Adaptive MC^3 and Gibbs algorithms for Bayesian Model Averaging in Linear Regression Models

The MC$^3$ (Madigan and York, 1995) and Gibbs (George and McCulloch, 1997) samplers are the most widely implemented algorithms for Bayesian Model Averaging (BMA) in linear regression models. These samplers draw a variable at random in each iteration using uniform selection probabilities and then propose to update that variable. This may be computationally inefficient if the number of variables is large and many variables are redundant. In this work, we introduce adaptive versions of these samplers that retain their simplicity in implementation and reduce the selection probabilities of the many redundant variables. The improvements in efficiency for the adaptive samplers are illustrated in real and simulated datasets

Different Patterns of Association of Body Fat Percent and Body Mass Index with Pre-adolescent and Adolescent Asthma: a Cross-sectional Study Amongst Cypriot Schoolchildren

IEA-EEF European Congress of Epidemiology, Porto, Portugal, September 201

Predictors of changing patterns of adherence to containment measures during the early stage of COVID-19 pandemic: an international longitudinal study

BackgroundIdentifying common factors that affect public adherence to COVID-19 containment measures can directly inform the development of official public health communication strategies. The present international longitudinal study aimed to examine whether prosociality, together with other theoretically derived motivating factors (self-efficacy, perceived susceptibility and severity of COVID-19, perceived social support) predict the change in adherence to COVID-19 containment strategies.MethodIn wave 1 of data collection, adults from eight geographical regions completed online surveys beginning in April 2020, and wave 2 began in June and ended in September 2020. Hypothesized predictors included prosociality, self-efficacy in following COVID-19 containment measures, perceived susceptibility to COVID-19, perceived severity of COVID-19 and perceived social support. Baseline covariates included age, sex, history of COVID-19 infection and geographical regions. Participants who reported adhering to specific containment measures, including physical distancing, avoidance of non-essential travel and hand hygiene, were classified as adherence. The dependent variable was the category of adherence, which was constructed based on changes in adherence across the survey period and included four categories: non-adherence, less adherence, greater adherence and sustained adherence (which was designated as the reference category).ResultsIn total, 2189 adult participants (82% female, 57.2% aged 31-59 years) from East Asia (217 [9.7%]), West Asia (246 [11.2%]), North and South America (131 [6.0%]), Northern Europe (600 [27.4%]), Western Europe (322 [14.7%]), Southern Europe (433 [19.8%]), Eastern Europe (148 [6.8%]) and other regions (96 [4.4%]) were analyzed. Adjusted multinomial logistic regression analyses showed that prosociality, self-efficacy, perceived susceptibility and severity of COVID-19 were significant factors affecting adherence. Participants with greater self-efficacy at wave 1 were less likely to become non-adherence at wave 2 by 26% (adjusted odds ratio [aOR], 0.74; 95% CI, 0.71 to 0.77; P < .001), while those with greater prosociality at wave 1 were less likely to become less adherence at wave 2 by 23% (aOR, 0.77; 95% CI, 0.75 to 0.79; P = .04).ConclusionsThis study provides evidence that in addition to emphasizing the potential severity of COVID-19 and the potential susceptibility to contact with the virus, fostering self-efficacy in following containment strategies and prosociality appears to be a viable public health education or communication strategy to combat COVID-19

Editorial : emerging markets' health and pharmaceutical sectors at the dawn of a potential global financial crisis of early 2020s

Essential pharmaceutical innovation in terms of market placement of new chemical entities featuring medicines with novel mechanisms continue to be dominated by Pharmaceutical multinational companies (Sadat Russel et al, 2014). This is gradually changing with the growth of emerging biopharma companies launching their new products rather than being bought over by major Pharma Companies (IQVIA Report 2022)

