Demographics

Underrepresented populations have historically been excluded from participating in biomedical science. 

This exclusion may be intentional or unintentional, but may impact data quality, how we perceive, understand, or report on public and population health, the equity of programs, funding, public policy, and much more.

In an effort to improve demographic data collection standards, Oregon Health Authority’s Race, Ethnicity, Language, and Disability (REALD) offers a validated tool for collecting demographic information on race, ethnicity, language, and disability.

Notable race & ethnicity questions from REALD:

• Question 1: an open-ended question encourages respondents to identify in the way they choose without the limitations of predetermined categories.

• Question 2: respondents are asked to select their racial and ethnic identities from 34 options. Another write-in option is available here to self-describe race and/or ethnicity. This helps to track emergent populations.

Considerations:

• Pros:  REALD is an inclusive instrument; REALD answer options can be easily mapped to NIH categories for grant reporting (START, 2022)

• Balance: May take longer to complete; privacy concerns with small sample sizes

Disabilities may be seen or unseen by others and may seriously impact an individual's quality of life. Individuals with disabilities may experience discrimination and challenges living in a society not built for them, which impacts health and function. Disability data collection helps to identify health and service differences to eliminate preventable social and health inequities.

Prompt for Participants:

• “Your answers will help us find health and service differences among people with and without functional difficulties. Your answers are confidential.”

• 2-10 questions about hearing, vision, movement, communication, daily living, cognition, and mental health.

Considerations:

• Pros:  Inclusive tool; REALD answer options could be upcoded to NIH disability definitions; provides important information about how to provide accommodations to participants.

• Balance: Takes longer to answer

Disadvantaged background often describes socioeconomic or environmental conditions that impact access to education or training environments. Disadvantaged background definitions have evolved over time. NIH issued definitions in 2018 and changed them in 2019 (NOT-OD-20-031).  NIH measures relate to socioeconomics and class.

• Experienced past or present homelessness

• Previously or presently in the foster care system

• Eligible for the Federal Free and Reduced Lunch Program for 2+ years

• First generation college students*

• Eligible for Federal Pell grants

• Received WIC as a parent or child

• Grew up in a rural area defined by either HRSA* or HPSA* locations (address/zip-related eligibility)

*Eligibility was underreported by students when verified (Marriott et al., 2022).

Considerations:

• Pros:  Definitions and general conditions are described. Suggested wording via START.

• Balance: No clearly defined approach.   Students under-report their eligibility when verified (Marriott et al., 2022). Some backgrounds are missing (e.g. immigrants, refugees)

• Consider adding:  “We realize we may have not captured everything about your background or experience. If you would like to say more, please feel free to share your story” [open-field] (Marr, 2021).

Conflating sex and gender in research practices excludes entire populations whose physical characteristics and/or identities do not fit within the constraints of male/female.  The erasure of sex and gender minority groups makes it impossible to provide services and accurate data for these populations. Treating sex and gender as independent variables allows researchers to collect more accurate data.

Sexual orientation exists on a spectrum and is independent from gender identity. The two are often conflated in research studies. Measuring sexual orientation separately from gender and sex provides more accurate representation of populations.

Area for Growth: Sexual orientation is not included among NIH definitions of underrepresentation. In the program sample we studied, most training programs did not ask about sexual orientation (Mekinda et al., in review). Research shows these populations face significant health disparities and discrimination; further inclusion is warranted.

Recommendations: Include options to select more than one item from the list; include options to self-describe to support the fluidity and complexity of people’s identities; provide a table containing sexual orientation definitions (example found on START website)

The survey question below is intended to support accurate identification and inclusive data collection for sexual and gender minorities (SGMs). 

• Straight or heterosexual
• Gay 
• Lesbian
• Bisexual
• Pansexual
• Asexual
• Queer
• Fluid
• Questioning/unsure
• Prefer to self-describe: ______
• I don't know what this question is asking
• Prefer not to say