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

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 “pin” Advance Health Equity 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 November 20, 2020 rename yourself for clarity if possible.

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) 2

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 3

Learning objectives and caveats Caveats Learning objective • Data examples are from the 2014-18 ACS • Understand the utility of the REALD data PUMS data unless otherwise specified. elements to identify and address inequities – The data is relatively “clean.” (The ACS did their own imputations for missingness, etc.) Next webinar will go into details on how to – Many of the disaggregated race/ethnicity set up the data to do these analyses. categories in REALD were imputed. – The “outcome” variables selected were based on role of SDOH in health outcomes – including COVID. 4

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 5

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 6

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? 7

Emerging populations ONE /OHP ACS Medicaid ACS All 23.2 15.6 15.3 14.4 11.4 7.2 5.7 5.1 5.1 4.3 3.8 2.9 2.5 1.9 1.7 1.4 0.9 0.4 Cambodian Communities-Myanmar Ethiopian Somali Communities-Micronesian Marshallese % of Native Hawaiian/Pacific % of Asian Identities % of Black Identities Islander Identities 8

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? 9

Poverty by aggregate identities Over 138% FPL Under 139% FPL 36.9 OTHER/MULTIRACIAL 63.1 36.4 BLACK/AFRICAN AMERICAN 63.6 35.3 LATINO/A/X 64.7 34.7 AMERICAN INDIAN/ALASKA NATIVE 65.3 MIDDLE EASTERN/NORTH AFRICAN 23.2 76.8 20.3 WHITE 79.7 19.0 ASIAN 81.0 (Enter) DEPARTMENT (ALL CAPS) (Enter) Division or Office (Mixed Case) 10

Poverty: Deeper dive among Asian identities Over 138% FPL Under 139% FPL 53.4 46.6 COMMUNITIES OF MYANMAR 33.9 SOUTH ASIAN 66.1 23.1 76.9 OTHER ASIAN 22.6 VIETNAMESE 77.4 22.6 CHINESE 77.4 21.8 78.2 LAOTIAN 19.0 ALL ASIAN 81.0 17 83 KOREAN 16 JAPANESE 84 15.8 FILIPINO 84.2 14.3 85.7 CAMBODIAN 12.3 HMONG 87.7 11.1 ASIAN INDIAN 88.9 11

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? 12

Poverty by home language and English proficiency 139% FPL + Under 139% FPL 64.9 35.1 LIMITED ENGLISH PROFICIENCY SPEAKS ENGLISH "VERY WELL" 72.7 27.3 81.1 18.9 SPEAKS ENGLISH AT HOME 13

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? 14

English proficiency by primary race (aggregated) Limited English Proficiency (LEP) Not LEP English at home ASIAN 39.6 25 35.4 LATINX 33.5 42.5 24 MENA 27.6 34.9 37.5 NHPI 17.1 27.3 55.6 AIAN 8.4 12.4 79.1 BLACK 6.7 14.9 78.4 OTHER 2.9 2.7 94.4 WHITE 1.3 2.9 95.8 15

English proficiency by primary race (disaggregated) 0 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 16

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? 17

Top 20 language groups: OHP members with language access needs 0 200 400 600 800 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 18

Preferred language by access needs Lang Access Need No Lang Access Need 0 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 19

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? 20

Public assistance by disability No Public Assistance Receives Public Assistance 26.5 73.5 DISABLED 32.6 NON-DISABLED 67.4 21

Public assistance by functional limitations No Public Assistance Receives Public Assistance 14 87 SELF-CARE 16 84 INDEP-LIVING 18 82 MOBILITY 24 76 HEARING 25 75 COGNITIVE 28 72 VISION 67 33 NON-DISABLED (Enter) DEPARTMENT (ALL CAPS) (Enter) Division or Office (Mixed Case) 22

Public assistance by disability profile variable No Public Assistance Receives Public Assistance 17 84 INDP LIVING/SELF-CARE 2+ DISABILITIES 21 79 MOBILITY ONLY 28 73 HEARING ONLY 34 66 COGNITIVE ONLY 40 60 VISION ONLY 52 48 NON-DISABLED 67 33 (Enter) DEPARTMENT (ALL CAPS) (Enter) Division or Office (Mixed Case) 23