Also in this issue
Letter From the Editor
What's New? AI in drug manufacturing, the first autonomous diagnostic tool and the latest research updates.
Underwriting Updates GUM’s guidelines and information on alcohol consumption
Case ReView A case of hemophilia
Claims Updates Claims management demands deep technical expertise and a human touch
Longer Life Foundation
RGA's Global Medical Newsletter
Behavioral Evolution of Cancer Codes: What it means for insurance by Dr. Adela Osman Health Technologies: The potential to innovate insurance delivery and reach by Dr. Karneen Tam, Dr. Steve Woh, Dr. SiNing Zhao
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Dr. Adela Osman
Senior Vice President, Head of Global Medical
adela.osman@rgare.com
I am pleased to share the latest installment of RGA’s medical newsletter, packed with valuable insights on a variety of medical issues impacting the insurance industry. In our feature articles, Dr. Karneen Tam, Dr. Steve Woh, and Dr. SiNing Zhao highlight emerging health technologies and the new healthcare approaches they make possible, and I explore the implications of new oncology codes for insurers and critical illness products. Additional insights in this volume: Case ReView by Dr. Sheetal Salgaonkar focuses on the underwriting considerations for a hemophilia case. In Health View, Dr. Steve Woh looks at the first AI skin cancer detection platform, as well as how AI is reimagining pharmaceutical manufacturing. Our Research Watch summarizes how a blood test may predict the timing of cognitive decline in early-stage Alzheimer’s disease patients, how AI can use a CT scan to detect a hidden heart risk, and how using an esketamine nasal spray may help with treatment resistant depression. The Underwriting Update provides links to revised guidelines and information to enhance risk assessment for alcohol consumption. In our new Claims Update section, Jennie Calder Brown spotlights the training and support available to RGA clients. I remain appreciative for the contribution and input from our Assistant Editors and RGA Asia Medical Directors, Dr. Karneen Tam and Dr. SiNing Zhao. As always, we invite you to provide feedback on ReFlections by using the star ratings to evaluate articles and submit comments for topics you would like to see in future volumes. Thank you, Adela Osman
Welcome to the October 2025 edition of ReFlections.
ReFlections
From the Editor
In this issue
Behavioral Evolution of Cancer Codes: What it means for insurance
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Watch the welcome to ReFlections video
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3
Case ReView A Case of Hemophilia
Health Technologies: The potential to innovate insurance delivery and reach
Dr. Karneen Tam, MBBCH Regional Medical Director Asia Markets
Dr. SiNing Zhao, MBBS, FANZCA, FHKCA, FHKAM Regional Medical Director Asia Markets
Assistant Editors
In oncology, a quiet revolution is reshaping how we classify cancer. It is not just about treatments or therapies; it is about the codes behind every diagnosis, claim, and policy decision. This article explores the evolving behavior codes within the International Classification of Diseases for Oncology (ICD-O) and what these changes mean for insurers, particularly in critical illness (CI) products.
Introduction
References
https://training.seer.cancer.gov/coding-primary/ https://www.who.int/standards/classifications/other-classifications/international-classification-of-diseases-for-oncology https://training.seer.cancer.gov/coding-primary/icd-o-3.2-intro.html WHO Classification of Tumors Online https://www.drewberryinsurance.co.uk/critical-illness-insurance/guides/critical-illness-cover-claim-payout-rates-by-insurer https://pmc.ncbi.nlm.nih.gov/articles/PMC9722384/ https://pmc.ncbi.nlm.nih.gov/articles/PMC11669403 https://www.sciencedirect.com/science/article/pii/S1556086421032585 https://www.pathologyoutlines.com/topic/softtissueGIST.html
About the author
Dr. Adela Osman is the Senior Vice President and Head of Global Medical at RGA, where she oversees the development and execution of the company’s Global Medical strategy. She provides strategic guidance to executive leadership on complex medical issues, particularly those with implications for biometric risk. She holds a Bachelor of Medicine and Surgery from the University of Witwatersrand and began her career as a Medical Officer in public hospitals before transitioning to private practice, focusing on family health. In the insurance industry, Adela brings extensive expertise across claims, underwriting, medical marketing, product development, pricing, and legal consulting on medical matters. She has completed specialized training in disability medicine and impairment through the American Board of Independent Medical Examiners, including medico-legal report writing and court testimony, qualifying her as an independent medical examiner and expert witness. Adela previously chaired the Medical and Underwriting Standing Committee of ASISA, where she advocated for underwriting rights and best practices in South Africa. She currently serves as President-Elect and Board Member of the International Committee for Insurance Medicine (ICLAM) and is the Chief Editor of ReFlections magazine.
Adela.Osman@rgare.com
A change in how tumors are classified can significantly impact the claims landscape.
The International Classification of Diseases for Oncology (ICD-O) has undergone significant updates which reclassify many tumors’ behavior codes based on current clinical and pathological knowledge, impacting how cancers are categorized and treated. These reclassifications have major implications for the insurance industry, particularly in critical illness (CI) products – affecting underwriting, claims adjudication, and pricing – as tumors previously considered benign or borderline may now qualify as malignant and vice versa. To adapt to these changes, insurers should review CI definitions, update underwriting manuals, monitor ICD-O updates, and educate claims teams to ensure fair, accurate, and sustainable insurance practices that align with the latest medical understanding of cancer.
