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In many research settings, the intervention implied by the average causal effect of a time-varying exposure is impractical or unrealistic, and we might instead prefer a more realistic target estimand. Instead of requiring all individuals to be always exposed versus unexposed, incremental effects quantify the impact of merely shifting each individual's probability of being exposed.We demonstrate the estimation of incremental effects in the time-varying setting, using data from the Effects of Aspirin in Gestation and Reproduction trial, which assessed the effect of preconception low-dose aspirin on pregnancy outcomes. Compliance to aspirin or placebo was summarized weekly and was affected by time-varying confounders such as bleeding or nausea. We sought to estimate what the incidence of pregnancy by 26 weeks postrandomization would have been if we shifted each participant's probability of taking aspirin or placebo each week by odds ratios (OR) between 0.30 and 3.00.Under no intervention (OR = 1), the incidence of pregnancy was 77% (95% CI: 74%, 80%). Decreasing women's probability of complying with aspirin had little estimated effect on pregnancy incidence. When we increased women's probability of taking aspirin, estimated incidence of pregnancy increased, from 83% (95% confidence interval [CI] = 79%, 87%) for OR = 2 to 89% (95% CI = 84%, 93%) for OR=3. We observed similar results when we shifted women's probability of complying with a placebo.These results estimated that realistic interventions to increase women's probability of taking aspirin would have yielded little to no impact on the incidence of pregnancy, relative to similar interventions on placebo.

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.

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.

BACKGROUND: Adherence to the Dietary Guidelines for Americans is often assessed using the Healthy Eating Index (HEI). The HEI total score reflects overall diet quality, with all aspects equally important. Using the traditional weighting scheme for the HEI, all components are generally weighted equally in the total score. However, there is limited empirical basis for applying the traditional weighting for pregnancy specifically. OBJECTIVES: We aimed to assess associations between the 12 HEI-2010 component scores and select pregnancy outcomes. METHODS: The Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be was a prospective pregnancy cohort (US multicenter, 2010–2013). Participants enrolled in the study between 6 and 13 weeks of gestation. An FFQ assessed usual dietary intake 3 months prior to pregnancy (n = 7880). Scores for the HEI-2010 components were assigned using prespecified standards based on densities (standard units per 1000 kcal) of relevant food groups for most components, a ratio (PUFAs and MUFAs to SFAs) for fatty acids, and the contribution to total energy for empty calories. Using binomial regression, we estimated risk differences between each component score and cases of small-for-gestational age (SGA) birth, preterm birth, preeclampsia, and gestational diabetes, controlling for total energy and scores for the other HEI-2010 components. RESULTS: Higher scores for greens and beans and total vegetables were associated with fewer cases of SGA birth, preterm birth, and preeclampsia. For instance, every 1-unit increase in the greens and beans score was associated with 1.2 fewer SGA infants (95% CI, 0.7–1.7), 0.7 fewer preterm births (95% CI, 0.3–1.1), and 0.7 fewer preeclampsia cases (95% CI, 0.2–1.1) per 100 deliveries. For gestational diabetes, the associations were null. CONCLUSIONS: Vegetable-rich diets were associated with fewer cases of SGA birth, preterm birth, and preeclampsia, controlling for overall diet quality. Examination of the equal weighting of the HEI components (and underlying guidance) is needed for pregnancy.

