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BackgroundCommunity-based recovery-oriented mental health services for people with severe mental disorders have not been fully implemented in Bulgaria, Croatia, Macedonia, Montenegro, and Romania. The RECOVER-E project facilitates the implementation of specialized mental health care delivered by setting up services, implementing the services, and evaluating multidisciplinary community mental health teams. The outcomes of the RECOVER-E project are assessed in a trial-based outcome evaluation in each of the participating countries with a health-economic evaluation linked to these trials. ObjectiveThe aim of this protocol paper is to describe the methodology that will be used for the health-economic evaluation alongside the trials. MethodsImplementation sites have been selected in each of the five countries where hospital-based mental health services are available (care as usual [CAU]) for patients with severe mental disorders (severe depression, bipolar disorder, schizophrenia, and other psychotic disorders). The newly implemented health care system will involve community-based recovery-oriented mental health care (CMHC). At each site, 180 consenting patients will be randomized to either CAU or CMHC. Patient-level outcomes are personal and social functioning and quality-adjusted life years (QALYs). Data on participants’ health care use will be collected and corresponding health care costs will be computed. This enables evaluation of health care costs of CMHC as compared with CAU, and these costs can be related to patient-level outcomes (functioning and QALY gains) in health-economic evaluation. ResultsData collection was started in December 2018 (Croatia), February 2019 (Montenegro), April 2019 (Romania), June 2019 (North Macedonia), and October 2019 (Bulgaria). The findings of the outcome evaluations will be reported for each of the five countries separately, and the five trials will be pooled for multilevel analysis on a combined dataset. ConclusionsThe results of the health-economic evaluation of the RECOVER-E project will contribute to the growing evidence base on the health and economic benefits of recovery-oriented and community-based service models for health systems in transition. Trial Registration(1) ClinicalTrials.gov NCT03922425 (Bulgaria); https://clinicaltrials.gov/ct2/show/NCT03922425 (2) ClinicalTrials.gov NCT03862209 (Croatia); https://clinicaltrials.gov/ct2/show/NCT03862209 (3) ClinicalTrials.gov NCT03892473 (Macedonia); https://clinicaltrials.gov/ct2/show/NCT03892473 (4) ClinicalTrials.gov NCT03837340 (Montenegro); https://clinicaltrials.gov/ct2/show/NCT03837340 (5) ClinicalTrials.gov NCT03884933 (Romania); https://clinicaltrials.gov/ct2/show/NCT03884933 International Registered Report Identifier (IRRID)DERR1-10.2196/17454

The COVID-19 pandemic has disrupted medical care systems, by decreasing patient addressability to outpatient care. The main objective of this study was to compare the patient’s addressability to breast imaging techniques for diagnosis, and follow-up in the Clinical Emergency County Hospital of Craiova, Romania. We selected the mammographies performed over a period of 4 years (2018-2021) in our clinic. We divided the patients into four groups, one for each year (2018, 2019, 2020, 2021). Furtherly, we merged the data into two groups, one group for the pre-pandemic years (2018 and 2019) and one for the pandemic years (2020 and 2021). In our clinic, the number of mammographies plummeted to 0 during the month of April 2020 due to the lockdown and closure of non-urgent outpatient services in hospitals treating COVID-19 patients, and slowly creeped to 11 in the month of May and peaked to 160 in July (for the rest of the year). There was a huge difference regarding the patient’s addressability to mammography immediately after the lockdown, with a 95.2% less addressability compared to the pre-pandemic period (May 2020 compared to May 2018). As an overall, by comparing both pre-pandemic years included in the study with the pandemic years, we obtained an addressability reduced with 37.3% suggesting the possible future delays in diagnosing breast tumors. [ABSTRACT FROM AUTHOR]

Background: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a clinical illness Covid-19, has had a major impact on mental health globally. Those diagnosed with major depressive disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings of loneliness or lack of access to care. This study seeks to assess the impact of the 1st lockdown – pre-, during and post – in adults with a recent history of MDD across multiple centres. Methods: This study is a secondary analysis of an on-going cohort study, RADAR-MDD project, a multi-centre study examining the use of remote measurement technology (RMT) in monitoring MDD. Self-reported questionnaire and passive data streams were analysed from participants who had joined the project prior to 1st December 2019 and had completed Patient Health and Self-esteem Questionnaires during the pandemic (n = 252). We used mixed models for repeated measures to estimate trajectories of depressive symptoms, self-esteem, and sleep duration. Results: In our sample of 252 participants, 48% (n = 121) had clinically relevant depressive symptoms shortly before the pandemic. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem changed between pre-, during- and post-lockdown. However, we found evidence that mean sleep duration (in minutes) decreased significantly between during- and post- lockdown (− 12.16; 95% CI − 18.39 to − 5.92; p < 0.001). We also found that those experiencing clinically relevant depressive symptoms shortly before the pandemic showed a decrease in depressive symptoms, self-esteem and sleep duration between pre- and during- lockdown (interaction p = 0.047, p = 0.045 and p < 0.001, respectively) as compared to those who were not. Conclusions: We identified changes in depressive symptoms and sleep duration over the course of lockdown, some of which varied according to whether participants were experiencing clinically relevant depressive symptoms shortly prior to the pandemic. However, the results of this study suggest that those with MDD do not experience a significant worsening in symptoms during the first months of the Covid − 19 pandemic. [ABSTRACT FROM AUTHOR]

Background: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features., Objective: We aimed to address these 3 challenges to inform future work in stratified analyses., Methods: Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model., Results: We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression., Conclusions: This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses., (©Shaoxiong Sun, Amos A Folarin, Yuezhou Zhang, Nicholas Cummins, Rafael Garcia-Dias, Callum Stewart, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Petroula Laiou, Heet Sankesara, Faith Matcham, Daniel Leightley, Katie M White, Carolin Oetzmann, Alina Ivan, Femke Lamers, Sara Siddi, Sara Simblett, Raluca Nica, Aki Rintala, David C Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda W J H Penninx, Srinivasan Vairavan, Vaibhav A Narayan, Peter Annas, Matthew Hotopf, Richard J B Dobson, RADAR-CNS Consortium. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.08.2023.)