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Background: The prevalence of smoking is higher among sexual minority (SM) individuals compared with heterosexuals. The impact of televised anti-tobacco mass media campaigns on smoking outcomes among SMs is not known. Aims: We examined whether televised anti-tobacco advertising was differentially associated with current smoking and smoking intensity for SM and heterosexual adults. Method: We combined county-level anti-tobacco advertising data (gross rating points) with restricted, geocoded individual-level National Health Interview Survey data on smoking (2013-2015). We estimated associations between advertising and smoking outcomes, including potential effect modification by SM status. Results: Greater anti-tobacco advertising exposure was associated with lower smoking prevalence (prevalence ratio [PR] = 0.97; 95% confidence interval [CI] = [0.94, 1.00]). Although the direction of the association differed for heterosexual and SM adults, differences were not statistically significant. No significant associations were observed with regard to smoking intensity. Conclusion: Associations between anti-tobacco advertising and smoking were not significantly different for heterosexual and SM adults.

(1) Background: Smoking restrictions have been shown to be associated with reduced smoking, but there are a number of gaps in the literature surrounding the relationship between smoke-free policies and cessation, including the extent to which this association may be modified by sociodemographic characteristics. (2) Methods: We analyzed data from the Tobacco Use Supplement to the Current Population Survey, 2003-2015, to explore whether multiple measures of smoking restrictions were associated with cessation across population subgroups. We examined area-based measures of exposure to smoke-free laws, as well as self-reported exposure to workplace smoke-free policies. We used age-stratified, fixed effects logistic regression models to assess the impact of each smoke-free measure on 90-day cessation. Effect modification by gender, education, family income, and race/ethnicity was examined using interaction terms. (3) Results: Coverage by workplace smoke-free laws and self-reported workplace smoke-free policies was associated with higher odds of cessation among respondents ages 40-54. Family income modified the association between smoke-free workplace laws and cessation for women ages 25-39 (the change in the probability of cessation associated with coverage was most pronounced among lower-income women). (4) Conclusions: Heterogeneous associations between policies and cessation suggest that smoke-free policies may have important implications for health equity.

INTRODUCTION: Neighborhood socioeconomic status (SES) has been linked to dementia, but the distribution of SES within a neighborhood may also matter. METHODS: Data from 460 (47% Black, 46% White) older adults from the Michigan Cognitive Aging Project were linked to census tract–level data from the National Neighborhood Data Archive (NaNDA). Neighborhood SES included two composites reflecting disadvantage and affluence. Neighborhood racial income inequality was the ratio of median incomes for White versus Black residents. Generalized estimating equations examined associations between neighborhood factors and cognitive domains. RESULTS: Neighborhood racial income inequality was uniquely associated with worse cognitive health, and these associations did not differ by participant race. Neighborhood disadvantage was only associated with worse cognitive health among Black participants. DISCUSSION: Both the level and racial distribution of SES within a neighborhood may be relevant for dementia risk. Racial differences in the level and impact of neighborhood SES contribute to dementia inequalities. Highlights: Black participants lived in neighborhoods with lower socioeconomic status (SES) than White participants, on average.Neighborhood SES and racial income inequality were associated with worse cognition.Effects of neighborhood racial income inequality did not differ across racial groups.Effects of neighborhood SES were only evident among Black participants. [ABSTRACT FROM AUTHOR]

