COVID-19 Risk Appears to Vary Across Different Alcoholic Beverages

Objectives: To evaluate the associations of status, amount, and frequency of alcohol consumption across different alcoholic beverages with coronavirus disease 2019 (COVID-19) risk and associated mortality.Methods: This study included 473,957 subjects, 16,559 of whom tested positive for COVID-19. Multivariate logistic regression analyses were used to evaluate the associations of alcohol consumption with COVID-19 risk and associated mortality. The non-linearity association between the amount of alcohol consumption and COVID-19 risk was evaluated by a generalized additive model.Results: Subjects who consumed alcohol double above the guidelines had a higher risk of COVID-19 (1.12 [1.00, 1.25]). Consumption of red wine above or double above the guidelines played protective effects against the COVID-19. Consumption of beer and cider increased the COVID-19 risk, regardless of the frequency and amount of alcohol intake. Low-frequency of consumption of fortified wine (1–2 glasses/week) within guidelines had a protective effect against the COVID-19. High frequency of consumption of spirits (≥5 glasses/week) within guidelines increased the COVID-19 risk, whereas the high frequency of consumption of white wine and champagne above the guidelines decreased the COVID-19 risk. The generalized additive model showed an increased risk of COVID-19 with a greater number of alcohol consumption. Alcohol drinker status, frequency, amount, and subtypes of alcoholic beverages were not associated with COVID-19 associated mortality.Conclusions: The COVID-19 risk appears to vary across different alcoholic beverage subtypes, frequency, and amount. Red wine, white wine, and champagne have chances to reduce the risk of COVID-19. Consumption of beer and cider and spirits and heavy drinking are not recommended during the epidemics. Public health guidance should focus on reducing the risk of COVID-19 by advocating healthy lifestyle habits and preferential policies among consumers of beer and cider and spirits.

Quantile Generalized Additive Model a Robust Alternative to Generalized Additive Model

Nonparametric regression is an approach used when the structure of the relationship between the response and the predictor variable is unknown. It tries to estimate the structure of this relationship since there is no predetermined form. The generalized additive model (GAM) and quantile generalized additive (QGAM) model provides an attractive framework for nonparametric regression. The QGAM focuses on the features of the response beyond the central tendency, while the GAM focuses on the mean response. The analysis was done using gam and qgam packages in R, using data set on live-births, fertility-rate and birth-rate, where, live-birth is the response with fertility-rate and birth-rate as the predictors. The spline basis function was used while selecting the smoothing parameter by marginal loss minimization technique. The result shows that the basis dimension used was sufficient. The QGAM results show the effect of the smooth functions on the response variable at 25th, 50th, 75th and 95th quantiles, while the GAM showed only the effect of the predictors on the mean response. The results also reveal that the QGAM have lower Akaike information criterion (AIC) and Generalized cross-validation (GVC) than the GAM, hence producing a better model. It was also observed that the QGAM and the GAM at the 50th quantile had the same R2adj(77%), meaning that both models were able to explain the same percentage of variation in the models, this we attribute to the fact that mean regression and median regression are approximately the same, hence the observation is in agreement with existing literature. The plots reveal that some of the residuals of the GAM were seen to fall outside the confidence band while in QGAM all the residuals fell within the confidence band producing a better smooth.

Seasonal lead release to drinking water and the effect of aluminum

Monitoring lead in drinking water is important for public health, but seasonality in lead concentrations can bias monitoring programs if it is not understood and accounted for. Here, we describe an apparent seasonal pattern in lead release to orthophosphate-treated drinking water, identified through point-of-use sampling at sites in Halifax, Canada, with various sources of lead. Using a generalized additive model, we extracted the seasonally-varying components of time series representing a suite of water quality parameters and we identified aluminum as a correlate of lead. To investigate aluminum’s role in lead release, we modeled the effect of variscite (AlPO4 · 2H2O) precipitation on lead solubility, and we evaluated the effects of aluminum, temperature, and orthophosphate concentration on lead release from new lead coupons. At environmentally relevant aluminum and orthophosphate concentrations, variscite precipitation increased predicted lead solubility by decreasing available orthophosphate. Increasing the aluminum concentration from 20–500 µg L-1 increased lead release from coupons by 41% and modified the effect of orthophosphate, rendering it less effective. We attributed this to a decrease in the concentration of soluble (<0.45 µm) phosphorus with increasing aluminum and an accompanying increase in particulate lead and phosphorus (>0.45 µm).

