Child Maltreatment, Delinquent Behavior, and School Factors as Predictors of Depressive Symptoms from Adolescence to Adulthood: A Growth Mixture Model

2017 ◽  
Vol 52 (1) ◽  
pp. 27-54 ◽  
Author(s):  
Gia Elise Barboza
Keyword(s):  
Depressive Symptoms ◽  
Child Maltreatment ◽  
Latent Class ◽  
Group Membership ◽  
Growth Mixture Model ◽  
School Factors ◽  
Growth Mixture ◽  
Broad Array ◽  
Drinking Frequency ◽  
Maltreatment Type

Previous methodological approaches have not been flexible enough to model the heterogeneity of depressive symptoms or to identify variations between prototypical trajectories conditional on risk and protective factors. The current study examined latent class trajectories of depressive symptoms using data from 3,819 respondents of the Adolescent Health Survey. Four trajectory profiles of depressive symptoms were identified: low-stable, high-decreasing, low-increasing, and moderate-decreasing. A broad array of risk factors were included into the modeling procedure to identify predictors of group membership. Relative to the low-stable group, membership in one of the three symptomatic groups (i.e., heightened depressive symptoms) was predicted by poverty, low self-esteem, gender, drinking frequency, poor academic outcomes, delinquency, and child maltreatment type. This study contributes to our understanding about the longitudinal manifestations of depression and identifies a broad array of factors significantly related to pathways of resilience.

scholarly journals Development of a Mixture Model (SMM) Allowing for Smoothing Functions of Trajectories

2019 ◽  
Author(s):  
Ming Ding ◽  
Jorge E. Chavarro ◽  
Garrett M. Fitzmaurice
Keyword(s):  
Mixture Model ◽  
Latent Class ◽  
Group Membership ◽  
Model Misspecification ◽  
Parametric Modeling ◽  
Growth Mixture Model ◽  
Growth Mixture ◽  
Additive Mixed Models ◽  
Smoothing Functions ◽  
Time Varying Covariates

ABSTRACTIn the health and social sciences, two types of mixture model have been widely used by researchers to identify heterogeneous trajectories of participants within a population: latent class growth analysis (LCGA) and the growth mixture model (GMM). Both methods parametrically model trajectories of individuals, and capture latent trajectory classes, by using an expectation-maximization (E-M) algorithm. However, parametric modeling of trajectories using polynomial functions or monotonic spline functions results in limited flexibility for modelling trajectories; as a result, group membership may not be classified accurately due to model misspecification. In this paper, we propose a mixture model (SMM) allowing for smoothing functions of trajectories using a modified E-M algorithm. In the E step, participants are reassigned to only one group for which the estimated trajectory is the most similar to the observed one; in the M step, trajectories are fitted using generalized additive mixed models (GAMM) with smoothing functions of time. This modified E-M algorithm is straightforward to implement using the recently released “gamm4” macro in R. The SMM can incorporate time-varying covariates and be applied to longitudinal data with normal, Bernoulli, and Poisson distributions. Simulation results show favorable performance of the SMM in terms of classification of group membership. The proposed method is illustrated by its application to body mass index data of individuals followed from adolescence to young adulthood and the relationship with incidence of cardiometabolic disease.

Abstract P342: Development of a Mixture Model With Smoothing Functions of Trajectories

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Ming Ding ◽  
Garrett Fitzmaurice
Keyword(s):  
Mixture Model ◽  
Latent Class ◽  
Group Membership ◽  
Model Misspecification ◽  
Parametric Modeling ◽  
Growth Mixture Model ◽  
Growth Mixture ◽  
Additive Mixed Models ◽  
Smoothing Functions ◽  
Time Varying Covariates

In the health and social sciences, two types of mixture model have been widely used by researchers to identify heterogeneous trajectories of participants within a population: latent class growth analysis (LCGA) and the growth mixture model (GMM). Both methods parametrically model trajectories of individuals, and capture latent trajectory classes, by using an expectation-maximization (E-M) algorithm. However, parametric modeling of trajectories using polynomial functions or monotonic spline functions results in limited flexibility for modelling trajectories; as a result, group membership may not be classified accurately due to model misspecification. In this paper, we propose a mixture model (SMM) allowing for smoothing functions of trajectories using a modified E-M algorithm. In the E step, participants are reassigned to only one group for which the estimated trajectory is the most similar to the observed one; in the M step, trajectories are fitted using generalized additive mixed models (GAMM) models with smoothing functions of time. This modified E-M algorithm is straightforward to implement using the recently released “gamm4” macro in R. The SMM can incorporate time-varying covariates and be applied to longitudinal data with normal, Bernoulli, and Poisson distributions. Simulation results show favorable performance of the SMM in terms of classification of group membership. The proposed method is illustrated by its application to body mass index data of individuals followed from adolescence to young adulthood and the relationship with incidence of cardiometabolic disease.

scholarly journals Adult Children’s Support and Trajectory of Depressive Symptoms Among Disabled Chinese Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 751-751
Author(s):  
Jianyun Wang ◽  
Renyao Zhong ◽  
Yaolin Pei ◽  
Bei Wu
Keyword(s):  
Older Adults ◽  
Depressive Symptoms ◽  
Lower Risk ◽  
Risk Group ◽  
Policy Implications ◽  
Instrumental Support ◽  
Growth Mixture Model ◽  
Chinese Older Adults ◽  
Growth Mixture

Abstract This study aimed to examine the trajectory of depressive symptoms among Chinese older adults with disabilities and the role of adult children’s support in predicting trajectory classes of depressive symptoms. Data were drawn from three waves of the China Health and Retirement Longitudinal Study (2011-2015). The sample included 1420 disabled older adults age 60+ at the baseline and completed all three waves of the data. Growth mixture model shows two-class depressive symptoms trajectories: the higher risk group (25.49%) and the lower risk group (74.51%). Logistic regression results showed that respondents who received a longer term of adult children’s instrumental support were more likely to be classified in a higher risk group after controlling the covariates (OR=1.184, p<0.05), while financial support and the frequency of contacts were not associated with the increased level of depressive symptoms. The policy implications were also discussed in this study.

