An international risk prediction algorithm for the onset of generalized anxiety and panic syndromes in general practice attendees: predictA

Development and validation of a prediction score to assess the risk of depression in primary care

BACKGROUND

Major depression is the most frequent psychiatric disorder and primary care is a crucial setting for its early recognition. This study aimed to develop and validate the DEP-HScore as a tool to predict depression risk in primary care and increase awareness and investigation of this condition among General Practitioners (GPs).

METHODS

The DEP-HScore was developed using data from the Italian Health Search Database (HSD). A cohort of 903,748 patients aged 18 years or older was selected and followed until the occurrence of depression, death or end of data availability (December 2019). Demographics, somatic signs/symptoms and psychiatric/medical comorbidities were entered in a multivariate Cox regression to predict the occurrence of depression. The coefficients formed the DEP-HScore for individual patients. Explained variance (pseudo-R2), discrimination (AUC) and calibration (slope estimating predicted-observed risk relationship) assessed the prediction accuracy.

RESULTS

The DEP-HScore explained 18.1 % of the variation in occurrence of depression and the discrimination value was equal to 67 %. With an event horizon of three months, the slope and intercept were not significantly different from the ideal calibration.

LIMITATIONS

The DEP-HScore has not been tested in other settings. Furthermore, the model was characterized by limited calibration performance when the risk of depression was estimated at the 1-year follow-up.

CONCLUSIONS

The DEP-HScore is reliable tool that could be implemented in primary care settings to evaluate the risk of depression, thus enabling prompt and suitable investigations to verify the presence of this condition.

Personalized online intervention based on a risk algorithm for the universal prevention of anxiety disorders: Design and development of the prevANS intervention

Objective

To describe the design and development of prevANS, a personalized online intervention for the universal prevention of anxiety disorders based on a predictive risk algorithm. A user-centered approach was followed, considering the feedback of potential users and mental health professionals.

Methods

The study had three phases: (a) designing the intervention based on existing scientific literature; (b) piloting and evaluating the beta version involving potential users and health professionals; and (c) refining the intervention based on participants' suggestions. This iterative process aimed to refine the prevANS intervention before testing in a randomized controlled trial.

Results

The prevANS intervention provides personalized anxiety risk reports and components tailored to individuals' needs. Participants at low risk receive psychoeducation had access to a set of tools enhance protective factors. Moderate/high-risk individuals also receive cognitive-behavioral training. Both groups have access to a reward system and forum. Results from the design evaluation indicate that the prevANS interface is attractive and user-friendly and the psychoeducational materials helpful and engaging. The cognitive-behavioral training module received positive feedback. Participants suggested changes related to usability, content clarity, attractiveness, and engagement, which were implemented afterwards.

Conclusions

This article describes the development of a personalized intervention for preventing anxiety disorders using a validated risk prediction algorithm. The prevANS intervention was designed based on current scientific literature by a team of experts employing a user-centered approach. Research on the effectiveness of information and communication technologies in mental health prevention interventions considering user needs and preferences is warranted.

Effectiveness of a universal personalized intervention for the prevention of anxiety disorders: Protocol of a randomized controlled trial (the prevANS project)

Background

To date, all preventive anxiety disorders interventions are one-fit-all and none of them are based on individual level and risk profile. The aim of this project is to design, develop and evaluate an online personalized intervention based on a risk algorithm for the universal prevention of anxiety disorders in the general population.

Methods

A randomized controlled trial (RCT) with two parallel arms (prevANS vs usual care) and 1-year follow-up including 2000 participants without anxiety disorders from Spain and Portugal will be conducted.The prevANS intervention will be self-guided and can be implemented from the prevANS web or from the participants' Smartphone (through an App). The prevANS intervention will have different intensities depending on the risk level of the population, evaluated from the risk algorithm for anxiety: predictA. Both low and moderate-high risk participants will receive information on their level and profile (risk factors) of anxiety disorders, will have access to stress management tools and psychoeducational information periodically. In addition, participants with a moderate-high risk of anxiety disorders will also have access to cognitive-behavioral training (problem-solving, decision-making, communication skills, and working with thoughts). The control group will not receive any intervention, but they will fill out the same questionnaires as the intervention group.Assessments will be completed at baseline, 6 and 12-month follow-up. The primary outcome is the cumulative incidence of anxiety disorders. Secondary outcomes include depressive and anxiety symptoms, risk probability of anxiety disorders (predictA algorithm) and depression (predictD algorithm), improvement in physical and mental quality of life, and acceptability and satisfaction with the intervention. In addition, cost-effectiveness and cost-utility analyses will also be carried out from two perspectives, societal and health system, and analyses of mediators and moderators will also be performed.

