Racial residential segregation shapes the relationship between early childhood lead exposure and fourth-grade standardized test scores.

Racial/ethnic disparities in academic performance may result from a confluence of adverse exposures that arise from structural racism and accrue to specific subpopulations. This study investigates childhood lead exposure, racial residential segregation, and early educational outcomes. Geocoded North Carolina birth data is linked to blood lead surveillance data and fourth-grade standardized test scores (n = 25,699). We constructed a census tract-level measure of racial isolation (RI) of the non-Hispanic Black (NHB) population. We fit generalized additive models of reading and mathematics test scores regressed on individual-level blood lead level (BLL) and neighborhood RI of NHB (RINHB). Models included an interaction term between BLL and RINHB. BLL and RINHB were associated with lower reading scores; among NHB children, an interaction was observed between BLL and RINHB. Reading scores for NHB children with BLLs of 1 to 3 µg/dL were similar across the range of RINHB values. For NHB children with BLLs of 4 µg/dL, reading scores were similar to those of NHB children with BLLs of 1 to 3 µg/dL at lower RINHB values (less racial isolation/segregation). At higher RINHB levels (greater racial isolation/segregation), children with BLLs of 4 µg/dL had lower reading scores than children with BLLs of 1 to 3 µg/dL. This pattern becomes more marked at higher BLLs. Higher BLL was associated with lower mathematics test scores among NHB and non-Hispanic White (NHW) children, but there was no evidence of an interaction. In conclusion, NHB children with high BLLs residing in high RINHB neighborhoods had worse reading scores.

Weekly prenatal PM2.5 and NO2 exposures in preterm, early term, and full term infants: Decrements in birth weight and critical windows of susceptibility.

BACKGROUND: Previous studies have observed associations between birth weight and prenatal air pollution exposure, but there is not consensus on timing of critical windows of susceptibility. OBJECTIVE: We estimated the difference in birth weight among preterm, early term and full term births associated with weekly exposure to PM2.5 and NO2 throughout gestation. METHODS: We included all singleton live births in the Lower Peninsula of Michigan (United States) between 2007 and 2012 occurring at or after 32 weeks gestational age (n = 497,897). Weekly ambient PM2.5 and NO2 concentrations were estimated at maternal residences using 1-km gridded data from ensemble-based models. We utilized a distributed lag nonlinear model to estimate the difference in birth weight associated with weekly exposures from the last menstrual period (week 0) through 31 weeks gestation for preterm births; through 36 weeks gestation for early term births; and through 38 weeks gestation for full term births. RESULTS: In single-pollutant models, a 5 µg/m3 increase in PM2.5 exposure was associated with a reduction in birth weight among preterm births (-37.1 g [95% confidence interval [CI]: 60.8 g, -13.5 g]); early term births (-13.5 g [95% CI: 26.2 g, -0.67 g]); and full term births (-8.23 g [95% CI: 15.8 g, -0.68 g])]. In single-pollutant models, a 10 ppb increase in NO2 exposure was associated with a -11.7 g (95% CI: 14.46 g, -8.92 g) decrement in birth weight among full term births only. In models co-adjusted for PM2.5 and NO2, PM2.5 exposure was associated with reduced birth weight among preterm births (-36.9 g [95% CI: 61.9 g, -11.8 g]) and NO2 exposure was associated with reduced birth weight among full term births (-11.8 g [95% CI: 14.7 g, -8.94 g]). The largest decrements in birth weight were associated with PM2.5 exposure between approximately 10 and 26 weeks of pregnancy; for NO2 exposure, the largest decrements in birth weight in full term births were associated with exposure between weeks 6-18. CONCLUSION: We observed the largest and most persistent adverse associations between PM2.5 exposure and birth weight in preterm infants, and between NO2 exposure and birth weight in full term infants. Exposure during the first half of pregnancy had a greater impact on birthweight.

Where Is Air Quality Improving, and Who Benefits? A Study of PM2.5 and Ozone Over 15 Years.

