Modeling Influence of Soil Properties in Different Gradients of Soil Moisture: The Case of the Valencia Anchor Station Validation Site, Spain

The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physio-hydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models.

Technology-induced job loss risk, disability and all-cause mortality in Norway

BackgroundOngoing shifts in economic structure from automation and globalisation can affect employment and mortality, yet these relations are not well described.ObjectiveWe assess whether long-term employment and health outcomes relate systematically to structural change in the labour market, using the occupational Routine Task Intensity (RTI) score as indicator of exposure is to risks of outsourcing and technology-induced job loss.MethodsUsing a cohort design and administrative data with national population coverage, we categorise all Norwegian employees in 2003 by the RTI score of their occupation and examine how this score correlates with employment and health outcomes measured in 2018 and 2019. The study sample counts 416 003 men and 376 413 women aged 33–52 in 2003.ResultsThe occupational RTI score at baseline is robustly associated with long-term employment, disability and mortality outcomes. Raw correlations are reduced after adjustment for potential confounders, but associations remain substantial in models controlling for individual covariates and in sibling comparisons. Working in an occupation with RTI score 1 SD above the mean in 2003 is associated with a raised probability of being deceased in 2019 of 0.24 percentage points (95% CI: 0.18 to 0.30) for men and 0.13 percentage points (95% CI: 0.02 to 0.24) for women, corresponding to raised mortality rates of 6.7% and 5.5%.ConclusionsIndividuals in occupations characterised by high routine intensity are less likely to remain employed in the long term, and have higher rates of disability and mortality.

Test negative designs with uncertainty, sensitivity, and specificity

Test-negative designs (TNDs) can be used to estimate vaccine effectiveness by comparing the relative rates of the target disease and symptomatically similar diseases among vaccinated and unvaccinated populations. However, the diagnostic tests used to identify the target disease typically suffer from imperfect sensitivity and specificity, leading to biased vaccine effectiveness estimates. Here we present a solution to this problem via a Bayesian statistical model which can either incorporate point estimates of test sensitivity and specificity, or can jointly infer them directly from laboratory validation data. This approach enables uncertainties in the performance characteristics of the diagnostic test to be correctly propagated to estimates, avoiding both bias and false precision in vaccine effectiveness. By further incorporating individual covariates of study participants, and by allowing data streams from multiple diagnostic test types to be rigorously combined, our approach provides a flexible model for the analysis of TNDs with explicitly stated assumptions.

Network approach in legislative studies: methodological prospects for qualitative and quantitative analysis of parliamentary activity

The paper provides extensive methodological discussion of the network approach to legislative studies and gives an overview to different methods and techniques that show great promise to the research of parliamentary politics. The key points of the proposed network theoretical framework are the informal interactions and collaborations of actors and their respective groups, that are tied by linkages of trust and mutual interests. We also keep the focus on the influence of the nodes (MPs) which is being accumulated due to the access to various resources, performance, and individual interests. This article also suggests description of the public data used to reveal the networks of legislative co-sponsorship, which is the well-developed method of legislative studies. In this context we also review some other approaches to obtain information about the ties between the MPs, that have been suggested in the academic literature: the voting data, personal interactions revealed by the interviews, range of connections in the online social networks, official mail, public speech, and others. We show that the network analysis appears to be very insightful for the legislative studies because it allows to perceive parliaments as the “small worlds” each with its own highly institutionalized composition of nodes and ties. We also argue that it is critical to take into consideration the influence of several exogenic forces – voters, the public, and other authorities on the MPs persistent interactions and the respective network structure of the parliament. Finally, we propose two methodological solutions to the research of complex network structures. We debate on the potential implications of the discourse-network analysis in legislative studies. It provides the opportunity to map the advocacy coalitions and model the relations between the nodes, which are based on the similarities and differences of their ideas in the public speeches. We also discuss the potential of the inferential network analysis in regard to the quantitative research in legislative studies. Specifically, we provide a critical review of the modern studies of the innerparliamentary networks, that are based on ERGMs and their variations (SAOM and TERGM). We show that dyadic interactions between the MPs and political parties can be modeled taking into account both individual covariates (exogenous and endogenous) and network parameters of the current structure of parliament as a whole.