Discussion

To the best of our knowledge, prevANS study will be the first to evaluate the effectiveness and cost-effectiveness of a personalized online intervention based on a risk predictive algorithm for the universal prevention of anxiety disorders.

Trial registration

ClinicalTrials.gov: NCT05682365.

Variables Associated with Emotional Symptom Severity in Primary Care Patients: The Usefulness of a Logistic Regression Equation to Help Clinical Assessment and Treatment Decisions

The aim of this study is to contribute to the evidence regarding variables related to emotional symptom severity and to use them to exemplify the potential usefulness of logistic regression for clinical assessment at primary care, where most of these disorders are treated. Cross-sectional data related to depression and anxiety symptoms, sociodemographic characteristics, quality of life (QoL), and emotion-regulation processes were collected from 1,704 primary care patients. Correlation and analysis of variance (ANOVA) tests were conducted to identify those variables associated with both depression and anxiety. Participants were then divided into severe and nonsevere emotional symptoms, and binomial logistic regression was used to identify the variables that contributed the most to classify the severity. The final adjusted model included psychological QoL (p < .001, odds ratio [OR] = .426, 95% CI [.318, .569]), negative metacognitions (p < .001, OR = 1.083, 95% CI [1.045, 1.122]), physical QoL (p < .001, OR = .870, 95% CI [.841, .900]), brooding rumination (p < .001, OR = 1.087, 95% CI [1.042, 1.133]), worry (p < .001, OR = 1.047, 95% CI [1.025, 1.070]), and employment status (p = .022, OR [.397, 2.039]) as independent variables, ρ2 = .326, area under the curve (AUC) = .857. Moreover, rumination and psychological QoL emerged as the best predictors to form a simplified equation to determine the emotional symptom severity (ρ2 = .259, AUC = .822). The use of statistical models like this could accelerate the assessment and treatment-decision process, depending less on the subjective point of view of clinicians and optimizing health care resources.

Mental Health Prevention and Promotion-A Narrative Review

Extant literature has established the effectiveness of various mental health promotion and prevention strategies, including novel interventions. However, comprehensive literature encompassing all these aspects and challenges and opportunities in implementing such interventions in different settings is still lacking. Therefore, in the current review, we aimed to synthesize existing literature on various mental health promotion and prevention interventions and their effectiveness. Additionally, we intend to highlight various novel approaches to mental health care and their implications across different resource settings and provide future directions. The review highlights the (1) concept of preventive psychiatry, including various mental health promotions and prevention approaches, (2) current level of evidence of various mental health preventive interventions, including the novel interventions, and (3) challenges and opportunities in implementing concepts of preventive psychiatry and related interventions across the settings. Although preventive psychiatry is a well-known concept, it is a poorly utilized public health strategy to address the population's mental health needs. It has wide-ranging implications for the wellbeing of society and individuals, including those suffering from chronic medical problems. The researchers and policymakers are increasingly realizing the potential of preventive psychiatry; however, its implementation is poor in low-resource settings. Utilizing novel interventions, such as mobile-and-internet-based interventions and blended and stepped-care models of care can address the vast mental health need of the population. Additionally, it provides mental health services in a less-stigmatizing and easily accessible, and flexible manner. Furthermore, employing decision support systems/algorithms for patient management and personalized care and utilizing the digital platform for the non-specialists' training in mental health care are valuable additions to the existing mental health support system. However, more research concerning this is required worldwide, especially in the low-and-middle-income countries.

Predictive models for first-onset and persistence of depression and anxiety among university students

BACKGROUND

Depression and anxiety are both prevalent among university students. They frequently co-occur and share risk factors. Yet few studies have focused on identifying students at highest risk of first-onset and persistence of either of these conditions.

METHODS

Multicenter cohort study among Spanish first-year university students. At baseline, students were assessed for lifetime and 12-month Major Depressive Episode and/or Generalized Anxiety Disorder (MDE-GAD), other mental disorders, childhood-adolescent adversities, stressful life events, social support, socio-demographics, and psychological factors using web-based surveys; 12-month MDE-GAD was again assessed at 12-month follow-up.