In the United States, concentrations of criteria air pollutants have declined in recent decades. Questions remain regarding whether improvements in air quality are equitably distributed across subpopulations. We assessed spatial variability and temporal trends in concentrations of particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5) and ozone (O3) across North Carolina from 2002-2016, and associations with community characteristics. Estimated daily PM2.5 and O3 concentrations at 2010 Census tracts were obtained from the Fused Air Quality Surface Using Downscaling archive and averaged to create tract-level annual PM2.5 and O3 estimates. We calculated tract-level measures of: racial isolation of non-Hispanic Black individuals, educational isolation of non-college educated individuals, the neighborhood deprivation index (NDI), and percentage of the population in urban areas. We fitted hierarchical Bayesian space-time models to estimate baseline concentrations of and time trends in PM2.5 and O3 for each tract, accounting for spatial between-tract correlation. Concentrations of PM2.5 and O3 declined by 6.4 µg/m3 and 13.5 ppb, respectively. Tracts with lower educational isolation and higher urbanicity had higher PM2.5 and more pronounced declines in PM2.5. Racial isolation was associated with higher PM2.5 but not with the rate of decline in PM2.5. Despite declines in pollutant concentrations, over time, disparities in exposure increased for racially and educationally isolated communities.

Exposure to heat during pregnancy and preterm birth in North Carolina: Main effect and disparities by residential greenness, urbanicity, and socioeconomic status.

BACKGROUND: Although previous literature suggested that several factors may be associated with higher risk of adverse health outcomes related to heat, research is limited for birth outcomes. OBJECTIVES: We investigated associations between exposure to heat/heat waves during the last week of gestation and preterm birth (PTB) in North Carolina (NC) and evaluated effect modification by residential greenness, urbanicity, and socioeconomic status (SES). METHODS: We obtained individual-level NC birth certificate data for May-September 2003-2014. We estimated daily mean temperature at each maternal residential address using Parameter-elevation Regressions on Independent Slopes Model (PRISM) data. We created 3 definitions of heat waves (daily temperature ≥95th, 97th, 99th percentile for NC warm season temperature, for ≥2 consecutive days). Normalized Difference Vegetation Index (NDVI) was used to assess residential greenness. Community-level modifiers (e.g., income, urbanicity) were considered. We applied Cox proportional hazard models to estimate the association between exposure to heat/heat waves and PTB, controlling for covariates. Stratified analyses were conducted to evaluate whether the association between heat and PTB varied by several individual and community characteristics. RESULTS: Of the 546,441 births, 8% were preterm. Heat exposure during the last week before delivery was significantly associated with risk of PTB. The hazard ratio for a 1 °C increase in temperature during the last week before delivery was 1.01 (95% CI: 1.00, 1.02). Higher heat-PTB risk was associated with some characteristics (e.g., areas that were urbanized, low SES, or in the Coastal Plain). We also found significant PTB-heat risk in areas with low greenness for urbanized area. For heat waves, we did not find significantly positive associations with PTB. DISCUSSION: Findings provide evidence that exposure to heat during pregnancy increases risk of PTB and suggest disparities in these risks. Our results have implications for future studies of disparity in heat and birth outcomes associations.

Economic and mental health impacts of multiple adverse events: Hurricane Harvey, other flooding events, and the COVID-19 pandemic.

