P. Johnelle Sparks

Do biological, sociodemographic, and behavioral characteristics explain racial/ethnic disparities in preterm births?

Many studies find racial/ethnic disparities in a diverse set of birth outcomes. However few empirical studies have examined the existence and possible explanations for racial/ethnic disparities in preterm births using a diverse set of racial/ethnic categories and a nationally representative sample of births. This research fills that gap. Using data from the US Early Childhood Longitudinal Study - Birth Cohort (ECLS-B), this research first explores the distribution of biological, sociodemographic, and behavioral characteristics of mothers and infants based on seven categories of maternal race/ethnicity. Next, multivariable logistic regression models are estimated in a nested manner to test for possible explanations for racial/ethnic disparities in preterm births. Lastly, race-stratified models are estimated to better elucidate the mechanism leading to racial/ethnic disparities in preterm births. Results from the chi-square tests of significance for racial/ethnic differences indicate that all variables used in this analysis, except for infants gender, differ significantly based on maternal race/ethnicity. Results from the full multivariable logistic regression model finds that the only racial/ethnic disparity found in preterm births is observed for infants born to Native American mothers compared to non-Hispanic white mothers, once all variables are controlled for in the model. Race-stratified models indicate that maternal health complications and prenatal care adequacy offer the most potential in explaining remaining racial/ethnic disparities in preterm births. Results from this research support the need to increase access to appropriate and timely prenatal care for women of all races/ethnicities in an effort to reduce racial/ethnic disparities in preterm births.

One size does not fit all: an examination of low birthweight disparities among a diverse set of racial/ethnic groups.

To examine disparities in low birthweight using a diverse set of racial/ethnic categories and a nationally representative sample. This research explored the degree to which sociodemographic characteristics, health care access, maternal health status, and health behaviors influence birthweight disparities among seven racial/ethnic groups. Binary logistic regression models were estimated using a nationally representative sample of singleton, normal for gestational age births from 2001 using the ECLS-B, which has an approximate sample size of 7,800 infants. The multiple variable models examine disparities in low birthweight (LBW) for seven racial/ethnic groups, including non-Hispanic white, non-Hispanic black, U.S.-born Mexican-origin Hispanic, foreign-born Mexican-origin Hispanic, other Hispanic, Native American, and Asian mothers. Race-stratified logistic regression models were also examined. In the full sample models, only non-Hispanic black mothers have a LBW disadvantage compared to non-Hispanic white mothers. Maternal WIC usage was protective against LBW in the full models. No prenatal care and adequate plus prenatal care increase the odds of LBW. In the race-stratified models, prenatal care adequacy and high maternal health risks are the only variables that influence LBW for all racial/ethnic groups. The race-stratified models highlight the different mechanism important across the racial/ethnic groups in determining LBW. Differences in the distribution of maternal sociodemographic, health care access, health status, and behavior characteristics by race/ethnicity demonstrate that a single empirical framework may distort associations with LBW for certain racial and ethnic groups. More attention must be given to the specific mechanisms linking maternal risk factors to poor birth outcomes for specific racial/ethnic groups.To examine disparities in low birthweight using a diverse set of racial/ethnic categories and a nationally representative sample. This research explored the degree to which sociodemographic characteristics, health care access, maternal health status, and health behaviors influence birthweight disparities among seven racial/ethnic groups. Binary logistic regression models were estimated using a nationally representative sample of singleton, normal for gestational age births from 2001 using the ECLS-B, which has an approximate sample size of 7,800 infants. The multiple variable models examine disparities in low birthweight (LBW) for seven racial/ethnic groups, including non-Hispanic white, non-Hispanic black, U.S.-born Mexican-origin Hispanic, foreign-born Mexican-origin Hispanic, other Hispanic, Native American, and Asian mothers. Race-stratified logistic regression models were also examined. In the full sample models, only non-Hispanic black mothers have a LBW disadvantage compared to non-Hispanic white mothers. Maternal WIC usage was protective against LBW in the full models. No prenatal care and adequate plus prenatal care increase the odds of LBW. In the race-stratified models, prenatal care adequacy and high maternal health risks are the only variables that influence LBW for all racial/ethnic groups. The race-stratified models highlight the different mechanism important across the racial/ethnic groups in determining LBW. Differences in the distribution of maternal sociodemographic, health care access, health status, and behavior characteristics by race/ethnicity demonstrate that a single empirical framework may distort associations with LBW for certain racial and ethnic groups. More attention must be given to the specific mechanisms linking maternal risk factors to poor birth outcomes for specific racial/ethnic groups.

Differential Neonatal and Postneonatal Infant Mortality Rates Across US Counties: The Role of Socioeconomic Conditions and Rurality

PURPOSE To examine differences in correlates of neonatal and postneonatal infant mortality rates, across counties, by degree of rurality. METHODS Neonatal and postneonatal mortality rates were calculated from the 1998 to 2002 Compressed Mortality Files from the National Center for Health Statistics. Bivariate analyses assessed the relationship between neonatal and postneonatal mortality by Urban Influence (UI) codes. Multivariable, weighted least-squares regression models included measures of county socioeconomic conditions, health services and environmental risks. FINDINGS The bivariate analysis indicated neonatal and postneonatal mortality was significantly higher in the most nonmetropolitan counties compared to the most metropolitan counties. However the relationship was not linear across the Urban Influence codes. In the multivariable models, a nonmetropolitan advantage was observed for counties not adjacent to metropolitan areas for neonatal mortality. However, postneonatal mortality rates were higher in the most rural nonmetropolitan counties. CONCLUSIONS Certain characteristics of nonmetropolitan counties not adjacent to metropolitan counties and with an urban area of 2,500 population or more are protective against neonatal mortality (UI = 7, UI = 8). This may indicate that just having access to health services is more important to creating a protective effect for these nonmetropolitan counties than having a high concentration of medical facilities. The nonmetropolitan, not adjacent (UI = 9) disadvantage observed for postneonatal mortality supports the idea that the isolation of these areas combined with the combination of risk factors across the most nonmetropolitan counties leads to poorer postneonatal health outcomes in these areas.

