Detecting depressive and anxiety disorders in distressed patients in primary care; comparative diagnostic accuracy of the Four-Dimensional Symptom Questionnaire (4DSQ) and the Hospital Anxiety and Depression Scale (HADS)

BACKGROUND: Depressive and anxiety disorders often go unrecognized in distressed primary care patients, despite the overtly psychosocial nature of their demand for help. This is especially problematic in more severe disorders needing specific treatment (e.g. antidepressant pharmacotherapy or specialized cognitive behavioural therapy). The use of a screening tool to detect (more severe) depressive and anxiety disorders may be useful not to overlook such disorders. We examined the accuracy with which the Four-Dimensional Symptom Questionnaire (4DSQ) and the Hospital Anxiety and Depression Scale (HADS) are able to detect (more severe) depressive and anxiety disorders in distressed patients, and which cut-off points should be used. METHODS: Seventy general practitioners (GPs) included 295 patients on sick leave due to psychological problems. They excluded patients with recognized depressive or anxiety disorders. Patients completed the 4DSQ and HADS. Standardized diagnoses of DSM-IV defined depressive and anxiety disorders were established with the Composite International Diagnostic Interview (CIDI). Receiver Operating Characteristic (ROC) analyses were performed to obtain sensitivity and specificity values for a range of scores, and area under the curve (AUC) values as a measure of diagnostic accuracy. RESULTS: With respect to the detection of any depressive or anxiety disorder (180 patients, 61%), the 4DSQ and HADS scales yielded comparable results with AUC values between 0.745 and 0.815. Also with respect to the detection of moderate or severe depressive disorder, the 4DSQ and HADS depression scales performed comparably (AUC 0.780 and 0.739, p 0.165). With respect to the detection of panic disorder, agoraphobia and social phobia, the 4DSQ anxiety scale performed significantly better than the HADS anxiety scale (AUC 0.852 versus 0.757, p 0.001). The recommended cut-off points of both HADS scales appeared to be too low while those of the 4DSQ anxiety scale appeared to be too high. CONCLUSION: In general practice patients on sick leave because of psychological problems, the 4DSQ and the HADS are equally able to detect depressive and anxiety disorders. However, for the detection of cases severe enough to warrant specific treatment, the 4DSQ may have some advantages over the HADS, specifically for the detection of panic disorder, agoraphobia and social phobi

Approximation in two-stage stochastic integer programming

Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solution value. However, efficiency in the complexity theoretical sense is usually not taken into account. Quality statements mostly remain restricted to convergence to an optimal solution without accompanying implications on the running time of the algorithms for attaining more and more accurate solutions.However, over the last thirty years also some studies on performance analysis of approximation algorithms for stochastic programming have appeared. In this direction we find both probabilistic analysis and worst-case analysis.Recently the complexity of stochastic programming problems has been addressed, indeed confirming that these problems are harder than most deterministic combinatorial optimization problems. Polynomial-time approximation algorithms and their performance guarantees for stochastic linear and integer programming problems have received increasing research attention only very recently.Approximation in the traditional stochastic programming sense will not be discussed in this chapter. The reader interested in this issue is referred to surveys on stochastic programming, like the Handbook on Stochastic Programming by Ruszczyński and Shapiro (2003) or the textbooks by Birge and Louveaux (1997), Kall and Wallace (1994), Prékopa (1995), and Shapiro etal. (2009). We concentrate on the studies of approximation algorithms in relation to computational complexity theory.With this survey we intend to give a flavor of the type of results existing in the literature on approximation algorithms in two-stage stochastic integer programming rather than a complete overview of the literature on the subject. We do so by exhibiting a representative selection of results, which we present in full detail. While presenting them we do not refer to the literature; these references, together with pointers to other relevant work in this field of research, are given in an extensive notes section at the end of the survey. © 2014 Elsevier Ltd

LBDA+: A loose Benders decomposition algorithm for approximating two-stage mixed-integer recourse models

The code for the Loose Benders Decomposition Algorithm and the data of the test instances used in van der Laan and Romeijnders (2020)

LBDA+: A loose Benders decomposition algorithm for approximating two-stage mixed-integer recourse models

The code for the Loose Benders Decomposition Algorithm and the data of the test instances used in van der Laan and Romeijnders (2020)