Health Highlights

Editor’s Note: COVID-19 has illuminated systemic racial and ethnic inequities in our health care system and catalyzed an unprecedented call to action to address their root causes.1 It also has revealed the dearth of reliable and standardized race and ethnicity (R/E) data available to identify multidimensional contributors to disparities, design effective initiatives to drive improvement, and measure progress. Health plans are well-positioned to collect R/E data and use this information to promote health equity. However, plans face several data collection challenges that result in significant reporting gaps. In a new white paper prepared in partnership with Blue Shield of California, summarized below, Manatt Health discusses common health plan R/E data collection challenges and how states can help mitigate these barriers and equip health system stakeholders with crucial information to support health equity. Click here to download a free copy of the full white paper. To learn how you can join Connecting for Better Health for an interactive discussion on April 28 that will focus on how data sharing can support efforts to promote health equity across California, click here.

Why States Need Payer Data to Identify Inequities and Address Disparities

The repercussions of limited R/E data have been felt throughout the public health crisis. R/E data gaps initially masked the disproportionate burden COVID-19 placed on Black or African American (Black), Hispanic or Latino (Latinx), Asian, Native Hawaiian or other Pacific Islander, and American Indian or Alaska Native communities and slowed public health responses to the pandemic.2 As state and federal R/E reporting requirements were introduced, reporting improved, highlighting the role targeted data collection mandates and standards can play in improving data quality and use.3

States, plans, consumer organizations and providers can use R/E data to identify barriers to health care access and monitor the impact program reforms have on health inequities. State Medicaid managed care programs and state-based health insurance marketplaces have pioneered such requirements, requiring plans to collect and use R/E data to identify and address disparities.4 California’s Department of Health Care Services (DHCS), for example, analyzes Medi-Cal Managed Care Plan (MCP) quality data received through its External Quality Review Organization (EQRO) process to assess potential differences in health outcomes between population groups, and shares those analyses with plans to guide interventions.5 DHCS has also emphasized the importance of increased data collection to reduce disparities and inequities, through its broader California Advancing & Innovating Medi-Cal (CalAIM) proposal.6 Meanwhile, Covered California continues to increase R/E data reporting and use expectations by contracted Qualified Health Plans (QHPs).7 States may also consider using health plan R/E data as part of cross-departmental efforts to better understand and monitor cross-payer population inequities, though such reporting has been limited to date.

Challenges in Race and Ethnicity Data Collection

Health plans have much to gain in promoting health equity by acquiring more consistent self-identified R/E data upon member enrollment but face several challenges to collection, including:

  • Reluctance to Self-Identify: Individuals are the source of truth regarding their R/E information, and many choose to not voluntarily share it with their health plans.8, 9 Reluctance to self-identify may stem from long-standing concerns about privacy and discrimination.
  • Limited and Uneven Regulatory Requirements: Health plans are often not required by federal or state authorities to collect R/E data for significant portions of their members; where they are, requirements can vary considerably by state and line of business. Limited and uneven regulatory requirements weaken plans’ abilities to develop comprehensive strategies for R/E data collection and use.
  • Inconsistent Use of Standards: While a federal standard for R/E data categorization was first established in 1977 by the U.S. Office of Management and Budget (OMB) with Statistical Policy Directive No. 15 and updated in 1997 with Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity, other federal agencies (e.g., Health Resources Services Administration (HRSA), Census Bureau), health plans and providers have retained alternative classifications or use their own codes.10, 11 Variation in the collection and reporting of R/E data prevents accurate comparisons of the quality of care being delivered to different population groups across multiple entities. Nationally, National Committee for Quality Assurance’s (NCQA’s) Multicultural Health Care Distinction certification has offered state payers, plans and providers standard approaches to R/E data collection, although adoption is nascent.12

In response to R/E self-identification challenges, many health plans are increasingly relying on indirect or proxy methodologies to collect R/E data for their membership. Indirect data sources may include patient experience surveys (e.g., CAHPS), clinical data from providers or health information exchanges (HIEs), or other external administrative data resources.13 Plans may also employ advanced analytics or probabilistic matching algorithms to approximate a member’s R/E based on information the plan does have about the member (e.g., surname, address) and how those characteristics correlate with the race and ethnicity of others.14 While indirect data acquisition can substantially improve R/E data collection and reporting, its accuracy can vary based on its source and how the plan integrates the data into its HIT systems.15

Recommendations for Improving Race and Ethnicity Data Collection and Reporting

Actions to advance standards and requirements for R/E data collection include:

