Examining the role of healthcare access in racial/ethnic disparities in receipt of provider-patient discussions about smoking: A latent class analysis

2021 ◽  
Vol 148 ◽  
pp. 106584
Author(s):  
Lihua Li ◽  
Serena Zhan ◽  
Liangyuan Hu ◽  
Karen M. Wilson ◽  
Madhu Mazumdar ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Healthcare Access ◽  
Ethnic Disparities ◽  
Class Analysis ◽  
Racial Ethnic

scholarly journals Mental disorders pattern in staff of a military unit in Iran: the role of metabolic syndrome on latent class membership

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Abbas Abbasi-Ghahramanloo ◽  
Mohammadkarim Bahadori ◽  
Esfandiar Azad ◽  
Nooredin Dopeykar ◽  
Parisa Mahdizadeh ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Mental Disorders ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Adult Population ◽  
Class Analysis ◽  
Class Membership ◽  
Anthropometric Measures ◽  
Military Unit

Abstract Introduction Mental disorders are among the most prevalent health problems of the adult population in the world. This study aimed to identify the subgroups of staff based on mental disorders and assess the independent role of metabolic syndrome (MetS) on the membership of participants in each latent class. Methods This cross-sectional study was conducted among 694 staff of a military unit in Tehran in 2017. All staff of this military unit was invited to participate in this study. The collected data included demographic characteristics, anthropometric measures, blood pressure, biochemical parameters, and mental disorders. We performed latent class analysis using a procedure for latent class analysis (PROC LCA) in SAS to identify class membership of mental disorders using Symptom Checklist-90. Results Three latent classes were identified as healthy (92.7%), mild (4.9%), and severe (2.4%) mental disorders. Having higher age significantly decreased the odds of belonging to the mild class (adjusted OR (aOR = 0.21; 95% confidence interval (CI): 0.05–0.83) compared to the healthy class. Also, obesity decreased the odds of membership in mild class (aOR = 0.10, 95% CI: 0.01–0.92) compared to healthy class. On the other hand, being female increased the odds of being in severe class (aOR = 9.76; 95% CI: 1.35–70.65) class in comparison to healthy class. Conclusion This study revealed that 7.3% of staff fell under mild and severe classes. Considering educational workshops in the workplace about mental disorders could be effective in enhancing staff’s knowledge of these disorders. Also, treatment of comorbid mental disorders may help reduce their prevalence and comorbidity.

scholarly journals Exploring symptoms of somatization in chronic widespread pain: latent class analysis and the role of personality

2017 ◽  
Vol Volume 10 ◽  
pp. 1733-1740 ◽  
Author(s):  
Andrea Burri ◽  
Peter Hilpert ◽  
Peter McNair ◽  
Frances Williams
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Chronic Widespread Pain ◽  
Widespread Pain ◽  
Class Analysis

scholarly journals Bourdieu in de buurt : Sociale klassendebat en de klassenstructuur van Rotterdam

Sociologie ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 117-147
Author(s):  
Gijs Custers ◽  
Godfried Engbersen
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Research Approach ◽  
Class Analysis ◽  
Social And Cultural Capital ◽  
New Class ◽  
Social Mix ◽  
The Social ◽  
New Research

Abstract Studies by Savage et al. (2013) and Vrooman, Gijsberts and Boelhouwer (2014) introduce new class typologies that combine Bourdieu’s work with latent class analysis. This paper identifies this new research approach as Bourdieusian latent class analysis. We discuss the role of these studies within the social class debate and we review the merits and limitations of this approach. In addition, we show how the class structure of Rotterdam can be empirically established by studying the distribution of economic, social and cultural capital. We use the Neighbourhood Profile data (N = 14,040; 71 neighbourhoods) to develop a class typology that includes eight social groups. This class typology complements conventional indicators of neighbourhood socioeconomic status and can be used to study ‘social mix’ and gentrification.

scholarly journals eHealth Practices in Cancer Survivors With BMI in Overweight or Obese Categories: Latent Class Analysis Study (Preprint)

