Tuesday, Aug 6: 2:00 PM - 3:50 PM
1400
Invited Paper Session
Oregon Convention Center
Room: CC-258
Applied
Yes
Main Sponsor
ENAR
Co Sponsors
ASA LGBTQ+ Advocacy Committee
Justice Equity Diversity and Inclusion Outreach Group
Scientific and Public Affairs Advisory Committee
Presentations
In observational and/or epidemiological research we often adjust for a host of socio-demographic variables to clarify the influence of a predictor on an outcome. However, we rarely utilize All possible covariates, instead we utilize what is available or the normative variables we have been taught to include. If we want to be more inclusive of underrepresented communities, we have to start making the invisible visible by showing representation in our statistical analyses. Thus, in this presentation I will present ways to be more inclusive in the ways in which we select and utilize covariates in statistical analyses.
Body mass index, or BMI, is frequently used in health sciences and clinical settings as a marker of health, with an emphasis on the adverse physical effects of overweight/obesity on the body. Less discussed in these arenas are the effects of stigma and size discrimination on the mental and physical health of individuals with higher BMIs, and how anti-fat attitudes of public health and medical practitioners may exacerbate the very symptoms health professionals are attempting to ameliorate. Using data from three distinct studies that explore the role of body size on mental health and health behavior in disparate populations (e.g., adolescents, young sexual minority women, young men who have sex with men), this talk will explore the relationships between body size, stigma, and well-being, and emphasize the critical need to consider the role of anti-fat bias and weight-based stigma in shaping population health.
There are many important, informative qualities about human subjects that are inherently immeasurable. Even qualities that are measurable, such as age, are often transformed into categories for various statistical design or analysis purposes. Despite scientific evidence to the contrary, biological sex and psychosocial gender are often categorized as binary qualities and only five to eight levels represent different race and ethnicity groups in the US Census. Translating complex, multidimensional constructs into discrete statistical variables inevitably results in information loss. This presentation delves into the statistical issues associated with this kind of information loss. We emphasize human sex as an example where the thoughtless adaptation of traditional categorizations and variable definitions can lead to dire consequences, statistical and otherwise. We also present examples where misinformation due to inappropriate categorization results in misleading statistical conclusions and suggest preventative measures beginning at the data collection stage. Finally, we reflect on the potential impact this has on gender and sex minorities in policy and the health sciences.
Recently, research studies have focused on quantifying suicidality in the autistic population. A crucial finding of this research is that autistics are four to eight times more likely to die by suicide, yet the risk is often missed by common diagnostic approaches. While this is important, studies overlook a demographic group with the highest suicidality rate: autistic women. Not only have studies shown autistic women are more likely to die, roughly 40% are misdiagnosed or get no diagnosis. This adds an additional layer of methodological challenges in aforementioned studies into suicidality and autism as study populations include only diagnosed autistics. This implies that future studies should aim to recruit participants not according to the demographics of the diagnosed autistic population but by the demographics of diagnosed and undiagnosed autistic population. Without this updated approach, the likelihood of undiagnosed suicidality in the highest risk category, autistic females, continues to exist and this can cause treatment delay in individuals with a high clinical need. This talk discusses the critical need for correct populations and targeted understanding of key subgroups.
A diverse biomedical research workforce benefits the health research agenda as it leads to increased innovation and scientific rigor. Diverse investigators play an integral role in identifying novel research questions, perspectives, and solutions. Yet, while underrepresented minorities make up the most rapidly growing segment of the US population, diversifying the biomedical research workforce is still a major challenge. Barriers to the recruitment, retention, and advancement of underrepresented trainees and faculty in the biomedical and/or STEM research workforce are often systemic in nature, however many proposed interventions, while well-intentioned, tend to target the individual rather than the impeding structures/systems themselves. In this talk, I will present the progress to date of the NIH FIRST Cohort Cluster Hiring Initiative at Mount Sinai, a rigorous, data-driven, structural approach aimed at transforming culture and facilitating the equitable recruitment, retention, research success, and advancement of biomedical investigators who are underrepresented and/or committed to diversity, equity, inclusion, and accessibility.
Speaker
Emma Benn, Icahn School of Medicine at Mount Sinai