ADVANCE system testing: Can safety studies be conducted using electronic healthcare data? An example using pertussis vaccination

Exploring the Italian Population's attitudes toward health data sharing for healthcare purpose and scientific research: a cross-sectional study

BACKGROUND

This study aimed to explore the Italian population's knowledge and perceptions regarding health data storage and sharing for treatment and research and to identify factors associated with citizens' attitudes toward data storage and sharing.

METHODS

A cross-sectional questionnaire, distributed to 1389 participants, collected sociodemographic information, assessed knowledge and gauged attitudes toward sharing data for treatment and research. Descriptive analyses and logistic regressions were performed to examine the associations between sociodemographic factors and knowledge/attitudes about data storage and sharing.

RESULTS

Most respondents wrongly believed that healthcare providers could access personal health-related data across the entire national territory, while 94% expressed willingness to share personal health data nationwide. A substantial percentage of respondents (73%) fully agreed that storing and sharing personal health-related data could improve research and quality of care.Males and younger individuals (<41 years) were likelier to have higher data-sharing knowledge. Lower educational-level respondents exhibited lower positive attitudes towards sharing health data for treatment and research purposes.

CONCLUSIONS

The results provide valuable insights for policymakers, healthcare professionals and researchers seeking to improve data management, promote collaboration and leverage the full potential of health data for personalized care and scientific advancements.

Real-World Evidence BRIDGE: A Tool to Connect Protocol With Code Programming

OBJECTIVE

To enhance documentation on programming decisions in Real World Evidence (RWE) studies.

MATERIALS AND METHODS

We analyzed several statistical analysis plans (SAP) within the Vaccine Monitoring Collaboration for Europe (VAC4EU) to identify study design sections and specifications for programming RWE studies. We designed a machine-readable metadata schema containing study sections, codelists, and time anchoring definitions specified in the SAPs with adaptability and user-friendliness.

RESULTS

We developed the RWE-BRIDGE, a metadata schema in form of relational database divided into four study design sections with 12 tables: Study Variable Definition (two tables), Cohort Definition (two tables), Post-Exposure Outcome Analysis (one table), and Data Retrieval (seven tables). We provide a guide to populate this metadata schema and a Shiny app that checks the tables. RWE-BRIDGE is available on GitHub (github.com/UMC-Utrecht-RWE/RWE-BRIDGE).

DISCUSSION

The RWE-BRIDGE has been designed to support the translation of study design sections from statistical analysis plans into analytical pipelines and to adhere to the FAIR principles, facilitating collaboration and transparency between researcher and programmers. This metadata schema strategy is flexible as it can support different common data models and programming languages, and it is adaptable to the specific needs of each SAP by adding further tables or fields, if necessary. Modified versions of the RWE-BRIGE have been applied in several RWE studies within VAC4EU.

CONCLUSION

RWE-BRIDGE offers a systematic approach to detailing variables, time anchoring, and algorithms for RWE studies. This metadata schema facilitates communication between researcher and programmers.

Methodological frontiers in vaccine safety: qualifying available evidence for rare events, use of distributed data networks to monitor vaccine safety issues, and monitoring the safety of pregnancy interventions

While vaccines are rigorously tested for safety and efficacy in clinical trials, these trials do not include enough subjects to detect rare adverse events, and they generally exclude special populations such as pregnant women. It is therefore necessary to conduct postmarketing vaccine safety assessments using observational data sources. The study of rare events has been enabled in through large linked databases and distributed data networks, in combination with development of case-centred methods. Distributed data networks necessitate common protocols, definitions, data models and analytics and the processes of developing and employing these tools are rapidly evolving. Assessment of vaccine safety in pregnancy is complicated by physiological changes, the challenges of mother-child linkage and the need for long-term infant follow-up. Potential sources of bias including differential access to and utilisation of antenatal care, immortal time bias, seasonal timing of pregnancy and unmeasured determinants of pregnancy outcomes have yet to be fully explored. Available tools for assessment of evidence generated in postmarketing studies may downgrade evidence from observational data and prioritise evidence from randomised controlled trials. However, real-world evidence based on real-world data is increasingly being used for safety assessments, and new tools for evaluating real-world evidence have been developed. The future of vaccine safety surveillance, particularly for rare events and in special populations, comprises the use of big data in single countries as well as in collaborative networks. This move towards the use of real-world data requires continued development of methodologies to generate and assess real world evidence.

Base de Datos para la Investigación Farmacoepidemiológica en Atención Primaria (BIFAP): A data resource for pharmacoepidemiology in Spain

PURPOSE

Base de Datos para la Investigación Farmacoepidemiológica en Atención Primaria (BIFAP) is a population based database administered by the AEMPS (Spanish Agency for Medicines) of longitudinal electronic medical records (EMR) of patients attended in primary care. Its main purpose is to serve as source of information for independent studies on drug safety and support of medicines regulation activities. This article aim is to describe the characteristics of BIFAP, how to access the database and a summary of its potential for research.

