Process reconstruction from incomplete and/or inconsistent data

We analyze how an action of a qubit channel (map) can be estimated from the measured data that are incomplete or even inconsistent. That is, we consider situations when measurement statistics is insufficient to determine consistent probability distributions. As a consequence either the estimation (reconstruction) of the channel completely fails or it results in an unphysical channel (i.e., the corresponding map is not completely positive). We present a regularization procedure that allows us to derive physically reasonable estimates (approximations) of quantum channels. We illustrate our procedure on specific examples and we show that the procedure can be also used for a derivation of optimal approximations of operations that are forbidden by the laws of quantum mechanics (e.g., the universal NOT gate).Comment: 9pages, 5 figure

How is patient activation related to healthcare service utilisation? Evidence from electronic patient records in England

Background: There is increasing awareness of the importance of patient activation (knowledge, skills, and confidence for managing one’s health and health care) among clinicians and policy makers, with emerging evidence showing higher levels of patient activation are associated with better health outcomes and experiences of health care. This study aimed to examine the association between patient activation and a wide range of specific types of healthcare service utilisation in England, including GP and non-GP primary care, elective and emergency hospital admissions, outpatient visits, and attendances at the Accident and Emergency department. Methods: Data were derived from linked electronic patient records collected by primary and secondary healthcare providers in North West London between January 2016 and November 2019. Our analyses focused on adults (18+) with a valid Patient Activation Measure (PAM). After excluding patients with missing data, we had an analytical sample of 15,877 patients. Data were analysed using negative binomial regression and logistic regression models depending on the outcome variable. Results: Patients had a mean activation score of 55.1 and a standard deviation (SD) of 17.7 (range: 0–100). They had an average of 5.4 GP visits (SD = 8.0), 26.8 non-GP visits (SD = 23.4) and 6.0 outpatient attendances (SD = 7.9) within a one-year follow-up. About 24.7% patients had at least one elective admission, 24.2% had one or more emergency admissions, and 42.3% had one or more A&E attendance within the follow-up. After accounting for a number of demographic and health factors, we found a linear (or proximately linear) association between patient activation and the number of GP visits, emergency admissions and A&E attendance, but a non-linear relationship between patient activation and the number of non-GP visits, the number of outpatient attendance and elective inpatient admission. Conclusions: This study has provided strong empirical evidence from England linking patient activation with healthcare service utilisation. It suggests the value of supporting patient activation as a potential pathway to ease the burden of healthcare system

Field testing of young breeding pigs. II. – The accuracy of field testing

International audienc

Relationship between loneliness, social isolation and modifiable risk factors for cardiovascular disease: a latent class analysis

Background: There is growing research into the effects of psychological and social factors such as loneliness and isolation on cardiovascular disease (CVD). However, it is unclear whether individuals with particular clusters of CVD risk factors are more strongly affected by loneliness and isolation. This study aimed to identify latent clustering of modifiable risk factors among adults aged 50+ and explore the relationship between loneliness, social isolation and risk factor patterns. Methods: Data from 8218 adults of English Longitudinal Study of Ageing were used in latent class analyses to identify latent classes of cardiovascular risk factors and predictors of class membership. Results: There were four latent classes: low-risk (30.2%), high-risk (15.0%), clinical-risk (42.6%) and lifestyle-risk (12.2%) classes. Loneliness was associated with a greater risk of being in the high-risk class (relative risk ratio (RRR) 2.40, 95%CI 2.40 to 1.96) and lifestyle-risk class (RRR 1.36, 95%CI 1.10 to 1.67) and a lower risk of being in the clinical-risk class (RRR 0.84, 95%CI 0.72 to 0.98) relative to the low-risk class. Social disengagement, living alone and low social contact were also differentially associated with latent class memberships. Conclusion: These findings supplement our existing knowledge of modifiable risk factors for CVD by showing how risk factors cluster together and how the risk patterns are related to social factors, offering important implications for clinical practice and preventive intervention

Diffusion copulas: Identification and estimation

We propose a new semiparametric approach for modelling nonlinear univariate diffusions, where the observed process is a nonparametric transformation of an underlying parametric diffusion (UPD). This modelling strategy yields a general class of semiparametric Markov diffusion models with parametric dynamic copulas and nonparametric marginal distributions. We provide primitive conditions for the identification of the UPD parameters together with the unknown transformations from discrete samples. Likelihood-based estimators of both parametric and nonparametric components are developed and we analyse their asymptotic properties. Kernel-based drift and diffusion estimators are also proposed and shown to be normally distributed in large samples. A simulation study investigates the finite sample performance of our estimators in the context of modelling US short-term interest rates. We also present a simple application of the proposed method for modelling the CBOE volatility index data

