Ninez A. Ponce, PhD, MPP, is the director of the UCLA Center for Health Policy Research, and Professor and Fred W. & Pamela K. Wasserman Endowed Chair in the Department of Health Policy and Management at the UCLA Fielding School of Public Health. She leads the California Health Interview Survey (CHIS), the nation’s largest state health survey, recognized as a national model for data collection on race and ethnicity, sexual orientation and gender identity (SOGI) and immigrant health.

CHIS is the only large-scale population survey that includes Tagalog, Vietnamese, Korean, Cantonese, and Mandarin, in addition to Spanish and English in administering the survey to a representative sample of California’s adults, adolescents, and young children. It is considered a gold standard for other state-level efforts on meaningful inclusion of Asian Americans and Pacific Islanders through oversampling or special samples, and by developing culturally and linguistically appropriate instruments. This approach has resulted in one of the richest datasets with sufficient subsample of the SOGI population, mixed immigrant status (citizen children with noncitizen parents) families, and several major Asian ethnic groups. CHIS is one of the few population datasets that collects information on American Indian/Alaska Native tribal enrollment and whether the tribe is state or federally-recognized.

Ponce is an elected member of the National Academy of Social Insurance and has served on the Board of Scientific Counselors, National Center for Health Statistics. She has participated in committees for the National Academy of Medicine and the National Quality Forum, where her expertise has focused on setting guidance for health systems in the measurement and use of social determinants of health as tools to monitor health equity.  She has received numerous awards from community organizations recognizing her work in community-engaged research. In 2019 Dr. Ponce and her team received the AcademyHealth Impact award for their contributions to population health measurement to inform public policies.

Ponce serves on the Data Disaggregation workgroup for the White House Asian American, Native Hawaiian, Pacific Islander Commission. Currently, she is an Associate Editor for Diversity, Equity and Inclusion at JAMA Health Forum. Her portfolio includes a mixture of scholarly work and real-time knowledge diffusion studies, with over 140 peer-reviewed publications, over 60 policy reports, and various creative data access tools to democratize health data.

Ponce champions better data, especially for people from marginalized racial and ethnic, sexual orientation and gender identity, and immigrant populations.  She firmly believes that equity-centered data will lead to more meaningful program and policy inferences and better care for overlooked groups.

Ponce earned her bachelor’s degree in science at UC Berkeley, her master’s degree in public policy at Harvard University, and her PhD in health services at UCLA.

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Journal Article
Journal Article

Data Disaggregation in Action: Filipino Americans Who Do Not Identify as Asian

The U.S. Office of Management and Budget (OMB) categorizes Filipino Americans as Asian; however, many may not identify as such, opting instead for "other" or Pacific Islander (PI). This study examines the extent to which Filipino Americans select PI or "other" rather than Asian, using a large population-based survey of Californians.

Authors analyzed data from the 2019, 2020, and 2021 California Health Interview Survey (CHIS), the largest state health survey in the U.S. that includes write-in prompts for detailed race and ethnicity data. The focus was on participants who identified as Pacific Islanders and wrote in 'Filipino,' those who selected 'Asian' and specified 'Filipino,' and respondents who chose 'other' and wrote in 'Filipino.'

Findings: The analysis included 1,859 Filipino respondents, revealing that 8.8% identified as Pacific Islander, 85.2% as Asian, and 6.1% as "other." Those identifying as PI were more likely to also identify as Latino/Hispanic, be older, and possess U.S. citizenship compared to those identifying as Asian.

Disaggregating Filipino Americans from the broader Asian category in surveys is vital for accurately identifying the community's unique needs. Authors recommend incorporating open-ended write-in prompts in surveys that ask respondents to first identify their broader race category (e.g. Asian). These prompts help identify and reclassify Filipino respondents who may have identified as PI. Such prompts are also important for other racial/ethnic communities who may be uncertain about how to categorize themselves. Ongoing, dynamic community-driven research is essential for understanding identities and effectively categorizing Filipino Americans and other communities.
 

