"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.

Implementing a digital system for contact tracing and case investigation during COVID-19 pandemic in San Francisco: a qualitative study.

During the COVID-19 pandemic, many health jurisdictions deployed digital informatics systems to support "manual" case investigation and contact tracing (CICT). This case study evaluates the implementation and use of a digital information system through the experiences of CICT workers in the City and County of San Francisco (CCSF). We conducted semi-structured, 90-min interviews with a sample of the CCSF CICT workforce (n = 37). Participants also completed standardized assessments of the digital system using the System Usability Scale (SUS). Qualitative analyses highlighted (1) the importance of digital tools to ensure rapid onboarding and effective data capture in a public health emergency; (2) the use of digital systems to support culturally sensitive care; and (3) the role of digitals tools in building supportive work environments. The mean SUS score was 70/100 (SD = 17), indicating relative ease of use. In summary, the analysis highlights the importance of digital tools to support manual CICT in the COVID-19 response.