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Background: Childhood maltreatment is associated with elevated adult weight. It is unclear whether this association extends to pregnancy, a critical window for the development of obesity. Methods: We examined associations of childhood maltreatment histories with prepregnancy body mass index (BMI) and gestational weight gain among women who had participated for >20 years in a longitudinal cohort. At age 26-35 years, participants reported childhood maltreatment (physical, sexual, and emotional abuse; emotional neglect) and, 5 years later, about prepregnancy weight and gestational weight gain for previous pregnancies (n = 656). Modified Poisson regression models were used to estimate associations of maltreatment history with prepregnancy BMI and gestational weight gain z-scores, adjusting for sociodemographics. We used multivariate imputation by chained equations to adjust outcome measures for misclassification using data from an internal validation study. Results: Before misclassification adjustment, results indicated a higher risk of prepregnancy BMI ≥30 kg/m² in women with certain types of maltreatment (e.g., emotional abuse risk ratio = 2.4; 95% confidence interval: 1.5, 3.7) compared with women without that maltreatment type. After misclassification adjustment, estimates were attenuated but still modestly elevated (e.g., emotional abuse risk ratio = 1.7; 95% confidence interval: 1.1, 2.7). Misclassificationadjusted estimates for maltreatment associations with gestational weight gain z-scores were close to the null and imprecise. Conclusions: Findings suggest an association of maltreatment with prepregnancy BMI ≥30 kg/m² but not with high gestational weight gain. Results suggest a potential need for equitable interventions that can support all women, including those with maltreatment histories, as they enter pregnancy. [ABSTRACT FROM AUTHOR]

Background: The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator. Methods: We used data from 10,038 pregnant women and a 10% subsample (N = 1004) to examine the extent to which the risk difference for the relation between fruit and vegetable consumption and preeclampsia risk changes under different seed values. We fit an augmented inverse probability weighted estimator with two Super Learner algorithms: a simple algorithm including random forests and single-layer neural networks and a more complex algorithm with a mix of tree-based, regression-based, penalized, and simple algorithms. We evaluated the distributions of risk differences, standard errors, and P values that result from 5000 different seed value selections. Results: Our findings suggest important variability in the risk difference estimates, as well as an important effect of the stacking algorithm used. The interquartile range width of the risk differences in the full sample with the simple algorithm was 13 per 1000. However, all other interquartile ranges were roughly an order of magnitude lower. The medians of the distributions of risk differences differed according to the sample size and the algorithm used. Conclusions: Our findings add another dimension of concern regarding the potential for "p-hacking," and further warrant the need to move away from simplistic evidentiary thresholds in empirical research. When empirical results depend on pseudo-random number generator seed values, caution is warranted in interpreting these results. [ABSTRACT FROM AUTHOR]

Pregnancy weight gain standards are charts describing percentiles of weight gain among participants with no risk factors that could adversely affect weight gain. This detailed information is burdensome to collect. We investigated the extent to which exclusion of various pre-pregnancy, pregnancy and postpartum factors impacted the values of pregnancy weight gain percentiles. We examined pregnancy weight gain (kg) among 3178 participants of the US nuMoM2b-Heart Health Study (HHS). We identified five groups of potential exclusion criteria for pregnancy weight gain standards: socio-economic characteristics (group 1), maternal morbidities (group 2), lifestyle/behaviour factors (group 3), adverse neonatal outcomes (group 4) and longer-term adverse outcomes (group 5). We established the impact of different exclusion criteria by comparing the median, 25th and 75th percentiles of weight gain in the full cohort with the values after applying each of the five exclusion criteria groups. Differences > 0·75 kg were considered meaningful. Excluding participants with group 1, 2, 3 or 4 exclusion criteria had no impact on the 25th, median or 75th percentiles of pregnancy weight gain. Percentiles were only meaningfully different after excluding participants in group 5 (longer-term adverse outcomes), which shifted the upper end of the weight gain distribution to lower values (e.g. 75th percentile decreased from 19·6 kg to 17·8 kg). This shift was due to exclusion of participants with excess postpartum weight retention > 5 kg or > 10 kg. Except for excess postpartum weight retention, most potential exclusion criteria for pregnancy weight gain standards did not meaningfully impact chart percentiles. [ABSTRACT FROM AUTHOR]

