Quality assurance of registries for health technology assessment

Objectives: The aim of this study was to identify guidelines and assessment tools used by health technology agencies for quality assurance of registries and investigate the current use of registry data by HTA organizations worldwide. Methods: As part of a European Network for Health Technology Assessment Joint Action work package, we undertook a literature search and sent a questionnaire to all partner organizations on the work package and all organizations listed in the International Society for Pharmaco-economics and Outcomes Research directory. Results: We identified thirteen relevant documents relating to quality assurance of registries. We received fifty-five responses from organizations representing twenty-one different countries, a response rate of 40.5 percent (43/110). Many agencies, particularly in Europe, are already drawing on a range of registries to provide data for their HTA. Less than half, however, use criteria or standards to assess the quality of registry data. Nearly all criteria or standards in use have been internally defined by organizations rather than referring to those produced by an external body. A comparison of internal and external standards identified consistency in several quality dimensions, which can be used as a starting point for the development of a standardized tool. Conclusion: The use of registry data is more prevalent than expected, strengthening the need for a standardized registry quality assessment tool. A user-friendly tool developed in conjunction with stakeholders will support the consistent application of approved quality standards, and reassure critics who have traditionally considered registry data to be unreliable

Making use of comparable health data to improve quality of care and outcomes in diabetes : The EUBIROD review of diabetes registries and data sources in Europe

Background: Registries and data sources contain information that can be used on an ongoing basis to improve quality of care and outcomes of people with diabetes. As a specific task of the EU Bridge Health project, we carried out a survey of diabetes-related data sources in Europe. Objectives: We aimed to report on the organization of different sources of diabetes information, including their governance, information infrastructure and dissemination strategies for quality control, service planning, public health, policy and research. Methods: Survey using a structured questionnaire to collect targeted data from a network of collaborating institutions managing registries and data sources in 17 countries in the year 2017. Results: The 18 data sources participating in the study were most frequently academic centres (44.4%), national (72.2%), targeting all types of diabetes (61.1%) covering no more than 10% of the target population (44.4%). Although population-based in over a quarter of cases (27.8%), sources relied predominantly on provider-based datasets (38.5%), fewer using administrative data (16.6%). Data collection was continuous in the majority of cases (61.1%), but 50% could not perform data linkage. Public reports were more frequent (72.2%) as well as quality reports (77.8%), but one third did not provide feedback to policy and only half published ten or more peer reviewed papers during the last 5 years. Conclusions: The heterogeneous implementation of diabetes registries and data sources hampers the comparability of quality and outcomes across Europe. Best practices exist but need to be shared more effectively to accelerate progress and deliver equitable results for people with diabetes.publishedVersionPeer reviewe

Proactive advising: a machine learning driven approach to vaccine hesitancy

Despite once being nearly eradicated, Measles cases in Europe have surged to a 20-year high with more than 60,000 cases in 2018, due to a dramatic decrease in vaccination rates. The decrease in Measles, Mumps, and Rubella (MMR) vaccination rates can be attributed to an increase in 'vaccine hesitancy', or the delay in acceptance or refusal of vaccines despite their availability. Vaccine hesitancy is a relatively new global problem for which effective interventions are not yet established. In this paper, a novel machine learning approach to identify children at risk of not being vaccinated against MMR is proposed, with the objective of facilitating proactive action by healthcare workers and policymakers. A use case of the approach is the provision of individualized informative guidance to families that may otherwise become or are already vaccine hesitant. Using a LASSO logistic regression model trained on 44,000 child Electronic Health Records (EHRs), vaccine hesitant families can be identified with a higher precision (0.72) than predicting vaccine uptake based on a child's infant vaccination record alone (0.63). The model uses a low number of attributes of the child and his or her family and community to produce a prediction, making it readily interpretable by healthcare professionals. The implementation of the machine learning model into an open source dashboard for use by healthcare providers and policymakers as an Early Warning and Monitoring System (EWS) against vaccine hesitancy is proposed. The EWS would facilitate a wide variety of proactive, anticipatory and therefore potentially more effective public health interventions, compared to reactive interventions taken after vaccine rejections.publishersversionpublishe

Making use of comparable health data to improve quality of care and outcomes in diabetes : the EUBIROD review of diabetes registries and data sources in Europe

Background: Registries and data sources contain information that can be used on an ongoing basis to improve quality of care and outcomes of people with diabetes. As a specific task of the EU Bridge Health project, we carried out a survey of diabetes-related data sources in Europe. Objectives: We aimed to report on the organization of different sources of diabetes information, including their governance, information infrastructure and dissemination strategies for quality control, service planning, public health, policy and research. Methods: Survey using a structured questionnaire to collect targeted data from a network of collaborating institutions managing registries and data sources in 17 countries in the year 2017. Results: The 18 data sources participating in the study were most frequently academic centres (44.4%), national (72.2%), targeting all types of diabetes (61.1%) covering no more than 10% of the target population (44.4%). Although population-based in over a quarter of cases (27.8%), sources relied predominantly on provider-based datasets (38.5%), fewer using administrative data (16.6%). Data collection was continuous in the majority of cases (61.1%), but 50% could not perform data linkage. Public reports were more frequent (72.2%) as well as quality reports (77.8%), but one third did not provide feedback to policy and only half published ten or more peer reviewed papers during the last 5 years. Conclusions: The heterogeneous implementation of diabetes registries and data sources hampers the comparability of quality and outcomes across Europe. Best practices exist but need to be shared more effectively to accelerate progress and deliver equitable results for people with diabetes.publishedVersionPeer reviewe

SCORE2-diabetes : 10-year cardiovascular risk estimation in type 2 diabetes in Europe

Aims To develop and validate a recalibrated prediction model (SCORE2-Diabetes) to estimate the 10-year risk of cardiovascular disease (CVD) in individuals with type 2 diabetes in Europe. Methods and results SCORE2-Diabetes was developed by extending SCORE2 algorithms using individual-participant data from four large-scale datasets comprising 229 460 participants (43 706 CVD events) with type 2 diabetes and without previous CVD. Sex-specific competing risk-adjusted models were used including conventional risk factors (i.e. age, smoking, systolic blood pressure, total, and HDL-cholesterol), as well as diabetes-related variables (i.e. age at diabetes diagnosis, glycated haemoglobin [HbA1c] and creatinine-based estimated glomerular filtration rate [eGFR]). Models were recalibrated to CVD incidence in four European risk regions. External validation included 217 036 further individuals (38 602 CVD events), and showed good discrimination, and improvement over SCORE2 (C-index change from 0.009 to 0.031). Regional calibration was satisfactory. SCORE2-Diabetes risk predictions varied several-fold, depending on individuals' levels of diabetes-related factors. For example, in the moderate-risk region, the estimated 10-year CVD risk was 11% for a 60-year-old man, non-smoker, with type 2 diabetes, average conventional risk factors, HbA1c of 50 mmol/mol, eGFR of 90 mL/min/1.73 m(2), and age at diabetes diagnosis of 60 years. By contrast, the estimated risk was 17% in a similar man, with HbA1c of 70 mmol/mol, eGFR of 60 mL/min/1.73 m(2), and age at diabetes diagnosis of 50 years. For a woman with the same characteristics, the risk was 8% and 13%, respectively. Conclusion SCORE2-Diabetes, a new algorithm developed, calibrated, and validated to predict 10-year risk of CVD in individuals with type 2 diabetes, enhances identification of individuals at higher risk of developing CVD across Europe