Predicting the onset of major depression in primary care: international validation of a risk prediction algorithm from Spain

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.

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.

[The Research Network on Preventive Activities and Health Promotion (redIAPP): a reference network and promoter of primary care research]

The Research Network on Preventive Activities and Health Promotion (redIAPP), a reference network and promoter of primary care research was created in 2003 thanks to the program Thematic Networks for Cooperative Research in Health (RETICS) of the Instituto de Salud Carlos III (ISCIII). Its creation has meant a radical change in the situation of research in primary care. Throughout its 19 years (2003-2021), different research groups and autonomous communities have participated, and different lines of research have been developed with numerous projects and publications. Despite the difficulties suffered, it has created a collaborative research experience between different autonomous communities with great vitality and with important results for primary care. The redIAPP, therefore, has been a great reference for research in primary care and for the deepening of its area of knowledge. Several lines of improvement are suggested for the future of primary care research.

Predicting adolescent depression and anxiety from multi-wave longitudinal data using machine learning

BACKGROUND

This study leveraged machine learning to evaluate the contribution of information from multiple developmental stages to prospective prediction of depression and anxiety in mid-adolescence.

METHODS

A community sample (N = 374; 53.5% male) of children and their families completed tri-annual assessments across ages 3-15. The feature set included several important risk factors spanning psychopathology, temperament/personality, family environment, life stress, interpersonal relationships, neurocognitive, hormonal, and neural functioning, and parental psychopathology and personality. We used canonical correlation analysis (CCA) to reduce the large feature set to a lower dimensional space while preserving the longitudinal structure of the data. Ablation analysis was conducted to evaluate the relative contributions to prediction of information gathered at different developmental periods and relative to previous disorder status (i.e. age 12 depression or anxiety) and demographics (sex, race, ethnicity).

RESULTS

CCA components from individual waves predicted age 15 disorder status better than chance across ages 3, 6, 9, and 12 for anxiety and 9 and 12 for depression. Only the components from age 12 for depression, and ages 9 and 12 for anxiety, improved prediction over prior disorder status and demographics.

CONCLUSIONS

These findings suggest that screening for risk of adolescent depression can be successful as early as age 9, while screening for risk of adolescent anxiety can be successful as early as age 3. Assessing additional risk factors at age 12 for depression, and going back to age 9 for anxiety, can improve screening for risk at age 15 beyond knowing standard demographics and disorder history.

Structured communication methods for mental health consultations in primary care: a scoping review

BACKGROUND

Majority of people with mental health problems attend primary care for support. Interventions that structure consultations have been found effective for physical health conditions and secondary mental health care. The aim of the review is to identify what tools or interventions exist to structure communication in primary care for appointments related to mental health problems and examine existing evidence for effectiveness for mental health and quality of life outcomes.

METHODS

Quantitative and qualitative studies were eligible for inclusion if staff was based in a primary care setting and the intervention involved bi-directional communication with adult patients. Six databases were searched (MEDLINE, Web of Knowledge, EMBASE, PsycINFO, The Cochrane Library, CINAHL) with no time restriction. Search terms combined four concepts with key words such as "structured" and "interaction" and "mental illness" and "primary care". Reference lists of eligible studies were searched.

RESULTS

After removing duplicates, 3578 records were found and underwent further screening. A total of 16 records were included, representing eight different interventions from five countries. The majority were delivered by primary care doctors and focused on patients experiencing psychological distress. Similarities across interventions' service delivery were that most were created for a broad patient population, used self-report assessments at the start and actions or plans as the end point, and employed group settings and didactic methods for training staff in the intervention. Booster and follow-up trainings were not offered in any of the interventions, and supervision was only part of the process for one. The evidence for effectiveness for mental health and quality of life outcomes was mixed with three out of five RCTs finding a positive effect.

CONCLUSIONS

Although the idea of structuring communication for mental health consultations has been around since the 1980s, relatively few interventions have attempted to structure the conversations within the consultation, rather than modifying pre-visit events. As the evidence-base showed feasibility and acceptability for a number of interventions, there is scope for those interventions to be developed further and tested more rigorously.

Utility of PHQ-2, PHQ-8 and PHQ-9 for detecting major depression in primary health care: a validation study in Spain

BACKGROUND

Primary health care (PHC) professionals may play a crucial role in improving early diagnosis of depressive disorders. However, only 50% of cases are detected in PHC. The most widely used screening instrument for major depression is the Patient Health Questionnaire (PHQ), including the two-, eight- and nine-item versions. Surprisingly, there is neither enough evidence about the validity of PHQ in PHC patients in Spain nor indications about how to interpret the total scores. This study aimed to gather validity evidence to support the use of the three PHQ versions to screen for major depression in PHC in Spain. Additionally, the present study provided information for helping professionals to choose the best PHQ version according to the context.

METHODS

The sample was composed of 2579 participants from 22 Spanish PHC centers participating in the EIRA-3 study. The reliability and validity of the three PHQ versions for Spanish PHC patients were assessed based on responses to the questionnaire.

RESULTS

The PHQ-8 and PHQ-9 showed high internal consistency. The results obtained confirm the theoretically expected relationship between PHQ results and anxiety, social support and health-related QoL. A single-factor solution was confirmed. Regarding to the level of agreement with the CIDI interview (used as the criterion), our results indicate that the PHQ has a good discrimination power. The optimal cut-off values were: ⩾2 for PHQ-2, ⩾7 for PHQ-8 and ⩾8 for PHQ-9.

CONCLUSIONS

PHQ is a good and valuable tool for detecting major depression in PHC patients in Spain.

