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A recent analysis from the Turnaway study focused on women who were just under the gestational limit of a clinic and received an abortion and those who had first trimester abortions to examine trends in decisional rightness and negative and positive emotions over 5 years after the abortion. Specifically, Rocca et al. (in press) analyzed these data and found that women were overwhemingly sure of their decision: 95% felt their decision was the right one at each assessment after their abortion, and the predicted probability of abortion being the right decision was 99% at 5 years afterwards. Relief was the most common emotion felt by women, and negative emotions or decision regret did not emerge over time. These results and others from studies conducted globally counter assertions by abortion opponents that women are not certain of their decisions, or that women regret or have mainly negative emotions about their abortions if not in the short run then after a long period of time. This commentary addresses not only these findings but also relevant U.S. abortion policies based on these unsubstantiated claims. Policies should not be based on the notions that women are unsure of their decision, come to regret, it or have negative emotions because there is no evidence to support these claims.

OBJECTIVE: Telomeres are protective sequences of DNA capping the ends of chromosomes that shorten over time. Leukocyte telomere length (LTL) is posited to reflect the replicative history of cells and general systemic aging of the organism. Chronic stress exposure leads to accelerated LTL shortening, which has been linked to increased susceptibility to and faster progression of aging-related diseases. This study examined longitudinal associations between LTL and experiences of racial discrimination, a qualitatively unique source of minority psychosocial stress, among African Americans. METHOD: Data are from 391 African Americans in the Coronary Artery Risk Development in Young Adults (CARDIA) Telomere Ancillary Study. We examined the number of domains in which racial discrimination was experienced in relation to LTL collected in Years 15 and 25 (Y15: 2000/2001; Y25: 2010/2011). Multivariable linear regression examined if racial discrimination was associated with LTL. Latent change score analysis (LCS) examined changes in racial discrimination and LTL in relation to one another. RESULTS: Controlling for racial discrimination at Y15, multivariable linear regression analyses indicated that racial discrimination at Y25 was significantly associated with LTL at Y25. This relationship remained robust after adjusting for LTL at Y15 (b = -.019, p = .015). Consistent with this finding, LCS revealed that increases in experiences of racial discrimination were associated with faster 10-year LTL shortening (b = -.019, p = .015). CONCLUSIONS: This study adds to evidence that racial discrimination contributes to accelerated physiologic weathering and health declines among African Americans through its impact on biological systems, including via its effects on telomere attrition. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Importance   Extreme heat events (EHEs) are increasing in frequency, duration, and intensity, and this trend is projected to continue as part of ongoing climate change. There is a paucity of data regarding how EHEs may affect highly vulnerable populations, such as patients with end-stage renal disease (ESRD). Such data are needed to inform ESRD patient management guidelines in a changing climate. Objectives   To investigate the association between EHEs and the risk of hospital admission or mortality among patients with ESRD and further characterize how this risk may vary among races/ethnicities or patients with preexisting comorbidities. Design, Setting, and Participants   This study used hospital admission and mortality records of patients with ESRD who underwent hemodialysis treatment at Fresenius Kidney Care clinics in Boston, Massachusetts; Philadelphia, Pennsylvania; or New York, New York, from January 1, 2001, to December 31, 2012. Data were analyzed using a time-stratified case-crossover design with conditional Poisson regression to investigate associations between EHEs and risk of hospital admission or mortality among patients with ESRD. Data were analyzed from July 1, 2017, to March 31, 2019. Exposures   Calendar day– and location-specific 95th-percentile maximum temperature thresholds were calculated using daily meteorological data from 1960 to 1989. These thresholds were used to identify EHEs in each of the 3 cities during the study. Main Outcomes and Measures   Daily all-cause hospital admission and all-cause mortality among patients with ESRD. Results   The study included 7445 patients with ESRD (mean [SD] age, 61.1 [14.1] years; 4283 [57.5%] men), among whom 2953 deaths (39.7%) and 44 941 hospital admissions (mean [SD], 6.0 [7.5] per patient) were recorded. Extreme heat events were associated with increased risk of same-day hospital admission (rate ratio [RR], 1.27; 95% CI, 1.13-1.43) and same-day mortality (RR, 1.31; 95% CI, 1.01-1.70) among patients with ESRD. There was some heterogeneity in risk, with patients in Boston showing statistically significant increased risk for hospital admission (RR, 1.15; 95% CI, 1.00-1.31) and mortality (RR, 1.45; 95% CI, 1.04-2.02) associated with cumulative exposure to EHEs, while such risk was absent among patients with ESRD in Philadelphia. While increases in risks were similar among non-Hispanic black and non-Hispanic white patients, findings among Hispanic and Asian patients were less clear. After stratifying by preexisting comorbidities, cumulative lag exposure to EHEs was associated with increased risk of mortality among patients with ESRD living with congestive heart failure (RR, 1.55; 95% CI, 1.27-1.89), chronic obstructive pulmonary disease (RR, 1.60; 95% CI, 1.24-2.06), or diabetes (RR, 1.83; 95% CI, 1.51-2.21). Conclusions and Relevance   In this study, extreme heat events were associated with increased risk of hospital admission or mortality among patients with ESRD, and the association was potentially affected by geographic region and race/ethnicity. Future studies with larger populations and broader geographic coverage are needed to better characterize this variability in risk and inform ESRD management guidelines and differential risk variables, given the projected increases in the frequency, duration, and intensity of EHEs.

