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Objective: This case-cohort study estimated associations between gestational weight gain (GWG) and small-for-gestational-age (SGA) and large-for-gestational-age (LGA) births stratified by obesity class (I: 30-34.9 kg/m 2 ; II: 35-39.9 kg/m 2 ; III: ≥40 kg/m 2 ) (Magee-Womens Hospital, Pittsburgh, Pennsylvania, 1998-2011)., Methods: First-trimester GWG was categorized as being below (<0.2 kg), within (0.2-2.0 kg), or above (>2.0 kg) the Institute of Medicine recommendations. For second- and third-trimester GWG, four linear trajectories were derived: approximating maintenance (slope -0.05 ± 0.03 kg/wk), approximating the recommendations (0.27 ± 0.01 kg/wk; reference), higher than the recommendations (0.54 ± 0.01 kg/wk), and highest among those above the recommendations (0.91 ± 0.02 kg/wk)., Results: For classes I, II, and III, respectively, there were 1290, 1247, and 1198 pregnancies in the subcohort; 262, 171, and 123 SGA cases; and 353, 286, and 257 LGA cases. First-trimester GWG was not associated with SGA/LGA births. Second- and third-trimester weight maintenance was associated with potentially lower LGA risk (risk ratio [RR]: 0.80; 95% confidence interval [CI]: 0.55-1.1) but not higher SGA risk (RR: 0.98; 95% CI: 0.64-1.5) for class III. In addition, some sensitivity analyses supported no increased SGA risk with second- and third-trimester weight maintenance for classes I and II., Conclusions: Second- and third-trimester weight maintenance may be associated with more optimal birth weight for gestational age. However, how this could be achieved (e.g., through diet and exercise interventions) is unclear, given the observational design of our study., (© 2023 The Obesity Society.)

Background: Associations between pregnancy weight gain and adverse outcomes may be spurious owing to confounding by factors not typically measured in cohort studies. We determined the extent to which the addition of detailed behavioral, psychosocial, and environmental measurements to commonly available covariates improved control of confounding., Methods: We used data from a prospective US pregnancy cohort study (2010-2013, n = 8978). We calculated two propensity scores for low and high pregnancy weight gain (vs. adequate gain) using 11 standard confounders (e.g., age and education). We examined the balance of characteristics between weight gain groups before and after propensity score matching. We used negative binomial regression to estimate the association between weight gain and small- and large-for-gestational-age birth, preterm birth, and unplanned cesarean delivery, controlling for propensity score. To this model, we then added 17 detailed behavioral, psychosocial, and environmental measurements ("fully adjusted"). We calculated the risk ratio owing to confounding as the ratio of the standard confounder-adjusted risk ratio to the fully adjusted risk ratio., Results: There were minimal imbalances between weight gain groups in detailed measures after matching for a propensity score of standard covariates. Accordingly, the inclusion of detailed covariates had minimal impact on estimated associations between low or high pregnancy weight gain and adverse pregnancy outcomes: risk ratios owing to confounding were null for all outcomes (e.g., 1.1 [95% CI = 1.0, 1.1] for low weight gain and preterm birth)., Conclusions: Adjustment for detailed behavioral, psychosocial, and environmental measurements had minimal impact on estimated associations between pregnancy weight gain and adverse perinatal outcomes., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Background: The Institute of Medicine has published national recommendations for optimal pregnancy weight gain ranges for singletons and twins but not for higher-order multiples. A common clinical resource suggests weight gain targets for triplet pregnancies, but they are based on a single, small study conducted over 20 years ago., Objective: We sought to describe contemporary maternal weight gain patterns in triplet gestations in the United States, the weight gain patterns associated with good neonatal outcomes, and how these patterns compare with those of healthy twin pregnancies., Study Design: We used data from 7705 triplet pregnancies drawn from the United States live birth and fetal death files (2012‒2018). We calculated total pregnancy weight gain as weight at delivery minus the prepregnancy weight. A good neonatal outcome was defined as delivery at ≥32 weeks' gestation of 3 liveborn infants weighing ≥1500 g with 5-minute Apgar scores of ≥3. We described the weight gain patterns of triplet pregnancies with good neonatal outcomes by calculating week-specific percentiles of the total weight gain distribution for deliveries at 32 to 37 weeks' gestation. For comparative purposes, we plotted these values against the percentiles of a previously published weight gain chart for monitoring and evaluating twin pregnancies from a referent cohort., Results: Most participants were over weight (26%) or obese (30%), and 42% were normal weight or underweight. The 50th percentile (25th-75th) of total weight gain in triplet pregnancies was 17 (11-23) kg. As the body mass index category increased, the total weight gain declined: underweight or normal weight, median 19 (14-25) kg; overweight, 17 (12-23) kg; obese, 14 (7.7-20) kg. Approximately 46% of triplet pregnancies had a good neonatal outcome (n=3562). For underweight or normal weight triplet pregnancies with good neonatal outcomes, the 50th percentiles of weight gain at 32 weeks' and 36 weeks' gestation were 12.3 kg and 22.7 kg, respectively. The 10th and 90th percentiles were 12.3 kg and 32.7 kg, respectively, at 32 weeks, and 15.0 kg and 34.1 kg, respectively, at 36 weeks. Triplet pregnancies with prepregnancy overweight or obesity and a good neonatal outcome had lower weight gains. Compared with the reference values for pregnancy weight gain from a twin-specific weight gain chart, the median total weight gain in triplet pregnancies with good neonatal outcomes was approximately 3 to 5 kg more than twins, regardless of body mass index., Conclusion: Our study fills an important gap in understanding how much weight gain can be expected among triplet pregnancies by body mass index category. These descriptive data are a necessary first step to inform science-based triplet gestational weight gain guidelines. Additional research is needed to determine whether monitoring triplet pregnancy weight gain is useful for promoting healthy outcomes for pregnant individuals and children and what targets should be used to optimize maternal and neonatal health., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The Dietary Guidelines for Americans rely on summaries of the effect of dietary pattern on disease risk, independent of other population characteristics. We explored the modifying effect of prepregnancy body mass index (BMI; weight (kg)/height (m)2) on the relationship between fruit and vegetable density (cup-equivalents/1,000 kcal) and preeclampsia using data from a pregnancy cohort study conducted at 8 US medical centers (n = 9,412; 2010-2013). Usual daily periconceptional intake of total fruits and total vegetables was estimated from a food frequency questionnaire. We quantified the effects of diets with a high density of fruits (≥1.2 cups/1,000 kcal/day vs. <1.2 cups/1,000 kcal/day) and vegetables (≥1.3 cups/1,000 kcal/day vs. <1.3 cups/1,000 kcal/day) on preeclampsia risk, conditional on BMI, using a doubly robust estimator implemented in 2 stages. We found that the protective association of higher fruit density declined approximately linearly from a BMI of 20 to a BMI of 32, by 0.25 cases per 100 women per each BMI unit, and then flattened. The protective association of higher vegetable density strengthened in a linear fashion, by 0.3 cases per 100 women for every unit increase in BMI, up to a BMI of 30, where it plateaued. Dietary patterns with a high periconceptional density of fruits and vegetables appear more protective against preeclampsia for women with higher BMI than for leaner women., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Gestational diabetes might be more common in twin versus singleton pregnancies, yet the reasons for this are unclear. We evaluated the extent to which this relationship is explained by higher mid-pregnancy weight gain within normal weight and overweight pre-pregnancy body mass index (BMI) strata., Methods: We analyzed serial weights and glucose screening and diagnostic data abstracted from medical charts for twin (n = 1397) and singleton (n = 3117) pregnancies with normal or overweight pre-pregnancy BMI delivered from 1998 to 2013 at Magee-Womens Hospital in Pennsylvania. We used causal mediation analyses to estimate the total effect of twin versus singleton pregnancy on gestational diabetes, as well as those mediated (natural indirect effect) and not mediated (natural and controlled direct effects) by pathways involving mid-pregnancy weight gain., Results: Odds of gestational diabetes were higher among twin pregnancies [odds ratios (ORs) for total effect = 2.83 (95% CI = 1.54, 5.19) for normal weight and 2.09 (95% CI = 1.16, 3.75) for overweight pre pregnancy BMI], yet there was limited evidence that this relationship was mediated by mid-pregnancy weight gain [ORs for natural indirect effect = 1.21 (95% CI = 0.90, 1.24) for normal weight and 1.06 (95% CI = 0.92, 1.21) for overweight pre-pregnancy BMI] and more evidence of mediation via other pathways [ORs for natural direct effect = 2.34 (95% CI = 1.24, 4.40) for normal weight and 1.97 (95% CI = 1.08, 3.60) for overweight pre-pregnancy BMI]., Conclusions: While twin pregnancies with normal weight or overweight pre-pregnancy BMI experienced higher odds of gestational diabetes versus singletons, most of this effect was explained by pathways not involving mid-pregnancy weight gain., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

