Ethical, Legal, and Social Issues (ELSI) of Responsible Data Sharing Involving Children in Genomics: A Systematic Literature Review of Reasons

BACKGROUND

Progress in precision medicine relies on the access to, use of, and exchange of genomic and associated clinical data, including from children. The ethical, legal, and social issues (ELSI) of such data access, use, and exchange may be accentuated in the pediatric context due in part to the highly sensitive nature of genomic data, children's consent-related vulnerabilities, and uncertain risks of reidentification. Systematic analyses of the ELSI and scientific reasons for why and how genomic data may be shared responsibly are, however, limited. Methods: We conducted a modified systematic review of reasons according to Sofaer and Strech to examine the ELSI and scientific reasons for "responsible" sharing of children's genomic and associated clinical data. Empirical articles, commentaries, and data-sharing policies indexed in Medline, Scopus, Web of Science, and BIOSIS were included in the analysis if they discussed ELSI and were published between 2003 and 2017 in English. Results: One hundred and fifty-one records met our inclusion criteria. We identified 11 unique reasons and 8 subreasons for why children's genomic data should or should not be shared. Enhancing the prospect of direct and indirect benefits and maximizing the utility of children's data were top reasons why data should be shared. Inadequate data privacy protection was the leading reason why it should not. We furthermore identified 8 reasons and 30 subreasons that support conditional data sharing, in which recontact for the continued use of children's data once they reach the age of majority was the most frequently endorsed condition. Conclusions: The complete list of ELSI reasons and responsible conditions provides an evidentiary basis upon which institutions can develop data-sharing policies. Institutions should encourage the sharing of children's data to advance genomic research, while heeding special reconsent and data protection mechanisms that may help mitigate uncertain longitudinal risks for children and families.

Knowledge and Practice of Patients' Data Sharing and Confidentiality Among Nurses in Jordan

BACKGROUND

The key patient rights entail respecting human decency, receiving healthcare services of high-quality, the right to information, the initial agreement of the patient to medical intervention, respecting privacy and personal life, and sustaining care and treatment. This study aims to survey the knowledge and practice of nurses in various healthcare industries toward sharing and confidentiality of patients' data.

METHODS

A descriptive cross-sectional design was employed through an online survey from May to June 2020. The authors sent a developed tool containing 19 statements reflecting the understanding of nurses' knowledge and practice of privacy and sharing of data required to safeguard patient privacy. A total of 800 nurses agreed to participate in the study out of 1000 nurses.

RESULTS

Roughly, all participants agreed that junior nurses should participate in a data sharing and confidentiality course before engaging in practice. Regarding institution policies for data sharing and protection, many nurses agreed that there are special recommendations and instructions from the institution in which they work to exchange patient information among nurses and the medical staff. The predictors of sharing practices and confidentiality among nurses include age, gender, marriage status, and attending a security course before practice. Young age, female, not attending a data sharing course, and single nurses are less engaging with data sharing and confidentiality of the patients for unauthorized patients.

CONCLUSION

A significant proportion of the staff had appropriate practices that ensured data security. However, practices that ensure patient confidentiality in the aspects of access, sharing, and transferring of patient data need improvement. Training is essential since it will have a beneficial relationship with knowledge, opinions, views, and actions. Thus, planning continuous training on policies and regulations about data safety and privacy may assist in improving healthcare setting practices.

High-quality cardiac surgery through teamwork

The high-quality cardiothoracic surgery program is primed for mindful effective surgery. The challenge lies in attaining mindful skills and efficiency. Herein is one journey toward high departmental quality over two decades.

Assessing the feasibility of introducing an electronic health information system into Tuberculosis clinics and laboratories in Myanmar

Myanmar has launched an advanced tuberculosis examination policy, which involves specimen exchanges among clinics and referral laboratories. However, with the current paper-based operation, it is difficult to trace information accurately. Therefore, since April 2017, we introduced a pilot operation consisting of an electronic health information system (HIS) that uses QR codes for data sharing in the tuberculosis laboratory at seven facilities. This study aimed to assess the feasibility of introducing the electronic HIS into tuberculosis clinics and laboratories based on staff perception, workload and workflow, and data accuracy, and to clarify its advantages and disadvantages. The analysis was descriptive, and it involved a semi-structured interview for the staff, workflow observations to evaluate the workload and describe the change in workflow, and evaluation of the data accuracy by comparing the numbers yielded by the paper-based and HIS-based reports. The HIS was positively accepted as it improved work efficiency, while the operation still depended on paper-based reports. Parallel data registration using both paper-based and HIS-based reports increased the workload. Data discrepancies were found when comparing the paper-based and HIS-based reports, and these discrepancies were not directly attributed to the HIS introduction but individual factors. Crucial facilitating factors of the HIS were its operability and user-friendliness, because it does not require specific training. The additional workload translates into the need for additional human resources, and the parallel data registration remains a challenge. However, we consider that these challenges could be overcome as coverage of the HIS expands.

