Placental Abruption and Cardiovascular Event Risk (PACER): Design, data linkage, and preliminary findings.

BACKGROUND: Obstetrical complications impact the health of mothers and offspring along the life course, resulting in an increased burden of chronic diseases. One specific complication is abruption, a life-threatening condition with consequences for cardiovascular health that remains poorly studied. OBJECTIVES: To describe the design and data linkage algorithms for the Placental Abruption and Cardiovascular Event Risk (PACER) cohort. POPULATION: All subjects who delivered in New Jersey, USA, between 1993 and 2020. DESIGN: Retrospective, population-based, birth cohort study. METHODS: We linked the vital records data of foetal deaths and live births to delivery and all subsequent hospitalisations along the life course for birthing persons and newborns. The linkage was based on a probabilistic record-matching algorithm. PRELIMINARY RESULTS: Over the 28 years of follow-up, we identified 1,877,824 birthing persons with 3,093,241 deliveries (1.1%, n = 33,058 abruption prevalence). The linkage rates for live births-hospitalisations and foetal deaths-hospitalisations were 92.4% (n = 2,842,012) and 70.7% (n = 13,796), respectively, for the maternal cohort. The corresponding linkage rate for the live births-hospitalisations for the offspring cohort was 70.3% (n = 2,160,736). The median (interquartile range) follow-up for the maternal and offspring cohorts was 15.4 (8.1, 22.4) and 14.4 (7.4, 21.0) years, respectively. We will undertake multiple imputations for missing data and develop inverse probability weights to account for selection bias owing to unlinked records. CONCLUSIONS: Pregnancy offers a unique window to study chronic diseases along the life course and efforts to identify the aetiology of abruption may provide important insights into the causes of future CVD. This project presents an unprecedented opportunity to understand how abruption may predispose women and their offspring to develop CVD complications and chronic conditions later in life.

Direct Effect of Life-Course Socioeconomic Status on Late-Life Cognition and Cognitive Decline in the Rush Memory and Aging Project.

The role of socioeconomic status (SES) across the life course in late-life cognition is unclear. We tested the hypotheses that: 1) High SES in childhood, young adulthood, midlife, and late life have independent causal effects on higher cognition level and slower cognitive decline; 2) Compared with stable low SES (referent), stable high SES has the largest estimated effect for higher cognition level and slower decline among life-course SES combinations. The Rush Memory and Aging Project enrolled 1,940 dementia-free older adults in northeastern Illinois (1997-2018). We used inverse probability-weighted marginal structural models to estimate the joint and independent effect of each life-course SES on global and domain-specific cognition. A total of 1,746 participants had, on average, 6 years of follow-up. High SES at each life-course stage starting in young adulthood had a protective estimated effect on global and domain-specific cognition intercepts. Compared with consistently low SES, consistently high SES (ß = 0.64, 95% confidence interval: 0.48, 0.93) and high SES beyond childhood (ß = 0.64, 95% confidence interval: 0.47, 0.83) had the largest benefit for global cognition intercepts. None of the life-course SES measures influenced rate of global or domain-specific decline. Additional understanding of life-course SES components influencing cognitive level is warranted.

The role of body mass index at diagnosis of colorectal cancer on Black-White disparities in survival: a density regression mediation approach.

The study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease. In the case of colorectal cancer (CRC), disparities in survival among non-Hispanic Whites and Blacks are well documented, and mechanisms leading to these disparities need to be studied formally. It has also been established that body mass index (BMI) is a risk factor for developing CRC, and recent literature shows BMI at diagnosis of CRC is associated with survival. Since BMI varies by racial/ethnic group, a question that arises is whether differences in BMI are partially responsible for observed racial/ethnic disparities in survival for CRC patients. This article presents new methodology to quantify the impact of the hypothetical intervention that matches the BMI distribution in the Black population to a potentially complex distributional form observed in the White population on racial/ethnic disparities in survival. Our density mediation approach can be utilized to estimate natural direct and indirect effects in the general causal mediation setting under stronger assumptions. We perform a simulation study that shows our proposed Bayesian density regression approach performs as well as or better than current methodology allowing for a shift in the mean of the distribution only, and that standard practice of categorizing BMI leads to large biases when BMI is a mediator variable. When applied to motivating data from the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium, our approach suggests the proposed intervention is potentially beneficial for elderly and low-income Black patients, yet harmful for young or high-income Black populations.