The average causal effect compares counterfactual outcomes if everyone had been exposed versus if everyone had been unexposed, which can be an unrealistic contrast. Alternatively, we can target effects that compare counterfactual outcomes against the factual outcomes observed in the sample (i.e., we can compare against the natural course). Here, we demonstrate how the natural course can be estimated and used in causal analyses for model validation and effect estimation. Our example is an analysis assessing the impact of taking aspirin on pregnancy, 26 weeks after randomization, in the Effects of Aspirin in Gestation and Reproduction trial (United States, 2006-2012). To validate our models, we estimated the natural course using g-computation and then compared that against the observed incidence of pregnancy. We observed good agreement between the observed and model-based natural courses. We then estimated an effect that compared the natural course against the scenario in which participants assigned to aspirin always complied. If participants had always complied, there would have been 5.0 (95% confidence interval: 2.2, 7.8) more pregnancies per 100 women than was observed. It is good practice to estimate the natural course for model validation when using parametric models, but whether one should estimate a natural course contrast depends on the underlying research questions., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Effect measure modification is often evaluated using parametric models. These models, although efficient when correctly specified, make strong parametric assumptions. While nonparametric models avoid important functional form assumptions, they often require larger samples to achieve a given accuracy. We conducted a simulation study to evaluate performance tradeoffs between correctly specified parametric and nonparametric models to detect effect modification of a binary exposure by both binary and continuous modifiers. We evaluated generalized linear models and doubly robust (DR) estimators, with and without sample splitting. Continuous modifiers were modeled with cubic splines, fractional polynomials, and nonparametric DR-learner. For binary modifiers, generalized linear models showed the greatest power to detect effect modification, ranging from 0.42 to 1.00 in the worst and best scenario, respectively. Augmented inverse probability weighting had the lowest power, with an increase of 23% when using sample splitting. For continuous modifiers, the DR-learner was comparable to flexible parametric models in capturing quadratic and nonlinear monotonic functions. However, for nonlinear, nonmonotonic functions, the DR-learner had lower integrated bias than splines and fractional polynomials, with values of 141.3, 251.7, and 209.0, respectively. Our findings suggest comparable performance between nonparametric and correctly specified parametric models in evaluating effect modification., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Severe maternal morbidity (SMM) is an important maternal health indicator, but existing tools to identify SMM have substantial limitations. Our objective was to retrospectively identify true SMM status using ensemble machine learning in a hospital database and to compare machine learning algorithm performance with existing tools for SMM identification., Methods: We screened all deliveries occurring at Magee-Womens Hospital, Pittsburgh, PA (2010-2011 and 2013-2017) using the Centers for Disease Control and Prevention list of diagnoses and procedures for SMM, intensive care unit admission, and/or prolonged postpartum length of stay. We performed a detailed medical record review to confirm case status. We trained ensemble machine learning (SuperLearner) algorithms, which "stack" predictions from multiple algorithms to obtain optimal predictions, on 171 SMM cases and 506 non-cases from 2010 to 2011, then evaluated the performance of these algorithms on 160 SMM cases and 337 non-cases from 2013 to 2017., Results: Some SuperLearner algorithms performed better than existing screening criteria in terms of positive predictive value (0.77 vs. 0.64, respectively) and balanced accuracy (0.99 vs. 0.86, respectively). However, they did not perform as well as the screening criteria in terms of true-positive detection rate (0.008 vs. 0.32, respectively) and performed similarly in terms of negative predictive value. The most important predictor variables were intensive care unit admission and prolonged postpartum length of stay., Conclusions: Ensemble machine learning did not globally improve the ascertainment of true SMM cases. Our results suggest that accurate identification of SMM likely will remain a challenge in the absence of a universal definition of SMM or national obstetric surveillance systems., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW, a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM, npcausal, tmle, and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Gestational diabetes in singleton pregnancies increases the risk for large for gestational age infants, hypertensive disorders of pregnancy, and neonatal morbidity. Compared with singleton gestations, twin gestations are at increased risk for fetal growth abnormalities, hypertensive disorders, and neonatal morbidity. Whether gestational diabetes further increases the risk for these outcomes is unclear., Objective: We sought to determine the relationship between gestational diabetes and the risk for preeclampsia, fetal growth abnormalities, and neonatal intensive care unit admissions in a large cohort of women with twin pregnancies., Study Design: We used a retrospective cohort of all twin gestations that were delivered at our institution from 1998 to 2013. We excluded pregnancies delivered before 24 weeks' gestation, monochorionic-monoamniotic twins, and patients with preexisting diabetes for a final cohort of 2573 twin deliveries. Gestational diabetes was defined as 2 abnormal values on a 100 g, 3-hour glucose challenge test as defined by the Carpenter-Coustan criteria or a 1-hour value of 200 mg/dL after a 50 g glucose test. Multivariable Poisson regression models were used to estimate the associations between gestational diabetes and preeclampsia, small for gestational age infants, large for gestational age infants, and admission to the neonatal intensive care unit after adjusting for prepregnancy body mass index, maternal race, maternal age, parity, use of in vitro fertilization, prepregnancy smoking status, and chronic hypertension as confounders., Results: The unadjusted incidence of gestational diabetes was 6.5% (n=167). Women with gestational diabetes were more likely to be aged 35 years or older, living with obesity, and have conceived via in vitro fertilization than women without gestational diabetes. Preeclampsia was more common among women with twin pregnancies complicated by gestational diabetes (31%) than among women with twin pregnancies without gestational diabetes (18%) (adjusted risk ratio, 1.5; 95% confidence interval, 1.1-2.1). A diagnosis of small for gestational age infant was less common among women with gestational diabetes (17%) than among women without gestational diabetes (24%), although the results were imprecise (adjusted risk ratio, 0.8; 95% confidence interval, 0.5-1.1). There was no association between gestational diabetes and the incidence of large for gestational age neonates or neonatal intensive care unit admissions. Among women with gestational diabetes who reached 35 weeks' gestation, 62% (n=60) required medical management., Conclusion: Gestational diabetes is a risk factor for preeclampsia among women with twin pregnancies. Close blood pressure monitoring and patient education are critical for this high-risk group. The association between gestational diabetes and neonatal outcomes among women with twin pregnancies is less precise, although it may reduce the incidence of small for gestational age infants. Prospective studies to determine if glycemic control decreases the risk for preeclampsia in twin pregnancies with gestational diabetes are needed., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Abstract: Recently the National Academies of Sciences, Engineering, and Medicine reported a summary of recommendations from high-income countries on what and how to feed infants and children from birth to 2 years of age. The report notes generally consistent recommendations against providing sugar-sweetened beverages (SSB), juice, and beverages that contain nonnutritive sweeteners to infants and children younger than 2 years. When counselling parents and other caregivers, pediatricians should feel empowered to advise against offering these beverages., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 by European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition.)

