Decision Conflicts in Clinical Care during COVID-19: A Multi-Perspective Inquiry.

Background: The early COVID-19-pandemic was characterized by changes in decision making, decision-relevant value systems and the related perception of decisional uncertainties and conflicts resulting in decisional burden and stress. The vulnerability of clinical care professionals to these decisional dilemmas has not been characterized yet. Methods: A cross-sectional questionnaire study (540 patients, 322 physicians and 369 nurses in 11 institutions throughout Germany) was carried out. The inclusion criterion was active involvement in clinical treatment or decision making in oncology or psychiatry during the first year of COVID-19. The questionnaires covered five decision dimensions (conflicts and uncertainty, resources, risk perception, perception of consequences for clinical processes, and the perception of consequences for patients). Data analysis was performed using ANOVA, Pearson rank correlations, and the Chi²-test, and for inferential analysis, nominal logistic regression and tree classification were conducted. Results: Professionals reported changes in clinical management (27.5%) and a higher workload (29.2%), resulting in decisional uncertainty (19.2%) and decisional conflicts (22.7%), with significant differences between professional groups (p < 0.005), including anxiety, depression, loneliness and stress in professional subgroups (p < 0.001). Nominal regression analysis targeting "Decisional Uncertainty" provided a highly significant prediction model (LQ p < 0.001) containing eight variables, and the analysis for "Decisional Conflicts" included six items. The classification rates were 64.4% and 92.7%, respectively. Tree analysis confirmed three levels of determinants. Conclusions: Decisional uncertainty and conflicts during the COVID-19 pandemic were independent of the actual pandemic load. Vulnerable professional groups for the perception of a high number of decisional dilemmas were characterized by individual perception and the psychological framework. Coping and management strategies should target vulnerability, enable the handling of the individual perception of decisional dilemmas and ensure information availability and specific support for younger professionals.

Differences in Stakeholders' Perception of the Impact of COVID-19 on Clinical Care and Decision-Making.

BACKGROUND: Pandemics are related to changes in clinical management. Factors that are associated with individual perceptions of related risks and decision-making processes focused on prevention and vaccination, but perceptions of other healthcare consequences are less investigated. Different perceptions of patients, nurses, and physicians on consequences regarding clinical management, decisional criteria, and burden were compared. STUDY DESIGN: Cross-sectional OnCoVID questionnaire studies. METHODS: Data that involved 1231 patients, physicians, and nurses from 11 German institutions that were actively involved in clinical treatment or decision-making in oncology or psychiatry were collected. Multivariate statistical approaches were used to analyze the stakeholder comparisons. RESULTS: A total of 29.2% of professionals reported extensive changes in workload. Professionals in psychiatry returned severe impact of pandemic on all major aspects of their clinical care, but less changes were reported in oncology (p < 0.001). Both patient groups reported much lower recognition of treatment modifications and consequences for their own care. Decisional and pandemic burden was intensively attributed from professionals towards patients, but less in the opposite direction. CONCLUSIONS: All of the groups share concerns about the impact of the COVID-19 pandemic on healthcare management and clinical processes, but to very different extent. The perception of changes is dissociated in projection towards other stakeholders. Specific awareness should avoid the dissociated impact perception between patients and professionals potentially resulting in impaired shared decision-making.

COVID-19 vaccination rates in hospitalized mentally ill patients compared to the general population in Germany: Results from the COVID Ψ Vac study.

BACKGROUND: Mental illness is known to come along with a large mortality gap compared to thegeneral population and it is a risk for COVID-19 related morbidity andmortality. Achieving high vaccination rates in people with mental illness is therefore important. Reports are conflicting on whether vaccination rates comparable to those of the general population can be achieved and which variables represent risk factors for nonvaccination in people with mental illness. METHODS: The COVID Ψ Vac study collected routine data on vaccination status, diagnostic groups, sociodemographics, and setting characteristics from in- and day-clinic patients of 10 psychiatric hospitals in Germany in August 2021. Logistic regression modeling was used to determine risk factors for nonvaccination. RESULTS: Complete vaccination rates were 59% (n = 776) for the hospitalized patients with mental illness versus 64% for the regionally and age-matched general population. Partial vaccination rates were 68% (n = 893) for the hospitalised patients with mental illness versus 67% for the respective general population and six percentage (n = 74) of this hospitalized population were vaccinated during the hospital stay. Rates showed a large variation between hospital sites. An ICD-10 group F1, F2, or F4 main diagnosis, younger age, and coercive accommodation were further risk factors for nonvaccination in the model. CONCLUSIONS: Vaccination rates were lower in hospitalized people with mental illness than in the general population. By targeting at-risk groups with low-threshold vaccination programs in all health institutions they get in contact with, vaccination rates comparable to those in the general population can be achieved.

