Introduction: The value of integrating federal HIV services data with HIV surveillance is currently unknown. Upstream and complete case capture is essential in preventing future HIV transmission. Methods: This study integrated Ryan White, Social Security Disability Insurance, Medicare, Children Health Insurance Programs and Medicaid demographic aggregates from 2005 to 2018 for people living with HIV and compared them with Centers for Disease Control and Prevention HIV surveillance by demographic aggregate. Surveillance Unknown, Service Known (SUSK) candidate aggregates were identified from aggregates where services aggregate volumes exceeded surveillance aggregate volumes. A distribution approach and a deep learning model series were used to identify SUSK candidate aggregates where surveillance cases exceeded services cases in aggregate. Results: Medicare had the most candidate SUSK aggregates. Medicaid may have candidate SUSK aggregates where cases approach parity with surveillance. Deep learning was able to detect candidate SUSK aggregates even where surveillance cases exceed service cases. Conclusions: Integration of CMS case level records with HIV surveillance records can increase case discovery and life course model quality; especially for cases who die after seeking HIV services but before they become surveillance cases. The ethical implications for both the availability and reuse of clinical HIV Data without the knowledge and consent of the persons described remains an opportunity for the development of big data ethics in public health research. Future work should develop big data ethics to support researchers and assure their subjects that information which describes them is not misused.

该研究通过整合 Ryan White、Social Security Disability Insurance、Medicare、Children Health Insurance Programs 和 Medicaid 数据的方法，利用监测和服务数据的比较来识别HIV病例，尤其是那些在接受服务后死亡但未被监测到的病例，并探讨了公共卫生研究中大数据伦理的发展机会。

疾病过重：联邦艾滋病服务数据能否提高联邦艾滋病监测工作？