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Despite being one of the wealthiest nations, disparities in adverse birth outcomes persist across racial and ethnic lines in the United States. We studied the association between historical redlining and preterm birth, low birth weight (LBW), small-for-gestational age (SGA), and perinatal mortality over a ten-year period (2006-2015) in Los Angeles, Oakland, and San Francisco, California. We used birth outcomes data from the California Office of Statewide Health Planning and Development between January 1, 2006 and December 31, 2015. Home Owners' Loan Corporation (HOLC) Security Maps developed in the 1930s assigned neighborhoods one of four grades that pertained to perceived investment risk of borrowers from that neighborhood: green (grade A) were considered \"Best\", blue (grade B) \"Still Desirable\", yellow (grade C) \"Definitely Declining\", and red (grade D, hence the term \"redlining\") \"Hazardous\". Geocoded residential addresses at the time of birth were superimposed on HOLC Security Maps to assign each birth a HOLC grade. We adjusted for potential confounders present at the time of Security Map creation by assigning HOLC polygons areal-weighted 1940s Census measures. We then employed propensity score matching methods to estimate the association of historical HOLC grades on current birth outcomes. Because tracts graded A had almost no propensity of receiving grade C or D and because grade B tracts had low propensity of receiving grade D, we examined birth outcomes in the three following comparisons: B vs. A, C vs. B, and D vs. C. The prevalence of preterm birth, SGA and mortality tended to be higher in worse HOLC grades, while the prevalence of LBW varied across grades. Overall odds of mortality and preterm birth increased as HOLC grade worsened. Propensity score matching balanced 1940s census measures across contrasting groups. Logistic regression models revealed significantly elevated odds of preterm birth (odds ratio (OR): 1.02, 95% confidence interval (CI): 1.00-1.05), and SGA (OR: 1.03, 95% CI: 1.00-1.05) in the C vs. B comparison and significantly reduced odds of preterm birth (OR: 0.93, 95% CI: 0.91-0.95), LBW (OR: 0.94-95% CI: 0.92-0.97), and SGA (OR: 0.94, 95% CI: 0.92-0.96) in the D vs. C comparison. Results differed by metropolitan area and maternal race. Similar to prior studies on redlining, we found that worsening HOLC grade was associated with adverse birth outcomes, although this relationship was less clear after propensity score matching and stratifying by metropolitan area. Higher odds of preterm birth and SGA in grade C versus grade B neighborhoods may be caused by higher-stress environments, racial segregation, and lack of access to resources, while lower odds of preterm birth, SGA, and LBW in grade D versus grade C neighborhoods may due to population shifts in those neighborhoods related to gentrification.

Objectives. We examined the separate and combined relations of neighborhood-level social norms and collective efficacy with individuals’ cigarette smoking cessation. Methods. We modeled the hazard of quitting over a 5-year period among 863 smokers who participated in the 2005 New York Social Environment Study. Results. In adjusted Cox proportional hazard models, prohibitive neighborhood smoking norms were significantly associated with higher rates of smoking cessation (second quartile hazard ratio [HR] = 1.17; 95% confidence interval [CI] = 0.59, 2.32; third quartile HR = 2.37; 95% CI = 1.17, 4.78; fourth quartile HR = 1.80; 95% CI = 0.85, 3.81). We did not find a significant association between neighborhood collective efficacy and cessation or significant evidence of a joint relation of collective efficacy and smoking norms with cessation. Conclusions. Neighborhood social norms may be more relevant than is collective efficacy to smoking cessation. The normative environment may shape health behavior and should be considered as part of public health intervention efforts.

