The longitudinal impact of maternal depression and neighborhood social context on adolescent mental health

PURPOSE: Maternal depression and neighborhood characteristics are known to be associated both with each other and with adolescent mental health outcomes. These exposures are also subject to change throughout the life of a child. This study sought to identify multi-trajectories of maternal depression (MD) and self-reported neighborhood collective efficacy (NCE) over a 12-year period and determine whether these trajectories are differentially associated with adolescent mental health. METHODS: Data from the Fragile Families and Child Wellbeing study, a longitudinal cohort study of new parents and their children, were used. Maternal depression (MD) and self-reported NCE when the child was 3, 5, 9, and 15 years of age were the primary exposures of interest. Adolescent depression and anxiety symptomology when the child was 15 years of age were the primary outcomes. Primary analyses were conducted using multi-trajectory modeling and linear regressions. RESULTS: Five multi-trajectories were identified, two of which were characterized by no MD but either high or low NCE, and three of which were characterized by similarly moderate levels of NCE but either increasing, decreasing, or consistently high MD. Children of mothers with increasing or consistently high depressive symptomology and moderate NCE had significantly higher depression and anxiety scores compared to children of mothers with no depressive symptomology and high NCE. CONCLUSION: Adolescents with consistent and proximal exposure to MD are most likely to suffer from adverse mental health and should be provided with appropriate support systems to mitigate these outcomes

Context matters: Construct framing in measures of physical activity engagement among African American women

Assessment of psychosocial factors influencing health behavior typically privileges conceptual consistency (framing constructs similarly across contexts) over conceptual specificity (context-specific framing). Modest statistical relationships between these factors and health behaviors, and persistent racial disparities in health outcomes raise questions about whether conceptually consistent framing fully captures relevant predictors. Ethnographic studies suggest not - that perceptions influencing health behaviors are multifaceted and contextual. To test this, we added items querying contextualized predictors of intention to engage in leisure-time physical activity (LTPA) to a Theory of Planned Behavior (TPB)-based survey and examined the psychometrics of the adapted subscales. We measured internal consistency (Cronbach’s alpha) and construct validity (exploratory factor analysis using polychoric correlations for ordinal data). Participants were a convenience sample of 200 African American women in a Midwestern, suburban University-affiliated family medicine practice. Reliability of the adapted subscales was notably lower than the original subscales. A two-factor model fit best for the attitudes subscale, but explained slightly less than 50% of the variance. The new items loaded strongly on one factor. A three-factor model best fit the norms subscale and accounted for around 57% of the variance. Two of the three new items loaded strongly on one factor. Factor analysis for the perceived control subscale was not possible due to low number of items; however, two of the three new items were highly correlated (.73). Including context-specific factors may improve assessment of intention to engage in LTPA. Further study of this question with a larger, representative sample is warranted

The silver bullet that wasn’t: Rapid agronomic weed adaptations to glyphosate in North America

The rapid adoption of glyphosate-resistant crops at the end of the 20th century caused a simplification of weed management that relied heavily on glyphosate for weed control. However, the effectiveness of glyphosate has diminished. A greater understanding of trends related to glyphosate use will shed new light on weed adaptation to a product that transformed global agriculture. Objectives were to (1) quantify the change in weed control efficacy from postemergence (POST) glyphosate use on troublesome weeds in corn and soybean and (2) determine the extent to which glyphosate preceded by a preemergence (PRE) improved the efficacy and consistency of weed control compared to glyphosate alone. Herbicide evaluation trials from 24 institutions across the United States of America and Canada from 1996 to 2021 were compiled into a single database. Two subsets were created; one with glyphosate applied POST, and the other with a PRE herbicide followed by glyphosate applied POST. Within each subset, mean and variance of control ratings for seven problem weed species were regressed over time for nine US states and one Canadian province. Mean control with POST glyphosate alone decreased over time while variability in control increased. Glyphosate preceded by a labeled PRE herbicide showed little change in mean control or variability in control over time. These results illustrate the rapid adaptation of agronomically important weed species to the paradigm-shifting product glyphosate. Including more diversity in weed management systems is essential to slowing weed adaptation and prolonging the usefulness of existing and future technologies

A comparison of marginal odds ratio estimators.

Uses of the propensity score to obtain estimates of causal effect have been investigated thoroughly under assumptions of linearity and additivity of exposure effect. When the outcome variable is binary relationships such as collapsibility, valid for the linear model, do not always hold. This article examines uses of the propensity score when both exposure and outcome are binary variables and the parameter of interest is the marginal odds ratio. We review stratification and matching by the propensity score when calculating the Mantel-Haenszel estimator and show that it is consistent for neither the marginal nor conditional odds ratio. We also investigate a marginal odds ratio estimator based on doubly robust estimators and summarize its performance relative to other recently proposed estimators under various conditions, including low exposure prevalence and model misspecification. Finally, we apply all estimators to a case study estimating the effect of Medicare plan type on the quality of care received by African-American breast cancer patients

Outcomes, Approaches, and Challenges to Developing and Passing a Countywide Mandatory Vaccination Policy: St. Louis County’s Experience with Hepatitis A Vaccine for Food Service Personnel

In the early 1990s, St. Louis County had multiple foodservice worker-related hepatitis A outbreaks uncontrolled by standard outbreak interventions. Restaurant interest groups and the general public applied political pressure to local public health officials for more stringent interventions, including a mandatory vaccination policy. Local health departments can enact mandatory vaccination policies, but this has rarely been done. The study objectives were to describe the approach used to pass a mandatory vaccination policy at the local jurisdiction level and illustrate the outcome from this ordinance 15 years later. A case study design was used. In-depth, semi-structured interviews using guided questions were conducted in spring, 2015, with six key informants who had direct knowledge of the mandatory vaccination policy process. Meeting minutes and/or reports were also analyzed. A Poisson distribution analysis was used to calculate the rate of outbreaks before and after mandatory vaccination policy implementation. The policy appears to have reduced the number of hepatitis A outbreaks, lowering the morbidity and economic burden in St. Louis County. The lessons learned by local public health officials in passing a mandatory hepatitis A vaccination policy are important and relevant in today’s environment. The experience and lessons learned may assist other local health departments when faced with the potential need for mandatory policies for any vaccine preventable disease

Individual- and county-level determinants of high breast cancer incidence rates

Background: Age-adjusted breast cancer rates vary across and within states. However, most statistical models inherently identify either individual- or area-level determinants to explain geographic disparities in breast cancer rates and ignore the effects of the other level of determinants. We present a micro-macro modelling approach that incorporates both levels of determinants to better explain this variability and to discover opportunities to reduce breast cancer rates. Methods: Individual-level data about breast cancer risk factors from eligible Arkansas Rural Community Health (ARCH) study participants (n=13,554) was supplemented with publicly available county-level data using a novel micro-macro statistical approach. This model uses individual-level data to account for aggregation-induced biases, to predict county-level breast cancer incidence rates across Arkansas. Results: County-level breast cancer incidence rates ranged from 80.9 to 161.6 per 100,000 population. The best-fit model, which included individual-level predicted risk based on the Gail/CARE models, county-level population density (log transformed), and lead exposure (log transformed), explained 14.1% of the county variance. Conclusions: Our results support theoretical models that maintain that area-level determinants of breast cancer incidence are key risk factors in addition to established individual risks