Flight plan for the future: floatplane pilots and researchers team up to predict invasive species dispersal in Alaska

Aircraft can transport aquatic invasive species (AIS) from urban sources to remote waterbodies, yet little is known about this long-distance pathway. In North America and especially Alaska, aircraft with landing gear for water called floatplanes are used for recreation access to remote, often road-less wilderness destinations. Human-mediated dispersal of AIS is particularly concerning for the conservation of pristine wildlands, yet resource managers are often challenged by limited monitoring and response capacity given the vast areas they manage. We collected pathway data through a survey with floatplane pilots and used a Bayesian hierarchical model to inform early detection in a data-limited situation. The study was motivated by Alaska’s first known AIS, Elodea spp. (Elodea) and its floatplane-related dispersal. For 682 identified floatplane destinations, a Bayesian hierarchical model predicts the chance of flights originating from AIS source locations in freshwater and estimates the expected number of flights from these sources. Model predictions show the potential for broad spread across remote regions currently not known to have Elodea and informed monitoring and early detection efforts. Our result underlines the small window of opportunity for Arctic conservation strategies targeting an AIS free Arctic. We recommend management that focuses on long-distance connectivity, keeping urban sources free of AIS. We discuss applicability of the approach for other data-limited situations supporting data-informed AIS management responses.

Synthesizing the impacts of baseflow contribution on concentration–discharge (<i>C</i>–<i>Q</i>) relationships across Australia using a Bayesian hierarchical model

Understanding concentration–discharge (C–Q) relationships can inform catchment solute and particulate export processes. Previous studies have shown that the extent to which baseflow contributes to streamflow can affect C–Q relationships in some catchments. However, the current understanding on the effects of baseflow contribution in shaping the C–Q patterns is largely derived from temperate catchments. As such, we still lack quantitative understanding of these effects across a wide range of climates (e.g. arid, tropical and subtropical). The study aims to assess how baseflow contributions, as defined by the median and the range of daily baseflow indices within individual catchments (BFI_m and BFI_range, respectively), influence C–Q slopes across 157 catchments in Australia spanning five climate zones. This study focuses on six water quality variables: electrical conductivity (EC), total phosphorus (TP), soluble reactive phosphorus (SRP), total suspended solids (TSS), the sum of nitrate and nitrite (NOx) and total nitrogen (TN). The impact of baseflow contributions is explored with a novel Bayesian hierarchical model. For sediments and nutrient species (TSS, NOx, TN and TP), we generally see largely positive C–Q slopes, which suggest a dominance of mobilization export patterns. Further, for TSS, NOx and TP we see stronger mobilization (steeper positive C–Q slopes) in catchments with higher values in both the BFI_m and BFI_range, as these two metrics are positively correlated for most catchments. The enhanced mobilization in catchments with higher BFI_m or BFI_range is likely due to the more variable flow pathways that occur in catchments with higher baseflow contributions. These variable flow pathways can lead to higher concentration gradients between low flows and high flows, where the former is generally dominated by groundwater/slow subsurface flow while the latter by surface water sources, respectively. This result highlights the crucial role of flow pathways in determining catchment exports of solutes and particulates. Our study also demonstrates the need for further studies on how the temporal variations of flow regimes and baseflow contributions influence flow pathways and the potential impacts of these flow pathways on catchment C–Q relationships.

Do Women Respond Less to Performance Pay? Building Evidence from Multiple Experiments

Existing empirical work raises the hypothesis that performance pay—whatever its output gains—may widen the gender earnings gap because women may respond less to incentives. We evaluate this possibility by aggregating evidence from existing experiments on performance incentives with male and female subjects. Using a Bayesian hierarchical model, we estimate both the average effect and heterogeneity across studies. We find that the gender response difference is close to zero and heterogeneity across studies is small, while performance pay increases output by 0.36 standard deviations on average. The data thus support agency theory for men and women alike. (JEL C11, C90, J16, J31, J33)

P.051 Do clinical confounders to the neurological examination modify the diagnostic accuracy of CT-angiography for death by neurological criteria/brain death?

Background: CT-angiography is an ancillary test used to diagnose death by neurological criteria (DNC), notably in cases of unreliable neurological examinations due to clinical confounders. We studied whether clinical confounders to the neurological examination modified CT-angiography diagnostic accuracy. Methods: Systematic review and meta-analysis of studies including deeply comatose patients undergoing DNC ancillary testing. We estimated pooled sensitivities and specificities using a Bayesian hierarchical model, including data on CT-angiography (4-point, 7-point, 10-point scales, and no intracranial flow), and performing a subgroup analysis on clinical confounders to the reference neurological examination. Results: Of 40 studies included in the meta-analysis, 7 involve CT-angiography (n=586). There was no difference between subgroups (Table). The degree of uncertainty involving sensitivity estimates was high in both subgroups. Conclusions: Statistical uncertainty in diagnostic accuracy estimates preclude any conclusion regarding the impact of clinical confounders on CT-angiography diagnostic accuracy. Further research is required to validate CT-angiography as an accurate ancillary test for DNC. Table. Pooled sensitivities and specificities of CT-angiography for death by neurological criteria

