scholarly journals Non-participation modestly increased with distance to the examination clinic among adults in Finnish health examination surveys

2017 ◽  
Vol 46 (7) ◽  
pp. 752-754
Author(s):  
Jaakko Reinikainen ◽  
Perttu Saarsalmi ◽  
Tommi Härkänen ◽  
Pekka Jousilahti ◽  
Juha Karvanen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Population Health ◽  
Logistic Regression Model ◽  
Additional Analysis ◽  
Health Examination ◽  
Participation Rates ◽  
Related Factors ◽  
Health Related ◽  
At Home

Aims: Health examination surveys (HES) provide important information about population health and health-related factors, but declining participation rates threaten the representativeness of collected data. It is hard to conduct national HESs at examination clinics near to every sampled individual. Thus, it is interesting to look into the possible association between the distance from home to the examination clinic and non-participation, and whether there is a certain distance after which the participation activity decreases considerably. Methods: Data from two national HESs conducted in Finland in 2011 and 2012 were used and a logistic regression model was fitted to investigate how distance was related to non-participation. Results: We found out that non-participation modestly increased with distance to the examination clinic. An additional analysis indicated that the option of having an examination at home may decrease the effect of distance to participation. Conclusions: Long distances from home to the examination clinic are one reason for low participation activity. Possible bias caused by these differences in participation could be decreased by providing the option of a home examination.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

P5710Clinical applications of machine learning for prediction of incident atrial fibrillation from the general population: a nationwide cohort study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
I.-S Kim ◽  
P S Yang ◽  
H T Yu ◽  
T H Kim ◽  
J S Uhm ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
General Population ◽  
Regression Model ◽  
National Health ◽  
Logistic Regression Model ◽  
Learning System ◽  
Health Examination ◽  
Clinical Variables

Abstract Background To evaluate the ability of machine learning algorithms to predict incident atrial fibrillation (AF) from the general population using health examination items. Methods We included 483,343 subjects who received national health examinations from the Korean National Health Insurance Service-based National Sample Cohort (NHIS-NSC). We trained deep neural network model (DNN) of a deep learning system and decision tree model (DT) of a machine learning system using clinical variables and health examination items (including age, sex, body mass index, history of heart failure, hypertension or diabetes, baseline creatinine, and smoking and alcohol intake habits) to predict incident AF using a training dataset of 341,771 subjects constructed from the NHIS-NSC database. The DNN and DT were validated using an independent test dataset of 141,572 remaining subjects. C-indices of DNN and DT for prediction of incident AF were compared with that of conventional logistic regression model. Results During 1,874,789 person·years (mean±standard-deviation age 47.7±14.4 years, 49.6% male), 3,282 subjects with incident AF were observed. In the validation dataset, 1,139 subjects with incident AF were observed. The c-indices of the DNN and DT for incident AF prediction were 0.828 [0.819–0.836] and 0.835 [0.825–0.844], and were significantly higher (p<0.01) than conventional logistic regression model (c-index=0.789 [0.784–0.794]). Conclusions Application of machine learning using simple clinical variables and health examination items was helpful to predict incident AF in the general population. Prospective study is warranted to construct an individualized precision medicine.

That-variation in German and Spanish L2 English

2014 ◽  
Vol 6 (2) ◽  
pp. 271-299 ◽  
Author(s):  
STEFANIE WULFF ◽  
NICHOLAS LESTER ◽  
MARIA T. MARTINEZ-GARCIA
Keyword(s):  
Logistic Regression ◽  
Second Language ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Native Speakers ◽  
Related Factors ◽  
Cue Validity ◽  
L2 Learners ◽  
Complement Clauses

abstractIn certain English finite complement clauses, inclusion of the complementizer that is optional. Previous research has identified various factors that influence when native speakers tend to produce or omit the complementizer, including syntactic weight, clause juncture constraints, and predicate frequency. The present study addresses the question to what extent German and Spanish learners of English as a second language (L2) produce and omit the complementizer under similar conditions. 3,622 instances of English adjectival, object, and subject complement constructions were retrieved from the International Corpus of English and the German and Spanish components of the International Corpus of Learner English. A logistic regression model suggests that L2 learners’ and natives’ production is largely governed by the same factors. However, in comparison with native speakers, L2 learners display a lower rate of complementizer omission. They are more impacted by processing-related factors such as complexity and clause juncture, and less sensitive to verb-construction cue validity.