RESULTS

A total of 1253 students participated in both surveys (59.2% of baseline respondents; mean age = 18.7 (SD = 1.3); 56.0% female). First-onset of MDE-GAD at follow-up was 13.3%. Also 46.7% of those with baseline MDE-GAD showed persistence at follow-up. Childhood/Adolescence emotional abuse or neglect (OR= 4.33), prior bipolar spectrum disorder (OR= 4.34), prior suicidal ideation (OR=4.85) and prior lifetime symptoms of MDE (ORs=2.33-3.63) and GAD (ORs=2.15-3.75) were strongest predictors of first-onset MDE-GAD. Prior suicidal ideation (OR=3.17) and prior lifetime GAD symptoms (ORs=2.38-4.02) were strongest predictors of MDE-GAD persistence. Multivariable predictions from baseline showed AUCs of 0.76 for first-onset and 0.81 for persistence. 74.9% of first-onset MDE-GAD cases occurred among 30% students with highest predicted risk at baseline.

LIMITATIONS

Self-report data were used; external validation of the multivariable prediction models is needed.

CONCLUSION

MDE-GAD among university students is frequent, suggesting the need to implement web-based screening at university entrance that identify those students with highest risk.

The impact of communicating personal mental ill-health risk: A randomized controlled non-inferiority trial

AIM

Risk algorithms predicting personal mental ill-health will form an important component of digital and personalized preventive interventions, yet it is unknown whether informing people of personal risk may cause unintended harm. This trial evaluated the comparative effect of communicating personal mental ill-health risk profiles on psychological distress.

METHODS

Australian participants using a mood-monitoring app were randomly allocated to receiving their current personal mental ill-health risk profile (n = 119), their achievable personal risk profile (n = 118) or to a control group (n = 118) in which no risk information was communicated, in a non-inferiority trial design. The primary outcome was psychological distress at four-weeks as assessed on the Kessler Psychological Distress Scale.

RESULTS

There was high attrition in the trial with 64% of data missing at follow up. Per-protocol (completer) analysis found that the lower bounds of the confidence intervals of the estimated mean change of the current risk (m = 0.19, 95% CI: -2.59- 2.98) and achievable risk (m = -0.09, 95% CI: -2.84 to 2.66) groups were within the non-inferiority margin of the control group's mean at follow up. Supplementary intention-to-treat analysis using Multivariate Imputation by Chained Equations (MICE) found that 98/100 imputed datasets of the current risk profile group, and all imputed datasets of the achievable risk profile group showed non-inferiority to the control group.

CONCLUSIONS

This study provides preliminary support that providing personal mental health risk profiles does not lead to unacceptable worsening of distress compared to no risk feedback, although this needs to be replicated in a fully powered RCT.

Preventive psychiatry: a blueprint for improving the mental health of young people

Preventive approaches have latterly gained traction for improving mental health in young people. In this paper, we first appraise the conceptual foundations of preventive psychiatry, encompassing the public health, Gordon's, US Institute of Medicine, World Health Organization, and good mental health frameworks, and neurodevelopmentally-sensitive clinical staging models. We then review the evidence supporting primary prevention of psychotic, bipolar and common mental disorders and promotion of good mental health as potential transformative strategies to reduce the incidence of these disorders in young people. Within indicated approaches, the clinical high-risk for psychosis paradigm has received the most empirical validation, while clinical high-risk states for bipolar and common mental disorders are increasingly becoming a focus of attention. Selective approaches have mostly targeted familial vulnerability and non-genetic risk exposures. Selective screening and psychological/psychoeducational interventions in vulnerable subgroups may improve anxiety/depressive symptoms, but their efficacy in reducing the incidence of psychotic/bipolar/common mental disorders is unproven. Selective physical exercise may reduce the incidence of anxiety disorders. Universal psychological/psychoeducational interventions may improve anxiety symptoms but not prevent depressive/anxiety disorders, while universal physical exercise may reduce the incidence of anxiety disorders. Universal public health approaches targeting school climate or social determinants (demographic, economic, neighbourhood, environmental, social/cultural) of mental disorders hold the greatest potential for reducing the risk profile of the population as a whole. The approach to promotion of good mental health is currently fragmented. We leverage the knowledge gained from the review to develop a blueprint for future research and practice of preventive psychiatry in young people: integrating universal and targeted frameworks; advancing multivariable, transdiagnostic, multi-endpoint epidemiological knowledge; synergically preventing common and infrequent mental disorders; preventing physical and mental health burden together; implementing stratified/personalized prognosis; establishing evidence-based preventive interventions; developing an ethical framework, improving prevention through education/training; consolidating the cost-effectiveness of preventive psychiatry; and decreasing inequalities. These goals can only be achieved through an urgent individual, societal, and global level response, which promotes a vigorous collaboration across scientific, health care, societal and governmental sectors for implementing preventive psychiatry, as much is at stake for young people with or at risk for emerging mental disorders.

Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population

The burden of depression and anxiety in the world is rising. Identification of individuals at increased risk of developing these conditions would help to target them for prevention and ultimately reduce the healthcare burden. We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing depression or anxiety using digitally obtainable information. From the initial 167 variables selected from UKB, processed into 429 features, iterative backward elimination using Cox proportional hazards model was performed to select predictors which account for the majority of its predictive capability. Baseline and reduced models were then trained for depression and anxiety using both Cox and DeepSurv, a deep neural network approach to survival analysis. The baseline Cox model achieved concordance of 0.7772 and 0.7720 on the validation dataset for depression and anxiety, respectively. For the DeepSurv model, respective concordance indices were 0.7810 and 0.7728. After feature selection, the depression model contained 39 predictors and the concordance index was 0.7769 for Cox and 0.7772 for DeepSurv. The reduced anxiety model, with 53 predictors, achieved concordance of 0.7699 for Cox and 0.7710 for DeepSurv. The final models showed good discrimination and calibration in the test datasets. We developed predictive risk scores with high discrimination for depression and anxiety using the UKB cohort, incorporating predictors which are easily obtainable via smartphone. If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.

Moderators of psychological and psychoeducational interventions for the prevention of anxiety: A systematic review

The aim of this study was to assess the available evidence on potential moderators of psychological and psychoeducational interventions for the prevention of anxiety. A systematic review using PubMed, PsycINFO, Web of Science, Embase, OpenGrey, and CENTRAL was performed up to October 2019. Two independent researchers assessed the fulfillment of eligibility criteria, extracted the data and performed a quality assessment of the included studies. Outcomes were moderators of the reduction of anxious symptoms or the incidence of anxiety disorders. Fourteen studies reporting results on moderator analyses performed in 13 randomized controlled trials were included. Twenty-seven potential moderators were organized into six categories: sociodemographic, clinical characteristics, cognitive variables, life events, interpersonal functioning and intervention characteristics. The most frequently examined variables were gender, age and baseline anxiety. We found insufficient evidence for all moderator categories studied. In children and adolescents, we found some studies with significant results for the low family support variable and higher levels of anxiety symptoms at baseline, which were both associated with higher effectiveness. Limited conclusions can be drawn about for whom and under what conditions interventions work in the prevention of anxiety. A strong need to improve the methodological quality and the number of moderator studies was identified.

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND

The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.

METHODS

PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.

FINDINGS

Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.

INTERPRETATION

To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach

A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The goal of this study was to utilize prospective survey data and ensemble machine learning to develop algorithms predicting adult onset internalizing disorders. The data were from Waves 1-2 of the National Epidemiological Survey on Alcohol and Related Conditions (n = 34,653). Outcomes were incident occurrence of DSM-IV generalized anxiety, panic, social phobia, depression, and mania between Waves 1-2. In total, 213 risk factors (features) were operationalized based on their presence/occurrence at the time of or before Wave 1. For each of the five internalizing disorder outcomes, super learning was used to generate a composite algorithm from several linear and non-linear classifiers (e.g., random forests, k-nearest neighbors). AUCs achieved by the cross-validated super learner ensembles were in the range of 0.76 (depression) to 0.83 (mania), and were higher than AUCs achieved by the individual algorithms. Individuals in the top 10% of super learner predicted risk accounted for 37.97% (depression) to 53.39% (social anxiety) of all incident cases. Thus, the algorithms achieved acceptable-to-excellent prediction accuracy with a high concentration of incident cases observed among individuals predicted to be highest risk. In parallel with the development of effective preventive interventions, further validation, expansion, and dissemination of algorithms predicting internalizing disorder onset/trajectory could be of great value.

Clinical and Cost-Effectiveness of Personalized Tele-Based Coaching for Farmers, Foresters and Gardeners to Prevent Depression: Study Protocol of an 18-Month Follow-Up Pragmatic Randomized Controlled Trial (TEC-A)