Background: Current pregnancy weight gain guidelines were developed based on implicit assumptions of a small group of experts about the relative seriousness of adverse health outcomes. Therefore, they will not necessarily reflect the values of women., Objective: To estimate the seriousness of 11 maternal and child health outcomes that have been consistently associated with pregnancy weight gain by engaging patients and health professionals., Methods: We collected data using an online panel approach with a modified Delphi structure. We selected a purposeful sample of maternal and child health professionals (n = 84) and women who were pregnant or recently postpartum (patients) (n = 82) in the United States as panellists. We conducted three concurrent panels: professionals only, patients only, and patients and professionals. During a 3-round online modified Delphi process, participants rated the seriousness of health outcomes (Round 1), reviewed and discussed the initial results (Round 2), and revised their original ratings (Round 3). Panellists assigned seriousness ratings (0, [not serious] to 100 [most serious]) for infant death, stillbirth, preterm birth, gestational diabetes, preeclampsia, small-for-gestational-age (SGA) birth, large-for-gestational-age (LGA) birth, unplanned caesarean delivery, maternal obesity, childhood obesity, and maternal metabolic syndrome., Results: Each panel individually came to a consensus on all seriousness ratings. The final median seriousness ratings combined across all panels were highest for infant death (100), stillbirth (95), preterm birth (80), and preeclampsia (80). Obesity in children, metabolic syndrome in women, obesity in women, and gestational diabetes had median seriousness ratings ranging from 55 to 65. The lowest seriousness ratings were for SGA birth, LGA birth, and unplanned caesarean delivery (30-40)., Conclusion: Professionals and women rate some adverse outcomes as being more serious than others. These ratings can be used to establish the range of pregnancy weight gain associated with the lowest risk of a broad range of maternal and child health outcomes., (© 2020 John Wiley & Sons Ltd.)

Objective: Current guidelines for maternal weight gain in twin pregnancy were established in the absence of evidence on its longer-term consequences for maternal and child health. We evaluated the association between weight gain in twin pregnancies and the risk of excess maternal postpartum weight increase, childhood obesity, and child cognitive ability., Methods: We used 5-year follow-up data from 1000 twins born to 450 mothers in the Early Childhood Longitudinal Study-Birth Cohort, a nationally representative U.S. cohort of births in 2001. Pregnancy weight gain was standardized into gestational age- and prepregnancy body mass index (BMI)-specific z-scores. Excess postpartum weight increase was defined as ≥10 kg increase from prepregnancy weight. We defined child overweight/obesity as BMI ≥ 85th percentile, and low reading and math achievement as scores one standard deviation below the mean. We used survey-weighted multivariable modified Poisson models with a log link to relate gestational weight gain z-score with each outcome., Results: Excess postpartum weight increase occurred in 40% of mothers. Approximately 28% of twins were affected by overweight/obesity, and 16 and 14% had low reading and low math scores. There was a positive linear relationship between pregnancy weight gain and both excess postpartum weight increase and childhood overweight/obesity. Compared with a gestational weight gain z-score 0 SD (equivalent to 20 kg at 37 weeks gestation), a weight gain z-score of +1 SD (27 kg) was associated with 6.3 (0.71, 12) cases of excess weight increase per 1000 women and 4.5 (0.81, 8.2) excess cases of child overweight/obesity per 100 twins. Gestational weight gain was not related to kindergarten academic readiness., Conclusions: The high prevalence of excess postpartum weight increase and childhood overweight/obesity within the recommended ranges of gestational weight gain for twin pregnancies suggests that these guidelines could be inadvertently contributing to longer-term maternal and child obesity.