A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study

The predictD is an intervention implemented by general practitioners (GPs) to prevent depression, which reduced the incidence of depression-anxiety and was cost-effective. The e-predictD study aims to design, develop, and evaluate an evolved predictD intervention to prevent the onset of major depression in primary care based on Information and Communication Technologies, predictive risk algorithms, decision support systems (DSSs), and personalized prevention plans (PPPs). A multicenter cluster randomized trial with GPs randomly assigned to the e-predictD intervention + care-as-usual (CAU) group or the active-control + CAU group and 1-year follow-up is being conducted. The required sample size is 720 non-depressed patients (aged 18-55 years), with moderate-to-high depression risk, under the care of 72 GPs in six Spanish cities. The GPs assigned to the e-predictD-intervention group receive brief training, and those assigned to the control group do not. Recruited patients of the GPs allocated to the e-predictD group download the e-predictD app, which incorporates validated risk algorithms to predict depression, monitoring systems, and DSSs. Integrating all inputs, the DSS automatically proposes to the patients a PPP for depression based on eight intervention modules: physical exercise, social relationships, improving sleep, problem-solving, communication skills, decision-making, assertiveness, and working with thoughts. This PPP is discussed in a 15-min semi-structured GP-patient interview. Patients then choose one or more of the intervention modules proposed by the DSS to be self-implemented over the next 3 months. This process will be reformulated at 3, 6, and 9 months but without the GP-patient interview. Recruited patients of the GPs allocated to the control-group+CAU download another version of the e-predictD app, but the only intervention that they receive via the app is weekly brief psychoeducational messages (active-control group). The primary outcome is the cumulative incidence of major depression measured by the Composite International Diagnostic Interview at 6 and 12 months. Other outcomes include depressive symptoms (PHQ-9) and anxiety symptoms (GAD-7), depression risk (predictD risk algorithm), mental and physical quality of life (SF-12), and acceptability and satisfaction ('e-Health Impact' questionnaire) with the intervention. Patients are evaluated at baseline and 3, 6, 9, and 12 months. An economic evaluation will also be performed (cost-effectiveness and cost-utility analysis) from two perspectives, societal and health systems.

Trial registration

ClinicalTrials.gov, identifier: NCT03990792.

A multiple health behaviour change intervention to prevent depression: A randomized controlled trial

OBJECTIVE

To examine the effectiveness of a 12-month MHBC intervention in the prevention of onset depression in primary health care (PHC).

METHODS

Twenty-two PHC centres took part in the cluster-randomized controlled trial. Patients were randomized to receive either usual care or an MHBC intervention. The endpoints were onset of major depression and reduction of depressive symptoms in participants without baseline depression at a 12-month follow-up.

RESULTS

2531 patients agreed and were eligible to participate. At baseline, around 43% were smokers, 82% were non-adherent to the Mediterranean diet and 55% did not perform enough physical activity. The intervention group exhibited a greater positive change in two or more behaviours (OR 1.75 [95%CI: 1.17 to 2.62]; p = 0.006); any behaviour (OR 1.58 [95%CI: 1.13 to 2.20]; p = 0.007); and adherence to the Mediterranean diet (OR 1.94 [95%CI: 1.29 to 2.94]; p = 0.002), while this increase was not statistically significant for smoking and physical activity. The intervention was not effective in preventing major depression (OR 1.17; [95% CI 0.53 to 2.59)]; p = 0.690) or reducing depressive symptoms (Mean difference: 0.30; [95% CI -0.77 to 1.36]; p = 0.726) during follow-up.

CONCLUSIONS

As compared to usual care, the MHBC intervention provided a non-significant reduction in the incidence of major depression.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03136211.

Does providing personalized depression risk information lead to increased psychological distress and functional impairment? Results from a mixed-methods randomized controlled trial

BACKGROUND

Multivariable risk algorithms (MVRP) predicting the personal risk of depression will form an important component of personalized preventive interventions. However, it is unknown whether providing personalized depression risk will lead to unintended psychological harms. The objectives of this study were to evaluate the impact of providing personalized depression risk on non-specific psychological distress and functional impairment over 12 months.

METHODS

A mixed-methods randomized controlled trial was conducted in 358 males and 354 females who were at high risk of having a major depressive episode according to sex-specific MVRPs, and who were randomly recruited across Canada. Participants were assessed at baseline, 6 and 12 months.

RESULTS

Over 93% of participants were interested in knowing their depression risk. The intervention group had a greater reduction in K10 score over 12 months than the control group; complete-case analysis found a significant between-group difference in mean K10 change score (d = 1.17, 95% CI 0.12-2.23) at 12 months. Participants in the intervention group also reported significantly less functional impairment in the domains of home and work/school activities, than did those in the control group. A majority of the qualitative interviewees commented that personalized depression risk information does not have a negative impact on physical and mental health.

CONCLUSIONS

This study found no evidence that providing personalized depression risk information will lead to worsening psychological distress, functional impairment, and absenteeism. Provision of personalized depression risk information may have positive impacts on non-specific psychological distress and functioning.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02943876.

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.

Users' perceptions about receiving personalized depression risk information: findings from a qualitative study

OBJECTIVES

To understand users' perceptions about receiving their personalized depression risk score and to gain an understanding about how to improve the efficiency of risk communication from the user perspective.

METHODS

A qualitative study embedded in a randomized controlled trial (RCT) on evaluating the impact of providing personalized depression risk information on psychological harms and benefits. The participants (20 males and 20 females) were randomly selected from the intervention arm of the RCT after the 12-month assessment. The qualitative interviews were conducted through telephone, audio recorded and transcribed verbatim. We conducted a content analysis to describe the content and contextual meaning of data collected from participants.

RESULTS

The first theme explained the motivation for receiving a risk score. Most participants chose to receive their personalised depression risk score with the goal of improving their self-awareness. The results revealed three sub-themes surrounding perceptions and implication of receiving their risk score: positive, negative, and neutral. Most participants found that receiving their score was positive because it improved their awareness of their mental health, but some participants could see that some people would have negative feelings when getting the score causing them to be more likely to get depression. The final theme focussed on improvements including: the best delivery methods, having resources and strategies, and targeting younger people.

CONCLUSION

The most significant motivation for, and benefit of receiving one's personalized depression risk score was improved awareness of one's mental health. A comprehensive risk communication program may improve the uptake and maximize the impact on behavior changes and risk reduction.

Effectiveness of exercise-based interventions in reducing depressive symptoms in people without clinical depression: systematic review and meta-analysis of randomised controlled trials

BACKGROUND

In most trials and systematic reviews that evaluate exercise-based interventions in reducing depressive symptoms, it is difficult to separate treatment from prevention.

