Slavery, Home Ownership, and Contemporary Perinatal Outcomes in the Southeast: A Test of Mediation and Moderation.

OBJECTIVE: To estimate the effect of geographic variation in historic slavery on perinatal outcomes [chronic hypertension, hypertensive disorders of pregnancy (HDP), very preterm birth (VPTB), or very low birth weight birth (VLBW)] among Black people living in states where slavery was legal in 1860 and test mediation by Black homeownership. METHODS: We linked data from the 1860 census (the proportion of enslaved residents) to natality data on outcomes (2013-2021) using resident county. The percent of Black residents in a county who owned their home was a potential mediator. We fit log binomial models to estimate risk ratios (RRs) representing total and controlled direct effects (accounting for Black homeownership) of proportion enslaved on outcomes, accounting for potential confounding using marginal structural models. RESULTS: Among 2,443,198 included births, 8.8% (213,829) experienced HDP, 4.1% (100,549) chronic hypertension, 3.3% (81,072) VPTB, and 2.6% (62,538) VLBW. There was an increase in chronic hypertension and VPTB risk, but not HDP or VLBW, in counties with a 10% greater proportion enslaved in 1860 [adjusted RR: 1.06, 95% CI: (1.02, 1.1); 1.02 (1.00, 1.05); 1.00 (0.98, 1.02); 1.01 (1.00, 1.03)]. There was not evidence of mediation by Black homeownership. CONCLUSIONS: Historic slavery remains relevant for perinatal health.

Neighborhood deprivation and racial differences in in vitro fertilization outcomes.

BACKGROUND: There are significant racial disparities in in vitro fertilization outcomes, which are poorly explained by individual-level characteristics. Environmental factors such as neighborhood-level socioeconomic factors may contribute to these disparities. However, few studies have directly addressed this research question in a large, racially diverse cohort. OBJECTIVE: This study aimed to investigate whether neighborhood deprivation is associated with differences in in vitro fertilization outcomes. STUDY DESIGN: Our retrospective cohort study included 1110 patients who underwent 2254 autologous in vitro fertilization cycles between 2014 and 2019 at an academic fertility center in the Southeastern United States. Neighborhood deprivation was estimated using the Neighborhood Deprivation Index, a composite variable measuring community levels of material capital based on poverty, occupation, housing, and education domains. Using multivariable log-binomial generalized estimating equations with cluster weighting, risk ratios and 95% confidence intervals were estimated for cycle cancellation, miscarriage (defined as spontaneous pregnancy loss before 20 weeks after a confirmed intrauterine gestation), and live birth according to patient Neighborhood Deprivation Index. RESULTS: There were positive associations between increasing Neighborhood Deprivation Index (indicating worsening neighborhood deprivation) and body mass index, as well as increasing prevalence of tubal and uterine factor infertility diagnoses. The crude probability of live birth per cycle was lower among Black (24%) than among White patients (32%), and the crude probability of miscarriage per clinical pregnancy was higher among Black (22%) than among White patients (12%). After adjustment, the Neighborhood Deprivation Index was not significantly associated with risk of cycle cancellation or live birth. Results were consistent when analyses were stratified by race. CONCLUSION: Our research demonstrates racial disparities between Black and White women in the incidence of miscarriage and live birth following in vitro fertilization. Although the level of neighborhood deprivation was closely related to race, it did not have strong associations with in vitro fertilization outcomes in our population as a whole or within strata of race.

Unpacking Breastfeeding Disparities: Baby-Friendly Hospital Designation Associated with Reduced In-Hospital Exclusive Breastfeeding Disparity Attributed to Neighborhood Poverty.

OBJECTIVES: To examine US in-hospital exclusive breastfeeding (EBF) and the associations with Baby-Friendly designation and neighborhood sociodemographic factors. METHODS: Hospital data from the 2018 Maternity Practices in Infant Nutrition and Care survey were linked to hospital zip code tabulation area (ZCTA) sociodemographic data from the 2014-2018 American Community Survey (n = 2,024). The percentages of residents in the hospital ZCTA were dichotomized based on the relative mean percentage of the hospital's metropolitan area, which were exposure variables (high/low Black hospitals, high/low poverty hospitals, high/low educational attainment hospitals) along with Baby-Friendly designation. Using linear regression, we examined the associations and effect measure modification between Baby-Friendly designation and hospital sociodemographic factors with in-hospital EBF prevalence. RESULTS: US mean in-hospital EBF prevalence was 55.1%. Baby-Friendly designation was associated with 9.1% points higher in-hospital EBF prevalence compared to non-designated hospitals [95% confidence interval (CI): 7.0, 11.2]. High Black hospitals and high poverty hospitals were associated with lower EBF prevalence (difference= -3.3; 95% CI: -5.1, -1.4 and - 3.8; 95% CI: -5.7, -1.8). High educational attainment hospitals were associated with higher EBF prevalence (difference = 6.7; 95% CI: 4.1, 9.4). Baby-Friendly designation was associated with significant effect measure modification of the in-hospital EBF disparity attributed to neighborhood level poverty (4.0% points higher in high poverty/Baby-Friendly designated hospitals than high poverty/non-Baby-Friendly designated hospitals).

