Use of RWE to Inform Regulatory, Public Health Policy, and Intervention Priorities for the Developing World

Real-World Research on Retinal Diseases Using Health Claims Database: A Narrative Review

Real-world data (RWD) has emerged as a crucial component in understanding and improving patient outcomes across various medical conditions, including retinal diseases. Health claims databases, generated from healthcare reimbursement claims, offer a comprehensive source of RWD, providing insights into patient outcomes, healthcare utilization, and treatment effectiveness. However, the use of these databases for research also presents unique challenges. This narrative review explores the role of real-world research on retinal diseases using health claims databases, highlighting their advantages, limitations, and potential contributions to advancing our understanding and management of the diseases. The review examines the applications of health claims databases in retinal disease research, including epidemiological studies, comparative effectiveness and safety analyses, economic burden assessments, and evaluations of patient outcomes and quality of care. Previous findings demonstrate the value of these databases in generating prevalence and incidence estimates, identifying risk factors and predictors, evaluating treatment effectiveness and safety, and understanding healthcare utilization patterns and costs associated with retinal diseases. Despite their strengths, health claims databases face challenges related to data limitations, biases, privacy concerns, and methodological issues. Accordingly, the review also explores future directions and opportunities, including advancements in data collection and analysis, integration with electronic health records, collaborative research networks and consortia, and the evolving regulatory landscape. These developments are expected to enhance the utility of health claims databases for retinal disease research, resulting in more comprehensive and impactful findings across diverse retinal disorders and robust real-world insights from a large population.

The role of real-world evidence for regulatory and public health decision-making for Accelerated Vaccine Deployment- a meeting report

The COVID-19 pandemic underscored the need for rapid evidence generation to inform public health decisions beyond the limitations of conventional clinical trials. This report summarises presentations and discussions from a conference on the role of Real-World Evidence (RWE) in expediting vaccine deployment. Attended by regulatory bodies, public health entities, and industry experts, the gathering was a collaborative exchange of experiences and recommendations for leveraging RWE for vaccine deployment. RWE proved instrumental in refining decision-making processes to optimise dosing regimens, enhance guidance on target populations, and steer vaccination strategies against emerging variants. Participants felt that RWE was successfully integrated into lifecycle management, encompassing boosters and safety considerations. However, challenges emerged, prompting a call for improvements in data quality, standardisation, and availability, acknowledging the variability and potential inaccuracies in data across diverse healthcare systems. Regulatory transparency should also be prioritised to foster public trust, and improved collaborations with governments are needed to streamline data collection and navigate data privacy regulations. Moreover, building and sustaining resources, expertise, and infrastructure in LMICs emerged as imperative for RWE-generating capabilities. Continued stakeholder collaboration and securing adequate funding emerged as vital pillars for advancing the use of RWE in shaping responsive and effective public health strategies.

The value of using real-world evidence as a source of clinical evidence in the European medical device regulations: a mixed methods study

OBJECTIVES

This study investigates the benefits, limitations and awareness of using Real World Evidence and Real World Data for post-market clinical follow-up studies and clinical evaluation reports in the European Medical Device Regulations.

METHODOLOGY

A mixed methods study was utilized with qualitative interviews and a quantitative survey.

RESULTS

The findings from the study demonstrate that in the case of the Medical Device Regulations, opportunities exist for manufacturers of legacy devices to conduct Real World Evidence studies to bridge gaps in clinical evidence. The primary value of Real World Evidence lies in its ability to provide an accurate and, therefore, more reliable measure of device safety and performance. As a measure of safety and performance, it supplements clinical evidence generated from pre and post-market clinical investigations, reducing the costs associated with these studies and supporting the manufacturer's benefit: risk conclusion.

CONCLUSION

This study provides insight into how the medical device industry could utilize Real World Evidence and have an initiative in the EU similar to the FDA-sponsored NESTcc partnership. This would aid medical device manufacturers in transitioning to the MDR clinical evaluation requirements and mitigate the impact on medical device availability in the EU.

