Influence of Incomplete Death Information on Cumulative Risk Estimates in United States Claims Data

Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event-equivalent to other withdrawal reasons-rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment-weighted cumulative risks of a composite cardiovascular outcome among 34,527 initiators of telmisartan (exposure) and ramipril (referent) ages ≥55 in Optum claims from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared to treating death as a competing event (sub-distribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users (selected results), 5-year cause-specific and sub-distribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI 15.3, 17.5) and 16.2 (95% CI 15.1, 17.3) among ages 55-64 (difference=0.2) and were 43.2 (95% CI 41.3, 45.2) and 39.7 (95% CI 37.9, 41.4) among ages ≥75 (difference=3.6). Plasmode simulation results demonstrated the differences in cause-specific versus sub-distribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern.

Use of Sodium-Glucose Cotransporter-2 Inhibitors and Angiotensin Receptor-Neprilysin Inhibitors in Patients With Atrial Fibrillation and Heart Failure From 2021 to 2022: An Analysis of Real-World Data

BACKGROUND

Contemporary use of sodium-glucose cotransporter-2 inhibitors (SGLT2i) and angiotensin receptor-neprilysin inhibitors (ARNi) in patients with atrial fibrillation (AF) and heart failure (HF) has not been described.

METHODS AND RESULTS

We analyzed the MarketScan databases for the period January 1, 2021 to July 30, 2022. Validated algorithms were used to identify patients with AF and HF, and to classify patients into HF with reduced ejection fraction (HFrEF) or HF with preserved ejection fraction (HFpEF). We assessed the prevalence of SGLT2i and ARNi use overall and by HF type. Additionally, we explored correlates of lower use, including demographics and comorbidities. The study population included 60 927 patients (mean age, 75 years; 43% women) diagnosed with AF and HF (85% with HFpEF, 15% with HFrEF). Prevalence of ARNi use was 11% overall (30% in HFrEF, 8% in HFpEF), whereas the corresponding figure was 6% for SGLT2i (13% in HFrEF, 5% in HFpEF). Use of both medications increased over the study period: ARNi from 9% to 12% (22%-29% in HFrEF, 6%-8% in HFpEF), and SGLT2i from 3% to 9% (6%-16% in HFrEF, 2%-7% in HFpEF). Female sex, older age, and specific comorbidities were associated with lower use of these 2 medication types overall and by HF type.

CONCLUSIONS

Use of ARNi and SGLT2i in patients with AF and HF is suboptimal, particularly among women and older individuals, though use is increasing. These results underscore the need for understanding reasons for these disparities and developing interventions to improve adoption of evidence-based therapies among patients with comorbid AF and HF.

Periconceptional Dietary Patterns and Adverse Pregnancy and Birth Outcomes

BACKGROUND

The periconceptional period is a critical window for the origins of adverse pregnancy and birth outcomes, yet little is known about the dietary patterns that promote perinatal health.

OBJECTIVE

We used machine learning methods to determine the effect of periconceptional dietary patterns on risk of preeclampsia, gestational diabetes, preterm birth, small-for-gestational-age (SGA) birth, and a composite of these outcomes.

METHODS

We used data from 8259 participants in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (8 US medical centers, 2010‒2013). Usual daily periconceptional intake of 82 food groups was estimated from a food frequency questionnaire. We used k-means clustering with a Euclidean distance metric to identify dietary patterns. We estimated the effect of dietary patterns on each perinatal outcome using targeted maximum likelihood estimation and an ensemble of machine learning algorithms, adjusting for confounders including health behaviors and psychological, neighborhood, and sociodemographic factors.

RESULTS

The 4 dietary patterns that emerged from our data were identified as "Sandwiches and snacks" (34% of the sample); "High fat, sugar, and sodium" (29%); "Beverages, refined grains, and mixed dishes" (21%); and "High fruits, vegetables, whole grains, and plant proteins" (16%). One-quarter of pregnancies had preeclampsia (8% incidence), gestational diabetes (5%), preterm birth (8%), or SGA birth (8%). Compared with the "High fat, sugar, and sodium" pattern, there were 3.3 to 4.3 fewer cases of the composite adverse outcome per 100 pregnancies among participants following the "Beverages, refined grains and mixed dishes" pattern (risk difference -0.043; 95% confidence interval -0.078, -0.009), "High fruits, vegetables, whole grains and plant proteins" pattern (-0.041; 95% confidence interval -0.078, -0.004), and "Sandwiches and snacks" pattern (-0.033; 95% confidence interval -0.065, -0.002).