Key takeaways
Behavior codes: The game changer Behavior codes are at the core of the ICD-O system. These codes – /0 (benign), /1 (uncertain or borderline malignancy), /2 (in situ), and /3 (malignant) – are critical in defining tumor behavior. They help determine whether a tumor is likely to remain localized, invade surrounding tissues, or metastasize. In ICD-O-3.2, many of these codes were updated to reflect current clinical, molecular and pathological knowledge.2 Tumors can move between categories based on emerging evidence. This is not a one-way progression. A tumor once classified as malignant may be reclassified as borderline, while others previously considered benign may now be labeled malignant. These changes aim to improve diagnostic accuracy and clinical relevance. Here are a few notable examples of behavior code transitions in ICD-O-3.2: Benign to Malignant (/0 → /3) – Pituitary adenoma (now classified as pituitary neuroendocrine tumor or PitNET), pancreatic neuroendocrine tumor (PanNET), and gastrointestinal stromal tumor (GIST).3 Borderline to Malignant (/1 → /3) – Carcinoid tumors, thymomas, paragangliomas.3 Malignant to Borderline (/3 → /1) – Dermatofibrosarcoma protuberans (DFSP), immature teratoma.3 These shifts affect cancer registries, clinical decision-making, and insurance frameworks. For insurers, staying current with these changes is critical to ensuring accurate underwriting, fair claims adjudication, and sustainable product pricing.
Why codes matter ICD codes are the foundation of modern healthcare data. They translate complex diagnoses into standardized formats, supporting billing, research, and care coordination. While ICD-10 has long been the global standard, ICD-11 now offers greater flexibility, integration, and specificity. For cancer, however, ICD alone is not enough. ICD-O adds layers for morphology, behavior, and grading, offering a more detailed view of tumor biology and behavior.
ICD-O: A brief history The International Classification of Diseases for Oncology (ICD-O) was introduced by the World Health Organization (WHO) in 1976 as a specialized system for coding cancer registries. Unlike the broader ICD system, which classifies diseases generally, ICD-O captures the unique characteristics of tumors, including their topography (anatomical site), morphology (cell type), and behavior. Over time, ICD-O has been revised to reflect advances in tumor biology and pathology: ICD-O-2 (1990) – Widely adopted by cancer registries, this edition maintained the dual classification of topography and morphology. It remained in use until the end of 2000 and aligned more closely with ICD-10 for malignant neoplasms.1 ICD-O-3 (2001) – A major revision, this version introduced substantial changes to lymphoma and leukemia classification and incorporated early molecular pathology insights from the WHO Blue Books, paving the way for more nuanced tumor categorization.1 ICD-O-3.1 (2013) – A minor update focused on terminology and code refinements. It was not widely adopted in the United States.1 ICD-O-3.2 (2019) – Finalized by the International Agency for Research on Cancer (IARC), this edition became the standard in 2021. It includes major updates to behavior codes, reflecting current knowledge of tumor aggressiveness, recurrence risk, and metastatic potential. It also aligns with the latest WHO Blue Book classifications, incorporating histopathological and molecular data.1 Each update of ICD-O has mirrored the field’s shift from purely histological classification to integrated molecular and genetic insights. This evolution ensures more precise diagnosis, better treatment planning, and consistent data for epidemiology and insurance decision-making.
1. Neuroendocrine tumors (NETs) including pituitary neuroendocrine tumors (PitNETs) Under ICD-O-3.2, all neuroendocrine tumors (NETs) are now classified as malignant (/3), regardless of grade or anatomical site. This includes both well-differentiated NETs, which grow slowly and often have favorable outcomes, and poorly differentiated neuroendocrine carcinomas (NECs), which are more aggressive and linked to worse prognoses. The Ki-67 index, a marker of cellular proliferation, is used to grade these tumors. Even low-grade NETs (G1 and G2) now carry a malignant classification, reflecting the growing view that all NETs present some risk of recurrence or metastasis.6 A notable example is the reclassification of pituitary adenomas as PitNETs, which are now coded as malignant (/3).7 Although this change aligns with the WHO Blue Books, it remains a subject of debate. Most pituitary adenomas are clinically benign, slow-growing, and often discovered incidentally. Only a small portion demonstrate invasive behavior. Despite this, the new classification seeks to standardize terminology across neuroendocrine tumors. Insurance impact: Reclassifying all NETs, including PitNETs, as malignant has major implications for critical illness (CI) insurance. These tumors may now qualify for claims, even if low-grade or asymptomatic. Insurers should reassess policy wording, particularly exclusions for non-metastatic or low-risk tumors, and consider updates to underwriting guidelines. Increased claims volume is possible, making it important to ensure consistent and medically informed adjudication practices. 2. Thymomas Thymomas are rare tumors that originate from the epithelial cells of the thymus, typically affecting individuals between ages 40 and 60. Many thymomas are indolent and discovered incidentally. Under ICD-O-3.2, they are now classified as malignant (/3), reflecting their potential for local invasion and, in some cases, metastasis. This reclassification aligns with updated pathological insights, although early-stage types – A, AB, and B1 – have excellent survival outcomes.8 Insurance impact: This malignant reclassification of thymomas may now qualify for CI claims under cancer definitions. Insurers should review exclusions for early-stage thymomas and update underwriting criteria to reflect their variable behavior and favorable early-stage prognosis. 3. Gastrointestinal stromal tumor (GIST) GISTs arise from the interstitial cells of Cajal in the gastrointestinal tract. Previously, they were variably classified as benign, borderline, or malignant based on tumor size and mitotic rate. ICD-O-3.2 now uniformly classifies all GISTs as malignant (/3). While many small GISTs with low mitotic activity are clinically indolent, the reclassification reflects their potential for recurrence and metastasis, particularly in larger or higher-grade cases.9 Insurance impact: In countries like Japan and Korea, routine screening may increase early-stage GIST detection. This could lead to more CI claims for tumors that were historically excluded. To manage this, insurers may need to introduce exclusions for early-stage or low-risk GISTs or adopt tiered definitions that consider tumor size and mitotic index. In markets without routine screening, the impact may be smaller but still warrants attention.
