Using Race, Ethnicity, While we wait to get started We are - - PowerPoint PPT Presentation

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Using Race, Ethnicity, While we wait to get started We are - - PowerPoint PPT Presentation

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Using Race, Ethnicity, While we wait to get started We are recording this webinar. Language & Disability To access captioning, click on captions show subtitles . (REALD) Data to For ASL interpreter access, you can

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Using Race, Ethnicity, Language & Disability (REALD) Data to Advance Health Equity

November 20, 2020

While we wait to get started…

  • We are recording this webinar.
  • To access captioning, click on captions –

show subtitles.

  • For ASL interpreter access, you can “pin”

the video on your screen to keep the interpreter view at all times.

  • Private chat to Tom Cogswell if you are

having technical challenges.

  • If your name is not visible / clear, please

rename yourself for clarity if possible.

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SLIDE 2

Welcome and structure for today

  • Introductions

– Tom Cogswell, OHA Transformation Center: THOMAS.COGSWELL@dhsoha.state.or.us – Marjorie McGee, Ph.D., OHA Equity and Inclusion Division: MARJORIE.G.MCGEE@dhsoha.state.or.us

  • Structure: Brief Q & A after each section (use Chatbox)

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SLIDE 3

REALD Learning (webinar) Series:

  • 10/9/2020:

REALD 101 – Introduction – What and Why*

  • 10/14/2020:

Implementing New REALD Data Collection for Providers*

  • 10/16/2020:

How to ask the questions*

  • 11/10/2020:

Implementing REALD for Providers: Updates and FAQs

  • 11/20/2020 (today):

Using REALD Data to Advance Health Equity

  • TBD – December:

Cleaning the REALD data

*Webinar registration, materials/recordings:

https://www.oregon.gov/oha/OEI/Pages/REALD.aspx

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SLIDE 4

Learning objectives and caveats

Learning objective

  • Understand the utility of the REALD data

elements to identify and address inequities Next webinar will go into details on how to set up the data to do these analyses.

Caveats

  • Data examples are from the 2014-18 ACS

PUMS data unless otherwise specified. – The data is relatively “clean.” (The ACS did their own imputations for missingness, etc.) – Many of the disaggregated race/ethnicity categories in REALD were imputed. – The “outcome” variables selected were based on role of SDOH in health

  • utcomes – including COVID.

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SLIDE 5

How to make REALD work for you…

  • At a demographic (community) level:

– Identify inequities (between/within/intersectionally) – Address inequities through community action, policy and legislative efforts – Make the case for additional resources and funds needed to effectively address inequities – Determine who are being served or surveyed – Ensure effective interpreter (spoken) and translation (written) services – Develop culturally specific and accessible programs, services and materials (such as health education materials and survey tools) – Determine if certain groups of people are underserved

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SLIDE 6

Starting with the question…

  • What do you want to know?

– Do you have what you need to answer the question? – Is the data good enough to answer the question?

  • For today:

– Want to show different ways of examining the same variables – Will show when a deeper dive could be helpful

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SLIDE 7

Question: Emerging populations

  • What do you want to know?

– Which “emerging” populations were masked by original REALD categories?

  • Do you have what you need to answer the question?

– Race/Ethnicity variables: Reopen HomeLangS, SpokLang, WritLang (ACS & ONE/OHP) – REALD template items:

  • Q1 – How do you identify your race, ethnicity, tribal affiliation, country of origin, or

ancestry?

  • Q4a (primary language), 4b (preferred spoken), 4c (preferred written)
  • Is the data good enough to answer the question?

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Emerging populations

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0.4 1.9 1.4 4.3 14.4 1.7 3.8 2.5 5.1 11.4 23.2 15.6 2.9 0.9 5.1 5.7 15.3 7.2 Cambodian Communities-Myanmar Ethiopian Somali Communities-Micronesian Marshallese

ONE /OHP ACS Medicaid ACS All

% of Asian Identities % of Black Identities % of Native Hawaiian/Pacific Islander Identities

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SLIDE 9

Question: Poverty and race/ethnicity

  • What do you want to know?

– Which groups are most likely to be under 139% of federal poverty levels (FPL)? – Who are masked when using aggregated identities?

  • Do you have what you need to answer the question?

– Race/ethnicity variables: Recat (note - using identities and not primary race) (ACS) – REALD template item Q2 – Disaggregated race/ethnicity categories

  • Is the data good enough to answer the question?

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Poverty by aggregate identities

(Enter) DEPARTMENT (ALL CAPS) (Enter) Division or Office (Mixed Case)

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81.0 79.7 76.8 65.3 64.7 63.6 63.1

19.0 20.3 23.2 34.7 35.3 36.4 36.9

ASIAN WHITE MIDDLE EASTERN/NORTH AFRICAN AMERICAN INDIAN/ALASKA NATIVE LATINO/A/X BLACK/AFRICAN AMERICAN OTHER/MULTIRACIAL

Over 138% FPL Under 139% FPL

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SLIDE 11

Poverty: Deeper dive among Asian identities

88.9 87.7 85.7 84.2 84 83 81.0 78.2 77.4 77.4 76.9 66.1 46.6

11.1 12.3 14.3 15.8 16 17 19.0 21.8 22.6 22.6 23.1 33.9 53.4

ASIAN INDIAN HMONG CAMBODIAN FILIPINO JAPANESE KOREAN ALL ASIAN LAOTIAN CHINESE VIETNAMESE OTHER ASIAN SOUTH ASIAN COMMUNITIES OF MYANMAR

Over 138% FPL Under 139% FPL

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SLIDE 12

Question: Poverty and language

  • What do you want to know?

