Medication-Induced Weight Change Across Common Antidepressant Treatments : A Target Trial Emulation Study.

BACKGROUND: Antidepressants are among the most commonly prescribed medications, but evidence on comparative weight change for specific first-line treatments is limited. OBJECTIVE: To compare weight change across common first-line antidepressant treatments by emulating a target trial. DESIGN: Observational cohort study over 24 months. SETTING: Electronic health record (EHR) data from 2010 to 2019 across 8 U.S. health systems. PARTICIPANTS: 183 118 patients. MEASUREMENTS: Prescription data determined initiation of treatment with sertraline, citalopram, escitalopram, fluoxetine, paroxetine, bupropion, duloxetine, or venlafaxine. The investigators estimated the population-level effects of initiating each treatment, relative to sertraline, on mean weight change (primary) and the probability of gaining at least 5% of baseline weight (secondary) 6 months after initiation. Inverse probability weighting of repeated outcome marginal structural models was used to account for baseline confounding and informative outcome measurement. In secondary analyses, the effects of initiating and adhering to each treatment protocol were estimated. RESULTS: Compared with that for sertraline, estimated 6-month weight gain was higher for escitalopram (difference, 0.41 kg [95% CI, 0.31 to 0.52 kg]), paroxetine (difference, 0.37 kg [CI, 0.20 to 0.54 kg]), duloxetine (difference, 0.34 kg [CI, 0.22 to 0.44 kg]), venlafaxine (difference, 0.17 kg [CI, 0.03 to 0.31 kg]), and citalopram (difference, 0.12 kg [CI, 0.02 to 0.23 kg]); similar for fluoxetine (difference, -0.07 kg [CI, -0.19 to 0.04 kg]); and lower for bupropion (difference, -0.22 kg [CI, -0.33 to -0.12 kg]). Escitalopram, paroxetine, and duloxetine were associated with 10% to 15% higher risk for gaining at least 5% of baseline weight, whereas bupropion was associated with 15% reduced risk. When the effects of initiation and adherence were estimated, associations were stronger but had wider CIs. Six-month adherence ranged from 28% (duloxetine) to 41% (bupropion). LIMITATION: No data on medication dispensing, low medication adherence, incomplete data on adherence, and incomplete data on weight measures across time points. CONCLUSION: Small differences in mean weight change were found between 8 first-line antidepressants, with bupropion consistently showing the least weight gain, although adherence to medications over follow-up was low. Clinicians could consider potential weight gain when initiating antidepressant treatment. PRIMARY FUNDING SOURCE: National Institutes of Health.

Causal Effects of Stochastic PrEP Interventions on HIV Incidence Among Men Who Have Sex With Men.

Antiretroviral preexposure prophylaxis (PrEP) is highly effective in preventing human immunodeficiency virus (HIV) infection, but uptake has been limited and inequitable. Although interventions to increase PrEP uptake are being evaluated in clinical trials among men who have sex with men (MSM), those trials cannot evaluate effects on HIV incidence. Estimates from observational studies of the causal effects of PrEP-uptake interventions on HIV incidence can inform decisions about intervention scale-up. We used longitudinal electronic health record data from HIV-negative MSM accessing care at Fenway Health, a community health center in Boston, Massachusetts, from January 2012 through February 2018, with 2 years of follow-up. We considered stochastic interventions that increased the chance of initiating PrEP in several high-priority subgroups. We estimated the effects of these interventions on population-level HIV incidence using a novel inverse-probability weighted estimator of the generalized g-formula, adjusting for baseline and time-varying confounders. Our results suggest that even modest increases in PrEP initiation in high-priority subgroups of MSM could meaningfully reduce HIV incidence in the overall population of MSM. Interventions tailored to Black and Latino MSM should be prioritized to maximize equity and impact.

Disparities in cancer incidence by sexual orientation.

