Estimation of Class Membership Probabilities by Using Multiple Classification Scores

Abstract Objective To identify possible subgroups of health care utilization (HCU) patterns among adolescents and young adults (AYAs) with a chronic medical condition (CMC), and examine how these patterns relate to transition readiness and health-related quality of life (HRQoL). Methods Undergraduates (N = 359; Mage=19.51 years, SD = 1.31) with a self-reported CMC (e.g., asthma, allergies, irritable bowel syndrome) completed measures of demographics, HCU (e.g., presence of specialty or adult providers, recent medical visits), transition readiness, and mental HRQoL (MHC) and physical HRQoL (PHC). Latent class analysis identified four distinct patterns of HCU. The BCH procedure evaluated how these patterns related to transition readiness and HRQoL outcomes. Results Based on seven indicators of HCU, a four-class model was found to have optimal fit. Classes were termed High Utilization (n = 95), Adult Primary Care Physician (PCP)-Moderate Utilization (n = 107), Family PCP-Moderate Utilization (n = 81), and Low Utilization (n = 76). Age, family income, and illness controllability predicted class membership. Class membership predicted transition readiness and PHC, but not MHC. The High Utilization group reported the highest transition readiness and the lowest HRQoL, while the Low Utilization group reported the lowest transition readiness and highest HRQoL. Conclusions The present study characterizes the varying degrees to which AYAs with CMCs utilize health care. Our findings suggest poorer PHC may result in higher HCU, and that greater skills and health care engagement may not be sufficient for optimizing HRQoL. Future research should examine the High Utilization subgroup and their risk for poorer HRQoL.

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Internet of Things (IoTs)- Review and It’s Multiple Classification

2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) ◽

10.1109/icicv50876.2021.9388464 ◽

2021 ◽

Author(s):

Ratna Priya ◽

Neelu Suman ◽

Ankur Utsav ◽

Amit Abhishek

Keyword(s):

Internet Of Things ◽

Multiple Classification

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Risk factors for tooth loss with a mean follow-up period of 13.9 years in supportive periodontal therapy patients

BMC Oral Health ◽

10.1186/s12903-021-01573-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Tomotaka Kato ◽

Natsuki Fujiwara ◽

Tomohisa Ogawa ◽

Yukihiro Numabe

Keyword(s):

Risk Factors ◽

Tooth Loss ◽

Periodontal Therapy ◽

Dental Hygienists ◽

Multiple Classification Analysis ◽

Hazard Ratios ◽

Dental Office ◽

Multiple Classification ◽

Supportive Periodontal Therapy

Abstract Background Clinical evidence indicates that there are various risk factors of tooth loss. However, the degree of this risk among other risk factors remains unclear. In this retrospective cohort study, the authors evaluated the hazard ratios of several risk factors for tooth loss. Methods Included patients had all been treated for dental disorders, were in the supportive phase of periodontal therapy by dental hygienists, and visited a Japanese dental office continually during a 10-year period. Periodontal parameters, tooth condition, and general status of all teeth (excluding third molars) at the initial visit and at least 10 years later were evaluated by using multiple classification analysis. Results The authors evaluated a total of 7584 teeth in 297 patients (average age: 45.3, mean follow-up time: 13.9 years) Non-vital pulp was the most significant predictor of tooth loss according to Cox hazards regression analysis (hazard ratio: 3.31). The 10-year survival rate was approximately 90% for teeth with non-vital pulp and 99% for teeth with vital pulp. Fracture was the most common reason for tooth loss. Conclusions Non-vital pulp had the most significant association with tooth loss among the parameters. Therefore, it is very important to minimize dental pulp extirpation.

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Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis

BMC Pediatrics ◽

10.1186/s12887-021-02640-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Molly Mattsson ◽

Deirdre M. Murray ◽

Mairead Kiely ◽

Fergus P. McCarthy ◽

Elaine McCarthy ◽

...

Keyword(s):

Physical Activity ◽

Latent Class Analysis ◽

Latent Class ◽

Eating Behaviour ◽

Overweight And Obesity ◽

Class Analysis ◽

Class Membership ◽

Active Play ◽

Food Avoidance ◽

Cluster Membership

Abstract Background Diet, physical activity, sedentary behaviours, and sleep time are considered major contributory factors of the increased prevalence of childhood overweight and obesity. The aims of this study were to (1) identify behavioural clusters of 5 year old children based on lifestyle behaviours, (2) explore potential determinants of class membership, and (3) to determine if class membership was associated with body measure outcomes at 5 years of age. Methods Data on eating behaviour, engagement in active play, TV watching, and sleep duration in 1229 5 year old children from the Cork BASELINE birth cohort study was obtained through in-person interviews with parent. Latent class analysis was used to identify behavioural clusters. Potential determinants of cluster membership were investigated using multinomial logistic regression. Associations between the identified classes and cardio metabolic body measures were examined using multivariate logistic and linear regression, with cluster membership used as the independent variable. Results 51% of children belonged to a normative class, while 28% of children were in a class characterised by high scores on food avoidance scales in combination with low enjoyment of food, and 20% experienced high scores on the food approach scales. Children in both these classes had lower conditional probabilities of engaging in active play for at least 1 hour per day and sleeping for a minimum of 10 h, and higher probability of watching TV for 2 hours or more, compared to the normative class. Low socioeconomic index (SEI) and no breastfeeding at 2 months were found to be associated with membership of the class associated with high scores on the food avoidance scale, while lower maternal education was associated with the class defined by high food approach scores. Children in the class with high scores on the food approach scales had higher fat mass index (FMI), lean mass index (LMI), and waist-to-height ratio (WtHR) compared to the normative class, and were at greater risk of overweight and obesity. Conclusion Findings suggest that eating behaviour appeared to influence overweight and obesity risk to a greater degree than activity levels at 5 years old. Further research of how potentially obesogenic behaviours in early life track over time and influence adiposity and other cardio metabolic outcomes is crucial to inform the timing of interventions.

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