Spotlight on specific tumors
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The relationship between ICD-O and Blue Books The WHO Blue Books, also known as the WHO Classification of Tumors, are authoritative guides that define and categorize tumors based on the latest consensus in pathology. They reflect both histological and molecular features and are updated regularly by subject matter experts. These volumes often introduce new tumor types, revised terminology, and behavior classifications before those changes are incorporated into ICD-O coding.4 This timing gap can create challenges for insurers. A pathology report may use a new Blue Book term that does not correspond with any existing ICD-O code, making it unclear whether the diagnosis qualifies under a policy’s cancer definition. This can lead to confusion, inconsistent claims decisions, or disputes between insurers and policyholders. To close this gap, insurers should implement a clinical review process that incorporates both ICD-O and the most recent WHO Blue Book guidance. Maintaining a reference table that maps new terminology to existing ICD-O codes, along with interim interpretations, supports consistent, medically aligned claims decisions.
Insurance implications: A new landscape Cancer accounts for 50% to 70%5 of CI claims. A change in how tumors are classified – particularly behavior code shifts in ICD-O – can significantly impact the claims landscape. A tumor previously coded as benign or borderline may now qualify as malignant, making it eligible for a cancer claim under existing CI definitions. For example, pancreatic neuroendocrine tumors (PanNETs), once considered benign, are now classified as malignant under ICD-O-3.2. Claims that were previously denied may now be payable. These reclassifications also affect underwriting. Tumors once seen as low-risk and accepted at standard rates may now require exclusions, loadings, or declines. Pituitary neuroendocrine tumors (PitNETs) once classified as benign and often discovered incidentally, are now coded as malignant. This shift requires a review of underwriting guidelines. Pricing is also impacted. While short-term effects may appear modest, the cumulative impact over time could be substantial – particularly in markets with high screening rates for certain gastrointestinal cancers, such as Japan and South Korea. Earlier detection in these regions may lead to more claims for tumors now classified as malignant but historically considered benign and excluded. Insurers must reassess definitions, update guidelines, and refine pricing to align with evolving classifications.
The evolution of cancer classification codes is not just a clinical shift – it carries significant operational and financial implications for the insurance industry. To stay aligned with medical advances and maintain sustainability, insurers should take the following strategic actions: Review CI definitions Ensure definitions reflect current medical classifications. Consider tiered definitions or exclusions for low-grade tumors. Update underwriting manuals Incorporate the latest ICD-O classifications and tumor behavior codes into underwriting guidelines. Provide clear, evidence-based criteria for evaluating reclassified tumors. Monitor ICD-O updates ICD-O is updated regularly. Insurers must stay current to anticipate changes that may affect claims or pricing. Educate claims teams Ensure adjudicators understand the nuances of behavior codes and how they impact claims eligibility.
What needs to happen next?
The reclassification of cancer behavior codes reflects meaningful progress in medical science. Our understanding of tumor biology has advanced significantly over the past decade, but it brings complexity and greater responsibility. For insurers, the challenge is to keep pace with these changes while maintaining clarity, fairness, and sustainability in product design, underwriting, and claim management. These changes may seem technical or minor, but their impact on insurance operations is significant.
Conclusion
Emerging health technologies offer significant potential for enhancing healthcare delivery and present untapped opportunities for insurers across the entire value chain. While these technologies provide benefits – such as more accurate risk profiling, streamlined processes, and personalized product offerings – their adoption requires careful consideration of data privacy, ethical implications, regulatory compliance, and integration challenges. Successful implementation of new health technologies in insurance requires a collaborative approach involving multiple stakeholders, robust assessment criteria, and thorough testing with end-users to ensure real-world fit and business success.
Chen W., et al. Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement. Frontiers in Bioengineering and Biotechnology. Volume 12, 2024. doi:10.3389/fbioe.2024.1420100 Curran T., et al. Camera-based remote photoplethysmography for blood pressure measurement: current evidence, clinical perspectives, and future applications. Connected Health and Telemedicine. 2023;2: 200004. doi: 10.20517/ch.2022.25 Odinaev I., et al. Robust Heart Rate Variability Measurement from Facial Videos. Bioengineering. 2023; 10(7):851. https://doi.org/10.3390/bioengineering10070851 Li H., Cao J., Grzybowski A., Jin K., Lou L., Ye J. Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images. Healthcare (Basel). 2023 Jun 13;11(12):1739. doi: 10.3390/healthcare11121739. PMID: 37372857; PMCID: PMC10298137 Zhu Z., Wang Y., Qi Z., et al. Oculomics: Current concepts and evidence, Progress in Retinal and Eye Research, Volume 106, 2025, 101350, ISSN 1350-9462, https://doi.org/10.1016/j.preteyeres.2025.101350 Aeyehealth.com [Internet]. [Cited 2025 June 05]. Available from: https://www.aeyehealth.com/research Huang L., Li Q., Lu Y., et al. Consensus on rapid screening for prodromal Alzheimer’s disease in China. General Psychiatry 2024;37:e101310. doi:10.1136/ gpsych-2023-101310 Magno M., Martins A.I., Pais J., et al. Diagnostic accuracy of digital solutions to screen for cognitive impairment: a systematic review and meta-analysis. DOI: https://doi.org/10.21203/rs.3.rs-3160170/v1 Li Y., Cui L., Wu J., et al. A Novel Three-minute Game-based Cognitive Risk Screening Tool—WeChat Mini-program-based Design and Large-sample Feasibility Studies. doi:10.3969/j.issn.1671-7104.2023.05.005
Novel health technologies can significantly impact various stages of the insurance value chain.
Part II: Lewy Body Dementias
A wave of emerging health technologies is reshaping the healthcare landscape. Some are designed for clinical use by healthcare professionals, while others are consumer-facing tools intended for direct use by individuals. These innovations can enhance healthcare delivery by improving assessments, diagnostics, monitoring, information access, and data management. For insurance providers, they offer countless potential crossover benefits. However, selection and implementation of these technologies present complex challenges.
