Yiscah Bracha, MS1; Kevin Larsen, MD2

1Center for Urban Health, Minneapolis Medical Research Foundation and University of Minnesota, 2Hennepin County Medical Center.

Background

National research shows:

Not all provider groups collect patient race data Those that do find querying is uncomfortable:

Patients feel invasion of privacy Patients not sure how data will be used Patient resistance makes registrars

uncomfortable

Goal at HCMC: Establish method to query patients about

Race & ethnicity Other personal & demographic characteristics

Qualities of method Respectful towards patients Quick & easy to administer Captures clinically important differences Enables reporting using OMB classification

Experiment showed the best way to ask at HCMC:

DO ASK, DO TELL. How to query patients about race and ethnicity

Patient identity questions

Birthplace Language(s)

Race or ethnicity Religious preference

Race or ethnicity Marital status

Asking Hispanic ethnicity first didn’t work Next Q about race confused patients Too many Qs if we also ask birthplace

Asking general ethnicity first didn’t work Too many choices Patients asked: “What is the difference

between race and ethnicity?”

Querying methods for race/ethnicity

Method One Method Two Method Three Method Four

1. Hispanic?

(y/n)

2. Race?

(OMB list)

3. Ethnicity?

(Open-ended)

1. Ethnicity?

(Open-ended)

2. Race?

(OMB list)

1. Race?

(OMB list + Hispanic)

2. Ethnicity?

(Open-ended)

1. Race?

(OMB list +

White Hispanic Black Hispanic)

2. Ethnicity?

(Open-ended)

Results

Outcomes of InterestMethod

One Two Three Four

Interviews (n) 76 56 59 39

No answer to race Q (%) 21.1 3.6 0.0 2.6

Chose from available responses to race Q (%)

78.9 87.5 100.0 92.3

Answered ethnicity Q (%) 85.5 100.0 94.9 92.3

Average administration time (mins)

1.1 0.9 1.0 1.2

“What is your race? (You may choose more than one)”

White Black or African American Hispanic Asian Native American Other

Researchers use the data that providers obtain:

Providers need

Ways to mitigate against patient resistance

Minimal administrative burden Obtaining data (staff

assessment common) Managing data

Response choices that reflect local environment

Researchers want

Rigor & standardization in how questions are asked

Clean data: “Accurate” “Rolled up” to common

categories Common response choices

for sites across the US

Office of Management and Budget Statistical Directive 15

Are you of Hispanic origin?

YesNo

What is your race?

WhiteBlack or African AmericanAsianPacific IslanderNative American or Alaskan nativeOther

OMB standard

Does not reflect how most people self-identify, BUT

Reified in the US through constant use: Census Applications for jobs, schools,

loans, housing, etc. Surveys & questionnaires

Unlikely to undergo massive change

“What is your ethnicity (your ethnic identity)?” 60+ choices suggested by nationality, religion, language, race. Examples:

Mexican Somali Hmong Russian ….

Some ruminations and conclusions:

Each method tested during actual registration process

Test conducted in Jan & Feb 2006Four registrars, 2 who staffed Spanish telephone lineEach method tested by 2+ registrars on 2+ daysTesting continued until at least 30 responses obtained

Asking race first (with OMB responses) Worked for U.S. born Worked for Hispanic if response

choices include Hispanic Did not work for foreign-born non-

Hispanic, but ….could overcome problems with followup ethnicity Q

What worked, what didn’t:

HCMC Experiment

The OMB Standard: Confusing or meaningless for those not born in the US. Blurs important distinctions, such as:

American- vs. African-born Black Hmong vs. Chinese vs. Indian

Creates strange bedfellows Iranian & Brit both classified as “White” Israelis classified as “White” (even though Israel is one of the most racially diverse countries in the world)

Nearly 2/3 of Hispanics do not answer the race question (known even before the standard was released) Ambiguous: Are Spaniards ‘Hispanic’? What about indigenous people of Ecuador? In health care settings, repeatedly cited as a source of confusion and distress

Conflicts revealed

Data and disparities: What data are required?

Discrimination: Need to know how the patient is perceived. Staff eyeball of race could be OK! Culturally influenced behavior & beliefs. Need to know how patient self-identifies, but … self-identification changes with circumstance & time. What does “accuracy” mean?!

Source of adverse disparities Data required

Adverse discrimination by others Ascribed identity

Behavior & beliefs shared with cultural group Self-identity

Biology, physiology Biological

Paradox:

No one wants ascribed identities, but… If disparities are due to discrimination,

ascribed identities matter Everyone wants “accuracy”, but… Self-identification can change, so

“accuracy” is only momentarily reliable

A PROPOSAL

Question 1:

“What race do most people think you are?”

Response choices: OMB categories plus Hispanic

Question 2:

“How do you think of yourself?”

Response choices: Open-ended, locally specific, evolving dynamically