Real-world evidence: experiences and challenges for decision making in Latin America

OBJECTIVE

The Health Technology Assessment (HTA) process aims to optimize health system funding of technologies. In recent years there has been an increase in what is known as Real-World Evidence (RWE) as a complement to clinical trials. The objective of Health Technology Assessment International's Latin American Policy Forum 2022 was to explore the utility of incorporating RWE into HTA and decision-making processes in the region.

METHODS

This article is based on a background document, survey, and the deliberative work of the country representatives who participated in the Forum.

RESULTS

There is a growing interest in the use of Real-World Data / Real-World Evidence in HTA processes in Latin America, although currently there are no specific local guidelines for RWE use by HTA agencies. At present, its use is limited to certain areas such as adding context to HTA reports, the evaluation of adverse events, or cost estimation.Potential future uses of RWE were identified, including the creation of risk-sharing agreements, the assessment of technology performance in routine practice, providing information on outcomes that are not so easily evaluated in clinical trials (e.g., the identification of specific subpopulations or quality of life), and the estimation of input parameters for economic evaluations.

CONCLUSIONS

The participants agreed that there are several areas presenting significant potential to expand the application of RWD/RWE and that the development of normative frameworks for its use could be helpful.

Machine Learning Methods in Real-World Studies of Cardiovascular Disease

Objective:Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application.

Methods:This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD.

Conclusion:ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.

Real-World Evidence: A Primer

Real-world evidence (RWE) is clinical evidence on a medical product's safety and efficacy that is generated using real-world data (RWD) resulting from routine healthcare delivery. There are several sources of RWD, including electronic health records (EHRs), registries, claims/billing data, and patient-generated data, as well as those from mobile health applications and wearable devices. Real-world data from these sources can be collected and analysed through different study designs such as prospective and retrospective cohort studies, case-control studies, and pragmatic clinical trials. Real-world evidence in the form of post-marketing surveillance has been extensively used to generate pharmacovigilance data. Of late, it has been realised that, apart from safety, RWE has additional applications in different stages of the drug approval cycle, and can be used to optimize the design of randomised controlled trials (RCTs). There has been an increasing awareness and acceptance of RWE from different stakeholders, including physicians, pharmaceutical companies, payers, regulators, and patients. Several regulatory authorities have also created frameworks and guidelines for efficient harnessing of RWE while acknowledging several challenges in RWD collection and analysis. The purpose of this review is to offer an outline of the current information on RWE, its advantages and disadvantages, as well as the associated challenges and ways to overcome them, while also throwing some light on the future of RWE.

Contemporary Databases in Real-world Studies Regarding the Diverse Health Care Systems of India, Thailand, and Taiwan: Protocol for a Scoping Review

BACKGROUND

Real-world data (RWD) related to patient health status or health care delivery can be broadly defined as data collected outside of conventional clinical trials, including those from databases, treatment and disease registries, electronic medical records, insurance claims, and information directly contributed by health care professionals or patients. RWD are used to generate real-world evidence (RWE), which is increasingly relevant to policy makers in Asia, who use RWE to support decision-making in several areas, including public health policy, regulatory health technology assessment, and reimbursement; set priorities; or inform clinical practice.

OBJECTIVE

To support the achievement of the benefits of RWE in Asian health care strategies and policies, we sought to identify the linked contemporary databases used in real-world studies from three representative countries-India, Thailand, and Taiwan-and explore variations in results based on these countries' economies and health care reimbursement systems by performing a systematic scoping review. Herein, we describe the protocol and preliminary findings of our scoping review.

