Authors examine how changes in federal health data infrastructure affect states’ capacity to advance data equity and how policymakers can respond. They review across state data disaggregation legislation, draw on the California Health Interview Survey (CHIS) as a model case, and synthesize evidence on data interoperability, governance, and dissemination.

Findings: Some states are already moving beyond federal minimums on race and ethnicity data collection. For example, Connecticut, Oregon, and Massachusetts have enacted expansive laws collecting detailed subgroups; Illinois, New York, and New Jersey have added Middle Eastern and North African and other categories across all state agencies. Colorado and Oregon extend collection to sexual orientation, gender identity, and disability status. Authors offer examples from California's recent legislation and show how CHIS operationalizes such mandates. They also identify persistent constraints, including incomplete demographic fields in administrative data, workforce and interoperability gaps, and the need for governance frameworks and data firewalls that safeguard privacy.

Authors conclude that state-led data infrastructure will help build effective public health practice. The aim is not 50 incompatible systems but an ecosystem of comprehensive and inclusive systems that are more granular, responsive, and community-accountable. For such an ecosystem to function, the systems must still communicate, which depends on shared standards and definitions that keep data comparable across states and over time. Realizing this vision requires diversified funding, cross-agency coordination, strong governance, and active roles for researchers, philanthropy, and communities, so that progress in measuring disparities endures across changing policy environments.