Profile of Data Sharing in the Clinical Neurosciences

Importance: In today’s climate of high healthcare costs and limited research resources, much attention has been given to inefficiency in research. Open access to research data has been proposed as a way to pool resources and make the most of research funding while also promoting transparency and scientific rigor. 

Objective: The clinical neurosciences stand to benefit greatly from the potential opportunities afforded by open data, and we sought to evaluate the current state of publicly available research findings and data sharing policies within the clinical neurosciences. 

Design: The Clarivate Analytics Web of Science journal citation reports for 2017 were used to sort journals in the category ‘Clinical Neurosciences’ by impact factor. The top 50 journals were selected and reviewed, but data was only collected from journals focused on original research (42/50). For each journal we reviewed the 10 most recent original research articles for 2016, 2017, and 2018 as designated by Scopus. 

Results: A data sharing policy existed for 60% (25/42) of the journals reviewed. Of the articles studied 41% (517/1255) contained source data, and the amount of articles with available source data increased from 2016 to 2018. Of all the articles reviewed, 49.4% (620/1255) were open access. Overall, 6.9% (87/1255) of articles had their source data accessible outside of the manuscript (e.g. registries, databases, etc.) and 8.9% (112/1255) addressed the availability of their source data within the publication itself. The availability of source data outside the manuscript and in-article discussion of source data availability both increased from 2016 to 2018. Only 3.9% (49/1255) of articles reviewed reported negative results for their primary outcome, and 7.6% (95/1255) of the articles could not be defined as primarily reporting positive or negative findings (characterization studies, census reporting, etc.). The distribution of negative versus positive results reported showed no significant trend over the years studied. 

Conclusion and Relevance: Our results demonstrate an opportunity for increased data sharing in neuroscience original research. These findings also suggest a trend towards increased adoption of open data sharing policies among journals and increased availability of unprocessed data in publications. This can increase the quality and speed at which new research is developed in the clinical neurosciences.

ACTION-EHR: Patient-Centric Blockchain-Based Electronic Health Record Data Management for Cancer Care

Background

With increased specialization of health care services and high levels of patient mobility, accessing health care services across multiple hospitals or clinics has become very common for diagnosis and treatment, particularly for patients with chronic diseases such as cancer. With informed knowledge of a patient’s history, physicians can make prompt clinical decisions for smarter, safer, and more efficient care. However, due to the privacy and high sensitivity of electronic health records (EHR), most EHR data sharing still happens through fax or mail due to the lack of systematic infrastructure support for secure, trustable health data sharing, which can also cause major delays in patient care.

Objective

Our goal was to develop a system that will facilitate secure, trustable management, sharing, and aggregation of EHR data. Our patient-centric system allows patients to manage their own health records across multiple hospitals. The system will ensure patient privacy protection and guarantee security with respect to the requirements for health care data management, including the access control policy specified by the patient.

Methods

We propose a permissioned blockchain-based system for EHR data sharing and integration. Each hospital will provide a blockchain node integrated with its own EHR system to form the blockchain network. A web-based interface will be used for patients and doctors to initiate EHR sharing transactions. We take a hybrid data management approach, where only management metadata will be stored on the chain. Actual EHR data, on the other hand, will be encrypted and stored off-chain in Health Insurance Portability and Accountability Act–compliant cloud-based storage. The system uses public key infrastructure–based asymmetric encryption and digital signatures to secure shared EHR data.

Results

In collaboration with Stony Brook University Hospital, we developed ACTION-EHR, a system for patient-centric, blockchain-based EHR data sharing and management for patient care, in particular radiation treatment for cancer. The prototype was built on Hyperledger Fabric, an open-source, permissioned blockchain framework. Data sharing transactions were implemented using chaincode and exposed as representational state transfer application programming interfaces used for the web portal for patients and users. The HL7 Fast Healthcare Interoperability Resources standard was adopted to represent shared EHR data, making it easy to interface with hospital EHR systems and integrate a patient’s EHR data. We tested the system in a distributed environment at Stony Brook University using deidentified patient data.