Highly public anti-Black violence is associated with poor mental health days for Black Americans.

Highly public anti-Black violence in the United States may cause widely experienced distress for Black Americans. This study identifies 49 publicized incidents of racial violence and quantifies national interest based on Google searches; incidents include police killings of Black individuals, decisions not to indict or convict the officer involved, and hate crime murders. Weekly time series of population mental health are produced for 2012 through 2017 using two sources: 1) Google Trends as national search volume for psychological distress terms and 2) the Behavioral Risk Factor Surveillance System (BRFSS) as average poor mental health days in the past 30 d among Black respondents (mean weekly sample size of 696). Autoregressive moving average (ARMA) models accounted for autocorrelation, monthly unemployment, season and year effects, 52-wk lags, news-related searches for suicide (for Google Trends), and depression prevalence and percent female (for BRFSS). National search interest varied more than 100-fold between racial violence incidents. Black BRFSS respondents reported 0.26 more poor mental health days during weeks with two or more racial incidents relative to none, and 0.13 more days with each log10 increase in national interest. Estimates were robust to sensitivity tests, including controlling for monthly number of Black homicide victims and weekly search interest in riots. As expected, racial incidents did not predict average poor mental health days among White BRFSS respondents. Results with national psychological distress from Google Trends were mixed but generally unsupportive of hypotheses. Reducing anti-Black violence may benefit Black Americans' mental health nationally.

Understanding the roles of state demographics and state policies in epidemiologic studies of maternal-child health disparities.

Disparities in maternal-child health outcomes by race and ethnicity highlight structural differences in the opportunity for optimal health in the United States. Examples of these differences include access to state-level social policies that promote maternal-child health. States vary in their racial and ethnic composition as a result of the complex history of policies and laws related to slavery, Indigenous genocide and relocation, segregation, immigration, and settlement in the United States. States also vary in the social policies they enact. As a result, correlations exist between the demographic makeup of a state's population and the presence or absence of social policies in that state. These correlations become a mechanism by which racial and ethnic disparities in maternal-child health outcomes can operate. In this commentary, we use the example of three labor-related policies actively under consideration at state and federal levels (paid parental leave, paid sick leave, and reasonable accommodations during pregnancy) to demonstrate how correlations between state demographics and presence of these state policies could cause or exacerbate racial and ethnic disparities in maternal-child health outcomes. We conclude with a call for researchers to consider how the geographic distribution of racialized populations and state policies could contribute to maternal-child health disparities.

Census Tracts Are Not Neighborhoods: Addressing Spatial Misalignment in Studies Examining the Impact of Historical Redlining on Present-day Health Outcomes.

BACKGROUND: Research examining the effects of historical redlining on present-day health outcomes is often complicated by the misalignment of contemporary census boundaries with the neighborhood boundaries drawn by the US Home Owners' Loan Corporation (HOLC) in the 1930s. Previous studies have used different approaches to assign historical HOLC grades to contemporary geographies, but how well they capture redlining exposure is unknown. METHODS: Our analysis included 7711 residences identified in the Multiple Listing Service database in Atlanta, Georgia (2017-2022). We evaluated the classification of HOLC grade assignment (A, B, C, D, or ungraded) when assigning exposure under four area-level approaches (centroid, majority land area, weighted score, and highest HOLC) compared with using complete address data (gold standard). We additionally compared approaches across three 2020 census geographies (tract, block group, and block). RESULTS: When comparing the use of census tracts to complete address data, sensitivity was highest for the weighted score approach, which correctly identified 77% of residences in truly A-D graded neighborhoods as compared with the majority land area (44%), centroid (54%), and highest HOLC (59%) approaches. Regarding specificity, the majority land area approach best-classified residences in truly ungraded neighborhoods (93%) as compared with the weighted score (65%), centroid (81%), and highest HOLC (54%) approaches. Classification improved regardless of approach when using census block compared with the census tract. CONCLUSIONS: Misclassification of historical redlining exposure is inevitable when using contemporary census geographies rather than complete address data. This study provides a framework for assessing spatial misalignment and selecting an approach for classification.