INTRODUCTION: Cancer risk factors are more common among sexual minority populations (e.g., lesbian, bisexual) than their heterosexual peers, yet little is known about cancer incidence across sexual orientation groups. METHODS: The 1989-2017 data from the Nurses' Health Study II, a longitudinal cohort of female nurses across the United States, were analyzed (N = 101,543). Sexual orientation-related cancer disparities were quantified by comparing any cancer incidence among four sexual minority groups based on self-disclosure-(1) heterosexual with past same-sex attractions/partners/identity; (2) mostly heterosexual; (3) bisexual; and (4) lesbian women-to completely heterosexual women using age-adjusted incidence rate ratios (aIRR) calculated by the Mantel-Haenszel method. Additionally, subanalyses at 21 cancer disease sites (e.g., breast, colon/rectum) were conducted. RESULTS: For all-cancer analyses, there were no statistically significant differences in cancer incidence at the 5% type I error cutoff among sexual minority groups when compared to completely heterosexual women; the aIRR was 1.17 (95% CI,0.99-1.38) among lesbian women and 0.80 (0.58-1.10) among bisexual women. For the site-specific analyses, incidences at multiple sites were significantly higher among lesbian women compared to completely heterosexual women: thyroid cancer (aIRR, 1.87 [1.03-3.41]), basal cell carcinoma (aIRR, 1.85 [1.09-3.14]), and non-Hodgkin lymphoma (aIRR, 2.13 [1.10-4.12]). CONCLUSION: Lesbian women may be disproportionately burdened by cancer relative to their heterosexual peers. Sexual minority populations must be explicitly included in cancer prevention efforts. Comprehensive and standardized sexual orientation data must be systematically collected so nuanced sexual orientation-related cancer disparities can be accurately assessed for both common and rare cancers.

Causal inference with recurrent and competing events.

Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports injuries in athletes. Competing events, such as death, complicate causal inference in studies of recurrent events because once a competing event occurs, an individual cannot have more recurrent events. Several statistical estimands have been studied in recurrent event settings, with and without competing events. However, the causal interpretations of these estimands, and the conditions that are required to identify these estimands from observed data, have yet to be formalized. Here we use a formal framework for causal inference to formulate several causal estimands in recurrent event settings, with and without competing events. When competing events exist, we clarify when commonly used classical statistical estimands can be interpreted as causal quantities from the causal mediation literature, such as (controlled) direct effects and total effects. Furthermore, we show that recent results on interventionist mediation estimands allow us to define new causal estimands with recurrent and competing events that may be of particular clinical relevance in many subject matter settings. We use causal directed acyclic graphs and single world intervention graphs to illustrate how to reason about identification conditions for the various causal estimands based on subject matter knowledge. Furthermore, using results on counting processes, we show that our causal estimands and their identification conditions, which are articulated in discrete time, converge to classical continuous time counterparts in the limit of fine discretizations of time. We propose estimators and establish their consistency for the various identifying functionals. Finally, we use the proposed estimators to compute the effect of blood pressure lowering treatment on the recurrence of acute kidney injury using data from the Systolic Blood Pressure Intervention Trial.

Considering Questions Before Methods in Dementia Research With Competing Events and Causal Goals.

Studying causal exposure effects on dementia is challenging when death is a competing event. Researchers often interpret death as a potential source of bias, although bias cannot be defined or assessed if the causal question is not explicitly specified. Here we discuss 2 possible notions of a causal effect on dementia risk: the "controlled direct effect" and the "total effect." We provide definitions and discuss the "censoring" assumptions needed for identification in either case and their link to familiar statistical methods. We illustrate concepts in a hypothetical randomized trial on smoking cessation in late midlife, and emulate such a trial using observational data from the Rotterdam Study, the Netherlands, 1990-2015. We estimated a total effect of smoking cessation (compared with continued smoking) on 20-year dementia risk of 2.1 (95% confidence interval: -0.1, 4.2) percentage points and a controlled direct effect of smoking cessation on 20-year dementia risk had death been prevented of -2.7 (95% confidence interval: -6.1, 0.8) percentage points. Our study highlights how analyses corresponding to different causal questions can have different results, here with point estimates on opposite sides of the null. Having a clear causal question in view of the competing event and transparent and explicit assumptions are essential to interpreting results and potential bias.

Longitudinal incremental propensity score interventions for limited resource settings.

Many real-life treatments are of limited supply and cannot be provided to all individuals in the population. For example, patients on the liver transplant waiting list usually cannot be assigned a liver transplant immediately at the time they reach highest priority because a suitable organ is not immediately available. In settings with limited supply, investigators are often interested in the effects of treatment strategies in which a limited proportion of patients receive an organ at a given time, that is, treatment regimes satisfying resource constraints. Here, we describe an estimand that allows us to define causal effects of treatment strategies that satisfy resource constraints: incremental propensity score interventions (IPSIs) for limited resources. IPSIs flexibly constrain time-varying resource utilization through proportional scaling of patients' natural propensities for treatment, thereby preserving existing propensity rank ordering compared to the status quo. We derive a simple class of inverse-probability-weighted estimators, and we apply one such estimator to evaluate the effect of restricting or expanding utilization of "increased risk" liver organs to treat patients with end-stage liver disease.

