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This study evaluated the discordance between Abbott Architect SARS-CoV-2 IgG and EUROIMMUN SARS-COV-2 ELISA in a seroprevalence study. From June 10 to August 15, 2020, 8,246 specimens were dually evaluated by the Abbott Architect SARS-CoV-2 IgG (Abbott) and the EUROIMMUN SARS-CoV-2 ELISA (EI) assays. Sex-stratified phi correlation coefficients were calculated to evaluate the concordance between Abbott and EI assay's quantitative results. Multivariable mixed-effect logistic models were implemented to evaluate the association between assay positivity and sex on a low prevalence sample while controlling for age, race, ethnicity, diabetes, cardiovascular disease, hypertension, immunosuppressive therapy, and autoimmune disease. EI positivity among males was 2.1-fold that of females; however, no significant differences in Abbott positivity were observed between sexes. At the manufacturer-recommended threshold, the phi correlation coefficient for the Abbott and EI qualitative results among females (Φ = 0.47) was 34% greater than males (Φ = 0.35). The unadjusted and fully adjusted models yielded a strong association between sex and positive EI result for the low prevalence subgroup (unadjusted OR: 2.24, CI: 1.63, 3.11, adjusted OR: 3.40, CI: 2.15, 5.39). A similar analysis of Abbott positivity in the low prevalence subgroup did not find an association with any of the covariates examined. Significant quantitative and qualitative discordance was observed between Abbott and EI throughout the seroprevalence study. Our results suggest the presence of sex-associated specificity limitations with the EI assay. As these findings may extend to other anti-S assays utilized for SARS-CoV-2 seroprevalence investigations, further investigation is needed to evaluate the generalizability of these findings.

Electronic case reporting (eCR) is a process designed to automate case reporting from healthcare organizations (HCOs) to public health agencies (PHAs). However, for this benefit to be realized, it requires adoption by HCOs, and practical solutions to be implemented at PHAs. The aims of this dissertation were 1) to evaluate the economic return on investment when expanding eCR to reportable conditions beyond COVID-19 in an academic health setting, 2) to understand critical organizational characteristics among PHAs contributing to the progression of eCR implementation, and 3) to clarify critical technological considerations and infrastructure for eCR ingestion when received at a PHA.An economic evaluation leveraging the return-on-investment (ROI) framework was used throughout Aim 1 to evaluate the ROI of eCR expansion beyond COVID-19 at UHealth. A multi-site qualitative study design was used for Aims 2 and 3 to evaluate eCR implementation at PHAs across the levels of the informatics stack. The economic analysis demonstrated value for the expansion of electronic case reporting beyond only reporting COVID-19, with a five-year ROI for the expansion of eCR for the additional 29 conditions beyond COVID-19 of 142% (net present value of savings: $7,166). A positive ROI was present throughout all performed sensitivity analyses. The results of Aim 2 identified three distinct patterns among barriers and facilitators across PHAs at varying stages of the eCR implementation. These included 1) themes that were described consistently as either a barrier or facilitator regardless of the implementation stages, 2) themes that were described as a barrier for those in the early stages but evolved into a facilitator for those in later stages, and 3) facilitators that were unique to late-stage implementation. Aim 3 highlighted technological hurdles that need to be overcome to use eICRs at a PHA and the implemented infrastructure to facilitate its ingestion and utility across PHAs at different implementation stages.Through the identification of the benefits, barriers, and facilitators associated with eCR implementation across healthcare organizations and public health agencies, critical insights and recommendations were distilled. While this dissertation focused on eCR, many of the findings are generalizable to the broader public health data modernization effort.

Infectious disease modeling and forecasting tools are crucial for outbreak management. However, variability exists in the capacity of state and local health departments to effectively utilize these tools, influenced by factors such as infrastructure, funding, staff capacity, and data access. This study aims to identify the current priorities, needs, and capacities of state and local public health departments regarding infectious disease modeling and forecasting tools. Key informant interviews were conducted with epidemiologists, informaticists, and leadership across state and local health departments from Montana, Utah, and Washington. Thematic coding and axial coding were used for thematic analysis. Three themes emerged: (1) models and tools must be adaptable based on the jurisdiction type (rural, urban, state); (2) building trust in models and tools is an important precursor to adoption; and (3) there are concerns about the availability and quality of data. This study highlights the need for adaptable modeling tools that are tailored to specific public health jurisdictions. Building trust in modeling and forecasting tools and addressing data quality issues are essential for successful tool implementation and adoption across diverse public health settings.

Objectives Determine the economic cost or benefit of expanding electronic case reporting (eCR) for 29 reportable conditions beyond the initial eCR implementation for COVID-19 at an academic health center. Materials and methods The return on investment (ROI) framework was used to quantify the economic impact of the expansion of eCR from the perspective of an academic health system over a 5-year time horizon. Sensitivity analyses were performed to assess key factors such as personnel cost, inflation, and number of expanded conditions. Results The total implementation costs for the implementation year were estimated to be $5031.46. The 5-year ROI for the expansion of eCR for the 29 conditions is expected to be 142% (net present value of savings: $7166). Based on the annual ROI, estimates suggest that the savings from the expansion of eCR will cover implementation costs in approximately 4.8 years. All sensitivity analyses yielded a strong ROI for the expansion of eCR. Discussion and conclusion Our findings suggest a strong ROI for the expansion of eCR at UHealth, with the most significant cost savings observed implementing eCR for all reportable conditions. An early effort to ensure data quality is recommended to expedite the transition from parallel reporting to production to improve the ROI for healthcare organizations. This study demonstrates a positive ROI for the expansion of eCR to additional reportable conditions beyond COVID-19 in an academic health setting, such as UHealth. While this evaluation focuses on the 5-year time horizon, the potential benefit could extend further. Lay Summary This study aimed to assess the economic impact of expanding electronic case reporting (eCR) for 29 reportable conditions beyond the initial eCR implementation for COVID-19 at an academic health center. The costs and benefits were calculated over a 5-year period using the return on investment (ROI) framework. The total implementation costs were estimated to be $5031.46, with the 5-year ROI for the eCR expansion estimated to be 142% (net present value of savings: $7166). The findings indicate a strong ROI for the expansion of eCR, with potential savings covering implementation costs in approximately 4.8 years. The study recommends early efforts to implement eCR for all reportable conditions while ensuring data quality could yield the most significant cost savings for the respective healthcare organizations.

