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Sexual and gender minority (SGM) youth are more likely to use alcohol than their heterosexual cisgender peers. At the same time, SGM youth experience sexuality- and gender identity-specific stressors known to exacerbate negative health outcomes. Though scholars have established a link between minority stressors (e.g., internalized stigma and victimization) and increased alcohol use for SGM youth as a whole, there is little indication of whether internalized stigma and victimization are more strongly associated with alcohol use for specific groups of SGM youth. A United States sample of 11,811 racially and geographically diverse 13–17 year old SGM youth was used to employ a series of gender-stratified multivariable regression models to examine the association among internalized stigma, victimization, and alcohol-related behaviors, and whether they differed for specific groups of sexual minority youth. Sexual orientation moderated several associations between sexual minority stressors (i.e., victimization and stigma) and youth’s alcohol use (i.e., recent use and heavy episodic drinking) across models stratified by gender (i.e., male, female, and non-binary). For example, bisexual boys had stronger associations between SGM-specific victimization and alcohol use frequency and heavy episodic drinking relative to gay boys; conversely, victimization and alcohol use frequency were more weakly associated among bisexual girls relative to lesbian/gay girls. Pansexual girls showed weaker associations between internalized stigma and alcohol use frequency compared to lesbian/gay girls. This paper demonstrates who among SGM youth are more likely to engage in alcohol-related behaviors as a function of differential forms of SGM-related victimization and stigma. These findings can inform substance use interventions that are tailored to youth of diverse sexual orientations and gender identities.

Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter’s streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males reporting higher intensity physical activities and lower caloric foods across 40 and 48 states, respectively. In addition, counties with the highest percentage of exercise and food tweets had lower male and female obesity prevalence. Lastly, our models separately captured overall male and female spatial trends in obesity prevalence. The average correlation between actual and estimated obesity prevalence was 0.797(95% CI, 0.796, 0.798) and 0.830 (95% CI, 0.830, 0.831) for males and females, respectively. Social media can provide timely community-level data on health information seeking and changes in behaviors, sentiments and norms. Social media data can also be combined with other data types such as, demographics, built environment variables, diet and physical activity indicators from other digital sources (e.g., mobile applications and wearables) to monitor health behaviors at different geographic scales, and to supplement delayed estimates from traditional surveillance systems.

Greywater is increasingly treated and reused for agricultural irrigation in off-grid communities in the Middle East and other water scarce regions of the world. However, there is a dearth of data regarding levels of antibiotics and herbicides in off-grid greywater treatment systems. To address this knowledge gap, we evaluated levels of these contaminants in two types of greywater treatment systems on four farms in the West Bank, Palestinian Territories. Samples of household greywater (influent, n = 23), treated greywater effluent intended for agricultural irrigation (n = 23) and pumped groundwater held in irrigation water ponds (n = 12) were collected from October 2017 to June 2018. Samples were analyzed using high performance liquid chromatography tandem mass spectrometry (LC-MS/MS) for the following antibiotics and herbicides: alachlor, ampicillin, atrazine, azithromycin, ciprofloxacin, erythromycin, linezolid, oxacillin, oxolinic acid, penicillin G, pipemidic acid, sulfamethoxazole, triclocarban, tetracycline, triflualin, and vancomycin. All tested antibiotics and herbicides were detected in greywater influent samples at concentrations ranging from 1.3 to 1592.9 ng/L and 3.1–22.4 ng/L, respectively. When comparing influent to effluent concentrations, removal was observed for azithromycin, alachlor, linezolid, oxacillin, penicillin G, pipemidic acid, sulfamethoxazole, triclocarban, and vancomycin. Removal was not observed for atrazine, ciprofloxacin, erythromycin, oxolinic acid, tetracycline, and trifluralin. Pond water also contained the majority of tested contaminants, but at generally lower concentrations. To our knowledge, this is the first description of an extensive array of antibiotics and herbicides detected in household greywater from off-grid treatment systems.

Background:  The  HIV  epidemic continues to grow among  MSM  in countries across sub-Saharan Africa including  Nigeria . To inform prevention efforts, we used a  phylogenetic cluster  method to characterize  HIV  genetic clusters and factors associated with cluster formation among  MSM  living with  HIV  in  Nigeria . Methods:  We analyzed  HIV -1 pol sequences from 417  MSM  living with  HIV  enrolled in the TRUST/RV368 cohort between 2013 and 2017 in Abuja and Lagos,  Nigeria . A genetically linked cluster was defined among participants whose sequences had pairwise genetic distance of 1.5% or less. Binary and multinomial logistic regressions were used to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for factors associated with  HIV  genetic cluster membership and size. Results:  Among 417  MSM  living with  HIV , 153 (36.7%) were genetically linked. Participants with higher viral load (AOR = 1.72 95% CI: 1.04–2.86), no female partners (AOR = 3.66; 95% CI: 1.97–6.08), and self-identified as male sex (compared with self-identified as bigender) (AOR = 3.42; 95% CI: 1.08–10.78) had higher odds of being in a genetic cluster. Compared with unlinked participants,  MSM  who had high school education (AOR = 23.84; 95% CI: 2.66–213.49), were employed (AOR = 3.41; 95% CI: 1.89–10.70), had bacterial sexually transmitted infections (AOR = 3.98; 95% CI: 0.89–17.22) and were not taking antiretroviral therapy (AOR = 6.61; 95% CI: 2.25–19.37) had higher odds of being in a large cluster (size > 4). Conclusion:  Comprehensive  HIV  prevention packages should include behavioral and biological components, including early diagnosis and treatment of both  HIV  and bacterial sexually transmitted infections to optimally reduce the risk of  HIV  transmission and acquisition.

Demography paper with External Affiliate Andrew Fenelon finds "remarkable increases"

Mónica Caudillo Demography article re-examines education outcomes in light of advanced school progression by age

Global Warming and the Demographic Future

Bootstraps of Oppression: A Theoretical Framework of Structural Inequality in Policy Decision Making

Alternative Trajectories in Body Weight, Mental and Cognitive Health among Older Americans: Roles of Genetics and Earlier SES