[Development of an algorithm to predict the incidence of major depression among primary care consultants]. / Desarrollo y validación de un algoritmo para predecir riesgo de depresión en consultantes de atención primaria en Chile.

BACKGROUND: The reduction of major depression incidence is a public health challenge. AIM: To develop an algorithm to estimate the risk of occurrence of major depression in patients attending primary health centers (PHC). MATERIAL AND METHODS: Prospective cohort study of a random sample of 2832 patients attending PHC centers in Concepción, Chile, with evaluations at baseline, six and twelve months. Thirty nine known risk factors for depression were measured to build a model, using a logistic regression. The algorithm was developed in 2,133 patients not depressed at baseline and compared with risk algorithms developed in a sample of 5,216 European primary care attenders. The main outcome was the incidence of major depression in the follow-up period. RESULTS: The cumulative incidence of depression during the 12 months follow up in Chile was 12%. Eight variables were identified. Four corresponded to the patient (gender, age, depression background and educational level) and four to patients' current situation (physical and mental health, satisfaction with their situation at home and satisfaction with the relationship with their partner). The C-Index, used to assess the discriminating power of the final model, was 0.746 (95% confidence intervals (CI = 0,707-0,785), slightly lower than the equation obtained in European (0.790 95% CI = 0.767-0.813) and Spanish attenders (0.82; 95% CI = 0.79-0.84). CONCLUSIONS: Four of the factors identified in the risk algorithm are not modifiable. The other two factors are directly associated with the primary support network (family and partner). This risk algorithm for the incidence of major depression provides a tool that can guide efforts towards design, implementation and evaluation of effectiveness of interventions to prevent major depression.

Desarrollo y validación de un algoritmo para predecir riesgo de depresión en consultantes de atención primaria en Chile / Development of an algorithm to predict the incidence of major depression among primary care consultants

Background: The reduction of major depression incidence is a public health challenge. Aim: To develop an algorithm to estimate the risk of occurrence of major depression in patients attending primary health centers (PHC). Material and Methods: Prospective cohort study of a random sample of 2832 patients attending PHC centers in Concepción, Chile, with evaluations at baseline, six and twelve months. Thirty nine known risk factors for depression were measured to build a model, using a logistic regression. The algorithm was developed in 2,133 patients not depressed at baseline and compared with risk algorithms developed in a sample of 5,216 European primary care attenders. The main outcome was the incidence of major depression in the follow-up period. Results: The cumulative incidence of depression during the 12 months follow up in Chile was 12%. Eight variables were identified. Four corresponded to the patient (gender, age, depression background and educational level) and four to patients' current situation (physical and mental health, satisfaction with their situation at home and satisfaction with the relationship with their partner). The C-Index, used to assess the discriminating power of the final model, was 0.746 (95% confidence intervals (CI = 0,707-0,785), slightly lower than the equation obtained in European (0.790 95% CI = 0.767-0.813) and Spanish attenders (0.82; 95% CI = 0.79-0.84). Conclusions: Four of the factors identified in the risk algorithm are not modifiable. The other two factors are directly associated with the primary support network (family and partner). This risk algorithm for the incidence of major depression provides a tool that can guide efforts towards design, implementation and evaluation of effectiveness of interventions to prevent major depression.

Does recognition of depression in primary care affect outcome? The PREDICT-NL study.

BACKGROUND: Detection rates of depression in primary care are <50%. Studies showed similar outcome after 12 months for recognized and unrecognized depression. Outcome beyond 12 months is less well studied. OBJECTIVE: We investigated recognition of depression in primary care and its relation to outcome after 6, 12 and 39 months. METHODS: Data were used from a prospective cohort study of 1293 consecutive general practice attendees (PREDICT-NL), who were followed up after 6 (n = 1236), 12 (n = 1179) and 39 (n = 752) months. We measured the presence and severity of major depressive disorder (MDD) according to DSM-IV criteria and Patient Health Questionnaire 9 (PHQ-9) and mental function with Short Form 12 (SF-12). Recognition of depression was assessed using international classification of primary care codes (P03 and P76) and Anatomical Therapeutic Chemical (N06A) codes from the GP records (6 months before/after baseline). RESULTS: At baseline, 170 (13%) of the participants had MDD, of whom 36% were recognized by their GP. The relative risk of being depressed after 39 months was 1.35 [95% confidence interval (CI) 0.7-2.7] for participants with recognized depression compared to unrecognized depression. At baseline, participants with recognized depression had more depressive symptoms (mean difference PHQ-9 2.7, 95% CI 1.6-3.9) and worse mental function (mean difference mental component summary -3.8, 95% CI -7.8 to 0.2) than unrecognized depressed participants. After 12 and 39 months, mean scores for both groups did not differ but were worse than those without depression. CONCLUSIONS: A minority of patients with MDD is recognized in primary care. Those who were unrecognized had comparable outcome after 12 and 39 months as participants with recognized depression.