Background The Deferred Action for Childhood Arrivals (DACA) program provides temporary relief from deportation and work permits for previously undocumented immigrants who arrived as children. DACA faced direct threats under the Trump administration. There is select evidence of the short-term impacts of DACA on population health, including on birth outcomes, but limited understanding of the long-term impacts. Methods We evaluated the association between DACA program and birth outcomes using California birth certificate data (2009–2018) and a difference-in-differences approach to compare post-DACA birth outcomes for likely DACA-eligible mothers to birth outcomes for demographically similar DACA-ineligible mothers. We also separately compared birth outcomes by DACA eligibility status in the first 3 years after DACA passage (2012–2015) and in the subsequent 3 years (2015–2018) - a period characterized by direct threats to the DACA program - as compared to outcomes in the years prior to DACA passage. Results In the 7 years after its passage, DACA was associated with a lower risk of small-for-gestational age (− 0.018, 95% CI: − 0.035, − 0.002) and greater birthweight (45.8 g, 95% CI: 11.9, 79.7) for births to Mexican-origin individuals that were billed to Medicaid. Estimates were consistent but of smaller magnitude for other subgroups. Associations between DACA and birth outcomes were attenuated to the null in the period that began with the announcement of the Trump U.S. Presidential campaign (2015-2018), although confidence intervals overlapped with estimates from the immediate post-DACA period. Conclusions These findings suggest weak to modest initial benefits of DACA for select birthweight outcomes during the period immediately following DACA passage for Mexican-born individuals whose births were billed to Medicaid; any benefits were subsequently attenuated to the null. The benefits of DACA for population health may not have been sufficient to counteract the impacts of threats to the program's future and heightened immigration enforcement occurring in parallel over time.

Background The largest poverty alleviation program in the US is the earned income tax credit (EITC), providing $60 billion to over 25 million families annually. While research has shown positive impacts of EITC receipt in pregnancy, there is little evidence on whether the timing of receipt may lead to differences in pregnancy outcomes. We used a quasi-experimental difference-in-differences design, taking advantage of EITC tax disbursement each spring to examine whether trimester of receipt was associated with perinatal outcomes. Methods We conducted a difference-in-differences analysis of California linked birth certificate and hospital discharge records. The sample was drawn from the linked CA birth certificate and discharge records from 2007–2012 ( N  = 2,740,707). To predict eligibility, we created a probabilistic algorithm in the Panel Study of Income Dynamics and applied it to the CA data. Primary outcome measures included preterm birth, small-for-gestational age (SGA), gestational diabetes, and gestational hypertension/preeclampsia. Results Eligibility for EITC receipt during the third trimester was associated with a lower risk of preterm birth compared with preconception. Eligibility for receipt in the preconception period resulted in improved gestational hypertension and SGA. Conclusion This analysis offers a novel method to impute EITC eligibility using a probabilistic algorithm in a data set with richer sociodemographic information relative to the clinical and administrative data sets from which outcomes are drawn. These results could be used to determine the optimal intervention time point for future income supplementation policies. Future work should examine frequent income supplementation such as the minimum wage or basic income programs.

Scholarly literature claims that health declines in populations when optimism about investing in the future wanes. This claim leads us to describe collective optimism as a predictor of selection in utero. Based on the literature, we argue that the incidence of suicide gauges collective optimism in a population and therefore willingness to invest in the future. Using monthly data from Sweden for the years 1973–2016, we test the hypothesis that the incidence of suicide among women of child-bearing age correlates inversely with male twin births, an indicator of biological investment in high-risk gestations. We find that, as predicted by our theory, the incidence of suicide at month t varies inversely with the ratio of twin to singleton male births at month t + 3. Our results illustrate the likely sensitivity of selection in utero to change in the social environment and so the potential for viewing collective optimism as a component of public health infrastructure.

BACKGROUND:Childhood adversities may play a key role in the onset of mental disorders and influence patterns by race/ethnicity. We examined the relations between childhood adversities and mental disorders by race/ethnicity in the National Comorbidity Survey-Adolescent Supplement. METHODS:Using targeted maximum likelihood estimation, a rigorous and flexible estimation procedure, we estimated the relationship of each adversity with mental disorders (behavior, distress, fear, and substance use), and estimated the distribution of disorders by race/ethnicity in the absence of adversities. Targeted maximum likelihood estimation addresses the challenge of a multidimensional exposure such as a set of adversities because it facilitates “learning” from the data the strength of the relationships between each adversity and outcome, incorporating any interactions or nonlinearity, specific to each racial/ethnic group. Cross-validation is used to select the best model without over fitting. RESULTS:Among adversities, physical abuse, emotional abuse, and sexual abuse had the strongest associations with mental disorders. Of all outcomes, behavior disorders were most strongly associated with adversities. Our comparisons of observed prevalences of mental disorders to estimates in the absence of adversities suggest lower prevalences of behavior disorders across all racial/ethnic groups. Estimates for distress disorders and substance use disorders varied in magnitude among groups, but some estimates were imprecise. Interestingly, results suggest that the adversities examined here do not play a major role in patterns of racial/ethnic differences in mental disorders. CONCLUSIONS:Although causal interpretation relies on assumptions, growing work on this topic suggests childhood adversities play an important role in mental disorder development in adolescents.