In randomized clinical trials (RCTs), per-protocol effects may be of interest in the presence of nonadherence with the randomized treatment protocol. Using machine learning in per-protocol effect estimation can help avoid model misspecification owing to strong parametric assumptions, as is common with standard methods (eg, logistic regression).To demonstrate the use of ensemble machine learning with augmented inverse probability weighting (AIPW) for per-protocol effect estimation in RCTs and to evaluate the per-protocol effect size of aspirin on pregnancy.This secondary analysis used data from 1227 women in the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial, a multicenter, block-randomized, double-blind, placebo-controlled clinical trial of the effect of daily low-dose aspirin on pregnancy outcomes in women at high risk of pregnancy loss. Participants were recruited at 4 university medical centers in the US from June 15, 2007, to July 15, 2012. Women were followed up for 6 menstrual cycles for attempted pregnancy and 36 weeks of gestation if pregnancy occurred. Follow-up was completed on August 17, 2012. Data analyses were performed on July 9, 2021.Daily low-dose (81 mg) aspirin taken at least 5 of 7 days per week for at least 80% of follow-up time relative to placebo.Pregnancy detected using human chorionic gonadotropin (hCG) levels.Among the 1227 women included in the analysis (mean SD age, 28.74 [4.80] years), 1161 (94.6%) were non-Hispanic White and 858 (69.9%) adhered to the protocol. Five machine learning models were combined into 1 meta-algorithm, which was used to construct an AIPW estimator for the per-protocol effect. Compared with adhering to placebo, adherence to the daily low-dose aspirin protocol for at least 5 of 7 days per week was associated with an increase in the probability of hCG-detected pregnancy of 8.0 (95% CI, 2.5-13.6) more hCG-detected pregnancies per 100 women in the sample, which is substantially larger than the estimated intention-to-treat estimate of 4.3 (95% CI, -1.1 to 9.6) more hCG-detected pregnancies per 100 women in the sample.These findings suggest that a low-dose aspirin protocol is associated with increased hCG-detected pregnancy in women who adhere to treatment for at least 5 days per week. With the presence of nonadherence, per-protocol treatment effect estimates differ from intention-to-treat estimates in the EAGeR trial. The results of this secondary analysis of clinical trial data suggest that machine learning could be used to estimate per-protocol effects by adjusting for confounders related to nonadherence in a more flexible way than traditional regressions.ClinicalTrials.gov Identifier: NCT00467363.

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

The Kaplan-Meier (KM) estimator of the survival function imputes event times for right-censored and left-truncated observations, but these imputations are hidden and therefore sometimes unrecognized by applied health scientists. Using a simple example data set and the redistribution algorithm, we illustrate how imputations are made by the KM estimator. We also discuss the assumptions necessary for valid analyses of survival data. Illustrating imputations hidden by the KM estimator helps to clarify these assumptions and therefore may reduce inappropriate inferences.

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.

Purpose We examined (1) if child maltreatment (CM) is associated with lower health-related quality of life (HRQoL) and fewer quality-adjusted life years (QALY) over a 9-year follow-up of midlife women and (2) if adulthood psychosocial mediators could explain these associations. Methods Women (n = 342) completed the Childhood Trauma Questionnaire. Longitudinal HRQoL and QALY outcomes measured at five study visits include 36-item Short-Form Health Survey mental component score and physical component score and the Short Form-6 Dimension health index. Aims 1 and 2 were investigated by generalized estimating equations and sequential structural nested mean models, respectively. Results Twenty percent reported 2+ CM types. Compared with women without CM, women who experienced 2+ CM types reported 5- and 4-points lower scores in mental component score and physical component score, respectively, and 28 fewer healthy days per year in QALY. Low optimism, sleep problems, and low social support each explained greater than 10% of the relationship between 2+ CM and HRQoL and QALY over time. Conclusions CM is a life-course social determinant of HRQoL and QALY throughout midlife, particularly in women who experienced 2+ CM types. Several mediators are modifiable and could be targets of interventions to mitigate the negative impact of CM on midlife HRQoL and QALY in women.

There are important challenges to the estimation and identification of average causal effects in longitudinal data with time-varying exposures. Here, we discuss the difficulty in meeting the positivity condition. Our motivating example is the per-protocol analysis of the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial. We estimated the average causal effect comparing the incidence of pregnancy by 26 weeks that would have occurred if all women had been assigned to aspirin and complied versus the incidence if all women had been assigned to placebo and complied. Using flexible targeted minimum loss-based estimation, we estimated a risk difference of 1.27% (95% CI: −9.83, 12.38). Using a less flexible inverse probability weighting approach, the risk difference was 5.77% (95% CI: −1.13, 13.05). However, the cumulative probability of compliance conditional on covariates approached 0 as follow-up accrued, indicating a practical violation of the positivity assumption, which limited our ability to make causal interpretations. The effects of nonpositivity were more apparent when using a more flexible estimator, as indicated by the greater imprecision. When faced with nonpositivity, one can use a flexible approach and be transparent about the uncertainty, use a parametric approach and smooth over gaps in the data, or target a different estimand that will be less vulnerable to positivity violations.