2020 ◽  
Author(s):  
Annie Wen Lin ◽  
Sharon H Baik ◽  
David Aaby ◽  
Leslie Tello ◽  
Twila Linville ◽  
...  
Keyword(s):  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Health Information ◽  
Latent Class ◽  
Electronic Device ◽  
Ethnic Disparities ◽  
Sociodemographic Characteristics ◽  
Class Analysis ◽  
Class Membership ◽  
Health Related

BACKGROUND eHealth technologies have been found to facilitate health-promoting practices among cancer survivors with BMI in overweight or obese categories; however, little is known about their engagement with eHealth to promote weight management and facilitate patient-clinician communication. OBJECTIVE The objective of this study was to determine whether eHealth use was associated with sociodemographic characteristics, as well as medical history and experiences (ie, patient-related factors) among cancer survivors with BMI in overweight or obese categories. METHODS Data were analyzed from a nationally representative cross-sectional survey (National Cancer Institute’s Health Information National Trends Survey). Latent class analysis was used to derive distinct classes among cancer survivors based on sociodemographic characteristics, medical attributes, and medical experiences. Logistic regression was used to examine whether class membership was associated with different eHealth practices. RESULTS Three distinct classes of cancer survivors with BMI in overweight or obese categories emerged: younger with no comorbidities, younger with comorbidities, and older with comorbidities. Compared to the other classes, the younger with comorbidities class had the highest probability of identifying as female (73%) and Hispanic (46%) and feeling that clinicians did not address their concerns (75%). The older with comorbidities class was 6.5 times more likely than the younger with comorbidities class to share eHealth data with a clinician (odds ratio [OR] 6.53, 95% CI 1.08-39.43). In contrast, the younger with no comorbidities class had a higher likelihood of using a computer to look for health information (OR 1.93, 95% CI 1.10-3.38), using an electronic device to track progress toward a health-related goal (OR 2.02, 95% CI 1.08-3.79), and using the internet to watch health-related YouTube videos (OR 2.70, 95% CI 1.52-4.81) than the older with comorbidities class. CONCLUSIONS Class membership was associated with different patterns of eHealth engagement, indicating the importance of tailored digital strategies for delivering effective care. Future eHealth weight loss interventions should investigate strategies to engage younger cancer survivors with comorbidities and address racial and ethnic disparities in eHealth use.

Profiling cancer-related needs and the role of physical, practical and psychological difficulties: A latent class analysis approach.

2018 ◽  
Vol 36 (15_suppl) ◽  
pp. e22161-e22161
Author(s):  
Alessandro Rossi ◽  
Maria Marconi ◽  
Stefania Mannarini ◽  
India Minelli ◽  
Chiara Rossini ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Analysis Approach ◽  
Class Analysis ◽  
Psychological Difficulties

scholarly journals The Role of Trust in Explaining Food Choice: Combining Choice Experiment and Attribute Best–Worst Scaling

Foods ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 45
Author(s):  
Ching-Hua Yeh ◽  
Monika Hartmann ◽  
Nina Langen
Keyword(s):  
Latent Class Analysis ◽  
Food Choice ◽  
Logit Model ◽  
Choice Experiment ◽  
Latent Class ◽  
Class Analysis ◽  
Class Membership ◽  
Credence Attributes ◽  
Best Worst Scaling

This paper presents empirical findings from a combination of two elicitation techniques—discrete choice experiment (DCE) and best–worst scaling (BWS)—to provide information about the role of consumers’ trust in food choice decisions in the case of credence attributes. The analysis was based on a sample of 459 Taiwanese consumers and focuses on red sweet peppers. DCE data were examined using latent class analysis to investigate the importance and the utility different consumer segments attach to the production method, country of origin, and chemical residue testing. The relevance of attitudinal and trust-based items was identified by BWS using a hierarchical Bayesian mixed logit model and was aggregated to five latent components by means of principal component analysis. Applying a multinomial logit model, participants’ latent class membership (obtained from DCE data) was regressed on the identified attitudinal and trust components, as well as demographic information. Results of the DCE latent class analysis for the product attributes show that four segments may be distinguished. Linking the DCE with the attitudinal dimensions reveals that consumers’ attitude and trust significantly explain class membership and therefore, consumers’ preferences for different credence attributes. Based on our results, we derive recommendations for industry and policy.

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