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.

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.

Politieke voorkeur en antwoordgedrag van respondenten in politieke opiniepeilingen

Res Publica ◽  
1994 ◽  
Vol 36 (2) ◽  
pp. 143-152
Author(s):  
Geert Loosveldt
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Response Category ◽  
The Political ◽  
Class Analysis ◽  
Background Characteristics ◽  
Class Model ◽  
Political Preference

In this article a typology of respondent's ability to participate in a survey interview is created by means of a latent class analysis. The indicators in the analysis are: the interviewer's evaluation of the respondent's ability, the use of the "don't know" response category and inconsistent answers. It was possible to fit a latent class model with three classes or types of respondents. The three types are clearly differentiated concerning ability. As expected, this typology is related to respondent's education and age. Ability to participate is higher for better educated and younger respondents. Given the fact that political preference is also related to these two background characteristics, there is a relationship between the respondent's typology and the political preference of the respondents.

scholarly journals Using Latent Class Analysis to Identify Different Risk Patterns for Patients With Masked Hypertension

2021 ◽  
Vol 8 ◽  
Author(s):  
Ming Fu ◽  
Xiangming Hu ◽  
Shixin Yi ◽  
Shuo Sun ◽  
Ying Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Fasting Blood Glucose ◽  
Low Risk ◽  
Biological Information ◽  
Masked Hypertension ◽  
Class Analysis ◽  
The Impact

Background: There is controversy whether masked hypertension (MHT) requires additional intervention. The aim of this study is to evaluate whether MHT accompanied with high-risk metabolic syndrome (MetS), as the subphenotype, will have a different prognosis from low-risk MetS.Methods: We applied latent class analysis to identify subphenotypes of MHT, using the clinical and biological information collected from High-risk Cardiovascular Factor Screening and Chronic Disease Management Programme. We modeled the data, examined the relationship between subphenotypes and clinical outcomes, and further explored the impact of antihypertensive medication.Results: We included a total of 140 patients with MHT for analysis. The latent class model showed that the two-class (high/low-risk MetS) model was most suitable for MHT classification. The high-risk MetS subphenotype was characterized by larger waist circumference, lower HDL-C, higher fasting blood glucose and triglycerides, and prevalence of diabetes. After four years of follow-up, participants in subphenotype 1 had a higher non-major adverse cardiovascular event (MACE) survival probability than those in subphenotype 2 (P = 0.016). There was no interaction between different subphenotypes and the use of antihypertensive medications affecting the occurrence of MACE.Conclusions: We have identified two subphenotypes in MHT that have different metabolic characteristics and prognosis, which could give a clue to the importance of tracing the clinical correlation between MHT and metabolic risk factors. For patients with MHT and high-risk MetS, antihypertensive therapy may be insufficient.

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 Estimating the Concomitant-Variable Latent-Class Model With the EM Algorithm

1996 ◽  
Vol 21 (3) ◽  
pp. 215-229 ◽  
Author(s):  
Peter G. M. van der Heijden ◽  
Jos Dessens ◽  
UIf Bockenholt
Keyword(s):  
Latent Class Analysis ◽  
Latent Variable ◽  
Latent Class ◽  
Latent Class Model ◽  
Estimation Procedure ◽  
Class Analysis ◽  
Class Model ◽  
Explanatory Variables ◽  
Concomitant Variable ◽  
The Em Algorithm

Latent class analysis assumes the existence of a categorical latent variable that explains the relations between a set of categorical manifest variables. Simultaneous latent class analysis deals with sets of multiway contingency tables simultaneously. In this way an explanatory categorical grouping variable is related to latent class results. In this article we discuss a tool called the concomitant-variable latent-class model, which generalizes this work to continuous explanatory variables. An EM estimation procedure to estimate the model is worked out in detail, and the model is applied to an example on juvenile delinquency.

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.

scholarly journals Evaluating Latent Tuberculosis Infection Test Performance Using Latent Class Analysis in a TB and HIV Endemic Setting

2019 ◽  
Vol 16 (16) ◽  
pp. 2912 ◽  
Author(s):  
Shahieda Adams ◽  
Rodney Ehrlich ◽  
Roslynn Baatjies ◽  
Nandini Dendukuri ◽  
Zhuoyu Wang ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Test Performance ◽  
Latent Class ◽  
Latent Tuberculosis Infection ◽  
Latent Class Model ◽  
Latent Tuberculosis ◽  
Tuberculosis Infection ◽  
Class Analysis ◽  
Predictive Values ◽  
Endemic Setting

Background: Given the lack of a gold standard for latent tuberculosis infection (LTBI) and paucity of performance data from endemic settings, we compared test performance of the tuberculin skin test (TST) and two interferon-gamma-release assays (IGRAs) among health-care workers (HCWs) using latent class analysis. The study was conducted in Cape Town, South Africa, a tuberculosis and human immunodeficiency virus (HIV) endemic setting Methods: 505 HCWs were screened for LTBI using TST, QuantiFERON-gold-in-tube (QFT-GIT) and T-SPOT.TB. A latent class model utilizing prior information on test characteristics was used to estimate test performance. Results: LTBI prevalence (95% credible interval) was 81% (71–88%). TST (10 mm cut-point) had highest sensitivity (93% (90–96%)) but lowest specificity (57%, (43–71%)). QFT-GIT sensitivity was 80% (74–91%) and specificity 96% (94–98%), and for TSPOT.TB, 74% (67–84%) and 96% (89–99%) respectively. Positive predictive values were high for IGRAs (90%) and TST (99%). All tests displayed low negative predictive values (range 47–66%). A composite rule using both TST and QFT-GIT greatly improved negative predictive value to 90% (range 80–97%). Conclusion: In an endemic setting a positive TST or IGRA was highly predictive of LTBI, while a combination of TST and IGRA had high rule-out value. These data inform the utility of LTBI-related immunodiagnostic tests in TB and HIV endemic settings.