AIMS

To evaluate the effectiveness of exercise-based interventions in reducing depressive symptoms in people without clinical depression.

METHOD

We searched PubMed, PsycINFO, Embase, WOS, SPORTDiscus, CENTRAL, OpenGrey and other sources up to 25 May 2020. We selected randomised controlled trials (RCTs) that compared exclusively exercise-based interventions with control groups, enrolling participants without clinical depression, as measured using validated instruments, and whose outcome was reduction of depressive symptoms and/or incidence of new cases of people with depression. Pooled standardised mean differences (SMDs) were calculated using random-effect models (registration at PROSPERO: CRD42017055726).

RESULTS

A total of 14 RCTs (18 comparisons) evaluated 1737 adults without clinical depression from eight countries and four continents. The pooled SMD was -0.34 (95% CI -0.51 to -0.17; P < 0.001) and sensitivity analyses confirmed the robustness of this result. We found no statistical evidence of publication bias and heterogeneity was moderate (I2 = 54%; 95% CI 22-73%). Only two RCTs had an overall low risk of bias and three had long-term follow-up. Multivariate meta-regression found that a larger sample size, country (Asia) and selective prevention (i.e. people exposed to risk factors for depression) were associated with lower effectiveness, although only sample size remained significant when adjustment for multiple tests was considered. According to the Grading of Recommendations Assessment, Development and Evaluation tool, the quality of evidence was low.

CONCLUSIONS

Exercise-based interventions have a small effect on the reduction of depressive symptoms in people without clinical depression. It could be an alternative to or complement psychological programmes, although further higher-quality trials with larger samples and long-term follow-up are needed.

Predicting clinically significant response to primary care treatment for depression from electronic health records of veterans

OBJECTIVE

To reduce delays in referral to specialty mental health care, we evaluated clinical prediction models estimating the likelihood of response to primary care treatment of depression in the VA healthcare system.

METHODS

We included patients with a primary care depression diagnosis between October 1, 2015 and December 31, 2017, an initial PHQ-9 score ≥ 10 within 30 days, a follow-up PHQ-9 score within 2-8 months, and no specialty mental health care within three months prior to depression diagnosis. We evaluated eight ordinary least squares regression models, each with a different procedure for selecting predictors of percentage change in PHQ-9 score from baseline to follow-up. Predictors included patient characteristics from electronic health records and neighborhood characteristics from US census data. We repeated each modeling procedure 1,000 times, using different training and validation sets of patients. We used R², RMSE, and MAE to evaluate model performance.

RESULTS

The final cohort included 3,464 patients. The two best performing models included multiple iterations of backwards stepwise variable selection with R² of 0.07, RMSE of 41.45, MAE of 33.30; and R² of 0.07, RMSE of 41.39, MAE of 33.28.

LIMITATIONS

Wide follow-up interval, possibility of misclassification error due to use of EHR data.

CONCLUSIONS

Model performance did not suggest its use as a guide in clinical decision-making. Future research should explore whether obtaining additional risk factor data from patients (e.g., duration of symptoms) or modeling PHQ-9 scores over a narrower time interval improves performance of clinical risk prediction tools for depression.

The impact of providing personalized depression risk information on self-help and help-seeking behaviors: Results from a mixed methods randomized controlled trial

OBJECTIVES

To evaluate the impact of providing personalized depression risk information on self-help and help-seeking behaviors among individuals who are at high risk of having a major depressive episode (MDE).

MATERIALS AND METHODS

In a mixed methods randomized controlled trial, participants who were at high risk of having a MDE, were recruited from across Canada, and were randomized into intervention (n = 358) and control (n = 354) groups. Participants in the intervention group received their personalized depression risk estimated by sex-specific risk prediction models for MDE. All participants were assessed at baseline, 6 and 12 months.

RESULTS

Repeated measure mixed effects modeling showed significant between group differences in self-help scores. In the complete case analysis, the between group difference in mean self-help change score was 1.13 at 12 months (effect size = 0.16). Among participants who reported "fair" or "poor health," the between group difference in mean self-help change score was 2.78 at 12 months (effect size = 0.35). The qualitative data revealed three themes and the findings are consistent with the quantitative results.

CONCLUSIONS

Providing personalized depression risk information has a positive impact on self-help in high-risk individuals, particularly in those with poor health.

Self-help behaviors partially mediate the relationship between personalized depression risk disclosure and psychological distress: A mediation analysis using data from a randomized controlled trial

Major depressive disorder (MDD) is one of the most prevalent forms of mental illness. Multivariate risk predictive (MVRP) algorithms provide a new means of identifying high-risk individuals for mental health disorders. Self-help behaviors may provide accessible methods to mitigate depression risk. The objective of this study is to investigate the mediating effects of self-help behavior on the relationship between depression risk disclosure and psychological distress. A sample (n = 556) of high-risk Canadians for a major depressive episode (MDE) were randomized into risk-disclosure or control groups and followed-up at 6 and 12 months. Mediation analysis using repeated measure mixed effects models was used to investigate the mediating effects of self-help behaviors on the relationship between depression risk disclosure and psychological distress over time. Self-help behavior was found to partially mediate the relationship between risk disclosure and psychological distress at month 12. Both unadjusted and adjusted associations were found to be negative and significant (ß_unadj = -0.16 [-0.30, -0.03]) (ß_adj = -0.15[-0.29, -0.02]). Self-help plays a partial mediating role in the relationship between depression risk disclosure and psychological distress over time. More research is required in this field to increase knowledge about the role of self-help in mental health treatment.

Character configuration, major depressive episodes, and suicide-related ideation among Japanese undergraduates

Aim

To enable early identification of university students at high risk for suicide, we examined personality as a predictive factor for major depressive episodes and suicide-related ideation.

Methods

From 2011 to 2013, we administered the Patient Health Questionnaire-9 (PHQ-9) and the Temperament and Character Inventory (TCI) to 1,997 university students at enrollment (T1). We previously conducted a study using the same data set; this is a re-analysis of the dataset. To prevent contamination of data, participants diagnosed with a depressive episode were excluded at T1. Three years after enrollment (T2), we re-administered the PHQ-9 to the same students. We statistically compared TCI scores at T1 among depressive episode groups and suicide-related ideation groups. Two-way ANOVA and Cochran-Armitage trend tests were used to analyze the relationships between personality traits, depressive episodes, and suicide-related ideation.