Background: Farmers show high levels of depressive symptoms and mental health problems in various studies. This study is part of a nationwide prevention project carried out by a German social insurance company for farmers, foresters, and gardeners (SVLFG) to implement internet- and tele-based services among others to improve mental health in this population. The aim of the present study is to evaluate the (cost-)effectiveness of personalized tele-based coaching for reducing depressive symptom severity and preventing the onset of clinical depression, compared to enhanced treatment as usual. Methods: In a two-armed, pragmatic randomized controlled trial (N = 312) with follow-ups at post-treatment (6 months), 12 and 18 months, insured farmers, foresters, and gardeners, collaborating family members and pensioners with elevated depressive symptoms (PHQ-9 ≥ 5) will be randomly allocated to personalized tele-based coaching or enhanced treatment as usual. The coaching is provided by psychologists and consists of up to 34 tele-based sessions for 25-50 min delivered over 6 months. Primary outcome is depressive symptom severity at post-treatment. Secondary outcomes include depression onset, anxiety, stress, and quality of life. A health-economic evaluation will be conducted from a societal perspective. Discussion: This study is the first pragmatic randomized controlled trial evaluating the (cost-)effectiveness of a nationwide tele-based preventive service for farmers. If proven effective, the implementation of personalized tele-based coaching has the potential to reduce disease burden and health care costs both at an individual and societal level. Clinical Trial Registration: German Clinical Trial Registration: DRKS00015655.

Identification of children at risk for mental health problems in primary care-Development of a prediction model with routine health care data

BACKGROUND

Despite being common and having long lasting effects, mental health problems in children are often under-recognised and under-treated. Improving early identification is important in order to provide adequate, timely treatment. We aimed to develop prediction models for the one-year risk of a first recorded mental health problem in children attending primary care.

METHODS

We carried out a population-based cohort study based on readily available routine healthcare data anonymously extracted from electronic medical records of 76 general practice centers in the Leiden area, the Netherlands. We included all patients aged 1-19 years on 31 December 2016 without prior mental health problems. Multilevel logistic regression analyses were used to predict the one-year risk of a first recorded mental health problem. Potential predictors were characteristics related to the child, family and healthcare use. Model performance was assessed by examining measures of discrimination and calibration.

FINDINGS

Data from 70,000 children were available. A mental health problem was recorded in 27•7% of patients during the period 2007-2017. Age independent predictors were somatic complaints, more than two GP visits in the previous year, one or more laboratory test and one or more referral/contact with other healthcare professional in the previous year. Other predictors and their effects differed between age groups. Model performance was moderate (c-statistic 0.62-0.63), while model calibration was good.

INTERPRETATION

This study is a first promising step towards developing prediction models for identifying children at risk of a first mental health problem to support primary care practice by using routine healthcare data. Data enrichment from other available sources regarding e.g. school performance and family history could improve model performance. Further research is needed to externally validate our models and to establish whether we are able to improve under-recognition of mental health problems.

External validation of the International Risk Prediction Algorithm for the onset of generalized anxiety and/or panic syndromes (The Predict A) in the US general population

INTRODUCTION

Multivariable risk prediction algorithms are useful for making clinical decisions and health planning. While prediction algorithms for new onset of anxiety disorders in Europe and elsewhere have been developed, the performance of these algorithms in the Americas is not known. The objective of this study was to validate the PredictA algorithm for new onset of anxiety and/or panic disorders in the US general population.

METHODS

Longitudinal study design was conducted with approximate 2-year follow-up data from a total of 24 626 individuals who participated in Wave 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and who did not have generalized anxiety disorder (GAD) and panic disorder in the past year at Wave 1. The PredictA algorithm was directly applied to the selected participants.

RESULTS

Among the participants, 5.4% developed GAD and/or panic disorder over two years. The PredictA algorithm had a discriminative power (C-statistics = 0.62, 95%CI: 0.61; 0.64), but poor calibration (p < 0.001) with the NESARC data. The observed and the mean predicted risk of GAD and/or panic disorders in the NESARC were 5.3% and 3.6%, respectively. Particularly, the observed and predicted risks of GAD and/or panic disorders in the highest decile of risk score in the NESARC participants were 13.3% and 10.4%, respectively.

CONCLUSION

The PredictA algorithm has acceptable discrimination, but the calibration with the NESARC data was poor. The PredictA algorithm is likely to underestimate the risk of GAD/panic disorders in the US population. Therefore, the use of PredictA in the US general population for predicting individual risk of GAD and/or panic disorders is not encouraged.

Derivation of a prediction model for a diagnosis of depression in young adults: a matched case-control study using electronic primary care records

BACKGROUND

Approximately 80 000 children and young people in the UK suffer from depression, but many are untreated because of poor identification of early warning signs and risk factors.

AIMS

This study aimed to derive and to investigate discrimination characteristics of a prediction model for a first recorded diagnosis of depression in young people aged 15-24 years.