Abstract Background The initial focus of the US public health response to coronavirus disease 2019 (COVID-19) was the implementation of numerous social distancing policies. While COVID-19 was the impetus for imposing these policies, it is not the only respiratory disease affected by their implementation. This study aimed to assess the impact of social distancing policies on non–severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) respiratory pathogens typically circulating across multiple US states. Methods Linear mixed-effect models were implemented to explore the effects of 5 social distancing policies on non-SARS-CoV-2 respiratory pathogens across 9 states from January 1 through May 1, 2020. The observed 2020 pathogen detection rates were compared week by week with historical rates to determine when the detection rates were different. Results Model results indicate that several social distancing policies were associated with a reduction in total detection rate, by nearly 15%. Policies were associated with decreases in pathogen circulation of human rhinovirus/enterovirus and human metapneumovirus, as well as influenza A, which typically decrease after winter. Parainfluenza viruses failed to circulate at historical levels during the spring. The total detection rate in April 2020 was 35% less than the historical average. Many of the pathogens driving this difference fell below the historical detection rate ranges within 2 weeks of initial policy implementation. Conclusions This analysis investigated the effect of multiple social distancing policies implemented to reduce transmission of SARS-CoV-2 on non-SARS-CoV-2 respiratory pathogens. These findings suggest that social distancing policies may be used as an impactful public health tool to reduce communicable respiratory illness.

Gonorrhea (caused by infection with Neisseria gonorrhoeae) is the second most commonly reported notifiable disease in the United States (1). Left untreated, gonorrhea is associated with serious long-term adverse health effects, including pelvic inflammatory disease, ectopic pregnancy, and infertility. Infection also facilitates transmission of human immunodeficiency virus (2,3). Effective gonorrhea control relies upon early detection and effective antimicrobial treatment. To assess gonorrhea rate trends in Utah, the Utah Department of Health (UDOH) analyzed Utah National Electronic Disease Surveillance System (UT-NEDSS) data for the state during 2009-2014. After declining during 2009-2011, the statewide gonorrhea rate increased fivefold to 49 cases per 100,000 population in 2014. During 2009-2014, the proportion of cases among women increased from 21% to 39% (decreasing among males from 79% to 61%). Among male patients, the proportion who identified as men who have sex with men (MSM) decreased from 67% to 42%. These demographic changes suggest that increased heterosexual transmission of gonorrhea in Utah might be occurring. Health departments need to work with providers to ensure populations at high risk are being screened and properly treated for gonorrhea. Clinicians need to be aware of increases in the risk for infection among women and non-MSM males when making screening and testing decisions and educate their patients regarding gonorrhea transmission and prevention practices.

Gonorrhea (caused by infection with Neisseria gonorrhoeae) is the second most commonly reported notifiable disease in the US. Left untreated, gonorrhea is associated with serious long-term adverse health effects, including pelvic inflammatory disease, ectopic pregnancy, and infertility. Infection also facilitates transmission of human immunodeficiency virus. Effective gonorrhea control relies upon early detection and effective antimicrobial treatment. To assess gonorrhea rate trends in Utah, the Utah Department of Health (UDOH) analyzed Utah National Electronic Disease Surveillance System (UT-NEDSS) data for the state during 2009-2014. After declining during 2009-2011, the statewide gonorrhea rate increased fivefold to 49 cases per 100,000 population in 2014. During 2009-2014, the proportion of cases among women increased from 21% to 39% (decreasing among males from 79% to 61%). Among male patients, the proportion who identified as men who have sex with men decreased from 67% to 42%.

Plasma cleaning is a technique that can be applied in superconducting radio-frequency (SRF) cavities in situ in cryomodules in order to decrease their level of field emission. We developed the technique for the Linac Coherent Light Source II (LCLS-II) cavities and we present in this paper the full development and application of plasma processing to the LCLS-II High Energy (HE) verification cryomodule (vCM). We validated our plasma processing procedure on the vCM, fully processing four out of eight cavities of this CM, demonstrating that cavities performance were preserved in terms of both accelerating field and quality factor. Applying plasma processing to this clean, record breaking cryomodule also showed that no contaminants were introduced in the string, maintaining the vCM field emission-free up to the maximum field reached by each cavity. We also found that plasma processing eliminates multipacting (MP) induced quenches that are typically observed frequently within the MP band field range. This suggests that plasma processing could be employed in situ in CMs to mitigate both field emission and multipacting, significantly decreasing the testing time of cryomodules, the linac commissioning time and cost and increasing the accelerator reliability.