Increasing value and reducing waste by optimizing the development of complex interventions: Enriching the development phase of the Medical Research Council (MRC) Framework

This is the final version of the article. Available from Elsevier via the DOI in this record.Background In recent years there has been much emphasis on ‘research waste’ caused by poor question selection, insufficient attention to previous research results, and avoidable weakness in research design, conduct and analysis. Little attention has been paid to the effect of inadequate development of interventions before proceeding to a full clinical trial. Objective We therefore propose to enrich the development phase of the MRC Framework by adding crucial elements to improve the likelihood of success and enhance the fit with clinical practice Methods Based on existing intervention development guidance and synthesis, a comprehensive iterative intervention development approach is proposed. Examples from published reports are presented to illustrate the methodology that can be applied within each element to enhance the intervention design. Results A comprehensive iterative approach is presented by combining the elements of the MRC Framework development phase with essential elements from existing guidance including: problem identification, the systematic identification of evidence, identification or development of theory, determination of needs, the examination of current practice and context, modelling the process and expected outcomes leading to final element: the intervention design. All elements are drawn from existing models to provide intervention developers with a greater chance of producing an intervention that is well adopted, effective and fitted to the context. Conclusion This comprehensive approach of developing interventions will strengthen the internal and external validity, minimize research waste and add value to health care research. In complex interventions in health care research, flaws in the development process immediately impact the chances of success. Knowledge regarding the causal mechanisms and interactions within the intended clinical context is needed to develop interventions that fit daily practice and are beneficial for the end-user

An approach to developing a prediction model of fertility intent among HIV-positive women and men in Cape Town, South Africa: a case study

As a ‘case-study’ to demonstrate an approach to establishing a fertility-intent prediction model, we used data collected from recently diagnosed HIV-positive women (N = 69) and men (N = 55) who reported inconsistent condom use and were enrolled in a sexual and reproductive health intervention in public sector HIV care clinics in Cape Town, South Africa. Three theoretically-driven prediction models showed reasonable sensitivity (0.70–1.00), specificity (0.66–0.94), and area under the receiver operating characteristic curve (0.79–0.89) for predicting fertility intent at the 6-month visit. A k-fold cross-validation approach was employed to reduce bias due to over-fitting of data in estimating sensitivity, specificity, and area under the curve. We discuss how the methods presented might be used in future studies to develop a clinical screening tool to identify HIV-positive individuals likely to have future fertility intent and who could therefore benefit from sexual and reproductive health counselling around fertility options

Probability of Major Depression Classification Based on the SCID, CIDI and MINI Diagnostic Interviews : A Synthesis of Three Individual Participant Data Meta-Analyses

Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results.To compare the odds of the major depression classification based on the SCID, CIDI, and MINI.We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis.In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80).Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics

An Efficient Way to Detect Poststroke Depression by Subsequent Administration of a 9-Item and a 2-Item Patient Health Questionnaire

Background and Purpose-The early detection of poststroke depression is essential for optimizing recovery after stroke. A prospective study was conducted to investigate the diagnostic value of the 9-item and the 2-item Patient Health Questionnaire (PHQ-9, PHQ-2). Methods-One hundred seventy-one consecutive patients with stroke who could communicate adequately were included. In the 6th to 8th weeks after stroke, depression was measured using the PHQ-9 and PHQ-2 and diagnosed using the Composite International Diagnostic Interview. Results-Of the participating patients, 20 (12.2%) were depressed. The PHQ- 9 performed best at a score >= 10, a sensitivity of 0.80 (95% CI, 0.62-0.98), and a specificity of 0.78 (95% CI, 0.72-0.85) and the PHQ-2 at a score >= 2 with a sensitivity of 0.75 (95% CI, 0.56-0.94) and a specificity of 0.76 (95% CI, 0.69-0.83). Administering the PHQ- 9 only to patients who scored >= 2 on the PHQ-2 improved the identification of depression (sensitivity, 0.87; 95% CI, 0.69-1.04). Conclusions-The diagnostic value is acceptable to good for PHQ-9 scores >= 10 and PHQ-2 scores >= 2. Conducting a PHQ-9 only in patients with a PHQ-2 score >2 generates the best results. (Stroke. 2012;43:854-856.)

Determinants of activation for self-management in patients with COPD

YJG Korpershoek,1–3 ID Bos-Touwen,2 JM de Man-van Ginkel,2,4 J-WJ Lammers,3 MJ Schuurmans,1,2 JCA Trappenburg2 1Research Group Chronic Illnesses, Faculty of Health Care, University of Applied Sciences Utrecht, 2Department of Rehabilitation, Nursing Science & Sports, University Medical Center Utrecht, 3Department of Respiratory Medicine, Division of Heart & Lungs, University Medical Center Utrecht, 4Nursing Science, Program in Clinical Health Science, University Medical Center Utrecht, Utrecht, the Netherlands Background: COPD self-management is a complex behavior influenced by many factors. Despite scientific evidence that better disease outcomes can be achieved by enhancing self-management, many COPD patients do not respond to self-management interventions. To move toward more effective self-management interventions, knowledge of characteristics associated with activation for self-management is needed. The purpose of this study was to identify key patient and disease characteristics of activation for self-management. Methods: An explorative cross-sectional study was conducted in primary and secondary care in patients with COPD. Data were collected through questionnaires and chart reviews. The main outcome was activation for self-management, measured with the 13-item Patient Activation Measure (PAM). Independent variables were sociodemographic variables, self-reported health status, depression, anxiety, illness perception, social support, disease severity, and comorbidities. Results: A total of 290 participants (age: 67.2±10.3; forced expiratory volume in 1 second predicted: 63.6±19.2) were eligible for analysis. While poor activation for self-management (PAM-1) was observed in 23% of the participants, only 15% was activated for self-management (PAM-4). Multiple linear regression analysis revealed six explanatory determinants of activation for self-management (P<0.2): anxiety (β: -0.35; -0.6 to -0.1), illness perception (β: -0.2; -0.3 to -0.1), body mass index (BMI) (β: -0.4; -0.7 to -0.2), age (β: -0.1; -0.3 to -0.01), Global Initiative for Chronic Obstructive Lung Disease stage (2 vs 1 β: -3.2; -5.8 to -0.5; 3 vs 1 β: -3.4; -7.1 to 0.3), and comorbidities (β: 0.8; -0.2 to 1.8), explaining 17% of the variance. Conclusion: This study showed that only a minority of COPD patients is activated for self-management. Although only a limited part of the variance could be explained, anxiety, illness perception, BMI, age, disease severity, and comorbidities were identified as key determinants of activation for self-management. This knowledge enables health care professionals to identify patients at risk of inadequate self-management, which is essential to move toward targeting and tailoring of self-management interventions. Future studies are needed to understand the complex causal mechanisms toward change in self-management. Keywords: COPD, self-management, patient activation, patient and disease characteristic