Associations of urinary metabolite concentrations of phthalates and phthalate replacements with body composition from mid-childhood to early adolescence.

BACKGROUND: Phthalates may adversely influence body composition by lowering anabolic hormones and activating peroxisome-proliferator activated receptor gamma. However, data are limited in adolescence when body mass distributions rapidly change and bone accrual peaks. Also, potential health effects of certain phthalate/replacements [e.g., di-2-ethylhexyl terephthalate (DEHTP)] have not been well studied. METHODS: Among 579 children in the Project Viva cohort, we used linear regression to evaluate associations of urinary concentrations of 19 phthalate/replacement metabolites from mid-childhood (median: 7.6 years; 2007-2010) with annualized change in areal bone mineral density (aBMD) and lean, total fat, and truncal fat mass as measured by dual-energy X-ray absorptiometry between mid-childhood and early adolescence (median: 12.8 years). We used quantile g-computation to assess associations of the overall chemical mixture with body composition. We adjusted for sociodemographics and tested for sex-specific associations. RESULTS: Urinary concentrations were highest for mono-2-ethyl-5-carboxypentyl phthalate [median (IQR): 46.7 (69.1) ng/mL]. We detected metabolites of most replacement phthalates in a relatively small number of participants [e.g., 28% for mono-2-ethyl-5-hydrohexyl terephthalate (MEHHTP; metabolite of DEHTP)]. Detectable (vs. non-detectable) MEHHTP was associated with less bone and greater fat accrual in males and greater bone and lean mass accrual in females [e.g., change in aBMD Z-score/year (95% CI): -0.049 (-0.085, -0.013) in males versus 0.042 (0.007, 0.076) in females; pinteraction<0.01]. Children with higher concentrations of mono-oxo-isononyl phthalate and mono-3-carboxypropyl phthalate (MCPP) had greater bone accrual. Males with higher concentrations of MCPP and mono-carboxynonyl phthalate had greater accrual of lean mass. Other phthalate/replacement biomarkers, and their mixtures, were not associated with longitudinal changes in body composition. CONCLUSIONS: Concentrations of select phthalate/replacement metabolites in mid-childhood were associated with changes in body composition through early adolescence. As use of phthalate replacements such as DEHTP may be increasing, further investigation can help better understand the potential effects of early-life exposures.

Marginal Structural Models for Life-Course Theories and Social Epidemiology: Definitions, Sources of Bias, and Simulated Illustrations.

Social epidemiology aims to identify social structural risk factors, thus informing targets and timing of interventions. Ascertaining which interventions will be most effective and when they should be implemented is challenging because social conditions vary across the life course and are subject to time-varying confounding. Marginal structural models (MSMs) may be useful but can present unique challenges when studying social epidemiologic exposures over the life course. We describe selected MSMs corresponding to common theoretical life-course models and identify key issues for consideration related to time-varying confounding and late study enrollment. Using simulated data mimicking a cohort study evaluating the effects of depression in early, mid-, and late life on late-life stroke risk, we examined whether and when specific study characteristics and analytical strategies may induce bias. In the context of time-varying confounding, inverse-probability-weighted estimation of correctly specified MSMs accurately estimated the target causal effects, while conventional regression models showed significant bias. When no measure of early-life depression was available, neither MSMs nor conventional models were unbiased, due to confounding by early-life depression. To inform interventions, researchers need to identify timing of effects and consider whether missing data regarding exposures earlier in life may lead to biased estimates.

Revisiting the g-null Paradox.

The (noniterative conditional expectation) parametric g-formula is an approach to estimating causal effects of sustained treatment strategies from observational data. An often-cited limitation of the parametric g-formula is the g-null paradox: a phenomenon in which model misspecification in the parametric g-formula is guaranteed in some settings consistent with the conditions that motivate its use (i.e., when identifiability conditions hold and measured time-varying confounders are affected by past treatment). Many users of the parametric g-formula acknowledge the g-null paradox as a limitation when reporting results but still require clarity on its meaning and implications. Here, we revisit the g-null paradox to clarify its role in causal inference studies. In doing so, we present analytic examples and a simulation-based illustration of the bias of parametric g-formula estimates under the conditions associated with this paradox. Our results highlight the importance of avoiding overly parsimonious models for the components of the g-formula when using this method.