Introduction Achieving cessation in people with established smoking patterns remains a challenge. Increasing cigarette prices has been one of the most successful strategies for lowering smoking rates. The extent to which it has remained effective in encouraging cessation among adults in recent years and how the effectiveness has varied by sociodemographic characteristics is unclear. Aims and Methods Using repeated cross-sectional data collected by the Tobacco Use Supplement of the Current Population Survey, we investigate the relationship between cigarette prices and cessation from 2003 to 2019 in adults at least 25 years old. We examine the associations between price and cessation in the population overall and by sex, race and ethnicity, and socioeconomic status. Results We found mixed support for associations between greater local prices and cessation. Unadjusted models showed that greater local prices were associated with greater odds of cessation, but the associations did not persist after controlling for sociodemographic characteristics. The associations did not significantly differ by respondent characteristics. Sensitivity analysis using alternative specifications and retail state price as the main predictor showed similar results. Sensitivity analysis with controls for e-cigarette use in the 2014–2019 period showed that greater local price was associated with cessation among adults with less than a high school degree. When stratified by year of data collection, results show that greater local prices were associated with cessation after 2009. Conclusions Overall, the study adds to the conflicting evidence on the effectiveness of increasing prices on smoking cessation among adults with established smoking patterns. Implications Higher cigarette prices have been one of the most successful tools for lowering smoking prevalence. It remains unclear how effective they have been in recent years in encouraging adults with established smoking patterns to quit. The study's results show that greater local prices were associated with higher odds of cessation, but the association did not persist after sociodemographic adjustment. In a sensitivity analysis, greater local price was associated with cessation among people with less than a high school degree in models controlling for e-cigarette use. We also found evidence that greater local price was associated with cessation after 2009. More comprehensive smoke-free coverage was also associated with greater odds of cessation. The study's results highlight that encouraging cessation among adults with an established smoking pattern remains a challenging policy problem even when cigarette prices rise. [ABSTRACT FROM AUTHOR]

Mixed-mode designs have become increasingly common in survey data collection. Although different modes often have different measurement properties, the standard practice is to treat mixed-mode data as if they had been collected with a single mode, neglecting the potential impact of mode effects. To account for potential mode effects when making inferences for mixed-mode samples, we propose (i) a Testimator approach, (ii) a Bayesian approach, and (iii) a model averaging approach. In the Testimator approach, we test whether the means and the variances of mixed-mode samples are the same. If the means are the same, we take the average of mode-specific estimates. If the means are different, we take the average when we have no prior information about preferred modes and take the smaller (or larger) estimate when we have prior information about preferred modes (e.g. a smaller estimate is better). In the Bayesian approach, we assume some prior information. We use a data-driven method to determine whether there are mode effects. If there are no mode effects, we draw inferences using a common mean model. If there are mode effects, we draw inferences using the data collected with the mode that produces smaller estimates. In the model averaging approach, we combine estimates of different models (characterized by whether assume same means and variances across modes) using marginal posteriors as weights. We evaluate the approaches in simulation studies and find that they achieve robust inferences compared to the standard approach. We apply the methods to the Arab Barometer study, which employs a randomized mixed-mode design. [ABSTRACT FROM AUTHOR]

High-quality survey data collection is getting more expensive to conduct because of decreasing response rates and rising data collection costs. Responsive and adaptive designs have emerged as a framework for targeting and reallocating resources during the data collection period to improve survey data collection efficiency. Here, we report on the implementation and evaluation of a responsive design experiment in the National Survey of College Graduates that optimizes the cost-quality tradeoff by minimizing a function of data collection costs and the root mean squared error of a key survey measure, self-reported salary. We used a Bayesian framework to incorporate prior information and generate predictions of estimated response propensity, self-reported salary, and data collection costs for use in our optimization rule. At three points during the data collection process, we implement the optimization rule and identify cases for which reduced effort would have minimal effect on the mean squared error (RMSE) of mean self-reported salary while allowing us to reduce data collection costs. We find that this optimization process allowed us to reduce data collection costs by nearly 10 percent, without a statistically or practically significant increase in the RMSE of mean salary or a decrease in the unweighted response rate. This experiment demonstrates the potential for these types of designs to more effectively target data collection resources to reach survey quality goals. [ABSTRACT FROM AUTHOR]