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NHPI woman CAPIWAVES report cover
Research Report
Research Report

Mental Health and Socioeconomic Impact of COVID-19 on California’s Native Hawaiians and Pacific Islanders

The COVID-19 pandemic had a devastating toll on the Native Hawaiian and Pacific Islander (NHPI) community. In 13 of the 19 states that disaggregated NHPI data, including California, NHPIs had the highest COVID-19 cases and death rates of any racial and ethnic group. However, in many data sources, NHPIs are absorbed into other racial and ethnic groups, such as Asian, masking COVID’s true impact on the NHPI community.

Working alongside NHPI community leaders and partners, researchers conducted the California Pacific Islander Well-Being and COVID-19 Economic Survey (CAPIWAVES) of 929 NHPI adults and developed a report examining the mental health and socioeconomic impact of the COVID-19 pandemic on NHPIs in California.

In addition to the total NHPI population, the report provides disaggregated estimates for seven NHPI groups in the survey: CHamoru, Fijian, Marshallese, Native Hawaiian, Samoan, Tongan, and other Pacific Islander. These disaggregated estimates may be relevant to specific populations but should be interpreted with caution when the sample size is smaller or when the population characteristic is not very common.

Selected mental health findings:

  • More than 1 in 4 NHPI adults (28.2%) reported experiencing “fair” or “poor” mental health.
  • Although poor mental health was common, relatively few NHPI Californians sought and were able to access mental health care. For instance, 26.8% of NHPI adults reported seeking mental health care in their lifetime, indicating that nearly three-quarters had never sought professional help for emotional or mental health problems.
  • About 1 in 3 NHPI adults (33.9%) reported needing mental health care in the past year for help with stress, depression, or emotions.
  • The COVID-19 pandemic was associated with worsening mental health problems among NHPIs in California, with 47.5% reporting moderate and 9.2% reporting high levels of distress. The major sources of stress included health-related concerns (43.9%), financial concerns (38.8%), and pandemic impact on family members (37.6%), their work (32.4%), and their elders (31.1%).

Selected socioeconomic findings:

  • About 1 in 3 NHPI adults (34%) reported a decrease in household income since the COVID-19 pandemic, with 46.3% of Fijian, 42.9% of Marshallese, 41.4% of Tongan, and 35.6% of Native Hawaiian adults reporting loss of income.
  • Nearly 1 in 3 NHPI adults (29.7%) reported having someone in their household lose their job or a significant amount of income due to the pandemic.
  • More than 1 in 4 NHPI adults (27.4%) had difficulty meeting basic financial necessities during the COVID-19 pandemic, including paying bills, paying tuition, affording groceries, or paying their rent or mortgage. More than 1 in 9 (11.4%) had difficulty obtaining child care.
  • Although NHPI adults in California experienced severe economic impacts due to the pandemic, fewer than expected NHPI adults were able to access financial assistance, even when they were eligible. For example, 1 in 5 NHPI adults (19.0%) received government health benefits including Medi-Cal (California’s Medicaid program) or Medicare. Of those who were eligible based on reported household income, less than one-third (31.2%) received government health benefits.

 

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Breaking Barriers with Data Equity: The Essential Role of Data Disaggregation in Achieving Health Equity
Journal Article
Journal Article

Breaking Barriers with Data Equity: The Essential Role of Data Disaggregation in Achieving Health Equity

Achieving health equity necessitates high-quality data to address disparities that have remained stagnant or even worsened over time despite public health interventions. Data disaggregation, the breakdown of data into detailed subcategories, is crucial in health disparities research. It reveals and contextualizes hidden trends and patterns about marginalized populations and guides resource allocation and program development for specific needs in these populations.

Data disaggregation underpins data equity, which uses community engagement to democratize data and develop better solutions for communities. Years of research on disaggregation show that researchers must collaborate closely with communities for adequate representation. However, despite generally positive support for this approach in health disparities research, data disaggregation faces methodological and political challenges.

This review offers a framework for understanding data disaggregation in the context of data equity and highlights critical aspects of implementation, including challenges, opportunities, and recent policy and community-based efforts to address hurdles.
 

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Racial and Ethnic Differences in Low-Value Care Among Older Adults in a Large Statewide Health System
Journal Article
Journal Article

Racial and Ethnic Differences in Low-Value Care Among Older Adults in a Large Statewide Health System

As value-based payment models incorporate both measures of health equity and low-value care (LVC), understanding how LVC varies by race is vital for interventions. Authors measured racial differences in LVC in a contemporary sample.