METHOD

This study used a matched case-control method using electronic primary care records. Stepwise conditional logistic regression modelling investigated 42 potential predictors including symptoms, co-morbidities, social factors and drug and alcohol misuse.

RESULTS

Of the socio-economic and symptomatic predictors identified, the strongest associations were with depression symptoms and other psychological conditions. School problems and social services involvement were prominent predictors in men aged 15-18 years, work stress in women aged 19-24 years.

CONCLUSION

Our model is a first step in the development of a predictive model identifying early warning signs of depression in young people in primary care.

Efficacy and moderators of psychological interventions in treating subclinical symptoms of depression and preventing major depressive disorder onsets: protocol for an individual patient data meta-analysis of randomised controlled trials

INTRODUCTION

The long-term effectiveness of psychological interventions for the treatment of subthreshold depression and the prevention of depression is unclear and effects vary among subgroups of patients, indicating that not all patients profit from such interventions. Randomised clinical trials are mostly underpowered to examine adequately subgroups and moderator effects. The aim of the present study is, therefore, to examine the short-term and long-term as well as moderator effects of psychological interventions compared with control groups in adults with subthreshold depression on depressive symptom severity, treatment response, remission, symptom deterioration, quality of life, anxiety and the prevention of major depressive disorder (MDD) onsets on individual patient level and study level using an individual patient data meta-analysis approach.

METHODS AND ANALYSIS

Systematic searches in PubMed, PsycINFO, Embase and the Cochrane Central Register of Controlled Trials were conducted. We will use the following types of outcome criteria: (A) onset of major depression; (B) time to major depression onset; (C) observer-reported and self-reported depressive symptom severity; (D) response; (E) remission; (F) symptom deterioration; (G) quality of life, (H) anxiety; and (I) suicidal thoughts and behaviours. Multilevel models with participants nested within studies will be used. Missing data will be handled using a joint modelling approach to multiple imputation. A number of sensitivity analyses will be conducted in order test the robustness of our findings.

ETHICS AND DISSEMINATION

The investigators of the primary trials have obtained ethical approval for the data used in the present study and for sharing the data, if this was necessary, according to local requirements and was not covered from the initial ethic assessment.This study will summarise the available evidence on the short-term and long-term effectiveness of preventive psychological interventions for the treatment of subthreshold depression and prevention of MDD onset. Identification of subgroups of patients in which those interventions are most effective will guide the development of evidence-based personalised interventions for patients with subthreshold depression.

PROSPERO REGISTRATION NUMBER

CRD42017058585.

Development and validation of a prediction algorithm for the onset of common mental disorders in a working population

OBJECTIVE

Common mental disorders are the most common reason for long-term sickness absence in most developed countries. Prediction algorithms for the onset of common mental disorders may help target indicated work-based prevention interventions. We aimed to develop and validate a risk algorithm to predict the onset of common mental disorders at 12 months in a working population.

METHODS

We conducted a secondary analysis of the Household, Income and Labour Dynamics in Australia Survey, a longitudinal, nationally representative household panel in Australia. Data from the 6189 working participants who did not meet the criteria for a common mental disorders at baseline were non-randomly split into training and validation databases, based on state of residence. Common mental disorders were assessed with the mental component score of 36-Item Short Form Health Survey questionnaire (score ⩽45). Risk algorithms were constructed following recommendations made by the Transparent Reporting of a multivariable prediction model for Prevention Or Diagnosis statement.

RESULTS

Different risk factors were identified among women and men for the final risk algorithms. In the training data, the model for women had a C-index of 0.73 and effect size (Hedges' g) of 0.91. In men, the C-index was 0.76 and the effect size was 1.06. In the validation data, the C-index was 0.66 for women and 0.73 for men, with positive predictive values of 0.28 and 0.26, respectively Conclusion: It is possible to develop an algorithm with good discrimination for the onset identifying overall and modifiable risks of common mental disorders among working men. Such models have the potential to change the way that prevention of common mental disorders at the workplace is conducted, but different models may be required for women.

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm

BACKGROUND

Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers.

AIM

To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care.

DESIGN AND SETTING

Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months.

METHOD

Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT.

RESULTS

From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9.

CONCLUSION

The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking.