Results

The PHQ-9 summary scores at baseline (T1) were 3.0 (±2.7), with female students scoring 4.6 (±2.9) and male students 2.9 (±2.6, p = 0.025). The major depressive episode group at T2 had lower self-directedness (SD) scores at T1 than the non-depressive episode control group. The suicide-related ideation (SI) group at T2 also had higher harm avoidance (HA), lower SD, and lower cooperativeness (C) scores than the non-SI group at T1. The Cochran-Armitage trend tests revealed significant associations between character configurations composed of SD and C, and both depressive episodes at T2 and SI at T2.

Conclusion

The temperament feature of high HA at baseline and character configurations of low SD and low C at baseline are the most contributory predictors for the novel development of depressive episodes and SI among Japanese university students.

Use of a personalised depression intervention in primary care to prevent anxiety: a secondary study of a cluster randomised trial

Background

In the predictD-intervention, GPs used a personalised biopsychosocial programme to prevent depression. This reduced the incidence of major depression by 21.0%, although the results were not statistically significant.

Aim

To determine whether the predictD-intervention is effective at preventing anxiety in primary care patients without depression or anxiety.

Design and setting

Secondary study of a cluster randomised trial with practices randomly assigned to either the predictD-intervention or usual care. This study was conducted in seven Spanish cities from October 2010 to July 2012.

Method

In each city, 10 practices and two GPs per practice, as well as four to six patients every recruiting day, were randomly selected until there were 26–27 eligible patients for each GP. The endpoint was cumulative incidence of anxiety as measured by the PRIME-MD screening tool over 18 months.

Results

A total of 3326 patients without depression and 140 GPs from 70 practices consented and were eligible to participate; 328 of these patients were removed because they had an anxiety syndrome at baseline. Of the 2998 valid patients, 2597 (86.6%) were evaluated at the end of the study. At 18 months, 10.4% (95% CI = 8.7% to 12.1%) of the patients in the predictD-intervention group developed anxiety compared with 13.1% (95% CI = 11.4% to 14.8%) in the usual-care group (absolute difference = −2.7% [95% CI = −5.1% to −0.3%]; P = 0.029).

Conclusion

A personalised intervention delivered by GPs for the prevention of depression provided a modest but statistically significant reduction in the incidence of anxiety.

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.

Effectiveness of online psychological and psychoeducational interventions to prevent depression: Systematic review and meta-analysis of randomized controlled trials

Although evidence exists for the efficacy of interventions to prevent depression, little is known about its prevention through online interventions. We aim to assess the effectiveness of online psychological and psychoeducational interventions to prevent depression in heterogeneous populations. A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted based on literature searches in eight electronic data bases and other sources from inception to 22 July 2019. Of the 4181 abstracts reviewed, 501 were selected for full-text review, and 21 RCTs met the inclusion criteria, representing 10,134 participants from 11 countries and four continents. The pooled SMD was -0·26 (95%CI: -0·36 to -0·16; p < 0.001) and sensitivity analyses confirmed the robustness of this result. We did not find publication bias but there was substantial heterogeneity (I² = 72%; 95%CI, 57% to 82%). A meta-regression including three variables explained 81% of the heterogeneity. Indicated prevention and interactive website delivery were statistically associated with higher effectiveness, and no association was observed with risk of bias. Online psychological and psychoeducational interventions have a small effect in reducing depressive symptoms in non-depressed and varied populations, and the quality of evidence is moderate. Given that these types of interventions are very accessible and can be applied on a wide scale, they should be further developed and implemented. Registration details: Registration number (PROSPERO): CRD42014014804.

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.

[Common mental disorders in primary care: diagnostic and therapeutic difficulties, and new challenges in prediction and prevention. SESPAS Report 2020]

In primary health care only chronic pain surpass depression and anxiety in loss of quality-adjusted life years. More than 70% of people suffering from common mental disorders consulted their GPs for this reason. However, 'the declining halves rule' is a reality: less than 50% of primary care attendees with common mental disorders were correctly diagnosed, of these less than 50% received adequate treatment (pharmacological or psychological) and of these less than 50% patients were adherent. Collaborative models of common mental disorders care in primary health care have demonstrated their effectiveness through clinical trials; however, its implementation in a more general and real context is difficult and its effectiveness remains unclear. Risk algorithms have been developed and validated in primary health care to predict the onset and prognosis of common mental disorders; which are useful for their treatment and prevention. There is evidence that psychological and psychoeducational interventions (and possibly those of physical exercise) are effective for the primary prevention of common mental disorders, even in primary health care; although their effects are small or moderate. These interventions have a high potential to be scalable in schools, workplace and primary health care; in addition, when they are administered through information and communication technologies (e.g. by App), in self-guided or minimally guided programs, they have demonstrated their effectiveness for the treatment and prevention of common mental disorders. They are also very accessible, have low cost and contribute to the massive implementation of these interventions in different settings.

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

Psychological and psychoeducational interventions have proven to be effective in preventing depression. However, the identification of the patients that benefit the most from each type of intervention has not yet been established. A systematic review was performed of the literature on moderators of preventive psychological and psychoeducational interventions for depression in all types of population. A search was performed on PubMed, PsycINFO, Web of Science, Embase, Cochrane Central Register of Controlled Trials and OpenGrey up to July 2019. Fulfillment of eligibility criteria, data collection, and study quality assessment were assessed by two independent researchers. Outcomes were moderators of the reduction of depressive symptoms or the incidence of depression. Twenty-seven moderator effect studies performed in 19 randomized controlled trials were included. Thirty-four potential sociodemographic, clinical, interpersonal, personality and life-event moderators were evaluated. Baseline depressive symptoms, gender, age, baseline parental depression and social support were the most frequently studied potential moderators. In interventions for children and adolescents, the moderator for which evidence was strongest was having parents free of depression at baseline. Psychological and psychoeducational interventions seem to be more effective in children and adolescents who exhibit a lower use of substances and whose parents do not have symptoms of depression at baseline. In adults, a lower age was associated with greater effects of preventive interventions. ETHICS: As this systematic review is based on published data, approval from the local ethics committee was not required.