IL-6 and IL-10 as predictors of disease severity in COVID 19 patients: Results from Meta-analysis and Regression

Aims: SARS-CoV-2, an infectious agent behind the ongoing COVID-19 pandemic, induces high levels of cytokines such as IL-1, IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ etc in infected individuals which contribute towards the underlying disease patho-physiology. Nonetheless, exact association and contribution of every cytokine towards COVID-19 pathology remains poorly understood. Delineation of the role of the cytokines during COVID-19 holds the key of efficient patient management in clinics. This study performed a comprehensive meta-analysis to establish association between induced cytokines and COVID-19 disease severity to help in prognosis and clinical care. Main methods: Scientific literature was searched to identify 13 cytokines (IL-1β, IL-2, IL-2R, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL-17, TNF-α and IFN-γ) from 18 clinical studies. Standardized mean difference (SMD) for selected 6 cytokines IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ between severe and non-severe COVID-19 patient groups were summarized using random effects model. A classifier was built using logistic regression model with cytokines having significant SMD as covariates. Key findings: Out of 13 cytokines, IL-6 and IL-10 showed statistically significant SMD across the studies synthesized. Classifier with mean values of both IL-6 and IL-10 as covariates performed well with accuracy of ~ 92% that was significantly higher than accuracy reported in literature with IL-6 and IL-10 as individual covariates. Significance: Simple panel proposed by us with only two cytokine markers can be used as predictors for fast diagnosis of patients with higher risk of COVID-19 disease deterioration and thus can be managed well for a favourable prognosis.

Association between use of aspirin use before hospital admission and sepsis outcomes: a population-based cohort study

ABSTRACTObjectivesAntiplatelet agents have been shown to reduce serum levels of inflammatory markers and may reduce sepsis mortality. A previous randomized clinical trial did not find that aspirin use after hospitalization, compared with placebo, improve acute respiratory distress syndrome within 7 days. We aimed to examine the association between preadmission use of aspirin and sepsis outcome.MethodsWe conducted a population-based cohort study based on the National Health Insurance Research Database of Taiwan. The association between aspirin use and 90-day mortality in sepsis patients was determined by Cox proportional hazard models, adjusting for either individual covariates or a propensity score. Restricted mean survival time (RMST) analysis was performed as a sensitivity analysis.ResultsOf 52,982 patients with sepsis, 12,776 received preadmission use of aspirin, while 39,081 did not receive any antiplatelets. Use of aspirin before sepsis admission was associated with a decreased risk of 90-day mortality (PS-adjusted HR: 0.89, 95% CI: 0.84-0.93). RMST analysis confirmed the beneficial effect of aspirin which was associated with a 2% increase in survival time (RMST ratio 1.02, 95% CI: 1.01-1.03), when compared to nonuse. On PS adjusted analysis, the odds ratios (OR) of respiratory failure with the preadmission use of aspirin was 0.98 (95% CI: 0.93-1.03), but we did not find any significant association between prior aspirin use and respiratory failure.ConclusionsOur study confirms that prehospital aspirin use was associated with a reduced 90-day mortality rate among sepsis patients, but we did not find any substantial association with respiratory or acute renal failure.

Is there a link between temperatures and COVID-19 contagions? Evidence from Italy

This study analyzes the link between temperatures and COVID-19 contagions in a sample of Italian regions during the period ranging from February 24 to April 15. To that end, Bayesian Model Averaging techniques are used to analyze the relevance of the temperatures together with a set of additional climate, environmental, demographic, social and policy factors. The robustness of individual covariates is measured through posterior inclusion probabilities. The empirical analysis provides conclusive evidence on the role played by the temperatures given that it appears as the most relevant determinant of contagions. This finding is robust to (i) the prior distribution elicitation, (ii) the procedure to assign weights to the regressors, (iii) the presence of measurement errors in official data due to under-reporting, (iv) the employment of different metrics of temperatures or (v) the inclusion of additional correlates. In a second step, relative importance metrics that perform an accurate partitioning of the R2 of the model are calculated. The results of this approach support the evidence of the model averaging analysis, given that temperature is the top driver explaining 45% of regional contagion disparities. The set of policy-related factors appear in a second level of importance, whereas factors related to the degree of social connectedness or the demographic characteristics are less relevant.

