Frailty and the impacts of the COVID-19 pandemic on community-living middle-aged and older adults:an analysis of data from the Canadian Longitudinal Study on Aging (CLSA)

BACKGROUND: frailty imparts a higher risk for hospitalisation, mortality and morbidity due to COVID-19 infection, but the broader impacts of the pandemic and associated public health measures on community-living people with frailty are less known. METHODS: we used cross-sectional data from 23,974 Canadian Longitudinal Study on Aging participants who completed a COVID-19 interview (Sept-Dec 2020). Participants were included regardless of whether they had COVID-19 or not. They were asked about health, resource, relationship and health care access impacts experienced during the pandemic. Unadjusted and adjusted prevalence of impacts was estimated by frailty index quartile. We further examined if the relationship with frailty was modified by sex, age or household income. RESULTS: community-living adults (50-90 years) with greater pre-pandemic frailty reported more negative impacts during the first year of the pandemic. The frailty gradient was not explained by socio-demographic or health behaviour factors. The largest absolute difference in adjusted prevalence between the most and least frail quartiles was 15.1% (challenges accessing healthcare), 13.3% (being ill) and 7.4% (increased verbal/physical conflict). The association between frailty and healthcare access differed by age where the youngest age group tended to experience the most challenges, especially for those categorised as most frail. CONCLUSION: although frailty has been endorsed as a tool to inform estimates of COVID-19 risk, our data suggest it may have a broader role in primary care and public health by identifying people who may benefit from interventions to reduce health and social impacts of COVID-19 and future pandemics.</p

Kendall's tau estimator for bivariate zero-inflated count data

This paper extends the work of Pimentel et al. (2015), presenting an estimator of Kendall's τ for bivariate zero-inflated count data. We provide achievable bounds of our proposed estimator and suggest how to estimate them, thereby making the estimator useful in practice.</p

Joint modeling with time-dependent treatment and heteroskedasticity: Bayesian analysis with application to the Framingham Heart Study

Medical studies for chronic disease are often interested in the relation between longitudinal risk factor profiles and individuals' later life disease outcomes. These profiles may typically be subject to intermediate structural changes due to treatment or environmental influences. Analysis of such studies may be handled by the joint model framework. However, current joint modeling does not consider structural changes in the residual variability of the risk profile nor consider the influence of subject-specific residual variability on the time-to-event outcome. In the present paper, we extend the joint model framework to address these two heterogeneous intra-individual variabilities. A Bayesian approach is used to estimate the unknown parameters and simulation studies are conducted to investigate the performance of the method. The proposed joint model is applied to the Framingham Heart Study to investigate the influence of anti-hypertensive medication on the systolic blood pressure variability together with its effect on the risk of developing cardiovascular disease. We show that anti-hypertensive medication is associated with elevated systolic blood pressure variability and increased variability elevates risk of developing cardiovascular disease.Comment: 34 pages, 4 figure

Mitigating the Cost of Anarchy in Supply Chain Systems

In a decentralized two-stage supply chain where a supplier serves a retailer who, in turn, serves end customers, operations decisions based on local incentives often lead to suboptimal system performance. Operating decisions based on local incentives may in such cases lead to a degree of system disorder or anarchy, wherein one party's decisions put the other party and/or the system at a disadvantage. While models and mechanisms for such problem classes have been considered in the literature, little work to date has considered such problems under nonstationary demands and fixed replenishment order costs. This paper models such two-stage problems as a class of Stackelberg games where the supplier announces a set of time-phased ordering costs to the retailer over a discrete time horizon of finite length, and the retailer then creates an order plan, which then serves as the supplier's demand. We provide metrics for characterizing the degree of efficiency (and anarchy) associated with a solution, and provide a set of easily understood and implemented mechanisms that can increase this efficiency and reduce the negative impacts of anarchic decisions

Model stability of COVID-19 mortality prediction with biomarkers

Coronavirus disease 2019 (COVID-19) is an unprecedented and fast evolving pandemic, which has caused a large number of critically ill patients and deaths globally. It is an acute public health crisis leading to overloaded critical care capacity. Timely prediction of the clinical outcome (death/survival) of hospital-admitted COVID-19 patients can provide early warnings to clinicians, allowing improved allocation of medical resources. In a recently published paper, an interpretable machine learning model was presented to predict the mortality of COVID-19 patients with blood biomarkers, where the model was trained and tested on relatively small data sets. However, the model or performance stability was not explored and assessed. By re-analyzing the data, we reveal that the reported mortality prediction performance was likely over-optimistic and its uncertainty was underestimated or overlooked, with a large variability in predicting deaths

Sharp Inequalities of Bienaym\'e-Chebyshev and Gau\ss Type for Possibly Asymmetric Intervals around the Mean

Gau\ss (1823) proved a sharp upper bound on the probability that a random variable falls outside a symmetric interval around zero when its distribution is unimodal with mode at zero. For the class of all distributions with mean at zero, Bienaym\'e (1853) and Chebyshev (1867) independently provided another, simpler sharp upper bound on this probability. For the same class of distributions, Cantelli (1928) obtained a strict upper bound for intervals that are a half line. We extend these results to arbitrary intervals for six classes of distributions, namely the general class of `distributions', the class of `symmetric distributions', of `concave distributions', of `unimodal distributions', of `unimodal distributions with coinciding mode and mean', and of `symmetric unimodal distributions'. For some of the known inequalities, such as the Gau\ss \, inequality, an alternative proof is given.Comment: 33 pages, 1 tabl