Needs and Challenges for. Race/Ethnicity Data
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1 Disaggregation of Data: Needs and Challenges for Collecting and Reporting Race/Ethnicity Data Suhaila Khan, MD PhD Marguerite Ro, DrPH August 20, 2009 Webinar
2 Topics covered in webinar What is disaggregation of data Importance (need) of disaggregated data Challenges of collecting disaggregated data Challenges of reporting disaggregated data 2
3 What is disaggregation of data? Breaking down aggregate (or average) data into specific subgroups Also called granular data 3
4 What to disaggregate (depends on purpose) Age : children, youth, adult, elderly Gender : male, female Socio-economic status : employment status, insurance status Location : national, state, county Race/ethnicity especially needed for communities of color and indigenous people that comprises of multiple race/ethnic groups 4
5 Importance (need) of disaggregated data See patterns (similarities, differences) Issues get lost in aggregated data Confirm or refute perceptions Identify and address specific needs (e.g. health, education, employment) Develop, monitor, and improve programs (e.g. quality of services) Provides evidence to inform and influence program and policy Advocate for, and enable appropriate allocation of resources at community, local, state, and federal levels Ensure equity Provide evidence for grant applications 5
6 History of disaggregation of data by race/ethnicity White, Other White, Native American, Other White, Native American, Black, Other White, Native American, Black, Hispanic, Asian Pacific Islander, Other White, Native American, Black, Hispanic, Asian American, Native Hawaiian and Pacific Islander, Other 6
7 Identifying race/ethnicity Who does the identifying? self, somebody else Single race/ethnicity Mixed race/ethnicity e.g. Person has Asian and White parents. S/he identifies her/himself as Asian, or White, or both.
8 Ask ethnicity first, race second The two-part question may look like this: Are you Hispanic/Latino? (Choose only one) No Yes What is the your race? (Choose one or more) American Indian or Alaska Native Asian Black or African American Native Hawaiian or Other Pacific Islander White
9 Identity within Hispanics or Latinos Hispanic or Latino Mexican Puerto Rican Cuban Dominican (Dominican Republic) Central American Costa Rican Guatemalan Honduran Nicaraguan Panamanian Salvadoran Other Central American South American Argentinean Bolivian Chilean Colombian Ecuadorian Paraguayan Peruvian Uruguayan Venezuelan Other South American Other Hispanic or Latino Spaniard Spanish Spanish American All Other Hispanic or Latino
10 Identity within Asian Americans, Native Hawaiians a a and Pacific c Islanders Over 50 race/ethnic groups who speak over 100 different languages Asian Americans East Asians Chinese, Japanese, Korean South Asians Bangladeshi, Indian, Pakistani, Nepalese South East Asians Thai, Cambodian, Laotian, Vietnamese Native Hawaiian Pacific Islanders Native Hawaiians Micronesians Chamorro, Guam Polynesians Samoan, Tongan
11 Disaggregated data show disparity e.g. education - less than high school National State t (e.g. CA) General population 16% 20% White 14% 16% Native American 21% 20% Black 20% 14% Hispanic 40% 44% Asian 14% 14% Hmong 39% 48% Laotian 38% 42% Cambodian 35% 37% Native Hawaiian Pacific Islander 14% 15% Chamorro 19% NA source: American Community Survey
12 Disaggregated data show disparity e.g. Limited English Proficient Asian Americans % of Population, Percent LEP US U.S. Total Asian Vietnamese Korean Japanese Adapted from APIAHF: Diverse Communities, Diverse Experiences: The Status of Asian Americans and Pacific Islanders in the U.S. (A Review of Six Economic Indicators and Their Impact on Health), 2005
13 Disaggregated g data show disparity e.g. uninsurance rates General population 15% White 11% Native American 32% Black 20% Hispanic 33% Asian 17% Korean* 31% Native Hawaiian Pacific Islander 21% source: American Community Survey 2006 *KFF and APIAHF 2008
14 Disaggregated data show disparity for Latinos, 1998 source: Ventura et al Births: final data for National vital statistics reports 30% 25% 24.3% Birth Rates by Ethnicity 26.4% 23.2% 20% 19.0% 15% 10% 12.1% 10.0% 5% 0% White Total Latino Mexican Puerto Cuban Central & Rican South American
15 Disaggregated data by race/ethnicity and place of birth for Latino Mothers source: Ventura et al Births: final data for National vital statistics reports Mothers Level of Education 12 Years of School or More 100% 87.6% 86.1% 90.2% 87.0% 80% 78.4% 60% 59.5% 63.6% 64.3% 62.7% 64.5% 41.4% 40% 32.7% 20% 0% Central & Cuban Puerto Rican Mexican Total Latino White South American Mothers Born Outside U. S. Mothers Born in U.S.
16 Challenges of collecting and reporting disaggregated data 1997 Office of Management and Budget s Revisions to the Standards for the Classifications of Federal Data on Race and Ethnicity created a new category, Native Hawaiian and Other Pacific Islander separate from Asian This is a federal law, not yet implemented across all federal agencies. At the state and local levels, the classification is rarely used. 16
17 Challenges of collecting and reporting disaggregated data (cont..) Why data not collected lack of visibility of the specific groups lack of awareness of differences among specific groups budget language - questionnaires are in English or Spanish small population groups Why data collected but not reported if numbers <65,000 U.S. Census does not report small population, so small sample size - issues of confidentiality, statistical stability budget lack of awareness of importance of disaggregation of data
18 Example of data collected but not reported: state level California Department of Health collects adolescent data on AIDS and Chlamydia Chlamydia data are reported for White, Black, Hispanic, Native American, and Asian Pacific Islander AIDS data collects for all races, but not reported for Asian Pacific Islanders (reason: data withheld to ensure confidentiality) source: California Adolescent Sexual Health Work Group (ASHWG) 2007 p( ) Data for California Adolescent Births, AIDS, STDs; August 2009)
19 What can be done to address the challenges in disaggregation of data Educate and advocate on the importance of disaggregation of data Implement the OMB classifications on race/ethnicity across data collecting agencies at national, state, and local levels Report the unstable data for small populations Standardize the definitions used for race/ethnicity Collect data at longer intervals, but only if there is oversampling of small populations
20 Take Away Disaggregation of data are important!!! The more specific the data, the more efficient the decisions! 20
21 igracias ~ Thank You! QUESTIONS & COMMENTS
22 Thank you Contact info: Suhaila Khan, MD PhD Community Capacity Program Director Asian & Pacific Islander American Health Forum 450 Sutter Street, Suite 600 San Francisco, CA E skhan@apiahf.org, T , F Marguerite Ro, DrPH Deputy Director Asian & Pacific Islander American Health Forum 450 Sutter Street, Suite 600 San Francisco, CA E mro@apiahf.org, T , F
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