Effects of cumulative trauma load on long-term trajectories of life satisfaction and health in a population-based study.

BACKGROUND: Lifetime traumatic events are known to have a detrimental long-term impact on both mental and physical health. Yet, heterogeneity in the stress response regarding well-being in adults is not well understood. This study investigates effects of cumulative trauma on latent trajectories of two indices of well-being, subjective health and life satisfaction in a large representative sample by means of latent variable modelling techniques. METHODS: Data from the pairfam study wave 2-9, a longitudinal representative survey was used (N = 10,825). Individuals reported on lifetime trauma type exposure on wave 7 and indicated levels of life satisfaction and health at each wave. Different types of latent Variable Mixture Models were applied in an iterative fashion. Conditional models investigated effects of cumulative trauma load. RESULTS: The best fitting model indicated three latent trajectories for life, and four for health, respectively. Trauma load significantly predicted class membership: Higher exposure was associated with non-stable trajectories for both indices but followed complex patterns of both improving and decreasing life satisfaction and health. Trauma load also explained variability within classes. CONCLUSIONS: The current study expands on evidence to the long-term development of health and life satisfaction in response to traumatic events from a latent variable modelling perspective. Besides detrimental effect, it also points to functional adaptation after initial decline and increased well-being associated with trauma exposure. Thus, response to traumatic stress is marked by great heterogeneity. Future research should focus on variables beyond exposure to trauma that can further identify individuals prone to trajectories of declining well-being.

[Clinical Utility and Global Applicability of Prolonged Grief Disorder in the ICD-11 from the Perspective of Chinese and German-Speaking Health Care Professionals].

BACKGROUND: Prolonged grief disorder (PGD) is included in the ICD-11 (11th edition of the International Classification of Diseases). The new PGD criteria reflect the requirements and recommendations of the World Health Organization for improved clinical utility and international applicability. Even though the ICD classification system is globally used, no research has investigated how healthcare professionals (HP) in non-Western countries may adopt this change for their own practice. OBJECTIVE: The present study explored the extent to which the new PGD criteria were accepted and perceived to meet the standards for clinical utility and international applicability among Chinese and German-speaking HP. METHODS: Individual semistructured interviews were conducted in person, by phone, or online (e.g., via Skype), with 24 Chinese (n = 10) and German-speaking (n = 14) HP working with bereaved populations in China and Switzerland, and analyzed using a qualitative framework analysis. Questions included "what items are currently missing from the PGD criteria?". RESULTS: Across all HP, the majority supported the inclusion of PGD and were generally aligned with the current criteria. HP found that the criteria distinguished between normal and abnormal grief and considered the criteria easy to use if their modifications were considered. Merits included, among others, improved clinical decision making, research promotion, and social acknowledgment. Main concerns included misdiagnosis, pathologization, and a lack of specificity of criteria. The importance of international applicability was emphasized across Chinese and German-speaking HP. Different grief-specific symptoms were identified by German-speaking and Chinese HP. CONCLUSIONS: These findings provide evidence for the clinical utility and international applicability of ICD-11 PGD criteria among German-speaking and Chinese HP, as well as cultural similarities and differences in the barriers to implementation of these criteria.

Prognostic Risk Factors in Randomized Clinical Trials of Face-to-Face and Internet-Based Psychotherapy for Depression: A Systematic Review and Meta-Regression Analysis.

