ABSTRACT
While SARS-CoV-2 serologic testing is used to measure cumulative incidence of COVID-19, appropriate signal-to-cut off (S/Co) thresholds remain unclear. We demonstrate S/Co thresholds based on known negative samples significantly increases seropositivity and more accurately estimates cumulative incidence of disease compared to manufacturer-based thresholds.
ABSTRACT
BackgroundAddressing COVID-19 is a pressing health and social concern. To date, many epidemic projections and policies addressing COVID-19 have been designed without seroprevalence data to inform epidemic parameters. We measured the seroprevalence of antibodies to SARS-CoV-2 in a community sample drawn from Santa Clara County. MethodsOn April 3-4, 2020, we tested county residents for antibodies to SARS-CoV-2 using a lateral flow immunoassay. Participants were recruited using Facebook ads targeting a sample of individuals living within the county by demographic and geographic characteristics. We estimate weights to adjust our sample to match the zip code, sex, and race/ethnicity distribution within the county. We report both the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We also adjust for test performance characteristics by combining data from 16 independent samples obtained from manufacturers data, regulatory submissions, and independent evaluations: 13 samples for specificity (3,324 specimens) and 3 samples for sensitivity (157 specimens). ResultsThe raw prevalence of antibodies to SARS-CoV-2 in our sample was 1.5% (exact binomial 95CI 1.1-2.0%). Test performance specificity in our data was 99.5% (95CI 99.2-99.7%) and sensitivity was 82.8% (95CI 76.0-88.4%). The unweighted prevalence adjusted for test performance characteristics was 1.2% (95CI 0.7-1.8%). After weighting for population demographics of Santa Clara County, the prevalence was 2.8% (95CI 1.3-4.7%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 54,000 (95CI 25,000 to 91,000 using weighted prevalence; 23,000 with 95CI 14,000-35,000 using unweighted prevalence) people were infected in Santa Clara County by early April, many more than the approximately 1,000 confirmed cases at the time of the survey. ConclusionsThe estimated population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection may be much more widespread than indicated by the number of confirmed cases. More studies are needed to improve precision of prevalence estimates. Locally-derived population prevalence estimates should be used to calibrate epidemic and mortality projections.
ABSTRACT
Community-engaged adaptations of evidence-based interventions are needed to improve cancer care delivery for low-income and minority populations with cancer. The objective of this study was to adapt an intervention to improve end-of-life cancer care delivery using a community-partnered approach. We used a two-step formative research process to adapt the evidence-based lay health workers educate engage and encourage patients to share (LEAPS) cancer care intervention. The first step involved obtaining a series of adaptations through focus groups with 15 patients, 12 caregivers, and 6 leaders and staff of the Unite Here Health (UHH) payer organization, and 12 primary care and oncology care providers. Focus group discussions were recorded, transcribed, and analyzed using the constant comparative method of qualitative analysis. The second step involved finalization of adaptations from a community advisory board comprised of 4 patients, 2 caregivers, 4 oncology providers, 2 lay health workers and 4 UHH healthcare payer staff and executive leaders. Using this community-engaged approach, stakeholders identified critical barriers and solutions to intervention delivery which included: (1) expanding the intervention to ensure patient recruitment; (2) including caregivers; (3) regular communication between UHH staff, primary care and oncology providers; and (4) selecting outcomes that reflect patient-reported quality of life. This systematic and community-partnered approach to adapt an end-of-life cancer care intervention strengthened this existing intervention to promote the needs and preferences of patients, caregivers, providers, and healthcare payer leaders. This approach can be used to address cancer care delivery for low-income and minority patients with cancer.