scholarly journals Evaluation of Workload and Human Errors in Nurses

2019 ◽  
Author(s):  
Mahshid Naseri Karimvand ◽  
Naser Hasheminejad ◽  
Ali Faghihi Zarandi ◽  
Yones Jahani
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Human Error ◽  
Logistic Regression Model ◽  
Control Strategies ◽  
Statistical Significance ◽  
Medical Sciences ◽  
Related Factors ◽  
Human Errors ◽  
Significance Level

Background: Identification of human errors and their related factors in nurses dealing with the health of humans is important. Considering that much workload can increase the risk of human error, this study aimed to investigate the relationship between workload and human errors among nurses working in educational hospitals of Kerman University of Medical Sciences. Methods: This descriptive-analytical study was performed on 145 nurses from educational hospitals affiliated to Kerman University of Medical Sciences in 20. The workload was evaluated using the NASA-TLX questionnaire and human errors with SHERPA technique. The statistical test used was logistic regression model and the statistical significance level was considered <0.05 and the samples were selected randomly. The sample size was selected based on the percentage of functional errors reported by a study on human error assessment related to the duties of nurses in Semnan. Results: A total of 138 probable errors were detected in the nursing staff of these hospitals, 74% of nurses committed errors in seven main duties during their service. Patient medication with the highest frequency (34%) followed by the injection of the drug to the patient with a frequency of 23% were the most frequently committed errors by nurses. The findings of the research showed that workload in 53.1% of the nurses was very high and in 43.1% of the nurses was high, the results of the logistic regression model showed that there was no significant relationship between errors and workload in nurses. Conclusion: The results of the study showed that the average workload and human error in the nurses were high. Therefore, control strategies such as holding training sessions, implementation of clinical governance program in all wards, recruitment of adequate nurses, reduction of workload, reduction of work hours and the appropriate patient/nurse ratio should be given attention by the hospital managers depending on conditions; as well as the prevention of the two reported errors should be given top priority in corrective measures.

scholarly journals Analysis of comparable academic performance: panel logistic regression model

2010 ◽  
Vol 51 ◽  
Author(s):  
Marijus Radavičius ◽  
Pavel Samusenko
Keyword(s):  
Logistic Regression ◽  
Academic Performance ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Conditional Logistic Regression ◽  
Related Factors ◽  
Conditional Logistic Regression Model ◽  
Student Academic Performance

Conditional logistic regression model is fitted to data of student academic performance. This enables one to compare difficulties of different examinations and to identify related factors.

scholarly journals The Current Status of Health Promotion Lifestyle and Its Related Factors in Chinese Residents

2021 ◽  
Author(s):  
Qianqian Liu ◽  
Shusheng Huang ◽  
Xiaoyuan Qu ◽  
Aitian Yin
Keyword(s):  
Health Promotion ◽  
Logistic Regression ◽  
Regression Model ◽  
Behavioral Intention ◽  
Logistic Regression Model ◽  
Pearson Correlation ◽  
Current Status ◽  
Related Factors ◽  
Affective Factors ◽  
Factors Influencing

Abstract Objectives: This study aimed to explore the current status of Chinese residents' health promotion lifestyle and its influencing factors, especially explore how health attitudes affect health promotion lifestyle, thus can make targeted recommendations for health promotion in China and similar areas.Study design: This study was based on a household survey of 1769 adults aged 18 and over from Shandong Province of China conducted in 2018.Methods: A cross-sectional, face-to-face survey design was used. The between-group measured data were compared by One-way ANOVA or t-tests. The correlation between the health attitude and health promotion lifestyle was examined by Pearson correlation. Logistic regression model was used to examine the related factors influencing Chinese residents’ health promotion lifestyle.Results: The health promotion lifestyle of residents in Chinese residents was not good. Significant differences existed in health promotion lifestyle among different genders, education levels, income levels, marital status, and health attitude (Ps <0.001). Multivariable Logistic regression model found that gender, education level, annual family per capita income, affection and behavioral intention in health attitude were key factors influencing Chinese residents’ health promotion lifestyle (Ps<0.001). Conclusions: Individual health responsibilities need to be further strengthened. More affective factors and operable measures should be added to enhance health affection and health behavioral intention, so as to enhance health promotion lifestyle.

