P. JOHNELLE SPAR KS, P HDDEPARTMEN T OF DEMOGR APHY

THE U N I VER SI TY OF TEXAS AT SAN AN TON I O

COR EY S. SPAR KS, PHDDEPARTMEN T OF DEMOGR APHY

THE U N I VER SI TY OF TEXAS AT SAN AN TON I O

JOSEPH CAMPBELL , PHDU SAA

MODELI N G SECTI ON

An Application of Bayesian Spatial Statistical Methods to the Study of

Poverty Segregation and Infant Mortality Rates in the United States

Outline

IntroductionInfant Mortality in the US

Risk factors at the individual level Risk factors at the population level

Linking Residential Segregation Methods and DataResultsFurther Issues to discuss

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 conditionsHere, we explore the connection between

residential segregation and infant mortality from a population perspective

Iceland Austria Cuba Albania Ukraine Thailand Libya Nicaragua Nepal Comoros Sudan Burundi0

20

40

60

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120

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Infant Mortality Rate, 2008

IMR

US: Ranked 48th

In the world

31st Among OECD Countries

Infant Deaths by Cause, 2008

Congressional Research Service R41378

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

US IMR by Race/Ethnicity of Mother, 2007

NCHS Data Brief #74, Sept 2011

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

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

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

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

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

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

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

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

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

Models

The model we used is the convolution model, or the Besag, York and Mollie model yi

| θi ~Pois(ei θi) 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~ ( , / )i j ju N u n

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 models12 Models were fit

6 just considered segregation 6 considered segregation, controlling for the SES

variables Models are compared with Deviance Information

Criterion (DIC)

Results: SMR Observed

Results: SMR Smoothed

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

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

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

Acknowledgements

My two co-authorsUTSA COPPThe TAMU Census RDC