Associations between unemployment and major depressive disorder: evidence from an international, prospective study (the predict cohort).

Unemployment is known to be associated with poor mental health, but it is not clear how strongly unemployment leads to onset of diagnosed clinical depression (causation), or if depression raises the risks of becoming unemployed (health selection), or indeed if both pathways operate. We therefore investigate the direction of associations between clinical depression and unemployment in a cross-cultural prospective cohort study. 10,059 consecutive general practice attendees (18-75 years) were recruited from six European countries and Chile between 2003 and 2004 and followed up at six, 12 and (in a subset) 24 months. The analysis sample was restricted to 3969 men and women who were employed or unemployed and seeking employment and had data on depression measures. The outcomes were depressive episodes, assessed using the Depression Section of the Composite International Diagnostic Interview (CIDI) and self-reported employment status. Among 3969 men and women with complete data on depression and unemployment, 10% (n = 393) had depression symptoms and a further 6% (n = 221) had major depression at 12 months. 11% (n = 423) of the sample were unemployed by 6 months. Participants who became unemployed between baseline and 6 months compared to those employed at both times had an adjusted relative risk ratio for 12-month depression of 1.58 (95% Confidence Interval 0.76, 3.27). Participants with depression at baseline and 6 months compared to neither time had an odds ratio for 6-month unemployment of 1.58 (95% Confidence Interval 0.97, 2.58). We found evidence that causation and (to a lesser extent) health selection raise the prevalence of depression in the unemployed. Unemployed adults are at particular risk for onset of major clinical depression and should be offered extra services or screened. Given the trend for adults with depression to perhaps be at greater risk of subsequent unemployment, employees with depressive symptoms should also be supported at work as a precautionary principle.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

BACKGROUND: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. METHODS: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. RESULTS: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). CONCLUSIONS: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Polymorphic variation at the serotonin 1-A receptor gene is associated with comorbid depression and generalized anxiety.

BACKGROUND: Serotonin 1-A receptors are key regulators of serotonin activity and their dysregulation might be implicated in the emergence of both major depression (MD) and generalized anxiety disorder (GAD). Previous studies have yielded inconclusive results as to whether the 5-HT1A receptor gene (HTR1A) has a role in the aetiology of MD and no study up to date has analysed this polymorphism on either pure MD or MD comorbid with GAD. METHODS: In this study, 1059 patients taking part in the PREDICT-Gene study were ascertained for Diagnostic and Statistical Manual of Mental Disorders-IV MD and GAD diagnoses using the Composite International Diagnostic Interview and the Primary Care Evaluation of Mental Disorders questionnaire, respectively. They were also genotyped for the C(-1019)G functional polymorphism at the promoter region of HTR1A gene. RESULTS: Genetic variability at HTR1A was significantly associated with MD [odds ratio (OR) = 1.67; 95% confidence interval (CI) = 1.14-2.44; P = 0.008], although this effect disappeared after adjusting for GAD (OR = 1.43; 95% CI = 0.96-2.14; P = 0.080). Similarly, a crude association between C(-1019)G polymorphism and GAD was found (OR = 2.54; 95% CI = 1.28-4.86; P = 0.003), but these results became no longer significant after adjusting for MD (OR = 1.97; 95% CI = 0.99-3.91; P = 0.050). However, a main effect of HTR1A G(-1019) allele on comorbid MD-GAD was found (OR = 3.41; 95% CI = 1.44-8.05; P = 0.005) and it remained robust and statistically significant after adjusting by sex, age and family history of psychological problems (OR = 2.82; 95% CI = 1.18-6.77; P = 0.020). CONCLUSION: In our study, the HTR1A C(-1019)G polymorphism was found to be associated to the frequent clinical presentation of comorbid MD and GAD, suggesting a common genetic background for mixed depression and anxiety states. These findings should be considered as preliminary. Future replications in independent samples would be needed to confirm or discard such association.