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Jessica Fish and her colleagues published a study presenting sexual minority youth as an overrepresented population in foster care, child welfare and out-of-home placement

Qualitative research helps to document stories of "stigma and survival"

PURPOSE: We aimed to estimate latent classes of concurrent polysubstance use and test for sexual orientation differences in latent class memberships with representative data from adolescents living in 19 U.S. states. We also tested whether sex, race/ethnicity, and age moderated the sexual identity differences in polysubstance use class memberships. METHODS: We analyzed data from 119,437 adolescents from 19 states who participated in the 2015 Youth Risk Behavior Survey. Latent class analysis characterized polysubstance use patterns based on self-reported frequency of lifetime and past-month use of alcohol (including heavy episodic drinking), tobacco (cigarettes, cigars, and smokeless tobacco), and marijuana. Multinomial logistic regression models tested differences in latent class memberships by sexual identity. Interaction terms tested whether sex, race/ethnicity, and age moderated the sexual identity differences in polysubstance use class memberships. RESULTS: A six-class model of polysubstance use fit the data best and included nonusers (61.5%), experimental users (12.2%), marijuana-alcohol users (14.8%), tobacco-alcohol users (3.8%), medium-frequency three-substance users (3.6%), and high-frequency three-substance users (4.1%). Gay/lesbian- and bisexual-identified adolescents had significantly higher odds than heterosexual-identified adolescents of being in all of the user classes compared with the nonuser class. These sexual identity differences in latent polysubstance use class memberships were generally larger for females than for males, varied occasionally by race/ethnicity, and were sometimes larger for younger ages. CONCLUSION: Compared with their heterosexual peers, gay/lesbian and bisexual adolescents-especially females-are at heightened risk of engaging in multiple types of polysubstance use. Designing, implementing, and evaluating interventions will likely reduce these sexual orientation disparities.

Air pollution is a persistent and well-established public health problem in India: emissions from coal-fired power plants have been associated with over 80,000 premature deaths in 2015. Premature deaths could rise by four to five times this number by 2050 without additional pollution controls. We site a model 500 MW coal-fired electricity generating unit at eight locations in India and examine the benefits and costs of retrofitting the plant with a flue-gas desulfurization unit to reduce sulfur dioxide emissions. We quantify the mortality benefits associated with the reduction in sulfates (fine particles) and value these benefits using estimates of the value per statistical life transferred to India from high income countries. The net benefits of scrubbing vary widely by location, reflecting differences in the size of the exposed population. They are highest at locations in the densely populated north of India, which are also among the poorest states in the country.

Plant phenology (e.g. timing of spring green-up, flowering) is among the most sensitive indicator of ecological response to ongoing climate variability and change. While previous studies have documented changes in the timing of spring green-up and flowering across different parts of the world, empirical evidence regarding how such ongoing ecological changes impact allergic disease burden at population level is lacking. Because earlier spring green-up may increase season length for tree pollen, we hypothesized that early onset of spring (negative anomaly in start of season (SOS)) will be associated with increased hay fever burden. To test this, we first calculated a median cardinal date for SOS for each county within the contiguous US for the years 2001-2013 using phenology data from the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer (MODIS). We categorized yearly deviations in SOS for each county from their respective long-term averages as: very early (>3 wks early), early (1-3 wks early), average (within 1 wk), late (1-3 wks late) and very late (> 3 wks late). We linked these data to 2002-2013 National Health Interview Survey data, and investigated the association between changes in SOS and hay fever prevalence using logistic regression. We observed that adults living in counties with a very early onset of SOS had a 14% higher odds of hay fever compared to the reference group, i.e. those living in counties where onset of spring was within the normal range (Odds Ratios (OR): 1.14. 95% Confidence Interval (CI): 1.03-1.27). Likewise, adults living in counties with very late onset of SOS had a 18% higher odds hay fever compared to the reference group (OR: 1.18, CI: 1.05-1.32). Our data provides the first-ever national scale assessment of the impact of changing plant phenology-linked to ongoing climate variability and change-on hay fever prevalence. Our findings are likely tied to changes in pollen dynamics, i.e early onset of spring increases the duration of exposure to tree pollen, while very late onset of spring increases the propensity of exposure because of simultaneous blooming.

Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county  health outcomes , controlling for county-level demographics, economics, and  population density . At the county level, greater presence of highways was related to lower chronic diseases and  premature mortality . Areas characterized by street view images as ‘rural’ (having limited infrastructure) had higher obesity,  diabetes , fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the  census  tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.

An aspect of citation behavior, which has received longstanding attention in research, is how articles’ received citations evolve as time passes since their publication (i.e., citation ageing). Citation ageing has been studied mainly by the formulation and fit of mathematical models of diverse complexity. Commonly, these models restrict the shape of citation ageing functions and explicitly take into account factors known to influence citation ageing. An alternative—and less studied—approach is to estimate citation ageing functions using data-driven strategies. However, research following the latter approach has not been consistent in taking into account those factors known to influence citation ageing. In this article, we propose a model-free approach for estimating citation ageing functions which combines quantile regression with a non-parametric specification able to capture citation inflation. The proposed strategy allows taking into account field of research effects, impact level effects, citation inflation effects and skewness in the distribution of cites effects. To test our methodology, we collected a large dataset consisting of more than five million citations to 59,707 research articles spanning 12 dissimilar fields of research and, with this data in hand, tested the proposed strategy.

Agricultural water withdrawals account for the largest proportion of global freshwater use. Increasing municipal water demands and droughts are straining agricultural water supplies. Therefore, alternative solutions to agricultural water crises are urgently needed, including the use of nontraditional water sources such as advanced treated wastewater or reclaimed water, brackish water, return flows, and effluent from produce processing facilities. However, it is critical to ensure that such usage does not compromise soil, crop, and public health. Here, we characterized five different nontraditional water types (n = 357 samples) for the presence of pharmaceuticals, herbicides, and disinfectants using ultra-high-pressure liquid chromatography tandem mass spectrometry based method (UPLC-MS/MS). We then evaluated whether the levels of these contaminants were influenced by season. The highest level of herbicides (atrazine) was detected in untreated pond water (median concentration 135.9 ng/L). Reclaimed water had the highest levels of antibiotics and stimulants including azithromycin (215 ng/L), sulfamethoxazole (232.1 ng/L), and caffeine (89.4 ng/L). Produce processing plant water also tended to have high levels of atrazine (102.7 ng/L) and ciprofloxacin (80.1 ng/L). In addition, we observed seasonal variability across water types, with the highest atrazine concentrations observed during summer months, while the highest median azithromycin concentrations were observed in reclaimed water during the winter season. Further studies are needed to evaluate if economically feasible on-farm water treatment technologies can effectively remove such contaminants from nontraditional irrigation water sources.

City police discharged weapons in 2018 more often than any other American city

Severe maternal morbidity continues to be on the rise in the US. Short birth spacing is a modifiable risk factor associated with maternal morbidity, yet few studies have examined this association, possibly due to few available data sources to examine these rare events. To examine the association between interpregnancy interval (IPI) and severe maternal morbidity using near‐national birth certificate data and account for known under‐reporting using probabilistic bias analysis. We used revised 2014‐2017 birth certificate data, restricting to resident women with a non–first‐born singleton birth. We examined the following: (a) maternal blood transfusion, (b) admission to intensive care unit (ICU), (c) uterine rupture (among women with a prior caesarean delivery) and (d) third‐ or fourth‐degree perineal laceration (among vaginal deliveries) by IPI categories (<6, 6‐11, 12‐17, 18‐23, 24‐59 and 60+ months). Risk ratios and 95% confidence intervals were estimated using log‐binomial regression, adjusting for select maternal characteristics. Probabilistic bias analyses were performed. Compared with IPI 18 to 23 months, adjusted models revealed that the risk of maternal transfusion followed a U‐shaped curve with IPI, while risk of ICU admission and perineal laceration increased with longer IPI. Risk of uterine rupture was highest among IPI <6 months. With the exception of maternal transfusion, these findings persisted regardless of the extent or type of misclassification examined in bias analyses. Associations between IPI and maternal morbidity varied by outcome, even after adjusting for misclassification of SMM. Differences across maternal health outcomes should be considered when counselling and making recommendations regarding optimal birth spacing.