Digital health technology includes an increasing vast range of tools, applications, software, and sensors that can be interconnected, typically developed to improve healthcare and health outcomes. While some modernize familiar medical tools, recent years have seen a sharp rise in consumer-centric solutions designed for everyday use – think wearable fitness watches. This shift is influencing attitudes around health tools and solutions. Many support a wide range of medical functions, including assessment, screening, monitoring, detection, diagnosis, and access to health information. They also offer secure data storage and privacy protection, enabling legitimate data-sharing. The scope of health tech now extends to tools that support not just physical wellbeing, but also mental health. Many newer health technologies prioritize user convenience with features that encourage frequent use and continuous data generation: Location-agnostic – Designed for personal use, many tools can be accessed remotely from any location. While some still require operation by trained healthcare personnel, they are no longer limited to fixed clinical settings, which have improved access. Time-agnostic – These tools can be used at any time (continuously, intermittently, symptom-triggered, or on-demand). This flexibility has resulted in a higher quantum of usage data. Ongoing – Longitudinal tracking of individual health parameters enables more personalized care and supports data-driven clinical decisions. Most modern health technologies include built-in data collection and storage capabilities. This allows for the capture of health data not only at the individual level, but also across different geographic regions and populations. When anonymized, such data can support meaningful analysis and advance scientific understanding. These growing datasets have also expanded the opportunities for applying artificial intelligence (AI). When used with data generated from either general populations or specialized clinical groups, AI has enabled breakthrough developments in diagnosis, medical risk evaluation, and predictive modelling. For insurers, these advances present promising but largely untapped opportunities. Yet integrating them into insurance processes remains uncharted territory and will require rethinking traditional evaluation frameworks. This article explores some of those considerations by highlighting recent developments in health technology and sharing insights from a real-world business case.
Defining health technologies
Remote photoplethysmography (rPPG) is a non-contact method for measuring physiological signals by analyzing subtle color changes in the skin captured through standard video cameras.1 Like traditional photoplethysmography (PPG), which detects blood volume changes in the microvascular bed of tissue, rPPG does so remotely without physical sensors, registering color changes that occur with each heartbeat. Sophisticated algorithms can then compute vital signs such as heart rate (HR), respiratory rate, and blood oxygen saturation – often by capturing a short video of the face.1 rPPG has attracted growing interest in healthcare2 due to its potential for: Remote patient monitoring – Enables continuous, non-invasive monitoring of patients without attached sensors. Telemedicine – Allows healthcare providers to assess vital signs during video consultations. Neonatal care – Offers a non-contact method for monitoring infants, reducing skin irritation from adhesive sensors. Stress and anxiety detection – Measures stress levels during therapy sessions or in workplace settings. Sleep studies – Provides a less intrusive way to monitor sleep patterns and quality. Fitness and wellness – Can be integrated into smart mirrors or fitness equipment to provide real-time health data during exercise. One promising metric measurable via rPPG is heart rate variability (HRV) – the variation in time intervals between heartbeats.3 HRV is a powerful indicator of: Autonomic nervous system function – HRV reflects the balance between the sympathetic and parasympathetic nervous systems. Stress and fatigue levels – Used in biofeedback techniques to improve stress resilience and emotional regulation. Athletes also use HRV to optimize training and prevent overexertion. Cardiovascular health – Low HRV is linked to higher cardiovascular risk and poorer outcomes for patients with existing cardiovascular diseases. Early signs of illness or physiological imbalance – Because HRV reflects the body’s ability to adapt to stress and environmental demands, it serves as an indicator of overall health. rPPG provides a non-invasive, continuous method for measuring HRV and other biomarkers, making it a compelling area of research. Combined with biomarker analysis, it enables health assessments without physical contact or wearable devices. Accurate measurement using rPPG requires controlled conditions, including consistent lighting, stable camera frame rates, and minimal subject movement. Despite these challenges, advances in computer vision and deep learning are steadily improving the reliability of rPPG-based analysis.
rPPG
The retinal vasculature mirrors the body’s general circulatory system, while retinal nerve fibers extend directly from the central nervous system. As such, examining the eye, fundus, and retina have long been part of systemic health evaluation.4,5 Advances in digital optics and imaging – including fundus photography, retinal CT, and MR scanning – have enabled wide adoption of non-invasive retinal imaging. Hand-held fundus cameras offer added portability, and high-resolution imaging now reveals retinal biomarkers that were previously invisible to the human eye. Oculomics – the use of ophthalmic biomarkers to detect, predict, and understand disease – has been made possible by these optical and digital innovations.4,5 Large volumes of retinal images have become fertile ground for AI. Machine learning and deep learning techniques have produced algorithms that accurately detect conditions like diabetic retinopathy, glaucoma, and macular degeneration.6 Some mature models can now make diagnoses without a human specialist, using only high-quality optical images. Achieving sensitivity and specificity of 93% and 91% respectively for diabetic retinopathy detection, FDA approval has been granted to specific AI models.6 Beyond eye disease, researchers are developing retinal biomarkers to detect and predict systemic conditions, including cardiovascular, neurodegenerative, chronic kidney, and hepatobiliary diseases. Researchers are also examining whether specific retinal features correlate with metrics like BMI, blood pressure, cholesterol, and hemoglobin levels – potentially offering a noninvasive alternative to traditional blood tests.4,5 Despite strong momentum, AI models still require validation in large, diverse, real-world populations. Much of the current research remains siloed. Best practice guidelines could help standardize protocols and improve consistency across imaging techniques. Still, even with user acceptance, cost-effectiveness must be demonstrated. As clinical adoption progresses, the overlap between clinical and insurance objectives may open a nascent path of multi-system risk segmentation through a single non-invasive assessment.
Oculomics
Developers worldwide have created online cognitive assessment tools, ranging from short-gamified tasks to comprehensive tests incorporating fine-motor response, reaction time, and voice biomarkers.7,8 Most evaluate key domains like memory, problem-solving, and executive function, with language adaptations for local markets. Performance is typically calibrated against clinically validated tools such as the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). Systemic reviews report sensitivities above 80% with varying specificities.8,9 Many tools have received FDA safety exemptions but are intended only for screening, not diagnosis of cognitive impairment or dementia. Most platforms do not require a trained professional, allowing for convenient single or repeated use.8 Depending on product strategy, sales channels, and risk evaluation needs, online cognitive screening tools may offer insurers meaningful value.