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.

Family physicians' views on participating in prevention of major depression. The predictD-EVAL qualitative study

Background

The predictD intervention, a multicomponent intervention delivered by family physicians (FPs), reduced the incidence of major depression by 21% versus the control group and was cost-effective. A qualitative methodology was proposed to identify the mechanisms of action of these complex interventions.

Purpose

To seek the opinions of these FPs on the potential successful components of the predictD intervention for the primary prevention of depression in primary care and to identify areas for improvement.

Method

Qualitative study with FPs who delivered the predictD intervention at 35 urban primary care centres in seven Spanish cities. Face-to-face semi-structured interviews adopting a phenomenological approach. The data was triangulated by three investigators using thematic analysis and respondent validation was carried out.

Results

Sixty-seven FPs were interviewed and they indicated strategies used to perform the predictD intervention, including specific communication skills such as empathy and the activation of patient resources. They perceived barriers such as lack of time and facilitators such as prior acquaintance with patients. FPs recognized the positive consequences of the intervention for FPs, patients and the doctor-patient relationship. They also identified strategies for future versions and implementations of the predictD intervention.

Conclusions

The FPs who carried out the predictD intervention identified factors potentially associated with successful prevention using this program and others that could be improved. Their opinions about the predictD intervention will enable development of a more effective and acceptable version and its implementation in different primary health care settings.

Prediction of major depressive disorder onset in college students

BACKGROUND

Major depressive disorder (MDD) in college students is associated with substantial burden.

AIMS

To assess 1-year incidence of MDD among incoming freshmen and predictors of MDD-incidence in a representative sample of students.

METHOD

Prospective cohort study of first-year college students (baseline: n = 2,519, 1-year follow-up: n = 958) RESULTS: The incidence of MDD within the first year of college was 6.9% (SE = 0.8). The most important individual-level predictors of onset were prior suicide plans and/or attempts (OR = 9.5). The strongest population-level baseline predictors were history of childhood-adolescent trauma, stressful experience in the past 12 months, parental psychopathology, and other 12-month mental disorder. Multivariate cross-validated prediction (cross-validated AUC = 0.73) suggest that 36.1% of incident MDD cases in a replication sample would occur among the 10% of students at highest predicted risk (24.5% predicted incidence in this highest-risk subgroup).

CONCLUSIONS

Screening at college entrance is a promising strategy to identify students at risk of MDD onset, which may improve the development and deployment of targeted preventive interventions.

Variability of the Prevalence of Depression in Function of Sociodemographic and Environmental Factors: Ecological Model

Major depression etiopathogenesis is related to a wide variety of genetics, demographic and psychosocial factors, as well as to environmental factors. The objective of this study is to analyze sociodemographic and environmental variables that are related to the prevalence of depression through correlation analysis and to develop a regression model that explains the behavior of this disease from an ecological perspective. This is an ecological, retrospective, cross-sectional study. The target population was 1,148,430 individuals over the age of 16 who were registered in Aragon (Spain) during 2010, with electronic medical records in the community’s primary health care centers. The spatial unit was the Basic Health Area (BHA). The dependent variable was the diagnosis of Depression and the ecological independent variables were: Demographic variables (gender and age), population distribution, typology of the entity, population structure by sex and age, by nationality, by education, by work, by salary, by marital status, structure of the household by number of members, and state of the buildings. The results show moderate and positive correlations with higher rates of depression in areas having a higher femininity index, higher population density, areas with a higher unemployment rate and higher average salary. The results of the linear regression show that aging +75 and rural entities act as protective factors for depression, while urban areas and deficient buildings act as risk factors. In conclusion, the ecological methodology may be a useful tool which, together with the statistical epidemiological analysis, can help in the political decision making process.

A personalized intervention to prevent depression in primary care: cost-effectiveness study nested into a clustered randomized trial

BACKGROUND

Depression is viewed as a major and increasing public health issue, as it causes high distress in the people experiencing it and considerable financial costs to society. Efforts are being made to reduce this burden by preventing depression. A critical component of this strategy is the ability to assess the individual level and profile of risk for the development of major depression. This paper presents the cost-effectiveness of a personalized intervention based on the risk of developing depression carried out in primary care, compared with usual care.

METHODS

Cost-effectiveness analyses are nested within a multicentre, clustered, randomized controlled trial of a personalized intervention to prevent depression. The study was carried out in 70 primary care centres from seven cities in Spain. Two general practitioners (GPs) were randomly sampled from those prepared to participate in each centre (i.e. 140 GPs), and 3326 participants consented and were eligible to participate. The intervention included the GP communicating to the patient his/her individual risk for depression and personal risk factors and the construction by both GPs and patients of a psychosocial programme tailored to prevent depression. In addition, GPs carried out measures to activate and empower the patients, who also received a leaflet about preventing depression. GPs were trained in a 10- to 15-h workshop. Costs were measured from a societal and National Health care perspective. Qualityadjustedlife years were assessed using the EuroQOL five dimensions questionnaire. The time horizon was 18 months.

RESULTS

With a willingness-to-pay threshold of €10,000 (£8568) the probability of cost-effectiveness oscillated from 83% (societal perspective) to 89% (health perspective). If the threshold was increased to €30,000 (£25,704), the probability of being considered cost-effective was 94% (societal perspective) and 96%, respectively (health perspective). The sensitivity analysis confirmed these results.

CONCLUSIONS

Compared with usual care, an intervention based on personal predictors of risk of depression implemented by GPs is a cost-effective strategy to prevent depression. This type of personalized intervention in primary care should be further developed and evaluated.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01151982. Registered on June 29, 2010.

Development of a prognostic model for predicting depression severity in adult primary patients with depressive symptoms using the diamond longitudinal study

BACKGROUND

Depression trajectories among primary care patients are highly variable, making it difficult to identify patients that require intensive treatments or those that are likely to spontaneously remit. Currently, there are no easily implementable tools clinicians can use to stratify patients with depressive symptoms into different treatments according to their likely depression trajectory. We aimed to develop a prognostic tool to predict future depression severity among primary care patients with current depressive symptoms at three months.

