Assessing Prenatal Depression and Childhood Overweight

Background: The prenatal environment is recognized as an important part of fetal development with serious implications for health in to the future. Epidemiological evidence suggests there is a deleterious biological link between prenatal depression and birth weight via the HPA axis, although results have been mixed. Little evidence exists on behavioral aspects determining prenatal depression that explain an association with birth weight. Study Question: Do three factors - child wantedness, utility change theory, and endogeneity of expected child health - explain prenatal depression and its association on child birth weight? Methods: Data were obtained from the NLSY 1979. 2448 women aged 14-37 who were pregnant (1380 women) or had recently given birth were sampled from the years when depression scores were available (1979-1994). Scores were normalized to the Center for Epidemiologic Studies-Depression 1992 scale. Child wantedness measures were collected to distinguish between possible differences in women who were pregnant versus those who had given birth before surveyed. Utility change theory measures the stable preferences assumption that inconsistencies between risky behaviors in a woman\u27s youth (sex, drinking) and her preference for risky behaviors in pregnancy (smoking, later prenatal care) might predict depression. These measures are included in a two-stage least squares model that tests the impact of prenatal depression on birth weight using the sub-sample of women surveyed when pregnant. Results: The endogeneity of expected birth weight on prenatal depression is negative and marginally significant (p\u3c.07) when accounting for two different preference reversals of pregnancy behaviors as well as time between these reversals. Both preference reversals are related to prenatal depression scores (p\u3c.12), although have opposite effects. Women with a greater wantedness also have a lower prenatal depression score. In both preference reversals with n\u3e600, prenatal depression has an unexpectedly positive, small and significant (p\u3c.05) impact on birth weight. Conclusion: When modeling prenatal depression, child wantedness, preference reversals, and the endogeneity of expected child birth weight significantly impact prenatal depression, which positively affects birth weight. Consideration of a woman\u27s experiences in youth as well as her discounting of health in the future can significantly affect birth outcomes. Public Health Implications: Because expected child health is endogenous to prenatal depression, the impact of the fetal origins hypothesis in 1995 on birth weight needs to be further investigated. Kyle Fluegge is a Ph.D. Student in Agricultural, Development, and Environmental Economics at the Ohio State University in Columbus, Ohio, USA. His research interests in health economics intersect a great deal with epidemiology and public health. He earned a BA in economics from the University of Michigan in 2007 and an MA in 2008 in economics at the Ohio State University

Air pollution and risk of hospitalization for epilepsy: the role of farm use of nitrogen fertilizers and emissions of the agricultural air pollutant, nitrous oxide

ABSTRACT The link between various air pollutants and hospitalization for epilepsy has come under scrutiny. We have proposed that exposure to air pollution and specifically the pervasive agricultural air pollutant and greenhouse gas, nitrous oxide (N2O), may provoke susceptibility to neurodevelopmental disorders. Evidence supports a role of N2O exposure in reducing epileptiform seizure activity, while withdrawal from the drug has been shown to induce seizure-like activity. Therefore, we show here that the statewide use of anthropogenic nitrogen fertilizers (the most recognized causal contributor to environmental N2O burden) is significantly negatively associated with hospitalization for epilepsy in all three pre-specified hospitalization categories, even after multiple pollutant comparison correction (p<.007), while the other identified pollutants were not consistently statistically significantly associated with hospitalization for epilepsy. We discuss potential neurological mechanisms underpinning this association between air pollutants associated with farm use of anthropogenic nitrogen fertilizers and hospitalization for epilepsy

Glyphosate Use Predicts ADHD Hospital Discharges in the Healthcare Cost and Utilization Project Net (HCUPnet): A Two-Way Fixed-Effects Analysis - Fig 3

<div><p>(A) Average glyphosate use (in kilograms) in the HCUPNET states in Model 1 (Nevada and Hawaii excluded). Data are presented as average percentages with 95% confidence interval across all reporting states for each year indicated (2006–2009). (B) Average all-listed hospital discharges for ADHD, as a percent of total all-listed mental health discharges, in HCUPNET reporting states (excluding Nevada and Hawaii) for years 2007–2010 used in Model 1.</p> <p>Data are presented as average percentages with 95% confidence interval across all reporting states for each year indicated.</p></div

A global schematic proposing an indirect mechanism for how glyphosate-based herbicides may be contributing to the rise of ADHD during the period, 2007 to 2010.

<p>Abbreviations: N₂O –nitrous oxide; GnG–greenhouse gassing; AOB–ammonia-oxidizing bacteria; AOA–ammonia-oxidizing archaea; ADHD–attention-deficit hyperactivity disorder.</p

Description of the covariate used, with a citation to explain the inclusion, in both the initial OLS screening and the fixed effects regression analysis for Models 1 and 2.

<p>Description of the covariate used, with a citation to explain the inclusion, in both the initial OLS screening and the fixed effects regression analysis for Models 1 and 2.</p

The empirical schematic for studying the association between glyphosate use and ADHD.

<p>The empirical schematic for studying the association between glyphosate use and ADHD.</p

Diagnostic plots for Model 1 show marked heteroskedasticity within the center of distribution.

<p>(A) Observed all-listed ADHD percent discharges (<i>N</i>_<i>1</i> = 127) plotted against predicted percentages (B) Model residuals were plotted against fitted values. Fitted values were tabulated in <i>R</i> by subtracting residuals from observed values.</p