Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling

Identifying latent subtypes of symptom trajectories in major depressive disorder patients and their predictors

This study aimed to identify different symptom trajectories based on the severity of depression symptoms within a 2-month follow-up, and to explore predictive factors for different symptom trajectories. Three hundred and ninety-two adults diagnosed with major depressive disorder (MDD) were recruited from two longitudinal cohorts. Patients received antidepressant treatment as usual, and the depression symptoms were evaluated by the 17-item Hamilton depression rating scale (HAMD-17) at baseline, two weeks, and eight weeks. Based on the HAMD-17 scores, different trajectories of symptom change were distinguished by applying Growth Mixture Modeling (GMM). Furthermore, the baseline sociodemographic, clinical, and cognitive characteristics were compared to identify potential predictors for different trajectories. Through GMM, three unique depressive symptom trajectories of MDD patients were identified: (1) mild-severity class with significant improvement (Mild, n = 255); (2) high-severity class with significant improvement (High, n = 39); (3) moderate-severity class with limited improvement (Limited, n = 98). Among the three trajectories, the Mild class had a relatively low level of anxiety symptoms at baseline, whereas the High class had the lowest education level and the worst cognitive performance. Additionally, participants in the Limited class exhibited an early age of onset and experienced a higher level of emotional abuse. MDD patients could be categorised into three distinct latent subtypes through different symptom trajectories in this study, and the characteristics of these subtype patients may inform identifications for trajectory-specific intervention targets.

Trajectories of Treatment Burden Among Primary Care Patients with Long-Term Conditions in Southern China: A Latent Class Growth Analysis

Background

Treatment burden is a patient-centred, dynamic concept. However, longitudinal data on the changing pattern of treatment burden among patients with one or more long-term conditions (LTCs) are relatively scanty. We aimed to explore the longitudinal trajectories of treatment burden and associated risk factors in a large, patient population in primary care settings.

Methods

We analysed data from 5573 primary care patients with long-term conditions (LTCs) recruited using a multistage sampling method in Shenzhen, southern China. The treatment burden was assessed by the Mandarin Chinese version of the Treatment Burden Questionnaire (TBQ). We used latent class growth mixture modelling (LCGMM) to determine trajectories of treatment burden across four time points, ie, at baseline, and at 6, 12, and 18 months. Predictors of trajectory classes were explored using multivariable logistic regression analysis.

Results

The mean TBQ scores of patients with a single LTC (n = 2756), 2 LTCs (n = 1871), 3 LTCs (n = 699), and ≥4 LTCs (n = 247) were 18.17, 20.28, 21.32, and 26.10, respectively, at baseline. LCGMM identified three discrete classes of treatment burden trajectories over time, ie, a high-increasing class, a low-stable class, and a high-decreasing class. When controlling for individual-level factors including age, education, monthly household income per head, smoking, alcohol consumption, and attendance in health education, patients who had a clinical diagnosis of 3 LTCs (adjusted odds ratio [aOR] = 1.49, 95% CI = 1.21-1.86, P < 0.001) or ≥4 LTCs (aOR = 1.97, 95% CI = 1.44-2.72, P < 0.001) were more likely to belong to the high-increasing class. Sensitivity analysis using propensity score methods obtained similar results.

Conclusion

Our study revealed the presence of discrete patterns of treatment burden over time in Chinese primary care patients with LTCs, providing directions for tailored interventions to optimise disease management. Patients with 3 or more LTCs should receive close attention in healthcare delivery as they tend to experience a greater treatment burden.