METHODS

Patient-reported data from the diamond study, a prospective cohort of 593 primary care patients with depressive symptoms attending 30 Australian general practices. Participants responded affirmatively to at least one of the first two PHQ-9 items. Twenty predictors were pre-selected by expert consensus based on reliability, ease of administration, likely patient acceptability, and international applicability. Multivariable mixed effects linear regression was used to build the model.

RESULTS

The prognostic model included eight baseline predictors: sex, depressive symptoms, anxiety, history of depression, self-rated health, chronic physical illness, living alone, and perceived ability to manage on available income. Discrimination (c-statistic =0.74; 95% CI: 0.70-0.78) and calibration (agreement between predicted and observed symptom scores) were acceptable and comparable to other prognostic models in primary care.

LIMITATIONS

More complex model was not feasible because of modest sample size. Validation studies needed to confirm model performance in new primary care attendees.

CONCLUSION

A brief, easily administered algorithm predicting the severity of depressive symptoms has potential to assist clinicians to tailor treatment for adult primary care patients with current depressive symptoms.

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.

Target-D: a stratified individually randomized controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice: study protocol for a randomized controlled trial

BACKGROUND

Depression is a highly prevalent and costly disorder. Effective treatments are available but are not always delivered to the right person at the right time, with both under- and over-treatment a problem. Up to half the patients presenting to general practice report symptoms of depression, but general practitioners have no systematic way of efficiently identifying level of need and allocating treatment accordingly. Therefore, our team developed a new clinical prediction tool (CPT) to assist with this task. The CPT predicts depressive symptom severity in three months' time and based on these scores classifies individuals into three groups (minimal/mild, moderate, severe), then provides a matched treatment recommendation. This study aims to test whether using the CPT reduces depressive symptoms at three months compared with usual care.

METHODS

The Target-D study is an individually randomized controlled trial. Participants will be 1320 general practice patients with depressive symptoms who will be approached in the practice waiting room by a research assistant and invited to complete eligibility screening on an iPad. Eligible patients will provide informed consent and complete the CPT on a purpose-built website. A computer-generated allocation sequence stratified by practice and depressive symptom severity group, will randomly assign participants to intervention (treatment recommendation matched to predicted depressive symptom severity group) or comparison (usual care plus Target-D attention control) arms. Follow-up assessments will be completed online at three and 12 months. The primary outcome is depressive symptom severity at three months. Secondary outcomes include anxiety, mental health self-efficacy, quality of life, and cost-effectiveness. Intention-to-treat analyses will test for differences in outcome means between study arms overall and by depressive symptom severity group.

DISCUSSION

To our knowledge, this is the first depressive symptom stratification tool designed for primary care which takes a prognosis-based approach to provide a tailored treatment recommendation. If shown to be effective, this tool could be used to assist general practitioners to implement stepped mental-healthcare models and contribute to a more efficient and effective mental health system.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR 12616000537459 ). Retrospectively registered on 27 April 2016. See Additional file 1 for trial registration data.

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.

Examining the relationship between physical illness and depression: Is there a difference between inflammatory and non inflammatory diseases? A cohort study

BACKGROUND

There is evidence that inflammation may play a role in the association between physical illness and depression. Our aim was to compare the impact of chronic medical conditions on incidence of depression and to examine if risk of depression varies in terms of the presence and degree of inflammation.

METHODS

This is a secondary analysis conducted within the Spanish sample of the predictD-study.

PARTICIPANTS

5437.

PRIMARY OUTCOME

Incident major depression measured with the Composite International Diagnostic Interview.

EXPOSURE

Presence of chronic medical conditions recorded by GPs using the International Classification of Primary Care, ICPC-2. All analyses were conducted using multivariable logistic regression to allow adjustment for confounders.

RESULTS

The odds of depression are higher in almost all inflammatory than in non-inflammatory illnesses. There is an increasing risk of depression as a consequence of an increasing inflammatory load, with higher odds of depression in the autoimmune group than in the cardio-metabolic group, while both had higher odds of depression than the non-inflammatory groups.

CONCLUSIONS

Inflammation may be part of the pathway by which chronic physical illness leads to depression. Future studies should examine the role of inflammation in the prevention and management of depression.

Suicidality in primary care patients who present with sadness and anhedonia: a prospective European study

BACKGROUND

Sadness and anhedonia (loss of interest in activities) are central symptoms of major depression. However, not all people with these symptoms meet diagnostic criteria for major depression. We aimed to assess the importance of suicidality in the outcomes for primary care patients who present with sadness and anhedonia.

METHOD

Cohort study of 2,599 unselected primary care attenders in six European countries followed up at 6 and 12 months.

RESULTS

1) In patients with sadness and/or anhedonia who were not depressed at entry to the study, suicide plans (OR = 3.05; 95 % CI = 1.50-6.24; p = 0.0022) and suicide attempts (OR = 9.08; 95 % CI = 2.57-32.03; p = 0.0006) were significant predictors of developing new onset depression at 6 or 12 months. 2) In patients with sadness and/or anhedonia who met CIDI criteria for major depression at entry, suicidal ideation (OR = 2.93; 95 % CI = 1.70-5.07; p = 0.0001), suicide plans (OR = 3.70; 95 % CI = 2.08-6.57; p < 0.0001), and suicide attempts (OR = 3.33; 95 % CI = 1.47-7.54; p = 0.0040) were significant predictors of persistent depression at 6 or 12 months.

CONCLUSIONS

Three questions on suicidality could help primary care professionals to assess such patients more closely without necessarily establishing whether they meet criteria for major depression.

