scholarly journals Household Smoking Restrictions, Time to First Cigarette and Tobacco Dependence

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Steven A. Branstetter ◽  
Nicolle Krebs ◽  
Joshua E. Muscat
Keyword(s):  
Environmental Factors ◽  
Nicotine Dependence ◽  
Regression Models ◽  
No Reduction ◽  
Tobacco Dependence ◽  
Nicotine Addiction ◽  
Smoking Restrictions ◽  
Smoking Behaviors ◽  
Reported Data ◽  
Better Than

Objective. Environmental factors, such as household smoking restrictions (HSR), may impact a range of smoking-related outcomes. The current study examined the effects of various levels of HSR on smoking behaviors, including the number of cigarettes smoked per day and levels of nicotine dependence in a population of adult smokers. (1) Having specific HSR reduces the urges to smoke (path A); (2) having specific HSR reduces CPD (path B); (3) having specific HSR results in lower overall nicotine addiction (path C), and later, TTFC will be associated with (4) lower urges to smoke in the morning (path A’), (5) fewer CPD (path B’), and (6) lower levels of nicotine addiction (path C’). Method. Regression models using self-reported data from the Pennsylvania Adult Smoking Study ( N = 353 ) were used. TTFC was measured minutes between waking and the first cigarette of the day. Household smoking restrictions were measured as follows: (1) full ban on smoking in the home, (2) partial ban, or (3) no ban. Results. Subjects with no household smoking restrictions had lower incomes and education than those with at least some household smoking restrictions; those with full bans smoked less and had an earlier TTFC than those with at least some household smoking restrictions. Smokers with a full ban had a later TTFC, mediated by fewer cigarettes per day and lower cravings. Among those with partial bans, there is no reduction in cigarettes per day and an increase in urges to smoke. Conclusions. Partial household smoking restrictions are no better than no household smoking restrictions with regard to cigarettes per day and TTFC, and may cause an increase in urges to smoke in the morning.

STUDY ON THRIVE SCORE IN THE PROGNOSIS OF ISCHEMIC STROKE

2017 ◽  
pp. 22-24
Author(s):  
Thi Thao Nhi Tran ◽  
Dinh Toan Nguyen
Keyword(s):  
Ischemic Stroke ◽  
Clinical Outcome ◽  
Regression Models ◽  
Cross Sectional Study ◽  
High Rate ◽  
Vascular Events ◽  
Cross Sectional ◽  
Key Words Ischemic Stroke ◽  
Outcome Mortality ◽  
Better Than

Background and Purpose: Stroke is the second cause of mortality and the leading cause of disability. Using the clinical scale to predict the outcome of the patient play an important role in clinical practice. The Totaled Health Risks in Vascular Events (THRIVE) score has shown broad utility, allowing prediction of clinical outcome and death. Methods: A cross-sectional study conducting on 102 patients with acute ischemic stroke using THRIVE score. The outcome of patient was assessed by mRankin in the day of 30 after stroke. Statistic analysis using SPSS 15.0. Results: There was 60.4% patient in the group with THRIVE score 0 – 2 points having a good outcome (mRS 0 - 2), patient group with THRIVE score 6 - 9 having a high rate of bad outcome and mortality. Having a positive correlation between THRIVE score on admission and mRankin score at the day 30 after stroke with r = 0.712. THRIVE score strongly predicts clinical outcome with ROC-AUC was 0.814 (95% CI 0.735 - 0.893, p<0.001), Se 69%, Sp 84% and the cut-off was 2. THRIVE score strongly predicts mortality with ROC-AUC was 0.856 (95% CI 0.756 - 0.956, p<0.01), Se 86%, Sp 77% and the cut-off was 3. Analysis of prognostic factors by multivariate regression models showed that THRIVE score was only independent prognostic factor for the outcome of post stroke patients. Conclusions: The THRIVE score is a simple-to-use tool to predict clinical outcome, mortality in patients with ischemic stroke. Despite its simplicity, the THRIVE score performs better than several other outcome prediction tools. Key words: Ischemic stroke, THRIVE, prognosis, outcome, mortality

scholarly journals Smartphone GPS Locations of Students’ Movements to and from Campus