CONCLUSIONS

Our results highlight that there are a variety of periconceptional dietary patterns that are associated with perinatal health and reinforce the negative health implications of diets high in fat, sugars, and sodium.

Characterizing Fit-for-Purpose Real-World Data: An Assessment of a Mother-Infant Linkage in the Japan Medical Data Center Claims Database

Purpose

Observational postapproval safety studies are needed to inform medication safety during pregnancy. Real-world databases can be valuable for supporting such research, but fitness for regulatory purpose must first be vetted. Here, we demonstrate a fit-for-purpose assessment of the Japan Medical Data Center (JMDC) claims database for pregnancy safety regulatory decision-making.

Patients and Methods

The Duke-Margolis framework considers a database's fitness for regulatory purpose based on relevancy (capacity to answer the research question based on variable availability and a sufficiently sized, representative population) and quality (ability to validly answer the research question based on data completeness and accuracy). To assess these considerations, we examined descriptive characteristics of infants and pregnancies among females ages 12-55 years in the JMDC between January 2005 and March 2022.

Results

For relevancy, we determined that critical data fields (maternal medications, infant major congenital malformations, covariates) are available. Family identification codes permitted linkage of 385,295 total mother-infant pairs, 57% of which were continuously enrolled during pregnancy. The prevalence of specific congenital malformation subcategories and maternal medical conditions were representative of the general population, but preterm births were below expectations (3.6% versus 5.6%) in this population. For quality, our methods are expected to accurately identify the complete set of mothers and infants with a shared health insurance plan. However, validity of gestational age information was limited given the high proportion (60%) of missing live birth delivery codes coupled with suppression of infant birth dates and inaccessibility of disease codes with gestational week information.

Conclusion

The JMDC may be well suited for descriptive studies of pregnant people in Japan (eg, comorbidities, medication usage). More work is needed to identify a method to assign pregnancy onset and delivery dates so that in utero medication exposure windows can be defined more precisely as needed for many regulatory postapproval pregnancy safety studies.

Use of SGLT2i and ARNi in patients with atrial fibrillation and heart failure in 2021-2022: an analysis of real-world data

Objective

To evaluate utilization of sodium-glucose cotransporter-2 inhibitors (SGLT2i) and angiotensin receptor neprilysin inhibitors (ARNi) in patients with atrial fibrillation (AF) and heart failure (HF).

Methods

We analyzed the MarketScan databases for the period 1/1/2021 to 6/30/2022. Validated algorithms were used to identify patients with AF and HF, and to classify patients into HF with reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). We assessed the prevalence of SGLT2i and ARNi use overall and by HF type. Additionally, we explored correlates of lower utilization, including demographics and comorbidities.

Results

The study population included 60,927 patients (mean age 75, 43% female) diagnosed with AF and HF (85% with HFpEF, 15% with HFrEF). Prevalence of ARNi use was 11% overall (30% in HFrEF, 8% in HFpEF), while the corresponding figure was 6% for SGLT2i (13% in HFrEF, 5% in HFpEF). Use of both medications increased over the study period: ARNi from 9% to 12% (from 22% to 29% in HFrEF, from 6% to 8% in HFpEF), and SGLT2i from 3% to 9% (from 6% to 16% in HFrEF, from 2% to 7% in HFpEF). Female sex, older age, and specific comorbidities were associated with lower utilization of these two medication types overall and by HF type.

Conclusion

Use of ARNi and SGLT2i in patients with AF and HF is suboptimal, particularly among females and older individuals, though utilization is increasing. These results underscore the need for understanding reasons for these disparities and developing interventions to improve adoption of evidence-based therapies among patients with comorbid AF and HF.

Is the Association Between Fruits and Vegetables and Preeclampsia Due to Higher Dietary Vitamin C and Carotenoid Intakes?

BACKGROUND

Diets dense in fruits and vegetables are associated with a reduced risk of preeclampsia, but pathways underlying this relationship are unclear. Dietary antioxidants may contribute to the protective effect.

OBJECTIVE

We determined the extent to which the effect of dietary fruit and vegetable density on preeclampsia is because of high intakes of dietary vitamin C and carotenoids.