Importance: Variables such as severe symptoms, comorbidity, and sociodemographic characteristics (eg, low educational attainment or unemployment) are associated with a poorer prognosis in adults treated for depressive symptoms. The exclusion of patients with a poor prognosis from RCTs is negatively associated with the generalizability of research findings. Objective: To compare the prognostic risk factors (PRFs) in patient samples of RCTs of face-to-face therapy (FTF) and internet-based therapy (IBT) for depression. Data Sources: PsycINFO, Cochrane CENTRAL, and reference lists of published meta-analyses were searched from January 1, 2000, to December 31, 2021. Study Selection: RCTs that compared FTF (individual or group therapy) and IBT (guided or self-guided interventions) against a control (waitlist or treatment as usual) in adults with symptoms of depression were included. Data Extraction and Synthesis: Data were extracted by 2 independent observers. The Cochrane revised risk-of-bias tool was used to assess the risk of bias. The study was preregistered with OSF Registries and followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Main Outcomes and Measures: The primary outcome was the standardized mean difference (Hedges g effect size) in depressive symptoms at treatment termination (assessed with standard patient self-report questionnaires), with a positive standardized mean difference indicating larger improvements in the intervention compared with those in the control group. Meta-regression analyses were adjusted for the type of control group. Three preregistered and 2 exploratory sensitivity analyses were conducted. A prognostic risk index (PROG) was created that calculated the sum of 12 predefined individual indicators, with scores ranging from 0 to 12 and higher scores indicating that a sample comprised patients with poorer prognoses. Results: This systematic review and meta-regression analysis identified 105 eligible RCTs that comprised 18 363 patients. In total, 48 studies (46%) examined FTF, and 57 studies (54%) examined IBT. The PROG was significantly higher in the RCTs of FTF than in the RCTs of IBT (FTF: mean [SD], 3.55 [1.75]; median [IQR], 3.5 [2.0-4.5]; IBT: mean [SD], 2.27 [1.66]; median [IQR], 2.0 [1.0-3.5]; z = -3.68, P < .001; Hedges g = 0.75; 95% CI, 0.36-1.15). A random-effects meta-regression analysis found no association of the PROG with the effect size. Sensitivity analyses with outliers excluded and accounting for risk of bias or small-study effects yielded mixed results on the association between the PROG and effect size. Conclusions and Relevance: The findings of this systematic review and meta-regression analysis suggest that samples of RCTs of FTF vs IBT differ with regard to PRFs. These findings have implications for the generalizability of the current evidence on IBT for depression. More RCTs of internet-based interventions with clinically representative samples are needed, and the reporting of PRFs must be improved.

Trajectories of change in the therapeutic alliance during Cognitive Analytic Therapy for depression.

BACKGROUND: Managing the alliance is considered to be a core competency and central therapeutic change process during cognitive analytic therapy (CAT). This study examined latent trajectories of change in the alliance and their relationship to depression treatment outcomes. DESIGN: Secondary analysis of a randomized controlled trial. METHODS: A sample of N = 79 depressed participants completed standardized alliance (WAI-SF) and depression symptom measures (PHQ-9) every session during an 8-session CAT intervention. Growth mixture modelling was applied to model alliance trajectories and to classify cases into different latent classes. Associations between alliance class and post-treatment PHQ-9 scores were examined using hierarchical linear regression, controlling for confounders. RESULTS: There were two classes of alliance trajectories. The majority class (91%) displayed stable alliance trajectories, whilst a minority class (9%) had initially poor alliance ratings that significantly improved during treatment. Baseline severity and early change in depression symptoms significantly predicted treatment outcomes, but early alliance and longitudinal alliance change did not. CONCLUSIONS: Alliance trajectories did not significantly predict depression treatment outcomes after controlling for initial symptom severity and early change. An important limitation concerns the small sample size, so future replication in larger samples is necessary.

Dynamic prediction of psychological treatment outcomes: development and validation of a prediction model using routinely collected symptom data.