Testing Black-White Disparities in Biological Aging Among Older Adults in the United States: Analysis of DNA-Methylation and Blood-Chemistry Methods.

Biological aging is a proposed mechanism through which social determinants drive health disparities. We conducted proof-of-concept testing of 8 DNA-methylation (DNAm) and blood-chemistry quantifications of biological aging as mediators of disparities in healthspan between Black and White participants in the 2016 wave of the Health and Retirement Study (n = 9,005). We quantified biological aging from 4 DNAm "clocks" (Horvath, Hannum, PhenoAge, and GrimAge clock), a DNAm pace-of-aging measure (DunedinPoAm), and 3 blood-chemistry measures (PhenoAge, Klemera-Doubal method biological age, and homeostatic dysregulation). We quantified Black-White disparities in healthspan from cross-sectional and longitudinal data on physical performance tests, self-reported limitations in activities of daily living, and physician-diagnosed chronic diseases, self-rated health, and survival. DNAm and blood-chemistry quantifications of biological aging were moderately correlated (Pearson's r = 0.1-0.4). The GrimAge clock, DunedinPoAm, and all 3 blood-chemistry measures were associated with healthspan characteristics (e.g., mortality effect-size hazard ratios were 1.71-2.32 per standard deviation of biological aging) and showed evidence of more advanced/faster biological aging in Black participants than in White participants (Cohen's d = 0.4-0.5). These measures accounted for 13%-95% of Black-White differences in healthspan-related characteristics. Findings suggest that reducing disparities in biological aging can contribute to building health equity.

COVID-19 Pandemic Factors and Depressive Symptoms Among Primary Care Workers in São Paulo, Brazil, October and November 2020.

Objectives. To investigate associations between COVID-19-related factors and depressive symptoms among primary care workers (PCWs) in São Paulo, Brazil, and to compare the prevalence of probable depression among PCWs before and during the pandemic. Methods. In a random sample of primary care clinics, we examined 6 pandemic-related factors among 828 PCWs. We used multivariate Poisson regression with robust variance to estimate prevalence ratios for probable depression. We assessed the prevalence of probable depression in PCWs before and during the pandemic in 2 comparable studies. Results. Adjusted prevalence ratios were substantial for insufficient personal protective equipment; experiences of discrimination, violence, or harassment; and lack of family support. Comparisons between PCWs before and during the pandemic showed that the prevalence of probable depression among physicians, nurses, and nursing assistants was higher during the pandemic and that the prevalence among community health workers was higher before the pandemic. Conclusions. Our findings indicate domains that may be crucial to mitigating depression among PCWs but that, with the exception of personal protective equipment, have not previously been examined in this population. It is crucial that governments and communities address discriminatory behaviors against PCWs, promote their well-being at work, and foster family support. (Am J Public Health. 2022;112(5):786-794. https://doi.org/10.2105/AJPH.2022.306723).

Bayesian data fusion: Probabilistic sensitivity analysis for unmeasured confounding using informative priors based on secondary data.

Bayesian causal inference offers a principled approach to policy evaluation of proposed interventions on mediators or time-varying exposures. Building on the Bayesian g-formula method introduced by Keil et al., we outline a general approach for the estimation of population-level causal quantities involving dynamic and stochastic treatment regimes, including regimes related to mediation estimands such as natural direct and indirect effects. We further extend this approach to propose a Bayesian data fusion (BDF), an algorithm for performing probabilistic sensitivity analysis when a confounder unmeasured in a primary data set is available in an external data source. When the relevant relationships are causally transportable between the two source populations, BDF corrects confounding bias and supports causal inference and decision-making within the main study population without sharing of the individual-level external data set. We present results from a simulation study comparing BDF to two common frequentist correction methods for unmeasured mediator-outcome confounding bias in the mediation setting. We use these methods to analyze data on the role of stage at cancer diagnosis in contributing to Black-White colorectal cancer survival disparities.

