COMPAR-EU Recommendations on Self-Management Interventions in Type 2 Diabetes Mellitus.

Self-management interventions (SMIs) offer a promising approach to actively engage patients in the management of their chronic diseases. Within the scope of the COMPAR-EU project, our goal is to provide evidence-based recommendations for the utilisation and implementation of SMIs in the care of adult individuals with type 2 diabetes mellitus (T2DM). A multidisciplinary panel of experts, utilising a core outcome set (COS), identified critical outcomes and established effect thresholds for each outcome. The panel formulated recommendations using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach, a transparent and rigorous framework for developing and presenting the best available evidence for the formulation of recommendations. All recommendations are based on systematic reviews (SR) of the effects and of values and preferences, a contextual analysis, and a cost-effectiveness analysis. The COMPAR-EU panel is in favour of using SMIs rather than usual care (UC) alone (conditional, very low certainty of the evidence). Furthermore, the panel specifically is in favour of using ten selected SMIs, rather than UC alone (conditional, low certainty of the evidence), mostly encompassing education, self-monitoring, and behavioural techniques. The panel acknowledges that, for most SMIs, moderate resource requirements exist, and cost-effectiveness analyses do not distinctly favour either the SMI or UC. Additionally, it recognises that SMIs are likely to enhance equity, deeming them acceptable and feasible for implementation.

Effectiveness and Cost-Effectiveness of Self-Management Interventions for Adults Living with Heart Failure to Improve Patient-Important Outcomes: An Evidence Map of Randomized Controlled Trials.

Self-management interventions (SMIs) may enhance heart failure (HF) outcomes and address challenges associated with disease management. This study aims to review randomized evidence and identify knowledge gaps in SMIs for adult HF patients. Within the COMPAR-EU project, from 2010 to 2018, we conducted searches in the databases MEDLINE, CINAHL, Embase, Cochrane, and PsycINFO. We performed a descriptive analysis using predefined categories and developed an evidence map of randomized controlled trials (RCTs). We found 282 RCTs examining SMIs for HF patients, comparing two to four interventions, primarily targeting individual patients (97%) globally (34 countries, only 31% from an European country). These interventions involved support techniques such as information sharing (95%) and self-monitoring (62%), often through a mix of in-person and remote sessions (43%). Commonly assessed outcomes included quality of life, hospital admissions, mortality, exercise capacity, and self-efficacy. Few studies have focused on lower socio-economic or minority groups. Nurses (68%) and physicians (30%) were the primary providers, and most studies were at low risk of bias in generating a random sequence for participant allocation; however, the reporting was noticeably unclear of methods used to conceal the allocation process. Our analysis has revealed prevalent support techniques and delivery methods while highlighting methodological challenges. These findings provide valuable insights for researchers, clinicians, and policymakers striving to optimize SMIs for individuals living with HF.

Contextual factors for the successful implementation of self-management interventions for chronic diseases: A qualitative review of reviews.

OBJECTIVES: To identify and describe the most relevant contextual factors (CFs) from the literature that influence the successful implementation of self-management interventions (SMIs) for patients living with type 2 diabetes mellitus, obesity, COPD and/or heart failure. METHODS: We conducted a qualitative review of reviews. Four databases were searched, 929 reviews were identified, 460 screened and 61 reviews met the inclusion criteria. CFs in this paper are categorized according to the Tailored Implementation for Chronic Diseases framework. RESULTS: A great variety of CFs was identified on several levels, across all four chronic diseases. Most CFs were on the level of the patient, the professional and the interaction level, while less CFs were obtained on the level of the intervention, organization, setting and national level. No differences in main themes of CFs across all four diseases were found. DISCUSSION: For the successful implementation of SMIs, it is crucial to take CFs on several levels into account simultaneously. Person-centered care, by tailoring SMIs to patients' needs and circumstances, may increase the successful uptake, application and implementation of SMIs in real-life practice. The next step will be to identify the most important CFs according to various stakeholders through a group consensus process.

Self-Management Interventions for Adults Living with Type II Diabetes to Improve Patient-Important Outcomes: An Evidence Map.