Neighborhood Deprivation and Neural Tube Defects.

BACKGROUND: Individual measures of socioeconomic status (SES) have been associated with an increased risk of neural tube defects (NTDs); however, the association between neighborhood SES and NTD risk is unknown. Using data from the National Birth Defects Prevention Study (NBDPS) from 1997 to 2011, we investigated the association between measures of census tract SES and NTD risk. METHODS: The study population included 10,028 controls and 1829 NTD cases. We linked maternal addresses to census tract SES measures and used these measures to calculate the neighborhood deprivation index. We used generalized estimating equations to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) estimating the impact of quartiles of census tract deprivation on NTDs adjusting for maternal race-ethnicity, maternal education, and maternal age at delivery. RESULTS: Quartiles of higher neighborhood deprivation were associated with NTDs when compared with the least deprived quartile (Q2: aOR = 1.2; 95% CI = 1.0, 1.4; Q3: aOR = 1.3, 95% CI = 1.1, 1.5; Q4 (highest): aOR = 1.2; 95% CI = 1.0, 1.4). Results for spina bifida were similar; however, estimates for anencephaly and encephalocele were attenuated. Associations differed by maternal race-ethnicity. CONCLUSIONS: Our findings suggest that residing in a census tract with more socioeconomic deprivation is associated with an increased risk for NTDs, specifically spina bifida.

Severe Maternal Morbidity in Georgia, 2009-2020.

BACKGROUND: The increasing focus of population surveillance and research on maternal-and not only fetal and infant-health outcomes is long overdue. The United States maternal mortality rate is higher than any other high-income country, and Georgia is among the highest rates in the country. Severe maternal morbidity (SMM) is conceived of as a "near miss" for maternal mortality, is 50 times more common than maternal death, and efforts to systematically monitor SMM rates in populations have increased in recent years. Much of the current population-based research on SMM has occurred in coastal states or large cities, despite substantial geographical variation with higher maternal and infant health burdens in the Southeast and rural regions. METHODS: This population-based study uses hospital discharge records linked to vital statistics to describe the epidemiology of SMM in Georgia between 2009 and 2020. RESULTS: Georgia had a higher SMM rate than the United States overall (189.2 vs. 144 per 10,000 deliveries in Georgia in 2014, the most recent year with US estimates). SMM was higher among racially minoritized pregnant persons and those at the extremes of age, of lower socioeconomic status, and with comorbid chronic conditions. SMM rates were 5 to 6 times greater for pregnant people delivering infants <1500 grams or <32 weeks' gestation as compared with those delivering normal weight or term infants. Since 2015, SMM has increased in Georgia. CONCLUSION: SMM represents a collection of life-threatening emergencies that are unevenly distributed in the population and require increased attention. This descriptive analysis provides initial guidance for programmatic interventions intending to reduce the burden of SMM and, subsequently, maternal mortality in the US South.

Racial differences in the association of undetectable HIV viral load and transportation to an HIV provider among men who have sex with men in Atlanta, Georgia: a health equity perspective.

There are inequities in HIV outcomes among Black gay, bisexual, and other sexual minority men who have sex with men (GBMSM) compared to GBMSM overall, including access to transportation to HIV care. It is unclear if the relationship between transportation and clinical outcomes extends to viral load. We assessed the relationship between transportation dependence to an HIV provider and undetectable viral load among Black and White GBMSM in Atlanta. We collected transportation and viral load information from GBMSM with HIV from 2016-2017 (n = 345). More Black than White GBMSM had a detectable viral load (25% vs. 15%) and took dependent (e.g. public) transportation (37% vs. 18%). Independent (e.g. car) transportation was associated with undetectable viral load for White GBMSM (cOR 3.61, 95% CI 1.45, 8.97) but was attenuated by income (aOR. 2.29, 95% CI 0.78, 6.71), and not associated for Black GBMSM (cOR 1.18, 95% CI 0.58, 2.24). One possible explanation for no association for Black GBMSM is that there are more competing barriers to HIV care for Black GBMSM than White GBMSM. Further investigation is needed to confirm whether 1) transportation is unimportant for Black GBMSM or 2) transportation interacts with additional factors not considered in this analysis.

