To review and synthesise prognostic indices that predict subsequent risk, prescriptive indices that moderate treatment response, and mechanisms that underlie each with respect to relapse and recurrence of depression in adults. Childhood maltreatment, post-treatment residual symptoms, and a history of recurrence emerged as strong prognostic indicators of risk and each could be used prescriptively to indicate who benefits most from continued or prophylactic treatment. Targeting prognostic indices or their "down-stream" consequences will be particularly beneficial because each is either a cause or a consequence of the causal mechanisms underlying risk of recurrence. The cognitive and neural mechanisms that underlie the prognostic indices are likely addressed by the effects of treatments that are moderated by the prescriptive factors. For example, psychosocial interventions that target the consequences of childhood maltreatment, extending pharmacotherapy or adapting psychological therapies to deal with residual symptoms, or using cognitive or mindfulness-based therapies for those with prior histories of recurrence. Future research that focuses on understanding causal pathways that link childhood maltreatment, or cognitive diatheses, to dysfunction in the neocortical and limbic pathways that process affective information and facilitate cognitive control, might result in more enduring effects of treatments for depression.

Autism has long been viewed as a paediatric condition, meaning that many autistic adults missed out on a diagnosis as children when autism was little known. We estimated numbers of diagnosed and undiagnosed autistic people in England, and examined how diagnostic rates differed by socio-demographic factors. This population-based cohort study of prospectively collected primary care data from IQVIA Medical Research Data (IMRD) compared the prevalence of diagnosed autism to community prevalence to estimate underdiagnosis. 602,433 individuals registered at an English primary care practice in 2018 and 5,586,100 individuals registered between 2000 and 2018 were included. Rates of diagnosed autism in children/young people were much higher than in adults/older adults. As of 2018, 2.94% of 10- to 14-year-olds had a diagnosis (1 in 34), vs. 0.02% aged 70+ (1 in 6000). Exploratory projections based on these data suggest that, as of 2018, 463,500 people (0.82% of the English population) may have been diagnosed autistic, and between 435,700 and 1,197,300 may be autistic and undiagnosed (59–72% of autistic people, 0.77%–2.12% of the English population). Age-related inequalities were also evident in new diagnoses (incidence): c.1 in 250 5- to 9-year-olds had a newly-recorded autism diagnosis in 2018, vs. c.1 in 4000 20- to 49-year-olds, and c.1 in 18,000 people aged 50+. Substantial age-related differences in the proportions of people diagnosed suggest an urgent need to improve access to adult autism diagnostic services. 10.13039/501100000377 Dunhill Medical Trust , 10.13039/501100000269 Economic and Social Research Council , 10.13039/501100000265 Medical Research Council , 10.13039/501100000272 National Institute for Health Research , the 10.13039/100010269 Wellcome Trust , and the 10.13039/100010248 Royal College of Psychiatrists .

Background: The COVID-19 pandemic and nationally mandated restrictions to control the virus have been associated with increased mental health issues. However, the differential impact of the pandemic and lockdown on groups of individuals, and the personal characteristics associated with poorer outcomes are unknown.Method: Data from 21 938 adults in England who participated in a stratified cohort study were analysed. Trajectories of depression and anxiety symptoms were identified using growth mixture modelling. Multinomial and logistic regression models were constructed to identify sociodemographic and personality-related risk factors associated with trajectory class membership.Results: Four trajectories of depression and five for anxiety were identified. The most common group presented with low symptom severity throughout, other classes were identified that showed: severe levels of symptoms which increased; moderate symptoms throughout; worsening mental health during lockdown but improvements after lockdown ended; and for anxiety only, severe initial anxiety that decreased quickly during lockdown. Age, gender, ethnicity, income, previous diagnoses, living situation, personality factors and sociability were associated with different trajectories.Conclusions: Nearly 30% of participants experienced trajectories with symptoms in the clinical range during lockdown, and did not follow the average curve or majority group, highlighting the importance of differential trajectories. Young, female, outgoing and sociable people and essential workers experienced severe anxiety around the announcement of lockdown which rapidly decreased. Younger individuals with lower incomes and previous mental health diagnoses experienced higher and increasing levels of symptoms. Recognising the likely symptom trajectories for such groups may allow for targeted care or interventions.