Self-management interventions (SMIs) may be promising in the treatment of Diabetes Mellitus Type 2 (T2DM). However, accurate comparisons of their relative effectiveness are challenging, partly due to a lack of clarity and detail regarding the intervention content being evaluated. This study summarizes intervention components and characteristics in randomized controlled trials (RCTs) related to T2DM using a taxonomy for SMIs as a framework and identifies components that are insufficiently incorporated into the design of the intervention or insufficiently reported. Following evidence mapping methodology, we searched MEDLINE, CINAHL, Embase, Cochrane, and PsycINFO from 2010 to 2018 for randomized controlled trials (RCTs) on SMIs for T2DM. We used the terms 'self-management', 'adult' and 'T2DM' for content. For data extraction, we used an online platform based on the taxonomy for SMIs. Two independent reviewers assessed eligible references; one reviewer extracted data, and a second checked accuracy. We identified 665 RCTs for SMIs (34% US, 21% Europe) including 164,437 (median 123, range 10-14,559) adults with T2DM. SMIs highly differed in design and content, and characteristics such as mode of delivery, intensity, location and providers involved were poorly described. The majority of interventions aimed to improve clinical outcomes like HbA1c (83%), weight (53%), lipid profile (45%) or blood pressure (42%); 27% (also) targeted quality of life. Improved knowledge, health literacy, patient activation or satisfaction with care were hardly used as outcomes (<16%). SMIs most often used education (98%), self-monitoring (56%), goal-setting (48%) and skills training (42%) to improve outcomes. Management of emotions (17%) and shared decision-making (5%) were almost never mentioned. Although diabetes is highly prevalent in some minority groups, in only 13% of the SMIs, these groups were included. Our findings highlight the large heterogeneity that exists in the design of SMIs for T2DM and the way studies are reported, making accurate comparisons of their relative effectiveness challenging. In addition, SMIs pay limited attention to outcomes other than clinical, despite the importance attached to these outcomes by patients. More standardized and streamlined research is needed to better understand the effectiveness and cost-effectiveness of SMIs of T2DM and benefit patient care.

Can peer effects explain prescribing appropriateness? a social network analysis.

BACKGROUND: Optimizing prescribing practices is important due to the substantial clinical and financial costs of polypharmacy and an increasingly aging population. Prior research shows the importance of social relationships in driving prescribing behaviour. Using social network analysis, we examine the relationship between a physician practices' connectedness to peers and their prescribing performance in two German regions. METHODS: We first mapped physician practice networks using links established between two practices that share 8 or more patients; we calculated network-level (density, average path length) and node-level measures (degree, betweenness, eigenvector). We defined prescribing performance as the total number of inappropriate medications prescribed or appropriate medications not prescribed (PIMs) to senior patients (over the age of 65) during the calendar year 2016. We used FORTA (Fit fOR The Aged) algorithm to classify medication appropriateness. Negative binomial regression models estimate the association between node-level measures and prescribing performance of physician practices controlling for patient comorbidity, provider specialization, percentage of seniors in practice, and region. We conducted two sensitivity analyses to test the robustness of our findings - i) limiting the network mapping to patients younger than 65; ii) limiting the network ties to practices that share more than 25 patients. RESULTS: We mapped two patient-sharing networks including 436 and 270 physician practices involving 28,508 and 20,935 patients and consisting of 217,126 and 154,274 claims in the two regions respectively. Regression analyses showed a practice's network connectedness as represented by degree, betweenness, and eigenvector centrality, is significantly negatively associated with prescribing performance (degree-bottom vs. top quartile aRR = 0.04, 95%CI: 0.035,0.045; betweenness-bottom vs. top quartile aRR = 0.063 95%CI: 0.052,0.077; eigenvector-bottom vs. top quartile aRR = 0.039, 95%CI: 0.034,0.044). CONCLUSIONS: Our study provides evidence that physician practice prescribing performance is associated with their peer connections and position within their network. We conclude that practices occupying strategic positions at the edge of networks with advantageous access to novel information are associated with better prescribing outcomes, whereas highly connected practices embedded in insulated information environments are associated with poor prescribing performance.

Identifying Factors to Facilitate the Implementation of Decision-Making Tools to Promote Self-Management of Chronic Diseases into Routine Healthcare Practice: A Qualitative Study.

