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Availability of Accessible Representative Health Data to Examine Sexual and Gender Minority Disparities in Incarceration and Its Health Implications in the United States, 2010-2020.

Tyler D HarveyJackie M White HughtoKirsty A Clark
Published in: LGBT health (2022)
Purpose: To facilitate identification of the impact of incarceration on the health of sexual and gender minority (SGM) populations, we sought to identify publicly accessible, representative health datasets that assessed SGM status and incarceration history from 2010 to 2020 and to examine SGM disparities in lifetime incarceration experiences. Methods: Datasets were identified and analyzed through a multistep process: (1) content search of 76 health datasets; (2) consultation with 14 subject matter experts; (3) a systematic review; and (4) a data analysis stage. Utilizing the identified health datasets, we produced representative estimates of sexual minority (SM) incarceration disparities. Results: Five publicly accessible databases were identified that assessed SM status and incarceration history; none assessed gender minority status and incarceration history. Across datasets, the weighted prevalence of lifetime incarceration among SM populations was substantially higher (range = 17.5%-26.3%) than among non-SM populations (range = 4.6%-21.2%). Conclusion: Few publicly accessible, representative health datasets collect standardized information regarding SM status and incarceration history, and none assess diverse gender identities and incarceration history. These data suggest that a disproportionate proportion of SM individuals may experience incarceration compared with non-SM individuals. Research assessing the health effects of incarceration on SGM populations remains limited; publicly accessible, representative health data are needed to address this gap.
Keyphrases
  • mental health
  • public health
  • healthcare
  • health information
  • data analysis
  • magnetic resonance
  • computed tomography
  • rna seq
  • big data
  • single cell
  • climate change
  • machine learning