Bayesian kernel machine regression-causal mediation analysis.

Greater understanding of the pathways through which an environmental mixture operates is important to design effective interventions. We present new methodology to estimate natural direct and indirect effects and controlled direct effects of a complex mixture exposure on an outcome through a mediator variable. We implement Bayesian Kernel Machine Regression (BKMR) to allow for all possible interactions and nonlinear effects of (1) the co-exposures on the mediator, (2) the co-exposures and mediator on the outcome, and (3) selected covariates on the mediator and/or outcome. From the posterior predictive distributions of the mediator and outcome, we simulate counterfactuals to obtain posterior samples, estimates, and credible intervals of the mediation effects. Our simulation study demonstrates that when the exposure-mediator and exposure-mediator-outcome relationships are complex, BKMR-Causal Mediation Analysis performs better than current mediation methods. We applied our methodology to quantify the contribution of birth length as a mediator between in utero co-exposure to arsenic, manganese, and lead, and children's neurodevelopmental scores, in a prospective birth cohort in Bangladesh. Among younger children, we found a negative (adverse) association between the metal mixture and neurodevelopment. We also found evidence that birth length mediates the effect of exposure to the metal mixture on neurodevelopment for younger children. If birth length were fixed to its 75th percentile value, the harmful effect of the metal mixture on neurodevelopment is attenuated, suggesting nutritional interventions to help increase fetal growth, and thus birth length, could potentially block the harmful effect of the metal mixture on neurodevelopment.

Effect of a solar lighting intervention on fuel-based lighting use and exposure to household air pollution in rural Uganda: A randomized controlled trial.

Solar lighting is an alternative to polluting kerosene and other fuel-based lighting devices relied upon by millions of families in resource-limited settings. Whether solar lighting provides sustained displacement of fuel-based lighting sources and reductions in personal exposure to fine particulate matter (PM2 .5 ) and black carbon (BC) has not been examined in randomized controlled trials. Eighty adult women living in rural Uganda who utilized fuel-based (candles and kerosene lamps) and/or clean (solar, grid, and battery-powered devices) lighting were randomized in a 1:1 ratio to receive a home solar lighting system at no cost to study participants (ClinicalTrials.gov NCT03351504). Among intervention group participants, kerosene lamps were completely displaced in 92% of households using them. The intervention led to an average exposure reduction of 36.1 µg/m3 (95% CI -70.3 to -2.0) in PM2 .5 and 10.8 µg/m3 (95% CI -17.6 to -4.1) in BC, corresponding to a reduction from baseline of 37% and 91%, respectively. Reductions were greatest among participants using kerosene lamps. Displacement of kerosene lamps and personal exposure reductions were sustained over 12 months of follow-up. Solar lighting presents an immediate opportunity for achieving sustained reductions in personal exposure to PM2.5 and BC and should be considered in household air pollution intervention packages.

The impact of the COVID-19 pandemic on the mental health of healthcare workers: study protocol for the COVID-19 HEalth caRe wOrkErS (HEROES) study.

BACKGROUND: Preliminary country-specific reports suggest that the COVID-19 pandemic has a negative impact on the mental health of the healthcare workforce. In this paper, we summarize the protocol of the COVID-19 HEalth caRe wOrkErS (HEROES) study, an ongoing, global initiative, aimed to describe and track longitudinal trajectories of mental health symptoms and disorders among health care workers at different phases of the pandemic across a wide range of countries in Latin America, Europe, Africa, Middle-East, and Asia. METHODS: Participants from various settings, including primary care clinics, hospitals, nursing homes, and mental health facilities, are being enrolled. In 26 countries, we are using a similar study design with harmonized measures to capture data on COVID-19 related exposures and variables of interest during two years of follow-up. Exposures include potential stressors related to working in healthcare during the COVID-19 pandemic, as well as sociodemographic and clinical factors. Primary outcomes of interest include mental health variables such as psychological distress, depressive symptoms, and posttraumatic stress disorders. Other domains of interest include potentially mediating or moderating influences such as workplace conditions, trust in the government, and the country's income level. RESULTS: As of August 2021, ~ 34,000 health workers have been recruited. A general characterization of the recruited samples by sociodemographic and workplace variables is presented. Most participating countries have identified several health facilities where they can identify denominators and attain acceptable response rates. Of the 26 countries, 22 are collecting data and 2 plan to start shortly. CONCLUSIONS: This is one of the most extensive global studies on the mental health of healthcare workers during the COVID-19 pandemic, including a variety of countries with diverse economic realities and different levels of severity of pandemic and management. Moreover, unlike most previous studies, we included workers (clinical and non-clinical staff) in a wide range of settings.

