If you build it, will they come? Is test site availability a root cause of geographic disparities in COVID-19 testing?

Objectives: The purpose of this study was to examine the relationship between test site availability and testing rate within the context of social determinants of health. Study design: A retrospective ecological investigation was conducted using statewide COVID-19 testing data between March 2020 and December 2021. Methods: Ordinary least squares and geographically weighted regression were used to estimate state and ZIP code level associations between testing rate and testing sites per capita, adjusting for neighbourhood-level confounders. Results: The findings indicate that site availability is positively associated with the ZIP code level testing rate and that this association is amplified in communities of greater economic deprivation. In addition, economic deprivation is a key factor for consideration when examining ethnic differences in testing in medically underserved states. Conclusion: The study findings could be used to guide the delivery of testing facilities in resource-constrained states.

If you build it, will they come? Is test site availability a root cause of geographic disparities in COVID-19 testing?

OBJECTIVES: The purpose of this study was to examine the relationship between test site availability and testing rate within the context of social determinants of health. STUDY DESIGN: A retrospective ecological investigation was conducted using statewide COVID-19 testing data between March 2020 and December 2021. METHODS: Ordinary least squares and geographically weighted regression were used to estimate state and ZIP code level associations between testing rate and testing sites per capita, adjusting for neighbourhood-level confounders. RESULTS: The findings indicate that site availability is positively associated with the ZIP code level testing rate and that this association is amplified in communities of greater economic deprivation. In addition, economic deprivation is a key factor for consideration when examining ethnic differences in testing in medically underserved states. CONCLUSION: The study findings could be used to guide the delivery of testing facilities in resource-constrained states.

Exit strategies: optimising feasible surveillance for detection, elimination, and ongoing prevention of COVID-19 community transmission.

BACKGROUND: Following implementation of strong containment measures, several countries and regions have low detectable community transmission of COVID-19. We developed an efficient, rapid, and scalable surveillance strategy to detect remaining COVID-19 community cases through exhaustive identification of every active transmission chain. We identified measures to enable early detection and effective management of any reintroduction of transmission once containment measures are lifted to ensure strong containment measures do not require reinstatement. METHODS: We compared efficiency and sensitivity to detect community transmission chains through testing of the following: hospital cases; fever, cough and/or ARI testing at community/primary care; and asymptomatic testing; using surveillance evaluation methods and mathematical modelling, varying testing capacities, reproductive number (R) and weekly cumulative incidence of COVID-19 and non-COVID-19 respiratory symptoms using data from Australia. We assessed system requirements to identify all transmission chains and follow up all cases and primary contacts within each chain, per million population. RESULTS: Assuming 20% of cases are asymptomatic and 30% of symptomatic COVID-19 cases present for testing, with R = 2.2, a median of 14 unrecognised community cases (8 infectious) occur when a transmission chain is identified through hospital surveillance versus 7 unrecognised cases (4 infectious) through community-based surveillance. The 7 unrecognised community upstream cases are estimated to generate a further 55-77 primary contacts requiring follow-up. The unrecognised community cases rise to 10 if 50% of cases are asymptomatic. Screening asymptomatic community members cannot exhaustively identify all cases under any of the scenarios assessed. The most important determinant of testing requirements for symptomatic screening is levels of non-COVID-19 respiratory illness. If 4% of the community have respiratory symptoms, and 1% of those with symptoms have COVID-19, exhaustive symptomatic screening requires approximately 11,600 tests/million population using 1/4 pooling, with 98% of cases detected (2% missed), given 99.9% sensitivity. Even with a drop in sensitivity to 70%, pooling was more effective at detecting cases than individual testing under all scenarios examined. CONCLUSIONS: Screening all acute respiratory disease in the community, in combination with exhaustive and meticulous case and contact identification and management, enables appropriate early detection and elimination of COVID-19 community transmission. An important component is identification, testing, and management of all contacts, including upstream contacts (i.e. potential sources of infection for identified cases, and their related transmission chains). Pooling allows increased case detection when testing capacity is limited, even given reduced test sensitivity. Critical to the effectiveness of all aspects of surveillance is appropriate community engagement, messaging to optimise testing uptake and compliance with other measures.

A scalable tool for adjudication of time sensitive cases during COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, prioritization of care and utilization of scarce resources are daily considerations in healthcare systems that have never experienced these issues before. Elective surgical cases have been largely postponed, and surgery departments are struggling to correctly and equitably determine which cases need to proceed. A resource to objectively prioritize and track time sensitive cases would be useful as an adjunct to clinical decision-making. METHODS: A multidisciplinary working group at Emory Healthcare developed and implemented an adjudication tool for the prioritization of time sensitive surgeries. The variables identified by the team to form the construct focused on the patient's survivability according to actuarial data, potential impact on function with delay in care, and high-level biology of disease. Implementation of the prioritization was accomplished with a database design to streamline needed communication between surgeons and surgical adjudicators. All patients who underwent time sensitive surgery between 4/10/20 and 6/15/20 across 5 campuses were included. RESULTS: The primary outcomes of interest were calculated patient prioritization score and number of days until operation. 1767 cases were adjudicated during the specified time period. The distribution of prioritization scores was normal, such that real-time adjustment of the empiric algorithm was not required. On retrospective review, as the patient prioritization score increased, the number of days to the operating room decreased. This confirmed the functionality of the tool and provided a framework for organization across multiple campuses. CONCLUSIONS: We developed an in-house adjudication tool to aid in the prioritization of a large cohort of canceled and time sensitive surgeries. The tool is relatively simple in its design, reproducible, and data driven which allows for an objective adjunct to clinical decision-making. The database design was instrumental in communication optimization during this chaotic period for patients and surgeons.