Cross-State Differences in the Processes Generating Black–White Disparities in Neonatal Mortality

The U.S. Black neonatal mortality rate is more than twice the White rate. This dramatic disparity can be decomposed into two components: (1) disparities due to differences in the distribution of birth weights, and (2) disparities due to differences in birth weight–specific mortality. I utilize this distinction to explore how the social context into which infants are born contributes to gaps in mortality between Black and White neonates. I analyze variation in Black–White differences in neonatal mortality across 33 states using 1995–2010 data. For each state, I calculate the contribution of differences in birth weight distribution versus differences in birth weight–specific mortality to the total disparity in mortality between White and Black neonates. Disparities are largely a product of different birth weight distributions between Black and White newborns (mirroring the pattern for the United States as a whole). However, in at least nine states, differences in birth weight–specific mortality make a notable contribution. This pattern is observed even among those from advantaged sociodemographic backgrounds and is driven by differences in mortality among very low birth weight neonates. This calls attention to inequality in medical care at birth as an importantcontributor to racial disparities in neonatal mortality.

Birth weight charts for a Chinese population: an observational study of routine newborn weight data from Chongqing

Background To construct birth weight charts for the Chongqing municipality, China and to identify whether differences in birth weight exist across urban/rural populations, thereby warranting separate charts. Methods Secondary analysis of routinely collected data from 338,454 live infants between 2014 and 2017 in Chongqing municipality. Sex-specific birth weight-for-gestational age centiles were constructed by the lambda-mu-sigma method via the GAMLSS R-based package. This method remodels the skewed birth weight distribution to estimate a normal distribution, allowing any birth weight centile to be generated. A separate set of centiles were created, accounting for urban/rural differences in birth weight. Results The centiles performed well across all gestational ages. For example, 2.37% (n = 4176) of males and 2.26% (n = 3656) of females were classified as below the 2nd centile (expected percentage = 2.28%), 49.75% of males (n = 87,756) and 50.73% of females (n = 82,203) were classified as below the 50th centile (expected proportion = 50%) and 97.52% of males (n = 172,021) and 97.48% of females (n = 157,967) were classified as below the 98th centile (expected proportion = 97.72%). The overall estimated centiles of birth weight for rural infants were higher than the centiles for urban infants at the earlier gestational ages (< 37 gestational weeks). However, this trend was reversed in infants born at term. Conclusion We have constructed a readily utilizable set of birth weight references from a large representative sample of births in Chongqing. The method used to construct the references allows for the calculation of the exact centile for any infant delivered between 28 and 42 completed weeks, which was not possible with previous charts.

MODELLING THE RISK FACTORS FOR BIRTH WEIGHT IN TWIN GESTATIONS: A QUANTILE REGRESSION APPROACH

SummaryBirth weight is used as a proxy for the general health condition of newborns. Low birth weight leads to adverse events and its effects on child growth are both short- and long-term. Low birth weight babies are more common in twin gestations. The aim of this study was to assess the effects of maternal and socio-demographic risk factors at various quantiles of the birth weight distribution for twin gestations using quantile regression, a robust semi-parametric technique. Birth records of multiple pregnancies from between 1991 and 2005 were identified retrospectively from the birth registry of the Christian Medical College and hospitals in Vellore, India. A total of 1304 twin pregnancies were included in the analysis. Demographic and clinical characteristics of the mothers were analysed. The mean gestational age of the twins was 36 weeks with 51% having preterm labour. As expected, the examined risk factors showed different effects at different parts of the birth weight distribution. Gestational age, chroniocity, gravida and child’s sex had significant effects in all quantiles. Interestingly, mother’s age had no significant effect at any part of the birth weight distribution, but both maternal and paternal education had huge impacts in the lower quantiles (10thand 25th), which were underestimated by the ordinary least squares (OLS) estimates. The study shows that quantile regression is a useful method for risk factor analysis and the exploration of the differential effects of covariates on an outcome, and exposes how OLS estimates underestimate and overestimate the effects of risk factors at different parts of the birth weight distribution.

Leisure-Time Physical Activity in Pregnancy and the Birth Weight Distribution: Where Is the Effect?

Background:Leisure-time physical activity (LTPA) is recommended during pregnancy and has been associated with lower risk of delivering a large infant. We sought to characterize the effect of LTPA across the entire birth weight distribution.Methods:Women enrolled in the Pregnancy Outcomes and Community Health (POUCH) Study (1998–2004) were followed-up in 2007. Follow-up efforts were extensive for a subcohort and minimal for the remainder (nonsubcohort). Thus, 596 subcohort and 418 nonsubcohort women who delivered at term participated. Offspring were categorized as small-, appropriate-, or large-for-gestational-age (SGA, AGA, and LGA, respectively) based on gender and gestational age-specific birth weight z-scores (BWz). At follow-up, women recalled pregnancy LTPA and were classified as inactive, insufficiently active or meeting LTPA recommendations. Linear, logistic, and quantile regression analyses were conducted separately by subcohort status.Results:Meeting LTPA recommendations decreased odds of LGA significantly among the nonsubcohort (aOR = 0.30, 95% CI: 0.14–0.64) and nonsignificantly among the subcohort (aOR = 0.68, 95% CI: 0.34–1.34). In quantile regression, meeting LTPA recommendations reduced BWz among the upper quantiles in the nonsubcohort.Conclusions:LTPA during pregnancy lowered odds of LGA and reduced BWz among the upper quantiles, without shifting the entire distribution. LTPA during pregnancy may be useful for reducing risks of large fetal size.

Small-for-Gestational-Age Births in Pregnant Women with HIV, due to Severity of HIV Disease, Not Antiretroviral Therapy

Objectives. To determine rate and factors associated with small-for-gestational-age (SGA) births to women with HIV.Methods. Prospective data were collected from 183 pregnant women with HIV in an urban HIV prenatal clinic, 2000–2011. An SGA birth was defined as less than the 10th or 3rd percentile of birth weight distribution based upon cut points developed using national vital record data. Bivariate analysis utilized chi-squared andt-tests, and multiple logistic regression analyses were used.Results. The prevalence of SGA was 31.2% at the 10th and 12.6% at the 3rd percentile. SGA at the 10th (OR 2.77; 95% CI, 1.28–5.97) and 3rd (OR 3.64; 95% CI, 1.12–11.76) percentiles was associated with cigarette smoking. Women with CD4 count >200 cells/mm3at the first prenatal visit were less likely to have an SGA birth at the 3rd percentile (OR 0.29; 95% CI, 0.10–0.86). Women taking NNRTI were less likely to have an SGA infant at the 10th (OR 0.28; 95% CI, 0.10–0.75) and 3rd (OR 0.16; 95% CI, 0.03–0.91) percentiles compared to those women on PIs.Conclusions. In this cohort with high rates of SGA, severity of HIV disease, not ART, was associated with SGA births after adjusting for sociodemographic, medication, and disease severity.

Separated at Girth: US Twin Estimates of the Effects of Birth Weight

The fetal origins hypothesis asserts that nutrient deprivation in utero can raise chronic disease risk. Within economics, this hypothesis has gained acceptance as a leading explanation for the correlations between birth weight, a proxy for fetal nutrient intake, and adult outcomes. Exploiting birth-weight differences between twins using (a) a newly-created dataset of twins from 1960–1982 California birth records and (b) the Early Childhood Longitudinal Study Birth Cohort, I find birth weight is related to educational attainment, later pregnancy complications, and the birth weight of the next generation. These effects are generally small. However, the protective effects of birth weight vary across the birth-weight distribution. (JEL: I12, I21, J13)