They analyzed claims from adults ≥ 55 years receiving care at five academic medical centers in California from 2019 to 2021. This sample included patients who received a service that could be classified as LVC. The primary outcome was whether a service was classified as LVC. Secondary outcomes included clinical categories of LVC (preventive screening, diagnostic testing, prescription drugs, and preoperative testing).

Findings: Among 15,720 members who received potentially LVC, non-Hispanic white older adults comprised 59% of the sample, followed by Asian (17%), unknown race (8%), Latino (8%), non-Hispanic Black (5%), and other race (2%). Asian, Black, and Latino older adults were less likely to receive LVC compared to white older adults, specifically preventive and preoperative services. Asian, Black, and Latino older adults, however, were more likely to receive low-value prescriptions.

These diverging racial patterns in LVC across different measures likely reflect differential mechanisms, underscoring the need to use clinically specific measures rather than composite measures, which obscure underlying heterogeneity and could lead to potentially harmful and inequity-producing interventions.
 

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Publications
NHPI woman CAPIWAVES report cover
Research Report
Research Report

Mental Health and Socioeconomic Impact of COVID-19 on California’s Native Hawaiians and Pacific Islanders

The COVID-19 pandemic had a devastating toll on the Native Hawaiian and Pacific Islander (NHPI) community. In 13 of the 19 states that disaggregated NHPI data, including California, NHPIs had the highest COVID-19 cases and death rates of any racial and ethnic group. However, in many data sources, NHPIs are absorbed into other racial and ethnic groups, such as Asian, masking COVID’s true impact on the NHPI community.

Working alongside NHPI community leaders and partners, researchers conducted the California Pacific Islander Well-Being and COVID-19 Economic Survey (CAPIWAVES) of 929 NHPI adults and developed a report examining the mental health and socioeconomic impact of the COVID-19 pandemic on NHPIs in California.

In addition to the total NHPI population, the report provides disaggregated estimates for seven NHPI groups in the survey: CHamoru, Fijian, Marshallese, Native Hawaiian, Samoan, Tongan, and other Pacific Islander. These disaggregated estimates may be relevant to specific populations but should be interpreted with caution when the sample size is smaller or when the population characteristic is not very common.

Selected mental health findings:

  • More than 1 in 4 NHPI adults (28.2%) reported experiencing “fair” or “poor” mental health.
  • Although poor mental health was common, relatively few NHPI Californians sought and were able to access mental health care. For instance, 26.8% of NHPI adults reported seeking mental health care in their lifetime, indicating that nearly three-quarters had never sought professional help for emotional or mental health problems.
  • About 1 in 3 NHPI adults (33.9%) reported needing mental health care in the past year for help with stress, depression, or emotions.
  • The COVID-19 pandemic was associated with worsening mental health problems among NHPIs in California, with 47.5% reporting moderate and 9.2% reporting high levels of distress. The major sources of stress included health-related concerns (43.9%), financial concerns (38.8%), and pandemic impact on family members (37.6%), their work (32.4%), and their elders (31.1%).

Selected socioeconomic findings:

  • About 1 in 3 NHPI adults (34%) reported a decrease in household income since the COVID-19 pandemic, with 46.3% of Fijian, 42.9% of Marshallese, 41.4% of Tongan, and 35.6% of Native Hawaiian adults reporting loss of income.
  • Nearly 1 in 3 NHPI adults (29.7%) reported having someone in their household lose their job or a significant amount of income due to the pandemic.
  • More than 1 in 4 NHPI adults (27.4%) had difficulty meeting basic financial necessities during the COVID-19 pandemic, including paying bills, paying tuition, affording groceries, or paying their rent or mortgage. More than 1 in 9 (11.4%) had difficulty obtaining child care.
  • Although NHPI adults in California experienced severe economic impacts due to the pandemic, fewer than expected NHPI adults were able to access financial assistance, even when they were eligible. For example, 1 in 5 NHPI adults (19.0%) received government health benefits including Medi-Cal (California’s Medicaid program) or Medicare. Of those who were eligible based on reported household income, less than one-third (31.2%) received government health benefits.