OBJECTIVES: To assess the economic and mental health impacts of COVID-19 in the presence of previous exposure to flooding events. METHODS: Starting in April 2018, the Texas Flood Registry (TFR) invited residents to complete an online survey regarding their experiences with Hurricane Harvey and subsequent flooding events. Starting in April 2020, participants nationwide were invited to complete a brief online survey on their experiences during the pandemic. This study includes participants in the TFR (N = 20,754) and the COVID-19 Registry (N = 8568) through October 2020 (joint N = 2929). Logistic regression and generalized estimating equations were used to examine the relationship between exposure to flooding events and the economic and mental health impacts of COVID-19. RESULTS: Among COVID-19 registrants, 21% experienced moderate to severe anxiety during the pandemic, and 7% and 12% of households had difficulty paying rent and bills, respectively. Approximately 17% of Black and 15% of Hispanic households had difficulty paying rent, compared to 5% of non-Hispanic white households. The odds of COVID-19 income loss are 1.20 (1.02, 1.40) times higher for those who previously had storm-related home damage compared to those who did not and 3.84 (3.25-4.55) times higher for those who experienced Harvey income loss compared to those who did not. For registrants for whom Harvey was a severe impact event, the odds of having more severe anxiety during the pandemic are 5.14 (4.02, 6.58) times higher than among registrants for whom Harvey was a no meaningful impact event. CONCLUSIONS: Multiple crises can jointly and cumulatively shape health and wellbeing outcomes. This knowledge can help craft emergency preparation and intervention programs.

Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina.

Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler ("downscaler") model, which provides spatial predictions of daily concentrations of fine particulate matter (PM2.5) and ozone (O3) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM2.5 and lower levels of O3. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM2.5 and O3 between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM2.5 and O3, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PEPM2.5 and PEO3) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM2.5 and O3 levels decreased and O3 predictions became more reliable. However, the predictive uncertainty for PM2.5 has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM2.5. In contrast, racial isolation was associated with a greater decline in the reliability of PM2.5 predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O3 within the same time frame.

A longitudinal study of exposure to fine particulate matter during pregnancy, small-for-gestational age births, and birthweight percentile for gestational age in a statewide birth cohort.

BACKGROUND: Previous studies observed associations between prenatal exposure to fine particulate matter (≤ 2.5 µm; PM2.5) and small-for-gestational-age (SGA) birth and lower birthweight percentile for gestational age. Few, if any, studies examine prenatal air pollution exposure and these pregnancy outcomes in neonates born to the same women. Here, we assess whether prenatal exposure to ambient fine particulate matter (PM2.5) is associated with small-for-gestational-age (SGA) birth or birthweight percentile for gestational age in a longitudinal setting. METHODS: Detailed birth record data were used to identify women who had singleton live births at least twice in North Carolina during 2002-2006 (n = 53,414 women, n = 109,929 births). Prenatal PM2.5 exposures were calculated using daily concentration estimates obtained from the US EPA Fused Air Quality Surface using Downscaling data archive. Associations between PM2.5 exposure and birthweight percentile and odds of SGA birth were calculated using linear and generalized mixed models, comparing successive pregnancies to the same woman. Odds ratios and associations were also estimated in models that did not account for siblings born to the same mother. RESULTS: Among NHW women, pregnancy-long PM2.5 exposure was associated with SGA (OR: 1.11 [1.06, 1.18]) and lower birthweight percentile (- 0.46 [- 0.74, - 0.17]). Trimester-specific PM2.5 was also associated with SGA and lower birthweight percentile. Among NHB women, statistically significant within-woman associations between PM2.5, SGA, and birthweight percentile were not observed. However, in models that did not account for births to the same mother, statistically significant associations were observed between some PM2.5 exposure windows and higher odds of SGA and lower birthweight percentile among NHB women. CONCLUSIONS: Findings suggest that a woman is at greater risk of delivering an SGA or low birthweight percentile neonate when she has been exposed to higher PM2.5 levels. The within-woman comparison implemented here better controls for factors that may differ between women and potentially confound the relationship between PM2.5 exposure and pregnancy outcomes. This adds to the evidence that PM2.5 exposure may be causally related to SGA and birthweight percentile, even at concentrations close to or below National Ambient Air Quality Standards.

Long-term Exposure to PM2.5 and Mortality for the Older Population: Effect Modification by Residential Greenness.