METHODS

We used data from 7572 participants in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (8 United States medical centers, 2010‒2013). Usual daily periconceptional intake of total fruits and total vegetables was estimated from a food frequency questionnaire. We estimated the indirect effect of ≥2.5 cups/1000 kcal of fruits and vegetables through vitamin C and carotenoid on the risk of preeclampsia. We estimated these effects using targeted maximum likelihood estimation and an ensemble of machine learning algorithms, adjusting for confounders, including other dietary components, health behaviors, and psychological, neighborhood, and sociodemographic factors.

RESULTS

Participants who consumed ≥2.5 cups of fruits and vegetables per 1000 kcal were less likely than those who consumed <2.5 cups/1000 kcal to develop preeclampsia (6.4% compared with 8.6%). After confounder adjustment, we observed that higher fruit and vegetable density was associated with 2 fewer cases of preeclampsia (risk difference: -2.0; 95% CI: -3.9, -0.1)/100 pregnancies compared with lower density diets. High dietary vitamin C and carotenoid intake was not associated with preeclampsia. The protective effect of high fruit and vegetable density on the risk of preeclampsia and late-onset preeclampsia was not mediated through dietary vitamin C and carotenoids.

CONCLUSIONS

Evaluating other nutrients and bioactives in fruits and vegetables and their synergy is worthwhile, along with characterizing the effect of individual fruits or vegetables on preeclampsia risk.

Experiences of Racial Discrimination and Periconceptional Diet Quality

BACKGROUND

Racism is a key determinant of perinatal health disparities. Poor diet may contribute to this effect, but research on racism and dietary patterns is limited.

OBJECTIVE

We aimed to describe the relation between experiences of racial discrimination and adherence to the 2015‒2020 Dietary Guidelines for Americans.

METHODS

We used data from a prospective pregnancy cohort study conducted at 8 United States medical centers (2010‒2013). At 6‒13 weeks of gestation, 10,038 nulliparous people with singleton pregnancies were enrolled. Participants completed a Block food frequency questionnaire, assessing usual diet in the 3 mo around conception, and the Krieger Experiences of Discrimination Scale, assessing the number of situational domains (e.g., at school and on the street) in which participants ever experienced racial discrimination. Alignment of dietary intake with the 2015-2020 Dietary Guidelines for Americans was assessed using the Healthy Eating Index (HEI)-2015.

RESULTS

The study showed that 49%, 44%, 35%, and 17% of the Asian, Black, Hispanic, and White participants reported experiences of racial discrimination in any domain. Most participants experienced discrimination in 1 or 2 situational domains. There were no meaningful differences in HEI-2015 total or component scores in any racial or ethnic group according to count of self-reported domains in which individuals experienced discrimination. For example, mean total scores were 57‒59 among Black, 61‒66 among White, 61‒63 among Hispanic, and 66‒69 among Asian participants across the count of racial discrimination domains.

CONCLUSIONS

This null association stresses the importance of going beyond interpersonal racial discrimination to consider the institutions, systems, and practices affecting racialized people to eliminate persistent inequalities in diet and perinatal health.

Does heterogeneity underlie differences in treatment effects estimated from SuperLearner versus logistic regression? An application in nutritional epidemiology

PURPOSE

A strength of SuperLearner is that it may accommodate key interactions between model variables without a priori specification. In prior research, protective associations between fruit intake and preeclampsia were stronger when estimated using SuperLearner with targeted maximum likelihood estimation (TMLE) compared with multivariable logistic regression without any interaction terms. We explored whether heterogeneity (i.e., differences in the effect estimate due to interactions between fruit intake and covariates) may partly explain differences in estimates from these two models.

METHODS

Using a U.S. prospective pregnancy cohort (2010-2013, n = 7781), we estimated preeclampsia risk differences (RDs) for higher versus lower fruit density using multivariable logistic regression and included two-way statistical interactions between fruit density and each of the 25 model covariates. We compared the RDs with those from SuperLearner with TMLE (gold standard) and logistic regression with no interaction.

RESULTS

From the logistic regression models with two-way statistical interactions, 48% of the preeclampsia RDs were ≤-0.02 (closer to SuperLearner with TMLE estimate); 40% equaled -0.01 (same as logistic regression with no interaction estimate); the minority of RDs were at or crossed the null.

