Background: Both incidence and geographical range of tick-borne disease has increased across the USA. Similar to people, dogs are hosts for Anaplasma spp., Babesia spp., Ehrlichia spp. and Borrelia burgdorferi. Dogs also share our homes and beds, making them both a sentinel for the ticks in our backyards but also increasing our exposure to ticks. Measures to better track, prevent, and/or treat tick-borne diseases in companion animals can lead to better control and prevention of human tick-borne disease. This study identifies demographic and co-infection risk factors for canine seropositivity to tick-borne infections in a cohort of hunting dogs across the USA. Results: Human patterns of tick-borne disease co-infection in the USA have been predominantly driven by the geographical distribution of the tick vector. Dogs who tested seropositive for Anaplasma spp. were 1.40 times more likely (P = 0.0242) to also test seropositive for Babesia spp. and vice versa (1.60 times more likely, P = 0.0014). Dogs living in the West had 5% lower risk (P = 0.0001) for Ehrlichia spp. seropositivity compared to other regions. Controlling for age and Anaplasma spp. seroprevalence, dogs in all three other regions were 2.30 times more likely (P = 0.0216) to test seropositive for B. burgdorferi than dogs in the West. Dogs seropositive for B. burgdorferi were 1.60 times more likely (P = 0.0473) to be seropositive for Anaplasma spp. Conclusions: Tick geographical distributions have a prominent impact on the regional distribution of hunting dog exposure to tick-borne diseases. Education concerning regional tick prevalence and disease risk is important for everyone, but particularly dog owners, regarding ticks in their region and protection from infection and co-infection of tick-borne pathogens as they travel or move with their dogs. Dogs are sentinel species for human exposure to ticks, and as such surveillance of canine tick-borne infections and understanding the probability that these infections might be seen together as co-infections helps predict emerging areas where people are more likely to be exposed as well.

Evaluate factors associated with United States (US) healthcare personnel (HCP) uptake of an updated COVID-19 vaccine. We analyzed data from 887 US HCP enrolled between September 25, 2023, and April 17, 2024, in a multisite case-control COVID-19 vaccine effectiveness study. Sociodemographic and attitudinal data were collected via standardized surveys, and vaccination status was verified using source documentation. Overall, 188 (21.2 %) HCP received the updated vaccine. HCP aged 50–64 years (adjusted odds ratio [aOR] 2.02, 95 % CI: 1.11–3.67) versus ages 18–29, and HCP with undergraduate (aOR 3.75, 95 % CI: 1.97–7.12) or graduate degrees (aOR 7.73, 95 % CI: 4.10–14.55) versus high school/some college had higher odds of vaccination. Nurses and nurse assistants had lower odds (aOR 0.48, 95 % CI: 0.30–0.76) versus other job roles. Vaccinated HCP were more likely than unvaccinated HCP to cite concerns about personal infection (89.9 % vs. 44.6 %), infecting family/friends (96.3 % vs. 56.0 %), vaccine availability (63.3 % vs. 18.9 %), and peer-reviewed literature (56.4 % vs. 28.3 %) as important influences. These factors remained consistent with findings from a prior vaccine season. Vaccine access, education, and peer-reviewed information remain drivers of HCP vaccine uptake. Strategies should address occupational disparities and emphasize transmission risks.

BACKGROUND: Detection of large vessel occlusions using a deep learning (DL) algorithm for the anterior circulation has shown promising results. However, the role of DL algorithms in detecting posterior circulation large vessel occlusion (PC-LVO) remains uncertain. We aimed to evaluate the diagnostic performance of a DL algorithm (Methinks PC-LVO) for detecting PC-LVO using noncontrast computed tomography. METHODS: This is a retrospective, multicenter, observational cohort study that included patients with PC-LVO who underwent both noncontrast computed tomography and computed tomography angiography. The diagnostic performance of the DL algorithm was assessed by analyzing sensitivity, specificity, and area under the curve for PC-LVO detection of consecutive PC-LVO strokes. For comparative analysis, the area under the curve of the DL algorithm was also evaluated against the performance of a neuroradiologist interpreting noncontrast computed tomography. Ground truth labels were established through consensus readings by expert neuroradiologists. Subgroup analyses were performed according to clot location (proximal posterior cerebral artery and basilar artery) and National Institutes of Health Stroke Scale (NIHSS) score (NIHSS score ≥6, NIHSS score ≥8, and NIHSS score ≥10). RESULTS: A total of 196 patients were included, of whom 74 patients had PC-LVO. Among these, 43 had basilar artery occlusions, and 34 had proximal posterior cerebral artery occlusions. The overall sensitivity and specificity of the software were 55.4% and 80.9%, respectively, with an area under the curve of 0.72. The neuroradiologist achieved a sensitivity of 27.4% and specificity of 91.8%. Among patients with proximal posterior cerebral artery occlusion, sensitivity was 55.9%, whereas for basilar artery occlusion, it was 53.5%. When stratified by NIHSS score ≥10, sensitivity in the proximal posterior cerebral artery was 56.2%, and for the basilar artery with NIHSS score ≥10, it increased to 61.5%. CONCLUSIONS: Our initial experience with a DL algorithm for the detection of PC-LVO showed promising results. However, further improvements are required before the algorithm can be implemented in clinical practice.