Conclusions

We studied and developed the critical technology components to enable patient-centric, blockchain-based EHR sharing to support cancer care. The prototype demonstrated the feasibility of our approach as well as some of the major challenges. The next step will be a pilot study with health care providers in both the United States and Switzerland. Our work provides an exemplar testbed to build next-generation EHR sharing infrastructures.

The Veterans Affairs Precision Oncology Data Repository, a Clinical, Genomic, and Imaging Research Database

The Veterans Affairs Precision Oncology Data Repository (VA-PODR) is a large, nationwide repository of de-identified data on patients diagnosed with cancer at the Department of Veterans Affairs (VA). Data include longitudinal clinical data from the VA's nationwide electronic health record system and the VA Central Cancer Registry, targeted tumor sequencing data, and medical imaging data including computed tomography (CT) scans and pathology slides. A subset of the repository is available at the Genomic Data Commons (GDC) and The Cancer Imaging Archive (TCIA), and the full repository is available through the Veterans Precision Oncology Data Commons (VPODC). By releasing this de-identified dataset, we aim to advance Veterans' health care through enabling translational research on the Veteran population by a wide variety of researchers.

Guidelines for the content and format of PET brain data in publications and archives: A consensus paper

It is a growing concern that outcomes of neuroimaging studies often cannot be replicated. To counteract this, the magnetic resonance (MR) neuroimaging community has promoted acquisition standards and created data sharing platforms, based on a consensus on how to organize and share MR neuroimaging data. Here, we take a similar approach to positron emission tomography (PET) data. To facilitate comparison of findings across studies, we first recommend publication standards for tracer characteristics, image acquisition, image preprocessing, and outcome estimation for PET neuroimaging data. The co-authors of this paper, representing more than 25 PET centers worldwide, voted to classify information as mandatory, recommended, or optional. Second, we describe a framework to facilitate data archiving and data sharing within and across centers. Because of the high cost of PET neuroimaging studies, sample sizes tend to be small and relatively few sites worldwide have the required multidisciplinary expertise to properly conduct and analyze PET studies. Data sharing will make it easier to combine datasets from different centers to achieve larger sample sizes and stronger statistical power to test hypotheses. The combining of datasets from different centers may be enhanced by adoption of a common set of best practices in data acquisition and analysis.

Special Section on Ethics in Health Informatics

OBJECTIVE

To summarize significant research contributions on ethics in medical informatics published in 2019.

METHODS

An extensive search using PubMed/Medline was conducted to identify the scientific contributions published in 2019 that address ethics issues in medical informatics. The selection process comprised three steps: 1) 15 candidate best papers were first selected by the two section editors; 2) external reviewers from internationally renowned research teams reviewed each candidate best paper; and 3) the final selection of three best papers was conducted by the editorial committee of the Yearbook.

RESULTS

The three selected best papers explore timely issues of concern to the community and demonstrate how ethics considerations influence applied informatics.

CONCLUSION

With regard to ethics in informatics, data sharing and privacy remain primary areas of concern. Ethics issues related to the development and implementation of artificial intelligence is an emerging topic of interest.

Experimental Pipeline (Expipe): A Lightweight Data Management Platform to Simplify the Steps From Experiment to Data Analysis

As experimental neuroscience is moving toward more integrative approaches, with a variety of acquisition techniques covering multiple spatiotemporal scales, data management is becoming increasingly challenging for neuroscience laboratories. Often, datasets are too large to practically be stored on a laptop or a workstation. The ability to query metadata collections without retrieving complete datasets is therefore critical to efficiently perform new analyses and explore the data. At the same time, new experimental paradigms lead to constantly changing specifications for the metadata to be stored. Despite this, there is currently a serious lack of agile software tools for data management in neuroscience laboratories. To meet this need, we have developed Expipe, a lightweight data management framework that simplifies the steps from experiment to data analysis. Expipe provides the functionality to store and organize experimental data and metadata for easy retrieval in exploration and analysis throughout the experimental pipeline. It is flexible in terms of defining the metadata to store and aims to solve the storage and retrieval challenges of data/metadata due to ever changing experimental pipelines. Due to its simplicity and lightweight design, we envision Expipe as an easy-to-use data management solution for experimental laboratories, that can improve provenance, reproducibility, and sharing of scientific projects.