Aims: To determine whether age, gender and marital status are associated with prognosis for adults with depression who sought treatment in primary care. Methods: Medline, Embase, PsycINFO and Cochrane Central were searched from inception to 1st December 2020 for randomised controlled trials (RCTs) of adults seeking treatment for depression from their general practitioners, that used the Revised Clinical Interview Schedule so that there was uniformity in the measurement of clinical prognostic factors, and that reported on age, gender and marital status. Individual participant data were gathered from all nine eligible RCTs (N = 4864). Two-stage random-effects meta-analyses were conducted to ascertain the independent association between: (i) age, (ii) gender and (iii) marital status, and depressive symptoms at 3–4, 6–8, and 9–12 months post-baseline and remission at 3–4 months. Risk of bias was evaluated using QUIPS and quality was assessed using GRADE. PROSPERO registration: CRD42019129512. Pre-registered protocol https://osf.io/e5zup/. Results: There was no evidence of an association between age and prognosis before or after adjusting for depressive ‘disorder characteristics’ that are associated with prognosis (symptom severity, durations of depression and anxiety, comorbid panic disorderand a history of antidepressant treatment). Difference in mean depressive symptom score at 3–4 months post-baseline per-5-year increase in age = 0(95% CI: −0.02 to 0.02). There was no evidence for a difference in prognoses for men and women at 3–4 months or 9–12 months post-baseline, but men had worse prognoses at 6–8 months (percentage difference in depressive symptoms for men compared to women: 15.08% (95% CI: 4.82 to 26.35)). However, this was largely driven by a single study that contributed data at 6–8 months and not the other time points. Further, there was little evidence for an association after adjusting for depressive ‘disorder characteristics’ and employment status (12.23% (−1.69 to 28.12)). Participants that were either single (percentage difference in depressive symptoms for single participants: 9.25% (95% CI: 2.78 to 16.13) or no longer married (8.02% (95% CI: 1.31 to 15.18)) had worse prognoses than those that were married, even after adjusting for depressive ‘disorder characteristics’ and all available confounders. Conclusion: Clinicians and researchers will continue to routinely record age and gender, but despite their importance for incidence and prevalence of depression, they appear to offer little information regarding prognosis. Patients that are single or no longer married may be expected to have slightly worse prognoses than those that are married. Ensuring this is recorded routinely alongside depressive ‘disorder characteristics’ in clinic may be important.

Many treatments for depression are effective, yet half of all patients do not recover with the first treatment they receive.¹ This can lead to disengagement and poor long-term outcomes.² Recently, studies have begun to report on pretreatment characteristics associated with prognosis regardless of treatment type, providing patients and clinicians with desired information³ and informing assessments and clinical decision-making before a choice of treatment is made.⁴ These include the severity of depressive symptoms; the duration of depression, comorbid anxiety, and comorbid panic disorder; a history of antidepressant treatment^4,5; marital status⁶; and social support.⁷ Despite such knowledge, most of the variance in prognosis for patients with depression remains unexplained, reflecting the field’s limited knowledge of how patients respond to treatment.⁵

Forty-percent of the variance in psychological treatment outcomes is estimated to be explained by symptom change by the third treatment session. However, change may not be uniform across patient groups and symptom domains. This study aimed to identify subgroups of patients with different trajectories of depression and anxiety symptom change during psychological therapy and identify baseline patient characteristics associated with these trajectories. 4394 patients attending two psychological treatment services completed sessional, self-report depression and anxiety measures. Trajectories of symptom change were investigated using latent class growth analysis. Multinomial logistic regression was used to explore associations between baseline patient characteristics and trajectory classes. A number of distinct trajectories were identified. Anxiety symptom trajectories could be distinguished by the third treatment session, but for depression symptoms there was a class displaying limited change until session six followed by rapid improvement in symptoms thereafter. Compared to the non-responding trajectories, depression and anxiety trajectories indicating treatment response were associated with lower baseline severity, better social functioning and lower incidence of phobic anxiety, but not with medication prescription status. Data came from two services, so wider generalisability is unknown. Predictors were limited to data routinely collected in the services; unmeasured factors may have improved the prediction of trajectories. Baseline characteristics and symptom change early in therapy can help identify different trajectories of symptom change. This knowledge could aid clinical decision making and help improve treatment outcomes. By ignoring distinct trajectories, clinicians may incorrectly consider patients as "not-on-track" and unnecessarily change or end therapy that would otherwise benefit patients.