Meta-analysis of Total Effect Decomposition in the Presence of Multiple Mediators: The Example of Schizophrenia Treatment.

BACKGROUND: Causal mediation analysis addresses mechanistic questions by decomposing and quantifying effects operating through different pathways. Because most individual studies are underpowered to detect mediating effects, we outlined a parametric approach to meta-analyzing causal mediation and interaction analyses with multiple mediators, compared it with a bootstrap-based alternative, and discussed its limitations. METHODS: We employed fixed- and random-effects multivariate meta-analyses to integrate evidence on treatment-mediators and mediators-outcome associations across trials. We estimated path-specific effects as functions of meta-analyzed regression coefficients; we obtained standard errors using the delta method. We evaluated the performance of this approach in simulations and applied it to assess the mediating roles of positive symptoms of schizophrenia and weight gain in the treatment effect of paliperidone ER on negative symptoms across four efficacy trials. RESULTS: Both simulations and the application showed that the meta-analytic approaches increased statistical power. In the application, we observed substantial mediating effects of positive symptoms (proportions mediated from fixed-effects meta-analysis: (Equation is included in full-text article.)). Weight gain may have beneficial mediating effects; however, such benefit may disappear at high doses when metabolic side effects were excessive. CONCLUSIONS: Meta-analyzing causal mediation analysis combines evidence from multiple sources and improves power. Targeting positive symptoms may be an effective way to reduce negative symptoms that are challenging to treat. Future work should focus on extending the existing methods to allow for more flexible modeling of mediation.

Neighborhood greenness and burden of non-communicable diseases in Sub-Saharan Africa: A multi-country cross-sectional study.

Population growth, demographic transitions and urbanization in sub-Saharan Africa (SSA) will increase non-communicable disease (NCD) burden. We studied the association between neighborhood greenness and NCDs in a multi-country cross-sectional study. Among 1178 participants, in adjusted models, a 0.11 unit NDVI increase was associated with lower BMI (ß: -1.01, 95% CI: -1.35, -0.67), and lower odds of overweight/obesity (aOR: 0.73, 95% CI: 0.62, 0.85), diabetes (aOR: 0.77, 95% CI: 0.62, 0.96), and having ≥3 allostatic load components compared to none (aOR: 0.66, 95% CI: 0.52, 0.85). Except for diabetes, these remained statistically significant after Bonferroni correction. We observed no association between NDVI and hypertension or cholesterol. Our findings are consistent with health benefits of neighborhood greenness reported in other countries, suggesting greening strategies could be considered as part of broader public health interventions for NCDs.

Impaired insulin signaling in unaffected siblings and patients with first-episode psychosis.

Patients with psychotic disorders are at high risk for type 2 diabetes mellitus, and there is increasing evidence that patients display glucose metabolism abnormalities before significant antipsychotic medication exposure. In the present study, we examined insulin action by quantifying insulin sensitivity in first-episode psychosis (FEP) patients and unaffected siblings, compared to healthy individuals, using a physiological-based model and comprehensive assessment battery. Twenty-two unaffected siblings, 18 FEP patients, and 15 healthy unrelated controls were evaluated using a 2-h oral glucose tolerance test (OGTT), with 7 samples of plasma glucose and serum insulin concentration measurements. Insulin sensitivity was quantified using the oral minimal model method. Lipid, leptin, free fatty acids, and inflammatory marker levels were also measured. Anthropometric, nutrient, and activity assessments were conducted; total body composition and fat distribution were determined using whole-body dual-energy X-ray absorptiometry. Insulin sensitivity significantly differed among groups (F = 6.01 and 0.004), with patients and siblings showing lower insulin sensitivity, compared to controls (P = 0.006 and 0.002, respectively). Body mass index, visceral adipose tissue area (cm2), lipids, leptin, free fatty acids, inflammatory markers, and activity ratings were not significantly different among groups. There was a significant difference in nutrient intake with lower total kilocalories/kilogram body weight in patients, compared to siblings and controls. Overall, the findings suggest that familial abnormal glucose metabolism or a primary insulin signaling pathway abnormality is related to risk for psychosis, independent of disease expression and treatment effects. Future studies should examine underlying biological mechanisms of insulin signaling abnormalities in psychotic disorders.

