scholarly journals Face mask wearing rate predicts country’s COVID-19 death rates

2020 ◽  
Author(s):  
Daisuke Miyazawa ◽  
Gen Kaneko
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Face Mask ◽  
Lasso Regression ◽  
Regression Analyses ◽  
Linear Regression Models ◽  
National Differences ◽  
Regression Approach ◽  
Independent Risk Factors

AbstractIdentification of biomedical and socioeconomic predictors for the number of deaths by COVID-19 among countries will lead to the development of effective intervention. While previous multiple regression studies have identified several predictors, little is known for the effect of mask non-wearing rate on the number of COVID-19-related deaths possibly because the data is available for limited number of countries, which constricts the application of traditional multiple regression approach to screen a large number of potential predictors. In this study, we used the hypothesis-driven regression to test the effect of limited number of predictors based on the hypothesis that the mask non-wearing rate can predict the number of deaths to a large extent together with age and BMI, other relatively independent risk factors for hospitalized patients of COVID-19. The mask non-wearing rate, percentage of age ≥ 80 (male), and male BMI showed Spearman’s correlations up to about 0.8, 0.7, and 0.6 with the number of deaths per million from 22 countries from mid-March to mid-June, respectively. The observed number of deaths per million were significantly correlated with the numbers predicted by the lasso regression model including four predictors, age ≥ 80 (male), male BMI, and mask non-wearing rates from mid-March and late April to early May (Pearson’s coefficient = 0.918). The multiple linear regression models including the mask non-wearing rates, age, and obesity-related predictors explained up to 79% variation of the number of deaths per million. Furthermore, 56.8% of the variation of mask non-wearing rate in mid-March, the strongest predictor of the number of deaths per million, was predicted by age ≥ 80 (male) and male BMI, suggesting the confounding role of these predictors. Although further verification is needed to identify causes of the national differences in COVID-19 mortality rates, these results highlight the importance of the mask, age, and BMI in predicting the COVID-19-related deaths, providing a useful strategy for future regression analyses that attempt to contribute to the mechanistic understanding of COVID-19.

scholarly journals Loneliness and COVID-19 preventive behaviours among Japanese adults

2020 ◽  
Author(s):  
Andrew Stickley ◽  
Tetsuya Matsubayashi ◽  
Michiko Ueda
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Odds Ratio ◽  
Regression Models ◽  
Online Survey ◽  
Health Behaviours ◽  
Regression Analyses ◽  
Linear Regression Models ◽  
Japanese Adults ◽  
The Relationship

Abstract Background There is some evidence that loneliness may be linked to poorer health behaviours. Despite this, there has been little research to date on the relationship between loneliness and COVID-19 preventive behaviours. We studied these associations in a sample of the Japanese population. Methods Data were analysed from an online survey of 2000 adults undertaken in April and May 2020. Loneliness was assessed with the Three-Item Loneliness Scale. Information was also collected on 13 COVID-19 preventive behaviours. Regression analyses were used to examine associations. Results In linear regression models adjusted for demographic and mental health variables, both dichotomous and continuous loneliness measures were negatively associated with engaging in COVID-19 preventive behaviours. Logistic regression analyses further showed that loneliness was also associated with reduced odds for a variety of individual preventive behaviours including wearing a mask (odds ratio [OR]: 0.77, 95% confidence interval [CI]: 0.62–0.95), disinfecting hands (OR: 0.80, 95% CI: 0.67–0.94) and social distancing when outdoors (OR: 0.75, 95% CI: 0.61–0.92). Conclusions Loneliness is associated with lower engagement in COVID-19 preventive behaviours. Interventions to prevent or ameliorate loneliness during the ongoing pandemic may be important in combating the spread of the coronavirus.

Violent audiovisual content and social consequences: The moderating role of aggression in adolescents

2015 ◽  
Vol 40 (2) ◽  
Author(s):  
Gaspar Brändle ◽  
Miguel Angel M. Cardaba ◽  
Reynaldo G. Rivera
Keyword(s):  
Multiple Regression ◽  
Behavioral Intentions ◽  
Social Consequences ◽  
European Countries ◽  
Regression Analyses ◽  
Subjective Experiences ◽  
Aggressive Cognitions ◽  
Initial Objective ◽  
Moderating Role

