Most staff stay healthy during humanitarian work, although some worsen. Mean scores on health indicators may be masking individual participants struggling with health issues.

To investigate different field assignment-related health trajectories among international humanitarian aid workers (iHAWs) and explore the mechanisms used to stay healthy.

Growth mixture modelling analyses for five health indicators using pre-/post-assignment and follow-up data.

Among 609 iHAWs three trajectories (profiles) were found for emotional exhaustion, work engagement, anxiety and depression. For post-traumatic stress disorder (PTSD) symptoms, four trajectories were identified. The ‘healthy/normative’ trajectory had the largest sample size for all health indicators (73–86%). A stable (moderate) ‘ill health’ trajectory was identified for all health indicators (7–17%), except anxiety. An ‘improving’ trajectory was found for PTSD and anxiety symptoms (5–14%). A minority of staff (4–15%) worsened on all health indicators. Deterioration continued for PTSD, depressive symptoms and work engagement 2 months post-assignment. A strong sense of coherence was associated with higher odds of belonging to the ‘healthy’ trajectory. Female biological sex was associated with higher odds of belonging to the ‘worsening’ depression and anxiety trajectories. Extended duration of field assignment was related to higher odds of belonging to the ‘worsening’ depressive symptoms trajectory.

Most iHAWs stayed healthy during their assignment; a stable ‘ill health’ trajectory was identified for most health indicators. Sense of coherence is an important mechanism for understanding the health of all iHAWs in the different health trajectories, including the ‘healthy’ profile. These findings give new possibilities to develop activities to prevent worsening health and help strengthen iHAWs’ ability to remain healthy under stress.

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.

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.

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.

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.

Psychotic experiences and negative symptoms (PENS) are common in non-clinical populations. PENS are associated with adverse outcomes, particularly when they persist. Little is known about the trajectories of PENS dimensions in young people, nor about the precursory factors associated with these trajectories.

We conducted growth mixture modelling of paranoia, hallucinations, and negative symptoms across ages 16, 17, and 22 in a community sample (N = 12 049–12 652). We then described the emergent trajectory classes through their associations with genome-wide polygenic scores (GPS) for psychiatric and educational phenotypes, and earlier childhood characteristics.

Three trajectory classes emerged for paranoia, two for hallucinations, and two for negative symptoms. Across PENS, GPS for clinical help-seeking, major depressive disorder, and attention deficit hyperactivity disorder were associated with increased odds of being in the most elevated trajectory class (OR 1.07–1.23). Lower education GPS was associated with the most elevated trajectory class for hallucinations and negative symptoms (OR 0.77–0.91). Conversely for paranoia, higher education GPS was associated with the most elevated trajectory class (OR 1.25). Trajectory class associations were not significant for schizophrenia, obsessive-compulsive disorder, bipolar disorder, or anorexia GPS. Emotional/behaviour problems and life events in childhood were associated with increased odds of being in the most elevated trajectory class across PENS.

Our results suggest latent heterogeneity in the development of paranoia, hallucinations, and negative symptoms in young people that is associated with specific polygenic scores and childhood characteristics.

It is generally accepted that antipsychotics are more effective than placebo. However, it remains unclear whether antipsychotics induce a pattern or trajectory of response that is distinct from placebo. We used a data-driven technique, called growth mixture modelling (GMM), to identify the different patterns of response observed in antipsychotic trials and to determine whether drug-treated and placebo-treated subjects show similar or distinct patterns of response.

We examined data on 420 patients with schizophrenia treated for 6 weeks in two double-blind placebo-controlled trials using haloperidol and olanzapine. We used GMM to identify the optimal number of response trajectories; to compare the trajectories in drug-treated versus placebo-treated patients; and to determine whether the trajectories for the different dimensions (positive versus negative symptoms) were identical or different.

Positive symptoms were found to respond along four distinct trajectories, with the two most common trajectories (‘Partial responder’ and ‘Responder’) accounting for 70% of the patients and seen proportionally in both drug- and placebo-treated. The most striking drug–placebo difference was in the ‘Dramatic responders’, seen only among the drug-treated. The response of negative symptoms was more modest and did not show such distinct trajectories.

Trajectory models of response, rather than the simple responder/non-responder dichotomy, provide a better statistical account of how antipsychotics work. The ‘Dramatic responders’ (those showing >70% response) were seen only among the drug-treated and make a significant contribution to the overall drug–placebo difference. Identifying and studying this subset may provide specific insight into antipsychotic action.

Response and remission defined by cut-off values on the last observed depression severity score are commonly used as outcome criteria in clinical trials, but ignore the time course of symptomatic change and may lead to inefficient analyses. We explore alternative categorization of outcome by naturally occurring trajectories of symptom change.

Growth mixture models were applied to repeated measurements of depression severity in 807 participants with major depression treated for 12 weeks with escitalopram or nortriptyline in the part-randomized Genome-based Therapeutic Drugs for Depression study. Latent trajectory classes were validated as outcomes in drug efficacy comparison and pharmacogenetic analyses.

The final two-piece growth mixture model categorized participants into a majority (75%) following a gradual improvement trajectory and the remainder following a trajectory with rapid initial improvement. The rapid improvement trajectory was over-represented among nortriptyline-treated participants and showed an antidepressant-specific pattern of pharmacogenetic associations. In contrast, conventional response and remission favoured escitalopram and produced chance results in pharmacogenetic analyses. Controlling for drop-out reduced drug differences on response and remission but did not affect latent trajectory results.

Latent trajectory mixture models capture heterogeneity in the development of clinical response after the initiation of antidepressants and provide an outcome that is distinct from traditional endpoint measures. It differentiates between antidepressants with different modes of action and is robust against bias due to differential discontinuation.