scholarly journals Association between Crash Attributes and Drivers’ Crash Involvement: A Study Based on Police-Reported Crash Data

2020 ◽  
Vol 17 (23) ◽  
pp. 9020
Author(s):  
Guofa Li ◽  
Weijian Lai ◽  
Xingda Qu
Keyword(s):  
Logistic Regression ◽  
Age Groups ◽  
Nearest Neighbors ◽  
Older Drivers ◽  
Driving Safety ◽  
Crash Data ◽  
Imbalance Problem ◽  
Different Types ◽  
Crash Types ◽  
Safety Enhancement

Understanding the association between crash attributes and drivers’ crash involvement in different types of crashes can help figure out the causation of crashes. The aim of this study was to examine the involvement in different types of crashes for drivers from different age groups, by using the police-reported crash data from 2014 to 2016 in Shenzhen, China. A synthetic minority oversampling technique (SMOTE) together with edited nearest neighbors (ENN) were used to solve the data imbalance problem caused by the lack of crash records of older drivers. Logistic regression was utilized to estimate the probability of a certain type of crashes, and odds ratios that were calculated based on the logistic regression results were used to quantify the association between crash attributes and drivers’ crash involvement in different types of crashes. Results showed that drivers’ involvement patterns in different crash types were affected by different factors, and the involvement patterns differed among the examined age groups. Knowledge generated from the present study could help improve the development of countermeasures for driving safety enhancement.

scholarly journals Entomological survey for identification of Aedes larval breeding sites and their distribution in Chattogram, Bangladesh

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Md. Sahidur Rahman ◽  
Md. Omar Faruk ◽  
Sumiya Tanjila ◽  
Nur Mohammad Sabbir ◽  
Najmul Haider ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Field Survey ◽  
Mosquito Control ◽  
Multiple Logistic Regression Analysis ◽  
Binary Logistic Regression ◽  
Breeding Sites ◽  
Entomological Survey ◽  
Different Types ◽  
Coconut Shells ◽  
Positive Properties

Abstract Background Studying the characteristics of Aedes mosquito habitats is essential to control the mosquito population. The objective of this study was to identify the breeding sites of Aedes larvae and their distribution in Chattogram, Bangladesh. We conducted an entomological survey in 12 different sub-districts (Thana) under Chattogram City, during the late monsoon (August to November) 2019. The presence of different wet containers along with their characteristics and immature mosquitoes was recorded in field survey data form. Larvae and/or pupae were collected and brought to the laboratory for identification. Results Different indices like house index, container index, and the Breteau index were estimated. The multiple logistic regression analysis was applied to identify habitats that were more likely to be positive for Aedes larvae/pupae. A total of 704 wet containers of 37 different types from 216 properties were examined, where 52 (7.39%) were positive for Aedes larvae or pupae. Tire, plastic buckets, plastic drums, and coconut shells were the most prevalent container types. The plastic group possessed the highest container productivity (50%) whereas the vehicle and machinery group was found as most efficient (1.83) in terms of immature Aedes production. Among the total positive properties, 8% were infested with Aedes aegypti, 2% with Aedes albopictus, and 1% contained both species Ae. aegypti and A. albopictus. The overall house index was 17.35%, the container index was 7%, and the Breteau index was 24.49. Containers in multistoried houses had significantly lower positivity compared to independent houses. Binary logistic regression represented that containers having shade were 6.7 times more likely to be positive than the containers without shade (p< 0.01). Conclusions These findings might assist the authorities to identify the properties, containers, and geographical areas with different degrees of risk for mosquito control interventions to prevent dengue and other Aedes-borne disease transmissions.

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

2021 ◽  
pp. 036119812110312
Author(s):  
Denis Elia Monyo ◽  
Henrick J. Haule ◽  
Angela E. Kitali ◽  
Thobias Sando
Keyword(s):  
Latent Class ◽  
Time Of Day ◽  
Older Drivers ◽  
Contributing Factors ◽  
Lighting Condition ◽  
Crash Data ◽  
Traffic Characteristics ◽  
Road Users ◽  
Traffic Conflicts ◽  
Penalized Logistic Regression

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

scholarly journals Review of Studies on Older Drivers’ Behavior and Stress—Methods, Results, and Outlook

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3503
Author(s):  
Yanning Zhao ◽  
Toshiyuki Yamamoto
Keyword(s):  
Age Groups ◽  
Older Drivers ◽  
Driving Simulators ◽  
Driving Experience ◽  
Older Driver ◽  
Vehicle Data ◽  
Vehicle Systems ◽  
Questionnaire Surveys ◽  
Definition Of ◽  
Short Time

This paper presents a review on relevant studies and reports related to older drivers’ behavior and stress. Questionnaires, simulators, and on-road/in-vehicle systems are used to collect driving data in most studies. In addition, research either directly compares older drivers and the other drivers or considers participants according to various age groups. Nevertheless, the definition of ‘older driver’ varies not only across studies but also across different government reports. Although questionnaire surveys are widely used to affordably obtain massive data in a short time, they lack objectivity. In contrast, biomedical information can increase the reliability of a driving stress assessment when collected in environments such as driving simulators and on-road experiments. Various studies determined that driving behavior and stress remain stable regardless of age, whereas others reported degradation of driving abilities and increased driving stress among older drivers. Instead of age, many researchers recommended considering other influencing factors, such as gender, living area, and driving experience. To mitigate bias in findings, this literature review suggests a hybrid method by applying surveys and collecting on-road/in-vehicle data.

scholarly journals Are the Acute Effects of THC Different in Aging Adults?