2021 ◽  
Vol 10 (8) ◽  
pp. 517
Author(s):  
Patricia K. Doyle-Baker ◽  
Andrew Ladle ◽  
Angela Rout ◽  
Paul Galpern
Keyword(s):  
Student Engagement ◽  
Environmental Factors ◽  
University Students ◽  
Regression Models ◽  
Bayesian Regression ◽  
Daily Movement ◽  
Campus Visit ◽  
Canadian City ◽  
Campus Activities ◽  
Ability And Willingness

For many university students, commuting to and from campus constitutes a large proportion of their daily movement, and therefore it may influence their ability and willingness to spend time on campus or to participate in campus activities. To assess student engagement on campus, we collected smartphone GPS location histories from volunteers (n = 280) attending university in a major Canadian city. We investigated how campus visit length and frequency were related to characteristics of the commute using Bayesian regression models. Slower commutes and commutes over longer distances were associated with more time spent but less frequent visits to campus. Our results demonstrate that exposure to campus life, and therefore the potential for student engagement, may relate not just to whether a student lives on or near campus, but also to urban environmental factors that interact to influence the commuting experience.

scholarly journals Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5906
Author(s):  
Xiaoting Zhou ◽  
Weicheng Wu ◽  
Ziyu Lin ◽  
Guiliang Zhang ◽  
Renxiang Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Landslide Susceptibility ◽  
Urban Areas ◽  
Support Vector ◽  
Susceptibility Map ◽  
Human Society ◽  
Learning Approaches ◽  
Prevention Measures ◽  
Landslide Occurrence ◽  
Better Than

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

scholarly journals Evidence of Nicotine Dependence in Adolescents Who Use Juul and Similar Pod Devices

2019 ◽  
Vol 16 (12) ◽  
pp. 2135 ◽  
Author(s):  
Rachel Boykan ◽  
Maciej L. Goniewicz ◽  
Catherine R. Messina
Keyword(s):  
Nicotine Dependence ◽  
Early Onset ◽  
Nicotine Addiction ◽  
Urinary Cotinine ◽  
Nicotine Content ◽  
The Past ◽  
Survey Responses

Background: The use of high-nicotine content e-cigarettes (so-called pods, such as Juul) among adolescents raises concerns about early onset of nicotine addiction. Methods: In this analysis of adolescents surveyed from April 2017–April 2018, we compare survey responses and urinary cotinine of pod vs. non-pod using past-week e-cigarette users aged 12–21. Results: More pod users categorized themselves as daily users compared to non-pod users (63.0% vs. 11.0%; p = 0.001); more pod than non-pod users had used e-cigarettes within the past day (76.2% vs. 29.6%; p = 0.001). More pod users responded affirmatively to nicotine dependence questions (21.4% vs. 7.1%; p = 0.04). Urinary cotinine levels were compared among those responding positively and negatively to dependence questions: those with positive responses had significantly higher urinary cotinine levels than those responding negatively. Conclusions: Adolescents who used pod products showed more signs of nicotine dependence than non-pod users. Pediatricians should be vigilant in identifying dependence symptoms in their patients who use e-cigarettes, particularly in those using pod devices.

BDNF and nicotine dependence: associations and potential mechanisms

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zeyi Huang ◽  
Daichao Wu ◽  
Xilin Qu ◽  
Meixiang Li ◽  
Ju Zou ◽  
...  
Keyword(s):  
Nicotine Dependence ◽  
Nicotinic Acetylcholine Receptors ◽  
Acetylcholine Receptors ◽  
Molecular Mechanisms ◽  
Public Health Problem ◽  
Nicotine Addiction ◽  
Tyrosine Kinase Receptor ◽  
Bdnf Expression ◽  
High Affinity Receptor ◽  
The Brain

AbstractSmoking is the leading preventable cause of death worldwide and tobacco addiction has become a serious public health problem. Nicotine is the main addictive component of tobacco, and the majority of people that smoke regularly develop nicotine dependence. Nicotine addiction is deemed to be a chronic mental disorder. Although it is well known that nicotine binds to the nicotinic acetylcholine receptors (nAChRs) and activates the mesolimbic dopaminergic system (MDS) to generate the pleasant and rewarding effects, the molecular mechanisms of nicotine addiction are not fully understood. Brain-derived neurotrophic factor (BDNF) is the most prevalent growth factor in the brain, which regulates neuron survival, differentiation, and synaptic plasticity, mainly through binding to the high affinity receptor tyrosine kinase receptor B (TrkB). BDNF gene polymorphisms are associated with nicotine dependence and blood BDNF levels are altered in smokers. In this review, we discussed the effects of nicotine on BDNF expression in the brain and summarized the underlying signaling pathways, which further indicated BDNF as a key regulator in nicotine dependence. Further studies that aim to understand the neurobiological mechanism of BDNF in nicotine addcition would provide a valuable reference for quitting smoking and developing the treatment of other addictive substances.