Background Sexual and gender minority (SGM) adolescents are more likely than their heterosexual and cisgender peers to smoke cigarettes. Family rejection has been associated with adverse health outcomes; however, few studies have examined whether SGM-specific family rejection is associated with cigarette smoking among SGM adolescents. Method A non-probability sample of 11,005 SGM adolescents (M = 15.58, SD = 1.27) completed an online cross-sectional survey. Bivariate and multivariable analyses were conducted to examine associations between SGM-specific family rejection, sociodemographic variables, and smoking. Results Approximately 7% of the sample currently smoked cigarettes. Pansexual, asexual, trans boys, and non-binary assigned female at birth adolescents had the highest SGM family rejection scores. In multivariable regression analyses, SGM-specific family rejection was independently associated with smoking after adjusting for covariates (AOR = 1.15, 95% CI 1.04, 1.28). Family support (AOR = 0.80, 95% CI 0.73, 0.88) and experiencing violence (AOR = 1.64, 95% CI 1.49, 1.82) were also associated with smoking in multivariable models. Adolescents who identified as bisexual versus gay/lesbian (AOR = 1.50, 95% CI 1.21, 1.85) and trans boys versus cisgender girls (AOR = 2.05, 95% CI 1.13, 3.71) had an increased odds of smoking. Those who disclosed their sexual orientation identity to most (AOR = 1.95, 95% CI 1.45, 2.63) and all (AOR = 1.60, 95% CI 1.21, 2.11) of their family/parents had increased odds of smoking. Conclusion Our findings underscore the importance of attending to the role of SGM-specific family rejection and distinctions with SGM adolescents in tobacco prevention and smoking cessation efforts.

Deep learning and, specifically, convoltional neural networks (CNN) represent a class of powerful models that facilitate the understanding of many problems in computer vision. When combined with a reasonable amount of data, CNNs can outperform traditional models for many tasks, including image classification. In this work, we utilize these powerful tools with imagery data collected through Google Street View images to perform virtual audits of neighborhood characteristics. We further investigate different architectures for chronic disease prevalence regression through networks that are applied to sets of images rather than single images. We show quantitative results and demonstrate that our proposed architectures outperform the traditional regression approaches.

Recent attention to the interrelationship between racism, socioeconomic status (SES) and health has led to a small, but growing literature of empirical work on the role of structural racism in population health. Area-level racial inequities in SES are an indicator of structural racism, and the associations between structural racism indicators and self-rated health are unknown. Further, because urban-rural differences have been observed in population health and are associated with different manifestations of structural racism, explicating the role of urban-rural classification is warranted. This study examined the associations between racial inequities in SES and self-rated health by county urban-rural classification. Using data from County Health Rankings and American Communities Surveys, black-white ratios of SES were regressed on rates of fair/poor health in U.S. counties. Racial inequities in homeownership were negatively associated with fair/poor health ( β  = −0.87, s.e. = 0.18), but racial inequities in unemployment were positively associated with fair/poor health ( β  = 0.03, s.e. = 0.01). The associations between structural racism and fair/poor health varied by county urban-rural classification. Potential mechanisms include the concentration of resources in racially segregated counties with high racial inequities that lead to better health outcomes, but are associated with extreme black SES disadvantage. Racial inequities in SES are a social justice imperative with implications for population health that can be targeted by urban-rural classification and other social contextual characteristics.