2021 ◽  
Vol 11 (5) ◽  
pp. 590
Author(s):  
Raeghan L. Mueller ◽  
Jarrod M. Ellingson ◽  
L. Cinnamon Bidwell ◽  
Angela D. Bryan ◽  
Kent E. Hutchison
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Age Groups ◽  
Young Group ◽  
Self Administration ◽  
Behavioral Measures ◽  
Younger Adults ◽  
Different Types ◽  
Before And After ◽  
Aging Adults

In recent years of expanding legalization, older adults have reported the largest increase in cannabis use of any age group. While its use has been studied extensively in young adults, little is known about the effects of THC in older adults and whether the risks of cannabis might be different, particularly concerning intoxication and cognition. The current study investigated whether age is associated with the deleterious effects of THC on cognitive performance and other behavioral measures before and after ad libitum self-administration of three different types of cannabis flower (THC dominant, THC + CBD, and CBD dominant). Age groups consisted of young adults (ages 21–25) and older adults (ages 55–70). Controlling for pre-use scores on all measures, the THC dominant chemovar produced a greater deleterious effect in younger adults compared with older adults in tests of learning and processing speed, whereas there were no differences between old and young in the effects of the other chemovars. In addition, the young group reported greater cannabis craving than the older group after using the THC chemovar. Consistent with some reports in the preclinical literature, the findings suggest that older adults may be less sensitive to the effects of THC on cognitive and affective measures.

Spontaneous Glomerulonephritis in Dogs: II. Correlation of Glomerulonephritis with Age, Chronic Interstitial Nephritis and Extrarenal Lesions

1977 ◽  
Vol 14 (2) ◽  
pp. 121-127 ◽  
Author(s):  
R. Müller-Peddinghaus ◽  
G. Trautwein
Keyword(s):  
Interstitial Nephritis ◽  
Membranoproliferative Glomerulonephritis ◽  
Age Groups ◽  
Renal Amyloidosis ◽  
Morphologic Study ◽  
Different Types ◽  
End Stage ◽  
Chronic Interstitial Nephritis ◽  
Slight Damage ◽  
Severe Fibrosis

A morphologic study of 103 dogs, including two with renal amyloidosis, showed that different types of diffuse glomerulonephritis are correlated with different age groups. Membranous and membranoproliferative glomerulonephritis were more common in middle-aged and older animals, whereas mesangial lesions were found predominantly in younger dogs and considered to be early glomerular changes. Glomerulonephritis largely occurred independently of interstitial nephritis. The incidence of interstitial lesions was 71%. Chronic interstitial nephritis was rare in dogs under 1 year old. Glomerulonephritis did not seem to induce interstitial nephritis. Glomerulonephritis occurred not only in kidneys with severe interstitial damage, but also in those with slight damage. This indicated that glomerulonephritis occurred independently of interstitial nephritis. In end-stage kidneys with severe fibrosis, mesangial changes seemed to predominate.

scholarly journals Epidemiological Distribution and Subtype Analysis of Premenstrual Dysphoric Disorder Syndromes and Symptoms Based on TCM Theories

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Mingqi Qiao ◽  
Peng Sun ◽  
Haijun Wang ◽  
Yang Wang ◽  
Xianghong Zhan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Clinical Diagnosis ◽  
Premenstrual Dysphoric Disorder ◽  
Diagnosis And Treatment ◽  
Epidemiological Investigation ◽  
Different Types

We performed an epidemiological investigation of subjects with premenstrual dysphoric disorder (PMDD) to identify the clinical distribution of the major syndromes and symptoms. The pathogenesis of PMDD mainly involves the dysfunction of liver conveyance and dispersion. Excessive liver conveyance and dispersion are associated with liver-qi invasion syndrome, while insufficient liver conveyance and dispersion are expressed as liver-qi depression syndrome. Additionally, a nonconditional logistic regression was performed to analyze the symptomatic features of liver-qi invasion and liver-qi depression. As a result of this analysis, two subtypes of PMDD are proposed, namely, excessive liver conveyance and dispersion (liver-qi invasion syndrome) and insufficient liver conveyance and dispersion (liver-qi depression syndrome). Our findings provide an epidemiological foundation for the clinical diagnosis and treatment of PMDD based on the identification of different types.

