Objectives

To examine the perspectives of key stakeholders involved in the return-to-work (RTW) process regarding the definition of successful RTW outcome after sickness absence related to common mental disorders (CMD’s).

Methods

A mixed-method design was used: First, we used qualitative methods (focus groups, interviews) to identify a broad range of criteria important for the definition of successful RTW (N = 57). Criteria were grouped into content-related clusters. Second, we used a quantitative approach (online questionnaire) to identify, among a larger stakeholder sample (N = 178), the clusters and criteria most important for successful RTW.

Results

A total of 11 clusters, consisting of 52 unique criteria, were identified. In defining successful RTW, supervisors and occupational physicians regarded “Sustainability” and “At-work functioning” most important, while employees regarded “Sustainability,” “Job satisfaction,” “Work-home balance,” and “Mental Functioning” most important. Despite agreement on the importance of certain criteria, considerable differences among stakeholders were observed.

Conclusions

Key stakeholders vary in the aspects and criteria they regard as important when defining successful RTW after CMD-related sickness absence. Current definitions of RTW outcomes used in scientific research may not accurately reflect these key stakeholder perspectives. Future studies should be more aware of the perspective from which they aim to evaluate the effectiveness of a RTW intervention, and define their RTW outcomes accordingly.

Basic aspects in the handling of fatty acid-data have remained largely underexposed. Of these, we aimed to address three statistical methodological issues, by quantitatively exemplifying their imminent confounding impact on analytical outcomes: (1) presenting results as relative percentages or absolute concentrations, (2) handling of missing/non-detectable values, and (3) using structural indices for data-reduction. Therefore, we reanalyzed an example dataset containing erythrocyte fatty acid-concentrations of 137 recurrently depressed patients and 73 controls. First, correlations between data presented as percentages and concentrations varied for different fatty acids, depending on their correlation with the total fatty acid-concentration. Second, multiple imputation of non-detects resulted in differences in significance compared to zero-substitution or omission of non-detects. Third, patients’ chain length-, unsaturation-, and peroxidation-indices were significantly lower compared to controls, which corresponded with patterns interpreted from individual fatty acid tests. In conclusion, results from our example dataset show that statistical methodological choices can have a significant influence on outcomes of fatty acid analysis, which emphasizes the relevance of: (1) hypothesis-based fatty acid-presentation (percentages or concentrations), (2) multiple imputation, preventing bias introduced by non-detects; and (3) the possibility of using (structural) indices, to delineate fatty acid-patterns thereby preventing multiple testing.

One of the major clinical problems in the treatment of people with schizophrenia is suboptimal medication adherence. Most research focusing on determinants of nonadherence use quantitative research methods. These studies have some important limitations in exploring the decision-making process of patients concerning medication. In this study we explore factors influencing medication adherence behavior in people with schizophrenia using concept mapping. Concept mapping is a structured qualitative method and was performed in 4 European countries. Participants were 27 patients with schizophrenia, 29 carers, and 28 professionals of patients with schizophrenia. Five clinically relevant themes were identified that affect adherence: medication efficacy, external factors (such as patient support and therapeutic alliance), insight, side effects, and attitudes toward medication. Importance ratings of these factors differed significantly between professionals and carers and patients. Professionals, carers, and patients do not have a shared understanding of which factors are important in patients' medication adherence behavior. Adherence may be positively influenced if professionals focus on the positive aspects of medication, on enhancing insight, and on fostering a positive therapeutic relationship with patients and carers.

Objective To determine whether supervised medical prescription of heroin can successfully treat addicts who do not sufficiently benefit from methadone maintenance treatment.

Design Two open label randomised controlled trials.

Setting Methadone maintenance programmes in six cities in the Netherlands.

Participants 549 heroin addicts.

Interventions Inhalable heroin (n = 375) or injectable heroin (n = 174) prescribed over 12 months. Heroin (maximum 1000 mg per day) plus methadone (maximum 150 mg per day) compared with methadone alone (maximum 150 mg per day). Psychosocial treatment was offered throughout.

Main outcome measures Dichotomous, multidomain response index, including validated indicators of physical health, mental status, and social functioning.

Results Adherence was excellent with 12 month outcome data available for 94% of the randomised participants. With intention to treat analysis, 12 month treatment with heroin plus methadone was significantly more effective than treatment with methadone alone in the trial of inhalable heroin (response rate 49.7% v 26.9%; difference 22.8%, 95% confidence interval 11.0% to 34.6%) and in the trial of injectable heroin (55.5% v 31.2%; difference 24.3%, 9.6% to 39.0%). Discontinuation of the coprescribed heroin resulted in a rapid deterioration in 82% (94/115) of those who responded to the coprescribed heroin. The incidence of serious adverse events was similar across treatment conditions.

Conclusions Supervised coprescription of heroin is feasible, more effective, and probably as safe as methadone alone in reducing the many physical, mental, and social problems of treatment resistant heroin addicts.

Background

To examine whether a simple Visual Analogue Mood Scale (VAMS) is able to predict time to relapse over 5.5-years.

Methodology/Principal Findings

187 remitted recurrently depressed out-patients were interviewed using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) and the 17-item Hamilton Depression rating scale (HAM-D) to verify remission status (HAM-D <10). All patients rated their current mood with the help of a Visual Analogue Mood Scale (VAMS) at baseline and at a follow-up assessment three months later. Relapse over 5.5-years was assessed by the SCID-I. Cox regression revealed that both the VAMS at baseline and three months later significantly predicted time to relapse over 5.5-years. Baseline VAMS even predicted time to relapse when the number of previous depressive episodes and HAM-D scores were controlled for. The baseline VAMS explained 6.3% of variance in time to relapse, comparable to the HAM-D interview.

Conclusions/Significance

Sad mood after remission appears to play a pivotal role in the course of depression. Since a simple VAMS predicted time to relapse, the VAMS might be an easy and time-effective way to monitor mood and risk of early relapse, and offers possibilities for daily monitoring using e-mail and SMS.

Trial Registration

International Standard Randomized Controlled Trial Register Identifier: ISRCTN68246470.

Background

Missing data is a common nuisance in eHealth research: it is hard to prevent and may invalidate research findings.

Objective

In this paper several statistical approaches to data “missingness” are discussed and tested in a simulation study. Basic approaches (complete case analysis, mean imputation, and last observation carried forward) and advanced methods (expectation maximization, regression imputation, and multiple imputation) are included in this analysis, and strengths and weaknesses are discussed.

Methods

The dataset used for the simulation was obtained from a prospective cohort study following participants in an online self-help program for problem drinkers. It contained 124 nonnormally distributed endpoints, that is, daily alcohol consumption counts of the study respondents. Missingness at random (MAR) was induced in a selected variable for 50% of the cases. Validity, reliability, and coverage of the estimates obtained using the different imputation methods were calculated by performing a bootstrapping simulation study.

Results

In the performed simulation study, the use of multiple imputation techniques led to accurate results. Differences were found between the 4 tested multiple imputation programs: NORM, MICE, Amelia II, and SPSS MI. Among the tested approaches, Amelia II outperformed the others, led to the smallest deviation from the reference value (Cohen’s d = 0.06), and had the largest coverage percentage of the reference confidence interval (96%).

Conclusions

The use of multiple imputation improves the validity of the results when analyzing datasets with missing observations. Some of the often-used approaches (LOCF, complete cases analysis) did not perform well, and, hence, we recommend not using these. Accumulating support for the analysis of multiple imputed datasets is seen in more recent versions of some of the widely used statistical software programs making the use of multiple imputation more readily available to less mathematically inclined researchers.