This study, as part of the COMPAR-EU project, utilized a mixed-methods approach involving 37 individual, semi-structured interviews and one focus group with 7 participants to investigate the factors influencing the implementation and use of self-management interventions (SMIs) decision tools in clinical practice. The interviews and focus group discussions were guided by a tailored interview and focus group guideline developed based on the Tailored Implementation for Chronic Diseases (TICD) framework. The data were analyzed using a directed qualitative content analysis, with a deductive coding system based on the TICD framework and an inductive coding process. A rapid analysis technique was employed to summarize and synthesize the findings. The study identified five main dimensions and facilitators for implementation: decision tool factors, individual health professional factors, interaction factors, organizational factors, and social, political, and legal factors. The findings highlight the importance of structured implementation through SMI decision support tools, emphasizing the need to understand their benefits, secure organizational resources, and gain political support for sustainable implementation. Overall, this study employed a systematic approach, combining qualitative methods and comprehensive analysis, to gain insights into the factors influencing the implementation of SMIs' decision-support tools in clinical practice.

Patients' and informal caregivers' perspectives on self-management interventions for type 2 diabetes mellitus outcomes: a mixed-methods overview of 14 years of reviews.

BACKGROUND: Self-management interventions (SMIs) are core components of high-quality care in type 2 diabetes mellitus (T2DM). We aimed to identify and summarise the scientific evidence exploring the perspectives of patients with T2DM and their informal caregivers on outcomes of SMIs, and the key themes to enhance T2DM patient-centred care. METHODS: We conducted a mixed-methods overview of reviews. We searched MEDLINE, CINAHL and PsycINFO, up to June 2021 for systematic reviews (SRs) exploring the perspectives of adults with T2DM and their informal caregivers, regarding self-management. Two reviewers conducted independently study selection, data extraction and quality assessment. We estimated the degree of overlap across SRs. We performed a qualitative analysis using a thematic synthesis approach. RESULTS: We identified 54 SRs, corresponding to 939 studies, with a slight overlap. Most SRs (47/54, 87%) were considered high quality. We developed summaries for 22 outcomes and identified six overarching themes: (1) diabetic identity; (2) accessing healthcare; (3) experience of care; (4) engagement with self-management; (5) outcomes awareness; and (6) challenges adhering to self-management. We found important variability in how patients with T2DM and their informal caregivers value critical outcomes influenced by the disease progression and several contextual factors. CONCLUSIONS: Our findings represent what matters most to patients with T2DM and their informal caregivers regarding outcomes of SMIs. Our results can facilitate the development and evaluation of SMIs, and guide decision-making in diabetes care, including the formulation of decisions and recommendations.

Cooperation Improvement in an Integrated Healthcare Network: A Social Network Analysis.

Background: Cooperation is a core feature of integrated healthcare systems and an important link in their value-creating mechanism. The premise is that providers who cooperate can promote more efficient use of health services while improving health outcomes. We studied the performance of an integrated healthcare system in improving regional cooperation. Methods: Using claims data and social network analysis, we constructed the professional network from 2004 to 2017. Cooperation was studied by analyzing the evolution of network properties at network and physician practice (node) level. The impact of the integrated system was studied with a dynamic panel model that compared practices that participated in the integrated system versus nonparticipants. Results: The regional network evolved favourably towards cooperation. Network density increased 1.4% on average per year, while mean distance decreased 0.78%. At the same time, practices participating in the integrated system became more cooperative compared to other practices in the region: Degree (1.64e-03, p = 0.07), eigenvector (3.27e-03, p = 0.06) and betweenness (4.56e-03, p < 0.001) centrality increased more for participating practices. Discussion: Findings can be explained by the holistic approach to patients' care needs and coordination efforts of integrated healthcare. The paper provides a valuable design for performance assessment of professional cooperation. Highlights: Using claims data and social network analysis, we identify a regional cooperation network and conduct a panel analysis to measure the impact of an integrated care initiative on enhancing professional cooperation.Physician practices participating in the integrated system became more cooperative and improved their influence in the regional network more than non-participating practices.Integrated healthcare systems effectively incentivize cooperation through a holistic approach to patient care needs and coordination efforts.

