The dataset used for the analysis in the publication, A Spectral Confounder Adjustment for Spatial Regression with Multiple Exposures and Outcomes (https://doi.org/10.1080/01621459.2026.2714568). Abstract Characterizing social vulnerability is fundamental to disaster response planning. Numerous vulnerability indicators have been developed, but they are typically not validated for their predictive power over the outcomes of interest. As with many environmental health studies where interventions are impractical or unethical, validation of social vulnerability against public health outcomes is observational and relies on spatially-dependent data. Observational studies are susceptible to bias induced by unmeasured confounders. This problem is exacerbated in spatial studies with multiple health outcomes and environmental exposure variables, as the source and magnitude of confounding bias may differ by spatial scale and across exposure/outcome pairs. We propose to mitigate confounding effects in multivariate spatial studies using a tensor-regression model that allows exposure effects to vary by exposure, outcome, and spatial scale. By extracting exposure effects that correspond to local spatial scales, we replace the strict causal assumption of no unmeasured confounders with a more realistic assumption of local unconfoundedness i.e., differences between nearby regions are unconfounded, allowing for causal interpretation. Our study of the Southern United States shows that economic resilience (Theme 1) demonstrates the strongest positive effect on diabetes and negative effect on hyperlipidemia. Our analysis reveals a concise representation of the effects of social vulnerability indices on an array of chronic health outcomes, offering interpretable epidemiological insights that adjust for multivariate spatial confounding. Keywords: Spatial confoundingcausal inferenceexposure mixturesunmeasured confoundertensor decomposition. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Contact Centers for Medicare & Medicaid Services for data access. Format: Datasets are in text format, csv. This dataset is associated with the following publication: Prim, S., Y. Guam, S. Yang, A. Rappold, L. Hill, W. Tsai, C. Keeler, and B.J. Reich. A spectral confounder adjustment for spatial regression with multiple exposures and outcomes. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. Taylor & Francis Group, London, UK, 2714568, (2026).