Separating Algorithms From Questions and Causal Inference With Unmeasured Exposures: An Application to Birth Cohort Studies of Early Body Mass Index Rebound.

Observational studies reporting on adjusted associations between childhood body mass index (BMI; weight (kg)/height (m)2) rebound and subsequent cardiometabolic outcomes have often not paid explicit attention to causal inference, including definition of a target causal effect and assumptions for unbiased estimation of that effect. Using data from 649 children in a Boston, Massachusetts-area cohort recruited in 1999-2002, we considered effects of stochastic interventions on a chosen subset of modifiable yet unmeasured exposures expected to be associated with early (

Parametric g-formula implementations for causal survival analyses.

The g-formula can be used to estimate the survival curve under a sustained treatment strategy. Two available estimators of the g-formula are noniterative conditional expectation and iterative conditional expectation. We propose a version of the iterative conditional expectation estimator and describe its procedures for deterministic and random treatment strategies. Also, because little is known about the comparative performance of noniterative and iterative conditional expectation estimators, we explore their relative efficiency via simulation studies. Our simulations show that, in the absence of model misspecification and unmeasured confounding, our proposed iterative conditional expectation estimator and the noniterative conditional expectation estimator are similarly efficient, and that both are at least as efficient as the classical iterative conditional expectation estimator. We describe an application of both noniterative and iterative conditional expectation to answer "when to start" treatment questions using data from the HIV-CAUSAL Collaboration.

Variance estimation in inverse probability weighted Cox models.

Inverse probability weighted Cox models can be used to estimate marginal hazard ratios under different point treatments in observational studies. To obtain variance estimates, the robust sandwich variance estimator is often recommended to account for the induced correlation among weighted observations. However, this estimator does not incorporate the uncertainty in estimating the weights and tends to overestimate the variance, leading to inefficient inference. Here we propose a new variance estimator that combines the estimation procedures for the hazard ratio and weights using stacked estimating equations, with additional adjustments for the sum of terms that are not independently and identically distributed in a Cox partial likelihood score equation. We prove analytically that the robust sandwich variance estimator is conservative and establish the asymptotic equivalence between the proposed variance estimator and one obtained through linearization by Hajage et al. in 2018. In addition, we extend our proposed variance estimator to accommodate clustered data. We compare the finite sample performance of the proposed method with alternative methods through simulation studies. We illustrate these different variance methods in both independent and clustered data settings, using a bariatric surgery dataset and a multiple readmission dataset, respectively. To facilitate implementation of the proposed method, we have developed an R package ipwCoxCSV.

Childhood patterns of overweight and wheeze and subsequent risk of current asthma and obesity in adolescence.

BACKGROUND: Obesity and asthma in childhood often co-occur. Few studies have examined this relationship using repeated measures of body mass index (BMI) or asthma symptoms (such as wheeze). OBJECTIVE: We compared two analytic approaches for repeated measures data to investigate this relationship. METHODS: Our baseline sample consisted of 1277 children enrolled in a Boston-area cohort with BMI or wheeze at age 1 year and no missing covariates. We used latent class growth models (LCGM) and inverse probability weighting (IPW) of marginal structural models to examine the extent to which presence of overweight across childhood was associated with early adolescent current asthma, and conversely of repeated measures of wheeze across childhood with early adolescent obesity. RESULTS: Using LCGM, a "persistent" childhood overweight class (vs "never") was associated with higher risk of asthma in early adolescence (RR 1.9; 95% CI 1.1, 3.0), while "persistent" childhood wheeze (vs "never") was associated with higher risk of obesity in early adolescence (RR 2.7; 95% CI 1.0, 6.4) after adjusting for baseline covariates. An IPW analysis treating childhood overweight and wheeze as time-varying exposures and adjusting for baseline and time-varying covariates resulted in weaker and less precise associations of "persistent" (vs "never") overweight with adolescent asthma (RR 1.3; 95% CI 0.3, 3.0), and of "persistent" (vs "never") wheeze with adolescent obesity (RR 2.3; 95% CI 0.4, 5.3). CONCLUSION: Our point estimates from both approaches suggest an association between "persistent" childhood overweight and adolescent asthma, and between "persistent" childhood wheeze and adolescent obesity. LCGM results were stronger and more precise, whereas IPW results were less conclusive with wider 95% confidence intervals containing the null. The precision gained from LCGM may be at the expense of bias, and the use of both approaches helps to shed some light on this tradeoff.

A generalized theory of separable effects in competing event settings.