Sociodemographic Characteristics and Psychological Adjustment Among Transsexuals in Spain

This study examined the sociodemographic characteristics and the psychological adjustment of transsexuals in Andalusia (Spain), and also analyzed the differences between female-to-male (FtM) and male-to-female (MtF) transsexuals. The sample included 197 transsexuals (101 MtF and 96 FtM) selected from those who visited the Transsexual and Gender Identity Unit at the Carlos Haya Hospital in Malaga between 2011 and 2012. Our analyses indicated that MtF transsexuals were more likely to have lower educational levels, live alone, have worked less frequently throughout their lifetime, and have engaged in prostitution. For FtM transsexuals, there were more frequent references to the mother's psychiatric history and more social avoidance and distress. Multivariate analysis showed that the number of personality dysfunctional traits and unemployment status were associated with depression in the entire sample. The following three conclusions can be made: there are significant differences between MtF and FtM transsexuals (mainly related to sociodemographic variables), depression was high in both groups, and a remarkable percentage of transsexuals have attempted suicide (22.8 %) or have had suicidal thoughts (52.3 %).

Effectiveness of psychological and/or educational interventions to prevent the onset of episodes of depression: A systematic review of systematic reviews and meta-analyses

OBJECTIVE

To determine the effectiveness of psychological and/or educational interventions to prevent the onset of episodes of depression.

METHODS

Systematic review of systematic reviews and meta-analyses (SR/MA). We searched PubMed, PsycINFO, Cochrane Database of Systematic Reviews, OpenGrey, and PROSPERO from their inception until February 2014. Two reviewers independently evaluated the eligibility criteria of all SR/MA, abstracted data, and determined bias risk (AMSTAR).

RESULTS

Twelve SR/MA (156 non-repeated trials and 56,158 participants) were included. Of these, 142 (91%) were randomized-controlled, 13 (8.3%) controlled trials, and 1 (0.6%) had no control group. Five SR/MA focused on children and adolescents, four on specific populations (women after childbirth, of low socioeconomic status, or unfavorable circumstances; patients with severe traumatic physical injuries or stroke) and three addressed the general population. Nine (75%) SR/MA concluded that interventions to prevent depression were effective. Of the 156 trials, 137 (87.8%) reported some kind of effect size calculation. Effect sizes were small in 45 (32.8%), medium in 26 (19.1%), and large in 25 (18.2%) trials; 41 (29.9%) trials were not effective. Of the 141 trials for which follow-up periods were available, only 34 (24.1%) exceeded 12 months.

CONCLUSION

Psychological and/or educational interventions to prevent onset of episodes of depression were effective, although most had small or medium effect sizes.

The risk for major depression conferred by childhood maltreatment is multiplied by BDNF and SERT genetic vulnerability: a replication study

BACKGROUND

There is limited evidence for a moderating role of both serotonin transporter (SERT) and brain-derived neurotrophic factor (BDNF) genes on the risk for major depression (MD) developing after childhood maltreatment. However, research on this topic remains inconclusive, and there is a lack of data from longitudinal studies with large and representative population samples. Our study aimed to clarify whether, in the presence of previous childhood maltreatment, individuals carrying low functional alleles for both SERT 5-HTTLPR and BDNF Val66Met polymorphisms had a higher risk for MD.

METHODS

We explored 2- and 3-way gene (SERT and BDNF) × environment (childhood maltreatment) interactions in a large sample of Spanish adults who were followed up over a 3-year period and assessed in person for both DSM-IV MD and exposure to childhood maltreatment.

RESULTS

Our study included 2679 participants. Those with both the 5-HTTLPR s allele and the BDNF Met allele showed the highest risk of MD if they had previously experienced emotional (z = 2.08, p = 0.037), sexual (z = 2.19, p = 0.029) or any kind of childhood abuse (z = 2.37, p = 0.018). These 3-way interactions remained significant regardless of whether the 5-HTTLPR triallelic or the 5-HTTLPR biallelic polymorphisms were included in the analyses.

LIMITATIONS

Retrospective assessment of childhood maltreatment may have resulted in a moderate degree of recall bias.

CONCLUSION

Our results confirm that the risk of depression conferred by childhood maltreatment is modified by variation at both SERT and BDNF genes.

A shared framework for the common mental disorders and Non-Communicable Disease: key considerations for disease prevention and control

BACKGROUND

Historically, the focus of Non Communicable Disease (NCD) prevention and control has been cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), cancer and chronic respiratory diseases. Collectively, these account for more deaths than any other NCDs. Despite recent calls to include the common mental disorders (CMDs) of depression and anxiety under the NCD umbrella, prevention and control of these CMDs remain largely separate and independent.

DISCUSSION

In order to address this gap, we apply a framework recently proposed by the Centers for Disease Control with three overarching objectives: (1) to obtain better scientific information through surveillance, epidemiology, and prevention research; (2) to disseminate this information to appropriate audiences through communication and education; and (3) to translate this information into action through programs, policies, and systems. We conclude that a shared framework of this type is warranted, but also identify opportunities within each objective to advance this agenda and consider the potential benefits of this approach that may exist beyond the health care system.

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.

Patients' opinions about knowing their risk for depression and what to do about it. The predictD-qualitative study

BACKGROUND

The predictD study developed and validated a risk algorithm for predicting the onset of major depression in primary care. We aimed to explore the opinion of patients about knowing their risk for depression and the values and criteria upon which these opinions are based.

METHODS

A maximum variation sample of patients was taken, stratified by city, age, gender, immigrant status, socio-economic status and lifetime depression. The study participants were 52 patients belonging to 13 urban health centres in seven different cities around Spain. Seven Focus Groups (FGs) were given held with primary care patients, one for each of the seven participating cities.

RESULTS

The results showed that patients generally welcomed knowing their risk for depression. Furthermore, in light of available evidence several patients proposed potential changes in their lifestyles to prevent depression. Patients generally preferred to ask their General Practitioners (GPs) for advice, though mental health specialists were also mentioned. They suggested that GPs undertake interventions tailored to each patient, from a "patient-centred" approach, with certain communication skills, and giving advice to help patients cope with the knowledge that they are at risk of becoming depressed.

CONCLUSIONS

Patients are pleased to be informed about their risk for depression. We detected certain beliefs, attitudes, values, expectations and behaviour among the patients that were potentially useful for future primary prevention programmes on depression.

