Gynecological Care Among Brazilian Women Who Have Sex with Women: A Respondent-Driven Sampling Study.

Purpose: This study aimed to describe the gynecological care provided to Brazilian women who have sex with women (WSW). Methods: Respondent-driven sampling was used to recruit Brazilian WSW. The survey questions, concerning gynecological care, were designed in Portuguese by medical professionals, medical students, and LGBTQIA+ community members, including the authors. The statistical analyses were weighted to account for the likelihood of recruitment. Results: From January to August of 2018, 299 participants were recruited in 14 recruitment waves. The mean age of the WSW was 25.3 years. Most (54.9%) identified as lesbian and had been involved in past-year sexual intercourse mainly with cisgender women (86.1%). The WSW also reported having sex with cisgender men (22.2%), transgender men (5.3%), nonbinary people (2.3%), and transgender women (5.3%) in the last year. More than a quarter of the WSW did not have regular appointments with a gynecologist: 8.0% (95% confidence interval [CI] = 4.2-11.6) and 19% (95% CI = 12.8-25.2) stated that they had never gone to the gynecologist or they had only gone for emergencies, respectively. Almost one-third had never had cervical cancer screening (cervical cytology, Pap test or Pap smear). Most women justified avoiding the test because they felt healthy, thought it would hurt, or feared a health professional might mistreat them. Conclusion: Gynecologists should avoid heteronormative assumptions, inquire about sexual practices, orientation, and identity separately, and provide Pap tests as advised to WSW.

Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods.

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.

Math versus meaning in MAIHDA: A commentary on multilevel statistical models for quantitative intersectionality.

RATIONALE: Intersectionality has been increasingly adopted as a theoretical framework within quantitative research, raising questions about the congruence between theory and statistical methodology. Which methods best map onto intersectionality theory, with regard to their assumptions and the results they produce? Which methods are best positioned to provide information on health inequalities and direction for their remediation? One method, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA), has been argued to provide statistical efficiency for high-dimensional intersectional analysis along with valid intersection-specific predictions and tests of interactions. However, the method has not been thoroughly tested in scenarios where ground truth is known. METHOD: We perform a simulation analysis using plausible data generating scenarios where intersectional effects are present. We apply variants of MAIHDA and ordinary least squares regression to each, and we observe how the effects are reflected in the estimates that the methods produce. RESULTS: The first-order fixed effects estimated by MAIHDA can be interpreted neither as effects on mean outcome when interacting variables are set to zero (as in a correctly-specified linear regression model), nor as effects on mean outcome averaged over the individuals in the population (as in a misspecified linear regression model), but rather as effects on mean outcome averaged over an artificial population where all intersections are of equal size. Furthermore, the values of the random effects do not reflect advantage or disadvantage of different intersectional groups. CONCLUSIONS: Because first-order fixed effects estimates are the reference point for interpreting random effects as intersectional effects in MAIHDA analyses, the random effects alone do not provide meaningful estimates of intersectional advantage or disadvantage. Rather, the fixed and random parts of the model must be combined for their estimates to be meaningful. We therefore advise caution when interpreting the results of MAIHDA in quantitative intersectional analyses.