When causal inference is of primary interest, a range of target parameters can be chosen to define the causal effect, such as average treatment effects (ATEs). However, ATEs may not always align with the research question at hand. Furthermore, the assumptions needed to interpret estimates as ATEs, such as exchangeability, consistency, and positivity, are often not met. Here, we present the incremental propensity score (PS) approach to quantify the effect of shifting each person's exposure propensity by some predetermined amount. Compared with the ATE, incremental PS may better reflect the impact of certain policy interventions and do not require that positivity hold. Using the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b), we quantified the relationship between total vegetable intake and the risk of preeclampsia and compared it to average treatment effect estimates. The ATE estimates suggested a reduction of between two and three preeclampsia cases per 100 pregnancies for consuming at least half a cup of vegetables per 1,000 kcal. However, positivity violations obfuscate the interpretation of these results. In contrast, shifting each woman's exposure propensity by odds ratios ranging from 0.20 to 5.0 yielded no difference in the risk of preeclampsia. Our analyses show the utility of the incremental PS effects in addressing public health questions with fewer assumptions., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Expert groups recommend that women set a pregnancy weight gain goal with their care provider to optimise weight gain., Objective: Our aim was to describe the concordance between first-trimester personal and provider pregnancy weight gain goals with the Institute of Medicine (IOM) recommendations and to determine the association between these goals and total weight gain., Methods: We used data from 9353 women in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be. In the first trimester, women reported their personal pregnancy weight gain goal and their provider weight gain goal, and we categorised personal and provider weight gain goals and total weight gain according to IOM recommendations. We used log-binomial or linear regression models to relate goals to total weight gain, adjusting for confounders including race/ethnicity, maternal age, education, smoking, marital status and planned pregnancy., Results: Approximately 37% of women reported no weight gain goals, while 24% had personal and provider goals, 31% had only a personal goal, and 8% had only a provider goal. Personal and provider goals were outside the recommended ranges in 12%-23% of normal-weight women, 31%-41% of overweight women and 47%-63% of women with obesity. Women with both personal and provider pregnancy weight gain goals were 6%-14% more likely than their counterparts to have a goal within IOM-recommended ranges. Having any goal or a goal within the IOM-recommended ranges was unrelated to pregnancy weight gain. Excessive weight gain occurred in approximately half of normal-weight or obese women and three-quarters of overweight women, regardless of goal setting group., Conclusions: These findings do not support the effectiveness of early-pregnancy personal or provider gestational weight gain goal setting alone in optimising weight gain. Multifaceted interventions that address a number of mediators of goal setting success may assist women in achieving weight gain consistent with their goals., (© 2020 John Wiley & Sons Ltd.)

Information bias is common in epidemiology and can substantially diminish the validity of study results. Validation studies, in which an investigator compares the accuracy of a measure with a gold standard measure, are an important way to understand and mitigate this bias. More attention is being paid to the importance of validation studies in recent years, yet they remain rare in epidemiologic research and, in our experience, they remain poorly understood. Many epidemiologists have not had any experience with validations studies, either in the classroom or in their work. We present an example of misclassification of a dichotomous exposure to elucidate some important misunderstandings about how to conduct validation studies to generate valid information. We demonstrate that careful attention to the design of validation studies is central to determining how the bias parameters (e.g. sensitivity and specificity or positive and negative predictive values) can be used in quantitative bias analyses to appropriately correct for misclassification. Whether sampling is done based on the true gold standard measure, the misclassified measure or at random will determine which parameters are valid and the precision of those estimates. Whether or not the validation is done stratified by other key variables (e.g. by the exposure) will also determine the validity of those estimates. We also present sample questions that can be used to teach these concepts. Increasing the presence of validation studies in the classroom could have a positive impact on their use and improve the validity of estimates of effect in epidemiologic research., (© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Epidemiologic research on severe maternal morbidity often relies on a screening definition of the outcome because a gold standard approach requires medical record review., Objective: To determine the validity of screening or identification criteria to classify cases of severe maternal morbidity using the definition of severe maternal morbidity proposed by the American College of Obstetricians and Gynecologists (ACOG)., Methods: From all singleton deliveries at Magee-Womens Hospital in Pittsburgh, Pennsylvania (2010-2011; n = 19 307), we selected all deliveries that had at least one screening or identification criteria for severe maternal morbidity (n = 349) and a random sample of deliveries with no case identification criteria (n = 349). Screen-positive deliveries were a delivery with any of the following: Centers for Disease Control and Prevention International Classification of Diseases 9th Revision diagnosis and procedure codes for the identification of severe maternal morbidity; prolonged post-partum length of stay; or maternal intensive care unit admission. We identified true cases through detailed chart review using the suggested diagnoses in the 2016 ACOG and SMFM Obstetric Care Consensus on severe maternal morbidity. We calculated the positive and negative predictive values of the screening criteria., Results: Approximately 1.8% of deliveries screened positive for severe maternal morbidity. After medical record review, 166 screen-positive deliveries were true cases (48% positive predictive value), and 347 screen-negative cases were true negatives (99% negative predictive value). Two screen-negative cases were false negatives. If we applied the negative predictive value to the population, 109 true cases would be missed with these criteria., Conclusion: The criteria we used to identify potential cases of severe acute maternal morbidity had poor performance in our cohort. In the absence of resources to apply the gold standard outcome definition to a large population, validation data and analytic strategies that incorporate measurement error are essential to estimate the direction and magnitude of the resulting bias., (© 2020 John Wiley & Sons Ltd.)

Background: Maternal weight gain during pregnancy is one of the few potentially modifiable risk factors for many adverse maternal and child health outcomes. Defining the optimal pregnancy weight gain range is difficult because, while lower weight gain may prevent some outcomes, such as maternal and child obesity, it may increase the risk of others such as fetal growth restriction and infant death. These health outcomes vary in their seriousness to mothers and their health care providers, and these differences in seriousness should be taken into account when determining optimal weight gain ranges. However, the relative seriousness that women and their care providers place on different health outcomes is unknown., Objective: We will determine the seriousness of 11 maternal and child health outcomes that have been consistently associated with pregnancy weight gain. We will achieve this by engaging patients and maternal and child health professionals using an online modified Delphi panel process., Methods: We aim to recruit a racially/ethnically and geographically diverse group of 90 US maternal and child health professionals and 90 women who are pregnant or less than 2 years postpartum. We will conduct 3 concurrent panels using the ExpertLens system, a previously evaluated online modified Delphi system that combines 2 rounds of rating with 1 round of feedback and moderated online discussion. In Round 1, panelists are asked to rate the seriousness of each health outcome on a scale of 0-100 and to provide a rationale for their scores. In Round 2, panelists will review their responses relative to those of other panelists. They will discuss their seriousness ratings anonymously using a moderated online discussion board. In Round 3, participants will revise their Round 1 responses based on group feedback and discussion. Each round will be open for 1-2 weeks., Results: The study protocol was reviewed by our ethics boards and did not require approval as human research. A pilot study of 6 professionals and 7 patients was completed in December 2019., Conclusions: Our numeric estimates of the seriousness of maternal and child health outcomes will enable future studies to determine pregnancy weight gain ranges that balance the risks of low and high weight gain for mothers and children., International Registered Report Identifier (irrid): DERR1-10.2196/16478., (©Lisa M Bodnar, Dmitry Khodyakov, Katherine P Himes, Jessica G Burke, Sara Parisi, Jennifer A Hutcheon. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 02.06.2020.)

