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Access to medicines is a global priority. Azerbaijan, Georgia, and Uzbekistan have different approaches to pricing policies for pharmaceuticals. The aim of this study was to analyze recent trends in the consumption and prices of non-communicable disease (NCD) medicines in Azerbaijan, Georgia, and Uzbekistan, in the outpatient setting. We included medicines for asthma and COPD, cancer, cardiovascular disease, diabetes, epilepsy, and mental disorders. Sales data for pharmaceutical products in community pharmacies were extracted from a commercial database. Changes in consumption and prices were analyzed across all included NCD medicines, by disease category and pharmacological group. Consumption of NCD medicines was highest in Georgia, at twice the levels in Azerbaijan, and four times levels in Uzbekistan. Average prices of NCD medicines, weighted by consumption, increased by 26% in Georgia, but decreased by 3% in Azerbaijan and by 0.1% in Uzbekistan. Prices increased for all disease groups in Georgia (from +13% for epilepsy medicines to +86% for cancer), varied by group in Uzbekistan (from -22% for epilepsy medicines to +47% for cancer), while changes in Azerbaijan were smaller in magnitude (from -4% for medicines for cardiovascular disease to +11% for cancer). Cancer medicines had markedly higher prices in Uzbekistan, and asthma and COPD medicines had markedly higher prices in Azerbaijan and Uzbekistan. Georgia showed the highest outpatient consumption of NCD medicines, suggesting the broadest access to treatment. However, Georgia also saw marked price increases, greater than in the other countries. In Georgia, where there was no price regulation, widespread price increases and increases in consumption both contribute to increasing pharmaceutical expenditures. In Azerbaijan and Uzbekistan, increases in outpatient pharmaceutical expenditures were primarily driven by increases in consumption, rather than increases in price. Comparing trends in consumption and pricing can identify gaps in access and inform future policy approaches.

To compare and summarize the literature regarding infodemics and health misinformation, and to identify challenges and opportunities for addressing the issues of infodemics. We searched MEDLINE®, Embase®, Cochrane Library of Systematic Reviews, Scopus and Epistemonikos on 6 May 2022 for systematic reviews analysing infodemics, misinformation, disinformation and fake news related to health. We grouped studies based on similarity and retrieved evidence on challenges and opportunities. We used the AMSTAR 2 approach to assess the reviews' methodological quality. To evaluate the quality of the evidence, we used the Grading of Recommendations Assessment, Development and Evaluation guidelines. Our search identified 31 systematic reviews, of which 17 were published. The proportion of health-related misinformation on social media ranged from 0.2% to 28.8%. Twitter, Facebook, YouTube and Instagram are critical in disseminating the rapid and far-reaching information. The most negative consequences of health misinformation are the increase of misleading or incorrect interpretations of available evidence, impact on mental health, misallocation of health resources and an increase in vaccination hesitancy. The increase of unreliable health information delays care provision and increases the occurrence of hateful and divisive rhetoric. Social media could also be a useful tool to combat misinformation during crises. Included reviews highlight the poor quality of published studies during health crises. Available evidence suggests that infodemics during health emergencies have an adverse effect on society. Multisectoral actions to counteract infodemics and health misinformation are needed, including developing legal policies, creating and promoting awareness campaigns, improving health-related content in mass media and increasing people's digital and health literacy.

Digital technologies change the healthcare environment, with several studies suggesting barriers and facilitators to using digital interventions by healthcare professionals (HPs). We consolidated the evidence from existing systematic reviews mentioning barriers and facilitators for the use of digital health technologies by HP. Electronic searches were performed in five databases (Cochrane Database of Systematic Reviews, Embase®, Epistemonikos, MEDLINE®, and Scopus) from inception to March 2023. We included reviews that reported barriers or facilitators factors to use technology solutions among HP. We performed data abstraction, methodological assessment, and certainty of the evidence appraisal by at least two authors. Overall, we included 108 reviews involving physicians, pharmacists, and nurses were included. High-quality evidence suggested that infrastructure and technical barriers (Relative Frequency Occurrence [RFO] 6.4% [95% CI 2.9–14.1]), psychological and personal issues (RFO 5.3% [95% CI 2.2–12.7]), and concerns of increasing working hours or workload (RFO 3.9% [95% CI 1.5–10.1]) were common concerns reported by HPs. Likewise, high-quality evidence supports that training/educational programs, multisector incentives, and the perception of technology effectiveness facilitate the adoption of digital technologies by HPs (RFO 3.8% [95% CI 1.8–7.9]). Our findings showed that infrastructure and technical issues, psychological barriers, and workload-related concerns are relevant barriers to comprehensively and holistically adopting digital health technologies by HPs. Conversely, deploying training, evaluating HP’s perception of usefulness and willingness to use, and multi-stakeholders incentives are vital enablers to enhance the HP adoption of digital interventions.