scholarly journals An analysis of Italian university students’ performance through segmented regression models: gender differences in STEM courses

Genus ◽  
2021 ◽  
Vol 77 (1) ◽  
Author(s):  
Andrea Priulla ◽  
Nicoletta D’Angelo ◽  
Massimo Attanasio
Keyword(s):  
Gender Differences ◽  
Regression Models ◽  
First Year ◽  
Strongly Correlated ◽  
Segmented Regression ◽  
Freshmen Students ◽  
Female Performance ◽  
And Mathematics ◽  
The Relationship ◽  
Better Than

AbstractThis paper investigates gender differences in university performances in Science, Technology, Engineering and Mathematics (STEM) courses in Italy, proposing a novel application through the segmented regression models. The analysis concerns freshmen students enrolled at a 3-year STEM degree in Italian universities in the last decade, with a focus on the relationship between the number of university credits earned during the first year (a good predictor of the regularity of the career) and the probability of getting the bachelor degree within 4 years. Data is provided by the Italian Ministry of University and Research (MIUR). Our analysis confirms that first-year performance is strongly correlated to obtaining a degree within 4 years. Furthermore, our findings show that gender differences vary among STEM courses, in accordance with the care-oriented and technical-oriented dichotomy. Males outperform females in mathematics, physics, chemistry and computer science, while females are slightly better than males in biology. In engineering, female performance seems to follow the male stream. Finally, accounting for other important covariates regarding students, we point out the importance of high school background and students’ demographic characteristics.

Abstract 3775: Average Blood Glucose and Mortality in Patients Hospitalized with Acute Myocardial Infarction

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Mikhail Kosiborod ◽  
Silvio Inzucchi ◽  
Harlan M Krumholz ◽  
Lan Xiao ◽  
Phillip G Jones ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Hospital Mortality ◽  
Regression Models ◽  
Independent Predictor ◽  
Laboratory Data ◽  
Logistic Regression Models ◽  
Average Blood Glucose ◽  
The Relationship ◽  
Multivariable Adjustment ◽  
Better Than

Background: Elevated blood glucose (BG) on admission is associated with higher mortality risk in patients (pts) hospitalized with AMI. However, the prognostic value of average BG, which reflects overall glycemic exposure much better than admission BG, is unknown. Furthermore, the nature of the relationship between average BG and mortality has not been determined. Methods: We evaluated a cohort of 16,871 AMI pts hospitalized from January 2000-December 2005, using Cerner Corporation’s Health Facts® database from 40 hospitals, which contains demographics, clinical and comprehensive laboratory data. Logistic regression models evaluated the nature of the relationship between mean BG during the entire AMI hospitalization and in-hospital mortality, after adjusting for multiple patient factors and confounders. Similar analyses were performed in subgroups of pts with and without diabetes (DM). Results: A J-shaped relationship was observed between mean BG and in-hospital mortality, which persisted after multivariable adjustment (Figure ). Mortality increased with each 10 mg/dL incremental rise in mean BG over >120 mg/dL, and with incremental decline in mean BG <80 mg/dL. The slope of these relationships was much steeper in pts without DM. Conclusions: Average BG during the entire AMI hospitalization is a powerful independent predictor of in-hospital mortality. Both persistent hyper- and hypoglycemia are associated with adverse prognosis. Whether strategies directed at optimizing BG control will improve survival remains to be established. Association Between Mean BG and In-Hospital Mortality After Multivariable Adjustment (Reference: Mean BG 100 to <110)

scholarly journals Environmental Factors Associated With Loa loa Microfilaria Prevalence and Intensity in Diverse Bioecological Zones of Cameroon

