Bmc Med
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The coronavirus disease 2019 (COVID-19) has been a pandemic worldwide. Old age and underlying illnesses are associated with poor prognosis among COVID-19 patients. However, whether frailty, a common geriatric syndrome of reduced reserve to stressors, is associated with poor prognosis among older COVID-19 patients is unknown. The aim of our study is to investigate the association between frailty and severe disease among COVID-19 patients aged ≥ 60 years. ⋯ Frailty, assessed by the FRAIL scale, was associated with a higher risk of developing severe disease among older COVID-19 patients. Our findings suggested that the use of a clinician friendly assessment of frailty could help in early warning of older patients at high-risk with severe COVID-19 pneumonia.
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Results from clinical trials can be susceptible to bias if investigators choose their analysis approach after seeing trial data, as this can allow them to perform multiple analyses and then choose the method that provides the most favourable result (commonly referred to as 'p-hacking'). Pre-specification of the planned analysis approach is essential to help reduce such bias, as it ensures analytical methods are chosen in advance of seeing the trial data. For this reason, guidelines such as SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and ICH-E9 (International Conference for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) require the statistical methods for a trial's primary outcome be pre-specified in the trial protocol. ⋯ Because there are many different ways to perform multiple imputation, this approach to pre-specification is ineffective, as it still allows investigators to analyse the data in different ways before deciding on a final approach. In this article, we describe a five-point framework (the Pre-SPEC framework) for designing a pre-specified analysis approach that does not allow p-hacking. This framework was designed based on the principles in the SPIRIT and ICH-E9 guidelines and is intended to be used in conjunction with these guidelines to help investigators design the statistical analysis strategy for the trial's primary outcome in the trial protocol.
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Randomized Controlled Trial
Cost-effectiveness of a lifestyle intervention in high-risk individuals for diabetes in a low- and middle-income setting: Trial-based analysis of the Kerala Diabetes Prevention Program.
Data on the cost-effectiveness of lifestyle-based diabetes prevention programs are mostly from high-income countries, which cannot be extrapolated to low- and middle-income countries. We performed a trial-based cost-effectiveness analysis of a lifestyle intervention targeted at preventing diabetes in India. ⋯ A community-based peer-support lifestyle intervention was cost-effective in individuals at high risk of developing diabetes in India over 2 years.
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New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. ⋯ Our findings indicate associations between neighborhoods with a large dependent youth population, densely populated, low-income, and predominantly black neighborhoods and COVID-19 test positivity rate. The study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the positivity rate, either in terms of the true number of cases or access to testing.