Rural Health Disparities

Rural-urban health disparities have been noted for a variety of mortality and morbidity conditions and are seen in the United States and internationally. Two conceptual arguments are often given to explain potential health disparities based on rural or urban residence, including composition arguments that focus on the characteristics of individuals in certain locations and contextual arguments that argue that characteristics of places themselves lead to potential disparities in health outcomes. This chapter first reviews literature on compositional and contextual explanations for health disparities in the United States and internationally. Next an empirical analysis of 2008 Behavioral Risk Factor Surveillance System (BRFSS) data for adults in the United States is provided that explores these two potential explanations in documenting rural-urban health disparities for several morbidity conditions. Policy makers interested in eliminating rural health disparities must acknowledge the complex interplay between compositional and contextual factors in influencing health outcomes for rural populations.

The Role of Education in Explaining Racial/Ethnic Allostatic Load Differentials in the United States

This study expands on earlier findings of racial/ethnic and education–allostatic load associations by assessing whether racial/ethnic differences in allostatic load persist across all levels of educational attainment. This study used data from four recent waves of the National Health and Nutrition Survey (NHANES). Results from this study suggest that allostatic load differs significantly by race/ethnicity and educational attainment overall, but that the race/ethnicity association is not consistent across education level. Analysis of interactions and education-stratified models suggest that allostatic load levels do not differ by race/ethnicity for individuals with low education; rather, the largest allostatic load differentials for Mexican Americans (p < .01) and non-Hispanic blacks (p < .001) are observed for individuals with a college degree or more. These findings add to the growing evidence that differences in socioeconomic opportunities by race/ethnicity are likely a consequence of differential returns to education, which contribute to higher stress burdens among minorities compared to non-Hispanic whites.

Does allostatic load calculation method matter? Evaluation of different methods and individual biomarkers functioning by race/ethnicity and educational level.

Using nationally representative data for adults of age 25 years and older from four waves of the National Health and Nutrition Examination Survey (NHANES), collected from 2003 through 2010, this study examines differences in individual health markers used to calculate allostatic load, with particular attention given to stratification by race/ethnicity and educational level.

Socioeconomic Position, Rural Residence, and Marginality Influences on Obesity Status in the Adult Mexican Population

This paper assesses individual and social environment determinants of obesity in the adult Mexican population based on socioeconomic position, rural residence, and areal deprivation. Using a nationally representative health and nutrition survey, this analysis considers individual and structural determinants of obesity from a socioeconomic position and health disparities conceptual framework using multilevel logistic regression models. We find that more than thirty percent of Mexican adults were obese in 2006 and that the odds of being obese were strongly associated with an individuals socioeconomic position, gender, place of residence, and the level of marginalization (areal deprivation) in the place of residence. Surprisingly, areas of the country where areal deprivation was highest had lower risks of individual obesity outcomes. We suggest that programs oriented towards addressing the health benefits of traditional food systems over high-energy dense refined foods and sugary beverages be promoted as part of a public health program aimed at curbing the rising obesity prevalence in Mexico.

Poverty Segregation in Nonmetro Counties: A Spatial Exploration of Segregation Patterns in the US

Most research on segregation focuses on racial residential segregation in metropolitan statistical areas and typically uses a-spatial measures of segregation. What is less clear is if segregation measures operate in a similar fashion in nonmetropolitan areas and if spatial patterns exist for poverty segregation in nonmetro counties. The purpose of this research was to examine multiple dimensions of poverty segregation in the United States the period 2006–2010 for metropolitan and nonmetropolitan counties. Data for this analysis come from the 2006–2010 American Community Survey 5 year estimates, the 2000 U.S. Census of Population and Housing, Summary File 3 and the USDA Economic Research Service. Four different measures of poverty segregation were calculated, including both aspatial and spatial measures. A nonparametric Kruskal-Wallis test was used to test for variation in the segregation indices across metro and nonmetro areas and spatially autoregressive models were used to examine the socioeconomic correlates of poverty segregation. Results indicate significant variation in poverty segregation patterns in metro and nonmetro counties in the US, and nonmetro counties outside of the South have significantly lower levels of poverty segregation. This research adds to the literature by exploring patterns of metro and nonmetro poverty segregation and measuring different dimensions of segregation with an explicit spatial referent across counties in the contiguous United States in an effort to note differences in how segregation works across rural and urban places.

Childhood morbidities among income- and categorically-eligible WIC program participants and non-participants

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is the largest supplemental food assistance program in the United States. WIC benefits include food and infant formula, nutrition counseling, health screenings, and health-care referrals to low-income, nutritionally at-risk pregnant and postpartum women, infants, and children up to age five. This research explored the associations between childhood morbidities among income- and categorically-eligible WIC participant and non-participant groups in a diverse, nationally representative sample of children. Results indicate significant differences in the maternal sociodemographic profiles of eligible child WIC participants and non-participants. After propensity score-matching methods were used to create more appropriate comparison groups among child WIC participants and non-participants, complete covariate balance was obtained for all sociodemographic characteristics. Further, no significant differences in child asthma, respiratory illness, severe gastrointestinal illness, or ear infection diagnosis, or with mothers rating their health as poor, were noted between child WIC participants and non-participants, once the matched pairs were compared. Government regulators formulating future policies around WIC would benefit from understanding the characteristics of eligible non-participants in order to offer appropriate food, health, and educational assistance beneficial to child health.