Annals of family medicine
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Context: Cancer is associated with many risk factors, including obesity. Specifically, abdominal obesity is linked to metabolic syndrome and is quantified by waist circumference (WC). Compared to WC, body mass index (BMI) is more accessible in the office to assess a patient's overall body fat composition. ⋯ We found that BMI and WC were significantly correlated in this cohort. Further follow-up is needed with a larger sample size and over a longer period. As body composition in this cohort has not been previously studied, further analyses may provide more biomarkers to aid in disease management.
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Annals of family medicine · Jan 2023
Baseline Inappropriate Antibiotic Use in Primary Care Patients with Viral Respiratory Tract Infections with 2019 and 2020.
Context: Respiratory tract infection (RTI) is the leading cause of avoidable antimicrobial use in primary care. How the COVID-19 pandemic has impacted antibiotic prescribing practices across Canada is unknown. The purpose of this study was to examine rates of antibiotic prescribing for RTI in primary care during the first year of the pandemic (2020), compared to baseline in 2019. ⋯ In comparison, antibiotic prescribing for visits related to UTI increased marginally between 2019 and 2020 (71.6% vs. 72.3%, p=0.007). Conclusions: A significant decrease in antibiotic prescribing for RTI across primary care was observed during the first year of the COVID-19 pandemic, likely related to the changes in epidemiology and care delivery models in primary care. CPCSSN can provide pan-Canadian surveillance of antibiotic prescribing practices in primary care that can be used for provider feedback and quality improvement.
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Annals of family medicine · Jan 2023
Evaluation of Childhood Beverage Consumption and Perceptions of Water Safety.
Context: Children ages 1-3 should consume about 35oz of plain water per day. The CDC and AAP recommend that children under 12 months do not receive any juice and children over 12 months should receive no more than 4oz of 100% juice with no sugar-sweetened beverages (SSBs). SSB consumption is associated with overweight and obesity, as well as an increase of tooth decay. 22% of preschool children do not regularly drink plain water and 46% regularly drink SSBs. ⋯ Conclusions: Children drank more juice and SSBs and less water than recommended. Previous assessment suggests that families may be more open to beverage consumption changes that other lifestyle changes that prevent childhood obesity. Improved perceptions of tap water safety may be a possible mechanism to decrease juice and SSB and increase water consumption.
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Annals of family medicine · Jan 2023
Development and Design Needs of Mobile Health (mHealth) Apps for Adolescents.
Context: Adolescent obesity remains a significant public health issue within the United States. Application (app) technology growth and popularity offer new opportunities for research and health improvement. The development of a consolidated mobile health application (mHealth app) for adolescents on these platforms has the potential to improve health outcomes. ⋯ Outcomes: Adolescent stakeholder feedback is crucial in the successful development of an adolescent-targeted mHealth app. Adolescents prefer vibrant colors, modern, easy-to-use interface, gamification and rewards, customizable and personalized, simple, and mature graphics. Adolescents were especially motivated by gamification techniques in maintaining their interest in the application and their health behavior goals.
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Annals of family medicine · Jan 2023
Explainable Machine Learning Model to Predict COVID-19 Severity Among Older Adults in the Province of Quebec.
Context: Patients over the age of 65 years are more likely to experience higher severity and mortality rates than other populations from COVID-19. Clinicians need assistance in supporting their decisions regarding the management of these patients. Artificial Intelligence (AI) can help with this regard. ⋯ Conclusions: The use of explainable machine learning models, to predict the severity of COVID-19 among older adults is feasible. We obtained a high-performance level as well as explainability in the prediction of COVID-19 severity in this population. Further studies are required to integrate these models into a decision support system to facilitate the management of diseases such as COVID-19 for (primary) health care providers and evaluate their usability among them.