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.

SummaryIn this commentary, we put forth the following argument: Anyone conducting machine learning in a health-related domain should educate themselves about structural racism. We argue that structural racism is a critical body of knowledge needed for generalizability in almost all domains of health research.

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 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.

PURPOSE OF REVIEW: Epidemiologists frequently must handle competing events, which prevent the event of interest from occurring. We review considerations for handling competing events when interpreting results causally. RECENT FINDINGS: When interpreting statistical associations as causal effects, we recommend following a causal inference “roadmap” as one would in an analysis without competing events. There are, however, special considerations to be made for competing events when choosing the causal estimand that best answers the question of interest, selecting the statistical estimand (e.g. the cause-specific or subdistribution) that will target that causal estimand, and assessing whether causal identification conditions (e.g., conditional exchangeability, positivity, and consistency) have been sufficiently met. SUMMARY: When doing causal inference in the competing events setting, it is critical to first ascertain the relevant question and the causal estimand that best answers it, with the choice often being between estimands that do and do not eliminate competing events.

When causal inference is of primary interest, a range of target parameters can be chosen to define the causal effect of interest, 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 (incremental PS) approach to quantify the effect of shifting each person’s exposure propensity by some pre-determined amount. Compared to 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 relation 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 1/2 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 propensity score effects in addressing public health questions with fewer assumptions.

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.

Background A previous large randomized trial indicated that preconception-initiated low-dose aspirin (LDA) therapy did not have a positive effect on pregnancy outcomes. However, this trial was subject to nonadherence, which was not taken into account by the intention-to-treat approach. Objective To estimate per protocol effects of preconception-initiated LDA on pregnancy loss and live birth. Design The EAGeR (Effects of Aspirin on Gestation and Reproduction) trial was used to construct a prospective cohort for a post hoc analysis. (ClinicalTrials.gov: NCT00467363). Setting 4 university medical centers in the United States. Participants 1227 women between the ages of 18 and 40 years who had 1 or 2 previous pregnancy losses and were attempting pregnancy. Measurements Adherence to LDA or placebo, assessed by measuring pill bottle weights at regular intervals during follow-up. Primary outcomes were human chorionic gonadotropin (hCG)-detected pregnancies, pregnancy losses, and live births, determined by pregnancy tests and medical records. Results Relative to placebo, adhering to LDA for 5 of 7 days per week led to 8 more hCG-detected pregnancies (95% CI, 4.64 to 10.96 pregnancies), 15 more live births (CI, 7.65 to 21.15 births), and 6 fewer pregnancy losses (CI, -12.00 to -0.20 losses) for every 100 women in the trial. In addition, compared with placebo, postconception initiation of LDA therapy led to a reduction in the estimated effects. Furthermore, effects were obtained in a minimum of 4 of 7 days per week. Limitation The EAGeR trial data for this study were analyzed as observational data, thus are subject to the limitations of prospective observational studies. Conclusion Per protocol results suggest that preconception use of LDA at least 4 days per week may improve reproductive outcomes for women who have had 1 or 2 pregnancy losses. Increasing adherence to daily LDA seems to be key to improving effectiveness. Primary funding source National Institutes of Health.

All statistical estimates from data have uncertainty due to sampling variability. A standard error is one measure of uncertainty of a sample estimate (such as the mean of a set of observations or a regression coefficient). Standard errors are usually calculated based on assumptions underpinning the statistical model used in the estimation. However, there are situations in which some assumptions of the statistical model including the variance or covariance of the outcome across observations are violated, which leads to biased standard errors. One simple remedy is to use robust standard errors, which are robust to violations of certain assumptions of the statistical model. Robust standard errors are frequently used in clinical papers (e.g. to account for clustering of observations), although the underlying concepts behind robust standard errors and when to use them are often not well understood. In this paper, we demystify robust standard errors using several worked examples in simple situations in which model assumptions involving the variance or covariance of the outcome are misspecified. These are: (i) when the observed variances are different, (ii) when the variance specified in the model is wrong and (iii) when the assumption of independence is wrong.