 

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Journal Article
Journal Article

Data Disaggregation in Action: Filipino Americans Who Do Not Identify as Asian

The U.S. Office of Management and Budget (OMB) categorizes Filipino Americans as Asian; however, many may not identify as such, opting instead for "other" or Pacific Islander (PI). This study examines the extent to which Filipino Americans select PI or "other" rather than Asian, using a large population-based survey of Californians.

Authors analyzed data from the 2019, 2020, and 2021 California Health Interview Survey (CHIS), the largest state health survey in the U.S. that includes write-in prompts for detailed race and ethnicity data. The focus was on participants who identified as Pacific Islanders and wrote in 'Filipino,' those who selected 'Asian' and specified 'Filipino,' and respondents who chose 'other' and wrote in 'Filipino.'

Findings: The analysis included 1,859 Filipino respondents, revealing that 8.8% identified as Pacific Islander, 85.2% as Asian, and 6.1% as "other." Those identifying as PI were more likely to also identify as Latino/Hispanic, be older, and possess U.S. citizenship compared to those identifying as Asian.

Disaggregating Filipino Americans from the broader Asian category in surveys is vital for accurately identifying the community's unique needs. Authors recommend incorporating open-ended write-in prompts in surveys that ask respondents to first identify their broader race category (e.g. Asian). These prompts help identify and reclassify Filipino respondents who may have identified as PI. Such prompts are also important for other racial/ethnic communities who may be uncertain about how to categorize themselves. Ongoing, dynamic community-driven research is essential for understanding identities and effectively categorizing Filipino Americans and other communities.
 

View more publications
Breaking Barriers with Data Equity: The Essential Role of Data Disaggregation in Achieving Health Equity
Journal Article
Journal Article

Breaking Barriers with Data Equity: The Essential Role of Data Disaggregation in Achieving Health Equity

Achieving health equity necessitates high-quality data to address disparities that have remained stagnant or even worsened over time despite public health interventions. Data disaggregation, the breakdown of data into detailed subcategories, is crucial in health disparities research. It reveals and contextualizes hidden trends and patterns about marginalized populations and guides resource allocation and program development for specific needs in these populations.

Data disaggregation underpins data equity, which uses community engagement to democratize data and develop better solutions for communities. Years of research on disaggregation show that researchers must collaborate closely with communities for adequate representation. However, despite generally positive support for this approach in health disparities research, data disaggregation faces methodological and political challenges.

This review offers a framework for understanding data disaggregation in the context of data equity and highlights critical aspects of implementation, including challenges, opportunities, and recent policy and community-based efforts to address hurdles.
 

View more publications
Blog
smoke coming from the hills in Los Angeles
Director's Messages

Director’s message about the California wildfires

Dear Colleagues and Friends,

This has been an extraordinarily difficult time for our California communities, and our hearts are with everyone affected by the devastating wildfires.

Thank you to those on the front lines, including our brave firefighters and first responders who are working around the clock to contain the fires and risking their lives to protect us.

Last week, I loaded my car with photos, art, and my father’s medal from when he took the CPA exam, and my cat, and evacuated my home in Topanga. Like many Californians, I didn’t know if my house would still be standing after the fires were over. But I considered myself lucky: my family is healthy and safe; we have housing, food, and essentials; and we are surrounded by a community that continues to amaze me. The inspiring acts of generosity and kindness, the spirit of unity, the strength and resilience of our community: You are what makes LA.

Tens of thousands of people in Southern California are displaced, struggling with poor air quality, and in urgent need of help. Recovering from the physical, emotional, financial, environmental, and ecological impacts of wildfires takes years.

Last October, the UCLA Center for Health Policy Research (CHPR) released data from the 2023 California Health Interview Survey (CHIS) on wildfires and extreme weather-related events:

Nearly 1 in 8 (12.2%) California adults said they experienced a wildfire in the past two years and about 2 in 5 (41.3%) California adults experienced smoke from a wildfire in the past two years.