CONCLUSIONS

Our exploratory analysis provided preliminary evidence that heterogeneity may partly explain differences in estimates from logistic regression versus SuperLearner with TMLE.

Longitudinal profiles of neighborhood socioeconomic vulnerability influence blood pressure changes across the female midlife period

PURPOSE

To examine whether longitudinal exposure to neighborhood socioeconomic vulnerability influences blood pressure changes throughout midlife in a racially, ethnically, and geographically-diverse cohort of women transitioning through menopause.

METHODS

We used longitudinal data on 2738 women (age 42-52 at baseline) living in six United States cities from The Study of Women's Health Across the Nation. Residential histories, systolic blood pressures (SBP), and diastolic blood pressures (DBP) were collected annually for ten years. We used longitudinal latent profile analysis to identify patterns of neighborhood socioeconomic vulnerability occurring from 1996 to 2007 in participant neighborhoods. We used linear mixed-effect models to determine if a woman's neighborhood profile throughout midlife was associated with blood pressure changes.

RESULTS

We identified four unique profiles of neighborhood socioeconomic vulnerability - differentiated by residential socioeconomic status, population density, and vacant housing conditions - which remained stable across time. Women residing in the most socioeconomically vulnerable neighborhoods experienced the steepest increase in annual SBP growth by 0.93 mmHg/year (95% CI: 0.65-1.21) across ten-year follow-up.

CONCLUSIONS

Neighborhood socioeconomic vulnerability was significantly associated with accelerated SBP increases throughout midlife among women.

Racial Differences in Fatal Out-of-Hospital Coronary Heart Disease and the Role of Income in the Atherosclerosis Risk in Communities Cohort Study (1987 to 2017)

Black patients have higher incident fatal coronary heart disease (CHD) rates than do their White counterparts. Racial differences in out-of-hospital fatal CHD could explain the excess risk in fatal CHD among Black patients. We examined racial disparities in in- and out-of-hospital fatal CHD among participants with no history of CHD, and whether socioeconomic status might play a role in this association. We used data from the ARIC (Atherosclerosis Risk in Communities) study, including 4,095 Black and 10,884 White participants, followed between 1987 and 1989 until 2017. Race was self-reported. We examined racial differences in in- and out-of-hospital fatal CHD with hierarchical proportional hazard models. We then examined the role of income in these associations, using Cox marginal structural models for a mediation analysis. The incidence of out-of-hospital and in-hospital fatal CHD was 1.3 and 2.2 in Black participants, and 1.0 and 1.1 in White participants, respectively, per 1,000 person-years. The gender- and age-adjusted hazard ratios comparing out-of-hospital and in-hospital incident fatal CHD in Black with that in White participants were 1.65 (1.32 to 2.07) and 2.37 (1.96 to 2.86), respectively. The income-controlled direct effects of race in Black versus White participants decreased to 1.33 (1.01 to 1.74) for fatal out-of-hospital and to 2.03 (1.61 to 2.55) for fatal in-hospital CHD in Cox marginal structural models. In conclusion, higher rates of fatal in-hospital CHD in Black participants than in their White counterparts likely drive the overall racial differences in fatal CHD. Income largely explained racial differences in both fatal out-of-hospital CHD and fatal in-hospital CHD.

A Simulation Study Comparing the Performance of Time-Varying Inverse Probability Weighting and G-Computation in Survival Analysis

Inverse probability weighting (IPW) and g-computation are commonly used in time-varying analyses. To inform decisions on which to use, we compared these methods using a plasmode simulation based on data from the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial (June 15, 2007-July 15, 2011). In our main analysis, we simulated a cohort study of 1,226 individuals followed for up to 10 weeks. The exposure was weekly exercise, and the outcome was time to pregnancy. We controlled for 6 confounding factors: 4 baseline confounders (race, ever smoking, age, and body mass index) and 2 time-varying confounders (compliance with assigned treatment and nausea). We sought to estimate the average causal risk difference by 10 weeks, using IPW and g-computation implemented using a Monte Carlo estimator and iterated conditional expectations (ICE). Across 500 simulations, we compared the bias, empirical standard error (ESE), average standard error, standard error ratio, and 95% confidence interval coverage of each approach. IPW (bias = 0.02; ESE = 0.04; coverage = 92.6%) and Monte Carlo g-computation (bias = -0.01; ESE = 0.03; coverage = 94.2%) performed similarly. ICE g-computation was the least biased but least precise estimator (bias = 0.01; ESE = 0.06; coverage = 93.4%). When choosing an estimator, one should consider factors like the research question, the prevalences of the exposure and outcome, and the number of time points being analyzed.

