Publications by authors named "J Lokkerbol"
Objectives: When health outcomes relevant for economic evaluations are unavailable, algorithms can be developed to map utilities using available clinical outcome measures. This study aims to develop two mapping algorithms estimating EuroQol-5 dimension-3 level (EQ-5D-3 L) utilities using the clinician-rated Health of the Nation Outcome Scores (HoNOS) and Positive and Negative Syndrome Scale (PANNS).
Methods: A dataset with 2,029 observations of patients with psychotic disorders included EQ-5D-3 L, HoNOS, PANSS item scores, and demographics.
Objective: Although evidence supports the effectiveness of psychological interventions for prevention of anxiety, little is known about their cost-effectiveness. The aim of this study was to conduct a systematic review of health-economic evaluations of psychological interventions for anxiety prevention.
Methods: PubMed, PsycInfo, Web of Science, Embase, Cochrane Central Register of Controlled Trials, EconLit, National Health Service (NHS) Economic Evaluations Database, NHS Health Technology Assessment, and OpenGrey databases were searched electronically on December 23, 2022.
Objective: Currently, there is a paucity of up-to-date estimates of the economic burden caused by mental disorders. Such information could provide vital insight into one of the most serious and costly-yet to some extent preventable-health challenges facing the world today.
Method: Data from a national psychiatric-epidemiological cohort study (NEMESIS-2, N = 6506) were used to provide reliable, relevant, and up-to-date cost estimates (in 2019 Euro) regarding healthcare costs, productivity losses, and patient and family costs associated with DSM-IV mental disorders both at individual level, but also in the general population and in the workforce of the Netherlands (per 1 million population).
Background: Predicting which treatment will work for which patient in mental health care remains a challenge.
Objective: The aim of this multisite study was 2-fold: (1) to predict patients' response to treatment in Dutch basic mental health care using commonly available data from routine care and (2) to compare the performance of these machine learning models across three different mental health care organizations in the Netherlands by using clinically interpretable models.
Methods: Using anonymized data sets from three different mental health care organizations in the Netherlands (n=6452), we applied a least absolute shrinkage and selection operator regression 3 times to predict the treatment outcome.