AbstractNumerous studies have linked the consumption of violent audiovisual content to the increase of aggressive cognitions and behaviors. This research aims to clarify whether the possible harmful consequences of violent videogames might vary depending on an individual variable such as trait aggressiveness. A correlational study was carried out among 6,130 teenagers (average age of 13.8 years) from two European countries, in which it became evident, by means of multiple regression analyses, that there was a positive correlation between the use of violent videogames and aggressive behavioral intentions. More relevantly, these correlations were greater amongst those subjects with higher scores on the Aggression Scale. Finally, when analyzing the subjective experiences of users of violent videogames, the more aggressive individuals manifested a greater desire to imitate the characters of the interactive content, admitting that they ended up more agitated even when their initial objective was to relieve tension or relax. Practical and theoretical implications (e.g., Catharsis theory) of those results are discussed.

scholarly journals Regression models for productivity prediction in cactus pear cv. Gigante

2020 ◽  
Vol 24 (11) ◽  
pp. 721-727
Author(s):  
Bruno V. C. Guimarães ◽  
Sérgio L. R. Donato ◽  
Ignacio Aspiazú ◽  
Alcinei M. Azevedo ◽  
Abner J. de Carvalho
Keyword(s):  
Linear Regression ◽  
Multiple Regression ◽  
Regression Models ◽  
Yield Prediction ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Ct Number ◽  
Cactus Pear ◽  
Quadratic Interaction ◽  
Multiple Regression Models

ABSTRACT The understanding of plant behavior and its reflexes on yield is essential for rural planning; thus, the biomathematical models are promising in the yield prediction of cactus pear cv. Gigante. This study aimed to adjust, through simple and multiple regression analysis, models for predicting the yield of cactus pear cv. Gigante. The study, using homogeneous treatments, was developed at the Instituto Federal Baiano, Campus of Guanambi, Bahia, Brazil. Data were collected in an area consisting of 384 basic units (plants), in which the yield, defined as a dependent variable, and the predictor variables: plant height (PH), cladode length (CL), cladode width (CW), and cladode thickness (CT), number of cladodes (NC), cladode area (CA), and total cladode area (TCA) were evaluated. Simple linear regression models, multiple regression models only with simple effects for the explanatory variables, and the multiple regression models considering the simple and quadratic effects, and all its possible interactions were adjusted. From this last model, a reduced model was obtained by discarding the less relevant effects, using the Stepwise methodology. The use of the vegetative traits, TCA, NC, CA, CL, CT, and CW, through the adoption of multiple linear regression, quadratic interaction or just the variable TCA by the use of simple linear regression, allows the yield prediction of cactus pear, with adjusted R² of 0.82, 0.76, and 0.74, respectively.

scholarly journals Grapevine bunch weight estimation using image-based features: comparing the predictive performance of number of visible berries and bunch area

OENO One ◽  
2021 ◽  
Vol 55 (4) ◽  
pp. 209-226
Author(s):  
Carlos Lopes ◽  
Jorge Cadima
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Quantitative Estimation ◽  
Explanatory Power ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Predictor Models ◽  
Single Predictor ◽  
2D Images

Recent advances in machine vision technologies have provided a multitude of automatic tools for recognition and quantitative estimation of grapevine bunch features in 2D images. However, converting them into bunch weight (BuW) is still a big challenge. This paper aims to compare the explanatory power of the number of visible berries (#vBe) and the bunch area (BuA) in 2D images, in order to predict BuW. A set of 300 bunches from four grapevine cultivars were picked at harvest and imaged using a digital RGB camera. Then each bunch was manually assessed for several morphological attributes and, from each image, the #vBe was visually assessed while BuA was segmented using manual labelling combined with an image processing software. Single and multiple regression analysis between BuW and the image-based variables were performed and the obtained regression models were subsequently validated with two independent datasets.The high goodness of fit obtained for all the linear regression models indicates that either one of the image-based variables can be used as an accurate proxy of actual bunch weight and that a general model is also suitable. The comparison of the explanatory power of the two image-based attributes for predicting bunch weight showed that the models based on the predictor #vBe had a slightly lower coefficient of determination (R2) than the models based on BuA. The combination of the two image-based explanatory variables in a multiple regression model produced predictor models with similar or noticeably higher R2 than those obtained for single-predictor models. However, adding a second variable produced a higher and more generalised gain in accuracy for the simple regression models based on the predictor #vBe than for the models based on BuA. Our results recommend the use of the models based on the two image-based variables, as they were generally more accurate and robust than the single variable models. When the gains in accuracy produced by adding a second image-based feature are small, the option of using only a single predictor can be chosen; in such a case, our results indicate that BuA would be a more accurate and less cultivar-dependent option than the #vBe.

scholarly journals The Influence of Sitting, Standing, and Stepping Bouts on Cardiometabolic Health Markers in Older Adults