Age-Period-Cohort Models

Outcomes of interest often depend on the age, period, or cohort of the individual observed, where cohort and age add up to period. An example is consumption: consumption patterns change over the lifecycle (age) but are also affected by the availability of products at different times (period) and by birth-cohort-specific habits and preferences (cohort). Age-period-cohort (APC) models are additive models where the predictor is a sum of three time effects, which are functions of age, period, and cohort, respectively. Variations of these models are available for data aggregated over age, period, and cohort, and for data drawn from repeated cross-sections, where the time effects can be combined with individual covariates. The age, period, and cohort time effects are intertwined. Inclusion of an indicator variable for each level of age, period, and cohort results in perfect collinearity, which is referred to as “the age-period-cohort identification problem.” Estimation can be done by dropping some indicator variables. However, dropping indicators has adverse consequences such as the time effects are not individually interpretable and inference becomes complicated. These consequences are avoided by instead decomposing the time effects into linear and non-linear components and noting that the identification problem relates to the linear components, whereas the non-linear components are identifiable. Thus, confusion is avoided by keeping the identifiable non-linear components of the time effects and the unidentifiable linear components apart. A variety of hypotheses of practical interest can be expressed in terms of the non-linear components.

Solitary Day, Solitary Activities, and Associations With Well-Being Among Older Adults

Objectives Drawing on activity theory of aging, we examined whether solitary activities may be associated with negative well-being, as they may reflect social isolation. Using American Time Use Surveys, with information on “with whom” individuals engaged in activities over a 24 hr period, we created measures capturing solitary days and solitary activities to understand their prevalence and associations with well-being. Methods At the daily level, we examined associations between solitary days and proportion of the day in solitary activities with life satisfaction. At the activity level, we examined associations between engaging in an activity alone versus with others and emotional state during the activity. Results Solitary days and higher proportion of the day spent in solitary activities were associated with lower life satisfaction. These associations were attenuated controlling for individual covariates. Engagement in activities alone was associated with lower levels of happiness and higher levels of sadness and pain during the activity, and association with happiness remained even adjusting for covariates. Discussion A sizable proportion of older adults reported solitary days, and proportion of the day spent in solitary activities increases by age. Examining lived experiences of older adults and presence of others during activities could contribute to research on social isolation.

Association of violence in schools’ vicinity and smoking in schools’ premises with tobacco use among Brazilian adolescents

This study aims to quantify the overall importance of schools in explaining the individual variance of tobacco use and to test the association between characteristics of the school environment and its vicinity with the experimentation and current use of cigarettes. We analyzed data from 102,072 Brazilian adolescents interviewed in the 2015 National School Health Survey (PeNSE). Multilevel logistic regression models were performed to estimate the between-schools variance and to test the association between school-level variables and the use of tobacco. Violence in the vicinity of the school and presence of teachers or students smoking on school premises were the school-level characteristics. The analyses were adjusted by individual covariates and stratified by gender. Around 12.5% of the individual variance in ever smoking was explained by between-school variation among girls (9.2% among boys). The figures were even higher for current smoking (14.9% girls; 12.2% boys) and current use of other tobacco products (27.7% girls; 17.8% boys). In general, the use of tobacco was associated with the existence of violence in the vicinity of the schools and was higher among students whose schools reported that students and teachers (teachers only for use of other tobacco products among girls) smoke on school premises. Tobacco use on school premises and the safety of the neighborhood where the school is located are associated with some smoking behaviors among adolescents. Such findings reinforce the necessity to effectively consider interventions in the school environment and neighborhood to fight smoking among adolescents.