Age at Smoking Onset and Risk of Heavy Cigarette Smoking Among Iranian Adult Male: An Application of Generalized Additive Model

Background: Because the age at which a person first starts smoking has such a strong correlation with future smoking behaviours, it's crucial to examine its relationship with smoking intensity. However, it is still challenging to accurately identify this relationship due to limitations in the methodology of the performed studies .Therefore the main purpose of this study is to evaluate this relationship and also to identify the other risk factors affecting smoking intensity using an appropriate model.Methods: Data from 913 Iranian male current smokers over the age of 18 was evaluated from a national cross-sectional survey of non-communicable disease (NCD) risk factors in 2016. Individuals were classified into: light, moderate, and heavy smokers. A generalized additive model (GAM) was used to assess the relationship.Results: 246 (26.9%) subjects were light smokers, 190 (20.8%) subjects were moderate smokers and 477 (52.2%) subjects were heavy smokers. According to the GAM results, the relationship was nonlinear and smokers who started smoking at a younger age were more likely to become heavy smokers. The factors of unemployment (OR = 1.364), retirement (OR = 1.217), and exposure to secondhand smoke at home (OR = 1.364) increased the risk of heavy smoking. but, smokers with high-income (OR = 0.742) had a low tendency to heavy smoking. Conclusions: GAM identified the nonlinear relationship between the age of onset of smoking and smoking intensity. Tobacco control programs should be focused on young and adolescent groups and poorer socio-economic communities.

The Effect of the COVID-19 Vaccine on Daily Cases and Deaths Based on Global Vaccine Data

Background: Coronavirus disease 2019 (COVID-19), a global pandemic, has caused over 216 million cases and 4.50 million deaths as of 30 August 2021. Vaccines can be regarded as one of the most powerful weapons to eliminate the pandemic, but the impact of vaccines on daily COVID-19 cases and deaths by country is unclear. This study aimed to investigate the correlation between vaccines and daily newly confirmed cases and deaths of COVID-19 in each country worldwide. Methods: Daily data on firstly vaccinated people, fully vaccinated people, new cases and new deaths of COVID-19 were collected from 187 countries. First, we used a generalized additive model (GAM) to analyze the association between daily vaccinated people and daily new cases and deaths of COVID-19. Second, a random effects meta-analysis was conducted to calculate the global pooled results. Results: In total, 187 countries and regions were included in the study. During the study period, 1,011,918,763 doses of vaccine were administered, 540,623,907 people received at least one dose of vaccine, and 230,501,824 people received two doses. For the relationship between vaccination and daily increasing cases of COVID-19, the results showed that daily increasing cases of COVID-19 would be reduced by 24.43% [95% CI: 18.89, 29.59] and 7.50% [95% CI: 6.18, 8.80] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. Daily increasing deaths of COVID-19 would be reduced by 13.32% [95% CI: 3.81, 21.89] and 2.02% [95% CI: 0.18, 4.16] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. Conclusions: These findings showed that vaccination can effectively reduce the new cases and deaths of COVID-19, but vaccines are not distributed fairly worldwide. There is an urgent need to accelerate the speed of vaccination and promote its fair distribution across countries.

Relationship between water iodine and children’s goiters

Excessive iodine can lead to goiters. However, the relationship between the water iodine concentration (WIC) and goiter rate (GR) is unclear. This study aims to explore the factors that influence children’s GR in areas with high WIC and analyse the threshold value of the GR increase associated with the WIC. According to the monitoring of the areas with high WIC in China in 2018–2020, a total of 54,050 children in eight high water iodine provinces were chosen. Drinking water, urine and edible salt samples of children were collected. The thyroid volume (Tvol) was measured. A generalized additive model (GAM) was used to analyse the relationship between the WIC and GR in children. Among the 54,050 children in areas with high WIC, the overall GR was 3.34%, the median of water iodine concentration (MWIC) was 127.0 µg/L, the median of urinary iodine concentration (MUIC) was 318 µg/L, and the noniodized salt coverage rate (NISCR) was 63.51%. According to the GAM analysis results, water iodine and urinary iodine are factors that influence the Tvol and GR, while the NISCR affects only the GR. When the WIC was more than 420 µg/L or the urinary iodine concentration (UIC) was more than 800 µg/L, the GR increased rapidly. When the NISCR reached more than 85%, the GR was the lowest. Thus, in areas with high WIC, WIC more than 420µg/L may increase the risk of goiter, and the NISCR should be increased to over 85% to avoid goiters in children.

When Enough Is Really Enough? On the Minimum Number of Landslides to Build Reliable Susceptibility Models

Mapping existing landslides is a fundamental prerequisite to build any reliable susceptibility model. From a series of landslide presence/absence conditions and associated landscape characteristics, a binary classifier learns how to distinguish potentially stable and unstable slopes. In data rich areas where landslide inventories are available, addressing the collection of these can already be a challenging task. However, in data scarce contexts, where geoscientists do not get access to pre-existing inventories, the only solution is to map landslides from scratch. This operation can be extremely time-consuming if manually performed or prone to type I errors if done automatically. This is even more exacerbated if done over large geographic regions. In this manuscript we examine the issue of mapping requirements for west Tajikistan where no complete landslide inventory is available. The key question is: How many landslides should be required to develop reliable landslide susceptibility models based on statistical modeling? In fact, for such a wide and extremely complex territory, the collection of an inventory that is sufficiently detailed requires a large investment in time and human resources. However, at which point of the mapping procedure, would the resulting susceptibility model produce significantly better results as compared to a model built with less information? We addressed this question by implementing a binomial Generalized Additive Model trained and validated with different landslide proportions and measured the induced variability in the resulting susceptibility model. The results of this study are very site-specific but we proposed a very functional protocol to investigate a problem which is underestimated in the literature.