Including Digital Sequence Data in the Nagoya Protocol Can Promote Data Sharing

The Nagoya Protocol (NP), a legal framework under the Convention on Biological Diversity (CBD), formalises fair and equitable sharing of benefits arising from biological diversity. It encompasses biological samples and associated indigenous knowledge, with equitable return of benefits to those providing samples. Recent proposals that the use of digital sequence information (DSI) derived from samples should also require benefit-sharing under the NP have raised concerns that this might hamper research progress. Here, we propose that formalised benefit-sharing for biological data use can increase willingness to participate in research and share data, by ensuring equitable collaboration between sample providers and researchers, and preventing exploitative practices. Three case studies demonstrate how equitable benefit-sharing agreements might build long-term collaborations, furthering research for global benefits.

Assessment of a demonstrator repository for individual clinical trial data built upon DSpace

Background: Given the increasing number and heterogeneity of data repositories, an improvement and harmonisation of practice within repositories for clinical trial data is urgently needed. The objective of the study was to develop and evaluate a demonstrator repository, using a widely used repository system (DSpace), and then explore its suitability for providing access to individual participant data (IPD) from clinical research.

Methods: After a study of the available options, DSpace (version 6.3) was selected as the software for developing a demonstrator implementation of a repository for clinical trial data. In total, 19 quality criteria were defined, using previous work assessing clinical data repositories as a guide, and the demonstrator implementation was then assessed with respect to those criteria.

Results: Generally, the performance of the DSpace demonstrator repository in supporting sensitive personal data such as that from clinical trials was strong, with 14 requirements demonstrated (74%), including the necessary support for metadata and identifiers. Two requirements could not be demonstrated (the ability to include de-identification tools and the availabiltiy of a self-attestation system) and three requirements were only partially demonstrated (ability to provide links to de-identification tools and requirements, incorporation of a data transfer agreement in system workflow, and capability to offer managed access through application on a case by case basis).

Conclusions: Technically, the system was able to support most of the pre-defined requirements, though there are areas where support could be improved. Of course, in a productive repository, appropriate policies and procedures would be needed to direct the use of the available technical features. A technical evaluation should therefore be seen as indicating a system’s potential, rather than being a definite assessment of its suitability. DSpace clearly has considerable potential in this context and appears a suitable base for further exploration of the issues around storing sensitive data.

Global academic response to COVID-19: Cross-sectional study

This study explores the response to COVID‐19 from investigators, editors, and publishers and seeks to define challenges during the early stages of the pandemic. A cross‐sectional bibliometric review of COVID‐19 literature was undertaken between 1 November 2019 and 24 March 2020, along with a comparative review of Middle East respiratory syndrome (MERS) literature. Investigator responsiveness was assessed by measuring the volume and type of research published. Editorial responsiveness was assessed by measuring the submission‐to‐acceptance time and availability of original data. Publisher‐responsiveness was assessed by measuring the acceptance‐to‐publication time and the provision of open access. Three hundred and ninety‐eight of 2,835 COVID‐19 and 55 of 1,513 MERS search results were eligible. Most COVID‐19 studies were clinical reports (n = 242; 60.8%). The submission‐to‐acceptance [median: 5 days (IQR: 3–11) versus 71.5 days (38–106); P < .001] and acceptance‐to‐publication [median: 5 days (IQR: 2–8) versus 22.5 days (4–48·5‐; P < .001] times were strikingly shorter for COVID‐19. Almost all COVID‐19 (n = 396; 99.5%) and MERS (n = 55; 100%) studies were open‐access. Data sharing was infrequent, with original data available for 104 (26.1%) COVID‐19 and 10 (18.2%) MERS studies (P = .203). The early academic response was characterized by investigators aiming to define the disease. Studies were made rapidly and openly available. Only one‐in‐four were published alongside original data, which is a key target for improvement.

Piloting a model-to-data approach to enable predictive analytics in health care through patient mortality prediction

OBJECTIVE

The development of predictive models for clinical application requires the availability of electronic health record (EHR) data, which is complicated by patient privacy concerns. We showcase the "Model to Data" (MTD) approach as a new mechanism to make private clinical data available for the development of predictive models. Under this framework, we eliminate researchers' direct interaction with patient data by delivering containerized models to the EHR data.

MATERIALS AND METHODS

We operationalize the MTD framework using the Synapse collaboration platform and an on-premises secure computing environment at the University of Washington hosting EHR data. Containerized mortality prediction models developed by a model developer, were delivered to the University of Washington via Synapse, where the models were trained and evaluated. Model performance metrics were returned to the model developer.