Digital cognitive screening test
Novel health technologies can significantly impact various stages of the insurance value chain. The following outlines the key areas of influence: Risk assessment and underwriting • More accurate risk profiling – Continuous health data enables more precise risk assessment and mitigates the risk of non-disclosure. • Dynamic underwriting – Real-time health metrics may allow insurers to adjust premiums, supporting more personalized and equitable pricing models. Policy issuance and management • Streamlined application process – Non-invasive health monitoring can reduce the need for medical exams, simplifying and accelerating policy issuance. • Customized policies – Insurers can offer tailored policies based on individual health profiles and lifestyle choices. Claims management • Fraud detection – Continuous health monitoring can help identify inconsistencies and fraud. • Faster claims processing – Real-time health data can expedite claims review and processing. Customer engagement and retention • Proactive health management – Insurers can offer value-added services such as wellness programs, lifestyle coaching, mental health support, and disease management based on collected data. These services boost customer satisfaction, promote healthier behavior, and reduce claims. • Incentive programs – Rewards for maintaining strong health metrics can strengthen customer loyalty. Product innovation • Personalized insurance products – Health data supports real-time customization, moving beyond one-size-fits-all models.
Insurance considerations
While the case to adopt health technologies is strong, insurers must carefully consider the following: Data privacy and security • Regulatory compliance – Ensure adherence to data protection regulations like GDPR, HIPAA, etc. • Data security – Implement robust encryption methods for data storage and transmission. • Consent management – Develop clear protocols for collecting and managing customer consent. Ethical considerations • Fairness and non-discrimination – Safeguard against bias in underwriting and pricing, particularly when using AI tools or algorithms. • Transparency – Clearly communicate how customer data is being used and its impact on coverage and pricing. Regulatory landscape • Compliance with regulations – Verify technology aligns with relevant insurance and health device standards. • Collaboration with regulators – Where applicable, work with regulators to shape appropriate frameworks for technology use in insurance. Technology integration and reliability • Accuracy and validation – Ensure there is robust testing and validation in terms of the accuracy of novel health technologies before implementation. • Integration with existing systems – Ideally, new technologies are seamlessly integrated with current underwriting, claims, and customer management systems for optimal efficiency. • Scalability – Consider whether the technology can support business growth and increased data volume. Customer education and engagement • Clear communication – Help customers understand the benefits of these tools, including how their data supports better outcomes and potential premium savings. Transparency builds trust. • User-friendly interfaces – Create intuitive, accessible platforms that allow customers to interact easily with their health data and policies. Cost-benefit analysis • Implementation costs – Carefully evaluate the costs of assessing, adopting, and maintaining these technologies against potential benefits. • Return on investment – Analyze the short- and long-term financial impact on risk assessment, claims, and customer retention. Continuous feedback and improvement • Regular audits – Periodically review technology performance and business impact across the insurance value chain. • Adaptability – Stay agile to accommodate new technologies and shifting customer needs.
Adoption considerations
Successful implementation of new medical technologies requires a clear, collaborative process across all relevant stakeholders. Project teams should include representatives from product development, medical, underwriting, claims, technology, pricing, marketing, and business development. Compliance and regulatory experts should also be involved to help shape the proposition. Early stakeholder engagement enables thorough and informed strategic decisions. A robust, verifiable and replicable assessment is essential. It should define, review, communicate, and document key adoption considerations, with clearly established criteria for what constitutes a “pass” in each critical area outlined in the preceding Insurance and Adoption Consideration sections. These benchmarks should be agreed upon before deeper research, development, or resource commitment. While detailed planning is important, testing the solution with end users such as customers and agents is invaluable during the pre-launch phase. Early feedback and acceptability can shape development and integration. Later-stage feedback, gathered through pilot testing or refined journey walkthroughs with target users, helps ensure real-world fit and business success. Depending on the research goals, this feedback may be collected through surveys, simulated scenarios, or formal proof-of-concept testing.
Business application and adoption
New health technologies may follow development and validation pathways that differ from traditional medical solutions. Evaluating their value, suitability, and feasibility requires a paradigm shift for insurers. Use cases vary by market and product and must be assessed through coordinated input from relevant insurance functions, aligned to clear business objectives. By addressing the full range of considerations, insurers can responsibly leverage novel health technologies to improve operations, enhance customer experience, and reduce risk. At the same time, it is essential to balance these opportunities with ethical obligations, regulatory compliance, and operational efficiency to ensure sustainable and responsible implementation.
Part of the RGA’s Asia Pacific CMO team since 2019, Dr. Karneen Tam has been supporting risk assessment and claims functions across multiple markets in the Asia Pacific region. Additionally, alongside product maintenance and review, Dr. Tam’s role also includes trend monitoring of medical and technological development, as well as novel product ideations to support business growth. Medical topic training, cross-function sharing and skill-uplifting are passionate areas of interest. Previous roles include CMO positions at various South African insurers and re-insurers. Dr. Tam received a Bachelor of Medicine, Bachelor of Surgery degree from the University of Witwatersrand South Africa. She also received a Master of Science in Diabetes Management from the University of South Wales after obtaining a Postgraduate Diploma in Diabetes Management from Cardiff University. Her previous clinical work focused on comprehensive diabetes care across all life stages, as well as chronic lifestyle diseases management. A regular contributor of medical articles to RGA’s ReFlections publication, Dr. Tam joined the ReFlections editing team in 2025.
