scholarly journals Latent class analysis for identification of occupational accident casualty profiles in the selected Polish manufacturing sector

2021 ◽  
Vol 16 (4) ◽  
pp. 485-499
Author(s):  
M. Nowakowska ◽  
M. Pajecki
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Manufacturing Sector ◽  
Statistical Modelling ◽  
Occupational Accidents ◽  
Latent Classes ◽  
Occupational Accident ◽  
Class Analysis ◽  
Data Set ◽  
Wood Processing

The objective of the analysis is identifying profiles of occupational accident casualties as regards production companies to provide the necessary knowledge to facilitate the preparation and management of a safe work environment. Qualitative data characterizing employees injured in accidents registered in Polish wood processing plants over a period of 10 years were the subject of the research. The latent class analysis (LCA) method was employed in the investigation. This statistical modelling technique, based on the values of selected indicators (observed variables) divides the data set into separate groups, called latent classes, which enable the definition of patterns. A procedure which supports the decision as regards the number of classes was presented. The procedure considers the quality of the LCA model and the distinguishability of the classes. Moreover, a method of assessing the importance of indicators in the patterns description was proposed. Seven latent classes were obtained and illustrated by the heat map, which enabled the profiles identification. They were labelled as follows: very serious, serious, moderate, minor (three latent classes), slight. Some recommendations were made regarding the circumstances of occupational accidents with the most severe consequences for the casualties.

scholarly journals Applying latent class analysis in the identification of occupational accident patterns

2020 ◽  
Vol 2020 (146) ◽  
pp. 339-355
Author(s):  
Marzena NOWAKOWSKA ◽  
◽  
Michał PAJĘCKI ◽  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Severe Accident ◽  
Occupational Accidents ◽  
Occupational Accident ◽  
Class Analysis ◽  
Data Set ◽  
Pilot Studies ◽  
Individual Record

Purpose: The objective of the study is to use selected data mining techniques to discover patterns of certain recurring mechanisms related to the occurrence of occupational accidents in relation to production processes. Design/methodology/approach: The latent class analysis (LCA) method was employed in the investigation. This statistical modeling technique enables discovering mutually exclusive homogenous classes of objects in a multivariate data set on the basis of observable qualitative variables, defining the class homogeneity in terms of probabilities. Due to a bilateral agreement, Statistics Poland provided individual record-level real data for the research. Then the data were preprocessed to enable the LCA model identification. Pilot studies were conducted in relation to occupational accidents registered in production plants in 2008-2017 in the Wielkopolskie voivodeship. Findings: Three severe accident patterns and two light accident patterns represented by latent classes were obtained. The classes were subjected to descriptive characteristics and labeling, using interpretable results presented in the form of probabilities classifying categories of observable variables, symptomatic for a given latent class. Research limitations/implications: The results from the pilot studies indicate the necessity to continue the research based on a larger data set along with the analysis development, particularly as regards selecting indicators for the latent class model characterization. Practical implications: The identification of occupational accident patterns related to the production process can play a vital role in the elaboration of efficient safety countermeasures that can help to improve the prevention and outcome mitigation of such accidents among workers. Social implications: Creating a safe work environment comprises the quality of life of workers, their families, thus affirming the enterprises' principles and values in the area of corporate social responsibility. Originality/value: The investigation showed that latent class analysis is a promising tool supporting the scientific research in discovering the patterns of occupational accidents. The proposed investigation approach indicates the importance for the research both in terms of the availability of non-aggregated occupational accident data as well as the type of value aggregation of the variables taken for the analysis.