BACKGROUND: Although many studies demonstrated reduced mortality risk with higher greenness, few studies examined the modifying effect of greenness on air pollution-health associations. We evaluated residential greenness as an effect modifier of the association between long-term exposure to fine particles (PM2.5) and mortality. METHODS: We used data from all Medicare beneficiaries in North Carolina (NC) and Michigan (MI) (2001-2016). We estimated annual PM2.5 averages using ensemble prediction models. We estimated mortality risk per 1 µg/m3 increase using Cox proportional hazards modeling, controlling for demographics, Medicaid eligibility, and area-level covariates. We investigated health disparities by greenness using the Normalized Difference Vegetation Index with measures of urbanicity and socioeconomic status. RESULTS: PM2.5 was positively associated with mortality risk. Hazard ratios (HRs) were 1.12 (95% confidence interval (CI) = 1.12 to 1.13) for NC and 1.01 (95% CI = 1.00 to 1.01) for MI. HRs were higher for rural than urban areas. Within each category of urbanicity, HRs were generally higher in less green areas. For combined disparities, HRs were higher in low greenness or low SES areas, regardless of the other factor. HRs were lowest in high-greenness and high-SES areas for both states. CONCLUSIONS: In our study, those in low SES and high-greenness areas had lower associations between PM2.5 and mortality than those in low SES and low greenness areas. Multiple aspects of disparity factors and their interactions may affect health disparities from air pollution exposures. Findings should be considered in light of uncertainties, such as our use of modeled PM2.5 data, and warrant further investigation.

Response to a COVID-19 Outbreak on a University Campus - Indiana, August 2020.

Institutions of higher education adopted different approaches for the fall semester 2020 in response to the coronavirus disease 2019 (COVID-19) pandemic. Approximately 45% of colleges and universities implemented online instruction, more than one fourth (27%) provided in-person instruction, and 21% used a hybrid model (1). Although CDC has published COVID-19 guidance for institutions of higher education (2-4), little has been published regarding the response to COVID-19 outbreaks on college and university campuses (5). In August 2020, an Indiana university with approximately 12,000 students (including 8,000 undergraduate students, 85% of whom lived on campus) implemented various public health measures to reduce transmission of SARS-CoV-2, the virus that causes COVID-19. Despite these measures, the university experienced an outbreak involving 371 cases during the first few weeks of the fall semester. The majority of cases occurred among undergraduate students living off campus, and several large off-campus gatherings were identified as common sources of exposure. Rather than sending students home, the university switched from in-person to online instruction for undergraduate students and instituted a series of campus restrictions for 2 weeks, during which testing, contact tracing, and isolation and quarantine programs were substantially enhanced, along with educational efforts highlighting the need for strict adherence to the mitigation measures. After 2 weeks, the university implemented a phased return to in-person instruction (with 85% of classes offered in person) and resumption of student life activities. This report describes the outbreak and the data-driven, targeted interventions and rapid escalation of testing, tracing, and isolation measures that enabled the medium-sized university to resume in-person instruction and campus activities. These strategies might prove useful to other colleges and universities responding to campus outbreaks.

Bayesian variable selection for understanding mixtures in environmental exposures.

Social and environmental stressors are crucial factors in child development. However, there exists a multitude of measurable social and environmental factors-the effects of which may be cumulative, interactive, or null. Using a comprehensive cohort of children in North Carolina, we study the impact of social and environmental variables on 4th end-of-grade exam scores in reading and mathematics. To identify the essential factors that predict these educational outcomes, we design new tools for Bayesian linear variable selection using decision analysis. We extract a predictive optimal subset of explanatory variables by coupling a loss function with a novel model-based penalization scheme, which leads to coherent Bayesian decision analysis and empirically improves variable selection, estimation, and prediction on simulated data. The Bayesian linear model propagates uncertainty quantification to all predictive evaluations, which is important for interpretable and robust model comparisons. These predictive comparisons are conducted out-of-sample with a customized approximation algorithm that avoids computationally intensive model refitting. We apply our variable selection techniques to identify the joint collection of social and environmental stressors-and their interactions-that offer clear and quantifiable improvements in prediction of reading and mathematics exam scores.

Effects of accumulated environmental, social and host exposures on early childhood educational outcomes.

