Championing the 2021 New York State Law: A Step Toward Data Disaggregation on Asian Americans, Native Hawaiians, and Pacific Islanders

Racial and ethnic data aggregating Asian American and Native Hawaiian and Pacific Islander (AA and NH/PI) individuals into one or two categories do not provide an accurate picture of social needs or health outcomes among communities that identify as such.^1–4 Addressing these gaps in data is part of advancing data equity, which Lee et al. define as “a transparent, critically grounded approach to race and ethnicity data (dis)aggregation . . . necessary to document, understand, and address the health effects of racism.”^5(p262) Despite the long-standing acknowledgment of the gaps in AA and NH/PI population data, the passage of laws and changes in government data practices to correct these gaps have been slow.^4,6 New York City and State have taken important steps in collecting better data on the full range of AA and NH/PI populations. But the work to generate fully descriptive data that provide complete insights into health outcomes and social determinants and to support data equity remains incomplete.

In this article, we highlight the challenges of achieving data equity by advocating policies to fully disaggregate data, especially those that render invisible populations with heritage in Asia, Hawaii, and the Pacific Islands, through a detailed discussion of the Coalition for Asian American Children and Families (CACF) Invisible No More (INM) campaign in New York. Led by CACF, the decade-long INM campaign for AA and NH/PI data disaggregation led to the first-ever data disaggregation law in New York City in 2016, followed by the enactment of a New York State law in 2021. INM provides an important template for how coalition-based advocacy can successfully push for the passage and implementation of laws that mandate better data collection and usage.

In the United States, 21 million people identifying as AA and NH/PI make up 6% of the population.⁷ Gaps in resources persist even though AA and NH/PI populations were the fastest-growing racial and ethnic minority group in the United States from 2000 to 2019.⁸ In New York State, AA and NH/PI populations together make up more than 17% of the overall population.⁹ Aggregated data on social determinants of health overstate the level of education, employment, and income among many populations that fall under this umbrella.¹⁰ Such data also mask health disparities requiring public health intervention and social services.¹⁰

For example, aggregated data on COVID-19’s impact on communities indicated that Asian Americans were the racial group least affected by COVID-19.^11–13 But research by New York University’s Center for the Study of Asian American Health showed that at the height of the pandemic in 2020, Chinese New Yorkers suffered from the highest COVID-19 mortality rate and that South Asian New Yorkers had the highest infection and hospitalization rates across all New Yorkers.¹¹ Subsequently, aggregated community estimates pointed to high rates of vaccine uptake among AA and NH/PI communities overall.¹⁴ Disaggregated data showed, however, that whereas vaccine acceptance among Nepali and Bangladeshi populations was high, vaccine uptake among these groups was relatively low, pointing to a missed opportunity for disease prevention.¹⁴

Effective data disaggregation for any population requires better infrastructure, practices, and procedures. In January 2023, the Office of Management and Budget issued initial proposed revisions to their policy directive that mandates how race and ethnicity data are collected at the federal level.¹⁵ Notably, they included mandating data disaggregation that is more detailed than the five minimum race categories and the addition of a Middle Eastern North African category. New standards are expected to be announced by summer 2024.

Increased data disaggregation at the national level will help reinforce local and state efforts, especially toward harmonizing multiple systems and data sets. In the meantime, state and local efforts are where this battle is advanced in concrete ways. CACF’s INM campaign provides a rich case study of a local effort seeking improved data disaggregation.

LESSONS AND RECOMMENDATIONS

For those seeking to advance this work in their state, we make the following recommendations.

Committing to This Work for the Long Term

Identifying allies, conducting public education, creating a demand for change, developing policy, and implementing changes take years. Community-based leadership with long institutional and programmatic memory requires sustained commitment and support from organization leadership and funders.

In 2009, New York State assembly member Grace Meng (D) first proposed an AA and NH/PI–focused state disaggregation law: it was signed into law in 2021. Concerted efforts to win a New York City law began in 2012 and went on until its passage in 2016. Work continues to monitor and advocate implementation that reflects data equity, as does work to revise legislation to address gaps in the initial laws. Governments, funders, and advocates must commit to a long fight for effective disaggregated data.