Among adults whose households experienced an extreme-weather related event in the past two years, 1 in 5 (19.9%) said their physical health was harmed by smoke from wildfire and nearly 1 in 7 (13.3%) said their mental health was harmed by smoke from wildfire.

Among adults who experienced smoke from wildfires in the past two years, 2 in 3 (65.3%) said they accessed filtered air in their home when exposed to wildfire smoke and 1 in 3 (34.7%) did not access filtered air in their home. There were significant differences across sociodemographic factors, including income: 69.6% of adults with incomes 300% of the federal poverty level (FPL) accessed filtered air in their homes, significantly higher than 53.2% of adults with incomes 0–99% FPL who accessed filtered air. U.S.-born citizens (67.8%) were more likely to access filtered air in their home compared to non-citizens (50.7%).

Among adolescents in California, 29.6% said climate change makes them feel nervous, depressed, or emotionally stressed.

We encourage you to explore these findings on our AskCHIS platforms.

As a community of researchers, advocates, health care organizations, agencies, funders, journalists, and policymakers, we must work together: to conduct more research on wildfires and better understand the broad effects on health, to mitigate risk, to inform policies, and to help each other recover. 

I am so grateful for the outpouring of support we’ve received from our colleagues, partners, and friends around the nation. 

Please continue to take care of each other. 

Ninez 

Ninez A. Ponce, PhD, MPP
Director, UCLA Center for Health Policy Research
Principal Investigator, California Health Interview Survey
Professor and Fred W. & Pamela K. Wasserman Endowed Chair, Department of Health Policy and Management, UCLA Fielding School of Public Health

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Ninez Ponce standing in front of a banner that says The White House Forum on Asian Americans, Native Hawaiians, and Pacific Islanders
Press Releases

Ninez Ponce discusses the importance of data equity at a White House panel

On May 3, UCLA Center for Health Policy Research Director Ninez A. Ponce, PhD, MPP, was featured on a panel of distinguished community leaders and researchers at the White House Forum on Asian Americans, Native Hawaiians, and Pacific Islanders.

Hosted by the White House Initiative on Asian Americans, Native Hawaiians, and Pacific Islanders (WHIAANHPI), the all-day forum included breakout convenings, panels, and artistic performances to celebrate Asian American, Native Hawaiian, and Pacific Islander (AANHPI) Heritage Month.

Ponce, who also serves as endowed chair and professor of health policy and management at the UCLA Fielding School of Public Health, was part of the Advancing Justice Through Data Equity breakout convening held from 9:30 a.m.–11:00 a.m. ET.

“This is an exciting opportunity to both celebrate the historic achievements of AANHPI communities and work toward a better future for all Asian Americans, Native Hawaiians, and Pacific Islanders,” Ponce says. “It is an honor to collaborate with leaders from across the nation who are passionate about advancing equity and justice for all AANHPIs.”

By 2060, AANHPI populations are projected to increase to 10% of the U.S. population, yet a lack of disaggregated data masks the experiences, priorities, and challenges of these diverse communities and hinders the allocation of federal resources. Ponce, a champion for data disaggregation and data equity, discussed the need for and importance of data equity, along with the work being done at the UCLA Center for Health Policy Research’s Native Hawaiian and Pacific Islander (NHPI) Data Policy Lab and California Health Interview Survey (CHIS).

The panel also included Gregg Orton, national director of the National Council of Asian Pacific Americans; Fontane Lo, deputy director of AAPI Data; Kham Moua, national deputy director of the Southeast Asia Resource Action Center; and Neil Ruiz, head of Pew Research Initiatives at the Pew Research Center.

The White House Forum featured remarks from Vice President Kamala Harris and other members of President Joe Biden’s Cabinet; convened diverse federal government leaders, state, and local elected officials as well as community advocates, business leaders, and influencers to celebrate the rich history and contributions of AA and NHPI communities; and highlighted the Biden-Harris Administration’s progress and commitments to advance equity, justice, and opportunity for AA and NHPI communities.

This year’s theme is “Visible Together,” in which the White House Office of Public Engagement invited everyone to “reflect on the power of community — and acknowledge the intense, generational challenges and opportunities that come with coalition building.”

