Association between religion, mental health and social distancing during the Covid-19 pandemic / Associação entre religião, saúde mental e distanciamento social na pandemia de Covid-19 / Asociación entre religión, salud mental y distanciamiento social en la pandemia Covid-19

This cross-sectional study aimed to analyze the association of religiosity with behaviors and perceptions in the context of social distancing measures during the COVID-19 pandemic, as well as mental health outcomes, in a university community in Central-West Brazil. A sample of 1,796 subjects responded to an online form with socio-demographic questions and the DASS-21 and PWBS scales. Religion was associated with the frequency of interactions, perceptions of the duration of the social distancing measures, changes in emotional state and history of psychological illness. The prevalence of symptoms of depression, anxiety and stress was lower among people with religion and their scores in psychological well-being were higher. (AU)

Trata-se de um estudo transversal com o objetivo de analisar a associação entre religiosidade e comportamentos e percepções frente ao distanciamento social decorrente da pandemia de Covid-19 e estado de saúde mental em uma comunidade universitária do Centro-Oeste brasileiro. Participaram do estudo 1796 sujeitos, os quais responderam a um formulário online com questões sociodemográficas e às escalas DASS-21 e EBEP. Observou-se associação entre religião e frequência de interações, percepção sobre a duração do distanciamento social e mudanças no estado emocional e histórico de alterações psicológicas. Constatou-se menor prevalência de sintomas relacionados à depressão, à ansiedade e ao estresse e maiores escores de bem-estar psicológico entre aqueles com religião. (AU)

Se trata de un estudio transversal con el objetivo de analizar la asociación entre religiosidad, comportamientos y percepciones frente al distanciamiento social resultante de la pandemia Covid-19 y el estado de salud mental en una comunidad universitaria de la región Centro-Oeste de Brasil. Participaron en el estudio 1796 sujetos, que respondieron un formulario en línea con preguntas sociodemográficas y las escalas DASS-21 y EBEP. Se observó asociación entre religión y frecuencia de interacciones, la percepción de la duración del aislamiento social y los cambios en el estado emocional y el historial de cambios psicológicos. Se observó una menor prevalencia de síntomas relacionados con la depresión, la ansiedad y el estrés, y puntuaciones más altas de bienestar psicológico entre quienes profesaban una religión. (AU)

Deviation in the Age Structure of Mortality as an Indicator of COVID-19 Pandemic Severity.

Objectives. To test whether distortions in the age distribution of deaths can track pandemic activity. Methods. We compared weekly distributions of all-cause deaths by age during the COVID-19 pandemic in the United States from March to December 2020 with corresponding prepandemic weekly baseline distributions derived from data for 2015 to 2019. We measured distortions via Kolmogorov-Smirnov (K-S) and χ2 goodness-of-fit statistics as well as deaths among individuals aged 65 years or older as a percentage of total deaths (PERC65+). We computed bivariate correlations between these measures and the number of recorded COVID-19 deaths for the corresponding weeks. Results. Elevated COVID-19-associated fatalities were accompanied by greater distortions in the age structure of mortality. Distortions in the age distribution of weekly US COVID-19 deaths in 2020 relative to earlier years were highly correlated with COVID fatalities (K-S: r = 0.71, P < .001; χ2: r = 0.90, P < .001; PERC65+: r = 0.85, P < .001). Conclusions. A population-representative sample of age-at-death data can serve as a useful means of pandemic activity surveillance when precise cause-of-death data are incomplete, inaccurate, or unavailable, as is often the case in low-resource environments. (Am J Public Health. 2022;112(1):165-168. https://doi.org/10.2105/AJPH.2021.306567).

The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model.

Happiness levels often fluctuate from one day to the next, and an exogenous shock such as a pandemic can likely disrupt pre-existing happiness dynamics. This paper fits a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state's mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we use the one-step method to predict the unobserved states' evolution over time. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand's daily happiness data for May 2019 -November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequency of time periods with a probability of being unhappy in 2020 mostly correspond to pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, lack of mobility is significantly and negatively related to the probability of being happy.

Rice quality and its impacts on food security and sustainability in Bangladesh.