scholarly journals Common Mental Disorders among Occupational Groups: Contributions of the Latent Class Model

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Kionna Oliveira Bernardes Santos ◽  
Fernando Martins Carvalho ◽  
Tânia Maria de Araújo
Keyword(s):  
Mental Disorders ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Common Mental Disorders ◽  
Latent Class Model ◽  
Class Analysis ◽  
Class Iii ◽  
Occupational Groups ◽  
Informal Workers ◽  
Self Reporting Questionnaire

Background. The Self-Reporting Questionnaire (SRQ-20) is widely used for evaluating common mental disorders. However, few studies have evaluated the SRQ-20 measurements performance in occupational groups. This study aimed to describe manifestation patterns of common mental disorders symptoms among workers populations, by using latent class analysis.Methods. Data derived from 9,959 Brazilian workers, obtained from four cross-sectional studies that used similar methodology, among groups of informal workers, teachers, healthcare workers, and urban workers. Common mental disorders were measured by using SRQ-20. Latent class analysis was performed on each database separately.Results. Three classes of symptoms were confirmed in the occupational categories investigated. In all studies, class I met better criteria for suspicion of common mental disorders. Class II discriminated workers with intermediate probability of answers to the items belonging to anxiety, sadness, and energy decrease that configure common mental disorders. Class III was composed of subgroups of workers with low probability to respond positively to questions for screening common mental disorders.Conclusions. Three patterns of symptoms of common mental disorders were identified in the occupational groups investigated, ranging from distinctive features to low probabilities of occurrence. The SRQ-20 measurements showed stability in capturing nonpsychotic symptoms.

scholarly journals An Iterative, Frequentist Approach for Latent Class Analysis to Evaluate Conditionally Dependent Diagnostic Tests

2021 ◽  
Vol 8 ◽  
Author(s):  
Clara Schoneberg ◽  
Lothar Kreienbrock ◽  
Amely Campe
Keyword(s):  
Veterinary Medicine ◽  
Latent Class Analysis ◽  
Bayesian Approach ◽  
Diagnostic Tests ◽  
Degrees Of Freedom ◽  
Latent Class ◽  
Latent Class Model ◽  
Test Accuracy ◽  
Class Analysis ◽  
The Bayesian Approach

Latent class analysis is a well-established method in human and veterinary medicine for evaluating the accuracy of diagnostic tests without a gold standard. An important assumption of this procedure is the conditional independence of the tests. If tests with the same biological principle are used, this assumption is no longer met. Therefore, the model has to be adapted so that the dependencies between the tests can be considered. Our approach extends the traditional latent class model with a term for the conditional dependency of the tests. This extension increases the number of parameters to be estimated and leads to negative degrees of freedom of the model, meaning that not enough information is contained in the existing data to obtain a unique estimate. As a result, there is no clear solution. Hence, an iterative algorithm was developed to keep the number of parameters to be estimated small. Given adequate starting values, our approach first estimates the conditional dependencies and then regards the resulting values as fixed to recalculate the test accuracies and the prevalence with the same method used for independent tests. Subsequently, the new values of the test accuracy and prevalence are used to recalculate the terms for the conditional dependencies. These two steps are repeated until the model converges. We simulated five application scenarios based on diagnostic tests used in veterinary medicine. The results suggest that our method and the Bayesian approach produce similar precise results. However, while the presented approach is able to calculate more accurate results than the Bayesian approach if the test accuracies are initially misjudged, the estimates of the Bayesian method are more precise when incorrect dependencies are assumed. This finding shows that our approach is a useful addition to the existing Bayesian methods, while it has the advantage of allowing simpler and more objective estimations.

Evaluating students' abilities to construct mathematical models from data using latent class analysis

2018 ◽  
Vol 19 (1) ◽  
pp. 375-391 ◽  
Author(s):  
Alexandra Brandriet ◽  
Charlie A. Rupp ◽  
Katherine Lazenby ◽  
Nicole M. Becker
Keyword(s):  
Latent Class Analysis ◽  
Large Scale ◽  
Latent Class ◽  
Latent Class Model ◽  
Similar Response ◽  
Class Analysis ◽  
Concentration Data ◽  
Class Model ◽  
Chemistry Students ◽  
Rate Laws

Analyzing and interpreting data is an important science practice that contributes toward the construction of models from data; yet, there is evidence that students may struggle with making meaning of data. The study reported here focused on characterizing students’ approaches to analyzing rate and concentration data in the context of method of initial rates tasks, a type of task used to construct a rate law, which is a mathematical model that relates the reactant concentration to the rate. Here, we present a large-scale analysis (n= 768) of second-semester introductory chemistry students’ responses to three open-ended questions about how to construct rate laws from initial concentration and rate data. Students’ responses were coded based on the level of sophistication in their responses, and latent class analysis was then used to identify groups (i.e.classes) of students with similar response patterns across tasks. Here, we present evidence for a five-class model that included qualitatively distinct and increasingly sophisticated approaches to reasoning about the data. We compared the results from our latent class model to the correctness of students’ answers (i.e.reaction orders) and to a less familiar task, in which students were unable to use the control of variables strategy. The results showed that many students struggled to engage meaningfully with the data when constructing their rate laws. The students’ strategies may provide insight into how to scaffold students’ abilities to analyze data.

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