BACKGROUND: Common mental disorders can be effectively treated with psychotherapy, but some patients do not respond well and require timely identification to prevent treatment failure. We aimed to develop and validate a dynamic model to predict psychological treatment outcomes, and to compare the model with currently used methods, including expected treatment response models and machine learning models. METHODS: In this prediction model development and validation study, we obtained data from two UK studies including patients who had accessed therapy via Improving Access to Psychological Therapies (IAPT) services managed by ten UK National Health Service (NHS) Trusts between March, 2012, and June, 2018, to predict treatment outcomes. In study 1, we used data on patient-reported depression (Patient Health Questionnaire 9 [PHQ-9]) and anxiety (Generalised Anxiety Disorder 7 [GAD-7]) symptom measures obtained on a session-by-session basis (Leeds Community Healthcare NHS Trust dataset; n=2317) to train the Oracle dynamic prediction model using iterative logistic regression analysis. The outcome of interest was reliable and clinically significant improvement in depression (PHQ-9) and anxiety (GAD-7) symptoms. The predictive accuracy of the model was assessed in an external test sample (Cumbria Northumberland Tyne and Wear NHS Foundation Trust dataset; n=2036) using the area under the curve (AUC), positive predictive values (PPVs), and negative predictive values (NPVs). In study 2, we retrained the Oracle algorithm using a multiservice sample (South West Yorkshire Partnership NHS Foundation Trust, North East London NHS Foundation Trust, Cheshire and Wirral Partnership NHS Foundation Trust, and Cambridgeshire and Peterborough NHS Foundation Trust; n=42 992) and compared its performance with an expected treatment response model and five machine learning models (Bayesian updating algorithm, elastic net regularisation, extreme gradient boosting, support vector machine, and neural networks based on a multilayer perceptron algorithm) in an external test sample (Whittington Health NHS Trust; Barnet Enfield and Haringey Mental Health Trust; Pennine Care NHS Foundation Trust; and Humber NHS Foundation Trust; n=30 026). FINDINGS: The Oracle algorithm trained using iterative logistic regressions generalised well to external test samples, explaining up to 47·3% of variability in treatment outcomes. Prediction accuracy was modest at session one (AUC 0·59 [95% CI 0·55-0·62], PPV 0·63, NPV 0·61), but improved over time, reaching high prediction accuracy (AUC 0·81 [0·77-0·86], PPV 0·79, NPV 0·69) as early as session seven. The performance of the Oracle model was similar to complex (eg, including patient profiling variables) and computationally intensive machine learning models (eg, neural networks based on a multilayer perceptron algorithm, extreme gradient boosting). Furthermore, the predictive accuracy of a more simple dynamic algorithm including only baseline and index-session scores was comparable to more complex algorithms that included additional predictors modelling sample-level and individual-level variability. Overall, the Oracle algorithm significantly outperformed the expected treatment response model (mean AUC 0·80 vs 0·70, p<0·0001]). INTERPRETATION: Dynamic prediction models using sparse and readily available symptom measures are capable of predicting psychotherapy outcomes with high accuracy. FUNDING: University of Sheffield.

Examination of the New ICD-11 Prolonged Grief Disorder Guidelines Across Five International Samples.

Background: Prolonged grief disorder (PGD) is a new disorder included in the 11th edition of the International classification of diseases (ICD-11). An important remit of the new ICD-11 is the global applicability of the mental health disorder guidelines or definitions. Although previous definitions and descriptions of disordered grief have been assessed worldwide, this new definition has not yet been systematically validated. Method: Here we assess the validity and applicability of core items of the ICD-11 PGD across five international samples of bereaved persons from Switzerland (N = 214), China (N = 325); Israel (N = 544), Portugal (N = 218) and Ireland (N = 830). Results: The results confirm that variation in the diagnostic algorithm for PGD can greatly impact the rates of disorder within and between international samples. Different predictors of PGD severity may be related to sample differences. Finally, a threshold for diagnosis of clinically relevant PGD symptoms using a new scale, the International Prolonged Grief Disorder Scale (IPGDS), in three samples was confirmed. Conclusions: Although this study was limited by lack of questionnaire data points across all five samples, the findings for the diagnostic threshold and algorithm iterations have implications for clinical use of the new ICD-11 PGD criteria worldwide.

Development of the international prolonged grief disorder scale for the ICD-11: Measurement of core symptoms and culture items adapted for chinese and german-speaking samples.

BACKGROUND: Prolonged grief disorder (PGD) is a new mental health disorder included in the WHO ICD-11 however, the operationalization of the guidelines still needs to be empirically validated, particularly in different cultural contexts. Here we provide a preliminary validation study of the new International Prolonged Grief Disorder Scale (IPGDS) that serves to be the first self-report questionnaire directly based on the ICD-11 PGD and contains culturally adapted items. METHODS: In addition to core symptom items new culturally specific items were developed in two phases. Phase 1: key informant interviews with 10 German-speaking and 14 Chinese experts in grief and mental health, followed by a focus group with four bereaved German-speaking participants. Phase 2: 214 German-speaking and 325 Chinese bereaved participants completed self-report questionnaires. RESULTS: Phase 1 resulted in 19 potential culturally relevant items (e.g. feeling stuck in grief). Phase 2 exploratory factor analysis confirmed the one-dimensional nature of the IPGDS, additionally the 32-item scale revealed two factors (core grief and culturally specific symptoms). Psychometric analysis revealed strong internal consistency, concurrent validity and criterion validity. LIMITATIONS: The German-speaking and Chinese samples significantly differed in terms of several demographic variables including age, gender and type of loss. CONCLUSIONS: This preliminary validity study confirms that the IPGDS is a valid and reliable measure of the new ICD-11 PGD guidelines. This is the first scale of disordered grief to contain both core items and culturally specific supplementary items and aims to improve the clinical utility of the ICD-11 narrative approach.