RESULTS

The model developer was able to develop 3 mortality prediction models under the MTD framework using simple demographic features (area under the receiver-operating characteristic curve [AUROC], 0.693), demographics and 5 common chronic diseases (AUROC, 0.861), and the 1000 most common features from the EHR's condition/procedure/drug domains (AUROC, 0.921).

DISCUSSION

We demonstrate the feasibility of the MTD framework to facilitate the development of predictive models on private EHR data, enabled by common data models and containerization software. We identify challenges that both the model developer and the health system information technology group encountered and propose future efforts to improve implementation.

CONCLUSIONS

The MTD framework lowers the barrier of access to EHR data and can accelerate the development and evaluation of clinical prediction models.

Transmission dynamics: Data sharing in the COVID-19 era

Problem

The current coronavirus disease 2019 (COVID‐19) pandemic underscores the need for building and sustaining public health data infrastructure to support a rapid local, regional, national, and international response. Despite a historical context of public health crises, data sharing agreements and transactional standards do not uniformly exist between institutions which hamper a foundational infrastructure to meet data sharing and integration needs for the advancement of public health.

Approach

There is a growing need to apply population health knowledge with technological solutions to data transfer, integration, and reasoning, to improve health in a broader learning health system ecosystem. To achieve this, data must be combined from healthcare provider organizations, public health departments, and other settings. Public health entities are in a unique position to consume these data, however, most do not yet have the infrastructure required to integrate data sources and apply computable knowledge to combat this pandemic.

Outcomes

Herein, we describe lessons learned and a framework to address these needs, which focus on: (a) identifying and filling technology “gaps”; (b) pursuing collaborative design of data sharing requirements and transmission mechanisms; (c) facilitating cross‐domain discussions involving legal and research compliance; and (d) establishing or participating in multi‐institutional convening or coordinating activities.

Next steps

While by no means a comprehensive evaluation of such issues, we envision that many of our experiences are universal. We hope those elucidated can serve as the catalyst for a robust community‐wide dialogue on what steps can and should be taken to ensure that our regional and national health care systems can truly learn, in a rapid manner, so as to respond to this and future emergent public health crises.

Sharing SRP data to reduce environmentally associated disease and promote transdisciplinary research

The National Institute of Environmental Health Sciences (NIEHS) Superfund Basic Research and Training Program (SRP) funds a wide range of projects that span biomedical, environmental sciences, and engineering research and generate a wealth of data resulting from hypothesis-driven research projects. Combining or integrating these diverse data offers an opportunity to uncover new scientific connections that can be used to gain a more comprehensive understanding of the interplay between exposures and health. Integrating and reusing data generated from individual research projects within the program requires harmonization of data workflows, ensuring consistent and robust practices in data stewardship, and embracing data sharing from the onset of data collection and analysis. We describe opportunities to leverage data within the SRP and current SRP efforts to advance data sharing and reuse, including by developing an SRP dataset library and fostering data integration through Data Management and Analysis Cores. We also discuss opportunities to improve public health by identifying parallels in the data captured from health and engineering research, layering data streams for a more comprehensive picture of exposures and disease, and using existing SRP research infrastructure to facilitate and foster data sharing. Importantly, we point out that while the SRP is in a unique position to exploit these opportunities, they can be employed across environmental health research. SRP research teams, which comprise cross-disciplinary scientists focused on similar research questions, are well positioned to use data to leverage previous findings and accelerate the pace of research. Incorporating data streams from different disciplines addressing similar questions can provide a broader understanding and uncover the answers to complex and discrete research questions.

Policy Decisions and Use of Information Technology to Fight COVID-19, Taiwan

Because of its proximity to and frequent travelers to and from China, Taiwan faces complex challenges in preventing coronavirus disease (COVID-19). As soon as China reported the unidentified outbreak to the World Health Organization on December 31, 2019, Taiwan assembled a taskforce and began health checks onboard flights from Wuhan. Taiwan’s rapid implementation of disease prevention measures helped detect and isolate the country’s first COVID-19 case on January 20, 2020. Laboratories in Taiwan developed 4-hour test kits and isolated 2 strains of the coronavirus before February. Taiwan effectively delayed and contained community transmission by leveraging experience from the 2003 severe acute respiratory syndrome outbreak, prevalent public awareness, a robust public health network, support from healthcare industries, cross-departmental collaborations, and advanced information technology capacity. We analyze use of the National Health Insurance database and critical policy decisions made by Taiwan’s government during the first 50 days of the COVID-19 outbreak.

