Modelling the impact of a high-uptake bivalent booster scenario on the COVID-19 burden and healthcare costs in New York City.

Background: Uptake of the COVID-19 bivalent booster vaccine (targeting the original SARS-CoV-2 strain and subvariants BA.4 and BA.5 of the Omicron variant) among eligible residents of New York City (NYC) has been modest and declining. Assessing the impact of improved population-level booster coverage with bivalent vaccines in NYC can help inform investment towards vaccination and potential cost-savings. Methods: We calibrated an agent-based model of disease transmission to confirmed and probable cases of COVID-19 in NYC and simulated it to project outcomes under two scenarios. In the base case scenario, we assumed that vaccination continued with the average daily rate of 92 vaccine doses per 100,000 administered during December 2022. In the counterfactual scenario, we modeled a high-uptake scenario between January 1, 2023 and March 31, 2023, with an average daily rate of 296 vaccine doses per 100,000 population that increased bivalent coverage in NYC to match the age-specific influenza vaccine coverage of the 2020-2021 season. Vaccination rate outside the campaign duration remained the same as the base case scenario. Findings: Compared to the base case, the high-uptake scenario averted 88,274 (95% Confidence Interval [CI]: 77,097-100,342) cases, and prevented 2,917 (95% CI: 2,557-3,267) hospitalizations between January 1 through the end of June 2023. Averted outcomes resulted in net savings of $217.2 (95% CI: 190.0-242.2) million in direct healthcare costs. We estimated that the high-uptake scenario would avert 72,879 (95% CI: 63,894-82,228) days of student absenteeism from schools due to COVID-19 illness. Interpretation: Our results illustrate the continued benefits of COVID-19 vaccines in preventing severe health outcomes, averting healthcare costs, and maintaining educational continuity in NYC. Funding: The Canadian Institutes of Health Research, The Natural Sciences and Engineering Research Council of Canada, NIH, Centers for Disease Control and Prevention (CDC), NSF, The Commonwealth Fund, and The Notsew Orm Sands Foundation.

Social distancing and mask-wearing could avoid recurrent stay-at-home restrictions during COVID-19 respiratory pandemic in New York City.

Stay-at-home restrictions such as closure of non-essential businesses were effective at reducing SARS-CoV-2 transmission in New York City (NYC) in the spring of 2020. Relaxation of these restrictions was desirable for resuming economic and social activities, but could only occur in conjunction with measures to mitigate the expected resurgence of new infections, in particular social distancing and mask-wearing. We projected the impact of individuals' adherence to social distancing and mask-wearing on the duration, frequency, and recurrence of stay-at-home restrictions in NYC. We applied a stochastic discrete time-series model to simulate community transmission and household secondary transmission in NYC. The model was calibrated to hospitalizations, ICU admissions, and COVID-attributable deaths over March-July 2020 after accounting for the distribution of age and chronic health conditions in NYC. We projected daily new infections and hospitalizations up to May 31, 2021 under the different levels of adherence to social distancing and mask-wearing after relaxation of stay-at-home restrictions. We assumed that the relaxation of stay-at-home policies would occur in the context of adaptive reopening, where a new hospitalization rate of ≥ 2 per 100,000 residents would trigger reinstatement of stay-at-home restrictions while a new hospitalization rate of ≤ 0.8 per 100,000 residents would trigger relaxation of stay-at-home restrictions. Without social distancing and mask-wearing, simulated relaxation of stay-at-home restrictions led to epidemic resurgence and necessary reinstatement of stay-at-home restrictions within 42 days. NYC would have stayed fully open for 26% of the time until May 31, 2021, alternating reinstatement and relaxation of stay-at-home restrictions in four cycles. At a low (50%) level of adherence to mask-wearing, NYC would have needed to implement stay-at-home restrictions between 8% and 32% of the time depending on individual adherence to social distancing. At moderate to high levels of adherence to mask-wearing without social distancing, NYC would have needed to implement stay-at-home restrictions. In threshold analyses, avoiding reinstatement of stay-at-home restrictions required a minimum of 60% adherence to mask-wearing at 50% adherence to social distancing. With low adherence to mask-wearing and social distancing, reinstatement of stay-at-home restrictions in NYC was inevitable. High levels of adherence to social distancing and mask-wearing could have attributed to avoiding recurrent surges without reinstatement of stay-at-home restrictions.

