Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network.

Contact tracing played a crucial role in minimizing the onward dissemination of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the recent pandemic. Previous studies had also shown the effectiveness of preventive measures such as mask-wearing, physical distancing, and exposure duration in reducing SARS-CoV-2 transmission. However, there is still a lack of understanding regarding the impact of various exposure settings on the spread of SARS-CoV-2 within the community, as well as the most effective preventive measures, considering the preventive measures adherence in different daily scenarios. We aimed to evaluate the effect of individual protective measures and exposure settings on the community transmission of SARS-CoV-2. Additionally, we aimed to investigate the interaction between different exposure settings and preventive measures in relation to such SARS-CoV-2 transmission. Routine SARS-CoV-2 contact tracing information was supplemented with additional data on individual measures and exposure settings collected from index patients and their close contacts. We used a case-control study design, where close contacts with a positive test for SARS-CoV-2 were classified as cases, and those with negative results classified as controls. We used the data collected from the case-control study to construct a Bayesian network (BN). BNs enable predictions for new scenarios when hypothetical information is introduced, making them particularly valuable in epidemiological studies. Our results showed that ventilation and time of exposure were the main factors for SARS-CoV-2 transmission. In long time exposure, ventilation was the most effective factor in reducing SARS-CoV-2, while masks and physical distance had on the other hand a minimal effect in this ventilation spaces. However, face masks and physical distance did reduce the risk in enclosed and unventilated spaces. Distance did not reduce the risk of infection when close contacts wore a mask. Home exposure presented a higher risk of SARS-CoV-2 transmission, and any preventive measures posed a similar risk across all exposure settings analyzed. Bayesian network analysis can assist decision-makers in refining public health campaigns, prioritizing resources for individuals at higher risk, and offering personalized guidance on specific protective measures tailored to different settings or environments.

Sex and age differences in the association of fatty liver index-defined non-alcoholic fatty liver disease with cardiometabolic risk factors: a cross-sectional study.

BACKGROUND: Despite the extensive scientific evidence accumulating on the epidemiological risk factors for non-alcoholic fatty liver disease (NAFLD), evidence exploring sex- and age-related differences remains insufficient. The present cross-sectional study aims to investigate possible sex differences in the prevalence of FLI-defined NAFLD as well as in its association with common risk factors across different age groups, in a large sample of Spanish working adults. METHODS: This cross-sectional study included data from 33,216 Spanish adult workers (18-65 years) randomly selected during voluntary routine occupational medical examinations. Sociodemographic characteristics (age and social class), anthropometric (height, weight, and waist circumference) and clinical parameters (blood pressure and serum parameters) were collected. NAFLD was determined by the validated fatty liver index (FLI) with a cut-off value of ≥ 60. The presence of metabolic syndrome (MetS) was assessed according to the diagnostic criteria of the International Diabetes Federation. Cardiovascular risk was determined using the REGICOR-Framingham equation. The association between FLI-defined NAFLD and risk factors by sex and age was evaluated by multivariate logistic regression. RESULTS: The prevalence of FLI-defined NAFLD (FLI ≥ 60) was 19.1% overall, 27.9% (95% CI 23.3-28.5%) for men and 6.8% (95% CI 6.4-7.3%) for women and increasing across age intervals. As compared to women, men presented worse cardiometabolic and anthropometric profiles. The multivariate analysis model showed that hepatic steatosis assessed by FLI was strongly associated with age, HDL-cholesterol, social class, prediabetes, diabetes, prehypertension, hypertension, and smoking status for both men and women. The association between diabetes and hypertension with FLI-defined NAFLD was stronger in women than in men at both univariate and multivariate analyses. CONCLUSIONS: Men presented a higher prevalence of NAFLD than women across all age intervals, as well as a worse cardiometabolic profile and a higher cardiovascular risk. Nevertheless, the association between FLI-defined NAFLD and diabetes or hypertension was significantly stronger in women than in men, possibly indicating that the presence of a dysmetabolic state might affect women more than men with regard to liver outcomes.

Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network.

Background: It is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a global public health concern, and it is associated with other diseases such as cardiovascular disease. Methods: This study aimed to determine those factors with high influence in the development of T2D once prediabetes has been diagnosed, through a Bayesian network (BN), which can help to prevent T2D. Furthermore, the set of features with the strongest influences on T2D can be determined through the Markov blanket. A BN model for T2D was built from a dataset composed of 12 relevant features of the T2D domain, determining the dependencies and conditional independencies from empirical data in a multivariate context. The structure and parameters were learned with the bnlearn package in R language introducing prior knowledge. The Markov blanket was considered to find those features (variables) which increase the risk of T2D. Results: The BN model established the different relationships among features (variables). Through inference, a high estimated probability value of T2D was obtained when the body mass index (BMI) was instantiated to obesity value, the glycosylated hemoglobin (HbA1c) to more than 6 value, the fatty liver index (FLI) to more than 60 value, physical activity (PA) to no state, and age to 48-62 state. The features increasing T2D in specific states (warning factors) were ranked. Conclusion: The feasibility of BNs in epidemiological studies is shown, in particular, when data from T2D risk factors are considered. BNs allow us to order the features which influence the most the development of T2D. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis.

A Bayesian Approach to Sport Injuries Likelihood: Does Player's Self-Efficacy and Environmental Factors Plays the Main Role?

The psychological factors of sports injuries constitute a growing field of study, even from the point of view of the prediction of their occurrence. Most of them, however, do not take into account the likelihood of the injuries' occurrence and the weight and role of the psychological variables on it. We conducted a study building up a Bayesian Network on a big sample of athletes, trying to assess these probabilistic links among several relevant psychological variables and the injuries' occurrence. The sample was constituted by 297 athletes (239 males, 58 females) from a wide range of sports: track and field; judo; fencing; karate; boxing; swimming; kayaking; artistic rollerskating, and team sports as football, basketball, and handball (Mean age: 25.10 ±-3.87; range: 21-38 years). Several psychological variables, such as anxiety, social support, and self-efficacy were studied. Also, we recorded the history of injuries as well the body mass index and personal epidemiological data. The overall picture of the generated graph and Bayesian Network and its analysis - including the use of hypothetical data by means of several instantiations - includes the nuclear role of the Self-Efficacy regarding the injuries' occurrence likelihood; the decreasing impact of the competitive anxiety previous to the injury; the probabilistic independence of the players' risk behaviors, and the relevance of the environmental clues such the use of coping strategies and social support in order to build up a good level of Self-Efficacy after the occurrence of an injury. All these data are relevant when designing both preventive and recovery interventions from the multidisciplinary as well as from the psychological point of view.

A comparison between multiple regression models and CUN-BAE equation to predict body fat in adults.

BACKGROUND: Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. METHODS: Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. RESULTS: The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). CONCLUSIONS: There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.