Evidence suggests core autism trait consistency in older children, but development of these traits is variable in early childhood. The Social Responsiveness Scale (SRS) measures autism‐related traits and broader autism phenotype, with two age‐dependent forms in childhood (preschool, 2.5–4.5 years; school age, 4–18 years). Score consistency has been observed within forms, though reliability across forms has not been evaluated. Using data from the Environmental Influences on Child Health Outcomes (ECHO) program (n = 853), preschool, and school‐age SRS scores were collected via maternal report when children were an average of 3.0 and 5.8 years, respectively. We compared reproducibility of SRS total scores (T‐scores) and agreement above a clinically meaningful cutoff (T‐scores ≥ 60) and examined predictors of discordance in cutoff scores across forms. Participant scores across forms were similar (mean difference: 3.3 points; standard deviation: 7), though preschool scores were on average lower than school‐age scores. Most children (88%) were classified below the cutoff on both forms, and overall concordance was high (92%). However, discordance was higher in cohorts following younger siblings of autistic children (16%). Proportions of children with an autism diagnoses were also higher among those with discordant scores (27%) than among those with concordant scores (4%). Our findings indicate SRS scores are broadly reproducible across preschool and school‐age forms, particularly for capturing broader, nonclinical traits, but also suggest that greater variability of autism‐related traits in preschool‐age children may reduce reliability with later school‐age scores for those in the clinical range. Lay Summary: The Social Responsiveness Scale (SRS) is a questionnaire used to measure traits and behaviors related to autism. Two SRS forms exist for children, the preschool form for younger children (2.5–4.5 years) and the school‐age form for older children (4–18 years). We found that SRS scores are fairly similar within individuals across these forms, but also that traits may unfold over time among those with neurodevelopmental diagnoses. [ABSTRACT FROM AUTHOR]

Backgrounds and aims4-6 million people die of enteric infections each year. After invading intestinal epithelial cells, enteric bacteria encounter phagocytes. However, little is known about how phagocytes internalize the bacteria to generate host responses. Previously, we have shown that BAI1 (Brain Angiogenesis Inhibitor 1) binds and internalizes Gram-negative bacteria through an ELMO1 (Engulfment and cell Motility protein 1)/Rac1-dependent mechanism. Here we delineate the role of ELMO1 in host inflammatory responses following enteric infection.MethodsELMO1-depleted murine macrophage cell lines, intestinal macrophages and ELMO1 deficient mice (total or myeloid-cell specific) was infected with Salmonella enterica serovar Typhimurium. The bacterial load, inflammatory cytokines and histopathology was evaluated in the ileum, cecum and spleen. The ELMO1 dependent host cytokines were detected by a cytokine array. ELMO1 mediated Rac1 activity was measured by pulldown assay.ResultsThe cytokine array showed reduced release of pro-inflammatory cytokines, including TNF-α and MCP-1, by ELMO1-depleted macrophages. Inhibition of ELMO1 expression in macrophages decreased Rac1 activation (~6 fold) and reduced internalization of Salmonella. ELMO1-dependent internalization was indispensable for TNF-α and MCP-1. Simultaneous inhibition of ELMO1 and Rac function virtually abrogated TNF-α responses to infection. Further, activation of NF-κB, ERK1/2 and p38 MAP kinases were impaired in ELMO1-depleted cells. Strikingly, bacterial internalization by intestinal macrophages was completely dependent on ELMO1. Salmonella infection of ELMO1-deficient mice resulted in a 90% reduction in bacterial burden and attenuated inflammatory responses in the ileum, spleen and cecum.ConclusionThese findings suggest a novel role for ELMO1 in facilitating intracellular bacterial sensing and the induction of inflammatory responses following infection with Salmonella.

Backgrounds and aims4-6 million people die of enteric infections each year. After invading intestinal epithelial cells, enteric bacteria encounter phagocytes. However, little is known about how phagocytes internalize the bacteria to generate host responses. Previously, we have shown that BAI1 (Brain Angiogenesis Inhibitor 1) binds and internalizes Gram-negative bacteria through an ELMO1 (Engulfment and cell Motility protein 1)/Rac1-dependent mechanism. Here we delineate the role of ELMO1 in host inflammatory responses following enteric infection.MethodsELMO1-depleted murine macrophage cell lines, intestinal macrophages and ELMO1 deficient mice (total or myeloid-cell specific) was infected with Salmonella enterica serovar Typhimurium. The bacterial load, inflammatory cytokines and histopathology was evaluated in the ileum, cecum and spleen. The ELMO1 dependent host cytokines were detected by a cytokine array. ELMO1 mediated Rac1 activity was measured by pulldown assay.ResultsThe cytokine array showed reduced release of pro-inflammatory cytokines, including TNF-α and MCP-1, by ELMO1-depleted macrophages. Inhibition of ELMO1 expression in macrophages decreased Rac1 activation (~6 fold) and reduced internalization of Salmonella. ELMO1-dependent internalization was indispensable for TNF-α and MCP-1. Simultaneous inhibition of ELMO1 and Rac function virtually abrogated TNF-α responses to infection. Further, activation of NF-κB, ERK1/2 and p38 MAP kinases were impaired in ELMO1-depleted cells. Strikingly, bacterial internalization by intestinal macrophages was completely dependent on ELMO1. Salmonella infection of ELMO1-deficient mice resulted in a 90% reduction in bacterial burden and attenuated inflammatory responses in the ileum, spleen and cecum.ConclusionThese findings suggest a novel role for ELMO1 in facilitating intracellular bacterial sensing and the induction of inflammatory responses following infection with Salmonella.

