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      Characterizing Transgender Health Issues in Twitter

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          Abstract

          Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide new opportunities for transgender people to overcome these barriers by sharing their personal health experiences. Our research employs a computational framework to collect tweets from self-identified transgender users, detect those that are health-related, and identify their information needs. This framework is significant because it provides a macro-scale perspective on an issue that lacks investigation at national or demographic levels. Our findings identified 54 distinct health-related topics that we grouped into 7 broader categories. Further, we found both linguistic and topical differences in the health-related information shared by transgender men (TM) as com-pared to transgender women (TW). These findings can help inform medical and policy-based strategies for health interventions within transgender communities. Also, our proposed approach can inform the development of computational strategies to identify the health-related information needs of other marginalized populations.

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          Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures.

          We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses--which is consistent with the effects of sleep and circadian rhythm--and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends.
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            Transgender Population Size in the United States: a Meta-Regression of Population-Based Probability Samples

            Background. Transgender individuals have a gender identity that differs from the sex they were assigned at birth. The population size of transgender individuals in the United States is not well-known, in part because official records, including the US Census, do not include data on gender identity. Population surveys today more often collect transgender-inclusive gender-identity data, and secular trends in culture and the media have created a somewhat more favorable environment for transgender people. Objectives. To estimate the current population size of transgender individuals in the United States and evaluate any trend over time. Search methods. In June and July 2016, we searched PubMed, Cumulative Index to Nursing and Allied Health Literature, and Web of Science for national surveys, as well as “gray” literature, through an Internet search. We limited the search to 2006 through 2016. Selection criteria. We selected population-based surveys that used probability sampling and included self-reported transgender-identity data. Data collection and analysis. We used random-effects meta-analysis to pool eligible surveys and used meta-regression to address our hypothesis that the transgender population size estimate would increase over time. We used subsample and leave-one-out analysis to assess for bias. Main results. Our meta-regression model, based on 12 surveys covering 2007 to 2015, explained 62.5% of model heterogeneity, with a significant effect for each unit increase in survey year ( F  = 17.122; df  = 1,10; b = 0.026%; P  = .002). Extrapolating these results to 2016 suggested a current US population size of 390 adults per 100 000, or almost 1 million adults nationally. This estimate may be more indicative for younger adults, who represented more than 50% of the respondents in our analysis. Authors’ conclusions. Future national surveys are likely to observe higher numbers of transgender people. The large variety in questions used to ask about transgender identity may account for residual heterogeneity in our models. Public health implications. Under- or nonrepresentation of transgender individuals in population surveys is a barrier to understanding social determinants and health disparities faced by this population. We recommend using standardized questions to identify respondents with transgender and nonbinary gender identities, which will allow a more accurate population size estimate.
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              Transgender health in Massachusetts: results from a household probability sample of adults.

              Despite higher rates of unemployment and poverty among transgender adults (n = 131; 0.5% weighted) than among nontransgender adults (n = 28,045) in our population-based Massachusetts household sample, few health differences were observed between transgender and nontransgender adults. Transgender adults who are stably housed and participated in a telephone health survey may represent the healthiest segment of the transgender population. Our findings demonstrate a need for diverse sampling approaches to monitor transgender health, including adding transgender measures to population-based surveys, and further highlight economic inequities that warrant intervention.
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                Author and article information

                Journal
                17 August 2018
                Article
                1808.06022
                a646c023-5509-4f2d-be84-cd8a14e7fdc0

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                cs.CY cs.CL stat.AP stat.ML

                Theoretical computer science,Applications,Applied computer science,Machine learning

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