scholarly journals Axial growth and refractive change in white European children and young adults: predictive factors for myopia

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sara McCullough ◽  
Gary Adamson ◽  
Karen M. M. Breslin ◽  
Julie F. McClelland ◽  
Lesley Doyle ◽  
...  
Keyword(s):  
Predictive Factors ◽  
Population Based ◽  
Growth Trajectories ◽  
Latent Growth ◽  
Growth Mixture ◽  
Growth Mixture Modelling ◽  
Spherical Equivalent Refraction ◽  
Refractive Change ◽  
Children And Young Adults ◽  
Axial Growth

Abstract This report describes development of spherical equivalent refraction (SER) and axial length (AL) in two population-based cohorts of white, European children. Predictive factors for myopic growth were explored. Participants were aged 6–7- (n = 390) and 12–13-years (n = 657) at baseline. SER and AL were assessed at baseline and 3, 6 and 9 years prospectively. Between 6 and 16 years: latent growth mixture modelling identified four SER classes (Persistent Emmetropes-PEMM, Persistent Moderate Hyperopes-PMHYP, Persistent High Hyperopes-PHHYP and Emerging Myopes-EMYO) as optimal to characterise refractive progression and two classes to characterise AL. Between 12 and 22-years: five SER classes (PHHYP, PMHYP, PEMM, Low Progressing Myopes-LPMYO and High Progressing Myopes-HPMYO) and four AL classes were identified. EMYO had significantly longer baseline AL (≥ 23.19 mm) (OR 2.5, CI 1.05–5.97) and at least one myopic parent (OR 6.28, CI 1.01–38.93). More myopic SER at 6–7 years (≤ + 0.19D) signalled risk for earlier myopia onset by 10-years in comparison to baseline SER of those who became myopic by 13 or 16 years (p ≤ 0.02). SER and AL progressed more slowly in myopes aged 12–22-years (− 0.16D, 0.15 mm) compared to 6–16-years (− 0.41D, 0.30 mm). These growth trajectories and risk criteria allow prediction of abnormal myopigenic growth and constitute an important resource for developing and testing anti-myopia interventions.

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.

scholarly journals Distinct trajectories of perinatal depression in Chinese women: application of latent growth mixture modelling

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Lan Hong ◽  
Tao Le ◽  
Yinping Lu ◽  
Xiang Shi ◽  
Ludan Xiang ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Protective Factor ◽  
Perinatal Depression ◽  
Gestational Hypertension ◽  
Depression Scale ◽  
Depression Symptoms ◽  
Latent Growth ◽  
Mixture Modelling ◽  
Growth Mixture ◽  
Growth Mixture Modelling

Abstract Background Current research on perinatal depression rarely pays attention to the continuity and volatility of depression symptoms over time, which is very important for the early prediction and prognostic evaluation of perinatal depression. This study investigated the trajectories of perinatal depression symptoms and aimed to explore the factors related to these trajectories. Methods The study recruited 550 women during late pregnancy (32 ± 4 weeks of gestation) and followed them up 1 and 6 weeks postpartum. Depressive symptoms were measured using the Edinburgh Postnatal Depression Scale (EPDS). Latent growth mixture modelling (LGMM) was used to identify trajectories of depressive symptoms during pregnancy. Results Two trajectories of perinatal depressive symptoms were identified: “decreasing” (n = 524, 95.3%) and “increasing” (n = 26, 4.7%). History of smoking, alcohol use and gestational hypertension increased the chance of belonging to the increasing trajectories, and a high level of social support was a protective factor for maintaining a decreasing trajectory. Conclusions This study identified two trajectories of perinatal depression and the factors associated with each trajectory. Paying attention to these factors and providing necessary psychological support services during pregnancy would effectively reduce the incidence of perinatal depression and improve patient prognosis.

scholarly journals Education and Successful Aging Trajectories: A Longitudinal Population-Based Latent Variable Modelling Analysis

2017 ◽  
Vol 36 (4) ◽  
pp. 427-434 ◽  
Author(s):  
Theodore D. Cosco ◽  
Blossom C.M. Stephan ◽  
Carol Brayne ◽  
Graciela Muniz ◽  
Keyword(s):  
Successful Aging ◽  
Latent Variable ◽  
Occupational Status ◽  
Later Life ◽  
Population Based ◽  
Logistic Regression Models ◽  
Growth Mixture ◽  
Growth Mixture Modelling ◽  
Higher Educational Attainment ◽  
Life Education