A prediction algorithm for first onset of major depression in the general population: development and validation

OBJECTIVE

Prediction algorithms are useful for making clinical decisions and for population health planning. However, such prediction algorithms for first onset of major depression do not exist. The objective of this study was to develop and validate a prediction algorithm for first onset of major depression in the general population.

METHODS

Longitudinal study design with approximate 3-year follow-up. The study was based on data from a nationally representative sample of the US general population. A total of 28 059 individuals who participated in Waves 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions and who had not had major depression at Wave 1 were included. The prediction algorithm was developed using logistic regression modelling in 21 813 participants from three census regions. The algorithm was validated in participants from the 4th census region (n=6246). Major depression occurred since Wave 1 of the National Epidemiologic Survey on Alcohol and Related Conditions, assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule-diagnostic and statistical manual for mental disorders IV.

RESULTS

A prediction algorithm containing 17 unique risk factors was developed. The algorithm had good discriminative power (C statistics=0.7538, 95% CI 0.7378 to 0.7699) and excellent calibration (F-adjusted test=1.00, p=0.448) with the weighted data. In the validation sample, the algorithm had a C statistic of 0.7259 and excellent calibration (Hosmer-Lemeshow χ(2)=3.41, p=0.906).

CONCLUSIONS

The developed prediction algorithm has good discrimination and calibration capacity. It can be used by clinicians, mental health policy-makers and service planners and the general public to predict future risk of having major depression. The application of the algorithm may lead to increased personalisation of treatment, better clinical decisions and more optimal mental health service planning.

Development and validation of prediction algorithms for major depressive episode in the general population

BACKGROUND

To develop and validate sex specific prediction algorithms for 4-year risk of major depressive episode (MDE) using data from a population-based longitudinal cohort.

METHODS

Household residents from 10 provinces were randomly recruited and interviewed by Statistics Canada. 10,601 participants who were aged 18 years and older and who did not meet the criteria for MDE in the 12 months prior to a baseline interview in 2000/01 were included in algorithm development; data from 7902 participants who were aged 18 and older and who were free of MDE in 2004/05 were used for validation. Validation was also conducted in sub-populations that are of practice and policy importance. MDE was assessed using the World Health Organization's Composite International Diagnostic Interview(CIDI)-Short Form for Major Depression (CIDI-SFMD).

RESULTS

In the training data, the C statistics for algorithms in men was 0.7953 and was 0.7667 for algorithm in women. The algorithms had good predictive power and calibrated well in the development and validation data.

LIMITATIONS

The data relied on self-report. MDE was assessed with CIDI-SFMD. It was not feasible to validate the algorithms in different populations from different countries.

CONCLUSIONS

More studies are needed to further validate and refine these algorithms. However, the ability of a small number of easily assessed variables to predict MDE risk indicates that algorithms are a promising strategy for identifying individuals in need of enhanced monitoring and preventive interventions. Ultimately, application of algorithms may lead to increased personalization of treatment, and better clinical outcomes.

Preventing the onset of major depression based on the level and profile of risk of primary care attendees: protocol of a cluster randomised trial (the predictD-CCRT study)

BACKGROUND

The 'predictD algorithm' provides an estimate of the level and profile of risk of the onset of major depression in primary care attendees. This gives us the opportunity to develop interventions to prevent depression in a personalized way. We aim to evaluate the effectiveness, cost-effectiveness and cost-utility of a new intervention, personalized and implemented by family physicians (FPs), to prevent the onset of episodes of major depression.

METHODS/DESIGN

This is a multicenter randomized controlled trial (RCT), with cluster assignment by health center and two parallel arms. Two interventions will be applied by FPs, usual care versus the new intervention predictD-CCRT. The latter has four components: a training workshop for FPs; communicating the level and profile of risk of depression; building up a tailored bio-psycho-family-social intervention by FPs to prevent depression; offering a booklet to prevent depression; and activating and empowering patients. We will recruit a systematic random sample of 3286 non-depressed adult patients (1643 in each trial arm), nested in 140 FPs and 70 health centers from 7 Spanish cities. All patients will be evaluated at baseline, 6, 12 and 18 months. The level and profile of risk of depression will be communicated to patients by the FPs in the intervention practices at baseline, 6 and 12 months. Our primary outcome will be the cumulative incidence of major depression (measured by CIDI each 6 months) over 18 months of follow-up. Secondary outcomes will be health-related quality of life (SF-12 and EuroQol), and measurements of cost-effectiveness and cost-utility. The inferences will be made at patient level. We shall undertake an intention-to-treat effectiveness analysis and will handle missing data using multiple imputations. We will perform multi-level logistic regressions and will adjust for the probability of the onset of major depression at 12 months measured at baseline as well as for unbalanced variables if appropriate. The economic evaluation will be approached from two perspectives, societal and health system.

DISCUSSION

To our knowledge, this will be the first RCT of universal primary prevention for depression in adults and the first to test a personalized intervention implemented by FPs. We discuss possible biases as well as other limitations.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01151982.

[Integral Care Guide for Early Detection and Diagnosis of Depressive Episodes and Recurrent Depressive Disorder in Adults. Integral Attention of Adults with a Diagnosis of Depressive Episodes and Recurrent Depressive Disorder: Part I: Risk Factors, Screening, Suicide Risk Diagnosis and Assessment in Patients with a Depression Diagnosis]

INTRODUCTION

Depression is an important cause of morbidity and disability in the world; however, it is under-diagnosed at all care levels.

OBJECTIVE

The purpose here is to present recommendations based on the evidence gathered to answer a series of clinical questions concerning risk factors, screening, suicide risk diagnosis and evaluation in patients undergoing a depressive episode and recurrent depressive disorder. Emphasis has been made upon the approach used at the primary care level so as to grant adult diagnosed patients the health care guidelines based on the best and more updated evidence available thus achieving minimum quality standards.

METHODOLOGY

A practical clinical guide was elaborated according to standards of the Methodological Guide of the Ministry of Social Protection. Recommendation from guides NICE90 and CANMAT were adopted and updated so as to answer the questions posed while de novo questions were developed.

RESULTS

Recommendations 1-22 corresponding to screening, suicide risk and depression diagnosis were presented. The corresponding degree of recommendation is included.