Identifying most important contextual factors for the implementation of self-management interventions: A Delphi study.

OBJECTIVE: To reach consensus amongst stakeholders about the most important contextual factors (CFs) that may influence the successful implementation of (components of) self-management interventions (SMIs) for type 2 diabetes, obesity, COPD and heart failure. METHODS: Building on our literature review that identified 31 CFs on different levels we conducted a Delphi with 44 stakeholders to identify which of these CFs, or additional ones, contribute most to successful implementation of SMIs. The Delphi consisted of three rounds in which the CFs were scored, prioritized and discussed. RESULTS: The most important CFs overlapped to a great extent across components of SMIs and diseases. Overall, stakeholders identified 'HCP's ability to adapt the advice, communication or intervention to patients' situation and level of knowledge' as most important CF. CONCLUSION: CFs need to be taken into account when implementing promising SMIs. According to stakeholders, the most important CFs are patient-, HCP- or interaction related. 'Tailoring' was selected as the most crucial aspect for HCPs. PRACTICE IMPLICATIONS: Stakeholders can make informed decisions on the adoption of the most suitable SMIs in a given context. These CFs are available through a self-management platform. Suggestions to implement self-management behaviour and to close the research-to-practice gap are made.

Improving the evaluation of an integrated healthcare system using entropy balancing: Population health improvements in Gesundes Kinzigtal.

Background: Evidence of integrated healthcare networks' effect on population health is scarce. Moreover, current designs for evaluating such networks have shortcomings that can result in misleading conclusions. Our paper evaluates Gesundes Kinzigtal, a best-practice integrated healthcare network, using an innovative design that enlightens the discussion about health gains produced by integrated healthcare. Research question: What is the effect of Gesundes Kinzigtal on population health? Methods: We evaluated the effect of the integrated healthcare initiative by performing a quasi-experimental evaluation based on entropy balancing. Integrated network participants were compared to a control group and followed for five years. Claims data from 2004 to 2018 was used. Population health outcomes correspond to survival (Cox hazard ratio, Kaplan-Meier curve), mortality ratio, mean age at the time of death, and years of life lost or gained. Design validity was evaluated by assessing group balance at baseline. Finally, we compared our results to those obtained using a previously published design for evaluating integrated networks. Results: The treatment group was composed of 9083 network participants, compared to an equivalent control group, showing, respectively, a mortality ratio of 5.4% vs 7.5% (p < 0.05), mean age at the time of death of 80.1 vs 80.3 (p > 0.05) and a gain of 0.2 years of life per person for the treatment group (p > 0.05). The Cox hazard ratio (0.72; p < 0.05) and mean survival time (1784 vs 1768 days; p < 0.05) showed better survival for treated participants. Results using the previously published design were more favorable for the treatment group; however, the design excluded participants significantly associated with greater healthcare needs. Discussion: The integrated network had a favorable effect on participants' mortality and survival risk. Previous evaluations based on propensity score matching might overestimate the network's impact on population health by excluding participants with greater healthcare needs.

Big Data Analytics to Reduce Preventable Hospitalizations-Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions.

The purpose of this study was to develop a prediction model to identify individuals and populations with a high risk of being hospitalized due to an ambulatory care-sensitive condition who might benefit from preventative actions or tailored treatment options to avoid subsequent hospital admission. A rate of 4.8% of all individuals observed had an ambulatory care-sensitive hospitalization in 2019 and 6389.3 hospital cases per 100,000 individuals could be observed. Based on real-world claims data, the predictive performance was compared between a machine learning model (Random Forest) and a statistical logistic regression model. One result was that both models achieve a generally comparable performance with c-values above 0.75, whereas the Random Forest model reached slightly higher c-values. The prediction models developed in this study reached c-values comparable to existing study results of prediction models for (avoidable) hospitalization from the literature. The prediction models were designed in such a way that they can support integrated care or public and population health interventions with little effort with an additional risk assessment tool in the case of availability of claims data. For the regions analyzed, the logistic regression revealed that switching to a higher age class or to a higher level of long-term care and unit from prior hospitalizations (all-cause and due to an ambulatory care-sensitive condition) increases the odds of having an ambulatory care-sensitive hospitalization in the upcoming year. This is also true for patients with prior diagnoses from the diagnosis groups of maternal disorders related to pregnancy, mental disorders due to alcohol/opioids, alcoholic liver disease and certain diseases of the circulatory system. Further model refinement activities and the integration of additional data, such as behavioral, social or environmental data would improve both model performance and the individual risk scores. The implementation of risk scores identifying populations potentially benefitting from public health and population health activities would be the next step to enable an evaluation of whether ambulatory care-sensitive hospitalizations can be prevented.

