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This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.
Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1–3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3–4 months.
Models 1–7 all outperformed the null model and model 8. Model performance was very similar across models 1–6, meaning that differential weights applied to the baseline sum scores had little impact.
Any of the modelling techniques (models 1–7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.
To determine whether age, gender and marital status are associated with prognosis for adults with depression who sought treatment in primary care.
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,<Vinod: Please carry out the deletion of serial commas throughout the article> 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/.
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.
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.
Emerging evidence suggests that sedentary behaviour, specifically time spent taking part in screen-based activities, such as watching television, may be associated with mental health outcomes in young people . However, recent reviews have found limited and conflicting evidence for both anxiety and depression .
The purpose of the study was to explore associations between screen time at age 16 years and anxiety and depression at 18.
Subjects (n = 1958) were from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK-based prospective cohort study. We assessed associations between screen time (measured via questionnaire at 16 years) and anxiety and depression (measured in a clinic at 18 years using the Revised Clinical Interview Schedule) using ordinal logistic regression, before and after adjustment for covariates (including sex, maternal education, family social class, parental conflict, bullying and maternal depression).
After adjusting for potential confounders, we found no evidence for an association between screen time and anxiety (OR = 1.02; 95% CI 0.95–1.09). There was weak evidence that greater screen time was associated with a small increased risk of depression (OR = 1.05, 95% CI 0.98–1.13).
Our results suggest that young people who spend more time on screen-based activities may have a small increased risk of developing depression but not anxiety. Reducing youth screen time may lower the prevalence of depression. The study was limited by screen time being self-reported, a small sample size due to attrition and non-response, and the possibility of residual confounding. Reverse causation cannot be ruled out.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
The Beck Depression Inventory, 2nd edition (BDI-II) is widely used in research on depression. However, the minimal clinically important difference (MCID) is unknown. MCID can be estimated in several ways. Here we take a patient-centred approach, anchoring the change on the BDI-II to the patient's global report of improvement.
We used data collected (n = 1039) from three randomized controlled trials for the management of depression. Improvement on a ‘global rating of change’ question was compared with changes in BDI-II scores using general linear modelling to explore baseline dependency, assessing whether MCID is best measured in absolute terms (i.e. difference) or as percent reduction in scores from baseline (i.e. ratio), and receiver operator characteristics (ROC) to estimate MCID according to the optimal threshold above which individuals report feeling ‘better’.
Improvement in BDI-II scores associated with reporting feeling ‘better’ depended on initial depression severity, and statistical modelling indicated that MCID is best measured on a ratio scale as a percentage reduction of score. We estimated a MCID of a 17.5% reduction in scores from baseline from ROC analyses. The corresponding estimate for individuals with longer duration depression who had not responded to antidepressants was higher at 32%.
MCID on the BDI-II is dependent on baseline severity, is best measured on a ratio scale, and the MCID for treatment-resistant depression is larger than that for more typical depression. This has important implications for clinical trials and practice.
Meta-analyses suggest that reboxetine may be less effective than other antidepressants. Such comparisons may be biased by lower adherence to reboxetine and subsequent handling of missing outcome data. This study illustrates how to adjust for differential non-adherence and hence derive an unbiased estimate of the efficacy of reboxetine compared with citalopram in primary care patients with depression.
A structural mean modelling (SMM) approach was used to generate adherence-adjusted estimates of the efficacy of reboxetine compared with citalopram using GENetic and clinical Predictors Of treatment response in Depression (GENPOD) trial data. Intention-to-treat (ITT) analyses were performed to compare estimates of effectiveness with results from previous meta-analyses.
At 6 weeks, 92% of those randomized to citalopram were still taking their medication, compared with 72% of those randomized to reboxetine. In ITT analysis, there was only weak evidence that those on reboxetine had a slightly worse outcome than those on citalopram [adjusted difference in mean Beck Depression Inventory (BDI) scores: 1.19, 95% confidence interval (CI) –0.52 to 2.90, p = 0.17]. There was no evidence of a difference in efficacy when differential non-adherence was accounted for using the SMM approach for mean BDI (–0.29, 95% CI –3.04 to 2.46, p = 0.84) or the other mental health outcomes.
There was no evidence of a difference in the efficacy of reboxetine and citalopram when these drugs are taken and tolerated by depressed patients. The SMM approach can be implemented in standard statistical software to adjust for differential non-adherence and generate unbiased estimates of treatment efficacy for comparisons of two (or more) active interventions.
An argument often used to support the view that psychotic experiences (PEs) in general population samples are a valid phenotype for studying the aetiology of schizophrenia is that risk factors for schizophrenia show similar patterns of association with PEs. However, PEs often co-occur with depression, and no study has explicitly tested whether risk factors for schizophrenia are shared between PEs and depression, or are psychopathology specific, while jointly modelling both outcomes.
We used data from 7030 subjects from a birth cohort study. Depression and PEs at age 18 years were assessed using self-report questionnaires and semi-structured interviews. We compared the extent to which risk factors for schizophrenia across sociodemographic, familial, neurodevelopmental, stress–adversity, emotional–behavioural and substance use domains showed different associations with PEs and depression within bivariate models that allowed for their correlation.
Most of the exposures examined were associated, to a similar degree, with an increased risk of both outcomes. However, whereas female sex and family history of depression showed some discrimination as potential risk factors for depression and PEs, with stronger associations in the former, markers of abnormal neurodevelopment showed stronger associations with PEs.
The argument that PEs are valid markers for studying the aetiology of schizophrenia, made simply on the basis that they share risk factors in common, is not well supported. PEs seem to be a weak index of genetic and environmental risk for schizophrenia; however, studies disentangling aetiological pathways to PEs from those impacting upon co-morbid psychopathology might provide important insights into the aetiology of psychotic disorders.
There is an ever-increasing body of literature examining gene–environment interactions in psychiatry, reflecting a widespread belief that such studies will aid identification of novel risk factors for disease, increase understanding about underlying pathological mechanisms, and aid identification of high-risk groups for targeted interventions. In this article we discuss to what extent studies of gene–environment interactions are likely to lead to any such benefits in the future.
Alcohol is commonly considered to be associated with persistence of common mental disorder (CMD; anxiety/depression). However no community-based longitudinal studies have investigated the direction of causality.
We examined the association between alcohol consumption and recovery from CMD using data on 706 community-based subjects with CMD who were followed for 18 months. Alcohol consumption at baseline was defined as hazardous drinking [Alcohol Use Disorders Identification Test (AUDIT) ⩾8], binge drinking (defined as six or more units of alcohol on one occasion, approximately two to three pints of commercially sold beer) and dependence.
When compared with a non-binge-drinking group, non-recovery at follow-up was associated with binge drinking on at least a monthly basis at baseline, although the confidence interval (CI) included unity [adjusted odds ratio (OR) 1.47, 95% CI 0.89–2.45]. There was also weak evidence that alcohol dependence was associated with non-recovery (adjusted OR 1.37, 95% CI 0.67–2.81). There was little evidence to support hazardous drinking as a risk factor for non-recovery (adjusted OR 1.12, 95% CI 0.67–1.88).
Binge drinking may be a potential risk factor for non-recovery from CMD, although the possibility of no effect cannot be excluded. Larger studies are required to refute or confirm this finding.
Clozapine has become a widely used drug in therapyresistant schizophrenia and, increasingly, in the levodopa-induced dyskinesias and the psychoses of chronic Parkinson's disease. We report the case of a 43 year old male patient who developed a reversible encephalopathy associated with clozapine therapy.
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