Aim:To inform the primary care community about priorities for research in primary care as came up from the European project TO-REACH and to discuss transferability of service and policy innovations between countries.Background:TO-REACH stands for Transfer of Organizational innovations for Resilient, Effective, equitable, Accessible, sustainable and Comprehensive Health services and systems. This EU-funded project has put health systems and services research higher on the European agenda and has led to the current development of a European ‘Partnership Transforming Health and Care Systems’.Methods:To identify research priorities, both qualitative and quantitative approaches were used. Policy documents and strategic roadmaps were searched, and priorities were mapped. Stakeholders were involved through national roundtable consultations and online consultations. Regarding transferability, we carried out a review of the literature, guided by a conceptual framework, and using a snowballing approach.Findings:Primary care emerged as an important priority from the inventory, as are areas that are conducive to strengthening primary care, such as workforce policies. The large variation in service organisation and policy around primary care in Europe is a huge potential for cross-country learning. However, the simple transfer of primary care service and policy arrangements from one health system to another has a big chance to fail, unless known conditions for successful transfer are taken into account and gaps in our knowledge about transfer are resolved.

Several systematic reviews evaluating the use of telemedicine by clinicians, patients, and health authorities to improve the delivery of care in the 53 member states of the World Health Organization (WHO) European Region have been conducted in recent years. However, a study summarizing the findings of these reviews has not been conducted. This overview of systematic reviews aimed to summarize findings regarding the use of telemedicine across the 53 member states and identify the medical fields and levels of care in and at which the effectiveness, feasibility, and applicability of telemedicine have been demonstrated. The barriers to and facilitators of telemedicine use were also evaluated and collated to help with the design and implementation of telemedicine interventions. Through a comprehensive systematic evaluation of the published and unpublished literature, we extracted clinical, epidemiological, and technology-related data from each review included in the study. We focused on evaluating the barriers to and facilitators of the use of telemedicine apps across the 53 member states considered. We rated the methodological quality of each of the included reviews based on A Measurement Tool to Assess Systematic Review 2 approach and judged the overall certainty of evidence by using the Grading of Recommendations, Assessment, Development, and Evaluations methodology. The entire process was performed by 2 independent authors. This overview drew on data from >2239 primary studies, with >20,000 enrolled patients in total, within the WHO European Region. On the basis of data from randomized trials, observational studies, and economic evaluations from several countries, the results show a clear benefit of telemedicine technologies in the screening, diagnosis, management, treatment, and long-term follow-up of a series of chronic diseases. However, we were unable to pool the results into a reliable numeric parameter because of the high heterogeneity of intervention methodologies, scheduling, primary study design discrepancies, settings, and geographical locations. In addition to the clinical outcomes of the interventions, the social and economic outcomes are highlighted. The application of telemedicine is well established across countries in the WHO European Region; however, some countries could still benefit from the many uses of these digital solutions. Barriers related to users, technology, and infrastructure were the largest. Conversely, the provision of health services using technological devices was found to significantly enhance patients' clinical outcomes, improve the long-term follow-up of patients by medical professionals, and offer logistical benefits for both patients and health workers. PROSPERO (International Prospective Register of Systematic Reviews) CRD42022309375; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=309375.

Background Governments across the World Health Organization (WHO) European Region have prioritised dashboards for reporting COVID-19 data. The ubiquitous use of dashboards for public reporting is a novel phenomenon. Objective This study explores the development of COVID-19 dashboards during the first year of the pandemic and identifies common barriers, enablers and lessons from the experiences of teams responsible for their development. Methods We applied multiple methods to identify and recruit COVID-19 dashboard teams, using a purposive, quota sampling approach. Semi-structured group interviews were conducted from April to June 2021. Using elaborative coding and thematic analysis, we derived descriptive and explanatory themes from the interview data. A validation workshop was held with study participants in June 2021. Results Eighty informants participated, representing 33 national COVID-19 dashboard teams across the WHO European Region. Most dashboards were launched swiftly during the first months of the pandemic, February to May 2020. The urgency, intense workload, limited human resources, data and privacy constraints and public scrutiny were common challenges in the initial development stage. Themes related to barriers or enablers were identified, pertaining to the pre-pandemic context, pandemic itself, people and processes and software, data and users. Lessons emerged around the themes of simplicity, trust, partnership, software and data and change. Conclusions COVID-19 dashboards were developed in a learning-by-doing approach. The experiences of teams reveal that initial underpreparedness was offset by high-level political endorsement, the professionalism of teams, accelerated data improvements and immediate support with commercial software solutions. To leverage the full potential of dashboards for health data reporting, investments are needed at the team, national and pan-European levels.

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.