Background: Conventional analytic approaches for studying diet patterns assume no dietary synergy, which can lead to bias if incorrectly modeled. Machine learning algorithms can overcome these limitations., Objectives: We estimated associations between fruit and vegetable intake relative to total energy intake and adverse pregnancy outcomes using targeted maximum likelihood estimation (TMLE) paired with the ensemble machine learning algorithm Super Learner, and compared these with results generated from multivariable logistic regression., Methods: We used data from 7572 women in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be. Usual daily periconceptional intake of total fruits and total vegetables was estimated from an FFQ. We calculated the marginal risk of preterm birth, small-for-gestational-age (SGA) birth, gestational diabetes, and pre-eclampsia according to density of fruits and vegetables (cups/1000 kcal) ≥80th percentile compared with <80th percentile using multivariable logistic regression and Super Learner with TMLE. Models were adjusted for confounders, including other Healthy Eating Index-2010 components., Results: Using logistic regression, higher fruit and high vegetable densities were associated with 1.1% and 1.4% reductions in pre-eclampsia risk compared with lower densities, respectively. They were not associated with the 3 other outcomes. Using Super Learner with TMLE, high fruit and vegetable densities were associated with fewer cases of preterm birth (-4.0; 95% CI: -4.9, -3.0 and -3.7; 95% CI: -5.0, -2.3), SGA (-1.7; 95% CI: -2.9, -0.51 and -3.8; 95% CI: -5.0, -2.5), and pre-eclampsia (-3.2; 95% CI: -4.2, -2.2 and -4.0; 95% CI: -5.2, -2.7) per 100 births, respectively, and high vegetable densities were associated with a 0.9% increase in risk of gestational diabetes., Conclusions: The differences in results between Super Learner with TMLE and logistic regression suggest that dietary synergy, which is accounted for in machine learning, may play a role in pregnancy outcomes. This innovative methodology for analyzing dietary data has the potential to advance the study of diet patterns., (Copyright © The Author(s) on behalf of the American Society for Nutrition 2020.)

Background: Literature is divided regarding the risk of neonatal ventricular septal defect (VSD) associated with first trimester ondansetron use in pregnancy., Methods: We evaluated the risk of VSD associated with first trimester exposure to intravenous or oral ondansetron in 33 677 deliveries at Magee-Womens Hospital in Pittsburgh, PA (2006-2014). Using log-binomial regression, we evaluated the risk: (1) in the full cohort, (2) using propensity score designs with both matching and inverse probability weighting and (3) utilizing clustered trajectory analysis evaluating the role of dose. Sensitivity analyses assessed the association between ondansetron and all recorded birth defects in aggregate., Results: A total of 3733 (11%) pregnancies were exposed to ondansetron in the first trimester (dose range: 2.4-1008 mg). Ondansetron was associated with increased risk of VSD with risk ratios ranging from 1.7 [95% confidence interval (CI) 1.0-2.9] to 2.1 (95% CI 1.1-4.0) across methods. Risks correspond to one additional VSD for approximately every 330 pregnancies exposed in the first trimester. The association was dose-dependent with increased risk in women receiving highest cumulative doses compared with lowest doses [adjusted risk ratio: 3.2 (95% CI 1.0-9.9)]. The association between ondansetron and congenital malformations was diluted as the outcome included additional birth defects., Conclusions: First trimester ondansetron use is associated with an increased risk of neonatal VSD potentially driven by higher doses. This risk should be viewed in the context of risks attributable to severe untreated nausea and vomiting of pregnancy., (© The Author(s) 2019; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Objective: To identify the association of newly developed prepregnancy overweight and obesity with stillbirth and infant mortality., Methods: We studied subsequent pregnancies of mothers who were normal weight at fertilization of their first identified pregnancy, from a population-based cohort that linked birth registry with death records in Pennsylvania, 2003-2013. Women with newly developed prepregnancy overweight and obesity were defined as those whose body mass index (BMI) before second pregnancy was between 25 and 29.9 or 30 or higher, respectively. Our main outcomes of interest were stillbirth (intrauterine death at 20 weeks of gestation or greater), infant mortality (less than 365 days after birth), neonatal death (less than 28 days after birth) and postneonatal death (29-365 days after birth). Associations of both prepregnancy BMI categories and continuous BMI with each outcome were estimated by nonparametric targeted minimum loss-based estimation and inverse-probability weighted dose-response curves, respectively, adjusting for race-ethnicity, smoking, and other confounders (eg, age, education)., Results: A cohort of 212,889 women were included for infant mortality analysis (192,941 women for stillbirth analysis). The crude rate of stillbirth and infant mortality in these final analytic cohorts were 3.3 per 1,000 pregnancies and 2.9 per 1,000 live births, respectively. Compared with women who stayed at a normal weight in their second pregnancies, those becoming overweight had 1.4 (95% CI 0.6-2.1) excess stillbirths per 1,000 pregnancies. Those becoming obese had 3.6 (95% CI 1.3-5.9) excess stillbirths per 1,000 pregnancies and 2.4 (95% CI 0.4-4.4) excess neonatal deaths per 1,000 live births. There was a dose-response relationship between prepregnancy BMI increases of more than 2 units and increased risk of stillbirth and infant mortality. In addition, BMI increases were associated with higher risks of infant mortality among women with shorter interpregnancy intervals (less than 18 months) compared with longer intervals., Conclusion: Transitioning from normal weight to overweight or obese between pregnancies was associated with an increased risk of stillbirth and neonatal mortality.