ObjectiveFew studies have evaluated factors that influence menstrual cycle length (MCL) during the menopausal transition (MT), a life stage during which very long cycles become more likely to occur. The objective of this article was to assess how body mass index and race/ethnicity--factors associated with MCL in young women--influence MCL during the MT.MethodsStudy of Women's Health Across the Nation menstrual calendar substudy data of African-American, white, Chinese, and Japanese women were available for three sites (southeastern Michigan, Los Angeles, and northern California). Self-recorded monthly menstrual calendars with end-of-the-month questions on hormone therapy use and smoking were collected from 1996 to 2006. Height and weight were measured at annual study visits. We used quantile regression to model MCL at the 25th, 50th, 75th, and 90th percentiles with bootstrap sampling to construct 95% CIs. Models evaluated MCL with time indexed to the start of the MT (n = 963) and to the final menstrual period (n = 431).ResultsDuring the MT, increases in MCL occurred mostly at the right tail of the distribution, reflecting a lengthening of long menstrual cycles, not of the median MCL. After adjustment for smoking, education, physical activity, and time, Chinese and Japanese women had 1 day to 6 days longer MCLs compared with white women. Obese women had 1 day to 5 days longer MCLs compared with nonobese women.ConclusionsAs occurs in younger women, menstrual characteristics during the MT are influenced by race/ethnicity and obesity. The long menstrual cycles characteristic of the MT are longer in obese women and in Chinese and Japanese women.

A major challenge in longitudinal built-environment health studies is the accuracy of commercial business databases that are used to characterize dynamic food environments. Different databases often provide conflicting exposure measures on the same subject due to different source credibilities. As on-site verification is not feasible for historical data, we suggest combining multiple databases to correct the bias in health effect estimates due to measurement error in any 1 datasource. We propose a joint model for the time-varying health outcomes, observed count exposures, and latent true count exposures. Our model estimates the time-specific quality of sources and incorporates time dependence of true count exposure by Poisson integer-valued first-order autoregressive process. We take a Bayesian nonparametric approach to flexibly account for location-specific exposures. By resolving the discordance between different databases, our method reduces the bias in the longitudinal health effect of the true exposures. Our method is demonstrated with childhood obesity data in California public schools with respect to convenience store exposures in school neighborhoods from 2001 to 2008. [ABSTRACT FROM AUTHOR]

A common practice in clinical trials is to evaluate a treatment effect on an intermediate outcome when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate outcomes in a causally‐valid way when the trial outcomes are time‐to‐event. Using counterfactual outcomes, those that would be observed if the counterfactual treatment had been given, the causal association paradigm assesses the relationship of the treatment effect on the surrogate outcome with the treatment effect on the true, primary outcome. In particular, we propose illness‐death models to accommodate the censored and semicompeting risk structure of survival data. The proposed causal version of these models involves estimable and counterfactual frailty terms. Via these multistate models, we characterize what a valid surrogate would look like using a causal effect predictiveness plot. We evaluate the estimation properties of a Bayesian method using Markov chain Monte Carlo and assess the sensitivity of our model assumptions. Our motivating data source is a localized prostate cancer clinical trial where the two survival outcomes are time to distant metastasis and time to death. [ABSTRACT FROM AUTHOR]