Dr. Karneen Tam
Regional Medical Director
Karneen.Tam@rgare.com
Dr. Steve Woh, Vice President, Global Medical Director, is a member of RGA’s Global Medical team. He is responsible for providing medical underwriting insight, medical trend analysis, and health insurance thought leadership for internal stakeholders and clients. Steve also serves as a health claims director, assisting RGA health offices globally in claims matters and delivering best practice guidelines, standards, and solutions for claims adjudication. Prior to assuming his global role, Steve joined RGA in 2017 as Director of Heath Claims Management for the Southeast Asia markets, where he worked closely with clients to develop claims solutions. He has been in the insurance industry since 2012, working with multinational insurers in various capacities, including roles such as medical advisor, claims trend analyst, claims operation manager, and cost containment strategist. He began his career in 2006 as a clinician and underwent professional training in the field of anaesthesia and critical care. Steve received a Bachelor of Medicine, Bachelor of Surgery (MBBS) degree from International Medical University, Kuala Lumpur, Malaysia.
Dr. Steve Woh
Vice President, Global Medical Director
Steve.Woh@rgare.com
Dr. Si Ning Zhao is Regional Medical Director, Asia, and a member of RGA’s Asia Pacific regional medical team. She is experienced in product underwriting and guidelines development, medical technology and innovations review, risk management, case consultation, and business development support. Based in Hong Kong, her particular focus is on the Hong Kong, Korea, and Japan markets. Prior to joining RGA, Dr. Zhao was a medical director with a direct insurer. She also has extensive clinical experience in tertiary teaching hospitals in Australia and Hong Kong, as well as specialty training in intensive care medicine. Dr. Zhao earned her Bachelor of Medicine, Bachelor of Surgery (MBBS) degree from the University of Western Australia. She has a specialist qualification in Anaesthesiology and is a fellow of the Australian and New Zealand College of Anaesthetists, the Hong Kong College of Anaesthesiologists, and the Hong Kong Academy of Medicine.
Dr. SiNing Zhao
SiNing.Zhao@rgare.com
Nicotine and the Cotinine Test: The cost of consumption
Cotinine testing, while widely used to detect nicotine exposure, has limitations in distinguishing between active smokers, passive smokers, and users of nicotine replacement therapies due to varying cut-off values and the influence of multiple factors on cotinine levels. The use of nicotine products is prevalent among elite athletes for perceived performance enhancement, despite the lack of scientific evidence supporting these claims and the associated health risks. Passive smoking poses significant health risks, increasing all-cause mortality and specific disease-related mortality rates, highlighting the importance of considering second-hand smoke exposure in health assessments and underwriting processes.
A smoker absorbs about half a milligram of nicotine per cigarette, which is then broken down into a metabolite called cotinine.7 Cotinine is oxidized in the liver and distributed through various bodily fluids such as blood, saliva, and urine; 10%-15% is excreted in urine.8 While nicotine serves as a biomarker for tobacco exposure, its sensitivity and specificity* are limited due to its short half-life of roughly two hours – the time it takes for the initial level to reduce by half. Cotinine, by contrast, has a much longer half-life of about 20 hours (ranging from 12 to 40 hours) and remains detectable in the body for up to 72 hours, and in some cases, up to one week from when a person was last exposed to nicotine (either directly or indirectly).9 Several factors influence nicotine metabolism and cotinine values, including distribution and elimination from the body. Additionally, cotinine values can vary based on gender, genetics, ethnicity, pregnancy status, hydration levels, and the type of nicotine product used. These variables complicate the distinction, particularly in underwriting, between smokers, non-smokers, and individuals exposed to second-hand smoke.10
Nicotine
Hilary Henly, FCII, is a Global Medical Researcher with RGA’s Strategic Research team. Based in Ireland, she is a Fellow of the Chartered Insurance Institute (FCII) and has more than 30 years of experience in underwriting, claims, and mortality and morbidity research.
Hilary Henly
Global Medical Researcher
hhenly@rgare.com
Nicotine, the addictive chemical in tobacco, is among the most widely used stimulants worldwide.
The World Health Organization (WHO) estimates that 22.3% of the global population use tobacco, with Southeast Asia and India being the largest consumers.1 Between 1990 and 2021, smoking led to more than 175 million deaths.2 Each year, tobacco use causes 8 million deaths, 1.3 million among non-smokers exposed to second-hand smoke.3 Nicotine, the addictive chemical in tobacco, is among the most widely used stimulants worldwide. Compared to other stimulants, such as caffeine, it has significantly higher toxicity. It arouses the peripheral and central nervous system, increasing alertness, coronary blood flow, and myocardial oxygen intake. However, it also elevates heart rate and blood pressure, leading to vascular damage and compromised cardiac activity.4 Nicotine enters the body through various methods, including smoking cigarettes, chewing or sucking tobacco leaf, using water pipes, inhaling snuff, mucosal absorption through snus, or absorbing it through nicotine replacement products – such as gum, lozenges, sprays, and patches – and e-cigarettes (vapes). Once absorbed, it circulates through the bloodstream, reaching peak concentrations within 30 to 60 minutes. Absorption is slower from nicotine replacement products (NRPs) than traditional tobacco products.5 To curb nicotine addiction, the US Food and Drug Administration (FDA) recently proposed limiting nicotine levels to 0.7 milligrams per gram in cigarettes and other combusted tobacco products, excluding e-cigarettes. This policy could prevent an estimated 48 million U.S. adolescents and young adults from taking up smoking by 2100, potentially saving 4.3 million lives.6
The cotinine test does not determine smoker status; rather, it measures nicotine absorption from direct or indirect tobacco smoke, or nicotine-based products. It cannot differentiate between smoking from other nicotine sources, such as NRTs. Furthermore, studies indicate a lack of standardization in optimal cotinine cut-off values, making it difficult to differentiate smokers, passive smokers, NRT users, and non-smokers. However, when cotinine values are assessed, they may provide insight into distinguishing true smokers from passive smokers, and passive smokers from individuals without second-hand smoke exposure.