Assessing the association of stigma and HIV service and prevention uptake among men who have sex with men and transgender women in South Africa.

HIV prevention for gay, bisexual, and other men who have sex with men (GBMSM) and transgender women (TGW) is critical to reducing health disparities and population HIV prevalence. To understand if different types of stigma impact engagement with HIV prevention services, we assessed associations between stigmas and use of HIV prevention services offered through an HIV prevention intervention. This analysis included 201 GBMSM and TGW enrolled in a prospective cohort offering a package of HIV prevention interventions. Participants completed a baseline survey that included four domains of sexual identity/behavior stigma, HIV-related stigma, and healthcare stigma. Impact of stigma on PrEP uptake and the number of drop-in visits was assessed. No domain of stigma was associated with PrEP uptake. In bivariate analysis, increased enacted sexual identity stigma increased number of drop-in visits. In a logistic regression analysis constrained to sexual identity stigma, enacted stigma was associated with increased drop-in visits (aIRR = 1.30, [95% CI: 1.02, 1.65]). Participants reporting higher enacted stigma were modestly more likely to attend additional services and have contact with the study clinics and staff. GBMSM and TGW with higher levels of enacted stigma may seek out sensitized care after negative experiences in their communities or other healthcare settings.

Combining crowd-sourcing, census data, and public review forums for real-time, high-resolution food desert estimation.

BACKGROUND: It has been hypothesized that low access to healthy and nutritious food increases health disparities. Low-accessibility areas, called food deserts, are particularly commonplace in lower-income neighborhoods. The metrics for measuring the food environment's health, called food desert indices, are primarily based on decadal census data, limiting their frequency and geographical resolution to that of the census. We aimed to create a food desert index with finer geographic resolution than census data and better responsiveness to environmental changes. MATERIALS AND METHODS: We augmented decadal census data with real-time data from platforms such as Yelp and Google Maps and crowd-sourced answers to questionnaires by the Amazon Mechanical Turks to create a real-time, context-aware, and geographically refined food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better food environments. RESULTS: We made 139,000 pull requests to Yelp, analyzing 15,000 unique food retailers in the metro Atlanta area. In addition, we performed 248,000 walking and driving route analyses on these retailers using Google Maps' API. As a result, we discovered that the metro Atlanta food environment creates a strong bias towards eating out rather than preparing a meal at home when access to vehicles is limited. Contrary to the food desert index that we started with, which changed values only at neighborhood boundaries, the food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the environment that occurred after the census data was collected. CONCLUSIONS: Research on the environmental components of health disparities is flourishing. New machine learning models have the potential to augment various information sources and create fine-tuned models of the environment. This opens the way to better understanding the environment and its effects on health and suggesting better interventions.

A dose-response relationship found between the Ten Steps to Successful Breastfeeding indicators and in-hospital exclusive breastfeeding in US hospitals.

BACKGROUND: In-hospital exclusive breastfeeding (EBF) is associated with longer breastfeeding durations, yet only 64% of US newborns are EBF for 7 days. The Ten Steps to Successful Breastfeeding (Ten Steps) are a set of evidenced-based maternity practices shown to improve breastfeeding outcomes; these were updated in 2018. METHODS: Using hospital-level data from the 2018 Maternity Practices in Infant Nutrition and Care Survey (n = 2045 hospitals), we examined the prevalence of implementation of Ten Steps indicators (each step and total number of steps implemented). Using linear regression, we also examined the association between the steps and EBF prevalence adjusted for hospital characteristics and all other steps. Discharge support was not included in the models since it primarily occurs after hospital discharge. RESULTS: The most frequently implemented step was the provision of prenatal breastfeeding education (95.6%). Steps with low implementation included rooming-in (18.9%), facility policies supportive of breastfeeding (23.4%), and limited formula supplementation (28.2%). After adjusting for hospital characteristics and all other steps, limited formula supplementation (difference = 14.4: 95% confidence interval [CI]: 12.6, 16.1), prenatal breastfeeding education (difference = 7.0; 95% CI: 3.3, 10.8), responsive feeding (difference = 6.3; 95% CI: 3.7, 9.0), care right after birth (skin-to-skin; difference = 5.8; 95% CI: 4.2, 7.4), and rooming-in (difference = 2.4; 95% CI: 0.4, 4.6) were associated with higher in-hospital EBF prevalence. We found a dose-response relationship between the number of steps implemented and in-hospital EBF prevalence. CONCLUSION: Increased implementation of the updated Ten Steps may improve EBF and infant and maternal health outcomes.