BACKGROUND: Persistent disparities in academic performance may result from a confluence of adverse exposures accruing disproportionately to specific subpopulations. OBJECTIVE: Our overarching objective was to investigate how multiple exposures experienced over time affect early childhood educational outcomes. We were specifically interested in whether there were: racial/ethnic disparities in prevalence of adverse exposures; racial/ethnic disparities in associations observed between adverse exposures and early childhood educational outcomes; and interactions between exposures, suggesting that one exposure augments susceptibility to adverse effects of another exposure. METHODS: We link geocoded North Carolina birth data for non-Hispanic white (NHW) and non-Hispanic black (NHB) children to blood lead surveillance data and 4th grade end-of-grade (EOG) standardized test scores (n = 65,151). We construct a local, spatial index of racial isolation (RI) of NHB at the block group level. We fit race-stratified multi-level models of reading and mathematics EOG scores regressed on birthweight percentile for gestational age, blood lead level, maternal smoking, economic disadvantage, and RI, adjusting for maternal- and child-level covariates and median household income. RESULTS: There were marked racial/ethnic disparities in prevalence of adverse exposures. Specifically, NHB children were more likely than NHW children to be economically disadvantaged (80% vs. 40%), live in block groups with the highest quintile of RI (46% vs. 5%), have higher blood lead levels (4.6 vs. 3.7 µg/dL), and lower birthweight percentile for gestational age (mean: 39th percentile vs. 51st percentile). NHB children were less likely to have mothers who reported smoking during pregnancy (11% and 22%). We observed associations between key adverse exposures and reading and math EOG scores in 4th grade. Higher birthweight percentile for gestational age was associated with higher EOG scores, while economic disadvantage, maternal smoking, and elevated blood lead levels were associated with lower EOG scores. Associations observed for NHB and NHW children were generally not statistically different from one another, with the exception of neighborhood RI. NHB children residing in block groups in the highest RI quintile had reading and math scores 1.54 (0.74, 2.34) and 1.12 (0.38, 1.87) points lower, respectively, compared to those in the lowest RI quintile; statistically significant decrements in EOG scores associated with RI were not observed for NHW children. We did not find evidence of multiplicative interactions between exposures for NHB or NHW children. DISCUSSION: Key adverse host, environmental, and social exposures accrue disproportionately to NHB children. Decrements in test scores associated with key adverse exposures were often but not always larger for NHB children, but were not significantly different from those estimated for NHW children. While we did not observe interactive effects, NHB children on average experience more deleterious combined exposures, resulting in larger decrements to test scores compared to NHW children.

Distribution of environmental justice metrics for exposure to CAFOs in North Carolina, USA.

BACKGROUND: Several studies have reported environmental disparities regarding exposure to concentrated animal feeding operations (CAFOs). Public health implications of environmental justice from the intensive livestock industry are of great concern in North Carolina (NC), USA, a state with a large number and extensive history of CAFOs. OBJECTIVES: We examined disparities by exposure to CAFOs using several environmental justice metrics and considering potentially vulnerable subpopulations. METHODS: We obtained data on permitted animal facilities from NC Department of Environmental Quality (DEQ). Using ZIP code level variables from the 2010 Census, we evaluated environmental disparities by eight environmental justice metrics (i.e., percentage of Non-Hispanic White, Non-Hispanic Black, or Hispanic; percentage living below the poverty level; median household income; percentage with education less than high school diploma; racial residential isolation (RI) for Non-Hispanic Black; and educational residential isolation (ERI) for population without college degree). We applied two approaches to assign CAFOs exposure for each ZIP code: (1) a count method based on the number of CAFOs within ZIP code; and (2) a buffer method based on the area-weighted number of CAFOs using a 15 km buffer. RESULTS: Spatial distributions of CAFOs exposure generally showed similar patterns between the two exposure methods. However, some ZIP codes had different estimated CAFOs exposure for the different approaches, with higher exposure when using the buffer method. Our findings indicate that CAFOs are located disproportionately in communities with higher percentage of minorities and in low-income communities. Distributions of environmental justice metrics generally showed similar patterns for both exposure methods, however starker disparities were observed using a buffer method. CONCLUSIONS: Our findings of the disproportionate location of CAFOs provide evidence of environmental disparities with respect to race and socioeconomic status in NC and have implications for future studies of environmental and health impacts of CAFOs.