METHODS

The PubMed search strategy covered 3 concepts. Concept 1 was designed to identify potential RWE and RWD studies by applying various Medical Subject Headings (MeSH) terms ("Treatment Outcome," "Evidence-Based Medicine," "Retrospective Studies," and "Time Factors") and related keywords (eg, "real-world," "actual life," and "actual practice"). Concept 2 introduced the three countries-India, Taiwan, and Thailand. Concept 3 focused on data types, using a combination of MeSH terms ("Electronic Health Records," "Insurance, Health," "Registries," "Databases, Pharmaceutical," and "Pharmaceutical Services") and related keywords (eg, "electronic medical record," "electronic healthcare record," "EMR," "EHR," "administrative database," and "registry"). These searches were conducted with filters for language (English) and publication date (publications in the last 5 years before the search). The retrieved articles will undergo 2 screening phases (phase 1: review of titles and abstracts; phase 2: review of full texts) to identify relevant and eligible articles for data extraction. The data to be extracted from eligible studies will include the characteristics of databases, the regions covered, and the patient populations.

RESULTS

The literature search was conducted on September 27, 2022. We retrieved 3,172,434, 1,094,125, and 672,794 articles for concepts 1, 2, and 3, respectively. After applying all 3 concepts and the language and publication date filters, 2277 articles were identified. These will be further screened to identify eligible studies. Based on phase 1 screening and our progress to date, approximately 44% (1003/2277) of articles have undergone phase 2 screening to judge their eligibility. Around 800 studies will be used for data extraction.

CONCLUSIONS

Our research will be crucial for nurturing advancement in RWD generation within Asia by identifying linked clinical RWD databases and new avenues for public-private partnerships and multiple collaborations for expanding the scope and spectrum of high-quality, robust RWE generation in Asia.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/43741.

Transferability of real-world data across borders for regulatory and health technology assessment decision-making

Recently, there has been increased consideration of real-world data (RWD) and real-world evidence (RWE) in regulatory and health technology assessment (HTA) decision-making. Due to challenges in identifying high-quality and relevant RWD sources, researchers and regulatory/HTA bodies may turn to RWD generated in locales outside of the locale of interest (referred to as "transferring RWD"). We therefore performed a review of stakeholder guidance as well as selected case studies to identify themes for researchers to consider when transferring RWD from one jurisdiction to another. Our review highlighted that there is limited consensus on defining decision-grade, transferred RWD; certain stakeholders have issued relevant guidance, but the recommendations are high-level and additional effort is needed to generate comprehensive guidance. Additionally, the case studies revealed that RWD transferability has not been a consistent concern for regulatory/HTA bodies and that more focus has been put on the evaluation of internal validity. To help develop transferability best practices (alongside internal validity best practices), we suggest that researchers address the following considerations in their justification for transferring RWD: treatment pathways, nature of the healthcare system, incidence/prevalence of indication, and patient demographics. We also recommend that RWD transferability should garner more attention as the use of imported RWD could open doors to high-quality data sources and potentially reduce methodological issues that often arise in the use of local RWD; we thus hope this review provides a foundation for further dialogue around the suitability and utility of transferred RWD in the regulatory/HTA decision-making space.

Global Health Strategies in the Face of the COVID-19 Pandemic and Other Unprecedented Threats

Global health is evolving as a discipline aiming at exploring needs and offering equitable health services for all people. Over the past four decades, several global initiatives have been introduced to improve the accessibility of primary health care (PHC) and solve most health issues at this level. Historically, the 1978 Alma-Ata and 2018 Astana Declarations were perhaps the most important documents for a comprehensive approach to PHC services across the world. With the introduction of the United Nations Sustainable Development Goals in 2015, developments in all spheres of human life and multi-sectoral cooperation became the essential action targets that could contribute to improved health, well-being, and safety of all people. Other global initiatives such as the Riyadh Declaration on Digital Health and São Paulo Declaration on Planetary Health called to urgent action to employ advanced digital technologies, improve health data processing, and invest more in research management. All these initiatives are put to the test in the face of the coronavirus disease 2019 (COVID-19) pandemic and other unprecedented threats to humanity.