Background: Severe maternal morbidity is an important proxy for maternal mortality. Population attributable fraction is the proportion of a disease that is attributable to a given risk factor and can be used to estimate the reduction in the disease that would be anticipated if a risk factor was reduced or eliminated., Objective: We sought to determine the population-attributable fraction (PAF) of potentially modifiable risk factors for severe maternal morbidity., Study Design: We used a retrospective cohort of 86,260 delivery hospitalizations from Magee-Womens Hospital, Pittsburgh, PA for this analysis (2003-2012). Severe maternal morbidity was defined as any of the following: Centers for Disease Control and Prevention International Classification of Diseases 9 th Revision diagnosis and procedure codes for the identification of maternal morbidity; prolonged postpartum length of stay (defined as >3 standard deviations beyond the mean length of stay: >3 days for vaginal deliveries and >5 days for Cesarean deliveries); or maternal intensive care unit admission. We used multivariable logistic regression with generalized estimating equations to estimate the association of prepregnancy overweight or obesity, maternal age ≥35 years, preexisting hypertension, preexisting diabetes, excessive gestational weight gain, smoking, education, and marital status with severe maternal morbidity. We then calculated the PAF for each risk factor. We also examined the impact of modest reductions and elimination of risk factors on the PAF of severe maternal morbidity., Results: The overall rate of severe maternal morbidity was 2.0%. Overweight and obesity, maternal age ≥35 years, preexisting hypertension, excessive gestational weight gain, and lack of a college degree had PAF ranging from 4.5% to 13%. If all risk factors were eliminated, 36% of cases could have been prevented. Modest reductions in the prevalence of excessive BMI and advanced maternal age had minimal impact on preventing severe maternal morbidity. Smoking during pregnancy and marital status were not associated with severe maternal morbidity., Conclusions: Our data suggest maternal morbidity can be reduced by modifying common, individual-level risk factors. Nevertheless, the majority of cases were not attributable to the patient level risk factors we examined. These data support the need for large studies of patient-, provider-, system- and population-level factors to identify high-impact interventions to reduce maternal morbidity., Competing Interests: The authors report no conflict of interests.

Competing Interests: Conflict-of-Interest Disclosures: Dr. Siega-Riz’s reports have received funding by the National Institute of Child Health and Human Development (NICHD) for her work related to obesity and gestational weight gain from NICHD specifically from a contract for Diet, Obesity, and Weight Change in Pregnancy. In addition, she has served as a scientific advisory board member for Happy Families, a subsidiary company of Danone, and is a board of trustees member for the International Food Information Council. Dr. Bodnar reports grants from the National Institutes of Health. All other authors have no conflicts to disclose.

Purpose: Inversed probability weighted (IPW) estimators are commonly used to adjust for time-fixed or time-varying confounders. However, in high-dimensional settings, including all identified confounders may result in unstable weights leading to higher variance. We aimed to develop a visualization tool demonstrating the impact of each confounder on the bias and variance of IPW estimates, as well as the propensity score overlap., Methods: A SAS macro was developed for this visualization tool and we demonstrate how this tool can be used to identify potentially problematic confounders of the association of statin use after myocardial infarction on one-year mortality in a plasmode simulation study using a cohort of 39,792 patients from the UK (1998-2012)., Results: Through the tool's output, we can identify problematic confounders (two instrumental variables) and important confounders by comparing the estimated psuedo MSE with that from the fully adjusted model and propensity score overlap plot., Conclusion: Our results suggest that the analytic impact of all confounders should be considered carefully when fitting IPW estimators., (Copyright © 2019. Published by Elsevier Inc.)

Objective: To evaluate the association between gestational weight gain in twin pregnancies and small-for-gestational-age (SGA) and large-for-gestational-age (LGA) birth, preterm birth before 32 weeks of gestation, cesarean delivery, and infant death within each prepregnancy body mass index (BMI) category., Methods: Data in this population-based study came from Pennsylvania-linked infant birth and death records (2003-2013). We studied 54,836 twins born alive before 39 weeks of gestation. Total pregnancy weight gain (kg) was converted to gestational age-standardized z scores. Multivariable modified Poisson regression models stratified by prepregnancy BMI were used to estimate associations between z scores and outcomes. A probabilistic bias analysis, informed by an internal validation study, evaluated the effect of BMI and weight gain misclassification., Results: Gestational weight gain z score was negatively associated with SGA and positively associated with LGA and cesarean delivery in all BMI groups. The relation between weight gain and preterm birth was U-shaped in nonobese women. An increased risk of infant death was observed for very low weight gain among normal-weight women and for high weight gain among women without obesity. Most excess risks of these outcomes were observed at weight gains at 37 weeks of gestation that are equivalent to less than 14 kg or more than 27 kg in underweight or normal-weight women, less than 11 kg or more than 28 kg in overweight women, and less than 6.4 kg or more than 26 kg in women with obesity. The bias analysis supported the validity of the conventional analysis., Conclusion: Very low or very high weight gains were associated with the adverse outcomes we studied. If the associations we observed are even partially reflective of causality, targeted modification of pregnancy weight gain in women carrying twins might improve pregnancy outcomes.

While prepregnancy obesity increases risk of stillbirth, few studies have evaluated the role of newly developed obesity independent of long-standing obesity. Additionally, researchers have relied almost exclusively on parametric models, which require correct specification of an unknown function for consistent estimation. We estimated the association between incident obesity and stillbirth in a cohort constructed from linked birth and death records in Pennsylvania (2003-2013). Incident obesity was defined as body mass index (weight (kg)/height (m)2) greater than or equal to 30. We used parametric G-computation, semiparametric inverse-probability weighting, and parametric/nonparametric targeted minimum loss-based estimation (TMLE) to estimate the association between incident prepregnancy obesity and stillbirth. Compared with pregnancies from women who stayed nonobese, women who became obese prior to their next pregnancy were estimated to have 2.0 (95% confidence interval (CI): 0.5, 3.5) more stillbirths per 1,000 pregnancies using parametric G-computation. However, despite well-behaved stabilized inverse probability weights, risk differences estimated from inverse-probability weighting, nonparametric TMLE, and parametric TMLE represented 6.9 (95% CI: 3.7, 10.0), 0.4 (95% CI: 0.1, 0.7), and 2.9 (95% CI: 1.5, 4.2) excess stillbirths per 1,000 pregnancies, respectively. These results, particularly those derived from nonparametric TMLE, were highly sensitive to covariates included in the propensity score models. Our results suggest that caution is warranted when using nonparametric estimators to quantify exposure effects., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: To link the INTERGROWTH-21st gestational weight gain standard with the risks of excess maternal postpartum weight retention, approximated by women's weight change between successive pregnancies., Methods: A population-based retrospective cohort study of 58,534 women delivering successive pregnancies in British Columbia, Canada (2000-2015) was conducted. Pregnancy weight gain (kg) in the index pregnancy was converted into a gestational age-standardized z-score using the INTERGROWTH-21st standard. Excess interpregnancy weight gain was defined as weight increases of 5 kg, 10 kg, or obesity (≥30 kg/m 2 ) at the next pregnancy. Weight gain z-scores and excess interpregnancy weight change were associated using logistic regression., Results: For all definitions of excess interpregnancy weight gain, risks remained low and stable below a weight gain z-score of 0 (50th percentile) but rose sharply with increasing z-scores above zero. Compared with women gaining -1 to 0 SD (16th to 50th percentiles), women gaining > 0 to +1 SD (51st to 84th percentiles) were 55% to 84% more likely to retain excess weight between pregnancies. Risks were three- to sixfold higher in women gaining >+1 SD., Conclusions: A large range of the INTERGROWTH-21st percentiles were associated with increased risks of excess interpregnancy weight gain. The standard may normalize high weight gains of women at increased risk of excess weight retention., (© 2017 The Authors. Obesity published by Wiley Periodicals, Inc. on behalf of The Obesity Society (TOS).)