Self-management interventions for adults living with obesity to improve patient-relevant outcomes: An evidence map.

OBJECTIVES: To conduct an evidence map on self-management interventions and patient-relevant outcomes for adults living with overweight/obesity. METHODS: Following Arksey and O'Malley methodology, we searched in five electronical databases including randomized controlled trials (RCTs) on SMIs for overweight/obesity. We used the terms "self-management", "adult" and "obesity" for content. Two independent reviewers assessed eligible references; one reviewer extracted data, a second checked accuracy. RESULTS: We identified 497 RCTs (58% US, 20% Europe) including 99,741 (median 112, range 11-5145) adults living with overweight/obesity. Most research evaluated clinical outcomes (617, 55%) and behaviors adherence (255, 23%). Empowerment skills, quality of life and satisfaction were less targeted (8%, 7%, 0.2%, respectively). The most frequent techniques included sharing information (858, 99%), goal setting (619, 72%) and self-monitoring training (614, 71%), provided face-to-face (386, 45%) or in combination with remote techniques (256, 30%). Emotional management, social support and shared-decision were less frequent (18%, 26%, 4%). Socio-economic status, minorities or health literacy were seldom reported. CONCLUSION: There is a need of widening the scope of research by focusing on outcomes important to patients, assessing emotional/social/share-decision support, exploring remote techniques and including vulnerable populations.

Assessment of the Effectiveness, Socio-Economic Impact and Implementation of a Digital Solution for Patients with Advanced Chronic Diseases: The ADLIFE Study Protocol.

Due to population ageing and medical advances, people with advanced chronic diseases (ACD) live longer. Such patients are even more likely to face either temporary or permanent reduced functional reserve, which typically further increases their healthcare resource use and the burden of care on their caregiver(s). Accordingly, these patients and their caregiver(s) may benefit from integrated supportive care provided via digitally supported interventions. This approach may either maintain or improve their quality of life, increase their independence, and optimize the healthcare resource use from early stages. ADLIFE is an EU-funded project, aiming to improve the quality of life of older people with ACD by providing integrated personalized care via a digitally enabled toolbox. Indeed, the ADLIFE toolbox is a digital solution which provides patients, caregivers, and health professionals with digitally enabled, integrated, and personalized care, supporting clinical decisions, and encouraging independence and self-management. Here we present the protocol of the ADLIFE study, which is designed to provide robust scientific evidence on the assessment of the effectiveness, socio-economic, implementation, and technology acceptance aspects of the ADLIFE intervention compared to the current standard of care (SoC) when applied in real-life settings of seven different pilot sites across six countries. A quasi-experimental trial following a multicenter, non-randomized, non-concurrent, unblinded, and controlled design will be implemented. Patients in the intervention group will receive the ADLIFE intervention, while patients in the control group will receive SoC. The assessment of the ADLIFE intervention will be conducted using a mixed-methods approach.

The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data.

Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high-cost patients in the following year. Therefore, we exploited routinely collected sickness funds claims and cost data of the years 2016, 2017 and 2018. Various specifications of each algorithm were trained and cross-validated on training data (n = 20,984) with claims and cost data from 2016 and outcomes from 2017. The best performing specifications of each algorithm were selected based on validation dataset performance. For performance comparison, selected models were applied to unforeseen data with features of the year 2017 and outcomes of the year 2018 (n = 21,146). The RF was the best performing algorithm measured by the area under the receiver operating curve (AUC) with a value of 0.883 (95% confidence interval (CI): 0.872-0.893) on test data, followed by the GBM (AUC = 0.878; 95% CI: 0.867-0.889). The ANN (AUC = 0.846; 95% CI: 0.834-0.857) and LR (AUC = 0.839; 95% CI: 0.826-0.852) were significantly outperformed by the GBM and the RF. All ML algorithms and the LR performed ´good´ (i.e. 0.9 > AUC ≥ 0.8). We were able to develop machine learning models that predict high-cost patients with 'good' performance facilitating routinely collected sickness fund claims and cost data. We found that tree-based models performed best and outperformed the ANN and LR.