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Qandaimage
Ask the Expert

Three Questions with Ninez Ponce

 

Ninez Ponce Ask the Expert


Ninez Ponce, CHIS principal investigator and professor in the Department of Health Policy and Management at the UCLA Fielding School of Public Health, became Center Director July 1. In a brief interview, Ponce discusses the path that led her to the Center, the roots of health inequality, and more.

Q: How did you begin your career and end up at the Center?

​I was a volunteer for the Berkeley Free Clinic. The Berkeley Free Clinic experience made me think about the importance of health care as a right and not a privilege. I was also very interested in public health policy, because … I thought that fixing the health care system and health populations required multiple sectors and not just the medical sector.

I worked in Thailand with a relief organization looking at development and child nutrition. When I came back to the United States and graduated, I started working on immigrant health, a transnational link between domestic and international health. I got very passionate about data disaggregation particularly for the Asian American and the Native Hawaiian Pacific Islander community and then began advocacy work in that I was very moved by making a difference in public health.

One day, I got a note from Dr. E Richard Brown. It felt like that was a big pivot in my career that planted the seed in wanting to be an academic, do better data collection, better evidence for public health, and that was the beginning of my career in public health that was based here at the Center.
 

Q: What do you see as the emerging public health trend in the future?

​I think it’s happening now. There is recognition that the population’s health problems as well as the inequities that occur in health are not produced solely by the health care system. Some of these problems are beyond the clinical walls and that they may have been generated in not just what the individual has been exposed to in their lifetime but it could have been generational disadvantages ― this notion of institutional racism and structural disadvantages.

I think what’s emerging is that trying to come up with a wider system of care that collaborates in addressing needs for patients with complex clinical problems and complex social problems.
 

Q: What would you be doing if you could go back and pick another line of work to go into?

​​I would be an architect, because I like structure and how design influences how people gather, live, and get together. I would want to design homes that are efficient and aesthetically pleasing that can solve our homelessness problem here. There is also a part of me, the data part of me, which wishes I were a computer scientist. I want to gather all the freely available data that is out there and come up with much humanized stories and public health insights.

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Training
Video

Disaggregating Data Decision-making: Who, What, When?

On Tuesday, August 31, the National Network of Health Surveys hosted Disaggregating Racial/Ethnic Data Decision-making: Who, What, When?, a panel discussion on approaching the key considerations when choosing to expand racial/ethnic categories in health data sets. Presenters shared some of their decision-making on: Who: what categories to include? What: what question-wording gets at the information desired? When: what conditions should be present to trigger expanded racial/ethnic data disaggregation?

Presenters:

Samantha Artiga, Vice President and Director, Racial Equity and Health Policy Program, Kaiser Family Foundation

Joshua Quint, PhD, MPH, Epidemiologist, Disease Outbreak Control Division, Hawai’i Department of Health

Eva Wong, PhD, Epidemiologist, Public Health – Seattle & King County

The panel was moderated by UCLA CHPR Director Ninez A. Ponce, PhD, MPP

About the National Network of Health Surveys' Advancing Health Equity Through Data Disaggregation Workshop Series

Disaggregated race/ethnicity data is needed to expose gaps in health equities and inform policies and programs and close those gaps. The National Network of Health Surveys, part of the UCLA Center for Health Policy Research, offers a series of workshops designed to improve the disaggregation of race and ethnicity measures in health data sources. Our goal is to boost the number of subpopulation categories made available to key constituencies working to improve health equity. This is especially important for representing communities that are often “hidden” in large health data sets.

Topics and Timestamps

Implications of Data Gaps and Limitations for Health Equity: Samantha Artiga, MHSA (8:45)

Comprehensive High-Quality Data is Central to Efforts to Advance Equity (10:21)

  • Why is having disaggregated data important?

Gaps and Limitations in Racial/Ethnic Data (11:25)

  • Understanding how gaps in data emerge and being able to identify them

Addressing Data Gaps and Limitations (15:05)

  • Key considerations when addressing gaps in data

Strategies to Address Data Gaps (17:35)
Explanation of strategies utilized by the Kaiser Family Foundation Intentional Survey Design (17:37)

Partnering with Community (18:47)

Highlighting Missing Data (20:15)

Key Factors that Influence Data Disaggregation Efforts (20:55)

  • Even with the best of intentions, what other factors can effect disaggregation efforts?