Moving Beyond a Scarcity Mindset

CACF found it important to address concerns that data disaggregation might lead to a diffusion of political power by creating divisions among different AA and NH/PI populations or by encouraging decision makers to focus on specific ethnic needs over shared needs.¹⁶ CACF believes these fears are rooted in the harmful “model minority” myth, which casts Asian Americans as a monolithic group that neither needs social services nor experiences struggles.¹⁵ To challenge these myths, CACF grounds their work in collective advocacy as well as the agency that AA and NH/PI communities have in continuing to advocate together. AA and NH/PI communities have depended on coalition-based advocacy across racial, ethnic, linguistic, and cultural differences to protect themselves from discrimination and to advocate government recognition of their needs.¹⁷

CACF focused on disaggregated data’s revelation of inequities as—instead of a cause for division—an opportunity for AA and NH/PI communities to strengthen their collective advocacy; this was done by better understanding each other’s unique needs, identifying where shared needs lie, and developing more specific demands to meet all communities’ needs.^17–20 For example, individual ethnic community needs that emerged through focused research during the COVID-19 pandemic supported coalition-based advocacy for language access and health care for all.²¹ CACF, working closely with advocates from across the country, developed explainers for advocates that reframe the zero–sum concern fed by a scarcity mindset as a unifying vision of solidarity.²²

Bearing Context in Mind

CACF had to address concerns in AA and NH/PI communities rooted in fears based on the history of US anti-Asian policy. At a time of increasing racial acrimony and anti-immigrant sentiment, any policy change related to data collection raises concerns about data privacy and security. An ongoing challenge in this work is establishing trust between community members and government agencies tasked with collecting their data.

Given the US history of promoting anti-Asian policies such as the Chinese Exclusion Act of 1882, the forced incarceration of Japanese Americans in World War II, and the post-9/11 surveillance and persecution of South Asian, Indo-Caribbean, Middle Eastern, and North African Americans, any data collection practice must include ample guardrails to ensure data privacy and security while balancing the need for more publicly available comprehensive data.^16,17 Framing data as the foundation of public policy and framing complete and accurate data as a civil rights issue helped make it more accessible and relevant to community audiences, which is especially critical to the public health response during a pandemic.²²

Visibility for Small Populations

Another obstacle to data disaggregation is the perception that AA and NH/PI populations are separately too small to be statistically significant. Many established institutions analyzing demographic trends include White, Black, and Hispanic/Latino but exclude or otherize Asian and NH/PI by lumping them into the catchall of Other. Populations with samples that are traditionally thought to be small often struggle in silence and require attention from public health researchers.⁵

Strategies for oversampling certain populations or for employing standardized and detailed race and ethnicity categories that may be combined with data from multiple surveys can facilitate inquiry into the health and social service needs of small populations.¹ Because AA and NH/PI populations are small but growing, disaggregation practices must contemplate ways to identify small populations and maximize the possibility of collecting data consistently across agencies and levels of government while balancing privacy concerns.^1,3

Working in Coalition

Successful advocacy for these changes requires a diverse set of stakeholders (e.g., legislators, government agency officials, and media) and, especially, community leadership from the populations whose data are collected. Strong bidirectional communication about the concerns of the communities that will be affected by changes to data are essential. Tensions or gaps in trust must be bridged as this work advances. AA and NH/PI–serving community-based organizations were the core members of the INM coalition. At critical times, especially during the height of the legislative season, INM met at least monthly and sometimes more often with a steering committee of approximately 15 to 20 AA and NH/PI–serving community-based organizations.

Community-based organizations are directly affected by the lack of disaggregated data. They depend on city, state, and federal data collection to reflect the needs of underserved communities to drive adequate resources and appropriate policies that can meet those needs. INM stakeholders and allies include elected officials, government agency personnel, researchers and academics, media, health advocates, and, most importantly, community members who wish to be represented in government data collection.²³

CACF was successful also because it worked with other populations invested in data equity, including advocates from the LGBTQ+ (lesbian, gay, bisexual, transgender/-sexual, queer or questioning, and all subsects) community interested in sexual orientation and gender identity data disaggregation. The need for disaggregated data and reform of government data collection practices are shared by Black, White, Latino, Middle Eastern and North African, American Indian, Alaska Native, immigrant, and LGBTQ+ communities. Each type of stakeholder brings a unique set of skills, best practices, perspective, power, network, and political influence to the campaign.

