Pihl Lausten (stringarm74)

Since March 11, 2020 when the World Health Organization (WHO) declared the COVID-19 pandemic, the number of infected cases, the number of deaths, and the number of affected countries have climbed rapidly. To understand the impact of COVID-19 on public health, many studies have been conducted for various countries. To complement the available work, in this article we examine Canadian COVID-19 data for the period of March 18, 2020 to August 16, 2020 with the aim to forecast the dynamic trend in a short term. We focus our attention on Canadian data and analyze the four provinces, Ontario, Alberta, British Columbia, and Quebec, which have the most severe situations in Canada. To build predictive models and conduct prediction, we employ three models, smooth transition autoregressive (STAR) models, neural network (NN) models, and susceptible-infected-removed (SIR) models, to fit time series data of confirmed cases in the four provinces separately. In comparison, we also analyze the data of daily infections in ta dated from March 18, 2020 to August 11, 2020 show that the STAR, NN, and SIR models may output different results, though the differences are small in some cases. Prediction over a short term period incurs smaller prediction variability than over a long term period, as expected. The NN method tends to outperform other two methods. All the methods forecast an upward trend in all the four Canadian provinces for the period of August 12, 2020 to August 23, 2020, though the degree varies from method to method. This research offers model-based insights into the pandemic evolvement in Canada. Polypharmacy is potentially harmful and under-researched amongst the fastest growing subpopulation, the very old (aged ≥85). read more We aimed to characterise polypharmacy using data from the Newcastle 85+ Study-a prospective cohort of people born in 1921 who turned 85 in 2006 (n = 845). The prevalence of polypharmacy at baseline (mean age 85.5) was examined using cut-points of 0, 1, 2-4, 5-9 and ≥10 medicines-so-called 'no polypharmacy', 'monotherapy', 'minor polypharmacy', 'polypharmacy' and 'hyperpolypharmacy.' Cross-tabulations and upset plots identified the most frequently prescribed medicines and medication combinations within these categories. Mixed-effects models assessed whether gender and socioeconomic position were associated with prescribing changes over time (mean age 85.5-90.5). Participant characteristics were examined through descriptive statistics. Complex multimorbidity (44.4%, 344/775) was widespread but hyperpolypharmacy was not (16.0%, 135/845). The median medication count was six (interquaring in North East England. Prescribing shows significant gender and selected socioeconomic differences. More support for managing preventative medicines, of uncertain benefit, might be helpful in this population. Hemp (Cannabis sativa subsp. sativa), commonly used for industrial purposes, is now being consumed by the public for various health promoting effects. As popularity of hemp research and claims of beneficial effects rises, a systematic collection of current scientific evidence on hemp's health effects and pharmacological properties is needed to guide future research, clinical, and policy decision making. To provide an overview and identify the present landscape of hemp research topics, trends, and gaps. A systematic search and analysis strategy according to the preferred reporting items for systematic review and meta-analysis-ScR (PRISMA-ScR) checklist on electronic databases including MEDLINE, OVID (OVFT, APC Journal Club, EBM Reviews), Cochrane Library Central and Clinicaltrials.gov was conducted to include and analyse hemp research articles from 2009 to 2019. 65 primary articles (18 clinical, 47 pre-clinical) were reviewed. Several randomised controlled trials showed hempseed pills (in Traditional Cfic interventions are still preliminary,