In trials with non-compliance to assigned treatment, researchers might be interested in estimating a per-protocol effect – a comparison of two counterfactual outcomes defined by treatment assignment and (often time-varying) compliance with a well-defined treatment protocol. Here, we provide a general counterfactual definition of a per-protocol effect and discuss examples of per-protocol effects that are of either substantive or methodologic interest. In doing so, we seek to make more concrete what per-protocol effects are and highlight that one can estimate per-protocol effects that are more than just a comparison of always taking treatment in two distinct treatment arms. We then discuss one set of identifiability conditions that allow for identification of a causal per-protocol effect, highlighting some potential violations of those conditions that might arise when estimating per-protocol effects.

Background In settings in which there are time-varying confounders affected by previous exposure and a time-varying mediator, natural direct and indirect effects cannot generally be estimated unbiasedly. In the present study, we estimate interventional direct effect and interventional indirect effect of cigarette smoking as a time-varying exposure on coronary heart disease while considering body weight as a time-varying mediator. Methods To address this problem, the parametric mediational g-formula was proposed to estimate interventional direct effect and interventional indirect effect. We used data from the Multi-Ethnic Study of Atherosclerosis to estimate effect of cigarette smoking on coronary heart disease, considering body weight as time-varying mediator. Results Over a 11-years period, smoking 20 cigarettes per day compared to no smoking directly (not through weight) increased risk of coronary heart disease by an absolute difference of 1.91% (95% CI: 0.49%, 4.14%), and indirectly decreased coronary heart disease risk by -0.02% (95% CI: -0.05%, 0.04%) via change in weight. The total effect was estimated as an absolute 1.89% increase (95% CI: 0.49%, 4.13%). Conclusion The overall absolute impact of smoking to incident coronary heart disease is modest, and we did not discern any important contribution to this effect relayed through changes to bodyweight. In fact, changes in weight because of smoking have no meaningful mediating effect on CHD risk.

Objectives The incidence of oral cancer has been rapidly increasing in India, calling for evidence contributing to a deeper understanding of its determinants. Although disadvantageous life‐course socioeconomic position (SEP) is independently associated with the risk of these cancers, the explanatory mechanisms remain unclear. Possible pathways may be better understood by testing which life‐course model most influences oral cancer risk. We estimated the association between life‐course SEP and oral cancer risk under three life‐course models: critical period, accumulation and social mobility. Methods We recruited incident oral cancer cases (N = 350) and controls (N = 371) frequency‐matched by age and sex from two main referral hospitals in Kozhikode, Kerala, India, between 2008 and 2012. We collected information on childhood (0‐16 years), early adulthood (17‐30 years) and late adulthood (above 30 years) SEP and behavioural factors along the life span using interviews and a life‐grid technique. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for the association between life‐course SEP and oral cancer risk using inverse probability weighted marginal structural models. Results Relative to an advantageous SEP in childhood and early adulthood, a disadvantageous SEP was associated with oral cancer risk [(OR = 2.76, 95% CI: 1.99, 3.81) and (OR = 1.84, 95% CI: 1.21, 2.79), respectively]. In addition, participants who were in a disadvantageous (vs advantageous) SEP during all three periods of life had an increased oral cancer risk (OR = 4.86, 95% CI: 2.61, 9.06). The childhood to early adulthood social mobility model and overall life‐course trajectories indicated strong influence of exposure to disadvantageous SEP in childhood on the risk for oral cancer. Conclusions Using novel approaches to existing methods, our study provides empirical evidence that disadvantageous childhood SEP is critical for oral cancer risk in this population from Kerala, India.