  1. Developing HIE capacity as a centralizing source of R/E data for plans and providers. HIEs are uniquely positioned and functionally equipped to securely collect, link and share R/E data among health plans, providers, and public and private purchasers. Building on their existing connections and processes, HIEs can use the data they receive from participants to create centralized R/E records for all members, improving data acquisition; apply centralized, standardized methods of R/E estimation, or use external data sources, to fill R/E data gaps while minimizing the impact on overall data integrity; and facilitate R/E data standardization among participants.
  2. Requiring health plans to collect standardized R/E data across all regulated lines of business. Consistent data standards are critical for identifying and monitoring responses to health system inequities. A cross-agency workgroup should be convened to establish R/E data collection standards and acquisition targets for contracted and regulated health plans. Requirements should be embedded in regulations and state-administered contracts. Acquisition requirements should elevate R/E data collection as a plan priority, ensuring the data is available to guide interventions. The state should also facilitate sharing of industry best practices for maximizing member self-identification and optimal use of indirect data.16


Addressing the barriers to R/E data collection is imperative for all health care stakeholders as we work together to address the historical and deeply entrenched structural and programmatic barriers to better care and health for all.

NOTE: Join Connecting for Better Health on April 28 at noon PDT for an interactive discussion that will focus on how data sharing can support efforts to promote health equity across California. Speakers include:

  • Hector Rodriguez, PhD, MPH, UC Berkeley School of Public Health, Director of the California Initiative for Health Equity and Action 
  • Julia Adler-Milstein, PhD, Professor of Medicine and Director of the Center for Clinical Informatics and Improvement Research, UC San Francisco
  • Alice Hm Chen, MD, MPH, Chief Medical Officer, Covered California
  • Rhonda Smith, MBA, Executive Director, California Black Health Network

1 “Something Must Change: Inequities in U.S. Policy and Society,” U.S. House Committee on Ways & Means. Jan. 2021. Available here.

2 New data on national vaccine distribution was similarly found to be missing R/E values for nearly half of all vaccinations, making it difficult to determine the depth of inequities in vaccine distribution and emphasizing the chronicity of missing critical data. “Race and Ethnicity Data Missing for Nearly Half of Coronavirus Vaccine Recipients, Federal Study Finds,” The Washington Post. Feb. 2021. Available here.

3 “Racial Disparities in COVID-19: Key Findings from Available Data and Analysis,” KFF. Aug. 2020. Available here.

4 Federally, CMS’ Medicare Advantage Chronic Care Improvement Program (CCIP) similarly seeks to guard against potential health disparities by requiring population analyses to target care interventions. See “Medicare Advantage CCIP,” CMS. 2020. Available here.

5 The EQRO aggregates results to allow for statewide interpretation. Results are shared back with MCPs to guide intervention through their performance improvement projects. For more information, see “Health Disparities Data as a Driver of Quality Improvement in Medi-Cal,” RWJF State Health Value Strategies. Feb. 2021. Available here.

6 “California Advancing & Innovating Medi-Cal (CalAIM) Proposal,” DHCS. Jan. 2021. Available here.

7 “Draft Attachment 7 to Covered California 2017 Individual Market QHP Issuer Contract: Quality,
Network Management, Delivery System Standards and Improvement Strategy,” Covered California. Jan. 2021. Available here.

8 Such information may be solicited at enrollment, during a health screening or when a member is engaged in a care management program.

9 Despite one plan’s concerted effort to maximize direct R/E data acquisition, the plan obtained R/E data for only a third of its members, emphasizing that while significant direct collection is possible, it may have upper limits. “Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement,” Institute of Medicine U.S. Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement. 2009. Available here.

10 OMB Standards, OMB. Available here. (Accessed Mar. 2021) “Patient Protection and Affordable Care Act,” U.S. Congress. 2010. Available here. ACA § 4302 requires the Department of Health and Human Services (HHS) to establish data standards for collecting ethnicity, race, sex, primary language and disability status data. The HHS Office of Minority Health developed and finalized R/E data standards in partnership with the Census Bureau and OMB.

11 CDC Race and Ethnicity Code Set expands on and may be combined to match OMB standards. LOINC codes are another commonly used R/E standard. (Accessed Mar. 2021)

12 As of November 2020, 57 organizations have been MHC certified. See “Plotting a Course to Address Disparities,” NCQA. Nov. 2020. Available here.

13 “Collection of Race and Ethnicity Data by Health Plans Has Grown Substantially, But Opportunities Remain to Expand Efforts,” Health Affairs. Oct. 2011.

14 One example is RAND Corporation’s Bayesian Improved Surname Geocoding (BISG) indirect estimation method, discussed here. (Accessed Mar. 2021)

15 “Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement,” Institute of Medicine Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement. 2009. Available here. (Accessed Mar. 2021)

16 Plans and providers may also discuss whether a new data field (e.g., “R/E Source”) would be beneficial and may be instituted to allow for assessments of data reliability and member confirmation as the data is exchanged.