2021 ◽  
Vol 2 ◽  
Author(s):  
Xavier Badia-Rius ◽  
Hannah Betts ◽  
Samuel Wanji ◽  
David Molyneux ◽  
Mark J. Taylor ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Regression Models ◽  
Polynomial Regression ◽  
Canopy Cover ◽  
Nonparametric Tests ◽  
Tree Canopy ◽  
Mean Annual Temperature ◽  
Loa Loa ◽  
Factors Associated ◽  
Tree Canopy Cover

Loiasis (African Eye Worm) is a filarial infection caused by Loa loa and transmitted by Chrysops vectors, which are confined to the tropical rainforests of Central and West Africa. Loiasis is a major impediment to control and elimination programmes that use the drug ivermectin due to the risk of serious adverse events. There is an urgent need to better refine and map high-risk communities. This study aimed to quantify and predict environmental factors associated with loiasis across five bioecological zones in Cameroon. The L. loa microfilaria (mf) prevalence (%) and intensity (mf number/ml) data from 42 villages within an Equatorial Rainforest and Savannah region were examined in relation to climate, topographic and forest-related data derived from satellite remote sensing sources. Differences between zones and regions were examined using nonparametric tests, and the relationship between L. loa mf prevalence, mf intensity, and the environmental factors using polynomial regression models. Overall, the L. loa mf prevalence was 11.6%, L. loa intensity 927.4 mf/ml, mean annual temperature 23.7°C, annual precipitation 2143.2 mm, elevation 790 m, tree canopy cover 46.7%, and canopy height 19.3m. Significant differences between the Equatorial Rainforest and Savannah region were found. Within the Equatorial Rainforest region, no significant differences were found. However, within the Savannah region, significant differences between the three bioecological zones were found, and the regression models indicated that tree canopy cover and elevation were significant predictors, explaining 85.1% of the L. loa mf prevalence (adjusted R2 = 0.851; p&lt;0.001) and tree cover alone was significant, explaining 58.1% of the mf intensity (adjusted R2 = 0.581; p&lt;0.001). The study highlights that environmental analysis can help delineate risk at different geographical scales, which may be practical for developing larger scale operational plans for mapping and implementing safe effective interventions.

scholarly journals Exploring the Health-Promoting Potential of the “parkrun” Phenomenon: What Factors are Associated With Higher Levels of Participation?

2018 ◽  
Vol 33 (1) ◽  
pp. 13-23 ◽  
Author(s):  
Verity Cleland ◽  
Meredith Nash ◽  
Melanie J. Sharman ◽  
Suzi Claflin
Keyword(s):  
Physical Activity ◽  
Environmental Factors ◽  
Regression Models ◽  
Online Survey ◽  
Multivariate Analyses ◽  
Health Gain ◽  
Cross Sectional ◽  
Physically Active ◽  
Health Promoting ◽  
Intentions To Participate

Purpose: “ parkrun” is a free and increasingly popular weekly 5-km walk/run international community event, representing a novel setting for physical activity (PA) promotion. However, little is known about who participates or why. This study aimed to identify sociodemographic, health, behavioral, individual, social, and environmental factors associated with higher levels of participation. Design: Cross-sectional. Setting: Tasmania, Australia; June 2016. Participants: Three hundred seventy two adult parkrun participants. Measures: Online survey measuring sociodemographic, health, individual, social and environmental factors, parkrun participation, and PA. Analysis: Descriptive statistics, zero-truncated Poisson regression models. Results: Respondents (n = 371) were more commonly women (58%), aged 35 to 53 years (54%), and occasional or nonwalkers/runners (53%) at registration. A total of 44% had overweight/obesity. Half had non-adult children, most spoke English at home, and 7% reported PA-limiting illness/injury/disability. Average run/walk time was 30.2 ± 7.4 minutes. Compared to regular walkers/runners at registration, nonwalkers/runners were less commonly partnered, more commonly had overweight/obesity, less physically active, and had poorer self-rated health. Multivariate analyses revealed relative parkrun participation was inversely associated with education level and positively associated with interstate parkrun participation, perceived social benefits, self-efficacy for parkrun, and intentions to participate. Conclusion: parkrun attracts nonwalkers/runners and population groups hard to engage in physical activity. Individual- and social-level factors were associated with higher relative parkrun participation. parkrun’s scalability, accessibility, and wide appeal confers a research imperative to investigate its potential for public health gain.

Sign in / Sign up

Export Citation Format

Share Document