In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However, effects of treatment on the competing event may indirectly contribute to this total effect, complicating its interpretation. We previously proposed the separable effects to define direct and indirect effects of the treatment on the event of interest. This definition was given in a simple setting, where the treatment was decomposed into two components acting along two separate causal pathways. Here we generalize the notion of separable effects, allowing for interpretation, identification and estimation in a wide variety of settings. We propose and discuss a definition of separable effects that is applicable to general time-varying structures, where the separable effects can still be meaningfully interpreted as effects of modified treatments, even when they cannot be regarded as direct and indirect effects. For these settings we derive weaker conditions for identification of separable effects in studies where decomposed, or otherwise modified, treatments are not yet available; in particular, these conditions allow for time-varying common causes of the event of interest, the competing events and loss to follow-up. We also propose semi-parametric weighted estimators that are straightforward to implement. We stress that unlike previous definitions of direct and indirect effects, the separable effects can be subject to empirical scrutiny in future studies.

Privacy-protecting multivariable-adjusted distributed regression analysis for multi-center pediatric study.

BACKGROUND: Privacy-protecting analytic approaches without centralized pooling of individual-level data, such as distributed regression, are particularly important for vulnerable populations, such as children, but these methods have not yet been tested in multi-center pediatric studies. METHODS: Using the electronic health data from 34 healthcare institutions in the National Patient-Centered Clinical Research Network (PCORnet), we fit 12 multivariable-adjusted linear regression models to assess the associations of antibiotic use <24 months of age with body mass index z-score at 48 to <72 months of age. We ran these models using pooled individual-level data and conventional multivariable-adjusted regression (reference method), as well as using the more privacy-protecting pooled summary-level intermediate statistics and distributed regression technique. We compared the results from these two methods. RESULTS: Pooled individual-level and distributed linear regression analyses produced virtually identical parameter estimates and standard errors. Across all 12 models, the maximum difference in any of the parameter estimates or standard errors was 4.4833 × 10-10. CONCLUSIONS: We demonstrated empirically the feasibility and validity of distributed linear regression analysis using only summary-level information within a large multi-center study of children. This approach could enable expanded opportunities for multi-center pediatric research, especially when sharing of granular individual-level data is challenging.

A causal framework for classical statistical estimands in failure-time settings with competing events.

In failure-time settings, a competing event is any event that makes it impossible for the event of interest to occur. For example, cardiovascular disease death is a competing event for prostate cancer death because an individual cannot die of prostate cancer once he has died of cardiovascular disease. Various statistical estimands have been defined as possible targets of inference in the classical competing risks literature. Many reviews have described these statistical estimands and their estimating procedures with recommendations about their use. However, this previous work has not used a formal framework for characterizing causal effects and their identifying conditions, which makes it difficult to interpret effect estimates and assess recommendations regarding analytic choices. Here we use a counterfactual framework to explicitly define each of these classical estimands. We clarify that, depending on whether competing events are defined as censoring events, contrasts of risks can define a total effect of the treatment on the event of interest or a direct effect of the treatment on the event of interest not mediated by the competing event. In contrast, regardless of whether competing events are defined as censoring events, counterfactual hazard contrasts cannot generally be interpreted as causal effects. We illustrate how identifying assumptions for all of these counterfactual estimands can be represented in causal diagrams, in which competing events are depicted as time-varying covariates. We present an application of these ideas to data from a randomized trial designed to estimate the effect of estrogen therapy on prostate cancer mortality.

Effects of intergenerational exposure interventions on adolescent outcomes: An application of inverse probability weighting to longitudinal pre-birth cohort data.