Maternal health equity in Georgia: a Delphi consensus approach to definition and research priorities.

BACKGROUND: Pregnancy-related mortality in the United States is the greatest among all high-income countries, and Georgia has one of the highest maternal mortality rates-almost twice the national rate. Furthermore, inequities exist in rates of pregnancy-related deaths. In Georgia, non-Hispanic Black women are nearly 3 times more likely to die from pregnancy-related complications than non-Hispanic White women. Unlike health equity, a clear definition of maternal health equity is lacking, overall and in Georgia specifically, but is needed to reach consensus and align stakeholders for action. Therefore, we used a modified Delphi method to define maternal health equity in Georgia and to determine research priorities based on gaps in understanding of maternal health in Georgia. METHODS: Thirteen expert members of the Georgia Maternal Health Research for Action Steering Committee (GMHRA-SC) participated in an iterative, consensus-driven, modified Delphi study comprised of 3 rounds of anonymous surveys. In round 1 (web-based survey), experts generated open-ended concepts of maternal health equity and listed research priorities. In rounds 2 (web-based meeting) and 3 (web-based survey), the definition and research priorities suggested during round 1 were categorized into concepts for ranking based on relevance, importance, and feasibility. Final concepts were subjected to a conventional content analysis to identify general themes. RESULTS: The consensus definition of maternal health equity created after undergoing the Delphi method is: maternal health equity is the ultimate goal and ongoing process of ensuring optimal perinatal experiences and outcomes for everyone as the result of practices and policies free of interpersonal or structural bias that tackle current and historical injustices, including social, structural, and political determinants of health impacting the perinatal period and life course. This definition highlights addressing the current and historical injustices manifested in the social determinants of health, and the structural and political structures that impact the perinatal experience. CONCLUSION: The maternal health equity definition and identified research priorities will guide the GMHRA-SC and the broader maternal health community for research, practice, and advocacy in Georgia.

Inconsistent Reporting of Discrimination Over Time Using the Experiences of Discrimination Scale: Potential Underestimation of Lifetime Burden.

Studies documenting self-reported experiences of discrimination over the life course have been limited. Such information could be important for informing longitudinal epidemiologic studies of discrimination and health. We characterized trends in self-reports of racial, socioeconomic status, and gender discrimination over time measured using the Experiences of Discrimination Scale, with a focus on whether individuals' reports of lifetime discrimination were consistent over time. Overall experiences of discrimination and the number of settings in which discrimination was reported in 1992, 2000, and 2010 were examined among 2,774 African-American and White adults in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Reports of "ever" experiencing discrimination decreased for all forms of discrimination across the 3 study visits. Approximately one-third (30%-41%) of the sample inconsistently reported ever experiencing any discrimination over time, which contributed to the observed decreases. Depending on the form of discrimination, inconsistent reporting patterns over time were more common among African-American, younger, less educated, and lower-income individuals and women-groups who are often most exposed to and severely impacted by the health effects of discrimination. Our findings highlight the possible underestimation of the lifetime burden of discrimination when utilizing the Experiences of Discrimination Scale to capture self-reports of discrimination over time.

Have Paved Trails and Protected Bike Lanes Led to More Bicycling in Atlanta?: A Generalized Synthetic-Control Analysis.

BACKGROUND: Bicycling is an important form of physical activity in populations. Research assessing the effect of infrastructure on bicycling with high-resolution smartphone data is emerging in several places, but it remains limited in low-bicycling US settings, including the Southeastern US. The Atlanta area has been expanding its bicycle infrastructure, including off-street paved trails such as the Atlanta BeltLine and some protected bike lanes. METHODS: Using the generalized synthetic-control method, we estimated effects of five groups of off-street paved trails and protected bike lanes on bicycle ridership in their corresponding areas. To measure bicycling, we used 2 years (October 1, 2016 to September 30, 2018) of monthly Strava data in Atlanta's urban core along with data from 15 on-the-ground counters to adjust for spatiotemporal variation in app use. RESULTS: Considering all infrastructure as one joint intervention, an estimated 1.10 (95% confidence interval [CI]: 0.99, 1.18) times more bicycle-distance was ridden than would have been expected in the same areas had the infrastructure not been built, when defining treatment areas by the narrower of two definitions (defined in text). The Atlanta BeltLine Westside Trail and Proctor Creek Greenway had especially strong effect estimates, e.g., ratios of 1.45 (95% CI: 1.12, 1.86) and 1.55 (1.10, 2.14) under each treatment-area definition, respectively. We estimated that other infrastructure had weaker positive or no effects on bicycle-distance ridden. CONCLUSIONS: This study advances research on the topic because of its setting in the US Southeast, simultaneous assessment of several infrastructure groups, and data-driven approach to estimating effects. See video abstract at, http://links.lww.com/EDE/B936.