Patient expectations do matter - Experimental evidence on antibiotic prescribing decisions among hospital-based physicians.

BACKGROUND: The global public health crisis of antibiotic resistance is being driven in part by over prescription of antibiotics. We aimed to assess the relative weight of patient expectations, clinical uncertainty, and past behaviour on hospital-based physicians' antibiotic prescribing decisions. METHODS: A discrete choice experiment was administered among hospital-based physicians in Tuscany, Italy. Respondents were asked to choose in which of two clinical scenarios they would be more likely to prescribe antibiotics, with the two cases differing in levels of clinical uncertainty, patient expectations, and the physician's past behaviour. We fitted a conditional logistic regression. RESULTS: Respondents included 1,436 hospital-based physicians. Results show that the odds of prescribing antibiotics decrease when a patient requests it (OR=0.80, 95%CI [0.72,0.89]) and increase when the physician has prescribed antibiotics to a patient under similar circumstances previously (OR=1.15, 95%CI [1.03,1.27]). We found no significant effect of clinical uncertainty on the odds of prescribing antibiotics (OR=0.96, 95%CI [0.87, 1.07]). CONCLUSIONS: We show that patient expectation has a significant negative association with antibiotic prescribing among hospital-based physicians. Our findings speak to the importance of cultural context in shaping the physician's disposition when confronted with patient expectations. We suggest shared decision-making to improve prudent prescribing without compromising on patient satisfaction.

"It depends on the people!" - A qualitative analysis of contextual factors, prior to the implementation of digital health innovations for chronic condition management, in a German integrated care network.

Objective: Integrated care and digital health technology interventions are promising approaches to coordinate services for people living with chronic conditions, across different care settings and providers. The EU-funded ADLIFE project intends to provide digitally integrated personalized care to improve and maintain patients' health with advanced chronic conditions. This study conducted a qualitative assessment of contextual factors prior to the implementation of the ADLIFE digital health platforms at the German pilot site. The results of the assessment are then used to derive recommendations for action for the subsequent implementation, and for evaluation of the other pilot sites. Methods: Qualitative interviews with healthcare professionals and IT experts were conducted at the German pilot site. The interviews followed a semi-structured interview guideline, based on the HOT-fit framework, focusing on organizational, technological, and human factors. All interviews were audio recorded, transcribed, and subsequently analysed following qualitative content analysis. Results: The results of the 18 interviews show the interviewees' high openness and motivation to use new innovative digital solutions, as well as an apparent willingness of cooperation between different healthcare professionals. Challenges include limited technical infrastructure and large variability of software to record health data, lacking standards and interfaces. Conclusions: Considering contextual factors on different levels is critical for the success of implementing innovations in healthcare and the transfer into other settings. In our study, the HOT-fit framework proved suitable for assessing contextual factors, when implementing IT innovations in healthcare. In a next step, the methodological approach will be transferred to the six other European pilot sites, participating in the project, for a cross-national assessment of contextual factors.

Using a Taxonomy to Systematically Identify and Describe Self-Management Interventions Components in Randomized Trials for COPD.

