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)

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.

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.

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.

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.

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.

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.

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.

Effectiveness of Psychological and Educational Interventions to Prevent Depression in Primary Care: A Systematic Review and Meta-Analysis

PURPOSE

Although evidence exists for the efficacy of psychosocial interventions to prevent the onset of depression, little is known about its prevention in primary care. We aimed to evaluate the effectiveness of psychological and educational interventions to prevent depression in primary care.

METHODS

We conducted a systematic review and meta-analysis of relevant randomized controlled trials (RCTs) examining the effect of psychological and educational interventions to prevent depression in nondepressed primary care attendees. We searched MEDLINE, PsycINFO, Web of Science, OpenGrey Repository, Cochrane Central Register of Controlled Trials, and other sources up to May 2016. At least 2 reviewers independently evaluated the eligibility criteria, extracted data, and assessed the risk of bias. We calculated standardized mean differences (SMD) using random-effects models.

RESULTS

We selected 14 studies (7,365 patients) that met the inclusion criteria, 13 of which were valid to perform a meta-analysis. Most of the interventions had a cognitive-behavioral orientation, and in only 4 RCTs were the intervention clinicians primary care staff. The pooled SMD was -0.163 (95%CI, -0.256 to -0.070; P = .001). The risk of bias and the heterogeneity (I² = 20.6%) were low, and there was no evidence of publication bias. Meta-regression detected no association between SMD and follow-up times or SMD and risk of bias. Subgroup analysis suggested greater effectiveness when the RCTs used care as usual as the comparator compared with those using placebo.

CONCLUSIONS

Psychological and educational interventions to prevent depression had a modest though statistically significant preventive effect in primary care. Further RCTs using placebo or active comparators are needed.

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.

Who Owns the Data? Open Data for Healthcare

Research on large shared medical datasets and data-driven research are gaining fast momentum and provide major opportunities for improving health systems as well as individual care. Such open data can shed light on the causes of disease and effects of treatment, including adverse reactions side-effects of treatments, while also facilitating analyses tailored to an individual's characteristics, known as personalized or "stratified medicine." Developments, such as crowdsourcing, participatory surveillance, and individuals pledging to become "data donors" and the "quantified self" movement (where citizens share data through mobile device-connected technologies), have great potential to contribute to our knowledge of disease, improving diagnostics, and delivery of -healthcare and treatment. There is not only a great potential but also major concerns over privacy, confidentiality, and control of data about individuals once it is shared. Issues, such as user trust, data privacy, transparency over the control of data ownership, and the implications of data analytics for personal privacy with potentially intrusive inferences, are becoming increasingly scrutinized at national and international levels. This can be seen in the recent backlash over the proposed implementation of care.data, which enables individuals' NHS data to be linked, retained, and shared for other uses, such as research and, more controversially, with businesses for commercial exploitation. By way of contrast, through increasing popularity of social media, GPS-enabled mobile apps and tracking/wearable devices, the IT industry and MedTech giants are pursuing new projects without clear public and policy discussion about ownership and responsibility for user-generated data. In the absence of transparent regulation, this paper addresses the opportunities of Big Data in healthcare together with issues of responsibility and accountability. It also aims to pave the way for public policy to support a balanced agenda that safeguards personal information while enabling the use of data to improve public health.

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.

Economic evaluations of interventions designed to prevent mental disorders: a systematic review

AIM

The prevention of mental disorders is a growing field and there are interventions that have been demonstrated to prevent some disorders, particularly depression, from developing. The aim of the current study is to update two existing reviews of the cost-effectiveness studies of preventive interventions for mental disorders in order to determine whether such interventions are good value-for-money.

METHODS

A search was undertaken in Medline, PsycInfo and Econlit. The search was limited to articles published in English covering the period from 2010 to September 2013. Inclusion criteria for the review comprised comparative economic evaluations of interventions designed to prevent mental disorders.

RESULTS

Ten new economic evaluations have been published since 2010, more than doubling the numbers of economic evaluations of preventive interventions for mental disorders published prior to 2010. Using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist, the majority of studies were of a good standard and used cost-utility frameworks.

CONCLUSIONS

Indicated types of interventions for the prevention of depression and anxiety appeared to be particularly good value-for-money and most of these studies were modelled evaluations. Unfortunately, many such interventions are still not routinely provided. Future trials of preventive interventions for mental disorders need to include robust economic evaluations so that the economic impact of such interventions from the individual study participant perspective can be determined.

Framing of scientific knowledge as a new category of health care research

RATIONALE

The new area of health system research requires a revision of the taxonomy of scientific knowledge that may facilitate a better understanding and representation of complex health phenomena in research discovery, corroboration and implementation.

METHOD

A position paper by an expert group following and iterative approach.

RESULTS

'Scientific evidence' should be differentiated from 'elicited knowledge' of experts and users, and this latter typology should be described beyond the traditional qualitative framework. Within this context 'framing of scientific knowledge' (FSK) is defined as a group of studies of prior expert knowledge specifically aimed at generating formal scientific frames. To be distinguished from other unstructured frames, FSK must be explicit, standardized, based on the available evidence, agreed by a group of experts and subdued to the principles of commensurability, transparency for corroboration and transferability that characterize scientific research. A preliminary typology of scientific framing studies is presented. This typology includes, among others, health declarations, position papers, expert-based clinical guides, conceptual maps, classifications, expert-driven health atlases and expert-driven studies of costs and burden of illness.

CONCLUSIONS

This grouping of expert-based studies constitutes a different kind of scientific knowledge and should be clearly differentiated from 'evidence' gathered from experimental and observational studies in health system research.

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.