A Cluster-based Method to Quantify Individual Heterogeneity in Tuberculosis Transmission.

BACKGROUND: Recent evidence suggests transmission of Mycobacterium tuberculosis (Mtb) may be characterized by extreme individual heterogeneity in secondary cases (i.e., few cases account for the majority of transmission). Such heterogeneity implies outbreaks are rarer but more extensive and has profound implications in infectious disease control. However, discrete person-to-person transmission events in tuberculosis (TB) are often unobserved, precluding our ability to directly quantify individual heterogeneity in TB epidemiology. METHODS: We used a modified negative binomial branching process model to quantify the extent of individual heterogeneity using only observed transmission cluster size distribution data (i.e., the simple sum of all cases in a transmission chain) without knowledge of individual-level transmission events. The negative binomial parameter k quantifies the extent of individual heterogeneity (generally, indicates extensive heterogeneity, and as transmission becomes more homogenous). We validated the robustness of the inference procedure considering common limitations affecting cluster size data. Finally, we demonstrate the epidemiologic utility of this method by applying it to aggregate US molecular surveillance data from the US Centers for Disease Control and Prevention. RESULTS: The cluster-based method reliably inferred k using TB transmission cluster data despite a high degree of bias introduced into the model. We found that the TB transmission in the United States was characterized by a high propensity for extensive outbreaks (; 95% confidence interval = 0.09, 0.10). CONCLUSIONS: The proposed method can accurately quantify critical parameters that govern TB transmission using simple, more easily obtainable cluster data to improve our understanding of TB epidemiology.

Does a Rising Median Income Lift All Birth Weights? County Median Income Changes and Low Birth Weight Rates Among Births to Black and White Mothers.

Policy Points Policies that increase county income levels, particularly for middle-income households, may reduce low birth weight rates and shrink disparities between Black and White infants. Given the role of aggregate maternal characteristics in predicting low birth weight rates, policies that increase human capital investments (e.g., funding for higher education, job training) could lead to higher income levels while improving population birth outcomes. The association between county income levels and racial disparities in low birth weight is independent of disparities in maternal risks, and thus a broad set of policies aimed at increasing income levels (e.g., income supplements, labor protections) may be warranted. CONTEXT: Low birth weight (LBW; <2,500 grams) and infant mortality rates vary among place and racial group in the United States, with economic resources being a likely fundamental contributor to these disparities. The goals of this study were to examine time-varying county median income as a predictor of LBW rates and Black-White LBW disparities and to test county prevalence and racial disparities in maternal sociodemographic and health risk factors as mediators. METHODS: Using national birth records for 1992-2014 from the National Center for Health Statistics, a total of approximately 27.4 million singleton births to non-Hispanic Black and White mothers were included. Data were aggregated in three-year county-period observations for 868 US counties meeting eligibility requirements (n = 3,723 observations). Sociodemographic factors included rates of low maternal education, nonmarital childbearing, teenage pregnancy, and advanced-age pregnancy; and health factors included rates of smoking during pregnancy and inadequate prenatal care. Among other covariates, linear models included county and period fixed effects and unemployment, poverty, and income inequality. FINDINGS: An increase of $10,000 in county median income was associated with 0.34 fewer LBW cases per 100 live births and smaller Black-White LBW disparities of 0.58 per 100 births. Time-varying county rates of maternal sociodemographic and health risks mediated the association between median income and LBW, accounting for 65% and 25% of this estimate, respectively, but racial disparities in risk factors did not mediate the income association with Black-White LBW disparities. Similarly, county median income was associated with very low birth weight rates and related Black-White disparities. CONCLUSIONS: Efforts to increase income levels-for example, through investing in human capital, enacting labor union protections, or attracting well-paying employment-have broad potential to influence population reproductive health. Higher income levels may reduce LBW rates and lead to more equitable outcomes between Black and White mothers.

