Differentiating Individuals Convicted of Sexual Offenses: A Two-Country Latent Class Analysis

Spaan, Pascalle; Blokland, Arjan; Blander, Rembert De; Robert, Luc; Maes, Eric; Blom, Martine; Wartna, B.S.J.

doi:10.1177/1079063219893370

Cited by 10 publications

(6 citation statements)

References 48 publications

(54 reference statements)

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“…According to previous similar research [23], we ensured that the smallest class size was ＞1.5% of the study population in the latent-class modeling process. Posterior probabilities were examined as an indicator of classification certainty [24]. Posterior probabilities represent the probability that a person is allocated to a certain class based on their response pattern.…”

Section: Discussionmentioning

confidence: 99%

Applying latent class analysis to risk stratification of incident diabetes among Chinese adults

Chen

et al. 2021

Diabetes Research and Clinical Practice

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To use latent class analysis to identify unobservable subpopulations amongst the heterogeneous population and explore the relationship between subpopulations and incident diabetes among Chinese adults. Methods: The retrospective study included 32,312 Chinese adults without diabetes at baseline. Latent class indicators included demographic and clinical variables. The outcome was incident diabetes. The relationship between latent class and outcome was evaluated with Cox proportional hazard regression analysis.Results: After screening, the two-class latent class model best fits the population. Participants in class 2 are characterized by higher age, body mass index, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, triglyceride, low-density lipoprotein cholesterol, serum creatinine, serum urea nitrogen, alanine aminotransferase, and a higher proportion of males, ever/current smokers and drinkers, but lower high-density lipoprotein cholesterol and a lower proportion of family history of diabetes. The risk of diabetes in class 2 was 5.451 times (HR: 6.451, 95%CI: 4.179-9.960, P < 0.00001) and 5.264 times (HR: 6.264, 95%CI: 4.680-8.385, P < 0.00001) higher than that in class 1 during 3-year and 5year follow-up, respectively. Conclusions:We used latent class analysis to identify two distinct subpopulations with differential risk of diabetes during 3-year and 5-year follow-up.

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Section: Discussionmentioning

confidence: 99%

Applying latent class analysis to risk stratification of incident diabetes among Chinese adults

Chen

et al. 2021

Diabetes Research and Clinical Practice

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“…Entropy was also used to define the best model as it can indicate how precisely the model defines classes ( 35 ). Posterior probabilities were used as indicators of classification certainty ( 36 ). We stopped fitting the model with an additional class when the posterior probability of the model was <90%.…”

Section: Methodsmentioning

confidence: 99%

A study on the correlation between family dynamic factors and depression in adolescents

Shi

Tao²,

Yan

et al. 2023

Front. Psychiatry

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ObjectivesTo evaluate the relationship between systemic family dynamics and adolescent depression.MethodsAn offline survey was distributed to 4,109 students in grades 6–12, with the final analysis including 3,014 students (1,524 boys and 1,490 girls) aged 10–18 years. The questionnaire included the Self-Rating Scale of Systemic Family Dynamics (SSFD), the Self-Rating Depression Scale (SDS), and demographic characteristics.ResultsFamily dynamics were negatively correlated with depressive symptoms, with better family dynamics (high scores) associated with lower levels of depression based on the SDS score. After adjusting for sociodemographic characteristics, an ordinal multiclass logistic regression analysis identified family atmosphere (OR = 0.952, 95% CI: 0.948–0.956, p < 0.001) as the most important protective family dynamic against depression, followed by individuality (OR = 0.964, 95% CI: 0.960–0.968, p < 0.001). Latent class analysis (LCA) created the low family dynamic and high family dynamic groups. There were significant differences in the mean SDS scores between the two groups (45.52 ± 10.57 vs. 53.78 ± 11.88; p < 0.001) that persisted after propensity matching. Family atmosphere and individuation had a favorable diagnostic value for depression, with AUCs of 0.778 (95% CI: 0.760–0.796) and 0.710 (95% CI: 0.690–0.730), respectively. The diagnostic models for depression performed well.ConclusionPoor family dynamics may be responsible for adolescent depression. A variety of early intervention strategies focused on the family may potentially avoid adolescent depression.

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“…The value of the best-fit class is close to 1, meanwhile the value of the other classes is close to 0, indicating a higher certainty of classification. When the posterior probability of the model is <90%, we ceased adding a class to fit the model ( 22 ).…”

Section: Methodsmentioning

confidence: 99%

A study on differences about the influencing factors of depressive symptoms between medical staff and residents during 2022 city-wide temporary static management period to fighting against COVID-19 pandemic in Shanghai

Zhao

Tao²,

Bao³

et al. 2023

Front. Public Health

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ObjectivesOur study aimed to identify the latent class of depressive symptoms in the Shanghai population during the city-wide temporary static management period and compare differences in the factors influencing depressive symptoms between medical staff and residents.MethodsAn online cross-sectional survey was conducted with 840 participants using questionnaires, including Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Pittsburgh Sleep Quality Index (PSQI), and self-compiled questionnaire (demographic characteristics and internet usage time). Latent class analysis (LCA) was performed based on participants' depressive symptoms. The latent class subgroups were compared using the chi-square test and t-test. Logistic regression was used in our study to analyze the factors influencing depressive symptoms within the medical staff group and residents group and then compare their differences.ResultsTwo distinct subgroups were identified based on the LCA: the group with low-depressive symptoms and the group with high-depressive symptoms. There were significant differences between the two groups (P < 0.05) on age, education level, marital status, internet usage time, identity characteristics (medical staff or residents), family income level, living style, overall quality of sleep, and anxiety levels. Furthermore, logistic regression analysis results showed that compared with the residents group, the participants in the group of medical staff with “increasing internet usage time” and the “daytime dysfunction” would have nearly two times the possibility of getting serious depressive symptoms.ConclusionsThere are differences in the factors influencing depression symptoms between medical staff and residents during the 2022 city-wide temporary static management period to fighting against the COVID-19 pandemic in Shanghai. We should pay special attention to those with increasing internet usage time and daytime dysfunction in medical staff working in a special environment such as the COVID-19 pandemic.

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Differentiating Individuals Convicted of Sexual Offenses: A Two-Country Latent Class Analysis

Cited by 10 publications

References 48 publications

Applying latent class analysis to risk stratification of incident diabetes among Chinese adults

Applying latent class analysis to risk stratification of incident diabetes among Chinese adults

A study on the correlation between family dynamic factors and depression in adolescents

A study on differences about the influencing factors of depressive symptoms between medical staff and residents during 2022 city-wide temporary static management period to fighting against COVID-19 pandemic in Shanghai

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