"I Have to Ask": A Mixed-Methods Study on the Collection of Sexual Orientation and Gender Identity Data Within the San Francisco County COVID-19 Case Investigation and Contact Tracing Program.

OBJECTIVE: To understand how the San Francisco (SF) COVID-19 case investigation and contact tracing (CICT) workforce documented sexual orientation and gender identity (SOGI) data, as well as a qualitative assessment of the workforce's capacity to successfully collect that data. METHODS: This mixed-methods project analyzed data from 2 sources: SOGI item completeness among adult completed/partially completed interviews in the SF digital CICT COVID-19 database, and a secondary data analysis of qualitative data from 16 semistructured 90-minute virtual interviews with the SF CICT workforce, between November 14, 2020, and April 14, 2021. RESULTS: Among 15 416 COVID-19 cases and 7836 close contacts, sexual orientation data are missing from 20% of cases and 17% of contacts. The proportion of transgender/nonbinary individuals was 0.32% and 0.5%, respectively. The SF CICTs participants discussed challenges in collecting SOGI data, not understanding SOGI measure rationale, and feeling uncomfortable asking the questions. CONCLUSION: Qualitative interviews with the COVID-19 CICT workforce and quantitative data on SOGI parameters in COVID-19 surveillance suggest that these data may have been underreported. Our results strongly suggest that comprehensive training is crucial in the collection of SOGI data among COVID-19 cases and their close contacts. If SOGI data are not collected accurately, the true impact of COVID-19 among lesbian, gay, bisexual, transgender, and queer populations remains unknown, preventing data-driven allocation of COVID-19 funds to lesbian, gay, bisexual, transgender, and queer communities.

A Mobile Health Tool for Peer Support of Individuals Reentering Communities After Incarceration.

Individuals just released from prison, or returning citizens (RCs), face high mortality rates during the reentry period, with cardiovascular disease (CVD) being a leading cause. Peer mentors can support RCs' health, but they traditionally work in person, which may not always be feasible, particularly during pandemic outbreaks such as COVID-19. We used human-centered design to build a prototype of RCPeer, a web/mobile application (app) to support peer-led reentry efforts through CVD risk screening, action planning, linkage to resources addressing reintegration needs (e.g., housing, transportation), and goal-setting. We assessed feasibility, acceptability, and usability of RCPeer using mixed-methods. System Usability Scale (SUS) scores were 68 for peers and 66 for RCs, indicating good usability. Qualitative data suggests that RCPeer can support reentry tasks through RCs and peers sharing data, strengthen RC-peer relationships, and facilitate RCs meeting their goals. Future work is needed to enhance usability for RCs with limited technology experience.