers
work at a local level
 Knowing more about the location of ones’
constituents may help public officials serve them
better
 Being able to visualize where levels of risk differ is a
powerful policy tool
 Translates the statistical skull drudgery into real world context
 Spatial methods help with this

13. Methods and Data
 Data Source
 2008 Area Resource File
 Outcome: 3 Year count of infant deaths in each county
 24,487 total deaths
 4,041,042 live births in the period
 Equates to a rate of 60.6 deaths per 10,000 live births
 Control variables
 Rural population
 Income inequality
 Neighborhood deprivation

14. Methods and Data
 Segregation Measures
 3 dimensions
 Evenness -> Dissimilarity Index
 Exposure -> Interaction Index
 Spatial Clustering -> Spatial Proximity Index
 Refer to Reardon and O’Sullivan (2004) Sociological Methodology
and Massey and Denton (1988) Social Forces
•Measured Black-White and Poor-Non
Poor segregation
•6 total measures

15. Bayesian Estimation
 When we combine the likelihood and the prior, we
form what is called the posterior distribution
 Thus we have Bayes Theorem which in the
continuous case is:
 Which states, the posterior distribution of
θ, conditional on y is the product of the likelihood
and the prior distribution of θ

16.  The denominator in Bayes theorem is a
constant, and this is generally written as:
 Which says the posterior is proportional to the
likelihood times the prior

17. Models
 The model we used is the convolution
model, ortheBesag, York and Mollie model
 yi|θi ~Pois(eiθi)
 log(θi)=α+ X’β + ui + vi
 θi is commonly thought of as the Standardized Mortality Ratio
(SMR)
 Where ui is a correlated heterogeneity (CH)
term and vi is an Uncorrelated Heterogeneity
(UH) term
 ui is given a Conditionally Autoregressive Normal
prior
ui ~ N (u j , / n j )

18. Model Estimation
 OpenBUGS
 150,000 MCMC samples, 100k burn in
 2 parallel chains
 Thinned every 50th sample to reduce autocorrelation
 Gelman-Rubin diagnostics revealed convergence of the models
 12 Models were fit
 6 just considered segregation
 6 considered segregation, controlling for the SES variables
 Models are compared with Deviance Information Criterion
(DIC)

19. Results: SMR Observed

20. Results: SMR Smoothed

21. Results
 Racial Segregation
 Without controlling for county SES, dissimilarity increases the
IMR, Interaction decreased the IMR and Spatial clustering
increased the IMR
 After controlling for SES, the effects of segregation are
diminished, except for interaction, but maintain significant
effects
 Poverty Segregation
 Similar trends are found for poverty segregation in the
baseline models
 After controlling for SES, poverty dissimilarity becomes
insignificant in the model

22. Discussion
 Results show the utility of Bayesian models for
modeling unstable rates
 Smoothed risk profile
 Interaction between both blacks and white and
poor/non-poor residents tends to decrease infant
mortality
 Net of SES controls
 However, the more spatially concentrated both
blacks and residents below the poverty line, the
higher the infant mortality risk

23. Future Issues
 We should consider a spatio-temporal approach
instead of the purely spatial one taken here
 Cross sectional associations are weaker
 Ideally, we could emulate this work with individual
level data using similar hierarchical models
 Currently we are applying these methods to a variety
of health outcomes
 Ethnic differences in cancer incidence
 Cardiovascular disease
 Crime rates

24. Acknowledgements
 My two co-authors
 UTSA COPP
 The TAMU Census RDC