Associations between prenatal maternal exposure to per- and polyfluoroalkyl substances (PFAS) and polybrominated diphenyl ethers (PBDEs) and birth outcomes among pregnant women in San Francisco.

BACKGROUND: Perfluoroalkyl substances (PFAS) and polybrominated diphenyl ethers (PBDEs) are used in consumer products for their water repellent and flame retardant properties, respectively. However, there is widespread prenatal exposure and concern about their potential harm to the developing fetus. Here, we utilized data from a demographically diverse cohort of women in San Francisco, CA to examine associations between prenatal exposure to PFAS and PBDEs with gestational age and birth weight for gestational age z-scores. METHODS: Women included in this analysis were enrolled in the Chemicals in our Bodies (CIOB) cohort study (N = 506). PFAS and PBDEs were measured in serum obtained during the second trimester of pregnancy. Linear regression models were used to calculate crude and adjusted ß coefficients for the association between PFAS and PBDE concentrations in tertiles and gestational age and birth weight z-scores. Individual PFAS and PBDE concentrations, as well as their sums, were examined in separate models. RESULTS: The highest compared to lowest tertile of BDE-47 was associated with shorter gestational age (ß = - 0.49, 95% confidence interval [CI] = - 0.95, - 0.02). Additionally, exposure to BDE-47 and BDE-99 in the middle tertile was also associated with a reduction in birth weight z-scores (ß = - 0.26, 95% CI = -0.48, - 0.04; ß = - 0.25, 95% CI = -0.47, - 0.04, respectively) compared to those in the lowest tertile of exposure. No consistent associations were observed between increasing PFAS concentrations and gestational age or birth weight z-scores. DISCUSSION: Among a diverse group of pregnant women in the San Francisco Bay Area, we found non-linear associations between prenatal exposure to PBDEs during the second trimester of pregnancy and birth weight z-scores. However, most PFAS congeners were not associated with adverse birth outcomes. PFAS and PBDE concentrations were lower in our cohort relative to other studies. Future research should assess the effects of emerging and persistent PFAS and PBDEs on birth outcomes, as some congeners are being phased out and replaced by chemically similar structures.

Mediation of associations between adiposity and colorectal cancer risk by inflammatory and metabolic biomarkers.

Inflammation and hyperinsulinemia may drive associations between adiposity and colorectal cancer (CRC) risk, but few studies have examined this hypothesis using mediation analysis. We used inverse odds ratio weighting and logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) for estimated total effects (ORTE ) of body mass index, waist circumference, and adult weight gain on CRC risk, and estimated effects operating through seven inflammatory and metabolic biomarkers (natural indirect effect; ORNIE ) or through paths independent of these biomarkers (natural direct effect; ORNDE ) among 209 CRC cases and 382 matched controls nested within the Health Professionals Follow-up Study, a prospective cohort of male health professionals. A one-interquartile range (IQR) increase in body mass index (3.6 kg/m2 ) was associated with an ORTE of 1.40 (95% CI: 1.13, 1.73), which decomposed into an ORNIE of 1.26 (95% CI: 0.97, 1.52) and an ORNDE of 1.11 (0.87, 1.42), with possibly stronger mediation by these biomarkers for adult weight gain (IQR = 10.4 kg; ORTE = 1.32 [95% CI: 1.06, 1.64]; ORNIE = 1.47 [95% CI: 1.01, 1.81]; ORNDE = 0.89 [95% CI: 0.72, 1.11]), but no mediation for waist circumference. Mediation appeared to be stronger for the metabolic biomarkers than the inflammatory biomarkers. Inflammatory and metabolic mechanisms may mediate associations between both body mass index and adult weight gain with CRC risk.

