Search

Background: Evidence about the effectiveness of psychosocial interventions to reduce the incidence of depression and anxiety and promote subjective well-being in older people is limited, particularly in Latin-American countries. This study thus aims to assess a program specifically designed to address this issue in persons aged 65 to 80 and attending primary health care centres., Method: Older people who use primary care centres are to be randomly assigned to the program or to a control group. Only independent users will be included; those having had a major depressive disorder or an anxiety disorder in the last 6 months will be excluded. The program is group based; it includes cognitive stimulation, expansion of social support networks and cognitive behaviour strategies. Depressive and anxiety symptoms and disorders, as well as psychological well-being, will be assessed using standardised instruments, once before implementing the program and later, after 18 and 36 weeks., Discussion: Primary care is a setting where interventions to improve mental health can be beneficial. Providing evidence-based programs that work with older people is a priority for public mental health., Trial Registration: A protocol for this study has been registered prospectively at ISRCTN registry on 25 July 2018. Identifier: ISRCTN32235611 .

Background: Screening instruments are required for the detection of depressive disorders by primary care practitioners., Aim: To develop a screening instrument to detect depression, based on data gathered interviewing patients attending primary health care settings., Material and Methods: The instrument was constructed with data about factors associated or triggering a depressive disorder obtained from 3,000 patients consulting for general morbidity. All patients answered the Composite International Diagnostic Interview, (version 2.1, section depression) and an inventory containing 39 risk factors for depression, obtained from the literature. A multiple imputation method using chained equations was carried out. Using a binary logistic regression with backward selection, an equation for depression screening was obtained. The c-index was calculated to estimate discriminating power of the model. A shrinkage factor was estimated to adjust the predictive model., Results: Estimations were carried out with data from 2,552 patients with a median age of 47 years (73% women). Fifty five percent lived with a partner and 45% had basic studies. The method selected 14 significant predictors, with a shrinkage value of 0.861 and a c-index of 0.838 (95% confidence intervals 0.82-0.86)., Conclusions: The instrument has adequate psychometric properties as a screening tool for depression in primary health care.

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.

Objectives: Perceived discrimination is a prevalent problem that has been linked to negative health outcomes for victims. The goal of this research was to examine whether perceived discrimination within the past 6 months was related to 6-month prevalence of problem drinking, illicit drug use, major depressive disorder (MDD), and posttraumatic stress disorder (PTSD) in a sample of primary care patients in Chile., Methods: Structured diagnostic assessments were administered to assess for MDD and PTSD using the Composite International Diagnostic Interview. The Alcohol Use Disorders Identification Test assessed hazardous alcohol use. Additional measures captured illegal drug use and discrimination in the past 6 months. Measures were administered to 2839 participants between the ages of 15 to 98 in primary care centers in the Chilean cities of Concepcion and Talcahuano., Results: Controlling for demographic variables and previous trauma victimization, patients who reported discrimination in the past 6 months were significantly more likely to engage in hazardous alcohol use, illegal drug use, be diagnosed with MDD, and PTSD within this same time period than patients not reporting discrimination., Conclusions: This study highlights the importance of considering discrimination as a potential contributing factor to substance use and mental health problems in a Latin American sample.

BackgroundThe different incidence rates of, and risk factors for, depression in different countries argue for the need to have a specific risk algorithm for each country or a supranational risk algorithm. We aimed to develop and validate a predictD-Spain risk algorithm (PSRA) for the onset of major depression and to compare the performance of the PSRA with the predictD-Europe risk algorithm (PERA) in Spanish primary care.MethodA prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multi-level logistic regression and inverse probability weighting to build the PSRA. In Spain (4574), Chile (2133) and another five European countries (5184), 11 891 non-depressed adult primary care attendees formed our at-risk population. The main outcome was DSM-IV major depression (CIDI).ResultsSix variables were patient characteristics or past events (sex, age, sex×age interaction, education, physical child abuse, and lifetime depression) and six were current status [Short Form 12 (SF-12) physical score, SF-12 mental score, dissatisfaction with unpaid work, number of serious problems in very close persons, dissatisfaction with living together at home, and taking medication for stress, anxiety or depression]. The C-index of the PSRA was 0.82 [95% confidence interval (CI) 0.79–0.84]. The Integrated Discrimination Improvement (IDI) was 0.0558 [standard error (s.e.)=0.0071, Zexp=7.88, p<0.0001] mainly due to the increase in sensitivity. Both the IDI and calibration plots showed that the PSRA functioned better than the PERA in Spain.ConclusionsThe PSRA included new variables and afforded an improved performance over the PERA for predicting the onset of major depression in Spain. However, the PERA is still the best option in other European countries. [ABSTRACT FROM PUBLISHER]

BackgroundThere are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.MethodFamily practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.ResultsThere were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724–0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837–1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654–0.809) in Estonia, 0.811 (95% CI 0.736–0.886) in The Netherlands and 0.707 (95% CI 0.671–0.742) in Chile.ConclusionsPredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression. [ABSTRACT FROM PUBLISHER]