Impact of autoimmune thyroiditis on primary hyperparathyroidism

Aim. Primary hyperparathyroidism (PHPT) often coexists with thyroid diseases. Current guidelines advise preoperative ultrasound (US) examination of the thyroid gland for thyroid nodular disease or concomitant malignancy but not evaluation for autoimmune thyroiditis (AIT). The impact of autoimmune thyroiditis on the clinical presentation and intraoperative course of PHPT is not clear. Material and methods. We retrospectively assessed the medical records of 21 patients with PHPT who underwent parathyroidectomy. Clinical, biochemical, ultrasonographic and intraoperative data were evaluated. Results. There was a longer duration of parathyroidectomy in patients with AIT than in those without (113.3 min vs. 93.9 min, P=0.03). A lower rate of kidney stones was noted in patients with autoimmune thyroiditis (44.4% vs. 0%, P=0.03). Patients with AIT were more symptomatic, but this was not significant. There was no difference between the two groups in the prevalence of osteoporosis or thyroid nodular disease. Conclusions. A significantly longer duration of parathyroidectomy was seen in PHPT patients with AIT. Patients with PHPT undergoing surgery should be investigated for autoimmune thyroiditis, as this may affect surgical planning

A cross-sectional study of explainable machine learning in Alzheimer’s disease: diagnostic classification using MR radiomic features

IntroductionAlzheimer’s disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD.MethodsIn this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated.ResultsThe model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively.DiscussionThese directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD

Illness Perceptions of COVID-19 in Europe: Predictors, Impacts and Temporal Evolution

Objective: Illness perceptions (IP) are important predictors of emotional and behavioral responses in many diseases. The current study aims to investigate the COVID-19-related IP throughout Europe. The specific goals are to understand the temporal development, identify predictors (within demographics and contact with COVID-19) and examine the impacts of IP on perceived stress and preventive behaviors. Methods: This was a time-series-cross-section study of 7,032 participants from 16 European countries using multilevel modeling from April to June 2020. IP were measured with the Brief Illness Perception Questionnaire. Temporal patterns were observed considering the date of participation and the date recoded to account the epidemiological evolution of each country. The outcomes considered were perceived stress and COVID-19 preventive behaviors. Results: There were significant trends, over time, for several IP, suggesting a small decrease in negativity in the perception of COVID-19 in the community. Age, gender, and education level related to some, but not all, IP. Considering the self-regulation model, perceptions consistently predicted general stress and were less consistently related to preventive behaviors. Country showed no effect in the predictive model, suggesting that national differences may have little relevance for IP, in this context. Conclusion: The present study provides a comprehensive picture of COVID-19 IP in Europe in an early stage of the pandemic. The results shed light on the process of IP formation with implications for health-related outcomes and their evolution

Impact of COVID-19 pandemic on mental health: An international study

Background The COVID-19 pandemic triggered vast governmental lockdowns. The impact of these lockdowns on mental health is inadequately understood. On the one hand such drastic changes in daily routines could be detrimental to mental health. On the other hand, it might not be experienced negatively, especially because the entire population was affected. Methods The aim of this study was to determine mental health outcomes during pandemic induced lockdowns and to examine known predictors of mental health outcomes. We therefore surveyed n = 9,565 people from 78 countries and 18 languages. Outcomes assessed were stress, depression, affect, and wellbeing. Predictors included country, sociodemographic factors, lockdown characteristics, social factors, and psychological factors. Results Results indicated that on average about 10% of the sample was languishing from low levels of mental health and about 50% had only moderate mental health. Importantly, three consistent predictors of mental health emerged: social support, education level, and psychologically flexible (vs. rigid) responding. Poorer outcomes were most strongly predicted by a worsening of finances and not having access to basic supplies. Conclusions These results suggest that on whole, respondents were moderately mentally healthy at the time of a population-wide lockdown. The highest level of mental health difficulties were found in approximately 10% of the population. Findings suggest that public health initiatives should target people without social support and those whose finances worsen as a result of the lockdown. Interventions that promote psychological flexibility may mitigate the impact of the pandemic