Examining Associations Between Race, Gender, Alcohol Use, School Performance, and Patterns of Bullying in the School Context: A Latent Class Analysis

2021 ◽  
pp. 088626052199912
Author(s):  
Valdemir Ferreira-Junior ◽  
Juliana Y. Valente ◽  
Zila M. Sanchez
Keyword(s):  
Alcohol Use ◽  
Latent Class Analysis ◽  
School Performance ◽  
Latent Class ◽  
School Context ◽  
Bullying Victimization ◽  
Latent Classes ◽  
Class Analysis ◽  
Seventh Grade ◽  
Sociodemographic Variables

Although many studies addressed bullying occurrence and its associations, they often use individual variables constructed from few items that probably are inadequate to evaluate bullying severity and type. We aimed to identify involvement patterns in bullying victimization and perpetration, and its association with alcohol use, school performance, and sociodemographic variables. Baseline assessment of a randomized controlled trial were used and a latent class analysis was conducted to identify bullying patterns among 1,742 fifth-grade and 2,316 seventh-grade students from 30 public schools in São Paulo, Brazil. Data were collected using an anonymous self-reported, audio-guided questionnaire completed by the participants on smartphones. Multinomial logistic regressions were performed to verify how covariant variables affected bullying latent classes. Both grades presented the same four latent classes: low bullying, moderate bullying victimization, high bullying victimization, and high bullying victimization and perpetration. Alcohol use was associated with all bullying classes in both grades, with odds ratio up to 5.36 (95% CI 3.05; 10.38) among fifth graders from the high bullying victimization and perpetration class. Poor school performance was also strongly associated with this class (aOR = 10.12, 95%CI = 4.19; 24.41). Black/brown 5th graders were 3.35 times more likely to fit into the high bullying victimization class (95% CI 1.34; 8.37). Lack of evidence for association of sociodemographic variables and bullying latent class among seventh-grade students was found. Bullying and alcohol use are highly harmful behaviors that must be prevented. However, prevention programs should consider how racial and gender issues are influencing the way students experience violence.

Risk Factors Explaining Military Deaths From Suicide, 2008–2017: A Latent Class Analysis

2021 ◽  
pp. 0095327X2110469
Author(s):  
Scott D. Landes ◽  
Janet M. Wilmoth ◽  
Andrew S. London ◽  
Ann T. Landes
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Latent Class Analysis ◽  
Military Service ◽  
Latent Class ◽  
Service Providers ◽  
Service Members ◽  
Latent Classes ◽  
Class Analysis ◽  
Military Service Members

Military suicide prevention efforts would benefit from population-based research documenting patterns in risk factors among service members who die from suicide. We use latent class analysis to analyze patterns in identified risk factors among the population of 2660 active-duty military service members that the Department of Defense Suicide Event Report (DoDSER) system indicates died by suicide between 2008 and 2017. The largest of five empirically derived latent classes was primarily characterized by the dissolution of an intimate relationship in the past year. Relationship dissolution was common in the other four latent classes, but those classes were also characterized by job, administrative, or legal problems, or mental health factors. Distinct demographic and military-status differences were apparent across the latent classes. Results point to the need to increase awareness among mental health service providers and others that suicide among military service members often involves a constellation of potentially interrelated risk factors.

Competence Differences in Literacy, Numeracy, and Problem Solving According to Sex

2019 ◽  
Vol 69 (2) ◽  
pp. 101-119 ◽  
Author(s):  
Seher Yalcin
Keyword(s):  
Problem Solving ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Classes ◽  
Class Analysis ◽  
Country Level ◽  
Successful Group ◽  
Study Population ◽  
Multilevel Latent Class Analysis ◽  
International Assessment

This study aimed to determine individual- and country-level latent classes in literacy, numeracy and problem-solving competencies of individuals participating in the Programme for the International Assessment of Adult Competencies 2015. Specifically, it sought to distinguish these classes in relation to individuals’ sex and to identify the state of prediction of the determined latent classes by each person’s level of education. The study population consisted of 116,301 adults aged 16 to 65 years in 20 countries. Multilevel latent class analysis was conducted to consider the nested data structure and determine the number of latent classes. According to the results of the multilevel latent class analysis, Turkey and Chile were in the low achievement group in all skills, while Japan was in the most successful group. Moreover, the results revealed that sex and education level had a considerable influence on certain competence levels.

scholarly journals Clustering of Modifiable Behavioral Risk Factors and Their Association with All-Cause Mortality in Taiwan’s Adult Population: a Latent Class Analysis