METHODS

Health problems are registered by primary care physicians as episodes of care and include socio-demographic data, results of diagnostic procedures, lifestyle data, general data, and interventions. A proportion of data on hospitalizations and specialist care are currently available through linkage with other data sources. EMRs of the Spanish healthcare system are provided by the regional administrations. Specific data extraction and standardization processes are performed.

RESULTS

BIFAP includes data from 12 million patients starting in 2001 and updated annually. Validation of drug and diagnosis definitions has been ascertained. Participation in international collaborative projects and a number of articles in peer reviewed journals reflect its contribution to the knowledge of the risks associated with medicines and drug utilization patterns.

CONCLUSIONS

BIFAP is a useful tool for generating scientific evidence on medicines related issues, helping regulatory decision making in Europe. The main strengths of BIFAP are related to large sample size, population-based, longitudinal nature and annual update of data. BIFAP shares common challenges with similar data sources including accurate and efficient identification of health outcomes and of treatment exposure.

ADVANCE database characterisation and fit for purpose assessment for multi-country studies on the coverage, benefits and risks of pertussis vaccinations

INTRODUCTION

The public-private ADVANCE consortium (Accelerated development of vaccine benefit-risk collaboration in Europe) aimed to assess if electronic healthcare databases can provide fit-for purpose data for collaborative, distributed studies and monitoring of vaccine coverage, benefits and risks of vaccines.

OBJECTIVE

To evaluate if European healthcare databases can be used to estimate vaccine coverage, benefit and/or risk using pertussis-containing vaccines as an example.

METHODS

Characterisation was conducted using open-source Java-based (Jerboa) software and R scripts. We obtained: (i) The general characteristics of the database and data source (meta-data) and (ii) a detailed description of the database population (size, representatively of age/sex of national population, rounding of birth dates, delay between birth and database entry), vaccinations (number of vaccine doses, recording of doses, pattern of doses by age and coverage) and events of interest (diagnosis codes, incidence rates). A total of nine databases (primary care, regional/national record linkage) provided data on events (pertussis, pneumonia, death, fever, convulsions, injection site reactions, hypotonic hypo-responsive episode, persistent crying) and vaccines (acellular pertussis and whole cell pertussis) related to the pertussis proof of concept studies.

RESULTS

The databases contained data for a total population of 44 million individuals. Seven databases had recorded doses of vaccines. The pertussis coverage estimates were similar to those reported by the World Health Organisation (WHO). Incidence rates of events were comparable in magnitude and age-distribution between databases with the same characteristics. Several conditions (persistent crying and somnolence) were not captured by the databases for which outcomes were restricted to hospital discharge diagnoses.

CONCLUSION

The database characterisation programs and workflows allowed for an efficient, transparent and standardised description and verification of electronic healthcare databases which may participate in pertussis vaccine coverage, benefit and risk studies. This approach is ready to be used for other vaccines/events to create readiness for participation in other vaccine related studies.

ADVANCE system testing: Benefit-risk analysis of a marketed vaccine using multi-criteria decision analysis and individual-level state transition modelling

BACKGROUND

The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public-private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines using electronic health record (eHR) databases in Europe. Proof-of-concept studies were designed to assess the proposed processes and system for generating the required evidence to perform B/R assessment and near-real time monitoring of vaccines. We aimed to test B/R methodologies for vaccines, using the comparison of the B/R profiles of whole-cell (wP) and acellular pertussis (aP) vaccine formulations in children as an example.

METHODS

We used multi-criteria decision analysis (MCDA) to structure the B/R assessment combined with individual-level state transition modelling to build the B/R effects table. In the state transition model, we simulated the number of events in two hypothetical cohorts of 1 million children followed from first pertussis dose till pre-school-entry booster (or six years of age, whichever occurred first), with one cohort receiving wP, and the other aP. The benefits were reductions in pertussis incidence and complications. The risks were increased incidences of febrile convulsions, fever, hypotonic-hyporesponsive episodes, injection-site reactions and persistent crying. Most model parameters were informed by estimates (coverage, background incidences, relative risks) from eHR databases from Denmark (SSI), Spain (BIFAP and SIDIAP), Italy (Pedianet) and the UK (RCGP-RSC and THIN). Preferences were elicited from clinical and epidemiological experts.

RESULTS

Using state transition modelling to build the B/R effects table facilitated the comparison of different vaccine effects (e.g. immediate vaccine risks vs long-term vaccine benefits). Estimates from eHR databases could be used to inform the simulation model. The model results could be easily combined with preference weights to obtain B/R scores.

CONCLUSION

Existing B/R methodology, modelling and estimates from eHR databases can be successfully used for B/R assessment of vaccines.