Methods: Utilizing data from the charts of 900 trans women aged 16+ enrolled in care in six primary care clinics in the Montreal Toronto Trans Study (MTTS), collected 2018-2019, we conducted bivariate and multivariate analyses using ordinal logistic regression analyses to determine sociodemographic (e.g., age), clinical (e.g., mental health history), and social/structural (e.g., source of income) factors associated with recency of last visit with their PCP (<1 year ago; ≥ 1 year and <3 years ago; ≥ 3 years ago). Analyses were conducted with STATA (version 18).
Results: Participants [mean age: 38 (SD= 13.94)] had an average time since last PCP visit of 2.09 years (SD =0.97), with 43% having had a PCP visit within the past year, 5% having had a PCP visit ≥ 1 year and <3 years ago and 52% having had a PCP visit ≥ 3 years ago. The average number of missed PCP visits within the last two years was 2.32 (SD=0.87). Bivariate analyses showed that having a mental health diagnosis was associated with 25% lower odds of having not had a PCP visit within the past year (OR: 0.75, 95% CI: 0.57, 0.99, p=0.04). In a multivariate model including all sociodemographic, clinical, and social/structural factors, only age was significant, such that for each additional year of age, the odds of having not had a PCP visit within the past year rose by 6% (OR: 1.06, 95% CI: 1.00, 1.12, p=0.05).
Conclusions: Our findings show that a concerning proportion - more than half - of trans women enrolled with a PCP had not seen them in the past three years. Findings should be contextualized within data limitations (e.g., patients may have moved and/or passed away). The negative association between PCP visits and advancing age was counterintuitive and should be further explored. Mental health-related primary care appointments may serve as opportunities for integrated preventative and gender-affirming healthcare with trans women. Future prospective studies may opt to collect variables related to transphobia and intersecting stigma as well as more comprehensive social/structural data (e.g., homelessness) to inform a deeper understanding of barriers to trans women’s uptake of primary care and potential solutions (e.g., provider training).