2021 ◽  
Author(s):  
Shikha Kukreti ◽  
Tsung Yu ◽  
Po Wei Chiu ◽  
Carol Strong
Keyword(s):  
Physical Activity ◽  
Alcohol Consumption ◽  
Health Behavior ◽  
Latent Class Analysis ◽  
Risk Behaviors ◽  
Latent Class ◽  
Latent Classes ◽  
Class Analysis ◽  
Unhealthy Behavior ◽  
All Cause Mortality

Abstract Background Modifiable risk behaviors, such as smoking, diet, alcohol consumption, physical activity, and sleep, are known to impact health. This study aims toward identifying latent classes of unhealthy lifestyle behavior, exploring the correlations between sociodemographic factors, identifying classes, and further assessing the associations between identified latent classes and all-cause mortality. Methods For this study, the data were obtained from a prospective cohort study in Taiwan. The participants’ self-reported demographic and behavioral characteristics (smoking, physical activity, alcohol consumption, fruit and vegetable intake, and sleep) were used. Latent class analysis was used to identify health-behavior patterns, and Cox proportional hazard regression analysis was used to find the association between the latent class of health-behavior and all-cause mortality. Results A complete dataset was obtained from 290,279 participants with a mean age of 40 (12.4). Seven latent classes were identified, characterized as having a 100% likelihood of at least one unhealthy behavior coupled with the probability of having the other four unhealthy risk behaviors. This study also shows that latent health-behavior classes are associated with mortality, suggesting that they are representative of a healthy lifestyle. Finally, it appeared that multiple risk behaviors were more prevalent in younger men and individuals with low socioeconomic status. Conclusions There was a clear clustering pattern of modifiable risk behaviors among the adults under consideration, where the risk of mortality increased with increases in unhealthy behavior. Our findings can be used to design customized disease prevention programs targeting specific populations and corresponding profiles identified in the latent class analysis.

Longitudinal Adaptive Pattern Analysis of Child Abuse Victims During Childhood and Adolescence

2019 ◽  
Vol 123 (6) ◽  
pp. 2125-2146
Author(s):  
In Shil Paik ◽  
Sungsik Ahn ◽  
Sang Min Lee
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Transition Probabilities ◽  
Latent Transition Analysis ◽  
Childhood And Adolescence ◽  
Class Analysis ◽  
Data Set ◽  
Transition Analysis ◽  
Korean Children ◽  
Latent Transition

Adopting a contextual, systematic perspective, the present study aimed to understand whether an individual’s adverse circumstances in one domain have a continuous effect on his or her maladaptation in another domain. The Korean Children and Youth Panel Survey data set, comprising a stratified sample of 1,932 students recruited from 95 schools from 16 cities in Korea, was used for pattern identification, latent class analysis, and latent transition analysis. Consistent with Masten’s resilience model, latent class analysis was used to identify the following four types of patterns: resilient, maladaptive, vulnerable, and competent-unchallenged. These four patterns were clearly identified at Time 1, Time 2, and Time 3. Latent transition analysis was used to identify the continuity and change patterns in the four groups across the developmental pathology. The transition probabilities from Time 1 to Time 2 were relatively unstable, with many variations observed among the latent classes; however, the transition probabilities from Time 2 to Time 3 remained relatively stable. An in-depth discussion of the findings and their implications is provided.

scholarly journals The trajectories of depressive symptoms expressed in early childhood differ between boys and girls

2018 ◽  
Keyword(s):  
Early Childhood ◽  
Depressive Symptoms ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Classes ◽  
Class Analysis ◽  
Washington University

A study by Diana Whalen and colleagues at Washington University has used latent class analysis (LCA) to identify and define the trajectories of latent classes of depressive symptoms in early childhood.

scholarly journals Subtypes of nurses’ mental workload and interaction patterns with fatigue and work engagement during coronavirus disease 2019 (COVID-19) outbreak: A latent class analysis

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jing Wu ◽  
Husheng Li ◽  
Zhaohui Geng ◽  
Yanmei Wang ◽  
Xian Wang ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Work Engagement ◽  
Latent Class ◽  
Mental Workload ◽  
Latent Classes ◽  
Interaction Patterns ◽  
Class Analysis ◽  
Cross Sectional ◽  
Self Evaluation ◽  
Evaluation Group

