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Objective: Trans and non-binary (TNB) immigrants, refugees, and newcomers (IRN) face intersecting challenges and barriers, including stigma and persecution in countries of origin, and others unique to the Canadian resettlement process. The present study aimed to investigate factors that are associated with having a primary healthcare provider among TNB IRN. Design: Trans PULSE Canada was a community-based, national study of health and wellbeing among 2,873 TNB people residing in Canada, aged 14 and older, who were recruited using a multi-mode convenience sampling approach.. The survey asked questions about identity, community, service access, health – and IRN were asked questions specific to immigration/settlement. Results: Of the 313 IRN participants who completed the full survey version (age M = 34.1, SE=0.75), 76.4 % had a primary healthcare provider. TNB IRN largely reported being Canadian citizens (59.8 %), gender non-binary or similar (46.9 %), currently living in Ontario (35.5 %), and having immigrated from the United States (32.1 %). Chi-square analyses revealed that having a primary healthcare provider was associated with age, gender identity, citizenship status, region of origin, current location in Canada, length of time since immigrating to Canada, status in gender affirming medical care, and having extended health insurance. With modified Poisson regression, we found that TNB IRN who were non-permanent residents, originating from European, African, and Oceania regions, or living in Quebec and the Prairie provinces were less likely to have a primary healthcare provider. Conclusion: Results may inform settlement organizations of the unique needs and barriers of TNB IRN. Schools and LGBTQ+ organizations may better serve this population – especially those originating from highlighted regions, who live in Quebec or the Prairie provinces, and/or are non-permanent residents – by offering programs that connect them to primary healthcare providers who are competent in cross-cultural trans health.

Transgender (trans) and non-binary people experience barriers to culturally competent healthcare and many have reported avoiding care. COVID-19 and related mitigation strategies may have exacerbated avoidance, and poor mental health may be bidirectionally related to avoiding care. This study estimated the prevalence of primary care avoidance during the pandemic in a national sample of trans and non-binary people in Canada with a primary care provider and examined the association between poorer self-rated mental health and avoidance. In Fall 2019, Trans PULSE Canada collected multi-mode survey data from trans and non-binary people. In September to October 2020, 820 participants completed a COVID-19-focused survey. In this cross-sectional analysis, multivariable logistic regression models estimated odds ratios adjusted for confounders and weighted to the 2019 sample. The analysis included 689 individuals with a primary healthcare provider, of whom 61.2% (95% CI: 57.2, 65.2) reported fair or poor mental health and 25.7% (95% CI: 22.3, 29.2) reported care avoidance during the pandemic. The most common reason for avoidance was having a non-urgent health concern (72.7%, 95% CI: 65.9, 79.5). In adjusted analyses, those with fair or poor mental health had higher odds of avoiding primary care as compared to those with good to excellent mental health (adjusted odds ratio [AOR] = 2.37; 95% CI: 1.50, 3.77). This relationship was similar when excluding COVID-related reasons for avoidance (AOR = 2.52; 95% CI: 1.52, 4.17). Expansion of virtual communication may enhance primary care accessibility, and proactively assessing mental health symptoms may facilitate connections to gender-affirming mental health services.

Intersectionality recognizes that in the context of sociohistorically shaped structural power relations, an individual's multiple social positions or identities (e.g., gender, ethnicity) can interact to affect health-related outcomes. Despite limited methodological guidance, intersectionality frameworks have increasingly been incorporated into epidemiological studies, both to describe health disparities and to examine their causes. This study aimed to advance methods in intersectional estimation of binary outcomes in descriptive health disparities research through evaluation of 7 potentially intersectional data analysis methods: cross-classification, regression with interactions, multilevel analysis of individual heterogeneity (MAIHDA), and decision trees (CART, CTree, CHAID, random forest). Accuracy of estimated intersection-specific outcome prevalence was evaluated across 192 intersections using simulated data scenarios. For comparison we included a non-intersectional main effects regression. We additionally assessed variable selection performance amongst decision trees. Example analyses using National Health and Nutrition Examination Study data illustrated differences in results between methods. At larger sample sizes, all methods except for CART performed better than non-intersectional main effects regression. In smaller samples, MAIHDA was the most accurate method but showed no advantage over main effects regression, while random forest, cross-classification, and saturated regression were the least accurate, and CTree and CHAID performed moderately well. CART performed poorly for estimation and variable selection. Sensitivity analyses examining the bias-variance tradeoff suggest MAIHDA as the preferred unbiased method for accurate estimation of high-dimensional intersections at smaller sample sizes. Larger sample sizes are more imperative for other methods. Results support the adoption of an intersectional approach to descriptive epidemiology.

Background: Intersectionality is a theoretical framework rooted in the premise that human experience is jointly shaped by multiple social positions (e.g. race, gender), and cannot be adequately understood by considering social positions independently. Used widely in qualitative studies, its uptake in quantitative research has been more recent. Objectives: To characterize quantitative research applications of intersectionality from 1989 to mid-2020, to evaluate basic integration of theoretical frameworks, and to identify innovative methods that could be applied to health research. Methods: Adhering to PRISMA guidelines, we conducted a systematic review of peer-reviewed articles indexed within Scopus, Medline, ProQuest Political Science and Public Administration, and PsycINFO. Original English-language quantitative or mixed-methods research or methods papers that explicitly applied intersectionality theoretical frameworks were included. Experimental studies on perception/stereotyping and measures development or validation studies were excluded. We extracted data related to publication, study design, quantitative methods, and application of intersectionality. Results: 707 articles (671 applied studies, 25 methods-only papers, 11 methods plus application) met inclusion criteria. Articles were published in journals across a range of disciplines, most commonly psychology, sociology, and medical/life sciences; 40.8% studied a health-related outcome. Results supported concerns among intersectionality scholars that core theoretical tenets are often lost or misinterpreted in quantitative research; about one in four applied articles (26.9%) failed to define intersectionality, while one in six (17.5%) included intersectional position components not reflective of social power. Quantitative methods were simplistic (most often regression with interactions, cross-classified variables, or stratification) and were often misapplied or misinterpreted. Several novel methods were identified. Conclusions: Intersectionality is frequently misunderstood when bridging theory into quantitative methodology. Further work is required to (1) ensure researchers understand key features that define quantitative intersectionality analyses, (2) improve reporting practices for intersectional analyses, and (3) develop and adapt quantitative methods.