ABSTRACTAs the population ages, interest is increasing in studying aging well. However, more refined means of examining predictors of biopsychosocial conceptualizations of successful aging (SA) are required. Existing evidence of the relationship between early-life education and later-life SA is unclear. The Successful Aging Index (SAI) was mapped onto the Cognitive Function and Aging Study (CFAS), a longitudinal population-based cohort (n = 1,141). SAI scores were examined using growth mixture modelling (GMM) to identify SA trajectories. Unadjusted and adjusted (age, sex, occupational status) ordinal logistic regressions were conducted to examine the association between trajectory membership and education level. GMM identified a three-class model, capturing high, moderate, and low functioning trajectories. Adjusted ordinal logistic regression models indicated that individuals in higher SAI classes were significantly more likely to have higher educational attainment than individuals in the lower SAI classes. These results provide evidence of a life course link between education and SA.

scholarly journals Trajectory of post-traumatic stress following traumatic injury: 6-year follow-up

2015 ◽  
Vol 206 (5) ◽  
pp. 417-423 ◽  
Author(s):  
Richard A. Bryant ◽  
Angela Nickerson ◽  
Mark Creamer ◽  
Meaghan O'Donnell ◽  
David Forbes ◽  
...  
Keyword(s):  
Traumatic Stress ◽  
Traumatic Injury ◽  
Female Gender ◽  
Post Traumatic Stress ◽  
Latent Growth ◽  
Traumatic Injuries ◽  
Growth Mixture ◽  
Growth Mixture Modelling ◽  
Post Traumatic

BackgroundTraumatic injuries affect millions of patients each year, and resulting post-traumatic stress disorder (PTSD) significantly contributes to subsequent impairment.AimsTo map the distinctive long-term trajectories of PTSD responses over 6 years by using latent growth mixture modelling.MethodRandomly selected injury patients (n = 1084) admitted to four hospitals around Australia were assessed in hospital, and at 3, 12, 24 and 72 months. Lifetime psychiatric history and current PTSD severity and functioning were assessed.ResultsFive trajectories of PTSD response were noted across the 6 years: (a) chronic (4%), (b) recovery (6%), (c) worsening/recovery (8%), (d) worsening (10%) and (e) resilient (73%). A poorer trajectory was predicted by female gender, recent life stressors, presence of mild traumatic brain injury and admission to intensive care unit.ConclusionsThese findings demonstrate the long-term PTSD effects that can occur following traumatic injury. The different trajectories highlight that monitoring a subset of patients over time is probably a more accurate means of identifying PTSD rather than relying on factors that can be assessed during hospital admission.

scholarly journals Growth mixture models: a case example of the longitudinal analysis of patient‐reported outcomes data captured by a clinical registry

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jae-Yung Kwon ◽  
Richard Sawatzky ◽  
Jennifer Baumbusch ◽  
Sandra Lauck ◽  
Pamela A. Ratner
Keyword(s):  
Atrial Fibrillation ◽  
Longitudinal Analysis ◽  
Model Building ◽  
Population Based ◽  
Patient Reported Outcome ◽  
Latent Classes ◽  
Clinical Registry ◽  
Growth Mixture ◽  
Growth Mixture Modelling ◽  
Patient Reported

Abstract Background An assumption in many analyses of longitudinal patient-reported outcome (PRO) data is that there is a single population following a single health trajectory. One approach that may help researchers move beyond this traditional assumption, with its inherent limitations, is growth mixture modelling (GMM), which can identify and assess multiple unobserved trajectories of patients’ health outcomes. We describe the process that was undertaken for a GMM analysis of longitudinal PRO data captured by a clinical registry for outpatients with atrial fibrillation (AF). Methods This expository paper describes the modelling approach and some methodological issues that require particular attention, including (a) determining the metric of time, (b) specifying the GMMs, and (c) including predictors of membership in the identified latent classes (groups or subtypes of patients with distinct trajectories). An example is provided of a longitudinal analysis of PRO data (patients’ responses to the Atrial Fibrillation Effect on QualiTy-of-Life (AFEQT) Questionnaire) collected between 2008 and 2016 for a population-based cardiac registry and deterministically linked with administrative health data. Results In determining the metric of time, multiple processes were required to ensure that “time” accounted for both the frequency and timing of the measurement occurrences in light of the variability in both the number of measures taken and the intervals between those measures. In specifying the GMM, convergence issues, a common problem that results in unreliable model estimates, required constrained parameter exploration techniques. For the identification of predictors of the latent classes, the 3-step (stepwise) approach was selected such that the addition of predictor variables did not change class membership itself. Conclusions GMM can be a valuable tool for classifying multiple unique PRO trajectories that have previously been unobserved in real-world applications; however, their use requires substantial transparency regarding the processes underlying model building as they can directly affect the results and therefore their interpretation.

scholarly journals Trajectories of functioning in a population-based sample of veterans: contributions of moral injury, PTSD, and depression

2020 ◽  
pp. 1-10
Author(s):  
Shira Maguen ◽  
Brandon J. Griffin ◽  
Laurel A. Copeland ◽  
Daniel F. Perkins ◽  
Cameron B. Richardson ◽  
...  
Keyword(s):  
Posttraumatic Stress ◽  
Psychological Symptoms ◽  
Population Based ◽  
Moral Injury ◽  
Latent Growth ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Trajectories Of Change ◽  
Relationship Health ◽  
Over Time

