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

Little is known about the relationships between demographic and economic social determinants of health and the probability of contracting COVID-19 in American Indian and Alaska Native (AI/AN) peoples. In addition, we do not know if and how tribal payments, unique to AI/AN peoples, are associated with the probability of contracting COVID-19. We surveyed 767 AI/AN patients of five geographically disparate health organizations that primarily served AI/AN peoples in urban settings between January and May of 2021. We used univariate modified Poisson regressions to estimate the influence of age, gender, household composition, education, household income, and tribal payments on risk of contracting COVID-19, with results presented as both risk and risk difference. Fifteen percent of the sample contracted COVID-19, and individuals who lived in households with two or more generations had an 11-percentage point elevated risk of contracting COVID-19 compared to those who lived alone. Twenty-seven percent of participants received tribal payments; receipt was associated with seven percentage points (change from 18% probability to 11% probability) lower risk of contracting COVID-19. Our findings showed interventions specifically designed to reduce the spread of COVID-19 in multigenerational households, and regular tribal payments may help improve health outcomes in urban AI/AN populations. [ABSTRACT FROM AUTHOR]

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

IntroductionEpidemiological studies generally have not found plasma total fibrinogen to be a risk factor for venous thromboembolism (VTE), but several have reported associations between variants in the fibrinogen gamma gene (FGG) and VTE. A case-control study in whites suggested plasma γ' fibrinogen concentration may be associated inversely with VTE, but this was not replicated in African Americans.ObjectiveTo examine the prospective association between γ' fibrinogen concentrations and occurrence of VTE.MethodsWe used the Longitudinal Investigation of Thromboembolism Etiology (LITE), involving two pooled population-based cohorts in the United States including 16,234 participants. The cohorts comprised white and African American men and women, aged 50years and older at study onset in the early 1990s. We identified VTEs during follow-up and documented they met standardized diagnostic criteria.ResultsDuring two decades of follow-up, neither γ' fibrinogen nor total fibrinogen nor their ratio was associated with VTE overall (n=521 VTEs), in subgroups defined by race, or in other subgroups. In both race groups, the minor allele of FGG rs2066865 was associated with lower γ' fibrinogen concentrations, but this allele was not associated with VTE.ConclusionsA lower plasma concentration of γ' fibrinogen in healthy adults does not appear to increase VTE risk.

Introduction: This study utilizes triadic data to examine the association between mother-father-adolescent concordance (agreement) and discordance (disagreement) on home environment factors (i.e., parental encouragement of dieting, family functioning) and adolescent unhealthy weight control behaviors and binge eating. Method: A subsample of adolescent-mother-father triads (n = 833; adolescents ages 10-22) from two coordinated population-based studies (EAT 2010 and F-EAT) were used. Poisson regression analysis was used to estimate the relative risks of each eating disordered behavior. Results: Triads were more concordant (range 9-42%) than discordant (range 4-24%). Triadic agreement that parents did not encourage dieting was associated with a lower risk of adolescent eating disordered behaviors in some triadic combinations. Additionally, triadic concordance on high family functioning was also associated with a lower risk of adolescent eating disordered behaviors among some triadic combinations. Discussion: Results suggest that triadic concordance on healthy home environment factors is associated with fewer adolescent disordered eating behaviors in some triads. Family based interventions may want to consider focusing on strategies to help move mother-father-adolescent triads closer on seeing the home environment more similarly. Future research is needed on triadic concordance/discordance and disordered eating behaviors to confirm study results and to inform the development of family based interventions. Keywords: concordance/discordance, unhealthy weight control behaviors, binge eating, encouragement of dieting, family functioning, Adolescent disordered eating behaviors, including unhealthy weight control behaviors (e.g., fasting, skipping meals, purging) and binge eating, are concerning, due to their high prevalence and damaging consequences (Neumark-Sztainer, Croll, et [...]

Background: Despite a vast air pollution epidemiology literature to date and the recognition that lower-socioeconomic status (SES) populations are often disproportionately exposed to pollution, there is little research identifying optimal means of adjusting for confounding by SES in air pollution epidemiology, nor is there a strong understanding of biases that may result from improper adjustment. Objective: We aim to provide a conceptualization of SES and a review of approaches to its measurement in the U.S. context and discuss pathways by which SES may influence health and confound effects of air pollution. We explore bias related to measurement and operationalization and identify statistical approaches to reduce bias and confounding. Discussion: Drawing on the social epidemiology, health geography, and economic literatures, we describe how SES, a multifaceted construct operating through myriad pathways, may be conceptualized and operationalized in air pollution epidemiology studies. SES varies across individuals within the contexts of place, time, and culture. Although no single variable or index can fully capture SES, many studies rely on only a single measure. We recommend examining multiple facets of SES appropriate to the study design. Furthermore, investigators should carefully consider the multiple mechanisms by which SES might be operating to identify those SES indicators that may be most appropriate for a given context or study design and assess the impact of improper adjustment on air pollution effect estimates. Last, exploring model contraction and expansion methods may enrich adjustment, whereas statistical approaches, such as quantitative bias analysis, may be used to evaluate residual confounding. https://doi.org/10.1289/EHP7980, Introduction Studies in the United States and elsewhere have reported that long-term exposures to air pollution (AP) are associated with increased risk of all-cause (Dockery et al. 1993; Laden et [...]

Systematic error from selection bias, uncontrolled confounding, and misclassification is ubiquitous in epidemiologic research but is rarely quantified using quantitative bias analysis (QBA). This gap may in part be due to the lack of readily modifiable software to implement these methods. Our objective is to provide computing code that can be tailored to an analyst's dataset. We briefly describe the methods for implementing QBA for misclassification and uncontrolled confounding and present the reader with example code for how such bias analyses, using both summary-level data and individual record-level data, can be implemented in both SAS and R. Our examples show how adjustment for uncontrolled confounding and misclassification can be implemented. Resulting bias-adjusted point estimates can then be compared to conventional results to see the impact of this bias in terms of its direction and magnitude. Further, we show how 95% simulation intervals can be generated that can be compared to conventional 95% confidence intervals to see the impact of the bias on uncertainty. Having easy to implement code that users can apply to their own datasets will hopefully help spur more frequent use of these methods and prevent poor inferences drawn from studies that do not quantify the impact of systematic error on their results. [ABSTRACT FROM AUTHOR]