scholarly journals Predictors of Post-stroke Cognition Among Geriatric Patients: The Role of Demographics, Pre-stroke Cognition, and Trajectories of Depression

2021 ◽  
Vol 12 ◽  
Author(s):  
Christiana Kang
Keyword(s):  
Cognitive Ability ◽  
Race And Ethnicity ◽  
Growth Mixture Modeling ◽  
Predictive Ability ◽  
Latent Growth ◽  
Geriatric Population ◽  
Post Stroke ◽  
Growth Mixture ◽  
Prevalent Disease ◽  
Activity Of Daily Life

Stroke is a prevalent disease among geriatric population, which tends to deteriorate cognitive ability and mental health. In such context, cognitive impairment and geriatric depression generate mutually deteriorating impacts on each other. Using the Health and Retirement Study, this study examined depression and cognition before, immediately after, and 2 years after the onset of stroke. Through latent growth mixture modeling, four different trajectories of depression were identified: resilience, recovery, emergent depression, and chronicity. We used demographics including gender, age, race, and ethnicity, activity of daily life, baseline cognition, and trajectories of depression to predict cognitive ability 2 years after the stroke. Both aforementioned demographic factors and pre-stroke cognition were predictive of post-stroke cognition, but the inclusion of depression trajectories further improved the predictive ability. Emergent depression and chronicity were two significant predictors of worse post-stroke cognition. This study showed the importance of considering a more specific trajectotrial interrelationship between depression and cognition in geriatric stroke patients.

Methodological Approaches to Modeling Criminal Trajectories

2019 ◽  
pp. 60-100
Author(s):  
David M. Day ◽  
Margit Wiesner
Keyword(s):  
Longitudinal Data ◽  
Growth Curve ◽  
Growth Mixture Modeling ◽  
Statistical Technique ◽  
Heterogeneous Population ◽  
Growth Curve Modeling ◽  
Latent Growth ◽  
Growth Mixture ◽  
Curve Modeling ◽  
Trajectory Models

Criminal offenders compose a heterogeneous population. Criminal trajectory research aims to capture this heterogeneity in terms of the frequency or severity of offending. This chapter describes the concept a criminal trajectory and the statistical technique used to derive trajectories from longitudinal data. Both the semiparametric group-based trajectory modeling (SGBTM) and latent growth mixture modeling (GMM) approaches are described in nontechnical terms, and the differences between them are noted. Despite some similarities, these approaches are also distinguished from conventional growth curve modeling. Guidelines and factors to consider in building and testing trajectory models are discussed. Last, extensions of SGBTM and GMM are presented.

Identifying trajectories of adolescent smoking: An application of latent growth mixture modeling.

2001 ◽  
Vol 20 (2) ◽  
pp. 127-135 ◽  
Author(s):  
Craig R. Colder ◽  
Paras Mehta ◽  
Kevin Balanda ◽  
Richard T. Campbell ◽  
Kathryn Mayhew ◽  
...  
Keyword(s):  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Adolescent Smoking ◽  
Latent Growth ◽  
Growth Mixture ◽  
Latent Growth Mixture Modeling

scholarly journals Heterogeneity in Trajectories of Depression in Response to Divorce Is Associated With Differential Risk for Mortality

2017 ◽  
Vol 5 (5) ◽  
pp. 843-850 ◽  
Author(s):  
Matteo Malgaroli ◽  
Isaac R. Galatzer-Levy ◽  
George A. Bonanno
Keyword(s):  
Growth Mixture Modeling ◽  
Population Based ◽  
Mortality Data ◽  
Stressful Event ◽  
Latent Growth ◽  
Major Life ◽  
Growth Mixture ◽  
Married Individuals ◽  
Distal Outcome ◽  
Differential Risk

Divorce is a common stressful event associated with both increased rates of depression and mortality. Given evidence of significant individual differences in depression following major life stressors, we examined if heterogeneous depression responses confer differential risk for mortality. Data from a population-based longitudinal study was utilized to identify individuals who experienced divorce ( n = 559). Prospective trajectories of depression severity from before to after divorce were identified using latent growth mixture modeling, and rates of mortality between trajectories were compared as a distal outcome. Four trajectories demonstrated strongest model fit: resilience (67%), emergent depression (10%), chronic pre- to postdivorce depression (12%), and decreasing depression (11%). Mortality base rate was 9.7% by 6 years postevent, and depression that emerged due to divorce was associated with significantly greater mortality risk compared to resilient (odds ratio [OR] = 2.46; 95% confidence interval [CI] = [1.05, 5.81]) and to married individuals, whereas chronic depression was not associated with greater risk.

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