Building Relationships With Champions

Over many years, INM’s relationships with New York City and New York State legislators enabled them to see progress on both city and state laws. Councilmember Daniel Dromm’s proposal to package data disaggregation based on ancestry, languages spoken, multiracial identity, and sexual orientation and gender identity led to the successful passage of three combined laws.²⁴ At the state level, advocates worked with Assembly Member Yuh-Line Niou (D) and Senator Julia Salazar (D) to build consistent support for disaggregation by framing it as a question of efficiency, asking legislators how they could hold agencies accountable for efficiently deploying resources if they did not understand the existing needs. The bill passed both houses in 2019 but was vetoed by then-governor Cuomo.

In the wake of increased anti-Asian violence in 2020 and 2021, CACF redoubled efforts to draw attention to the root causes of violence and elevated data equity as a critical component of addressing health, wellness, and safety for AA and NH/PI communities. With a mobilizing letter to Governor Cuomo in 2021, INM cultivated broad-based support for AA and NH/PI data disaggregation among legislators, especially those whose constituents included a significant proportion of AA and NH/PI. INM sought bipartisan supporters, many of whom responded to arguments for the cost-saving efficiency of government systems: 2 Republican assembly members sponsored the bill, and half of all Republican senators voted to pass the law.

In December 2021, New York State enacted a historic state data disaggregation law that was signed by Governor Kathy Hochul (D). This win laid the groundwork for follow-up successes. In 2023, the New York State Senate also passed S.6584, which, if enacted, will mandate the disaggregation for Middle Eastern and North African populations, who up until now have been categorized as White.²⁵

Securing Government Implementation Funding

Data equity and meaningful disaggregation require system-level changes to the governmental agency data systems. These changes require government investment for successful implementation. Changing laws without an accompanying budget allocation to support infrastructure improvements across government agencies may lead to inaccurate data or challenges in implementation. Although it may be costly to implement better data infrastructure and capacity, communities that are not recognized, and in turn society, pay for data gaps and invisibility every day.

Ensuring Implementation

The nuts and bolts of disaggregation advocacy happen not only when the bill is written and signed into law, or only when a regulation is adopted, but also when it is implemented by a government agency. Without the effective and equitable implementation of data disaggregation, systems risk sharing and using inaccurate or biased data of communities that are assumed to be truthful.

Recognizing the importance of a community-informed process to ensure accurate implementation of data disaggregation laws, once the city law was passed, INM engaged with city agency officials to support the work to disaggregate city data. Improving data quality and descriptiveness requires transparency, dialogue, and relationship building among service providers, government officials, and researchers to be able to ascertain the processes of disaggregating data, including collection and information sharing with the community.

INM sought administrators familiar with data collection, infrastructure, and equity-related policies and processes in agencies and asked questions such as, What language is the data collected in? How are government systems training staff to collect the data? Who is involved in survey creation? How is the community involved in efforts to collect and disseminate their data? When are data being collected? Is data collection tied to existing forms or a voluntary form (which may elicit different responses from individuals than a required one)? It was important for advocates to understand the internal technical, financial, and political obstacles that might slow government agencies’ implementation of data disaggregation. Understanding the challenges of unfunded mandates and establishing consistency across varying jurisdictions help to establish trust and lead to better advocacy.

CHALLENGES AND OPPORTUNITIES AHEAD

Resistance can act as a drag on the momentum needed to secure resources, build national partnerships, and win policy changes for data disaggregation. The notion that these efforts lack meaningful support can become a self-fulfilling prophecy, impeding local and state efforts for better data. Progress in New York serves as a model to activate comparable campaigns for data disaggregation as part of broader efforts for data equity.

CACF is already building on its initial successes by being part of a data disaggregation network that was developed and assembled by The Leadership Conference Education Fund, in which groups share their knowledge and experiences and work with other state and local groups to foster change from the community level to the national level. As public health seeks to be at the forefront of efforts for data equity, centering the fight for disaggregated data for all populations is essential. Winning descriptive and illuminating data are possible with tenacity, relationships, and by working in coalition.