Phone-Related Distracted Walking Injuries as a Function of Age and Walking Environment

2021 ◽  
Vol 65 (1) ◽  
pp. 611-615
Author(s):  
Zheng Haolan ◽  
Isabella M. Campbell ◽  
Wayne C.W. Giang*
Keyword(s):  
Emergency Department ◽  
Surveillance System ◽  
Age Groups ◽  
Injury Surveillance ◽  
Future Research ◽  
Home Environments ◽  
Injury Surveillance System ◽  
Different Types ◽  
Ed Visits ◽  
Walking Environment

Using phones while walking has been a factor that has led to accidents and injuries. However, few studies have analyzed the propensity of injuries due to distracted walking for different age groups and in different types of walking environments. This study aims to examine the number of emergency department (ED) visits due to distracted walking across different age groups and walking environments using a publicly available dataset, the National Electronic Injury Surveillance System (NEISS) database. The results suggest that there were an estimated 29140 distracted walking injuries between the years 2011-2019. Individuals between 11 and 20 years old had the most injuries, followed by 21 to 30, and 31 to 40. Furthermore, the proportion of estimated injuries that occurred in different walking environments differed across age groups. Safety-orient interventions for future research for stairs and home environments were also recommended in the present study.

Phonological Profile of Patients With Velopharyngeal Dysfunction and Palatal Anomalies

2021 ◽  
pp. 1-15
Author(s):  
Ariela Nachmani ◽  
Muhamed Masalha ◽  
Firas Kassem
Keyword(s):  
Cleft Palate ◽  
Cleft Lip ◽  
Cleft Lip And Palate ◽  
Age Groups ◽  
Phonological Process ◽  
Phonological Processes ◽  
Process Error ◽  
Submucous Cleft Palate ◽  
Different Types

Purpose This purpose of this study was to assess the frequency and types of phonological process errors in patients with velopharyngeal dysfunction (VPD) and the different types of palatal anomalies. Method A total of 808 nonsyndromic patients with VPD, who underwent follow-up at the Center for Cleft Palate and Craniofacial Anomalies, from 2000 to 2016 were included. Patients were stratified into four age groups and five subphenotypes of palatal anomalies: cleft lip and palate (CLP), cleft palate (CP), submucous cleft palate (SMCP), occult submucous cleft palate (OSMCP), and non-CP. Phonological processes were compared among groups. Results The 808 patients ranged in age from 3 to 29 years, and 439 (54.3%) were male. Overall, 262/808 patients (32.4%) had phonological process errors; 80 (59.7%) ages 3–4 years, 98 (40, 0%) ages 4.1–6 years, 48 (24.7%) 6.1–9 years, and 36 (15.3%) 9.1–29 years. Devoicing was the most prevalent phonological process error, found in 97 patients (12%), followed by cluster reduction in 82 (10.1%), fronting in 66 (8.2%), stopping in 45 (5.6%), final consonant deletion in 43 (5.3%), backing in 30 (3.7%), and syllable deletion and onset deletion in 13 (1.6%) patients. No differences were found in devoicing errors between palatal anomalies, even with increasing age. Phonological processes were found in 61/138 (44.20%) with CP, 46/118 (38.1%) with SMCP, 61/188 (32.4%) with non-CP, 70/268 (26.1%) with OSMCP, and 25/96 (26.2%) with CLP. Phonological process errors were most frequent with CP and least with OSMCP ( p = .001). Conclusions Phonological process errors in nonsyndromic VPD patients remained relatively high in all age groups up to adulthood, regardless of the type of palatal anomaly. Our findings regarding the phonological skills of patients with palatal anomalies can help clarify the etiology of speech and sound disorders in VPD patients, and contribute to general phonetic and phonological studies.

scholarly journals Predicting hospitalization following psychiatric crisis care using machine learning

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Matthijs Blankers ◽  
Louk F. M. van der Post ◽  
Jack J. M. Dekker
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Ensemble Model ◽  
K Nearest Neighbors ◽  
Crisis Care

Abstract Background Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate the accuracy of ten machine learning algorithms, including the generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact. We also evaluate an ensemble model to optimize the accuracy and we explore individual predictors of hospitalization. Methods Data from 2084 patients included in the longitudinal Amsterdam Study of Acute Psychiatry with at least one reported psychiatric crisis care contact were included. Target variable for the prediction models was whether the patient was hospitalized in the 12 months following inclusion. The predictive power of 39 variables related to patients’ socio-demographics, clinical characteristics and previous mental health care contacts was evaluated. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared and we also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis and the five best performing algorithms were combined in an ensemble model using stacking. Results All models performed above chance level. We found Gradient Boosting to be the best performing algorithm (AUC = 0.774) and K-Nearest Neighbors to be the least performing (AUC = 0.702). The performance of GLM/logistic regression (AUC = 0.76) was slightly above average among the tested algorithms. In a Net Reclassification Improvement analysis Gradient Boosting outperformed GLM/logistic regression by 2.9% and K-Nearest Neighbors by 11.3%. GLM/logistic regression outperformed K-Nearest Neighbors by 8.7%. Nine of the top-10 most important predictor variables were related to previous mental health care use. Conclusions Gradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was in most cases modest. The results show that a predictive accuracy similar to the best performing model can be achieved when combining multiple algorithms in an ensemble model.

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