scholarly journals Nature contact by window opening and depressive symptoms among Older Adults: A cross-sectional analysis of the Chinese Longitudinal Healthy Longevity Survey

2020 ◽  
Author(s):  
Xurui Jin ◽  
Hao Zhang ◽  
Kehan Zhang ◽  
Yu Leng ◽  
Yali Zhao ◽  
...  
Keyword(s):  
Older Adults ◽  
Logistic Regression ◽  
Depressive Symptoms ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Window Opening ◽  
Older Populations ◽  
At Home

BACKGROUND: Recent evidence suggests that window-view and window-ventilation may contribute to mental wellbeing. Compared to their younger counterparts, older adults spend more time at home and have less contact with natural environments due to social isolation and physical decline. However, the association of residential window-opening with depressive symptoms is understudied among older populations. METHODS: We used data from a community-based cohort study conducted in 23 provinces of China including 13,125 adults age 65 years or older (mean age: 83.8 years [SD: 11.1]). We created the window opening index (WOI) as a proxy of window-view and window-ventilation, leveraging relevant data on self-reported frequencies of manual window opening at home. Depressive symptoms were assessed by the 10-item version of the Centre for Epidemiological Studies Depression scale no fewer than 10. We used multivariate logistic regression model to examine the association between window opening and depressive symptoms. RESULTS: In the multivariate logistic regression model controlling for a set of well-designed mood-influencing environmental and individual-level covariates, a protective effect of window opening on depressive symptoms was observed, with 16% lower odds per interquartile increment in WOI (odds ratio: 0.84, 95%-CI: 0.81-0.87). Subgroup analyses indicated that the beneficial effects were more pronounced among those with higher socioeconomic status (higher levels of education, urban residents) and better surrounding environments (lower exposure of ambient fine particular matter, proximity to park, or higher levels of residential greenness). DISCUSSION: The results point to the benefits of well-designed household window-opening environments on the mental health of older populations and suggest a synergistic effect of window-opening and favorable surrounding natural environment.

scholarly journals Which Factors Affect Probability of Women Using Prenatal Care? The Case of Bangladesh

2014 ◽  
Vol 2 (2) ◽  
pp. 39-46
Author(s):  
Sharjil Muktafi Haque ◽  
A. K Enamul Haque
Keyword(s):  
Logistic Regression ◽  
Prenatal Care ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cross Sectional ◽  
Policy Makers ◽  
Factors Affecting ◽  
Health Related ◽  
Insight Into

This paper determines the probability of women in Bangladesh taking prenatal care based on changes in socioeconomic and health-related variables. Insight into factors affecting prenatal care usage will help policy-makers redirect health-related strategies and policies in more equitable directions. We used a total of 1,099 cross-sectional observations from Bangladesh Bureau of Statistics (2000) to estimate a logistic regression model. Our results show that education and income is positively associated with odds of women taking prenatal care while increase in age reduces odds of taking this service. We end by making several policy-relevant recommendations.

scholarly journals The Prevalence of Glaucoma and Its Related Factors in Rural Residents: A Cross-Sectional Study in Jiangxi, China

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xiaojun Zhou ◽  
Qin Zhu ◽  
Jinglin Yi ◽  
Jiajunni Li ◽  
Qi Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Alcohol Consumption ◽  
Physical Exercise ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Vegetable Consumption ◽  
Jiangxi Province ◽  
Cluster Sampling ◽  
Rural Residents ◽  
Related Factors

Background. This study aims to investigate the prevalence of glaucoma and its related factors among residents aged 40 and over in Jiangxi Province, China, and provide a scientific basis for the prevention and control of glaucoma. Methods. The cluster sampling method was used to randomly select six townships. Similarly, eight villages were randomly selected from each sample township. A total of 5385 rural residents from 48 villages were collected for a questionnaire survey. A logistic regression model was used to explore the personal behavioral factors related to glaucoma. Results. Among the 5385 participants, the prevalence rate of glaucoma was 1.4%. The logistic regression model found that alcohol consumption, vegetable consumption, physical exercise, daily reading time, and frequent reading environment were related to glaucoma. Conclusion. To prevent the occurrence of glaucoma, it is important for rural residents to reduce the frequency of alcohol consumption, increase the frequency of vegetable consumption and physical exercise, control the length of daily reading, and read in a moderately lit environment.

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