2021 ◽  
pp. 1-9
Author(s):  
Simone J.J.M. Verswijveren ◽  
Cormac Powell ◽  
Stephanie E. Chappel ◽  
Nicola D. Ridgers ◽  
Brian P. Carson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Body Composition ◽  
Linear Regression ◽  
Regression Models ◽  
Fasting Glucose ◽  
Cardiometabolic Health ◽  
Linear Regression Models ◽  
Cross Sectional

Aside from total time spent in physical activity behaviors, how time is accumulated is important for health. This study examined associations between sitting, standing, and stepping bouts, with cardiometabolic health markers in older adults. Participants from the Mitchelstown Cohort Rescreen Study (N = 221) provided cross-sectional data on activity behaviors (assessed via an activPAL3 Micro) and cardiometabolic health. Bouts of ≥10-, ≥30-, and ≥60-min sitting, standing, and stepping were calculated. Linear regression models were fitted to examine the associations between bouts and cardiometabolic health markers. Sitting (≥10, ≥30, and ≥60 min) and standing (≥10 and ≥30 min) bouts were detrimentally associated with body composition measures, lipid markers, and fasting glucose. The effect for time spent in ≥60-min sitting and ≥30-min standing bouts was larger than shorter bouts. Fragmenting sitting with bouts of stepping may be targeted to benefit cardiometabolic health. Further insights for the role of standing need to be elicited.

The Impact of Discrimination Beyond Sense of Belonging: Predicting College Students’ Confidence in Their Ability to Graduate

2020 ◽  
pp. 152102512095760
Author(s):  
Zachary A. Jackson ◽  
I. Shevon Harvey ◽  
Ledric D. Sherman
Keyword(s):  
College Students ◽  
Multiple Regression ◽  
Sense Of Belonging ◽  
Initial Step ◽  
Extracurricular Activity ◽  
Regression Analyses ◽  
Hierarchical Multiple Regression ◽  
Final Sample ◽  
The Impact

Data from the Healthy Mind Study were analyzed using hierarchical multiple regression analyses to determine the role of discriminatory experience in students’ confidence in their ability to persist through graduation, controlling for age, extracurricular activity participation, housing, years in their degree program, and their sense of belonging. The final sample consists of 4,708 college students—57.1% women, 70.8% Whites, 7.4% Blacks, 10.4% Asians, and 10.4% Latinx. A final hierarchical multiple regression with discrimination and covariates revealed an overall model that explained 15.5% of the total variance of confidence to persist (F [12, 4574] = 76.762, p < .001). The frequency of discriminatory experiences explains a statistically significant percentage of the variance in students’ confidence in their ability to persist. Thus, efforts to minimize students’ discriminatory experiences need to be increased. This study offers an initial step that institutions can implement to serve and retain their students better.

Self-concept clarity as a predictor of self-disclosure in romantic relationships

2020 ◽  
Vol 37 (6) ◽  
pp. 1873-1891
Author(s):  
Maryam Tajmirriyahi ◽  
William Ickes
Keyword(s):  
Romantic Relationships ◽  
Multiple Regression ◽  
Regression Models ◽  
Experimental Studies ◽  
Self Esteem ◽  
Self Concept ◽  
Future Research ◽  
Self Disclosure ◽  
Multiple Regression Models

Several studies have examined the role of self-esteem in self-disclosure while overlooking a potentially important confounding variable: self-concept clarity. Across three studies, we found an association between self-concept clarity and self-disclosure to one’s romantic partner. This incremental effect held even when the variance attributable to self-esteem was statistically controlled in a multiple regression analysis. Moreover, in two of the three studies, self-esteem was no longer a significant predictor of self-disclosure after controlling for the variance in self-concept clarity. These data suggest that self-concept clarity is an important predictor of self-disclosure—one that is conceptually and empirically distinct from self-esteem. That self-concept clarity tended to supplant self-esteem in the multiple regression models suggests that disclosing the specific aspects of the self that one clearly perceives (one’s attributes, goals, motives, values, etc.) might be more essential to everyday self-disclosure than disclosing only whether one has a globally positive or negative self-view. Future research should explore the causal relationships involved with the aid of experimental studies.

