No Data, No Problem

Latine communities in the United States experience disproportionately high uninsurance rates because of systemic barriers, including limited language equity, lack of provider (clinical or nonclinical) concordance, discrimination, misinformation, and immigration-related fears. Data on individuals eligible for but not enrolled in insurance programs are lacking, which prevents the identification of barriers, population impacted, and tailored approaches to meet specific needs of vulnerable communities. We propose community-informed policy strategies, including culturally tailored outreach, involvement of trusted community health workers, and improved health equity data collection. Framing data in terms of eligible but not enrolled individuals shifts the focus to existing coverage gaps and the potential for improvement, encouraging states to take more proactive enrollment actions.