Background: Considering its deleterious effects on health, as well as the importance of information to support actions, strategies and public policies, this study was aimed to identify and classify the risk factors for consumption of ultra-processed foods among Brazilian adolescents. Data sources: targeting observational studies involving samples of Brazilian adolescents (11 to 19 years old), which evaluated possible associations between the consumption of ultra-processed foods and individual, interpersonal, environmental and public policies variables, in October 2022, a systematic review was conducted, consulting electronic databases (Lilacs, Pubmed, Scielo, Scopus and Web of Science), Google Scholar and the reference lists of included articles. Data synthesis: The descriptive synthesis consisted of 11 papers, representing nine original studies. In general, the consumption of ultra-processed foods was associated with different individual, interpersonal and environmental variables. More specifically, the following variables can be highlighted: sedentary behavior (specially screen time), studying at a private school, having a higher Body Mass Index and being female. Conclusions: Based on this evidence, it is important to direct actions, strategies and public policies aimed at confronting the consumption of ultra-processed foods for these groups.

The incidence of carbapenem-resistant Enterobacteriaceae (CRE) has been increasing considerably annually since being first reported in 2008. Gyeonggi Province has the largest number of healthcare facilities in South Korea, which contribute to its regional vulnerability to CRE infections. Thus, there is a need to obtain up to date information on the occurrence of CRE infections in the Gyeonggi Province to take corrective measures. Therefore, in this study, we aimed to investigate the overall trends of CRE occurrence in Gyeonggi Province over the past 4 years and the epidemiological characteristics of CRE-related mortality. Patients admitted to a healthcare facility in Gyeonggi Province from January 2018 to December 2021 with positive blood cultures to CRE were included in the study and investigated. Additionally, the risk factors for CRE-related death were determined using data from CRE-positive patients who died within 30 days of last blood sampling. Older adults aged 70 and over accounted for the highest percentage of both survivors and non-survivors with CRE bacteremia. The number of non-survivors was higher in tertiary hospitals and among patients in intensive care unit. There were no significant differences in the mortality risk according to the use of antibiotics. Moreover, non-survivors were predominant among patients with hypertension, malignant tumor, and those having two or more underlying diseases. Klebsiella pneumoniae was the most common CRE strain in both survivors and non-survivors. Among the survivors, carbapenemase-producing Enterobacteriaceae was detected in 151 cases (71.2%) and 49 cases (23.1%) in the KPC and NDM groups, respectively. Among the non-survivors, it was detected in 25 cases (86.2%) and 5 cases (17.2%) in the KPC and NDM groups, respectively. The endemicity of CRE in South Korea has been proposed for several years; our results have suggested endemicity of CRE in the Gyeonggi Province. Despite the strengthened infection control policies in healthcare facilities during the pandemic, there has been an increasing trend in the isolation of CRE strains in South Korean long-term care hospitals and hospitals within Gyeonggi Province. Factors such as history of hospitalization and underlying diseases have been identified as risk factors for CRE-related deaths. However, to address this issue strictly, it is important to establish infection control infrastructure tailored based on each type of healthcare facility, and elaborate specific government support measures, accordingly.

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, support quantative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation and multi-year daily use for public health and clinical support decision-making of a particle filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian Sequential Monte Carlo algorithm of Particle Filtering allows the model to be re-grounded daily and adapt to new trends within daily incoming data – including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of dynamic model state, support probabilistic model projection of epidemiology and health system capacity utilization and service demand and probabilistically evaluate trade-offs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision making across Canada, such methods offer strong support for evidence-based public health decision making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

One of the most important problems in the presence of epidemics and pandemics is an accurate prediction and preparedness. Scholars and experts argue that future pandemics and/or epidemics are almost inevitable events and is not whether next pandemics will happen, but when a new heath emergency will emerge. Epidemiologic models for prediction of Coronavirus Disease 2019 (COVID-19) have shown many limitations because of unpredictable dynamics of the new viral agent SARS-CoV-2 in environment and society. The main goals of this study are twofold: first, the analysis of anthropogenic activities and factors that may trigger pandemic threats; second, the planning of new directions for strategies to reduce risks that a pandemic threat emerges and/or in the initial phase to reduce vast diffusion and negative impact of new viral agents that can generate hazards and problems in public health, environment and socioeconomic systems. In particular, the investigation and understanding of sources and driving factors concerning the emergence and diffusion of new pandemics have critical aspects for strategic actions of forecast, prevention and preparation of effective policy responses to cope with next pandemic crises and health emergencies. Insights here endeavor, whenever possible, to clarify these problems to increase the knowledge of the sources and factor determining the emergence of new viral agents in order to design optimal response policies to face next pandemic diseases similar to COVID-19. .

Heart failure (HF) management in type 1 diabetes (T1D) is particularly challenging due to its increased prevalence and the associated risks of hospitalization and mortality, driven by diabetic cardiomyopathy. Sodium-glucose cotransporter-2 inhibitors (SGLT2-i) offer a promising avenue for treating HF, specifically the preserved ejection fraction variant most common in T1D, but their utility is hampered by the risk of euglycemic diabetic ketoacidosis (DKA). This review investigates the potential of SGLT2-i in T1D HF management, alongside the emergent Continuous Ketone Monitoring (CKM) technology as a means to mitigate DKA risk, through a comprehensive analysis of clinical trials, observational studies, and reviews. The evidence suggests SGLT2-i significantly reduce HF hospitalization and enhance cardiovascular outcomes. However, their application in T1D patients remains limited due to DKA concerns. CKM technology emerges as a crucial tool in this context, offering real-time monitoring of ketone levels, which enables the safe incorporation of SGLT2-i into treatment regimes by allowing for early detection and intervention in the development of ketosis. The synergy between SGLT2-i and CKM has the potential to revolutionize HF treatment in T1D, promising improved patient safety, quality of life, and reduced HF-related morbidity and mortality. Future research should aim at clinical trials directly assessing this integrated approach, potentially guiding new management protocols for HF in T1D.