The ADC API: A Web API for the Programmatic Query of the AIRR Data Commons

The Adaptive Immune Receptor Repertoire (AIRR) Community is a research-driven group that is establishing a clear set of community-accepted data and metadata standards; standards-based reference implementation tools; and policies and practices for infrastructure to support the deposit, curation, storage, and use of high-throughput sequencing data from B-cell and T-cell receptor repertoires (AIRR-seq data). The AIRR Data Commons is a distributed system of data repositories that utilizes a common data model, a common query language, and common interoperability formats for storage, query, and downloading of AIRR-seq data. Here is described the principal technical standards for the AIRR Data Commons consisting of the AIRR Data Model for repertoires and rearrangements, the AIRR Data Commons (ADC) API for programmatic query of data repositories, a reference implementation for ADC API services, and tools for querying and validating data repositories that support the ADC API. AIRR-seq data repositories can become part of the AIRR Data Commons by implementing the data model and API. The AIRR Data Commons allows AIRR-seq data to be reused for novel analyses and empowers researchers to discover new biological insights about the adaptive immune system.

SPHN/PHRT - MedCo in Action: Empowering the Swiss Molecular Tumor Board with Privacy-Preserving and Real-Time Patient Discovery

MedCo is the first operational system that makes sensitive medical-data available for research in a simple, privacy-conscious and secure way. It enables a consortium of clinical sites to collectively protect their data and to securely share them with investigators, without single points of failure. In this short paper, we report on our ongoing effort for the operational deployment of MedCo within the context of the Swiss Personalized Health Network (SPHN) for the Swiss Molecular Tumor Board.

Specification and Distribution of Vocabularies Among Consortial Partners

Due to the variety of different software systems and disparate observational databases, the need for a uniform data representation rises. Common data models (CDM) support the harmonisation of data. A powerful but compact software setup and a minimum vocabulary set has been composed via Docker to facilitate analysis of data across ten university hospitals. The presented approach also creates the possibility to use a concise database which is easy to deploy.

Data Integration Approaches for Representing Stem Cell Studies

The aim of this study was to examine existing methods for sharing results of stem cell research via online data repositories. To identify the relevant repositories, a PubMed search was conducted using standard MeSH terms which was followed by a web-based search of relevant databases. The search yielded 16 databases created between 2010 and 2019. The review of databases identified 35 major rubrics and their sub-rubrics organized in a five-module system. Data integration approaches were characterized by three domains (common data elements, data visualization and analysis tools, and ontology mapping) which varied widely across the databases. Current state of stem cell data integration lacks reproducibility and standardization. Standardization of data integration approaches for representing stem cell studies is necessary to facilitate data sharing.

African Swine Fever: Lessons to Learn From Past Eradication Experiences. A Systematic Review

Prevention, early detection, prompt reaction, and communication play a crucial role in African swine fever (ASF) control. Appropriate surveillance capable of early detection of the disease in both domestic and wild animals, and the implementation of consolidated contingency plans, are currently considered the best means of controlling this disease. The purpose of this study was to understand the lessons to be learned through the global disease eradication history. To establish which strategies were successful for prevention, control, and eradication of ASF, and which errors should not be repeated, we conducted a systematic review. A query was defined to search for surveillance and control strategies applied by countries worldwide for ASF eradication in the past. Inclusion and exclusion criteria were defined. Decisions on study eligibility and data extraction were performed by two independent reviewers and the differences were resolved by consensus or by a third reviewer. From 1,980 papers, 23 were selected and included in the qualitative analysis. Reports from Belgium, Brazil, Cuba, the Dominican Republic and Haiti, France, mainland Italy, Malta, Portugal, and Spain were included. Despite the economic resources allocated and the efforts made, eradication was possible in only eight countries, between the 50s and 90s in the twentieth century, in different epidemiological and cultural contexts, in some instances within <1 year, and in others in about 40 years. Classical surveillance strategies, such as active and passive surveillance, both at farm and slaughterhouse levels, targeted surveillance, together with conventional biosafety and sanitary measures, led to eradication even in countries in which the tick's epidemiological role was demonstrated. Historical surveillance data analysis indicated that eradication was possible even when technological tools either were not available or were used less than they are currently. This emphasizes that data on surveillance and on animal population are crucial for planning effective surveillance, and targeting proper control and intervention strategies. This paper demonstrates that some strategies applied in the past were effective; these could be implemented and improved to confront the current epidemiological wave. This offers encouragement for the efforts made particularly in Europe during the recent epidemics.