Decision Conflicts in Clinical Care during COVID-19: A Patient Perspective.

(1) Background: Uncertainty is typical for a pandemic or similar healthcare crisis. This affects patients with resulting decisional conflicts and disturbed shared decision making during their treatment occurring to a very different extent. Sociodemographic factors and the individual perception of pandemic-related problems likely determine this decisional dilemma for patients and can characterize vulnerable groups with special susceptibility for decisional problems and related consequences. (2) Methods: Cross-sectional data from the OnCoVID questionnaire study were used involving 540 patients from 11 participating institutions covering all major regions in Germany. Participants were actively involved in clinical treatment in oncology or psychiatry during the COVID-19 pandemic. Questionnaires covered five decision dimensions (conflicts and uncertainty, resources, risk perception, perception of consequences for clinical processes, perception of consequences for patients) and very basic demographic data (age, gender, stage of treatment and educational background). Decision uncertainties and distress were operationalized using equidistant five-point scales. Data analysis was performed using descriptive and various multivariate approaches. (3) Results: A total of 11.5% of all patients described intensive uncertainty in their clinical decisions that was significantly correlated with anxiety, depression, loneliness and stress. Younger and female patients and those of higher educational status and treatment stage had the highest values for these stressors (p < 0.001). Only 15.3% of the patients (14.9% oncology, 16.2% psychiatry; p = 0.021) considered the additional risk of COVID-19 infections as very important for their disease-related decisions. Regression analysis identified determinants for patients at risk of a decisional dilemma, including information availability, educational level, age group and requirement of treatment decision making. (4) Conclusions: In patients, the COVID-19 pandemic induced specific decisional uncertainty and distress accompanied by intensified stress and psychological disturbances. Determinants of specific vulnerability were related to female sex, younger age, education level, disease stages and perception of pandemic-related treatment modifications, whereas availability of sufficient pandemic-related information prevented these problems. The most important decisional criteria for patients under these conditions were expected side effects/complications and treatment responses.

Pharmacological and non-pharmacological interventions for adults with ADHD: protocol for a systematic review and network meta-analysis.

INTRODUCTION: It is unclear how pharmacological and non-pharmacological interventions compare with each other in terms of efficacy and tolerability for core symptoms and additional problems in adults with attention-deficit/hyperactivity disorder (ADHD). We aim to conduct the first network meta-analysis (NMA) comparing pharmacological and non-pharmacological interventions (or their combinations) in adults with ADHD. METHODS AND ANALYSIS: We will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for NMAs. We will search a broad set of electronic databases/registries and contact drug companies and experts in the field to retrieve published and unpublished randomised controlled trials (RCTs) (parallel or cross-over) of medications (either licensed or unlicensed) and any non-pharmacological intervention in adults (≥18 years) with ADHD. Primary outcomes will be: (1) change in severity of ADHD core symptoms, and (2) acceptability (all-cause discontinuation). Secondary outcomes will include tolerability (drop-out due to side effects) and change in the severity of emotional dysregulation, executive dysfunctions and quality of life. The risk of bias in each individual RCT included in the NMA will be assessed using the Cochrane Risk of Bias tool-version 2. We will evaluate the transitivity assumption comparing the distribution of possible effect modifiers across treatment comparisons. We will perform Bayesian NMA for each outcome with random-effects model in OpenBUGS. Pooled estimates of NMA will be obtained using the Markov Chains Monte Carlo method. We will judge the credibility in the evidence derived from the NMA using the CINeMA tool (which includes assessment of publication bias). We will conduct a series of sensitivity analyses to assess the robustness of the findings. ETHICS AND DISSEMINATION: As this is the protocol for an aggregate-data level NMA, ethical approval will not be required. Results will be disseminated at national/international conferences and in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42021265576.