Policy Points
Disruptions to federal health data infrastructure threaten states’ ability to identify health disparities, target interventions, and evaluate programs, making state-level investment in data infrastructure and systems especially urgent.
States can build resilient, equity-centered data systems by enacting data disaggregation legislation, investing in data systems with bridging methodologies for data continuity, and establishing governance frameworks that protect privacy while facilitating data access.
Context
Health equity depends on data equity: the representation of communities in the data used to identify disparities, target interventions, and hold systems accountable. Recent shifts in federal health data infrastructure, including changes to datasets and reductions in agency capacity, have introduced uncertainty for state officials who bear primary responsibility for population health outcomes. Even apart from these shifts, robust state capacity matters in its own right, making investment in resilient data infrastructure a timely priority.
Methods
We examine how changes in federal health data infrastructure affect states’ capacity to advance data equity and how policymakers can respond. We 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. We offer examples from California’s recent legislation and show how CHIS operationalizes such mandates. We 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.
Conclusions
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.