Patterns of Intimate Partner Violence victimization among Australia and New Zealand female university students: An initial examination of child maltreatment and self-reported depressive symptoms across profiles

2016 ◽  
Vol 50 (4) ◽  
pp. 582-601 ◽  
Author(s):  
Jesse Cale ◽  
Stacy Tzoumakis ◽  
Benoit Leclerc ◽  
Jan Breckenridge
Keyword(s):  
Intimate Partner Violence ◽  
Depressive Symptoms ◽  
New Zealand ◽  
Child Maltreatment ◽  
University Students ◽  
Partner Violence ◽  
Latent Class ◽  
Intimate Partner ◽  
Violence Victimization ◽  
Intimate Partner Violence Victimization

The aim of this study was to examine the relationship between child abuse, depression, and patterns of Intimate Partner Violence victimization among female university students in Australia and New Zealand. Data were based on the Australia/New Zealand portion of the International Dating Violence Study (2001–2005) (n = 293). Using Latent Class Analysis, Low-, Moderate-, and High-level Intimate Partner Violence profiles were identified that differed according to the variety, degree, and severity of Intimate Partner Violence. Furthermore, the combination of child maltreatment and self-reported depressive symptoms differed across profiles. The results highlighted differential pathways from child maltreatment to specific Intimate Partner Violence victimization patterns. These findings provide further evidence for the importance of early intervention strategies to prevent Intimate Partner Violence, and specifically for children who experience abuse and neglect to help prevent subsequent victimization experiences in intimate relationship contexts.

scholarly journals Does Money Matter? A Theory-Driven Growth Mixture Model to Explain Travel-Mode Choice with Experimental Data

Methodology ◽  
2006 ◽  
Vol 2 (3) ◽  
pp. 124-134 ◽  
Author(s):  
Eldad Davidov ◽  
Kajsa Yang-Hansen ◽  
Jan-Eric Gustafsson ◽  
Peter Schmidt ◽  
Sebastian Bamberg
Keyword(s):  
Mixture Model ◽  
Latent Class ◽  
Mode Choice ◽  
Growth Mixture Model ◽  
Travel Mode ◽  
High Importance ◽  
Growth Mixture ◽  
Travel Mode Choice ◽  
Changes Over Time ◽  
Over Time

In the present article we apply a growth mixture model using Mplus via STREAMS to delineate the mechanism underlying travel-mode choice. Three waves of an experimental field study conducted in Frankfurt Main, Germany, are applied for the statistical analysis. Five major questions are addressed: (1) whether the choice of public transport rather than the car changes over time; (2) whether a soft policy intervention to change travel mode choice has any effect on the travel-mode chosen; (3) whether one can identify different groups of people regarding the importance allocated to monetary and time considerations for the decision of which travel mode to use; (4) whether the different subgroups of people have different initial states and rates of change in their travel-model choices; (5) whether sociodemographic variables have an additional effect on the latent class variables and on the changes in travel-mode choice over time. We also found that choice of public transportation in our study is stable over time. Moreover, the intervention has an effect only on one of the classes. We identify four classes of individuals. One class allocates a low importance to both monetary and time considerations, the second allocates high importance to money and low importance to time, the third allocates high importance to both, and the fourth allocates a low importance to money and a high importance to time. We found no difference in the patterns of travel-mode changes over time in the four classes. We also found some additional effects of sociodemographic characteristics on the latent class variables and on behavior in the different classes. The model specification and the empirical findings are discussed in light of the theory of the allocation of time of Gary Becker.

scholarly journals Development of a mixture model allowing for smoothing functions of longitudinal trajectories

2020 ◽  
pp. 096228022096601
Author(s):  
Ming Ding ◽  
Jorge E. Chavarro ◽  
Garrett M. Fitzmaurice
Keyword(s):  
Mixture Model ◽  
Growth Analysis ◽  
Latent Class ◽  
Parametric Modeling ◽  
Latent Class Growth Analysis ◽  
Growth Mixture Model ◽  
Longitudinal Trajectories ◽  
Growth Mixture ◽  
Smoothing Functions ◽  
Latent Class Growth

In the health and social sciences, two types of mixture models have been widely used by researchers to identify participants within a population with heterogeneous longitudinal trajectories: latent class growth analysis and the growth mixture model. Both methods parametrically model trajectories of individuals, and capture latent trajectory classes, using an expectation–maximization algorithm. However, parametric modeling of trajectories using polynomial functions or monotonic spline functions results in limited flexibility for modeling trajectories; as a result, group membership may not be classified accurately due to model misspecification. In this paper, we propose a smoothing mixture model allowing for smoothing functions of trajectories using a modified algorithm in the M step. Specifically, participants are reassigned to only one group for which the estimated trajectory is the most similar to the observed one; trajectories are fitted using generalized additive mixed models with smoothing functions of time within each of the resulting subsamples. The smoothing mixture model is straightforward to implement using the recently released “ gamm4” package (version 0.2–6) in R 3.5.0. It can incorporate time-varying covariates and be applied to longitudinal data with any exponential family distribution, e.g., normal, Bernoulli, and Poisson. Simulation results show favorable performance of the smoothing mixture model, when compared to latent class growth analysis and growth mixture model, in recovering highly flexible trajectories. The proposed method is illustrated by its application to body mass index data on individuals followed from adolescence to young adulthood and its relationship with incidence of cardiometabolic disease.

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