– What are the % of poverty (139% FPL) by home language and English proficiency status?

  • Do you have what you need to answer the question?

– Language variables: Language: PrefLang; ENG; POVPIP (ACS) – REALD template items: Q4a (primary language), Q6 (English proficiency)

  • Is the data good enough to answer the question?

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Poverty by home language and English proficiency

81.1 72.7 64.9 18.9 27.3 35.1

SPEAKS ENGLISH AT HOME SPEAKS ENGLISH "VERY WELL" LIMITED ENGLISH PROFICIENCY

139% FPL + Under 139% FPL

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SLIDE 14

Question: English proficiency and race

  • What do you want to know?

– What is the profile of Oregonians by English proficiency and primary race (aggregated and disaggregated)?

  • Do you have what you need to answer the question?

– Variables: PriREcd; ENG (ACS) – REALD template items: Q3, Q6

  • Is the data good enough to answer the question?

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English proficiency by primary race (aggregated)

39.6 33.5 27.6 17.1 8.4 6.7 2.9 1.3 25 42.5 34.9 27.3 12.4 14.9 2.7 2.9 35.4 24 37.5 55.6 79.1 78.4 94.4 95.8 ASIAN LATINX MENA NHPI AIAN BLACK OTHER WHITE

Limited English Proficiency (LEP) Not LEP English at home

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English proficiency by primary race (disaggregated)

10 20 30 40 50 60 70 80 90 100 South Asian Chinese Other Asian Micronesian/COFA Korean Laotian Hmong Asian Indian Latinx South American Slavic Samoan Japanese Eastern European Other AIAN Other race Other Black Other White Guamanian or Chamorro

Limited English Proficiency (LEP) Not LEP English at home

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Question: Language access needs

  • What do you want to know?

– Excluding Spanish speakers, what are the top 20 language groups with access needs? – Which groups have highest language access needs?

  • Do you have what you need to answer the question?

– Variables: SpokLang, WritLang; ENG, Interp (ONE/OHP) – REALD template items: Created composite variables

  • Preferred language from combination of 4b, 4c
  • LEPdiMod category using PrefLang, LEPdi, InterpN
  • Is the data good enough to answer the question?

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Top 20 language groups: OHP members with language access needs

200 400 600 800 1000 1200 Vietnamese Russian Cantonese Arabic Mandarin Somali Korean Burmese Chinese Amharic Swahili Farsi Marshallese Karen Thai Nepali Tigrinya Other Pac Islndr language Romanian Afghan

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Preferred language by access needs

10 20 30 40 50 60 70 80 90 100 Guatamalan Indian Dialect Marshallese Other Pac Islndr language Mandarin Mayan Hindi Persian Punjabi Japanese Gujarati Portuguese Amharic Thai Korean Farsi Tigrinya Cambodian Hmong Afghan Tagalog Lang Access Need No Lang Access Need

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Question: Public assistance and disability

  • What do you want to know?

– How does the profile of people receiving public assistance (e.g., SSI, OHP, food stamps) vary by how the disability variables are handled in analyses?

  • Do you have what you need to answer the question?

– Variables: Calculated field – DA7comp (Composite var); DISdi (dichotomous var) (base vars: DEAR, DEYE, DREM, DPHY, DDRS, DOUT) (ACS) – REALD template items: Q7-Q11; Q14 (the other disability variables are not in ACS datasets)

  • Is the data good enough to answer the question?

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SLIDE 21

Public assistance by disability

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67.4

26.5 32.6 73.5

NON-DISABLED DISABLED

No Public Assistance Receives Public Assistance

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SLIDE 22

Public assistance by functional limitations

(Enter) DEPARTMENT (ALL CAPS) (Enter) Division or Office (Mixed Case)

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14 16 18 24 25 28 67 87 84 82 76 75 72 33

SELF-CARE INDEP-LIVING MOBILITY HEARING COGNITIVE VISION NON-DISABLED

No Public Assistance Receives Public Assistance

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SLIDE 23

Public assistance by disability profile variable

(Enter) DEPARTMENT (ALL CAPS) (Enter) Division or Office (Mixed Case)

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67 52 40 34 28 21 17 33 48 60 66 73 79 84

NON-DISABLED VISION ONLY COGNITIVE ONLY HEARING ONLY MOBILITY ONLY 2+ DISABILITIES INDP LIVING/SELF-CARE

No Public Assistance Receives Public Assistance

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Question: Race/ethnicity and disability

  • What do you want to know?

– Among which racial/ethnic groups is disability most common? – How does that vary by number of disabilities?

  • Do you have what you need to answer the question?

– Variables: Calculated field – DA7comp (Composite var); DISdi (dichotomous var) (base vars: DEAR, DEYE, DREM, DPHY, DDRS, DOUT) (ACS) – REALD template items: Q7-Q11; Q14 (the other disability variables are not in ACS datasets)

  • Is the data good enough to answer the question?

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Intersections: Granular race and disability

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10 20 30 40 50 60 70 80 90 100 American Indian Other AIAN Other race Western European African Other White Alaska Native Eastern European Other Latinx Other Black South Asian Tongan Slavic Indigenous Mex/Cen/So American Laotian Middle Eastern Native Hawaiian Other Pacific Islander

2+ disabilities 1 disability Non-disabled

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SLIDE 26

Questions

Using REALD Data to Advance Health Equity (Race, Ethnicity, Language & Disability)

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