Background: Prepregnancy body mass index (BMI) and gestational weight gain (GWG) are determinants of maternal and child health. However, many studies of these factors rely on error-prone self-reported measures. Methods: Using data from Life-course Experiences And Pregnancy (LEAP), a US-based cohort, we assessed the validity of prepregnancy BMI and GWG recalled on average 8 years postpartum against medical record data treated as alloyed gold standard (“true”) values. We calculated probabilities of being classified into a self-reported prepregnancy BMI or GWG category conditional on one’s true category (analogous to sensitivities and specificities) and probabilities of truly being in each prepregnancy BMI or GWG category conditional on one’s self-reported category (analogous to positive and negative predictive values). Results: There was a tendency toward under-reporting prepregnancy BMI. Self-report misclassified 32% (95% confidence interval [CI] = 19%, 48%) of those in LEAP with truly overweight and 13% (5%, 27%) with obesity into a lower BMI category. Self-report correctly predicted the truth for 72% (55%, 84%) with self-reported overweight to 100% (90%, 100%) with self-reported obesity. For GWG, both under- and over-reporting were common; self-report misclassified 32% (15%, 55%) with truly low GWG as having moderate GWG and 50% (28%, 72%) with truly high GWG as moderate or low GWG. Self-report correctly predicted the truth for 45% (25%, 67%) with self-reported high GWG to 85% (76%, 91%) with selfreported moderate GWG. Misclassification of BMI and GWG varied across maternal characteristics. Conclusion: Findings can be used in quantitative bias analyses to estimate bias-adjusted associations with prepregnancy BMI and GWG. [ABSTRACT FROM AUTHOR]

Objective While twin gestations are at increased risk of severe maternal morbidity (SMM), there is limited information about timing and causes of SMM in twins. Furthermore, existing data rely on screening definitions of SMM because a gold standard approach requires chart review. We sought to determine the timing and cause of SMM in twins using a gold standard definition outlined by the American College of Obstetricians and Gynecologists (ACOG). Study Design We used a perinatal database to identify all twin deliveries from 1998 to 2013 at a single academic medical center (n = 2,367). Deliveries were classified as screen positive for SMM if they met any of the following criteria: (1) one of the Centers for Disease Control and Prevention (CDC) International Classification of Diseases Ninth Revision diagnosis and procedure codes for SMM; (2) a prolonged postpartum length of stay (>3 standard deviations beyond mean length of stay by mode of delivery); or (3) maternal intensive care unit admission. We identified true cases of SMM through medical record review of all screen-positive deliveries using the definition of SMM outlined in the ACOG Obstetric Care Consensus. We also determined cause and timing of SMM. Results A total of 165 (7%) of twin deliveries screened positive for SMM. After chart review of all screen-positive cases, 2.4% (n = 56) were classified as a true case of SMM using the ACOG definition for a positive predictive value of 34%. The majority of SMM occurred postpartum (65%). Hemorrhage was the most common cause of SMM, followed by hypertensive and pulmonary etiologies. Conclusion Commonly used approaches to screen for SMM perform poorly in twins. This has important implications for quality initiatives and epidemiologic studies that rely on screening definitions of maternal morbidity. Our study demonstrates that the immediate postpartum period is a critical time for maternal health among women with twin pregnancies. Key Points Screening approaches for SMM have low positive predictive value in twins. Hemorrhage, hypertensive, and pulmonary complications were the most common morbidities. SMM was most common postpartum. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Severe maternal morbidity (SMM) affects 50,000 women annually in the United States, but its consequences are not well understood. We aimed to estimate the association between SMM and risk of adverse cardiovascular events during the 2 years postpartum. We analyzed 137,140 deliveries covered by the Pennsylvania Medicaid program (2016–2018), weighted with inverse probability of censoring weights to account for nonrandom loss to follow-up. SMM was defined as any diagnosis on the Centers for Disease Control and Prevention list of SMM diagnoses and procedures and/or intensive care unit admission occurring at any point from conception through 42 days postdelivery. Outcomes included heart failure, ischemic heart disease, and stroke/transient ischemic attack up to 2 years postpartum. We used marginal standardization to estimate average treatment effects. We found that SMM was associated with increased risk of each adverse cardiovascular event across the follow-up period. Per 1,000 deliveries, relative to no SMM, SMM was associated with 12.1 (95% confidence interval (CI): 6.2, 18.0) excess cases of heart failure, 6.4 (95% CI: 1.7, 11.2) excess cases of ischemic heart disease, and 8.2 (95% CI: 3.2, 13.1) excess cases of stroke/transient ischemic attack at 26 months of follow-up. These results suggest that SMM identifies a group of women who are at high risk of adverse cardiovascular events after delivery. Women who survive SMM may benefit from more comprehensive postpartum care linked to well-woman care. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns., Competing Interests: Conflict of Interest: RML is a statistical editor for the British Journal of Nutrition. Other authors have none to declare.