Alcohol Use Among Older Adults in the National Epidemiologic Survey on Alcohol and Related Conditions: A Latent Class Analysis

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.

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The Patterns of Caregiving Activities for Family Caregivers of Older Adults in Hong Kong: An Exploratory Latent Class Analysis

The Journals of Gerontology Series B ◽

10.1093/geronb/gbaa203 ◽

2020 ◽

Author(s):

Jing Huang ◽

Pui Hing Chau ◽

Edmond Pui Hang Choi ◽

Bei Wu ◽

Vivian W Q Lou

Keyword(s):

Older Adults ◽

Hong Kong ◽

Activities Of Daily Living ◽

Latent Class Analysis ◽

Family Caregivers ◽

Caregiver Burden ◽

Daily Living ◽

Latent Class ◽

Class Analysis ◽

High Demand

Abstract Objectives This study identified the classes (i.e., patterns) of caregivers’ activities, based on their engagements in caregiving activities, and explored the characteristics and the caregiver burden of these classes. Methods This study was a secondary analysis of a cross-sectional survey on the profiles of family caregivers of older adults in Hong Kong. A latent class analysis approach was adopted to classify family caregivers (N = 932) according to their routine involvements in 17 daily caregiving activities: 6 activities of daily living (ADLs) and 8 instrumental activities of daily living activities (IADLs) in addition to emotional support, decision making, and financial support. Multinomial logistic regression and multiple linear regression illuminated the characteristics of the classes and compared their levels of caregiver burden. Results The family caregivers fell into 5 classes: All-Round Care (High Demand, 19.5%), All-Round Care (Moderate Demand, 8.2%), Predominant IADLs Care (High Demand, 23.8%), Predominant IADLs Care (Moderate Demand, 32.5%), and Minimal ADLs and IADLs Care (Low Demand, 16.0%). These classes exhibited different characteristics in terms of care recipients’ cognitive statuses and caregiver backgrounds. The levels of caregiver burden differed across classes; the All-Round Care (High Demand) class experienced the highest levels of caregiver burden. Discussion This study contributes to existing scholarship by turning away from a predefined category of care tasks to explore the patterns of caregiving activities. By identifying caregiving activity patterns and understanding their associated characteristics and caregiver burden, prioritizing and targeting caregiver support interventions better is possible.

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