Effect of Reliable Recovery on Health Care Costs and Productivity Losses in Emotional Disorders.

Despite the high economic costs associated with emotional disorders, relatively few studies have examined the variation in costs according to whether the patient has achieved a reliable recovery. The aim of this study was to explore differences in health care costs and productivity losses between primary care patients from a previous randomized controlled trial (RCT)-PsicAP-with emotional symptoms who achieved a reliable recovery and those who did not after transdiagnostic cognitive-behavioral therapy (TD-CBT) plus treatment as usual (TAU) or TAU alone. Sociodemographic and cost data were obtained for 134 participants treated at five primary care centers in Madrid for the 12-month posttreatment period. Reliable recovery rates were higher in the patients who received TD-CBT + TAU versus TAU alone (66% vs. 34%, respectively; chi-square = 13.78, df = 1, p < .001). Patients who did not achieve reliable recovery incurred more costs, especially associated with general practitioner consultations (t = 3.01, df = 132, p = .003), use of emergency departments (t = 2.20, df = 132, p = .030), total health care costs (t = 2.01, df = 132, p = .040), and sick leaves (t = 1.97, df = 132, p = .048). These findings underscore the societal importance of achieving a reliable recovery in patients with emotional disorders, and further support the value of adding TD-CBT to TAU in the primary care setting.

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

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

Factor structure and measurement invariance across various demographic groups and over time for the PHQ-9 in primary care patients in Spain.

The Patient Health Questionnaire (PHQ-9) is a widely-used screening tool for depression in primary care settings. The purpose of the present study is to identify the factor structure of the PHQ-9 and to examine the measurement invariance of this instrument across different sociodemographic groups and over time in a sample of primary care patients in Spain. Data came from 836 primary care patients enrolled in a randomized controlled trial (PsicAP study) and a subsample of 218 patients who participated in a follow-up assessment at 3 months. Confirmatory factor analysis (CFA) was used to test one- and two-factor structures identified in previous studies. Analyses of multiple-group invariance were conducted to determine the extent to which the factor structure is comparable across various demographic groups (i.e., gender, age, marital status, level of education, and employment situation) and over time. Both one-factor and two-factor re-specified models met all the pre-established fit criteria. However, because the factors identified in the two-factor model were highly correlated (r = .86), the one-factor model was preferred for its parsimony. Multi-group CFA indicated measurement invariance across different demographic groups and across time. The present findings suggest that physicians in Spain can use the PHQ-9 to obtain a global score for depression severity in different demographic groups and to reliably monitor changes over time in the primary care setting.