Marginal Time-Dependent Causal Effects in Mediation Analysis With Survival Data.

The main aim of mediation analysis is to study the direct and indirect effects of an exposure on an outcome. To date, the literature on mediation analysis with multiple mediators has mainly focused on continuous and dichotomous outcomes. However, the development of methods for multiple mediation analysis of survival outcomes is still limited. Here we extend to survival outcomes a method for multiple mediation analysis based on the computation of appropriate weights. The approach considered has the advantages of not requiring specific models for mediators, allowing nonindependent mediators of any nature, and not relying on the assumption of rare outcomes. Simulation studies show good performance of the proposed estimator in terms of bias and coverage probability. The method is further applied to an example from a published study on prostate cancer mortality aimed at understanding the extent to which the effect of DNA methyltransferase 3b (DNMT3b) genotype on mortality was explained by DNA methylation and tumor aggressiveness. This approach can be used to quantify the marginal time-dependent direct and indirect effects carried by multiple indirect pathways, and software code is provided to facilitate its application.

Counterpoint: Mediation Formulas With Binary Mediators and Outcomes and the "Rare Outcome Assumption".

In their accompanying article, Samoilenko and Lefebvre (Am J Epidemiol. 2019;188(7):1203-1205) correctly note 2 typographical errors in the formulas presented in a 2011 paper on placental abruption by Ananth and VanderWeele (Am J Epidemiol. 2011;174(1):99-108). Fortunately, to the best of our knowledge, researchers are using our methods papers and Dr. VanderWeele's 2015 book on mediation analysis (Explanation in Causal Inference; Oxford University Press, New York, New York), rather than the paper on placental abruption, to carry out their direct and indirect effect analyses; and in our methods papers and the book, the formulas are correct. The formulas discussed by Samoilenko and Lefebvre and in our work make reference to a "rare outcome assumption." In evaluating this assumption, it is important to note that the outcome is to be relatively rare across all strata defined by the exposure and the mediator-a point that is often neglected.

Social deprivation is associated with poor kidney transplantation outcome in children.

Socioeconomic status is an important determinant of health. Its impact on kidney transplantation outcome has been studied among adults but data in children are scarce, especially in Europe. Here, we investigate the association between the level of social deprivation (determined by the continuous score European Deprivation Index) and graft failure risk in pediatric kidney transplant recipients. All patients listed under 18 years of age who received a first kidney transplant between 2002 and 2014 in France were included. Of 1050 kidney transplant recipients (males 59%, median age at transplantation 13.2 years, preemptive transplantation 23%), 211 graft failures occurred within a median followup of 5.9 years. Thirty-seven percent of these patients belong to the most deprived quintile, suggesting that deprivation is more frequent in pediatric patients with end-stage kidney disease (ESKD) than in the general population. Five- and ten-year graft survival were 85% and 69%, respectively, in the most deprived quintile vs. 90% and 83%, respectively, in the least deprived quintile. At any time after transplantation, patients in the most deprived quintile had almost a two-fold higher hazard of graft failure compared with the least deprived quintile, after adjustment for age at renal replacement therapy, duration of dialysis, primary kidney disease, and rural/urban living environment (hazard ratio 1.99; 95% confidence interval 1.20-3.28). The hazard of graft failure did not differ significantly between girls and boys. Thus, our findings suggest a lower socioeconomic status is independently associated with poor graft outcome in pediatric kidney transplantation.

Distinct Trajectories of Cortisol Response to Prolonged Acute Stress Are Linked to Affective Responses and Hippocampal Gray Matter Volume in Healthy Females.