Birthweight is often used as a proxy for fetal weight. Problems with this practice have recently been brought to light. We explore whether data available at birth can be used to predict estimated fetal weight using linear and quantile regression, random forests, Bayesian additive regression trees, and generalized boosted models. We train and validate each approach using 18,517 pregnancies (31,948 ultrasound visits) from the Magee-Womens Obstetric Maternal and Infant data and 240 pregnancies in a separate dataset of high-risk pregnancies. We also quantify the relation between smoking and small-for-gestational-age birth, defined as a birthweight in the lower 10th percentile of a population birthweight standard and estimated and predicted fetal weight standard. Using mean squared error and median absolute deviation criteria, quantile regression performed best among the regression-based approaches, but generalized boosted models performed best overall. Using the birthweight standard, smoking during pregnancy increased the risk of small-for-gestational-age 3.84-fold (95% CI: 2.70, 5.47). This ratio dropped to 1.65 (95% CI: 1.50, 1.81) when using the correct fetal weight standard, which was no different from the machine learning-based predicted standards, but higher than the regression-based predicted standards. Machine learning algorithms show promise in recovering missing fetal weight information. See video abstract at, http://links.lww.com/EDE/B314.

BACKGROUND Short interpregnancy intervals (IPI) are associated with poor birth outcomes. Often, only livebirths are considered to estimate IPI. The objective of our work is to explore whether the associations between demographic, behavioural, and pregnancy variables and IPI change when events other than livebirth are included. METHODS We used data from the 2006-10 and 2011-13 period of the National Survey of Family Growth (NSFG). We defined IPI using the conception date of the index pregnancy and the event date of the previous one ending in (i) livebirth; (ii) stillbirth; (iii) miscarriage; (iv) abortion; or (v) any of these events. Risk ratios (RR) were estimated for short IPI (

BACKGROUND: Pregnant women treated with methadone as opioid maintenance therapy are more likely than women treated with buprenorphine to deliver preterm. Preterm birth is associated with less risk of neonatal abstinence syndrome (NAS). We sought to assess the role of preterm birth as a mediator of the relationship between in utero exposure to methadone and NAS compared with buprenorphine. METHODS: We studied 716 women receiving methadone or buprenorphine and delivering live-born infants at Magee-Womens Hospital, Pittsburgh, Pennsylvania (2013-2015). We implemented inverse probability weighted marginal structural models to isolate the role of preterm birth (

Background and Purpose— Standard analytic approaches (eg, logistic regression) fail to adequately control for time-dependent confounding and, therefore, may yield biased estimates of the total effect of the exposure on the outcome. In the present study, we estimate the effect of body mass index, intentional physical activity, HDL (high-density lipoprotein) cholesterol, LDL (low-density lipoprotein) cholesterol, hypertension, and cigarette smoking on the 11-year risk of ischemic stroke by sex using the parametric g-formula to control time-dependent confounders. Methods— Using data from the MESA (Multi-Ethnic Study of Atherosclerosis), we followed 6809 men and women aged 45 to 84 years. We estimated the risk of stroke under 6 hypothetical interventions: maintaining body mass index 2 , maintaining normotension (systolic blood pressure 1.55 mmol/L, maintaining LDL Results— In men, the 11-year risk of ischemic stroke would be reduced by 85% (95% CI, 66–96) for all 6 hypothetical interventions. In women, this same effect was estimated as 55% (95% CI, 6–82). Conclusions— The hypothetical interventions explored in our study resulted in risk reduction in both men and women.

OBJECTIVES The aim of this study was to determine the impact of food insecurity (FI) on HIV viral load and CD4 count among people coinfected with HIV and hepatitis C virus (HCV). METHODS This study was conducted using data from the Food Security & HIV-HCV Sub-Study of the Canadian Co-Infection Cohort study. FI was measured using the adult scale of Health Canada's Household Food Security Survey Module and was classified into three categories: food security, moderate food insecurity and severe food insecurity. The association between FI, HIV viral load, and CD4 count was assessed using a stabilized inverse probability weighted marginal structural model. RESULTS A total of 725 HIV/HCV-coinfected people with 1973 person-visits over 3 years of follow-up contributed to this study. At baseline, 23% of participants experienced moderate food insecurity and 34% experienced severe food insecurity. The proportion of people with undetectable HIV viral load was 75% and the median CD4 count was 460 [interquartile range (IQR): 300-665] cells/μL. People experiencing severe food insecurity had 1.47 times [95% confidence interval (CI): 1.14, 1.88] the risk of having detectable HIV viral load and a 0.91-fold (95% CI: 0.84, 0.98) increase in CD4 count compared with people who were food secure. CONCLUSIONS These findings provide evidence of the negative impact of food insecurity on HIV viral load and CD4 count among HIV/HCV-coinfected people.