Rice market efficiency is important for food security in countries where rice is a staple. We assess the impact of rice quality on rice prices, food security, and environmental sustainability in Bangladesh. We find that while price varies as expected for most quality attributes, it is unaffected by a broken percentage below 24.9 percent. This reveals a potential inefficiency, considering the average 5 percent broken rate observed in the market. An increase in the broken rate of milled rice within the limits supported by our findings can, ceteris paribus, increase rice rations by 4.66 million a year, or conversely, yield the current number of rice rations using 170.79 thousand fewer hectares and cutting emissions by 1.48 million metric tons of CO2 equivalent. Thus, producing rice based on quality assessment can improve food security and its sustainability.

Racial/Ethnic and Age Differences in the Direct and Indirect Effects of the COVID-19 Pandemic on US Mortality.

Objectives. To estimate the direct and indirect effects of the COVID-19 pandemic on overall, race/ethnicity‒specific, and age-specific mortality in 2020 in the United States. Methods. Using surveillance data, we modeled expected mortality, compared it to observed mortality, and estimated the share of "excess" mortality that was indirectly attributable to the pandemic versus directly attributed to COVID-19. We present absolute risks and proportions of total pandemic-related mortality, stratified by race/ethnicity and age. Results. We observed 16.6 excess deaths per 10 000 US population in 2020; 84% were directly attributed to COVID-19. The indirect effects of the pandemic accounted for 16% of excess mortality, with proportions as low as 0% among adults aged 85 years and older and more than 60% among those aged 15 to 44 years. Indirect causes accounted for a higher proportion of excess mortality among racially minoritized groups (e.g., 32% among Black Americans and 23% among Native Americans) compared with White Americans (11%). Conclusions. The effects of the COVID-19 pandemic on mortality and health disparities are underestimated when only deaths directly attributed to COVID-19 are considered. An equitable public health response to the pandemic should also consider its indirect effects on mortality. (Am J Public Health. 2022;112(1):154-164. https://doi.org/10.2105/AJPH.2021.306541).

Comparison of Five Methods to Estimate the Parameters for the Three-Parameter Lindley Distribution with Application to Life Data.

We have studied one of the most common distributions, namely, Lindley distribution, which is an important continuous mixed distribution with great ability to represent different systems. We studied this distribution with three parameters because of its high flexibility in modelling life data. The parameters were estimated by five different methods, namely, maximum likelihood estimation, ordinary least squares, weighted least squares, maximum product of spacing, and Cramér-von Mises. Simulation experiments were performed with different sample sizes and different parameter values. The different methods were compared on the generated data by mean square error and mean absolute error. In addition, we compared the methods for real data, which represent COVID-19 data in Iraq/Anbar Province.

Impacts of COVID-19 on rural livelihoods in Bangladesh: Evidence using panel data.

Rapid assessments have been emerging on the effects of COVID-19, yet rigorous analyses remain scant. Here, rigorous evidence of the impacts of COVID-19 on several livelihood outcomes are presented, with a particular focus on heterogenous effects of COVID-19. We use a household-level panel dataset consisting of 880 data points collected in rural Bangladesh in 2018 and 2020, and employ difference-in-differences with fixed effects regression techniques. Results suggest that COVID-19 had significant and heterogenous effects on livelihood outcomes. Agricultural production and share of production sold were reduced, especially for rice crops. Further, diet diversity and education expenditure were reduced for the total sample. Households primarily affected by (fear of) sickness had a significantly lower agricultural production, share of crop market sales, and lower health and education expenditure, compared to households affected by other COVID-19 effects, such as travel restrictions. In turn, (fear of) sickness and the correlated reduced incidence of leaving the house, resulted in higher off-farm incomes suggesting that households engage in less physically demanding and localized work. Policy-makers need to be cognizant of these heterogenous COVID-19 effects and formulate policies that are targeted at those households that are most vulnerable (e.g., unable/willing to leave the house due to (fear of) sickness).

An equation to calculate UPDRS motor severity for online and rural assessments of Parkinson's.