BACKGROUND: There is great interest in understanding whether interventions on sugar-sweetened beverage (SSB) consumption through pregnancy and early childhood affect adolescent body mass index (BMI). Without data from randomised trials, unbiased estimation of such effects might be achieved with observational data given sufficient and appropriate adjustment for both baseline and time-varying confounders. OBJECTIVES: To illustrate the use of inverse probability (IP) weighting of marginal structural models (MSM) for estimating the effects of SSB consumption through pregnancy and early childhood on the mean early adolescent BMI z-score. METHODS: Our baseline sample consisted of 1584 pregnant women from a pre-birth cohort. We defined 6 intervention intervals: early pregnancy, late pregnancy, 3, 4, 5, and 6 years. We fitted a MSM via a weighted linear regression with IP exposure and censoring weights to estimate the mean difference in BMI z-score under interventions: "maintain SSB consumption below (vs above) 0.5 servings/day in all intervals." RESULTS: The estimated difference in mean BMI z-score under interventions maintaining SSB consumption at or below (vs above) 0.5 servings/day from pregnancy to 6 years was -0.94 (95% confidence interval [CI] -1.52, -0.08). The effect estimate in pregnancy, while fixing the exposure range in childhood, was -0.05 (95% CI -0.34, 0.23), and in early childhood, while fixing the range in pregnancy was -0.89 (95% CI -1.46, -0.11). The effect estimates were largely unchanged under sensitivity analyses to different implementation choices except for the choice of time interval length. CONCLUSIONS: Under assumptions that include no unmeasured confounding and selection bias, and no model misspecification, results of this IP weighting application are in line with a lower mean BMI z-score in early adolescence under interventions ensuring lower, vs greater, SSB consumption in early life. This application provides a resource for researchers working with longitudinal birth cohort studies and interested in similar causal questions.

Associations of prenatal exposure to impaired glucose tolerance with eating in the absence of hunger in early adolescence.

OBJECTIVE: Exposure to impaired gestational glucose tolerance has been shown to have sex-specific associations with offspring obesity risk, perhaps by affecting the development of appetite regulation. We examined the extent to which prenatal exposure to impaired glucose tolerance was associated with eating in the absence of hunger (EAH) in early adolescent offspring, and in turn, whether EAH was cross-sectionally associated with body composition. METHODS: We included data from 1097 adolescents participating in Project Viva, a pre-birth longitudinal cohort. We obtained the results of two-stage prenatal glycemic screening (50 g glucose challenge test, followed if abnormal by 100 g oral glucose tolerance test) at 26-28 weeks of gestation, and categorized mothers as having normal glucose tolerance, isolated hyperglycemia (IH, n = 92, 8.4%), impaired glucose tolerance (IGT, n = 36, 3.3%), or gestational diabetes mellitus (GDM, n = 52, 4.7%). At a median age of 13 years, offspring reported on two modified items of the Eating in the Absence of Hunger in Children and Adolescents questionnaire, we measured height and weight, and performed dual X-ray absorptiometry scans to assess fat and fat-free mass. We used multivariable linear regression analyses adjusted for sociodemographic and prenatal covariates, including maternal pre-pregnancy BMI. RESULTS: On a ten-point scale, the mean (SD) EAH score was 4.4 points (SD = 1.5) in boys and 4.4 (SD = 1.4) in girls. In girls, prenatal exposure to both IH and IGT was associated with more EAH compared with normal glucose tolerance (e.g., for IH: 0.56 points, 95% CI: 0.17, 0.96), whereas in boys, prenatal exposure to IGT was associated with less EAH (-0.81 points, 95% CI: -1.41, -0.21). We did not observe an association between exposure to GDM and EAH, nor did we observe associations between EAH and body composition in early adolescence. CONCLUSIONS: These findings suggest sex-specific associations of exposure to impaired gestational glucose tolerance with offspring EAH in early adolescence.

Maternal antibiotic use during pregnancy and childhood obesity at age 5 years.

OBJECTIVE: The benefits of antibiotic treatment during pregnancy are immediate, but there may be long-term risks to the developing child. Prior studies show an association between early life antibiotics and obesity, but few have examined this risk during pregnancy. SUBJECTS: To evaluate the association of maternal antibiotic exposure during pregnancy on childhood BMI-z at 5 years, we conducted a retrospective cohort analysis. Using electronic health record data from seven health systems in PCORnet, a national distributed clinical research network, we included children with same-day height and weight measures who could be linked to mothers with vital measurements during pregnancy. The primary independent variable was maternal outpatient antibiotic prescriptions during pregnancy (any versus none). We examined dose response (number of antibiotic episodes), spectrum and class of antibiotics, and antibiotic episodes by trimester. The primary outcome was child age- and sex-specific BMI-z at age 5 years. RESULTS: The final sample was 53,320 mother-child pairs. During pregnancy, 29.9% of mothers received antibiotics. In adjusted models, maternal outpatient antibiotic prescriptions during pregnancy were not associated with child BMI-z at age 5 years (ß = 0.00, 95% CI -0.03, 0.02). When evaluating timing during pregnancy, dose-response, spectrum and class of antibiotics, there were no associations of maternal antibiotics with child BMI-z at age 5 years. CONCLUSION: In this large observational cohort, provision of antibiotics during pregnancy was not associated with childhood BMI-z at 5 years.