BACKGROUND: Strategies to reduce hospital readmission have been studied mainly at the local level. We assessed associations between population-wide policies supporting team-based primary care delivery models and short-term outcomes after hospital discharge. METHODS: We extracted claims data on hospital admissions for any cause from 2002 to 2009 in the province of Quebec. We included older or chronically ill patients enrolled in team-based or traditional primary care practices. Outcomes were rates of readmission, emergency department visits and mortality in the 90 days following hospital discharge. We used inverse probability weighting to balance exposure groups on covariates and used marginal structural survival models to estimate rate differences and hazard ratios. RESULTS: We included 620 656 index admissions involving 312 377 patients. Readmission rates at any point in the 90-day post-discharge period were similar between primary care models. Patients enrolled in team-based primary care practices had lower 30-day rates of emergency department visits not associated with readmission (adjusted difference 7.5 per 1000 discharges, 95% confidence interval [CI] 4.2 to 10.8) and lower 30-day mortality (adjusted difference 3.8 deaths per 1000 discharges, 95% CI 1.7 to 5.9). The 30-day difference for mortality differed according to morbidity level (moderate morbidity: 1.0 fewer deaths per 1000 discharges in team-based practices, 95% CI 0.3 more to 2.3 fewer deaths; very high morbidity: 4.2 fewer deaths per 1000 discharges, 95% CI 3.0 to 5.3; p INTERPRETATION: Our study showed that enrolment in the newer team-based primary care practices was associated with lower rates of postdischarge emergency department visits and death. We did not observe differences in readmission rates, which suggests that more targeted or intensive efforts may be needed to affect this outcome.

Background Outpatient follow-up has been a key intervention point in addressing gaps in care after hospital discharge. We sought to estimate the association between enrolment in new team-based primary care practices and 30-day postdischarge physician follow-up among older patients and patients with chronic illnesses who were admitted to hospital in Quebec, Canada. Methods Patients were selected into this cohort if a primary care physician enrolled them as a "vulnerable patient" between November 2002 and January 2005. Data for this analysis included province-wide health insurance claims for inpatient and outpatient services delivered between November 2002 and January 2009 in Quebec. The primary analysis examined time to the first outpatient postdischarge follow-up service provided by either a primary care physician or a medical specialist. We used marginal structural models to estimate adjusted rates of follow-up with a primary care physician or with a medical specialist by primary care delivery models. Results We extracted billing data for 312 377 patients that represented 620 656 index admissions for any cause from 2002 to 2009. Rates of 30-day follow-up were 374 visits to primary care physicians and 422 visits to medical specialists per 1000 discharges. Rates of primary care physician follow-up were similar across primary care delivery models, except for patients with very high morbidity; these patients had significantly higher rates of follow-up with a primary care physician if they were enrolled in team-based primary care practices (30-d rate difference [RD] 13.3 more follow-up visits per 1000 discharges, 95% confidence interval [CI] 6.8 to 19.8). Rates of follow-up with a medical specialist were lower among patients enrolled in team-based practices, particularly within 15 days of hospital discharge (15-d RD 25.1 fewer follow-up visits per 1000 discharges, 95% CI 21.1 to 29.1). Interpretation Our study found lower rates of postdischarge follow-up with a medical specialist among older patients and patients with chronic illness who were enrolled in team-based primary care practices compared with those enrolled in traditional primary care practices. Future research is needed to better understand the role of primary health care service organization in improving acute postdischarge care.