OBJECTIVE: Management of PD has largely been affected by COVID-19. Due to the restrictions posed by COVID-19, there has been a shift from in-person to online forms of assessment. This presents a challenge as not all motor symptoms can be assessed virtually. Two of the four cardinal symptoms of PD (rigidity and postural instability) cannot be assessed virtually using the gold-standard Unified Parkinson's Disease Rating Scale (UPDRS-III). As a result, an accurate total motor severity score can not be computed from the remaining subsections. Recently, one study stated that in order for accurate scores to be calculated, only three sections could be absent. Virtually, six sections are unable to be evaluated with online assessments. This inability to compute a total motor severity score may result in poor disease management. Thus, in this study a regression equation was developed to predict total motor severity scores from partial scores. METHODS: Total motor severity scores (UPDRS-III) from N = 234 individuals with idiopathic Parkinson's were retrospectively analyzed. In order to conduct a linear regression analysis predictor and outcome variables were created. The variables were then used for the linear regression. The equation was then tested on an independent data set N = 1168. RESULTS: The regression analysis resulted in the equation to predict total motor symptom severity of PD. CONCLUSIONS: In conclusion, the developed equation will be very useful for outreach in rural communities, as well as the continued remote management of PD during COVID-19 and beyond.

A New Clinical Model to Estimate the Pre-Test Probability of Cushing's Syndrome: The Cushing Score.

Background: Hypercortisolism accounts for relevant morbidity and mortality and is often a diagnostic challenge for clinicians. A prompt diagnosis is necessary to treat Cushing's syndrome as early as possible. Objective: The aim of this study was to develop and validate a clinical model for the estimation of pre-test probability of hypercortisolism in an at-risk population. Design: We conducted a retrospective multicenter case-control study, involving five Italian referral centers for Endocrinology (Turin, Messina, Naples, Padua and Rome). One hundred and fifty patients affected by Cushing's syndrome and 300 patients in which hypercortisolism was excluded were enrolled. All patients were evaluated, according to current guidelines, for the suspicion of hypercortisolism. Results: The Cushing score was built by multivariable logistic regression, considering all main features associated with a clinical suspicion of hypercortisolism as possible predictors. A stepwise backward selection algorithm was used (final model AUC=0.873), then an internal validation was performed through ten-fold cross-validation. Final estimation of the model performance showed an average AUC=0.841, thus reassuring about a small overfitting effect. The retrieved score was structured on a 17.5-point scale: low-risk class (score value: ≤5.5, probability of disease=0.8%); intermediate-low-risk class (score value: 6-8.5, probability of disease=2.7%); intermediate-high-risk class (score value: 9-11.5, probability of disease=18.5%) and finally, high-risk class (score value: ≥12, probability of disease=72.5%). Conclusions: We developed and internally validated a simple tool to determine pre-test probability of hypercortisolism, the Cushing score, that showed a remarkable predictive power for the discrimination between subjects with and without a final diagnosis of Cushing's syndrome.

Key factors affecting people's unwillingness to be confined during the COVID-19 pandemic in Spain: a large-scale population study.

Population confinements have been one of the most widely adopted non-pharmaceutical interventions (NPIs) implemented by governments across the globe to help contain the spread of the SARS-CoV-2 virus. While confinement measures have been proven to be effective to reduce the number of infections, they entail significant economic and social costs. Thus, different policy makers and social groups have exhibited varying levels of acceptance of this type of measures. In this context, understanding the factors that determine the willingness of individuals to be confined during a pandemic is of paramount importance, particularly, to policy and decision-makers. In this paper, we study the factors that influence the unwillingness to be confined during the COVID-19 pandemic by the means of a large-scale, online population survey deployed in Spain. We perform two types of analyses (logistic regression and automatic pattern discovery) and consider socio-demographic, economic and psychological factors, together with the 14-day cumulative incidence per 100,000 inhabitants. Our analysis of 109,515 answers to the survey covers data spanning over a 5-month time period to shed light on the impact of the passage of time. We find evidence of pandemic fatigue as the percentage of those who report an unwillingness to be in confinement increases over time; we identify significant gender differences, with women being generally less likely than men to be able to sustain long-term confinement of at least 6 months; we uncover that the psychological impact was the most important factor to determine the willingness to be in confinement at the beginning of the pandemic, to be replaced by the economic impact as the most important variable towards the end of our period of study. Our results highlight the need to design gender and age specific public policies, to implement psychological and economic support programs and to address the evident pandemic fatigue as the success of potential future confinements will depend on the population's willingness to comply with them.