737. Geographic Clustering of Travel-acquired Infections in Ontario, Canada, 2008-2020

Background As rates of international travel increase, more individuals are at risk of travel-acquired infections (TAIs). We aimed to review all microbiologically confirmed cases of malaria, dengue, chikungunya, and enteric fever (Salmonella enterica serovar Typhi/Paratyphi) in Ontario, Canada between 2008-2020 to identify high-resolution geographical clusters that could be targeted for pre-travel prevention. Methods Retrospective cohort study of over 174,000 unique tests for the four above TAIs from Public Health Ontario Laboratories. Test-level data were processed to calculate annual case counts and crude population-standardized incidence ratios (SIRs) at the forward sortation area (FSA) level. Moran’s I statistic was used to test for global spatial autocorrelation. Smoothed SIRs and 95% posterior credible intervals (CIs) were estimated using a spatial Bayesian hierarchical model, which accounts for statistical instability and uncertainty in small-area incidence. Posterior CIs were used to identify high- and low-risk areas, which were described using sociodemographic data from the 2016 Census. Finally, a second model was used to estimate the association between drivetime to the nearest travel clinic and risk of TAI within high-risk areas. Results There were 5962 cases of the four TAIs across Ontario over the study period. Smoothed FSA-level SIRs are shown in Figure 1a, with an inset for the Greater Toronto Area (GTA) in 1b. There was spatial clustering of TAIs (Moran’s I=0.61, p&lt; 2.2e-16). Identified high- and low-risk areas are shown in panels c and d. Compared to low-risk areas, high-risk areas were significantly more likely to have higher proportions of immigrants (p&lt; 0.0001), lower household after-tax income (p=0.04), more university education (p&lt; 0.0001), and were less knowledgeable of English/French (p&lt; 0.0001). In the high-risk GTA, each minute increase in drivetime to the closest travel clinic was associated with a 4% reduction in TAI risk (95% CI 2 - 6%). Bayesian hierarchical model (BHM) smoothed standardized incidence ratios (SIRs) for travel-acquired infections (TAIs) and estimated risk levels (a and c) with insets for the Greater Toronto Area (b and d). High-risk areas are defined as those with smoothed SIR 95% CIs greater than 2, and low-risk areas with smoothed SIR 95% CIs less than 0.25. Conclusion Urban neighbourhoods in the GTA had elevated risks of becoming ill with TAIs. However, geographic proximity to a travel clinic was not associated with an area-level risk reduction in TAI, suggesting other barriers to seeking and adhering to pre-travel advice. Disclosures Isaac Bogoch, MD, MSc, BlueDot (Consultant)National Hockey League Players' Association (Consultant) Andrea Boggild, MSc MD DTMH FRCPC, Nothing to disclose Shaun Morris, MD, MPH, DTM&H, FRCPC, FAAP, GSK (Speaker's Bureau)Pfizer (Advisor or Review Panel member)Pfizer (Grant/Research Support)

Estimating COVID-19 Hospitalizations in the United States with surveillance data using a Bayesian Hierarchical model

Introduction: In the United States, COVID-19 is a nationally notifiable disease, cases and hospitalizations are reported to the CDC by states. Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating burden of COVID-19 from established sentinel surveillance systems is becoming more important. We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. Methods: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. We created a model for six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years), separately. We identified covariates from multiple data sources that varied by age, state, and/or month, and performed covariate selection for each age group based on two methods, Least Absolute Shrinkage and Selection Operator (LASSO) and Spike and Slab selection methods. We validated our method by checking sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. Results: We estimated 3,569,500 (90% Credible Interval:3,238,000 - 3,934,700) hospitalizations for a cumulative incidence of 1,089.8 (988.6 - 1,201.3) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 352 - 1,821per 100,000 between states. The age group with the highest cumulative incidence was aged greater than or equal to 85 years (5,583.1; 5,061.0 - 6,157.5). The monthly hospitalization rate was highest in December (183.8; 154.5 - 218.0). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks and timing of peaks between states. Conclusions: Our novel approach to estimate COVID-19 hospitalizations has potential to provide sustainable estimates for monitoring COVID-19 burden, as well as a flexible framework leveraging surveillance data.

Emerging data on the treatment of multiple sclerosis

Background and objectives. In the past few years, a myriad of new studies were aimed to find better ways to manage MS. As a result, a bunch of new molecules were found to have good efficacy, therefore FDA and EMA approved a series of treatments in the last few years, the last one receiving green light from EMA on March 30th, 2021 (Ofatumumab – Kesimpta®). The aim of this study was to evaluate and classify three of the newest drugs approved by the FDA and EMA. Material and methods. All the studies were chosen on the basis of pre-determined inclusion criteria and in accordance with PRISMA guidelines. We searched Pubmed and Cochrane Library for all studies published up until the end of 2020. For the data analysis we used MetaInsight®, a statistical web-based tool for meta-analyses and NMAs performing both Frequentist and Bayesian hierarchical model analyses, each one being seen as a sensitivity check for the other. Outcomes. The best therapeutic agent in reported efficacy amongst the three analyzed was Ofatumumab, ranked first in hierarchy, Ozanimod and Cladribine following in the second and third place, respectively. Conclusions. According to ABN’s 2015 guidelines, Cladribine was ranked between the most effective medicines for the treatment of MS; given the results from this study, other two may be considered as high efficacy alongside Natalizumab, Alemtuzumab and Ocrelizumab.