Gutierrez-Mock L, Burgess H, Pardo S, Persson M, Gagliano J, Ho YX, Reid MJA. "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.
JOURNAL OF PUBLIC HEALTH MANAGEMENT AND PRACTICE 2023;
29:71-76. [PMID:
36070579 DOI:
10.1097/phh.0000000000001584]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
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.
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