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The value of integrating federal HIV services data with HIVsurveillance is currently unknown. Upstream and complete case capture isessential in preventing future HIV transmission. Methods: This study integratedRyan White, Social Security Disability Insurance, Medicare, Children HealthInsurance Programs and Medicaid demographic aggregates from 2005 to 2018 forpeople living with HIV and compared them with Centers for Disease Control andPrevention HIV surveillance by demographic aggregate. Surveillance Unknown,Service Known (SUSK) candidate aggregates were identified from aggregates whereservices aggregate volumes exceeded surveillance aggregate volumes. Adistribution approach and a deep learning model series were used to identifySUSK candidate aggregates where surveillance cases exceeded services cases inaggregate. Results: Medicare had the most candidate SUSK aggregates. Medicaidmay have candidate SUSK aggregates where cases approach parity withsurveillance. Deep learning was able to detect candidate SUSK aggregates evenwhere surveillance cases exceed service cases. Conclusions: Integration of CMScase level records with HIV surveillance records can increase case discoveryand life course model quality; especially for cases who die after seeking HIVservices but before they become surveillance cases. The ethical implicationsfor both the availability and reuse of clinical HIV Data without the knowledgeand consent of the persons described remains an opportunity for the developmentof big data ethics in public health research. Future work should develop bigdata ethics to support researchers and assure their subjects that informationwhich describes them is not misused.

Too sick for surveillance: Can federal HIV service data improve federal HIV surveillance efforts?