Background Identifying preterm infants at risk for mortality or major morbidity traditionally relies on gestational age, birth weight, and other clinical characteristics that offer underwhelming utility. We sought to determine whether a newborn metabolic vulnerability profile at birth can be used to evaluate risk for neonatal mortality and major morbidity in preterm infants. Methods This was a population-based retrospective cohort study of preterm infants born between 2005 and 2011 in California. We created a newborn metabolic vulnerability profile wherein maternal/infant characteristics along with routine newborn screening metabolites were evaluated for their association with neonatal mortality or major morbidity. Results Nine thousand six hundred and thirty-nine (9.2%) preterm infants experienced mortality or at least one complication. Six characteristics and 19 metabolites were included in the final metabolic vulnerability model. The model demonstrated exceptional performance for the composite outcome of mortality or any major morbidity (AUC 0.923 (95% CI: 0.917–0.929). Performance was maintained across mortality and morbidity subgroups (AUCs 0.893–0.979). Conclusions Metabolites measured as part of routine newborn screening can be used to create a metabolic vulnerability profile. These findings lay the foundation for targeted clinical monitoring and further investigation of biological pathways that may increase the risk of neonatal death or major complications in infants born preterm. Impact We built a newborn metabolic vulnerability profile that could identify preterm infants at risk for major morbidity and mortality. Identifying high-risk infants by this method is novel to the field and outperforms models currently in use that rely primarily on infant characteristics. Utilizing the newborn metabolic vulnerability profile for precision clinical monitoring and targeted investigation of etiologic pathways could lead to reductions in the incidence and severity of major morbidities associated with preterm birth.

To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.

Background Few studies have explored the relationship between air pollution and fertility. We used a natural experiment in California when coal and oil power plants retired to estimate associations with nearby fertility rates. Methods We used a difference-in-differences negative binomial model on the incident rate ratio scale to analyze the change in annual fertility rates among California mothers living within 0-5 km and 5-10 km of 8 retired power plants between 2001 and 2011. The difference-in-differences method isolates the portion of the pre- versus post-retirement contrast in the 0-5 km and 5-10 km bins, respectively, that is due to retirement rather than secular trends. We controlled for secular trends with mothers living 10-20 km away. Adjusted models included fixed effects for power plant, proportion Hispanic, Black, high school educated, and aged > 30 years mothers, and neighborhood poverty and educational attainment. Results Analyses included 58,909 live births. In adjusted models, we estimated that after power plant retirement annual fertility rates per 1000 women aged 15–44 years increased by 8 births within 5 km and 2 births within 5-10 km of power plants, corresponding to incident rate ratios of 1.2 (95% CI: 1.1–1.4) and 1.1 (95% CI: 1.0–1.2), respectively. We implemented a negative exposure control by randomly selecting power plants that did not retire and repeating our analysis with those locations using the retirement dates from original 8 power plants. There was no association, suggesting that statewide temporal trends may not account for results. Conclusions Fertility rates among nearby populations appeared to increase after coal and oil power plant retirements. Our study design limited the possibility that our findings resulted from temporal trends or changes in population composition. These results require confirmation in other populations, given known methodological limitations of ecologic study designs.

Background Knowledge on how well we can predict primary postpartum hemorrhage (PPH) can help policy makers and health providers design current delivery protocols and PPH case management. The purpose of this paper is to identify risk factors and determine predictive probabilities of those risk factors for primary PPH among women expecting singleton vaginal deliveries in Egypt. Methods From a prospective cohort study, 2510 pregnant women were recruited over a six-month period in Egypt in 2004. PPH was defined as blood loss ≥ 500 ml. Measures of blood loss were made every 20 minutes for the first 4 hours after delivery using a calibrated under the buttocks drape. Using all variables available in the patients' charts, we divided them in ante-partum and intra-partum factors. We employed logistic regression to analyze socio-demographic, medical and past obstetric history, and labor and delivery outcomes as potential PPH risk factors. Post-model predicted probabilities were estimated using the identified risk factors. Results We found a total of 93 cases of primary PPH. In multivariate models, ante-partum hemoglobin, history of previous PPH, labor augmentation and prolonged labor were significantly associated with PPH. Post model probability estimates showed that even among women with three or more risk factors, PPH could only be predicted in 10% of the cases. Conclusions The predictive probability of ante-partum and intra-partum risk factors for PPH is very low. Prevention of PPH to all women is highly recommended.