Intermediate or Intensive Care Unit Admission across Race and Ethnicity.

OBJECTIVES: The aim of this study was to assess the association between race and ethnicity and admission to intermediate (IMCUs) or intensive care units (ICUs) among hospitalized patients. METHODS: Using Florida hospital discharge data from the Agency for Healthcare Research and Quality-sponsored State Inpatient Database in 2017, we assessed the relationship between race (White, Black, Other) and Hispanic ethnicity and IMCU or ICU admission. Demographic covariates included age, sex, quartile of household income for patient ZIP code, insurance status, and patient residence. An adjusted model assessed the association between race and ethnicity and IMCU or ICU admission using log binomial regression with generalized estimating equations after controlling for demographic characteristics and the Elixhauser Comorbidity Index. RESULTS: After controlling for demographics and comorbidities, the prevalence of IMCU or ICU admission was higher among non-Hispanic Blacks (adjusted prevalence ratio [aPR] 1.04; 95% confidence interval [CI] 1.02-1.05) and non-Hispanic patients of other races (aPR 1.03; 95% CI 1.01-1.04) compared with non-Hispanic Whites. The prevalence of IMCU or ICU use was lower among Hispanic Whites (aPR 0.98; 95% CI 0.86-1.00) and Hispanics of other races (aPR 0.96; 95% CI 0.95-0.98) compared with non-Hispanic Whites after controlling for other demographic characteristics and comorbidities. CONCLUSIONS: Among hospitalized patients, racial minorities are slightly more likely to use higher levels of care, whereas Hispanic patients are generally slightly less likely than non-Hispanic White patients to use higher levels of care. Further evaluation is needed to identify reasons for disparate IMCU or ICU admission.

A nation-wide study on the common reasons for infant formula supplementation among healthy, term, breastfed infants in US hospitals.

In-hospital infant formula supplementation of breastfed infants reduces breastfeeding duration, yet little is known about common reasons for infant formula supplementation. We examined the three most common reasons for in-hospital infant formula supplementation of healthy, term, breastfed infants in the US reported by hospital staff. Hospital data were obtained from the 2018 Maternity Practices in Infant Nutrition and Care survey (n = 2045), which is completed by hospital staff. An open-ended question on the top three reasons for in-hospital infant formula supplementation was analyzed using thematic qualitative analysis and the frequencies for each reason were reported. The top three most common reasons for in-hospital infant formula supplementation reported by hospital staff included medical indications (70.0%); maternal request/preference/feelings (55.9%); lactation management-related issues (51.3%); physical but non-medically indicated reasons (36.1%); social influences (18.8%); perceived cultural/societal/demographic factors (8.2%) and medical staff/institutional practices (4.7%). These findings suggest that a variety of factors should be considered to address unnecessary infant formula supplementation. Lactation management support delivered in a timely and culturally sensitive manner and targeted to mother-infant dyads with potential medical and physical indications may reduce unnecessary in-hospital infant formula supplementation.

At-risk-measure Sampling in Case-Control Studies with Aggregated Data.

Transient exposures are difficult to measure in epidemiologic studies, especially when both the status of being at risk for an outcome and the exposure change over time and space, as when measuring built-environment risk on transportation injury. Contemporary "big data" generated by mobile sensors can improve measurement of transient exposures. Exposure information generated by these devices typically only samples the experience of the target cohort, so a case-control framework may be useful. However, for anonymity, the data may not be available by individual, precluding a case-crossover approach. We present a method called at-risk-measure sampling. Its goal is to estimate the denominator of an incidence rate ratio (exposed to unexposed measure of the at-risk experience) given an aggregated summary of the at-risk measure from a cohort. Rather than sampling individuals or locations, the method samples the measure of the at-risk experience. Specifically, the method as presented samples person-distance and person-events summarized by location. It is illustrated with data from a mobile app used to record bicycling. The method extends an established case-control sampling principle: sample the at-risk experience of a cohort study such that the sampled exposure distribution approximates that of the cohort. It is distinct from density sampling in that the sample remains in the form of the at-risk measure, which may be continuous, such as person-time or person-distance. This aspect may be both logistically and statistically efficient if such a sample is already available, for example from big-data sources like aggregated mobile-sensor data.