BACKGROUND Metabolic syndrome is associated with increases in both inflammation and aspirin resistance, but effectiveness of aspirin in improving reproductive health among women with metabolic syndrome is unknown. We evaluated the effectiveness of low-dose aspirin in improving reproductive outcomes across metabolic syndrome score. METHODS The EAGeR trial randomly assigned 1228 women with a history of pregnancy loss to receive 81 mg aspirin or placebo for up to six menstrual cycles of attempting pregnancy and, if they became pregnant, throughout pregnancy. We assessed components of metabolic syndrome at enrollment, including: waist circumference ≥88 cm, triglycerides ≥150 mg/dl, high-density lipoprotein ≤50 mg/dl, blood pressure ≥130 mmHg systolic or ≥85 mmHg diastolic, and glucose ≥100 mg/dl. We summed components to calculate metabolic syndrome score. RESULTS A total of 229 participants (20%) met full criteria for metabolic syndrome, 207 (18%) had two components, 366 (31%) one component, and 372 (32%) no components. Among those without any component of metabolic syndrome, aspirin was associated with 10.7 [95% confidence interval (CI) = 1.2, 20.2] more pregnancies and 13.7 (95% CI = 3.3, 24.0) more live births per 100 couples. Effects were attenuated as metabolic syndrome score increased and we observed no clear effect of aspirin on pregnancy or live birth among women with metabolic syndrome. CONCLUSIONS Low-dose aspirin is most effective in increasing pregnancy and live birth among women with no or few components of metabolic syndrome. Reduced effectiveness among women with metabolic syndrome may be due to differences in effective dose or aspirin resistance.

Purpose We assessed the extent of cardiovascular benefit of renin–angiotensin system (RAS) inhibition beyond lowering blood pressure (BP) and albuminuria in type 1 diabetes (T1D). Methods This cohort study included 605 T1D participants from the Pittsburgh Epidemiology of Diabetes Complications study without baseline coronary artery disease (CAD). Participant follow-up extended through 25 years. We implemented marginal structural models to estimate total effect of and controlled direct effect by isolating the role of BP or albuminuria in mediating the relation between RAS inhibitors and CAD. Results Total effect of longitudinal RAS inhibition treatment was associated with 38% decreased CAD risk (HR [95% CI]: 0.62 [0.23, 1.77]). The controlled direct effect of RAS inhibition was a 27% risk reduction (HR: 0.73 [0.20, 2.59]) when isolating the role of BP and 26% risk reduction (HR: 0.74 [0.16, 3.35]) when isolating the role of albuminuria. The mediation proportion for each 10 mm Hg systolic BP and each 1 log unit of albumin excretion rate were 34% and 37%, respectively. Conclusion Our findings suggest that BP regulation and albuminuria reduction can only partially explain cardiovascular benefit of RAS inhibition on CAD in T1D, supporting the assertion that RAS inhibitors provide additional cardioprotection beyond lowering BP and albuminuria.

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.

Background: Birth weight and weight gain in infancy and early childhood are commonly studied as risk factors for later cardiometabolic diseases. In this study, we explore methods for quantifying weight gain during different age periods and for comparing the magnitude of the associations with later blood pressure.Methods: Based on data from a birth cohort study nested within a large cluster-randomized trial with repeated measures of weight from birth to 16 years of age, we compared the results of four analytic approaches to assess sensitive periods of growth on blood pressure at age 16 years. Results: Approaches based on z-scores of weight or weight gain velocity (both standardized for age and sex), or on regression-based conditional weight standardized residuals, yielded more coherent results than an approach based on absolute weight gain velocity. Weight gain standardized by sex and age was positively associated with blood pressure at 16 years at all postnatal age periods, but the magnitude of association was larger during adolescence (11.5 to 16 years) than during earlier intervals (0-3 months, 3-12 months, 1-6.5 years, or 6.5-11.5 years). Conclusions: Standardization of weight and weight gain by age and sex, or regression-based standardized residuals based on conditional weight, reflects relative gain and thus accounts for the rapid weight gains normally observed in early infancy and puberty. Adolescence appears to be a more sensitive period for relative weight gain effects on later blood pressure than earlier periods, even those of similar duration.