Associations Between Residential Proximity to Traffic and Vascular Disease in a Cardiac Catheterization Cohort.

OBJECTIVE: Exposure to mobile source emissions is nearly ubiquitous in developed nations and is associated with multiple adverse health outcomes. There is an ongoing need to understand the specificity of traffic exposure associations with vascular outcomes, particularly in individuals with cardiovascular disease. APPROACH AND RESULTS: We performed a cross-sectional study using 2124 individuals residing in North Carolina, United States, who received a cardiac catheterization at the Duke University Medical Center. Traffic-related exposure was assessed via 2 metrics: (1) the distance between the primary residence and the nearest major roadway; and (2) location of the primary residence in regions defined based on local traffic patterns. We examined 4 cardiovascular disease outcomes: hypertension, peripheral arterial disease, the number of diseased coronary vessels, and recent myocardial infarction. Statistical models were adjusted for race, sex, smoking, type 2 diabetes mellitus, body mass index, hyperlipidemia, and home value. Results are expressed in terms of the odds ratio (OR). A 23% decrease in residential distance to major roadways was associated with higher prevalence of peripheral arterial disease (OR=1.29; 95% confidence interval, 1.08-1.55) and hypertension (OR=1.15; 95% confidence interval, 1.01-1.31). Associations with peripheral arterial disease were strongest in men (OR=1.42; 95% confidence interval, 1.17-1.74) while associations with hypertension were strongest in women (OR=1.21; 95% confidence interval, 0.99-1.49). Neither myocardial infarction nor the number of diseased coronary vessels were associated with traffic exposure. CONCLUSIONS: Traffic-related exposure is associated with peripheral arterial disease and hypertension while no associations are observed for 2 coronary-specific vascular outcomes.

Residential Racial Isolation and Spatial Patterning of Hypertension in Durham, North Carolina.

INTRODUCTION: Neighborhood characteristics such as racial segregation may be associated with hypertension, but studies have not examined these relationships using spatial models appropriate for geographically patterned health outcomes. The objectives of our study were to 1) evaluate the geographic heterogeneity of hypertension; 2) describe whether and how patient-level risk factors and racial isolation relate to geographic heterogeneity in hypertension; and 3) examine cross-sectional associations of hypertension with racial isolation. METHODS: We obtained electronic health records from the Duke Medicine Enterprise Data Warehouse for 2007-2011. We linked patient data with data on racial isolation determined by census block of residence. We constructed a local spatial index of racial isolation for non-Hispanic black patients; the index is scaled from 0 to 1, with 1 indicating complete isolation. We used aspatial and spatial Bayesian models to assess spatial variation in hypertension and estimate associations with racial isolation. RESULTS: Racial isolation ranged from 0 (no isolation) to 1 (completely isolated). A 0.20-unit increase in racial isolation was associated with 1.06 (95% credible interval, 1.03-1.10) and 1.11 (95% credible interval, 1.07-1.16) increased odds of hypertension among non-Hispanic black and non-Hispanic white patients, respectively. Across Durham, census block-level odds of hypertension ranged from 0.62 to 1.88 among non-Hispanic black patients and from 0.32 to 2.41 among non-Hispanic white patients. Compared with spatial models that included patient age and sex, residual heterogeneity in spatial models that included age, sex, and block-level racial isolation was 33% lower for non-Hispanic black patients and 20% lower for non-Hispanic white patients. CONCLUSION: Racial isolation of non-Hispanic black patients was associated with increased odds of hypertension among both non-Hispanic black and non-Hispanic white patients. Further research is needed to identify latent spatially patterned factors contributing to hypertension.