Loneliness and Risk for Cardiovascular Disease: Mechanisms and Future Directions

PURPOSE OF REVIEW: In this review, we synthesise recent research on the association between loneliness and cardiovascular disease (CVD). We present evidence for mechanisms underlying this association and propose directions for future research. RECENT FINDINGS: Loneliness is related to increased risk of early mortality and CVD comparable to other well-established risk factors such as obesity or smoking. Loneliness has been linked to higher rates of incident CVD, poorer CVD patient outcomes, and early mortality from CVD. Loneliness likely affects risk for these outcomes via health-related behaviours (e.g. physical inactivity and smoking), biological mechanisms (e.g. inflammation, stress reactivity), and psychological factors (e.g. depression) to indirectly damage health

Loneliness during a strict lockdown: Trajectories and predictors during the COVID-19 pandemic in 38,217 United Kingdom adults

Rationale: There are increasing worries that lockdowns and “stay-at-home” orders due to the COVID-19 pandemic could lead to a rise in loneliness, which is recognised as a major public health concern. But profiles of loneliness during the pandemic and risk factors remain unclear. / Objective: The current study aimed to examine if and how loneliness levels changed during the strict lockdown and to explore the clustering of loneliness growth trajectories. / Methods: Data from 38,217 UK adults in the UCL COVID -19 Social Study (a panel study collecting data weekly during the pandemic) were analysed during the strict lockdown period in the UK (23/03/2020-10/05/2020). The sample was well-stratified and weighted to population proportions of gender, age, ethnicity, education and geographical location. Growth mixture modelling was used to identify the latent classes of loneliness growth trajectories and their predictors. / Results: Analyses revealed four classes, with the baseline loneliness level ranging from low to high. In the first a few weeks of lockdown, loneliness levels increased in the highest loneliness group, decreased in the lowest loneliness group, and stayed relatively constant in the middle two groups. Younger adults (OR=2.17-6.81), women (OR=1.59), people with low income (OR=1.3), the economically inactive (OR=1.3-2.04) and people with mental health conditions (OR=5.32) were more likely to be in highest loneliness class relative to the lowest. Further, living with others or in a rural area, and having more close friends or greater social support were protective. / Conclusions: Perceived levels of loneliness under strict lockdown measures due to COVID-19 were relatively stable in the UK, but for many people these levels were high with no signs of improvement. Results suggest that more efforts are needed to address loneliness

Rates and predictors of uptake of mental health support during the COVID-19 pandemic: an analysis of 26,720 adults in the UK in lockdown

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has put a great strain on people's mental health. A growing number of studies have shown worsening mental health measures globally during the pandemic. However, there is a lack of empirical study on how people support their mental health during the COVID-19 pandemic. This study aimed to examine a number of formal and informal mental health support. Further, it explored factors that might be associated with the use of different types mental health support. METHODS: Data from 26,720 adults in the UCL COVID-19 Social Study were analysed between 13th April 2020 and 3rd July 2020. Data were analysed using logistic and Poisson regression models. RESULTS: About 45% of people reported talking to friends or family members to support their mental health, 43% engaging in self-care activities, 20% taking medication, 9% speaking to mental health professionals, 8% talking to a GP or other health professional, and another 8% using helpline or online services. Gender, education, living status, loneliness, pre-existing mental health conditions, general depression and anxiety, coping and personality were found to be associated with the use of mental health support. CONCLUSION: While the negative impacts caused by the COVID-19 pandemic are inevitable, people can play an active role in managing their mental health. Understanding the patterns and predictors of various kinds of mental health support during the pandemic is crucial for future service planning and delivery through recognising potential barriers to mental health care faced by certain groups

Social isolation and loneliness as risk factors for hospital admissions for respiratory disease among older adults

Rising hospital admissions due to respiratory disease (RD) are a major challenge to hospitals. This study explored modifiable social risk factors among 4478 older adults from the English Longitudinal Study of Ageing. Data were linked with administrative hospital records and mortality registry data (follow-up 9.6 years) and analysed using survival analysis accounting for competing risks. Living alone and social disengagement but not social contact or loneliness were associated with an increased risk of RD admissions, independent of socio-demographic, health and behaviour factors. Providing support for disengaged adults living alone who are at risk of RD admissions should be explored