Abstract Background Although research has shown that exposure to potentially traumatic and morally injurious events is associated with psychological symptoms among veterans, knowledge regarding functioning impacts remains limited. Methods A population-based sample of post-9/11 veterans completed measures of intimate relationship, health, and work functioning at approximately 9, 15, 21, and 27 months after leaving service. Moral injury, posttraumatic stress, and depression were assessed at ~9 months post-separation. We used Latent Growth Mixture Models to identify discrete classes characterized by unique trajectories of change in functioning over time and to examine predictors of class membership. Results Veterans were assigned to one of four functioning trajectories: high and stable, high and decreasing, moderate and increasing, and moderate and stable. Whereas posttraumatic stress, depression, and moral injury associated with perpetration and betrayal predicted worse outcomes at baseline across multiple functioning domains, moral injury associated with perpetration and depression most reliably predicted assignment to trajectories characterized by relatively poor or declining functioning. Conclusions Moral injury contributes to functional problems beyond what is explained by posttraumatic stress and depression, and moral injury due to perpetration and depression most reliably predicted assignment to trajectories characterized by functional impairment over time.

Early childhood aggression trajectories

2015 ◽  
Vol 39 (3) ◽  
pp. 221-234 ◽  
Author(s):  
Andrea Wildeboer ◽  
Sandra Thijssen ◽  
Marinus H. van IJzendoorn ◽  
Jan van der Ende ◽  
Vincent W. V. Jaddoe ◽  
...  
Keyword(s):  
Early Childhood ◽  
Aggressive Behaviour ◽  
Population Based ◽  
Attention Problems ◽  
Childhood Aggression ◽  
Problem Behaviour ◽  
Child Aggression ◽  
Growth Mixture ◽  
Growth Mixture Modelling ◽  
Rule Breaking

High and stable levels of aggression and the presence of aggressive behaviour in multiple settings according to different informants are risk factors for later problems. However, these two factors have not been investigated in early childhood. The present study investigates trajectories of parent-reported child aggression from 1.5 up to 6 years of age and their association with aggressive behaviour, attention problems and rule-breaking behaviour in a different setting, as reported by the teacher. In a longitudinal population-based cohort study, parent-reported measures of aggressive behaviour were obtained using the CBCL when children were 1.5, 3, and 6 years of age ( n = 4,781). Teacher-reported problem behaviour at school was assessed at age 6.5, using the TRF questionnaire ( n = 2,756). Growth mixture modelling yielded three aggression trajectories, with high increasing (3.0%), intermediate (21.3%), and low decreasing (75.7%) aggression levels. Children in trajectories with higher and increasing levels of aggression showed more teacher-reported aggressive behaviour, attention problems and rule breaking behaviour. However, parent-reported aggression at age six predicted problem behaviour at school to the same extent as did the aggression trajectories, suggesting that the incremental value of trajectories is not always self-evident.

scholarly journals Loneliness during lockdown: trajectories and predictors during the COVID-19 pandemic in 35,712 adults in the UK

2020 ◽  
Author(s):  
Feifei Bu ◽  
Andrew Steptoe ◽  
Daisy Fancourt
Keyword(s):  
Low Income ◽  
Panel Study ◽  
Geographical Location ◽  
Social Study ◽  
Health Concern ◽  
Growth Trajectories ◽  
Younger Adults ◽  
Growth Mixture ◽  
Growth Mixture Modelling ◽  
The Uk

AbstractThere are increasing worries that lockdowns and “stay-at-home” orders due to the COVID-19 pandemic could lead to a rise in loneliness, which is recognised as a major public health concern. But profiles of loneliness during the pandemic and risk factors remain unclear.Data from 35,712 UK adults in the UCL COVID-19 Social Study (a panel study collecting data weekly during the pandemic) were analysed from 21/03/2020-03/05/2020. The sample was well-stratified and weighted to population proportions of gender, age, ethnicity, education and geographical location. Growth mixture modelling was used to identify the latent classes of loneliness growth trajectories and their predictors.Analyses revealed four classes, with the baseline loneliness level ranging from low to high. In the first six weeks of lockdown, loneliness levels increased in the highest loneliness group, decreased in the lowest loneliness group, and stayed relatively constant in the middle two groups. Younger adults (OR = 2.17–6.81), women (OR = 1.59), people with low income (OR = 1.3), the economically inactive (OR = 1.3–2.04) and people with mental health conditions (OR = 5.32) were more likely to be in highest loneliness class relative to the lowest. Further, living with others or in a rural area, and having more close friends or greater social support were protective.Perceived levels of loneliness in the first few weeks of lockdown during COVID-19 were relatively stable in the UK, but for many people these levels were high with no signs of improvement. Results suggest that more efforts are needed to address loneliness, especially amongst young people.

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