Prevention of Mental Health Disorders Using Internet- and Mobile-Based Interventions: A Narrative Review and Recommendations for Future Research

Although psychological interventions might have a tremendous potential for the prevention of mental health disorders (MHD), their current impact on the reduction of disease burden is questionable. Possible reasons include that it is not practical to deliver those interventions to the community en masse due to limited health care resources and the limited availability of evidence-based interventions and clinicians in routine practice, especially in rural areas. Therefore, new approaches are needed to maximize the impact of psychological preventive interventions. Limitations of traditional prevention programs could potentially be overcome by providing Internet- and mobile-based interventions (IMIs). This relatively new medium for promoting mental health and preventing MHD introduces a fresh array of possibilities, including the provision of evidence-based psychological interventions that are free from the restraints of travel and time and allow reaching participants for whom traditional opportunities are not an option. This article provides an introduction to the subject and narratively reviews the available evidence for the effectiveness of IMIs with regard to the prevention of MHD onsets. The number of randomized controlled trials that have been conducted to date is very limited and so far it is not possible to draw definite conclusions about the potential of IMIs for the prevention of MHD for specific disorders. Only for the indicated prevention of depression there is consistent evidence across four different randomized trial trials. The only trial on the prevention of general anxiety did not result in positive findings in terms of eating disorders (EDs), effects were only found in post hoc subgroup analyses, indicating that it might be possible to prevent ED onset for subpopulations of people at risk of developing EDs. Future studies need to identify those subpopulations likely to profit from preventive. Disorders not examined so far include substance use disorders, bipolar disorders, stress-related disorders, phobic disorders and panic disorder, obsessive-compulsive disorder, impulse-control disorders, somatic symptom disorder, and insomnia. In summary, there is a need for more rigorously conducted large scale randomized controlled trials using standard clinical diagnostic instruments for the selection of participants without MHD at baseline and the assessment of MHD onset. Subsequently, we discuss future directions for the field in order to fully exploit the potential of IMI for the prevention of MHD.

Risk factors for the onset of panic and generalised anxiety disorders in the general adult population: a systematic review of cohort studies

BACKGROUND

We aimed to assess available evidence on risk factors associated with the onset of panic disorder (PD) and/or generalised anxiety disorder (GAD) in cohort studies in the general adult population.

METHODS

Systematic review using MEDLINE, PsycINFO and Embase. Search terms included panic disorder, generalised anxiety disorder, cohort studies and risk factors.

RESULTS

We finally selected 21 studies, involving 163,366 persons with a median follow-up of 5 years. 1) Sociodemographic factors: PD was associated with age, female gender, and few economic resources. GAD was associated with age, non-Hispanics and Blacks, being divorced or widowed, and few economic resources. 2) Psychosocial factors: PD was associated with smoking and alcohol problems. GAD was associated with stressful life events in childhood and adulthood, and personality. 3) Physical and mental health factors: PD was associated with the number of physical diseases suffered and the joint hypermobility syndrome. PD was also associated with a parental history of mental disorders, as well as with other anxiety disorders and other mental health problems in the person affected. GAD was associated with a parental history of mental disorders, as well as with other anxiety disorders and other mental health problems in the person affected, plus already having received psychiatric care.

LIMITATIONS

Few studies examined the same risk factors.

CONCLUSIONS

Sociodemographic, psychosocial and mental-physical health risk factors were determinant for the onset of PD and GAD in the general adult population. These findings could be useful for developing preventive interventions in PD and GAD.

Predicting the onset of anxiety syndromes at 12 months in primary care attendees. The predictA-Spain study

Background

There are no risk algorithms for the onset of anxiety syndromes at 12 months in primary care. We aimed to develop and validate internally a risk algorithm to predict the onset of anxiety syndromes at 12 months.

Methods

A prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multilevel logistic regression and inverse probability weighting to build the risk algorithm. Our main outcome was generalized anxiety, panic and other non-specific anxiety syndromes as measured by the Primary Care Evaluation of Mental Disorders, Patient Health Questionnaire (PRIME-MD-PHQ). We recruited 3,564 adult primary care attendees without anxiety syndromes from 174 family physicians and 32 health centers in 6 Spanish provinces.

Results

The cumulative 12-month incidence of anxiety syndromes was 12.2%. The predictA-Spain risk algorithm included the following predictors of anxiety syndromes: province; sex (female); younger age; taking medicines for anxiety, depression or stress; worse physical and mental quality of life (SF-12); dissatisfaction with paid and unpaid work; perception of financial strain; and the interactions sex*age, sex*perception of financial strain, and age*dissatisfaction with paid work. The C-index was 0.80 (95% confidence interval = 0.78–0.83) and the Hedges' g = 1.17 (95% confidence interval = 1.04–1.29). The Copas shrinkage factor was 0.98 and calibration plots showed an accurate goodness of fit.