This study examined the changes in sleep duration (total sleep time, night‐time sleep and daytime naps) after retirement transitions in China using a panel dataset of the China Health and Retirement Longitudinal Study in 2011, 2013 and 2015 with a total of 48,458 respondents. Linear regression analysis with generalized estimating equations was employed to examine the changes in sleep duration after transitions between different types of employment status. After controlling for the confounders, the results showed that the retired population and the population working in agricultural sectors slept 8.02 ( p  < .01) and 5.19 ( p  < .01) minutes longer than the population working in non‐agricultural sectors, respectively. Employment transition also had significant effects on sleep duration. Transition from non‐agricultural sectors to retirement increased total sleep time by 13.58 ( p  < .01) minutes and also raised the probability of daytime naps by 18% (OR = 1.18,  p  < .01). Transition from agricultural employment to retirement did not significantly affect the total sleep time, but significantly increased the probability of daytime naps (OR = 1.12,  p  = .02). Reentering the non‐agricultural sectors for the retirees did not significantly affect night‐time sleep, but decreased the probability of daytime naps (OR = 0.73,  p  < .01) and daytime nap duration (by 5.26 min,  p  = .01). In conclusion, people in China increased their sleep duration after transitions to retirement, but the magnitudes were much smaller than those in Western countries. Differences may be attributed to an abundant amount of Chinese people working in agricultural sectors, the high volume of retired people reentering the work force and the large proportion of people in China that had daytime naps at baseline.

Background Recent years have seen increased interest in the role of neighborhood factors in chronic diseases such as cancers. Less is known about the role of neighborhood factors beyond individual demographics such as age or education. It is particularly important to examine neighborhood effects on health among African American men and women, considering the disproportionate impact of cancer on this group. This study evaluated the unique contribution of neighborhood characteristics (e.g., racial/ethnic diversity, income) beyond individual demographics, to cancer control behaviors in African American men and women. Methods Individual-level data were drawn from a national survey (N = 2,222). Participants’ home addresses were geocoded and merged with neighborhood data from the American Community Survey. Multi-level regressions examined the unique contribution of neighborhood characteristics beyond individual demographics, to a variety of cancer risk, prevention, and screening behaviors. Results Neighborhood racial/ethnic diversity, median income, and percentage of home ownership made modest significant contributions beyond individual factors, in particular to smoking status where these factors were associated with lower likelihood of smoking (ps < .05). Men living in neighborhoods with older residents, and greater income and home ownership were significantly more likely to report prostate specific antigen testing (ps < .05). Regional analyses suggested different neighborhood factors were associated with smoking status depending on the region. Conclusion Findings provide a more nuanced understanding of the interplay of social determinants of health and neighborhood social environment among African American men and women, with implications for cancer control interventions to eliminate cancer disparities.

Rural coastal areas are highly vulnerable to climate change impacts. In the USA, much energy is devoted to conserving rural coastal ecosystems by promoting their adaptation to climate change. However, these areas are also home to vulnerable and underserved communities who can be challenging to engage in climate adaptation discussions. Churches—as trusted social institutions—may offer a structure through which government decision-makers and rural residents can engage to improve the resilience of these rural coastal regions. We employed collaborative learning to engage government decision-makers and rural church members on the topic of climate impacts on Maryland’s Eastern Shore of the Chesapeake Bay. We analyzed the collaborative learning process and its outcomes using ethnographic methods. In this paper, we present our approach and discuss the benefits and challenges of collaborative learning with rural churches. We found that this approach yielded major benefits including greater understanding of capacities and limitations in addressing environmental challenges, increased trust and social networks, expanded engagement with a greater diversity of stakeholders, increased opportunities for new conversations, new pathways toward interventions, and stakeholder empowerment. Collaborating with churches is not without challenges though; it requires considerable time and effort and presents difficulties in navigating social hierarchies and specialized language, identifying common goals, grappling with the newness of climate change, and overcoming institutional barriers. Despite these challenges, we conclude that collaborative learning with churches is a valuable approach for information exchange and network-building toward more resilient rural coasts.

Objective: This study aimed to examine the explanatory role of health behaviors, socioeconomic position (SEP), and psychosocial stressors on racial/ethnic obesity disparities in a multiethnic and multiracial sample of adults. Methods: Using data from the Chicago Community Adult Health Study (2001-2003), Oaxaca-Blinder decomposition analysis was conducted to quantify the extent to which health behaviors (fruit and vegetable consumption and physical activity), SEP, and cumulative stressors (e.g., perceived discrimination, financial strain) each explained differences in obesity prevalence in Black, US-born Hispanic, and non-US-born Hispanic compared with non-Hispanic White participants. Results: SEP and health behaviors did not explain obesity differences between racial/ethnic minorities and White individuals. Having high levels of stress in four or more domains explained 4.46% of the differences between Black and White individuals, whereas having high levels of stress in three domains significantly explained 14.13% of differences between US-born Hispanic and White. Together, the predictors explained less than 20% of differences between any racial/ethnic minority group and White individuals. Conclusions: Exposure to stressors may play a role in obesity disparities, particularly among Black and US-born Hispanic individuals. Other obesity-related risk factors need to be examined to understand the underlying mechanisms explaining obesity disparities.