Estimation of the Time-Varying Incremental Effect of Low-dose Aspirin on Incidence of Pregnancy

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Estimation of the Average Causal Effect in Longitudinal Data With Time-Varying Exposures: The Challenge of Nonpositivity and the Impact of Model Flexibility

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.

Sexually transmitted infections and risk of hypertensive disorders of pregnancy

Hypertensive disorders of pregnancy (HDP) result in maternal morbidity and mortality but are rarely examined in perinatal studies of sexually transmitted infections. We examined associations between common sexually transmitted infections and HDP among 38,026 singleton pregnancies. Log-binomial regression calculated relative risk (RRs) and 95% confidence intervals (CIs) for associations with gestational hypertension, preeclampsia with severe features, mild preeclampsia, and superimposed preeclampsia. All models were adjusted for insurance type, maternal age, race/ethnicity, and education. Additional adjustments resulted in similar effect estimates. Chlamydia was associated with preeclampsia with severe features (RR_adj. 1.4, 95% CI 1.1, 1.9). Effect estimates differed when we examined first prenatal visit diagnosis only (RR_adj. 1.3, 95% CI 0.9, 1.9) and persistent or recurrent infection (RR_adj. 2.0, 95% CI 1.1, 3.4). For chlamydia (RR_adj. 2.0, 95% CI 1.3, 2.9) and gonorrhea (RR_adj. 3.0, 95% CI 1.1, 12.2), women without a documented treatment were more likely to have preeclampsia with severe features. Among a diverse perinatal population, sexually transmitted infections may be associated with preeclampsia with severe features. With the striking increasing rates of sexually transmitted infections, there is a need to revisit the burden in pregnant women and determine if there is a link between infections and hypertensive disorders of pregnancy.

Equal Weighting of the Healthy Eating Index-2010 Components May Not be Appropriate for Pregnancy

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.

Use of a Doubly Robust Machine-Learning-Based Approach to Evaluate Body Mass Index as a Modifier of the Association Between Fruit and Vegetable Intake and Preeclampsia

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

Use of Machine Learning to Estimate the Per-Protocol Effect of Low-Dose Aspirin on Pregnancy Outcomes: A Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

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).

OBJECTIVES

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.

DESIGN, SETTING, AND PARTICIPANTS

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.

EXPOSURES

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.

MAIN OUTCOMES AND MEASURES

Pregnancy detected using human chorionic gonadotropin (hCG) levels.

RESULTS

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.

CONCLUSIONS AND RELEVANCE

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.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00467363.

The Role of the Natural Course in Causal Analysis

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.

Does weight mediate the effect of smoking on coronary heart disease? Parametric mediational g-formula analysis

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.

Performance Evaluation of Parametric and Nonparametric Methods When Assessing Effect Measure Modification

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.

Can Ensemble Machine Learning Improve the Accuracy of Severe Maternal Morbidity Screening in a Perinatal Database?

BACKGROUND

Severe maternal morbidity (SMM) is an important maternal health indicator, but existing tools to identify SMM have substantial limitations. Our objective was to retrospectively identify true SMM status using ensemble machine learning in a hospital database and to compare machine learning algorithm performance with existing tools for SMM identification.

METHODS

We screened all deliveries occurring at Magee-Womens Hospital, Pittsburgh, PA (2010-2011 and 2013-2017) using the Centers for Disease Control and Prevention list of diagnoses and procedures for SMM, intensive care unit admission, and/or prolonged postpartum length of stay. We performed a detailed medical record review to confirm case status. We trained ensemble machine learning (SuperLearner) algorithms, which "stack" predictions from multiple algorithms to obtain optimal predictions, on 171 SMM cases and 506 non-cases from 2010 to 2011, then evaluated the performance of these algorithms on 160 SMM cases and 337 non-cases from 2013 to 2017.

RESULTS

Some SuperLearner algorithms performed better than existing screening criteria in terms of positive predictive value (0.77 vs. 0.64, respectively) and balanced accuracy (0.99 vs. 0.86, respectively). However, they did not perform as well as the screening criteria in terms of true-positive detection rate (0.008 vs. 0.32, respectively) and performed similarly in terms of negative predictive value. The most important predictor variables were intensive care unit admission and prolonged postpartum length of stay.