Skipping breakfast and mood: The role of sleep

2021 ◽  
pp. 026010602098486
Author(s):  
Zon-Shuan Chang ◽  
Ali Boolani ◽  
Deirdre A. Conroy ◽  
Tom Dunietz ◽  
Erica C. Jansen
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Mood States ◽  
Poor Sleep ◽  
Linear Regression Models ◽  
Night Time ◽  
Breakfast Skipping ◽  
Time Awake ◽  
Skipping Breakfast

Background: Breakfast skipping has been related to poor mood, but the role of sleep in this relationship remains unclear. Aim: To evaluate whether breakfast skipping associated with mood independently of sleep, and whether sleep interacted with breakfast skipping. Methods: During an in-person research visit, a sample of 329 adults completed questionnaires regarding last night’s sleep, current morning breakfast intake, and mid-morning mood states. Sex-stratified linear regression models examined associations between breakfast skipping and mood and interactions with sleep. Results: Among males, those who did not consume breakfast had less vigor independent of sleep (β=−2.72 with 95% CI −4.91, −0.53). Among females, those who did not consume breakfast had higher feelings of anxiety (β=1.21 with 95% CI −0.04, 2.47). Interaction analyses revealed that males with longer time to fall asleep and longer night-time awake time had higher depression scores in the presence of breakfast skipping, and females with more night-time awake time and shorter duration had higher fatigue and less vigor if they were also breakfast skippers. Conclusion: Breakfast skipping and poor sleep may jointly affect mood.

scholarly journals A Novel Method for Computationally Efficacious Linear and Polynomial Regression Analytics of Big Data in Medicine

2020 ◽  
Vol 14 (5) ◽  
pp. 1
Author(s):  
Ahmed Al-Imam
Keyword(s):  
Big Data ◽  
Linear Regression ◽  
Regression Models ◽  
Rank Test ◽  
Regression Analyses ◽  
Linear Regression Models ◽  
Signed Rank ◽  
Wilcoxon Signed Rank Test ◽  
Scale Down ◽  
Signed Rank Test

Background: Machine learning relies on a hybrid of analytics, including regression analyses. There have been no attempts to deploy a scale-down transformation of data to enhance linear regression models. Objectives: We aim to optimize linear regression models by implementing data transformation function to scale down all variables in an attempt to minimize the sum of squared error. Materials and Methods: We implemented non-Bayesian statistics using SPSS and MatLab. We used Excel to generate 40 trials of linear regression models, and each has 1,000 observations. We utilized SPSS to conduct regression analyses, Wilcoxon signed-rank test, and Cronbach&rsquo;s alpha statistics to evaluate the performance of the optimization model. Results: The scale-down transformation succeeded by significantly reducing the sum of squared errors [absolute Z-score=5.511, effect size=0.779, p-value&lt;0.001, Wilcoxon signed-rank test]. Inter-item reliability testing confirmed the robust internal consistency of the model [Cronbach&rsquo;s alpha=0.993]. Conclusions: The optimization model is valuable for high-impact research based on regression. It can reduce the computational processing demands for powerful real-time and predictive analytics of big data.

scholarly journals SELF-RATED HEALTH AS A PREDICTOR OF DEPRESSION AND ANXIETY IN OLDER ADULT INMATES

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S166-S166
Author(s):  
Carlyn E Vogel ◽  
Lisa C Barry
Keyword(s):  
Regression Models ◽  
Continuous Variable ◽  
High Risk Population ◽  
Correctional Facilities ◽  
Depression And Anxiety ◽  
Linear Regression Models ◽  
Face To Face ◽  
Self Rated Health ◽  
Older Inmates

Abstract Inmates age ≥50 years (older inmates) are a rapidly growing population within the U.S. correctional system with the highest suicide rate among adult prisoners. Although depression and anxiety are strong precursors of subsequent suicide, little is known regarding factors associated with these outcomes in older inmates. To inform suicide prevention efforts in this high-risk population, we evaluated the role of older inmates’ self-rated health (SRH) in relation to depression and anxiety. We utilized data from the ongoing Aging Inmates Suicidal Ideation and Depression study (Aging INSIDE). Participants (N=175) included men age ≥50 (M=56.5, SD=6.3, range=50-79 years) from eight correctional facilities in Connecticut who completed face-to-face interviews. The outcomes, depression and anxiety, were assessed using the PHQ-9 (range 0-27) and GAD-7 (range 0-21); higher scores on each scale indicated worsening severity. SRH, operationalized as a pseudo-continuous variable (1=excellent; 5=Poor), was correlated with depression (r=0.379; p &lt;.001) and anxiety (r=0.260; p =.001) in unadjusted analyses. Two linear regression models were conducted to determine if SRH was associated with depression and/or anxiety after controlling for age, race (white versus non-white), years of education, visitors (yes versus no), and number of chronic conditions. Increasingly worse SRH was significantly associated with more depressive symptoms (β=1.92, SE=.43, p &lt;.001) and higher anxiety scores (β=1.41, SE=.41, p=.001). SRH explained 10.0% and 6.2% of the variance in depression and anxiety scores, respectively. SRH may be useful for identifying older inmates who are more likely to have depression or anxiety, and thus may be at higher risk for suicide.

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