Disaggregating Race/Ethnicity COVID-19 Data for Hawai’i – Joshua Quint, PhD, MPH (23:31)

Principles of Epidemiological Analysis (25:39)

  • Fundamentals of epidemiology as guiding factors in data disaggregation

Key Process Factors in Working with Target Populations (26:55)

  • Process model for engagement with population

Explanation of Case Study – COVID-19 in Hawai’i (28:16)

  • Establishing background (28:16)
  • Identifying opportunities for disaggregation (31:30)
  • Example of disaggregated public dashboard (32:03)
  • Barriers encountered and strategies utilized (32:38)
  • Working example of process model for engagement (34:11)
  • Additional guiding principles, questions, and considerations of case study (35:09)
  • Key outcomes (36:33)
  • Scope of future work (37:03)

Data Disaggregation for Equity: Example from King County, WA – Eva Wong, PhD (42:25)

Importance of Equity and Community Involvement (44:29)

  • Discussion of racism as a public health crisis, and how best to center equity

The BSK Health Survey (45:28)

  • Participants and coding (46:01)
  • Examples of equity in action (47:04)
    • Presenting data by race (48:15)
  • Adding categories based on write-in responses
  • Why disaggregation matters (49:16)
  • More key considerations in disaggregating (50:06)
  • Accessing their dashboard (51:45)
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Video

Advanced Weighting Strategies for Disaggregated Racial/Ethnic Data

This workshop shares the ways in which survey weighting processes can and cannot be used to improve the representativeness of data on small and disaggregated populations within population surveys. The presentations cover the purpose of providing survey weights that account for specific subpopulations, things to consider when selecting a control population to use for calibration, and methods of accounting for small subgroups in weighting data.

Presenters:
Ninez A. Ponce, PhD, MPP, Director, UCLA Center for Health Policy Research
Brian Wells, PhD, Former Survey Methodologist, California Health Interview Survey
Tara Becker, PhD, Senior Public Administration Analyst, UCLA Center for Health Policy Research

About the National Network of Health Surveys' Advancing Health Equity Through Data Disaggregation Workshop Series

Disaggregated race/ethnicity data is needed to expose gaps in health equities and inform policies and programs and close those gaps. The National Network of Health Surveys, part of the UCLA Center for Health Policy Research, offers a series of workshops designed to improve the disaggregation of race and ethnicity measures in health data sources. Our goal is to boost the number of subpopulation categories made available to key constituencies working to improve health equity. This is especially important for representing communities that are often “hidden” in large health data sets.

Topics and Timestamps

Why is a weighting session included in data disaggregation series? (6:25)

  • Survey weights are a critical component to the discussion of data disaggregation.

The purpose of weighting (8:56)

  • Weights help reflect the complexity of sample design, and reduce bias reduction.

When are weights unnecessary? (10:44)

  • Weights are not always necessary, but certain conditions must hold.

Why we need survey weights (11:51)

  • Weights are used when the sample of respondent distribution is not aligned with the population distribution.

Conditions that affect representativeness (13:19)

  • Sampling frame is incomplete/contain errors
  • Unequal probability of being sampled
  • Nonresponse error

General form of weights (13:49)

  • Weight = selection probability x sample nonresponse x population adjustment
  • What weighting does (27:19)

Selection probability (14:30)

  • Understanding relationship between the sample and the framework.
    • Selection probability and simple random sample designs (15:24)
    • Selection probability and complex designs (16:08)      
    • Selection probability and data disaggregation (17:46)
      • If a study is oversampling a small group, we want to account for this different from the population.

Sampling frame limitations (18:55)

  • Sampling frame may underrepresent a subpopulation within the target population, exclude a subpopulation or cover more than the desired population.
    • Discussion includes examples of each.

Adjusting for nonresponse (20:33)

  • Nonresponse and data disaggregation (22:03)
    • If a subgroup of interest responds to a survey at a lower (or higher) rate, you want to account for this difference. Discussion includes examples.

