Impact of pre-exposure prophylaxis uptake among gay, bisexual, and other men who have sex with men in urban centers in Brazil: a modeling study.

BACKGROUND: Men who have sex with men (MSM) in Brazil remain disproportionately affected by HIV. We estimated the potential incidence reduction by five years with increased uptake of publicly-funded, daily, oral tenofovir/emtricitabine (TDF/FTC) for HIV pre-exposure prophylaxis (PrEP) among MSM using the Cost Effectiveness of Preventing AIDS Complications microsimulation model. We used national data, local studies, and literature to inform model parameters for three cities: Rio de Janeiro, Salvador, and Manaus. RESULTS: In Rio de Janero, a PrEP intervention achieving 10% uptake within 60 months would decrease incidence by 2.3% whereas achieving 60% uptake within 24 months would decrease incidence by 29.7%; results were similar for Salvador and Manaus. In sensitivity analyses, decreasing mean age at PrEP initiation from 33 to 21 years increased incidence reduction by 34%; a discontinuation rate of 25% per year decreased it by 12%. CONCLUSION: Targeting PrEP to young MSM and minimizing discontinuation could substantially increase PrEP's impact.

Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context.

Language Models (LMs) have performed well on biomedical natural language processing applications. In this study, we conducted some experiments to use prompt methods to extract knowledge from LMs as new knowledge Bases (LMs as KBs). However, prompting can only be used as a low bound for knowledge extraction, and perform particularly poorly on biomedical domain KBs. In order to make LMs as KBs more in line with the actual application scenarios of the biomedical domain, we specifically add EHR notes as context to the prompt to improve the low bound in the biomedical domain. We design and validate a series of experiments for our Dynamic-Context-BioLAMA task. Our experiments show that the knowledge possessed by those language models can distinguish the correct knowledge from the noise knowledge in the EHR notes, and such distinguishing ability can also be used as a new metric to evaluate the amount of knowledge possessed by the model.

Analysis of Mammography Screening Schedules under Varying Resource Constraints for Planning Breast Cancer Control Programs in Low- and Middle-Income Countries: A Mathematical Study.

Background. Low-and-middle-income countries (LMICs) have higher mortality-to-incidence ratio for breast cancer compared to high-income countries (HICs) because of late-stage diagnosis. Mammography screening is recommended for early diagnosis, however, the infrastructure capacity in LMICs are far below that needed for adopting current screening guidelines. Current guidelines are extrapolations from HICs, as limited data had restricted model development specific to LMICs, and thus, economic analysis of screening schedules specific to infrastructure capacities are unavailable. Methods. We applied a new Markov process method for developing cancer progression models and a Markov decision process model to identify optimal screening schedules under a varying number of lifetime screenings per person, a proxy for infrastructure capacity. We modeled Peru, a middle-income country, as a case study and the United States, an HIC, for validation. Results. Implementing 2, 5, 10, and 15 lifetime screens would require about 55, 135, 280, and 405 mammography machines, respectively, and would save 31, 62, 95, and 112 life-years per 1000 women, respectively. Current guidelines recommend 15 lifetime screens, but Peru has only 55 mammography machines nationally. With this capacity, the best strategy is 2 lifetime screenings at age 50 and 56 years. As infrastructure is scaled up to accommodate 5 and 10 lifetime screens, screening between the ages of 44-61 and 41-64 years, respectively, would have the best impact. Our results for the United States are consistent with other models and current guidelines. Limitations. The scope of our model is limited to analysis of national-level guidelines. We did not model heterogeneity across the country. Conclusions. Country-specific optimal screening schedules under varying infrastructure capacities can systematically guide development of cancer control programs and planning of health investments.