Obtaining Prevalence Estimates of Coronavirus Disease 2019: A Model to Inform Decision-Making.

We evaluated whether randomly sampling and testing a set number of individuals for coronavirus disease 2019 (COVID-19) while adjusting for misclassification error captures the true prevalence. We also quantified the impact of misclassification error bias on publicly reported case data in Maryland. Using a stratified random sampling approach, 50,000 individuals were selected from a simulated Maryland population to estimate the prevalence of COVID-19. We examined the situation when the true prevalence is low (0.07%-2%), medium (2%-5%), and high (6%-10%). Bayesian models informed by published validity estimates were used to account for misclassification error when estimating COVID-19 prevalence. Adjustment for misclassification error captured the true prevalence 100% of the time, irrespective of the true prevalence level. When adjustment for misclassification error was not done, the results highly varied depending on the population's underlying true prevalence and the type of diagnostic test used. Generally, the prevalence estimates without adjustment for misclassification error worsened as the true prevalence level increased. Adjustment for misclassification error for publicly reported Maryland data led to a minimal but not significant increase in the estimated average daily cases. Random sampling and testing of COVID-19 are needed with adjustment for misclassification error to improve COVID-19 prevalence estimates.

Prediction of two novel overlapping ORFs in the genome of SARS-CoV-2.

Six candidate overlapping genes have been detected in SARS-CoV-2, yet current methods struggle to detect overlapping genes that recently originated. However, such genes might encode proteins beneficial to the virus, and provide a model system to understand gene birth. To complement existing detection methods, I first demonstrated that selection pressure to avoid stop codons in alternative reading frames is a driving force in the origin and retention of overlapping genes. I then built a detection method, CodScr, based on this selection pressure. Finally, I combined CodScr with methods that detect other properties of overlapping genes, such as a biased nucleotide and amino acid composition. I detected two novel ORFs (ORF-Sh and ORF-Mh), overlapping the spike and membrane genes respectively, which are under selection pressure and may be beneficial to SARS-CoV-2. ORF-Sh and ORF-Mh are present, as ORF uninterrupted by stop codons, in 100% and 95% of the SARS-CoV-2 genomes, respectively.

What we have learned from the COVID-19 pandemic: Time to think outside the box, maybe far outside.

The coronavirus disease 2019 pandemic has had a profound effect on our lives and careers; this presentation explores some of the lessons we have learned from it and others that it may yet teach us. Socioeconomic effects have been profound, not all of them favorable. Travel and meeting activities, as well as many other activities, have been severely restricted. Social unrest has become intense, and it may have questionable political consequences, as the United States is undergoing a contested election result.

Optimization of reaction condition of recombinase polymerase amplification to detect SARS-CoV-2 DNA and RNA using a statistical method.

Recombinase polymerase amplification (RPA) is an isothermal reaction that amplifies a target DNA sequence with a recombinase, a single-stranded DNA-binding protein (SSB), and a strand-displacing DNA polymerase. In this study, we optimized the reaction conditions of RPA to detect SARS-CoV-2 DNA and RNA using a statistical method to enhance the sensitivity. In vitro synthesized SARS-CoV-2 DNA and RNA were used as targets. After evaluating the concentration of each component, the uvsY, gp32, and ATP concentrations appeared to be rate-determining factors. In particular, the balance between the binding and dissociation of uvsX and DNA primer was precisely adjusted. Under the optimized condition, 60 copies of the target DNA were specifically detected. Detection of 60 copies of RNA was also achieved. Our results prove the fabrication flexibility of RPA reagents, leading to an expansion of the use of RPA in various fields.

Improved individual and population-level HbA1c estimation using CGM data and patient characteristics.

Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19.