Prospective trajectories of depression predict mortality in cancer patients

An ever-growing body of empirical evidence has demonstrated the relationship between depression and cancer. The objective of this study was to examine whether depression trajectories predict mortality risk above and beyond demographics and other general health-related factors. Participants (n = 2,345) were a part of the Health and Retirement Study. The sample consisted of patients who were assessed once before their cancer diagnosis and thrice after. Depressive symptoms and general health-related factors were based on self-reports. Mortality risk was determined based on whether the patient was alive or not at respective time points. Latent Growth Mixture Modeling was performed to map trajectories of depression, assess differences in trajectories based on demographics and general health-related factors, and predict mortality risk. Four trajectories of depression symptoms emerged: resilient (69.7%), emerging (13.5%), recovery (9.5%), and chronic (7.2%). Overall, females, fewer years of education, higher functional impairment at baseline, and high mortality risk characterized the emerging, recovery, and chronic trajectories. In comparison to the resilient trajectory, mortality risk was highest for the emerging trajectory and accounted for more than half of the deaths recorded for the participants in emerging trajectory. Mortality risk was also significantly elevated, although to a lesser degree, for the recovery and chronic trajectories. The data highlights clinically relevant information about the depression-cancer association that can have useful implications towards cancer treatment, recovery, and public health.

Exploration of frailty trajectories and their associations with health outcomes in older gastric cancer survivors undergoing radical gastrectomy: A prospective longitudinal observation study

BACKGROUND

Frailty is commom among gastric cancer survivors and increases the burden of care.

AIMS

Our aims were to identify the frailty trajectories and investigate their associations with health outcomes in older gastric cancer survivors.

METHODS

We finally recruited 381 patients aged ≧60 who underwent radical gastrectomy and recorded frailty at discharge from the hospital, 1, 3, 6, and 12 months after surgery. Growth mixture modeling was used to investigate the frailty trajectories and linear regression models were used to examine their associations with health outcomes.

RESULTS

Three classes of frailty trajectories were identified: the "improving frailty", "maintaining frailty" and "deteriorating frailty". Compared with class 1, patients who followed class 2 and class 3 frailty trajectories were more likely to have more severe disability (β = -14.22, 95 % CI: -17.92, -10.61, P < 0.001; β = -48.34, 95 % CI: -52.25, -44.42, P < 0.001), worse quality of life (β = 10.89, 95 % CI: 7.71,14.08, P < 0.001; β = 34.82, 95 % CI: 31.46, 38.19, P < 0.001), and more frequency readmission within 1 year (β = 1.02, 95 % CI: 0.98, 1.06, P < 0.001; β = 2.10, 95 % CI: 2.01, 2.14, P < 0.001) after controlling potential confounders. However, class 2 and class 3 have no significant difference from class 1 in the total hospitalization costs (β = 1672.12, 95 % CI: -7145.95, 10496.19, P = 0.709; β = 7651.60, 95 % CI: -1670.28, 16793.47, P = 0.107).

CONCLUSIONS

Our study suggested the significant prognostic heterogeneity in frailty trajectories, and what we need to do is to identify patients with heterogeneous trajectory and intervene in them to reduce adverse outcomes, promote rational use of resources, and reduce the burden of care.

Predictors and Functional Outcomes Associated With Longitudinal Trajectories of Anxiety and Depression from 2 to ≥36 Months After Moderate to Severe Traumatic Brain Injury

This study investigated longitudinal trajectories of anxiety and depressive symptoms following moderate-severe traumatic brain injury (TBI), predictors of the trajectories, and associations with 1-year return to productivity. One hundred forty-eight patients with moderate-severe TBI were assessed at 2, 5, 12, and ≥36 months post-injury on the Beck Anxiety Inventory and the Beck Depression Inventory. Clinical interviews obtained information about demographics, injury characteristics, and 1-year return to productivity. Latent growth mixture modeling identified trajectories of anxiety and depression across time. The three-step method identified predictors of trajectories, and χ2 analyses determined associations between trajectories and 1-year return to productivity. Analyses revealed that four-class models of anxiety and depression best fit the data. Most individuals had stable minimal (67%) or low (18%) levels of anxiety over time. Two other subsets of individuals were classified by anxiety that worsened rapidly (7%) or improved in the 1st year but worsened by 3 years post-injury (9%). Similarly for the depression trajectories, most individuals had stable minimal (70%) or low (10%) levels of depression over time. Others had depression that worsened rapidly (12%) or was delayed, with onset 1-year post-injury (8%). Predictors of worsening anxiety and depression included younger age, less education, and male gender. Those with worsening anxiety or depression were less likely to return to productivity by 1-year post-injury. There is a significant burden of anxiety (15%) and depression (20%) in the 3 years after moderate-severe TBI. Future research targeting at-risk patients may help to improve quality of life and functional recovery.