Conclusions
WHO (2024). WHO global report on trends in prevalence of tobacco use 2000-2030. Geneva: World Health Organization 2024. Available from: WHO global report on trends in prevalence of tobacco use 2000–2030 GBD 2021 Tobacco Forecasting Collaborators (2024). Forecasting the effects of smoking prevalence scenarios on years of life lost and life expectancy from 2022 to 2050: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet Public Health; 9(10): e729-e744. Available from: Lancet - Export Citations WHO (2023). Tobacco. Available from: Tobacco Akpa, O.M. et al. (2021). Passive smoking exposure and the risk of hypertension among non-smoking adults: the 2015-2016 NHANES data. Clinical Hypertension; 27: 1 (2021). Available from: Passive smoking exposure and the risk of hypertension among non-smoking adults: the 2015–2016 NHANES data | Clinical Hypertension | Full Text Mundel, T. (2017). Nicotine: sporting friend or foe? A review of athlete use, performance consequences and other considerations. Sports Med (2017); 47: 2497-2506. Available from: Nicotine: Sporting Friend or Foe? A Review of Athlete Use, Performance Consequences and Other Considerations FDA (2025). FDA proposes significant step toward reducing nicotine to minimally or nonaddictive level in cigarettes and certain other combusted tobacco products. Available from: FDA Proposes Significant Step Toward Reducing Nicotine to Minimally or Nonaddictive Level in Cigarettes and Certain Other Combusted Tobacco Products | FDA Duque, A. et al. (2017). Accuracy of cotinine serum test to detect the smoking habit and its association with periodontal disease in a multicenter study. Med Oral Patol Oral Cir Bucal 2017 Jul 1; 22(4): e425-e431. Available from: Accuracy of cotinine serum test to detect the smoking habit and its association with periodontal disease in a multicenter study - PubMed Anderson, A. et al. (2022). DNA methylation differentiates smoking from vaping and non-combustible tobacco use. Epigenetics 2021 Feb 25; 17(2): 178-190. Available from: DNA methylation differentiates smoking from vaping and non-combustible tobacco use - PMC Ab Manan, N. et al. (2019). Self-reported smoking among adolescents: how accurate is it with the urine cotinine strip test? International Journal of Pediatrics and Adolescent Medicine; 7(2): 78-82. Available from: Self-reported smoking among adolescents: How accurate is it with the urine cotinine strip test? - ScienceDirect Kawasaki, Y. et al. (2020). Effects of smoking cessation on biological monitoring markers in urine. Genes and Environment; 42: 26 (2020). Available from: Effects of smoking cessation on biological monitoring markers in urine | Genes and Environment | Full Text Salimetrics (2025). Guidelines for interpreting cotinine levels: United States. Available from: Guidelines for Interpreting Cotinine Levels: United States – Salimetrics Sharma, P. et al. (2019). Assessment of cotinine in urine and saliva of smokers, passive smokers, and nonsmokers: method validation using liquid chromatography and mass spectrometry. Indian Journal of Psychiatry, 2019 May-Jun; 61(3): 270-276. Available from: Assessment of cotinine in urine and saliva of smokers, passive smokers, and nonsmokers: Method validation using liquid chromatography and mass spectrometry - PubMed Raja, M. et al. (2016). Diagnostic methods for detection of cotinine level in tobacco users: a review. Journal of Clinical and Diagnostic Research 2016 Mar; 10(3): ZEO4-6. Available from: jcdr-10-ZE04.pdf Kim, S. (2016). Overview of cotinine cutoff values for smoking status classification. International Journal of Environmental Research and Public Health 2016; 13(12): 1236. Available from: Overview of Cotinine Cutoff Values for Smoking Status Classification Jarvis, M.J. et al. (2008). Assessing smoking status in children, adolescents, and adults: cotinine cut-point revisited. Addiction, 2008 Sept; 103(9): 1553-61. Available from: Assessing smoking status in children, adolescents and adults: cotinine cut-points revisited - PubMed Anderson, A.M. et al. (2018). A droplet digital PCR assay for smoking predicts all-cause mortality. Journal of Insurance Medicine 2018; 47: 220-229. Available from: A Droplet Digital PCR Assay for Smoking Predicts All-Cause Mortality - PMC Wang, X. et al. (2024). Passive smoking and risk of pancreatic cancer: an updated systematic review and meta-analysis. PeerJ 2024 Oct 8; 12e18017. Available from: Passive smoking and risk of pancreatic cancer: an updated systematic review and meta-analysis - PubMed Read, D. et al. (2024). Snus use in football: the threat of a new addiction? Biology of Sport 2024 Jan; 41 (1): 201-205. Available from: Snus use in football: the threat of a new addiction? - PubMed Bartik, P. et al. (2023). The effect of high nicotine dose on maximum anaerobic performance and perceived pain in health non-smoking athletes: crossover pilot study. The International Journal of Environmental Research and Public Health, 2023 Jan 5: 20(2): 1009. Available from: The Effect of High Nicotine Dose on Maximum Anaerobic Performance and Perceived Pain in Healthy Non-Smoking Athletes: Crossover Pilot Study - PMC
Cotinine cut-off values
Cotinine can be measured in urine, blood plasma, and saliva and has a high sensitivity and specificity. However, cotinine test cut-off values vary, and there is no standardized value to distinguish true smokers from true non-smokers or passive smokers. Cut-off values typically range from 10 to 20 nanograms per milliliter (ng/ml) for serum or salivary cotinine, and 50-200 ng/ml for urinary cotinine.8 In the US, the average salivary cotinine level for adult smokers exceeds 100 ng/ml.11 Mean saliva cotinine test results are reported at 9.53 ng/ml for non-smokers, 18.31 ng/ml for passive smokers, and 327.39 ng/ml for true smokers.12 Urinary cotinine levels correlate with daily nicotine intake absorbed and are typically four to six times higher than salivary cotinine levels, ranging from 20 to 550 ng/ml.13 Passive smokers exhibit elevated urinary cotinine levels, averaging approximately three times higher than that of non-smokers, reported at 13.6 ng/ml in non-smokers, 36.63 ng/ml in passive smokers, and 1043.7 ng/ml in smokers.12
Sensitivity and specificity of cotinine tests
The sensitivity and specificity of cotinine tests vary based on the cut-off value used. Sensitivity can range between 69% and 99%, and specificity ranges between 74% and 99%. For example, with a cut-off value of 14.2 ng/ml, sensitivity is 99% and specificity is 96.4%.14 Lowering the cut-off value to 12 ng/ml slightly reduces sensitivity to 96.7% and specificity to 96.9%. Studies suggest that a cut-off value of 15 ng/ml for plasma or saliva cotinine provides the best distinction among current smokers, non-smokers, and passive smokers, while 20 ng/ml represents the upper limit typically associated with passive smoking.15 A lower cut-off value results in higher sensitivity but lower specificity, meaning more passive smokers may test positive. Conversely, a higher cut-off value increases specificity but reduces sensitivity, meaning some irregular light smokers could test negative.14 In simple terms, raising the cut-off value decreases the likelihood of detecting nicotine exposure, while lowering it increases the likelihood of a positive test result. Studies show that light, passive smokers typically have cotinine levels below 5 ng/ml, whereas heavy, passive smokers may reach 10 ng/ml or slightly higher. Light, irregular smokers generally fall between 10 and 100 ng/ml, while regular smoker values are 100 ng/ml or more.14 Cotinine test cut-off values may also vary based on smoking prevalence in different countries. In regions where smoking rates are lower, more non-smokers have non-detectable levels due to reduced exposure to second-hand smoke. As a result, there is no universal cut-off value reliably distinguishing smokers from passive smokers.