Objective: To estimate the extent to which prepregnancy obesity explains the Black-White disparity in stillbirth and infant mortality., Methods: A population-based study of linked Pennsylvania birth-infant death certificates (2003-2011; n = 1,055,359 births) and fetal death certificates (2006-2011; n = 3,102 stillbirths) for all singleton pregnancies in non-Hispanic (NH) White and NH Black women was conducted. Inverse probability weighted regression was used to estimate the role of prepregnancy obesity in explaining the race-infant/fetal death association., Results: Compared with NH White women, NH Black women were more likely to have obesity (≥30 kg/m 2 ) and experienced a higher rate of stillbirth (8.3 vs. 3.6 stillbirths per 1,000 live-born and stillborn infants) and infant death (8.5 vs. 3.0 infant deaths per 1,000 live births). When the contribution of prepregnancy obesity was removed, the difference in risk between NH Blacks and NH Whites decreased from 6.2 (95% CI: 5.6-6.7) to 5.5 (95% CI: 4.9-6.2) excess stillbirths per 1,000 and 5.8 (95% CI: 5.3-6.3) to 5.2 (95% CI: 4.7-5.7) excess infant deaths per 1,000., Conclusions: For every 10,000 live births in Pennsylvania (2003-2011), 6 of the 61 excess infant deaths in NH Black women and 5 of the 44 excess stillbirths (2006-2011) were attributable to prepregnancy obesity., Competing Interests: No conflicts of interest, (© 2016 The Obesity Society.)

Background: Our objective was to estimate associations between gestational weight gain z scores and preterm birth, neonatal intensive care unit admission, large- and small-for-gestational age birth, and cesarean delivery among grades 1, 2, and 3 obese women., Methods: We included singleton infants born in Pennsylvania (2003-2011) to grade 1 (body mass index 30-34.9 kg/m, n = 148,335), grade 2 (35-39.9 kg/m, n = 72,032), or grade 3 (≥40 kg/m, n = 47,494) obese mothers. Total pregnancy weight gain (kg) was converted to gestational age-standardized z scores. Multivariable Poisson regression models stratified by obesity grade were used to estimate associations between z scores and outcomes. A probabilistic bias analysis, informed by an internal validation study, evaluated the impact of body mass index and weight gain misclassification., Results: Risks of adverse outcomes did not substantially vary within the range of z scores equivalent to 40-week weight gains of -4.3 to 9 kg for grade 1 obese, -8.2 to 5.6 kg for grade 2 obese, and -12 to -2.3 kg for grade 3 obese women. As gestational weight gain increased beyond these z score ranges, there were slight declines in risk of small-for-gestational age birth but rapid rises in cesarean delivery and large-for-gestational age birth. Risks of preterm birth and neonatal intensive care unit admission were weakly associated with weight gain. The bias analysis supported the validity of the conventional analysis., Conclusions: Gestational weight gain below national recommendations for obese mothers (5-9 kg) may not be adversely associated with fetal growth, gestational age at delivery, or mode of delivery., Competing Interests: The authors declare no conflicts of interest.

The American College of Obstetricians and Gynecologists recommends early treatment of nausea and vomiting of pregnancy to stop progression to hyperemesis gravidarum. Nausea and vomiting and hyperemesis gravidarum typically occur during the first trimester, the sensitive time for exposure to teratogens because organogenesis is occurring in the embryo. An efficacious treatment used widely across the United States for both nausea and vomiting of pregnancy and hyperemesis gravidarum is ondansetron. Recent studies have provided conflicting findings on the safety of ondansetron during pregnancy. There are numerous limitations in the current literature on ondansetron safety including exposure to the medication is not limited to sensitive windows of organogenesis, there is a lack of information on dosing and compliance, self-reports of exposure are commonly used, an inadequate accounting exists for other factors that may explain the relationship between ondansetron exposure and the adverse outcome, and there exists a lack of biologic plausibility by which ondansetron might cause harm. It is the authors' opinion that current data do not support a reluctance to treat women with ondansetron in clinical practice.

The objective of this study is unknown if fetal sex and race modify the impact of maternal pre-pregnancy body mass index (BMI), and smoking on fetal growth. The authors studied markers of fetal growth in singleton offspring of 8,801 primiparous, normotensive women, enrolled in the Collaborative Perinatal Project. The authors tested for departures from additivity between sex/race and each predictor. The head-to-chest circumference ratio (HCC) decreased more, while birthweight and ponderal index (PI) increased more for each 1 kg/m(2) increase in pre-pregnancy BMI among term females versus males (P = 0.07, P < 0.01 and P = 0.08, interaction respectively). For term offspring of White compared with Black women, smoking independent of "dose" was associated with larger reductions in growth (165 g vs. 68 g reduction in birthweight, P < 0.01, interaction), greater reduction in fetal placental ratio (P < 0.01, interaction), PI (P < 0.01, interaction), and greater increase in HCC (P = 0.02), respectively. The association of BMI and smoking with fetal size appeared to be reversed in term versus preterm infants. Our study provides evidence that the associations of pre-pregnancy BMI and smoking are not constant across sex and race. This finding may be relevant to sex and race differences in neonatal and long term health outcomes.

Objective: To estimate the association between maternal 25-hydroxyvitamin D concentrations and risk of preterm birth subtypes., Methods: We performed a case-cohort study using data and banked samples from patients at a teaching hospital in Pittsburgh, Pennsylvania. Eligible participants were women with a prenatal aneuploidy screening serum sample at or before 20 weeks of gestation who subsequently delivered a singleton, liveborn neonate. Of the 12,861 eligible women, we selected 2,327 at random as well as all remaining preterm birth cases for a total of 1,126 cases. Serum 25-hydroxyvitamin D was measured using liquid chromatography-tandem mass spectrometry. Multivariable log-binomial regression models were used to estimate associations between maternal vitamin D status and preterm birth at 37 weeks of gestation (separately by spontaneous or indicated) and preterm birth at less than 34 weeks of gestation., Results: The incidence of preterm birth at less than 37 weeks of gestation was 8.6% overall and 11.3%, 8.6%, and 7.3% among mothers with serum 25-hydroxyvitamin D less than 50, 50-74.9, and 75 nmol/L or greater, respectively (P<.01). After adjustment for maternal race and ethnicity, prepregnancy body mass index, season, smoking, and other confounders, the risk of preterm birth at less than 37 weeks of gestation significantly decreased as 25-hydroxyvitamin D increased to approximately 90 nmol/L and then plateaued (test of nonlinearity P<.01). Results were similar when limiting to cases that were medically indicated or occurred spontaneously and cases occurring at less than 34 weeks of gestation., Conclusion: Our data support a protective association maternal vitamin D sufficiency and preterm birth that combined with extant epidemiologic data may provide justification for a randomized clinical trial of maternal vitamin D replacement or supplementation to prevent preterm birth.