The Rate Stabilizing Tool: Generating Stable Local-Level Measures of Chronic Disease.

Accurate and precise estimates of local-level epidemiologic measures are critical to informing policy and program decisions, but they often require advanced statistical knowledge, programming/coding skills, and extensive computing power. In response, we developed the Rate Stabilizing Tool (RST), an ArcGIS-based tool that enables users to input their own record-level data to generate more reliable age-standardized measures of chronic disease (eg, prevalence rates, mortality rates) or other population health outcomes at the county or census tract levels. The RST uses 2 forms of empirical Bayesian modeling (nonspatial and spatial) to estimate age-standardized rates and 95% credible intervals for user-specified geographic units. The RST also provides indicators of the reliability of point estimates. In addition to reviewing the RST's statistical techniques, we present results from a simulation study that illustrates the key benefit of smoothing. We demonstrate the dramatic reduction in root mean-squared error (rMSE), indicating a better compromise between accuracy and stability for both smoothing approaches relative to the unsmoothed estimates. Finally, we provide an example of the RST's use. This example uses heart disease mortality data for North Carolina census tracts to map the RST output, including reliability of estimates, and demonstrates a subsequent statistical test.

Variation in Gastrostomy Tube Placement in Premature Infants in the United States.

OBJECTIVE: To describe the variation in surgical gastrostomy tube (SGT) placement in premature infants among neonatal intensive care units (NICUs) in the United States. STUDY DESIGN: We identified 8,781 premature infants discharged from 114 NICUs in the Pediatrix Medical Group from 2010 to 2012. The outcome of interest was SGT placement prior to discharge home from an NICU. Unadjusted proportions and adjusted risk estimates were calculated to quantify variation observed among individual NICUs. RESULTS: SGT placement occurred in 360 of 8,781 (4.1%) of infants. Across NICUs, any gastrostomy tube placement ranged from none in 45 NICUs up to 19.6%. Adjusted risk estimates for factors associated with SGT placement included gestational age at birth (odds ratio [OR]: 0.7/week, 95% confidence interval[CI]: [0.65, 0.75]), small for gestational age status (OR: 2.78 [2.09, 3.71]), administration of antenatal steroids (OR: 0.69 [0.52, 0.92]), Hispanic ethnicity (OR: 0.54 [0.37, 0.78]), and higher 5-minute Apgar scores (7-10, OR: 0.54 [0.37, 0.79]). CONCLUSION: Individual NICU center has a strong clinical effect on the probability of SGT placement relative to other medical factors. Future work is needed to understand the cause of this variation and the degree to which it represents over or under use of gastrostomy tubes.

Residential Racial Isolation and Spatial Patterning of Type 2 Diabetes Mellitus in Durham, North Carolina.

Neighborhood characteristics such as racial segregation may be associated with type 2 diabetes mellitus, but studies have not examined these relationships using spatial models appropriate for geographically patterned health outcomes. We constructed a local, spatial index of racial isolation (RI) for black residents in a defined area, measuring the extent to which they are exposed only to one another, to estimate associations of diabetes with RI and examine how RI relates to spatial patterning in diabetes. We obtained electronic health records from 2007-2011 from the Duke Medicine Enterprise Data Warehouse. Patient data were linked to RI based on census block of residence. We used aspatial and spatial Bayesian models to assess spatial variation in diabetes and relationships with RI. Compared with spatial models with patient age and sex, residual geographic heterogeneity in diabetes in spatial models that also included RI was 29% and 24% lower for non-Hispanic white and black residents, respectively. A 0.20-unit increase in RI was associated with an increased risk of diabetes for white (risk ratio = 1.24, 95% credible interval: 1.17, 1.31) and black (risk ratio = 1.07, 95% credible interval: 1.05, 1.10) residents. Improved understanding of neighborhood characteristics associated with diabetes can inform development of policy interventions.

Predictors of Prolonged Breast Milk Provision to Very Low Birth Weight Infants.