Conclusions

The predictA-Spain risk algorithm is valid to predict anxiety syndromes at 12 months. Although external validation is required, the predictA-Spain is available for use as a predictive tool in the prevention of anxiety syndromes in primary care.

Risk factors for onset of multiple or long major depressive episodes versus single and short episodes

PURPOSE

Major depressive disorder may vary according to number and duration of episodes. It is unclear whether risk factors for onset of multiple or long episodes of depression (MDE) differ from risk factors for the onset of single and short ones.

METHODS

Data were used from a cohort study of 5,256 GP attendees without major depressive disorder at baseline, who were followed up three times (predictD). The numbers and duration of MDE were noted and categorized into no episodes, single and short (≤3 months), and multiple or long (>3 months) episodes at follow-up. Log-binomial regression models were used to calculate relative risks between the groups for 18 risk factors examined at baseline.

RESULTS

165 persons (3 %) had a single and short MDE and 328 (6 %) had multiple or long MDE at follow-up. Lower education, anxiety, problems at work and financial strain significantly increased the risk of multiple or long MDE when compared to single and short MDE. Younger people were at reduced risk of multiple or long MDE.

CONCLUSIONS

Our findings suggest that several risk factors can be identified that may help to predict onset of different types of MDE. These factors are easy to assess and may be used in the prevention of depression.

Recent trends in the incidence of anxiety diagnoses and symptoms in primary care

BACKGROUND

Anxiety is common, with significant morbidity, but little is known about presentations and recording of anxiety diagnoses and symptoms in primary care. This study aimed to determine trends in incidence and socio-demographic variation in General Practitioner (GP) recorded diagnoses of anxiety, mixed anxiety/depression, panic and anxiety symptoms.

METHODOLOGY/PRINCIPAL FINDINGS

Annual incidence rates of anxiety diagnoses and symptoms were calculated from 361 UK general practices contributing to The Health Improvement Network (THIN) database between 1998 and 2008, adjusted for year of diagnosis, gender, age, and deprivation. Incidence of GP recorded anxiety diagnosis fell from 7.9 to 4.9/1000PYAR from 1998 to 2008, while incidence of anxiety symptoms rose from 3.9 to 5.8/1000PYAR. Incidence of mixed anxiety/depression fell from 4.0 to 2.2/1000PYAR, and incidence of panic disorder fell from 0.9/1000PYAR in 1998 to 0.5/1000PYAR in 2008. All these entries were approximately twice as common in women and more common in deprived areas. GP-recorded anxiety diagnoses, symptoms and mixed anxiety/depression were commonest aged 45-64 years, whilst panic disorder/attacks were more common in those 16-44 years. GPs predominately use broad non-specific codes to record anxiety problems in the UK.

CONCLUSIONS/SIGNIFICANCE

GP recording of anxiety diagnoses has fallen whilst recording of anxiety symptoms has increased over time. The incidence of GP recorded diagnoses of anxiety diagnoses was lower than in screened populations in primary care. The reasons for this apparent under-recording and whether it represents under-detection in those being seen, a reluctance to report anxiety to their GP, or a reluctance amongst GPs to label people with anxiety requires investigation.

Recent life events pose greatest risk for onset of major depressive disorder during mid-life

BACKGROUND

The authors examined an additive model for the association of life events and age with onset of major depressive disorder (MDD) and whether the combination of life events and age posed greater risk than the sum of their independent effects.

METHODS

Data were used from a prospective cohort study of 10,045 general practice attendees (PredictD). We included those without MDD at baseline (N=8293). We examined age divided into tertiles and into 10 year groups. Life events were assessed at baseline using the List of Threatening Life Experiences Questionnaire and categorized according to type. Main outcome measure was onset of DSM-IV MDD at 6 or 12 months of follow-up. The authors calculated Relative Excess Risks due to Interaction (RERI).

RESULTS

6910 persons (83.3%) had a complete follow-up, of whom 589 (8.5%) had an onset of MDD (166 younger, 254 middle aged and 169 older). The combined effect of personal problems (RERI=1.30; 95% CI 0.29 to 2.32), events in family or friends (RERI=1.23; 95% CI 0.28 to 2.19), or problems with law (RERI=1.57; 95% CI 0.33 to 2.82) and middle age was larger than the sum of individual effects.

LIMITATIONS

Lower response to recruitment in the UK and the Netherlands.

CONCLUSIONS

Recent life events carry the largest risk of onset of MDD in mid-life. Understanding the different vulnerability to life events according to age may help to indicate groups at a particular risk and assist in preventive strategies.