A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging: Is Artificial Intelligence-Based Fat-Suppressed Imaging Feasible?

MATERIALS AND METHODS: This single-center study was approved by the institutional review board. Artificial intelligence-based FS MRI scans were created from non-FS images using a deep learning system with a modified convolutional neural network-based U-Net that used a training set of 25,920 images and validation set of 16,416 images. Three musculoskeletal radiologists reviewed 88 knee MR studies in 2 sessions, the original (proton density [PD] + FSPD) and the synthetic (PD + AFSMRI). Readers recorded AFSMRI quality (diagnostic/nondiagnostic) and the presence or absence of meniscal, ligament, and tendon tears; cartilage defects; and bone marrow abnormalities. Contrast-to-noise rate measurements were made among subcutaneous fat, fluid, bone marrow, cartilage, and muscle. The original MRI sequences were used as the reference standard to determine the diagnostic performance of AFSMRI (combined with the original PD sequence). This is a fully balanced study design, where all readers read all images the same number of times, which allowed the determination of the interchangeability of the original and synthetic protocols. Descriptive statistics, intermethod agreement, interobserver concordance, and interchangeability tests were applied. A P value less than 0.01 was considered statistically significant for the likelihood ratio testing, and P value less than 0.05 for all other statistical analyses. RESULTS: Artificial intelligence-based FS MRI quality was rated as diagnostic (98.9% [87/88] to 100% [88/88], all readers). Diagnostic performance (sensitivity/specificity) of the synthetic protocol was high, for tears of the menisci (91% [71/78], 86% [84/98]), cruciate ligaments (92% [12/13], 98% [160/163]), collateral ligaments (80% [16/20], 100% [156/156]), and tendons (90% [9/10], 100% [166/166]). For cartilage defects and bone marrow abnormalities, the synthetic protocol offered an overall sensitivity/specificity of 77% (170/221)/93% (287/307) and 76% (95/125)/90% (443/491), respectively. Intermethod agreement ranged from moderate to substantial for almost all evaluated structures (menisci, cruciate ligaments, collateral ligaments, and bone marrow abnormalities). No significant difference was observed between methods for all structural abnormalities by all readers (P > 0.05), except for cartilage assessment. Interobserver agreement ranged from moderate to substantial for almost all evaluated structures. Original and synthetic protocols were interchangeable for the diagnosis of all evaluated structures. There was no significant difference for the common exact match proportions for all combinations (P > 0.01). The conspicuity of all tissues assessed through contrast-to-noise rate was higher on AFSMRI than on original FSPD images (P < 0.05). CONCLUSIONS: Artificial intelligence-based FS MRI (3D AFSMRI) is feasible and offers a method for fast imaging, with similar detection rates for structural abnormalities of the knee, compared with original 3D MR sequences.

It's Not All Deep Vein Thrombosis: Sonography of the Painful Lower Extremity With Multimodality Correlation.

The purpose of this pictorial essay is to review different etiologies for lower extremity pain encountered on lower extremity venous sonography including acute deep venous thrombosis, chronic postthrombotic change, central venous disease, common arterial pathologies, and nonvascular abnormalities.

Investigating a cholera outbreak in Kaiso Fishing Village, Hoima District, Uganda, October 2015.

Globally, even though improvements have been made to effective surveillance and response, communicable diseases such as cholera remain high priorities for national health programs, especially in Africa. High-quality surveillance information coupled with adequate laboratory facilities are effective in curbing outbreaks from such diseases, ultimately reducing morbidity and mortality. One way of building this capacity is through simulation of response to such health events. This case study based on a cholera outbreak investigated by FETP trainees in October 2015 in Uganda can be used to reinforce skills of frontline FETP trainees and other novice public health practitioners through a practical simulation approach. This activity should be completed in 2.5 hours.