Sampling bias in Brazilian studies on transgender and gender diverse populations: the two-step measure for assessing gender identity in surveys.
Angelo Brandelli CostaLetícia de Oliveira RosaAnna Martha Vaitses FontanariPublished in: Cadernos de saude publica (2022)
Correctly recognizing gender identity in population-based surveys is essential to develop effective public health strategies to improve the living conditions of transgender and gender-diverse populations, as well as to adequately collect data on cisgender individuals. This study aims to present the two-step measure as the best strategy for assessing gender identity in Brazilian surveys, thus we performed two separate analyses. Firstly, we conducted a systematic review concerning HIV-related care among Brazilian transgender and gender-diverse populations to assess the strategy used to identify participants' gender identity. Secondly, we re-analyzed data from a recent survey that included Brazilian transgender populations, comparing characteristics and health outcomes from the sample identified by single-item and by the two-step measure. Concerning the systematic review, from 6,585 references, Brazilian research teams published seven articles, and only one study used the two-step measure. Regarding this survey, the two-step measure recognized 567 cisgender and 773 transgender and gender diverse participants among the 1,340 participants who answered the questionnaire, whereas the single-item measure was able to recognize only 540 transgender and gender diverse people. Furthermore, 31 transgender women self-identified as "transgender men" on the single-item measure. Therefore, although scarcely used in Brazil, the two-step measure is a more accurate strategy to recognize gender identity.
Keyphrases
- hiv testing
- mental health
- public health
- systematic review
- cross sectional
- healthcare
- men who have sex with men
- randomized controlled trial
- palliative care
- meta analyses
- type diabetes
- machine learning
- antiretroviral therapy
- mass spectrometry
- chronic pain
- hiv aids
- social media
- artificial intelligence
- drug induced
- health information
- pregnancy outcomes