CONCLUSIONS

Ensemble machine learning did not globally improve the ascertainment of true SMM cases. Our results suggest that accurate identification of SMM likely will remain a challenge in the absence of a universal definition of SMM or national obstetric surveillance systems.

Adverse Cardiovascular Events Following Severe Maternal Morbidity

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.

AIPW: An R Package for Augmented Inverse Probability-Weighted Estimation of Average Causal Effects

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.

Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms

Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data generating mechanisms. As such, numerous authors have advocated for ML methods to estimate causal effects. Unfortunately, ML algorithmscan perform worse than parametric regression. We demonstrate the performance of ML-based single- and double-robust estimators. We use 100 Monte Carlo samples with sample sizes of 200, 1200, and 5000 to investigate bias and confidence interval coverage under several scenarios. In a simple confounding scenario, confounders were related to the treatment and the outcome via parametric models. In a complex confounding scenario, the simple confounders were transformed to induce complicated nonlinear relationships. In the simple scenario, when ML algorithms were used, double-robust estimators were superior to single-robust estimators. In the complex scenario, single-robust estimators with ML algorithms were at least as biased as estimators using misspecified parametric models. Double-robust estimators were less biased, but coverage was well below nominal. The use of sample splitting, inclusion of confounder interactions, reliance on a richly specified ML algorithm, and use of doubly robust estimators was the only explored approach that yielded negligible bias and nominal coverage. Our results suggest that ML based singly robust methods should be avoided.

Simulation as a Tool for Teaching and Learning Epidemiologic Methods

In aspiring to be discerning epidemiologists, we must learn to think critically about the fundamental concepts in our field and be able to understand and apply many of the novel methods being developed today. We must also find effective ways to teach both basic and advanced topics in epidemiology to graduate students, in a manner that goes beyond simple provision of knowledge. Here, we argue that simulation is one critical tool that can be used to help meet these goals, by providing examples of how simulation can be used to address 2 common misconceptions in epidemiology. First, we show how simulation can be used to explore nondifferential exposure misclassification. Second, we show how an instructor could use simulation to provide greater clarity on the correct definition of the P value. Through these 2 examples, we highlight how simulation can be used to both clearly and concretely demonstrate theoretical concepts, as well as to test and experiment with ideas, theories, and methods in a controlled environment. Simulation is therefore useful not only in the classroom but also as a skill for independent self-learning.

The Effect of Preconception-Initiated Low-Dose Aspirin on Human Chorionic Gonadotropin-Detected Pregnancy, Pregnancy Loss, and Live Birth : Per Protocol Analysis of a Randomized Trial

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.

Reflection on modern methods: demystifying robust standard errors for epidemiologists

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.

Hidden Imputations and the Kaplan-Meier Estimator

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.

Vaginal bleeding and nausea in early pregnancy as predictors of clinical pregnancy loss

BACKGROUND

Although nausea and vaginal bleeding are commonly experienced in early pregnancy, their prognostic value in predicting clinical pregnancy loss is not well understood.

OBJECTIVE

This study aimed to understand whether timing of bleeding and nausea symptoms can be used to predict risk of pregnancy loss among women with ultrasound-confirmed pregnancies.

STUDY DESIGN

A cohort of 701 women with clinically confirmed pregnancies and 1 to 2 previous pregnancy losses were preconceptionally enrolled in the Effects of Aspirin in Gestation and Reproduction trial (2006-2012). Participants completed daily symptom diaries from 2 to 8 weeks' gestation and were prospectively monitored for detection of pregnancy loss. The risk of pregnancy loss was estimated for each observed bleeding and nausea pattern, and positive and negative predictive values for each pattern were calculated.