Experience, experts, statistics, or just science? Predictors and consequences of reliance on different evidence types during the COVID-19 infodemic.

As an unprecedented global disease outbreak, the COVID-19 pandemic is also accompanied by an infodemic. To better cope with the pandemic, laypeople need to process information in ways that help guide informed judgments and decisions. Such information processing likely involves the reliance on various evidence types. Extending the Risk Information Seeking and Processing model via a two-wave survey (N = 1284), we examined the predictors and consequences of US-dwelling Chinese's reliance on four evidence types (i.e. scientific, statistical, experiential, and expert) regarding COVID-19 information. Overall, Risk Information Seeking and Processing variables such as information insufficiency and perceived information gathering capacity predicted the use of all four evidence types. However, other Risk Information Seeking and Processing variables (e.g. informational subjective norms) did not emerge as important predictors. In addition, different evidence types had different associations with subsequent disease prevention behaviors and satisfaction with the US government's action to address the pandemic. Finally, discrete emotions varied in their influences on the use of evidence types, behaviors, and satisfaction. The findings provide potentially valuable contributions to science and health communication theory and practice.

Estimation of Incubation Period and Serial Interval for SARS-CoV-2 in Jiangxi, China, and an Updated Meta-Analysis.

INTRODUCTION: This paper aims to estimate the incubation period and serial intervals for SARS-CoV-2 based on confirmed cases in Jiangxi Province of China and meta-analysis method. METHODOLOGY: Distributions of incubation period and serial interval of Jiangxi epidemic data were fitted by "fitdistrplus" package of R software, and the meta-analysis was conducted by "meta" package of R software. RESULTS: Based on the epidemic data of Jiangxi, we found the median days of incubation period and serial interval were 5.9 days [IQR: 3.8 - 8.6] and 5.7 days [IQR: 3.6 - 8.3], respectively. The median days of the infectivity period at pre-symptomatic was 1.7 days [IQR: 1.1 - 2.4]. The meta-analysis based on 64 papers showed the pooled means of the incubation period and serial interval were 6.25 days (95% CrI: 5.75 - 6.75) and 5.15 days (95% CrI: 4.73 - 5.57), respectively. CONCLUSIONS: Our results contribute to a better understanding of COVID-19 and provide useful parameters for modelling the dynamics of disease transmission. The serial interval is shorter than the incubation period, which indicates that the patients are infectious at pre-symptomatic period, and isolation of detected cases alone is likely to be difficult to halt the spread of SARS-CoV-2.

An Algorithm for Gene Fragment Reconstruction.

Gene sequencing technology has been playing an important role in many aspects, such as life science, disease medicine and health medicine, particularly in the extremely tough process of fighting against 2019-novel coronavirus. Drawing DNA restriction map is a particularly important technology in genetic biology. The simplified partial digestion method (SPDP), a biological method, has been widely used to cut DNA molecules into DNA fragments and obtain the biological information of each fragment. In this work, we propose an algorithm based on 0-1 planning for the location of restriction sites on a DNA molecule, which is able to solve the problem of DNA fragment reconstruction just based on data of fragments' length. Two specific examples are presented in detail. Furthermore, based on 1000 groups of original DNA sequences randomly generated, we define the coincidence rate and unique coincidence rate between the reconstructed DNA sequence and the original DNA sequence, and then analyze separately the effect of the number of fragments and the maximum length of DNA fragments on the coincidence rate and unique coincidence rate as defined. The effectiveness of the algorithm is proved. Besides, based on the existing optimization solution obtained, we simulate and discuss the influence of the error by computation method. It turns out that the error of position of one restriction site does not affect other restriction sites and errors of most restriction sites may lead to the failure of sequence reconstruction. Matlab 7.1 program is used to solve feasible solutions of the location of restriction sites, derive DNA fragment sequence and carry out the statistical analysis and error analysis. This paper focuses on basic computer algorithm implementation of rearrangement and sequencing rather than biochemical technology. The innovative application of the mathematical idea of 0-1 planning to DNA sequence mapping construction, to a certain extent, greatly simplifies the difficulty and complexity of calculation and accelerates the process of 'jigsaw' of DNA fragments.