BMI trajectories, associations with outcomes and predictors in elderly gastric cancer patients undergoing radical gastrectomy: a prospective longitudinal observation study

OBJECTIVES

Elderly gastric cancer patients undergoing radical gastrectomy are prone to experience unexpected weight loss. Preoperative weight risk prediction may be a promising way to prevent weight loss and improve prognosis. The objectives of this study were to explore the BMI trajectory of elderly gastric cancer patients one year after surgery, evaluate theirs the association with outcomes, and explore their related predictors, so as to provide evidence for weight management and prognosis improvement.

METHODS

412 gastric cancer patients were included and recorded BMI at 6 time points. The trajectories of BMI were analyzed by growth mixture modeling, and the associations of BMI trajectories with outcomes as well as their predictors were investigated by regression models.

RESULTS

We identified 3 classes of BMI trajectories: the "slow-decreasing BMI", "rapid-decreasing BMI" and "maintaining BMI". Compared with class1, patients in class 2 were more likely to have a higher frequency of readmission within 1-year(β = 0.59, 95%CI: 0.29, 0.89, P < 0.001) and a higher rate of mortality within 1-year(β = 24.74, 95%CI: 9.60, 63.74, P < 0.001) ; patients in class 3 were more likely to have a higher quality of life (β=-10.46, 95%CI: -17.70, -3.22, P = 0.005) and fewer readmission times within one year (β=-0.43, 95%CI: -0.77, -0.09, P = 0.015). Predictors of decreasing BMI trajectories were TNM stage, comorbidity, anxiety, family cohesion and social support(P < 0.05).

CONCLUSIONS

Our findings can provide a basis for screening high-risk elderly gastric cancer patients with poor prognosis, implementing risk stratification, formulating accurate weight management programs and improving prognosis.

IMPLICATIONS FOR CANCER SURVIVORS

The results of our study can provide gastric cancer survivors with preoperative risk screening based on predictive factors so that nutritional support and weight management can be implemented in a timely manner to improve prognosis.

The association between trajectories of change in social functioning and psychological treatment outcome in university students: a growth mixture model analysis

BACKGROUND

The transition to university and resultant social support network disruption can be detrimental to the mental health of university students. As the need for mental health support is becoming increasingly prevalent in students, identification of factors associated with poorer outcomes is a priority. Changes in social functioning have a bi-directional relationship with mental health, however it is not clear how such measures may be related to effectiveness of psychological treatments.

METHODS

Growth mixture models were estimated on a sample of 5221 students treated in routine mental health services to identify different trajectories of change in self-rated impairment in social leisure activities and close relationships during the course of treatment. Multinomial regression explored associations between trajectory classes and treatment outcomes.

RESULTS

Five trajectory classes were identified for social leisure activity impairment while three classes were identified for close relationship impairment. In both measures most students remained mildly impaired. Other trajectories included severe impairment with limited improvement, severe impairment with delayed improvement, and, in social leisure activities only, rapid improvement, and deterioration. Trajectories of improvement were associated with positive treatment outcomes while trajectories of worsening or stable severe impairment were associated with negative treatment outcomes.

CONCLUSIONS

Changes in social functioning impairment are associated with psychological treatment outcomes in students, suggesting that these changes may be associated with treatment effectiveness as well as recovery experiences. Future research should seek to establish whether a causal link exists to understand whether integrating social support within psychological treatment may bring additional benefit for students.