The development of robust laboratory procedures for acute stress induction over the last decades has greatly advanced our understanding of stress responses in humans and their underlying neurobiological mechanisms. Nevertheless, attempts to uncover linear relationships among endocrine, neural, and affective responses to stress have generally yielded inconsistent results. Here, 79 healthy females completed a well established laboratory procedure of acute stress induction that was modified to prolong its effect. Endocrinological and subjective affect assessments revealed stress-induced increases in cortisol release and negative affect that persisted 65 and 100 min after stress onset, respectively, confirming a relatively prolonged acute stress induction. Applying latent class linear mixed modeling on individuals' patterns of cortisol responses identified three distinct trajectories of cortisol response: the hyper-response (n = 10), moderate-response (n = 21), and mild-response (n = 48) groups. Notably, whereas all three groups exhibited a significant stress-induced increase in cortisol release and negative affect, the hyper-response and mild-response groups both reported more negative affect relative to the moderate-response group. Structural MRI revealed no group differences in hippocampal and amygdala volumes, yet a continuous measure of cortisol response (area under the curve) showed that high and low levels of stress-induced cortisol release were associated with less hippocampal gray matter volume compared with moderate cortisol release. Together, these results suggest that distinct trajectories of cortisol response to prolonged acute stress among healthy females may not be captured by conventional linear analyses; instead, quadratic relations may better describe links between cortisol response to stress and affective responses, as well as hippocampal structural variability.SIGNIFICANCE STATEMENT Despite substantial research, it is unclear whether and how individual neuroendocrine stress response patterns are linked to affective responses to stress and structural variability in neuroendocrine regulatory brain regions. By applying latent class linear mixed modeling on individuals' patterns of cortisol responses to a prolonged acute stressor, we identified three distinct trajectories of cortisol response. Relative to the group showing a moderate cortisol response, groups characterized by hyper and mild cortisol response were both associated with more negative affect. Moreover, a continuous measure of cortisol response showed that high and low levels of stress-induced cortisol release correlated with reduced hippocampal gray matter volume. Given that neuroendocrine stress responses are conceptualized as biomarkers of stress susceptibility, these insights may have clinical implications.

Decomposition of the Total Effect in the Presence of Multiple Mediators and Interactions.

Mediation analysis allows decomposing a total effect into a direct effect of the exposure on the outcome and an indirect effect operating through a number of possible hypothesized pathways. Recent studies have provided formal definitions of direct and indirect effects when multiple mediators are of interest and have described parametric and semiparametric methods for their estimation. Investigating direct and indirect effects with multiple mediators, however, can be challenging in the presence of multiple exposure-mediator and mediator-mediator interactions. In this paper we derive a decomposition of the total effect that unifies mediation and interaction when multiple mediators are present. We illustrate the properties of the proposed framework in a secondary analysis of a pragmatic trial for the treatment of schizophrenia. The decomposition is employed to investigate the interplay of side effects and psychiatric symptoms in explaining the effect of antipsychotic medication on quality of life in schizophrenia patients. Our result offers a valuable tool to identify the proportions of total effect due to mediation and interaction when more than one mediator is present, providing the finest decomposition of the total effect that unifies multiple mediators and interactions.

Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation.

In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficiency of these estimators. Furthermore, we compare 2 different model selection strategies based on 1) Akaike's Information Criterion and the Bayesian Information Criterion and 2) machine learning algorithms, and we illustrate double-robust estimators' performance in a real-world setting. In simulations with correctly specified models and near-positivity violations, all but the naive estimators had relatively good performance. However, the augmented inverse-probability-of-treatment weighting estimator showed the largest relative bias. Under dual model misspecification and near-positivity violations, all double-robust estimators were biased. Nevertheless, the targeted maximum likelihood estimator showed the best bias-variance trade-off, more precise estimates, and appropriate 95% confidence interval coverage, supporting the use of the data-adaptive model selection strategies based on machine learning algorithms. We applied these methods to estimate adjusted 1-year mortality risk differences in 183,426 lung cancer patients diagnosed after admittance to an emergency department versus persons with a nonemergency cancer diagnosis in England (2006-2013). The adjusted mortality risk (for patients diagnosed with lung cancer after admittance to an emergency department) was 16% higher in men and 18% higher in women, suggesting the importance of interventions targeting early detection of lung cancer signs and symptoms.