RESULTS

Among 701 women, 211 (30.1%) reported any vaginal bleeding, and 639 (91.2%) reported any nausea. Most bleeding experienced by women was spotting and contained within a single episode. Within 2 to <4, 4 to <6, and 6 to 8 weeks' gestation, vaginal bleeding occurred in 5.9% (41) (5.7% live birth, 7.1% clinical pregnancy loss), 14.6% (102) (13.9% live birth, 18.6% clinical pregnancy loss), and 20.8% (146) (18.4% live birth, 32.4% clinical pregnancy loss) of women, respectively. Within the same gestational periods, nausea was reported in 22.7% (159) (23.2% live birth, 20.4% clinical pregnancy loss), 65.9% (462) (67.5% live birth, 58.4% clinical pregnancy loss), and 87.0% (610) (90.6% live birth, 69.0% clinical pregnancy loss) of women. Women who had bleeding without nausea between 6 and 8 weeks' gestation (3.6% prevalance) had the greatest risk of clinical pregnancy loss (risk difference=56.1%; 95% confidence interval, 37.6-74.7), a positive predictive value of 68.0% (49.7%, 86.3%), negative predictive value of 85.8% (83.2%, 88.4%), positive likelihood ratio of 11.1 (2.04, 20.1), and negative likelihood ratio of 0.86 (0.79, 0.93). Nausea and bleeding are clinical factors that predicted clinical pregnancy loss (area under the curve, 0.87; 95% confidence interval, 0.81-0.88) similar to age, body mass index, blood pressure, and waist-to-hip ratio (area under the curve, 0.81; 95% confidence interval, 0.78-0.88) measured preconceptionally.

CONCLUSION

Women experiencing bleeding without nausea between 6 and 8 weeks' gestation had an increased risk of clinical pregnancy loss. Bleeding and nausea were not predictive risk factors of clinical pregnancy loss prior to 6 weeks' gestation.

Defining and Identifying Per-protocol Effects in Randomized Trials

In trials with noncompliance 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.

Causal inference in the face of competing events

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.

Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes

BACKGROUND

Conventional analytic approaches for studying diet patterns assume no dietary synergy, which can lead to bias if incorrectly modeled. Machine learning algorithms can overcome these limitations.

OBJECTIVES

We estimated associations between fruit and vegetable intake relative to total energy intake and adverse pregnancy outcomes using targeted maximum likelihood estimation (TMLE) paired with the ensemble machine learning algorithm Super Learner, and compared these with results generated from multivariable logistic regression.

METHODS

We used data from 7572 women in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be. Usual daily periconceptional intake of total fruits and total vegetables was estimated from an FFQ. We calculated the marginal risk of preterm birth, small-for-gestational-age (SGA) birth, gestational diabetes, and pre-eclampsia according to density of fruits and vegetables (cups/1000 kcal) ≥80th percentile compared with <80th percentile using multivariable logistic regression and Super Learner with TMLE. Models were adjusted for confounders, including other Healthy Eating Index-2010 components.

RESULTS

Using logistic regression, higher fruit and high vegetable densities were associated with 1.1% and 1.4% reductions in pre-eclampsia risk compared with lower densities, respectively. They were not associated with the 3 other outcomes. Using Super Learner with TMLE, high fruit and vegetable densities were associated with fewer cases of preterm birth (-4.0; 95% CI: -4.9, -3.0 and -3.7; 95% CI: -5.0, -2.3), SGA (-1.7; 95% CI: -2.9, -0.51 and -3.8; 95% CI: -5.0, -2.5), and pre-eclampsia (-3.2; 95% CI: -4.2, -2.2 and -4.0; 95% CI: -5.2, -2.7) per 100 births, respectively, and high vegetable densities were associated with a 0.9% increase in risk of gestational diabetes.

CONCLUSIONS

The differences in results between Super Learner with TMLE and logistic regression suggest that dietary synergy, which is accounted for in machine learning, may play a role in pregnancy outcomes. This innovative methodology for analyzing dietary data has the potential to advance the study of diet patterns.

Life-course impact of child maltreatment on midlife health-related quality of life in women: longitudinal mediation analysis for potential pathways

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.

Association of Overweight and Obesity Development Between Pregnancies With Stillbirth and Infant Mortality in a Cohort of Multiparous Women

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.

Analysis of 'sensitive' periods of fetal and child growth

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 in relation to 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 (11.5-16 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.

Mediation analysis for estimating cardioprotection of longitudinal RAS inhibition beyond lowering blood pressure and albuminuria in type 1 diabetes

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.

Effects of Hypothetical Interventions on Ischemic Stroke Using Parametric G-Formula

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 <25 kg/m 2 , maintaining normotension (systolic blood pressure <140 and diastolic <90 mm Hg), quitting smoking, maintaining HDL >1.55 mmol/L, maintaining LDL <3.11 mmol/L, and exercising at least 210 minutes per week. The effects of joint hypothetical interventions were also simulated.

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.