OBJECTIVE: To identify factors associated with prolonged maternal breast milk (BM) provision in very low birth weight (VLBW) infants. STUDY DESIGN: This was a cohort study of VLBW infants who initially received maternal BM and were born at one of 197 neonatal intensive care units managed by the Pediatrix Medical Group from 2010 to 2012. We used multivariable logistic regression to identify demographic, clinical, and maternal factors associated with provision of maternal BM on day of life (DOL) 30 and at discharge. RESULTS: Median gestational age for all infants was 28 weeks (25th, 75th percentiles: 26, 30), and median maternal age was 28 years (23, 33). Of 8806 infants, 6261 (71%) received maternal BM on DOL 30, and 4003 of 8097 (49%) received maternal BM at discharge to home. Predictors of maternal BM provision at DOL 30 included increased maternal age, white maternal race, absence of history of necrotizing enterocolitis or late-onset sepsis, higher household income, lower education level, lack of donor BM exposure, and lower gestational age. CONCLUSIONS: Our results suggest that maternal-infant demographic and clinical factors and household neighborhood socioeconomic characteristics were associated with provision of maternal BM at 30 postnatal days to VLBW infants. Identification of these factors allows providers to anticipate mothers' needs and develop tailored interventions designed to improve rates of prolonged maternal BM provision and infant outcomes.

The association of single-nucleotide polymorphisms in the oxytocin receptor and G protein-coupled receptor kinase 6 (GRK6) genes with oxytocin dosing requirements and labor outcomes.

BACKGROUND: Oxytocin is a potent uterotonic agent that is widely used for induction and augmentation of labor. Oxytocin has a narrow therapeutic index and the optimal dosing for any individual woman varies widely. OBJECTIVE: The objective of this study was to determine whether genetic variation in the oxytocin receptor (OXTR) or in the gene encoding G protein-coupled receptor kinase 6 (GRK6), which regulates desensitization of the oxytocin receptor, could explain variation in oxytocin dosing and labor outcomes among women being induced near term. STUDY DESIGN: Pregnant women with a singleton gestation residing in Durham County, NC, were prospectively enrolled as part of the Healthy Pregnancy, Healthy Baby cohort study. Those women undergoing an induction of labor at 36 weeks or greater were genotyped for 18 haplotype-tagging single-nucleotide polymorphisms in OXTR and 7 haplotype-tagging single-nucleotide polymorphisms in GRK6 using TaqMan assays. Linear regression was used to examine the relationship between maternal genotype and maximal oxytocin infusion rate, total oxytocin dose received, and duration of labor. Logistic regression was used to test for the association of maternal genotype with mode of delivery. For each outcome, backward selection techniques were utilized to control for important confounding variables and additive genetic models were used. Race/ethnicity was included in all models because of differences in allele frequencies across populations, and Bonferroni correction for multiple testing was used. RESULTS: DNA was available from 482 women undergoing induction of labor at 36 weeks or greater. Eighteen haplotype-tagging single-nucleotide polymorphisms within OXTR and 7 haplotype-tagging single-nucleotide polymorphisms within GRK6 were examined. Five single-nucleotide polymorphisms in OXTR showed nominal significance with maximal infusion rate of oxytocin, and two single-nucleotide polymorphisms in OXTR were associated with total oxytocin dose received. One single-nucleotide polymorphism in OXTR and two single-nucleotide polymorphisms in GRK6 were associated with duration of labor, one of which met the multiple testing threshold (P = .0014, rs2731664 [GRK6], mean duration of labor, 17.7 hours vs 20.2 hours vs 23.5 hours for AA, AC, and CC genotypes, respectively). Three single-nucleotide polymorphisms, two in OXTR and one in GRK6, showed nominal significance with mode of delivery. CONCLUSION: Genetic variation in OXTR and GRK6 is associated with the amount of oxytocin required as well as the duration of labor and risk for cesarean delivery among women undergoing induction of labor near term. With further research, pharmacogenomic approaches may potentially be utilized to develop personalized treatment to improve safety and efficacy outcomes among women undergoing induction of labor.