An Application of Bayesian Spatial   Statistical Methods to the Study ofPoverty Segregation and Infant Mortality       Rat...
Outline Introduction Infant Mortality in the US   Risk factors at the individual level   Risk factors at the populatio...
Introduction The United States has an Infant Mortality Rate (IMR)  higher than one would expect, given the stature of our...
20                                                                     120                                 0              ...
Infant Deaths by Cause, 2008Congressional Research Service R41378
Infant Mortality in the US Contributing Factors:   Inconsistent recording of births       Are we just bad at counting? ...
US IMR by Race/Ethnicity of Mother, 2007NCHS 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 pos...
Residential Segregation Racial and socioeconomic residential segregation generally exposes minority groups to negative st...
Residential Segregation Less work has focused on the explicit role of poverty segregation on infant health    Outside of...
Residential Segregation Regardless of whether segregation is racial or economic, it is multidimensional    It is not cle...
Spatial is Special When considering health outcomes, policy makers  work at a local level Knowing more about the locatio...
Methods and Data Data Source   2008 Area Resource File       Outcome: 3 Year count of infant deaths in each county     ...
Methods and Data Segregation Measures   3 dimensions   Evenness -> Dissimilarity Index   Exposure -> Interaction Index...
Bayesian Estimation When we combine the likelihood and the prior, we  form what is called the posterior distribution Thu...
 The denominator in Bayes theorem is a constant, and this is generally written as: Which says the posterior is proportio...
Models The model we used is the convolution model, ortheBesag, York and Mollie model    yi|θi ~Pois(eiθi)    log(θi)=α+...
Model Estimation OpenBUGS   150,000 MCMC samples, 100k burn in   2 parallel chains   Thinned every 50th sample to redu...
Results: SMR Observed
Results: SMR Smoothed
Results Racial Segregation   Without controlling for county SES, dissimilarity increases the    IMR, Interaction decreas...
Discussion Results show the utility of Bayesian models for modeling unstable rates    Smoothed risk profile Interaction...
Future Issues We should consider a spatio-temporal approach instead of the purely spatial one taken here    Cross sectio...
Acknowledgements My two co-authors UTSA COPP The TAMU Census RDC
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Infant mortality paper

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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

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  • The oecd figure places the US 4th from the bottom behind Turkey Mexico and Chile
  • Infant mortality paper

    1. 1. An Application of Bayesian Spatial Statistical Methods to the Study ofPoverty 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. 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. 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. 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 ThailandSt. 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. 5. Infant Deaths by Cause, 2008Congressional Research Service R41378
    6. 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. 7. US IMR by Race/Ethnicity of Mother, 2007NCHS Data Brief #74, Sept 2011
    8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. Results: SMR Observed
    20. 20. Results: SMR Smoothed
    21. 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. 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. 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. 24. Acknowledgements My two co-authors UTSA COPP The TAMU Census RDC

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