Self-management interventions (SMIs) may improve outcomes in Chronic Obstructive Pulmonary Disease (COPD). However, accurate comparisons of their relative effectiveness are challenging, partly due to a lack of clarity and detail regarding the intervention content being evaluated. This study systematically describes intervention components and characteristics in randomized controlled trials (RCTs) related to COPD self-management using the COMPAR-EU taxonomy as a framework, identifying components that are insufficiently incorporated into the design of the intervention or insufficiently reported. Overall, 235 RCTs published between 2010 and 2018, from a systematic review were coded using the taxonomy, which includes 132 components across four domains: intervention characteristics, expected patient (or caregiver) self-management behaviours, patient relevant outcomes, and target population characteristics. Risk of bias was also assessed. Interventions mainly focused on physical activity (67.4%), and condition-specific behaviours like breathing exercise (63.5%), self-monitoring (50.8%), and medication use (33.9%). Support techniques like education and skills-training, self-monitoring, and goal setting (over 35% of the RCTs) were mostly used for this. Emotional-based techniques, problem-solving, and shared decision-making were less frequently reported (less than 15% of the studies). Numerous SMIs components were insufficiently incorporated into the design of COPD SMIs or insufficiently reported. Characteristics like mode of delivery, intensity, location, and providers involved were often not described. Only 8% of the interventions were tailored to the target population's characteristics. Outcomes that are considered important by patients were hardly taken into account. There is still a lot to improve in both the design and description of SMIs for COPD. Using a framework such as the COMPAR-EU SMI taxonomy may contribute to better reporting and to better informing of replication efforts. In addition, prospective use of the taxonomy for developing and reporting intervention content would further aid in building a cumulative science of effective SMIs in COPD.

Assessing the outcomes and experiences of care from the perspective of people living with chronic conditions, to support countries in developing people-centred policies and practices: study protocol of the International Survey of People Living with Chronic Conditions (PaRIS survey).

INTRODUCTION: In view of growing populations with chronic conditions, many countries are redesigning their health systems. However, little information is available about how health systems perform from the perspective of people living with chronic conditions. The Organisation for Economic Co-operation and Development (OECD) Member States therefore mandated the OECD to initiate the International Survey of People Living with Chronic Conditions (PaRIS survey), which aims to provide insight in outcomes and experiences of care as reported by people living with chronic conditions. The PaRIS-SUR consortium has been tasked by the OECD to support the development and implementation of the survey. METHODS AND ANALYSIS: As primary care services play a pivotal role in the management of chronic conditions, the PaRIS survey will be implemented in the primary care setting. Data will be collected with a survey among users of primary care services aged 45 years or older, of whom many have chronic conditions. An additional survey is conducted among their primary care providers. The nested study design will allow analysis of the patient-reported data in relation to characteristics of and care provided by primary care providers within and across countries. In 2022, the survey will be tested in a Field Trial in participating countries. Data for cross-country comparison will be collected by the Main Survey in 2023. ETHICS AND DISSEMINATION: Informed consent will be obtained from primary care providers and service users. National Project Managers search ethical approval of the survey in their country, if required. Reporting by the OECD will focus on questions for international comparison. A secured information technology platform will be developed for participants and stakeholders in countries to receive feedback and answer their own questions. Findings will also be disseminated through an international OECD flagship report, conferences, scientific papers and policy briefs, to inform strategies to improve care for people living with chronic conditions throughout the world.

The experiences of 33 national COVID-19 dashboard teams during the first year of the pandemic in the World Health Organization European Region: A qualitative study.

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.

Changes in Acute Myocardial Infarction, Stroke, and Heart Failure Hospitalizations During COVID-19 Pandemic in Tuscany-An Interrupted Time Series Study.

Objectives: We evaluate the impact of the COVID-19 pandemic on unplanned hospitalization rates for patients without COVID-19, including their length of stay, and in-hospital mortality, overall, and for acute myocardial infarction (AMI), stroke, and heart failure in the Tuscany region of Italy. Methods: We carried out a population-based controlled interrupted time series study using segmented linear regression with an autoregressive error term based on admissions data from all public hospitals in Tuscany. The primary outcome measure was weekly hospitalization rates; secondary outcomes included length of stay, and in-hospital mortality. Results: The implementation of the pandemic-related mitigation measures and fear of infection was associated with large decreases in inpatient hospitalization rates overall (-182 [-234, -130]), unplanned hospitalization (-39 [-51, -26]), and for AMI (-1.32 [-1.98, -0.66]), stroke (-1.51 [-2.56, -0.44]), and heart failure (-8.7 [-11.1, -6.3]). Average length of stay and percent in-hospital mortality for select acute medical conditions did not change significantly. Conclusion: In Tuscany, Italy, the COVID-19 pandemic was associated with large reductions in hospitalization rates overall, as well as for heart failure, and the time sensitive conditions of AMI and stroke during the months January to July 2020.