Limitations of sample-based adjustments (23:13)

  • Sample based adjustments require knowing information about both respondents AND nonrespondents.

Population-based adjustments (23:55)

  • Using information known about the population to make the respondent pool look like the population.
  • Benchmark Comparison example (24:29)
  • Population adjustments and data disaggregation (26:36)
    • Regardless of what our final sample looks like, you want it to reflect the population.

Limitations of weighting (29:08)

  • The effectiveness of weighting is constrained by survey methodology and content – it cannot make a sample representative of a missing subpopulation.
    • Example of Limitations (30:50)

Weighting considerations and benchmark data (33:18) 

  • Why do we need a benchmark population? (33:45)
    • Tells us what the population SHOULD look like.
  • How is the benchmark population used? (34:26)
  • Choosing benchmark data (34:55)
  • Commonly used benchmark data (37:11)
    • Practical example of benchmark data usage (38:20)
  • Benchmark data from multiple sources (39:38)
    • Practical examples of utilizing benchmark data from multiple sources (40:07)

Weighting dimensions

  • What are weighting dimensions? (41:57)
    • Set of characteristics that are used to standardize the sample data
  • Choosing characteristics for weighting (42:14)
  • Defining dimensions (43:13)
  • Sample size constraints (44:22)
    • Small samples may require collapsing categories, preventing adjustments for disaggregated categories
  • Limitations of weighting dimensions (45:48)
    • Weighting dimensions might not fully account for differential nonparticipation

Coding race and ethnicity (47:15)

  • Measuring race and ethnicity (47:21)
  • Constraints on coding race/ethnicity (48:45)
    • Practical example (50:23)
  • Weighting characteristics: A look at six federal surveys (51:18)
    • Visualization of differences based on weighting (53:05)
  • Potential solutions for small groups (55:50)
    • Suggestions for coding of weighting dimensions and increasing sample size
  • Weighting and disaggregated racial/ethnic data (58:25)
  • Weighting methodologies matter (58:43)
  • The process of weighting survey data (59:20)
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Video

Disaggregated Racial/Ethnic Data: Considerations for Data Collection and Processing

The workshop explores effective strategies to ensure your data represents the true diversity of your population. The discussion includes decision-making approaches, community engagement strategies, and case studies in survey science. Following the presentation, Dr. Ninez Ponce, CHPR Director, leads activities and provide opportunities to discuss specific project needs.
 

About the National Network of Health Surveys' Advancing Health Equity Through Data Disaggregation Workshop Series

Disaggregated race/ethnicity data is needed to expose gaps in health equities and inform policies and programs and close those gaps. The National Network of Health Surveys, part of the UCLA Center for Health Policy Research, offers a series of workshops designed to improve the disaggregation of race and ethnicity measures in health data sources. Our goal is to boost the number of subpopulation categories made available to key constituencies working to improve health equity. This is especially important for representing communities that are often “hidden” in large health data sets.

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In the News
Center in the News

New Survey Finds 2.6 Million Californians Experienced Acts of Hate in Just One Year

Findings come from an examination of California Health Interview Survey data by the California Civil Rights Department. News https://www.nbcpalmsprings.com/2025/04/11/new-survey-finds-26-million-californians-experienced-acts-of-hate-in-just-one-year
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Center in the News

Report offers deeper look at hate incidents across California

California Health Interview Survey data showed that more than 2 million Californians experienced acts of hate between 2022 and 2023. News https://www.axios.com/local/san-diego/2025/04/10/survey-hate-crimes-incidents-california-san-diego
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Center in the News

UCLA study finds greater health inequities exist in minority communities

Ninez Ponce, director of the UCLA Center for Health Policy Research, and principal investigator of the center's California Health Interview Survey, provides insight into some of the key findings from the most recent edition. “Measurement is what detects where the needs are, and then policies can help shape programs,” Ponce said. News https://dailybruin.com/2025/02/27/ucla-study-finds-greater-health-inequities-exist-in-minority-communities
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Events
Online

Navigating the Post-Pandemic Surge in Mental Health and Economic Distress Among NHPIs

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In-Person

2024 E.R. Brown Symposium

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Online

California Health Interview Survey (CHIS) Annual Data Release

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