Objective: To explore whether state restrictions on Medicaid funding for pregnancy termination of anomalous fetuses could be contributing to the black-white disparity in infant death resulting from congenital anomalies., Methods: Data on deaths resulting from anomalies were obtained from U.S. vital statistics records (1983-2004) and the Nationwide Inpatient Sample (2003-2007). We conducted an ecological study using Poisson and logistic regression to explore the association between state Medicaid funding for pregnancy terminations of anomalous fetuses and infant death resulting from anomalies by calendar time, race, and individual Medicaid status., Results: Since 1983, a gap in anomaly-related infant death has developed between states without compared with those with Medicaid funding for pregnancy termination (rate ratio in 2004 1.21, 95% confidence interval [CI] 1.18-1.24; crude risks: 146.8 compared with 121.7/100,000). Blacks were significantly more likely than whites to be on Medicaid (60.2% compared with 29.2%) and to live in a state without Medicaid funding for pregnancy termination (65.8% compared with 59.6%). The increased risk of anomaly-related death associated with lack of state Medicaid funding for pregnancy termination was most pronounced among black women on Medicaid (relative risk 1.94, 95% CI 1.52-2.36; crude risks: 245.5 compared with 129.3/100,000)., Conclusion: States without Medicaid funding for pregnancy termination of anomalous fetuses have higher rates of infant death resulting from anomalies than those with funding, and this difference is most pronounced among black women on Medicaid. Restrictions on Medicaid funding for termination of anomalous fetuses potentially could be contributing to the black-white disparity in anomaly-related infant death., Level of Evidence: II.

Background: Conventional measures of gestational weight gain (GWG) are correlated with pregnancy duration, and may induce bias to studies of GWG and perinatal outcomes. A maternal weight-gain-for-gestational-age z-score chart is a new tool that allows total GWG to be classified as a standardised z-score that is independent of gestational duration. Our objective was to compare associations with perinatal outcomes when GWG was assessed using gestational age-standardised z-scores and conventional GWG measures., Methods: We studied normal-weight (n=522 120) and overweight (n=237 923) women who delivered liveborn, singleton infants in Pennsylvania, 2003-11. GWG was expressed using gestational age-standardised z-scores and three traditional measures: total GWG (kg), rate of GWG (kg per week of gestation), and the GWG adequacy ratio (observed GWG/GWG recommended by the Institute of Medicine). Log-binomial regression models were used to assess associations between GWG and preterm birth, and small- and large-for-gestational-age births, while adjusting for race/ethnicity, education, smoking, and other confounders., Results: The association between GWG z-score and preterm birth was approximately U-shaped. The risk of preterm birth associated with weight gain <10th percentile of each measure was substantially overestimated when GWG was classified using total kilogram and was moderately overestimated using rate of GWG or GWG adequacy ratio. All GWG measures had similar associations with small- or large-for-gestational-age birth., Conclusions: Our findings suggest that studies of gestational age-dependent outcomes misspecify associations if total GWG, rate of GWG, or GWG adequacy ratio are used. The potential for gestational age-related bias can be eliminated by using z-score charts to classify total GWG., (© 2014 John Wiley & Sons Ltd.)

To estimate the effects of gestational weight gain (GWG), central adiposity and subcutaneous fat on maternal post-load glucose concentration, pregnant women [n = 413, 62% black, 57% with pregravid body mass index (BMI) ≥25] enrolled in a cohort study at ≤13 weeks gestation. GWG was abstracted from medical records. In a sub-sample of women (n = 214), waist circumference (WC), and biceps and triceps skinfold thicknesses were measured at enrollment. At 24-28 weeks gestation, post-load glucose concentration was measured using a 50-g 1-h oral glucose tolerance test. After adjustment for pre-pregnancy BMI, age, parity, race/ethnicity, smoking, marital status, annual family income, education, family history of diabetes, and gestational age of GDM screening, each 0.3-kg/week increase in weight in the first trimester was associated with a 2.2 (95% CI 0.1, 4.3)-mg/dl increase in glucose concentration. Each 8.6-mm increase in biceps skinfold thickness and each 11.7-mm increase in triceps skinfold thickness was associated with 4.3 (95% CI 0.2, 8.5)-mg/dl increase in maternal glucose, independent of BMI and other confounders. Neither GWG in the second trimester nor WC at ≤13 weeks was significantly associated with glucose concentration after confounder adjustment. Independent of pre-pregnancy BMI, high early pregnancy GWG and maternal subcutaneous body fat may be positively associated with maternal glucose concentrations at 24-28 weeks.

We explored the relationship between neighborhood socioeconomic disadvantage (NSED) and gestational weight gain and loss and if the association differed by race. A census tract level NSED index (categorized as low, mid-low, mid-high, and high) was generated from 12 measures from the 2000 US Census data. Gestational weight gain and other individual-level characteristics were derived from vital birth records for Allegheny County, PA for 2003-2010 (n = 55,608). Crude and adjusted relative risks were estimated using modified multilevel Poisson regression models to estimate the association between NSED and excessive and inadequate gestational weight gain (GWG) and weight loss (versus adequate GWG). Black women lived in neighborhoods that were more likely to be socioeconomically disadvantaged compared to white women. Almost 55% of women gained an excessive amount of weight during pregnancy, and 2% lost weight during pregnancy. Black women were more likely than white women to have inadequate weight gain or weight loss. Mid-high (aRR = 1.3, 95% CI 1.2, 1.3) and high (aRR = 1.5, 95% CI 1.5, 1.6) NSED compared to low NSED was associated with inadequate weight gain while NSED was not associated with excessive weight gain. Among black women, high versus low NSED was associated with weight loss during pregnancy (RR = 1.6, 95% CI 1.1, 2.5). Among white women, each level of NSED compared to low NSED was associated with weight loss during pregnancy. This study demonstrates how neighborhood socioeconomic characteristics can contribute to our understanding of inadequate weight gain and weight loss during pregnancy, having implications for future research and interventions designed to advance pregnancy outcomes.