Introduction

This study investigated associations between maternal prepregnancy BMI and child behaviors at ages 9-11 years and examine interaction by race and gender.

Methods

The National Longitudinal Survey of Youth and the Children and Young Adults surveys are U.S.-based, ongoing longitudinal studies, initiated in 1979 and 1986, respectively. Mothers (n=2,952) reported pregnancy and child (n=5,660) developmental information at multiple time points. Child total, internalizing, and externalizing problems at ages 9-11 years were assessed using the Behavior Problems Index (BPI), collected biennially until 2012. Associations between prepregnancy BMI and child BPI outcomes were examined, as well as two- and three-way interactions by race and gender. Analyses were conducted in 2017.

Results

Boys whose mothers had higher prepregnancy weights exhibited higher total BPI and externalizing scores at ages 9-11 years versus those with normal-weight mothers. Boys with severely obese mothers had higher total BPI (mean difference=7.99, 95% CI=3.53, 12.46) and externalizing (mean difference=5.77, 95% CI=1.50, 10.04) scores. Prepregnancy underweight was associated with boys' higher total BPI (mean difference=2.34, 95% CI=0.02, 4.66) and externalizing (mean difference=3.30, 95% CI=0.69, 5.91); these associations were not significant in sensitivity analyses. No associations emerged for girls or internalizing problems. Two-way interactions by race and three-way interactions by race and gender were not significant.

Conclusions

Maternal prepregnancy weight was associated with BPI level among boys. Boys with severely obese mothers exhibited markedly higher behavioral problems at ages 9-11 years versus those with normal-weight mothers, regardless of race. Maintaining healthy prepregnancy weight may be important for preventing boys' deleterious behavior outcomes in middle childhood.

Aim: Community-based participatory research (CBPR) is an increasingly common approach in the USA, but still relatively rare in Europe. In the industrial zone of Marseille, there is a long history of pollution, but little is known about the health implications. This study documented the prevalence of different health issues in two heavily polluted towns in the industrial zone using a CBPR approach. Subject and methods: This study used a CBPR approach and epidemiologic methods to answer community members’ questions about the health of residents in Marseille’s industrial zone by randomly sampling a cross-section of residents to systematically document health issues in Fos-sur-Mer and Port-Saint-Louis-du-Rhône, two towns in the industrial port area of Marseille, France. Results: Many chronic illnesses were elevated in these communities, as compared to regional and national prevalences, including chronic skin problems, asthma, cancer, and diabetes. Chronic skin problems and asthma were among the most common chronic illnesses reported. A majority of respondents also reported acute symptoms that affected daily life, including eye irritation or nose and throat problems. Conclusion: There is likely an environmental explanation for why, even after direct standardization, the prevalences of many diseases were higher in these communities than elsewhere. The combination of CBPR and rigorous epidemiologic methods helps make our findings relevant to both community members and researchers.

Background

Estimates of effect heterogeneity (i.e. the extent to which the causal effect of one exposure varies across strata of a second exposure) can be biased if the exposure-outcome relationship is subject to uncontrolled confounding whose severity differs across strata of the second exposure.

Methods

We propose methods, analogous to the E-value for total effects, that help to assess the sensitivity of effect heterogeneity estimates to possible uncontrolled confounding. These E-value analogues characterize the severity of uncontrolled confounding strengths that would be required, hypothetically, to 'explain away' an estimate of multiplicative or additive effect heterogeneity in the sense that appropriately controlling for those confounder(s) would have shifted the effect heterogeneity estimate to the null, or alternatively would have shifted its confidence interval to include the null. One can also consider shifting the estimate or confidence interval to an arbitrary non-null value. All of these E-values can be obtained using the R package EValue.

Results

We illustrate applying the proposed E-value analogues to studies on: (i) effect heterogeneity by sex of the effect of educational attainment on dementia incidence and (ii) effect heterogeneity by age on the effect of obesity on all-cause mortality.

Conclusion

Reporting these proposed E-values could help characterize the robustness of effect heterogeneity estimates to potential uncontrolled confounding.

The vast majority of studies investigating participation in, persistence through, and consequences of postsecondary education focus on educational attainment status among the so-called traditional population of collegegoers between the ages of 18 and 24. This narrow focus leaves largely invisible the role that an expanding set of educational trajectories throughout adulthood plays in shaping social stratification. Using 35-plus and 20 years of follow-up data from the US National Longitudinal Survey of Youth (NLSY)’s 1979 and 1997 cohorts, we find that a substantial share within each cohort is attaining education well into adulthood, and that these trajectories are patterned according to key social and demographic characteristics. In both cohorts, racial/ethnic differences in educational attainment grew over time and, for those attaining the same degree, members of historically disadvantaged groups did so at an older age. Cohort differences in trajectories emerged, however, when considering the intersection of race/ethnicity and socialized gender. Through careful descriptive analysis of two generational cohorts, our study makes clear the role of educational trajectories in the process of cumulative (dis)advantage across the life course, as well as across generations.

The health and safety of incarcerated persons and correctional personnel have been prominent in the U.S. news media discourse during the COVID-19 pandemic. Examining changing attitudes toward the health of the incarcerated population is imperative to better assess the extent to which the general public favors criminal justice reform. However, existing natural language processing lexicons that underlie current sentiment analysis (SA) algorithms may not perform adequately on news articles related to criminal justice due to contextual complexities. News discourse during the pandemic has highlighted the need for a novel SA lexicon and algorithm (i.e., an SA package) tailored for examining public health policy in the context of the criminal justice system. We analyzed the performance of existing SA packages on a corpus of news articles at the intersection of COVID-19 and criminal justice collected from state-level outlets between January and May 2020. Our results demonstrated that sentence sentiment scores provided by three popular SA packages can differ considerably from manually-curated ratings. This dissimilarity was especially pronounced when the text was more polarized, whether negatively or positively. A randomly selected set of 1,000 manually scored sentences, and the corresponding binary document term matrices, were used to train two new sentiment prediction algorithms (i.e., linear regression and random forest regression) to verify the performance of the manually-curated ratings. By better accounting for the unique context in which incarceration-related terminologies are used in news media, both of our proposed models outperformed all existing SA packages considered for comparison. Our findings suggest that there is a need to develop a novel lexicon, and potentially an accompanying algorithm, for analysis of text related to public health within the criminal justice system, as well as criminal justice more broadly.