Abstract Background Nurses play critical roles when providing health care in high-risk situations, such as during the COVID-19 outbreak. However, no previous study had systematically assessed nurses’ mental workloads and its interaction patterns with fatigue, work engagement and COVID-19 exposure risk. Methods A cross-sectional study was conducted via online questionnaire. The NASA Task Load Index, Fatigue Scale-14, and Utrecht Work Engagement Scale were used to assess nurses’ mental workload, fatigue and work engagement, respectively. A total of 1337 valid questionnaires were received and analyzed. Nurses were categorized into different subgroups of mental workload via latent class analysis (LCA). Cross-sectional comparisons, analysis of covariance (ANCOVA), and multivariate (or logistic) regression were subsequently performed to examine how demographic variables, fatigue and work engagement differ among nurses belonging to different subgroups. Results Three latent classes were identified based on the responses to mental workload assessment: Class 1 – low workload perception & high self-evaluation group (n = 41, 3.1%); Class 2 – medium workload perception & medium self-evaluation group (n = 455, 34.0%); and Class 3 – high workload perception & low self-evaluation group (n = 841, `62.9%). Nurses belonging into class 3 were most likely to be older and have longer professional years, and displayed higher scores of fatigue and work engagement compared with the other latent classes (p < 0.05). Multivariate analysis showed that high cognitive workload increased subjective fatigue, and mental workload may be positively associated with work engagement. Group comparison results indicated that COVID-19 exposure contributed to significantly higher mental workload levels. Conclusions The complex scenario for the care of patients with infectious diseases, especially during an epidemic, raises the need for improved consideration of nurses’ perceived workload, as well as their physical fatigue, work engagement and personal safety when working in public health emergencies.

scholarly journals Physical frailty and cognitive impairment in older nursing home residents: a latent class analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yiyang Yuan ◽  
Kate L. Lapane ◽  
Jennifer Tjia ◽  
Jonggyu Baek ◽  
Shao-Hsien Liu ◽  
...  
Keyword(s):  
Nursing Home ◽  
Cognitive Impairment ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Nursing Home Residents ◽  
Class Analysis ◽  
Physical Frailty ◽  
Data Set ◽  
Severe Cognitive Impairment ◽  
Moderate Cognitive Impairment

Abstract Background Little is known about the heterogeneous clinical profile of physical frailty and its association with cognitive impairment in older U.S. nursing home (NH) residents. Methods Minimum Data Set 3.0 at admission was used to identify older adults newly-admitted to nursing homes with life expectancy ≥6 months and length of stay ≥100 days (n = 871,801). Latent class analysis was used to identify physical frailty subgroups, using FRAIL-NH items as indicators. The association between the identified physical frailty subgroups and cognitive impairment (measured by Brief Interview for Mental Status/Cognitive Performance Scale: none/mild; moderate; severe), adjusting for demographic and clinical characteristics, was estimated by multinomial logistic regression and presented in adjusted odds ratios (aOR) and 95% confidence intervals (CIs). Results In older nursing home residents at admission, three physical frailty subgroups were identified: “mild physical frailty” (prevalence: 7.6%), “moderate physical frailty” (44.5%) and “severe physical frailty” (47.9%). Those in “moderate physical frailty” or “severe physical frailty” had high probabilities of needing assistance in transferring between locations and inability to walk in a room. Residents in “severe physical frailty” also had greater probability of bowel incontinence. Compared to those with none/mild cognitive impairment, older residents with moderate or severe impairment had slightly higher odds of belonging to “moderate physical frailty” [aOR (95%CI)moderate cognitive impairment: 1.01 (0.99–1.03); aOR (95%CI)severe cognitive impairment: 1.03 (1.01–1.05)] and much higher odds to the “severe physical frailty” subgroup [aOR (95%CI)moderate cognitive impairment: 2.41 (2.35–2.47); aOR (95%CI)severe cognitive impairment: 5.74 (5.58–5.90)]. Conclusions Findings indicate the heterogeneous presentations of physical frailty in older nursing home residents and additional evidence on the interrelationship between physical frailty and cognitive impairment.

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