Other tests for exposure to nicotine
Urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) is a tobacco-specific nitrosamine with a half-life of 10 to 40 days, making it a useful indicator of smoking status. NNAL is not detected in non-smokers unless they have been exposed to second-hand smoke. However, levels are significantly lower in passive smokers – 5.19 picograms per milligram** (pg/mg) of creatinine in urine – compared to active smokers (183 pg/mg) and may also be a useful indicator of non-smoker status in passive smokers who test positive on a cotinine test.10 Other testing methods can also distinguish smokers from non-smokers. Hair testing can accurately detect cotinine for up to three months after the last nicotine exposure. Epigenetic markers provide another means of differentiation. For example, DNA methylation status at cg05575921 is strongly associated with cigarette smoking, whereas e-cigarette use and smokeless tobacco use do not demethylate cg05575921. This makes it the most sensitive and specific epigenetic indicator of smoking. However, using epigenetic testing in the underwriting process is time-consuming and costly and presents a barrier to widespread implementation.16
Passive smoking
Exposure to second-hand smoke can result in a positive cotinine test, which may create challenges during the underwriting process when true non-smoker applicants test positive.4 Passive smoke contains more than 50 carcinogens, and studies show that concentrations of carcinogenic chemicals are much higher than in directly inhaled smoke. Research indicates that exposure to tobacco smoke increases all-cause mortality by 10%, cardiovascular disease (CVD)-related mortality by 12%, cancer-related mortality by 9%, and respiratory-related mortality by 14% compared to non-smokers.17
Nicotine replacement therapy (NRT)
A growing problem is the use of nicotine replacement therapies (NRTs) – such as gum, patches, and other tobacco products, including snus in non-smokers. Snus, a smokeless tobacco product originating in Sweden, comes in a pouch that is placed between the upper lip and gums for approximately 30 minutes before being removed. Nicotine is rapidly absorbed across the mucosal membrane into the bloodstream. Each pouch contains approximately 15mg of nicotine, similar to a traditional cigarette, but prolonged exposure leads to higher nicotine concentrations in users.18 Nicotine products are often used to enhance aerobic performance, with many elite athletes using them to improve concentration and reaction times, control weight, and promote relaxation. When taken at higher doses, nicotine enhances the effect of serotonin and reduces feelings of anxiety and stress. However, studies do not support claims that nicotine improves athletic performance.19 Its use is associated with adverse effects, including nausea, vomiting, nicotine addiction, periodontal disease, heat intolerance, impaired cardiac function, and an increased risk of pancreatic cancer.18 An estimated 25%-50% of elite athletes on professional teams and in strength sports use nicotine products for performance enhancement. The rate is 28% among rugby players, while rates are even higher in other sports – up to 34% in major league baseball players, 50% in ice hockey, 56% in American football. These figures are substantially higher than the 25% nicotine detection rate in the general population.5,19 Reports suggest that the use of snus among elite athletes is rising.
Cotinine levels (ng/ml)
1500 1250 1000 750 500 250 0
Cotinine levels (urine)
Non-smokers (n=27)
Passive smokers (n=15)
Smokers (n=56)
Cotinine levels (saliva)
500 400 300 200 100 0
Non-smokers (n=26)
Smokers (n=49)
P<0.001. Comparison of mean urine and saliva continine levels among nonsmokers, passive smokers and smokers
* Sensitivity: the percentage of self-reported non-smokers classified as smokers (true positives); specificity: the percentage of self-reported non-smokers classified as non-smokers (true negatives) ** Picogram per milligram is equal to one trillionth of a milligram
Figure 1: Comparison of mean urine and saliva cotinine levels among non-smokers, passive smokers, and smokers.12
What’s New?
Digital twins in healthcare and insurance
Dr. Lauren Acton, MBChb, has joined RGA South Africa as Chief Medical Officer. Lauren completed her medical studies and obtained her Bachelor of Medicine, Bachelor of Surgery degree at the University of Pretoria in South Africa in 2006. After her internship and community service in Johannesburg, she pursued a Master’s degree in bioethics at the University of Stellenbosch, also in South Africa. Her experience encompasses both clinical and insurance medicine, having worked in a private practice as well as for a direct insurer and a reinsurer before coming to RGA.
After a quarter-century with RGA South Africa, Dr. Anthony (Tony) Crosley, the branch’s Chief Medical Officer, is retiring. Tony came to the South Africa office soon after its 1999 launch, and has played a major role in the success and market standing of the underwriting and medical teams. A physician and veteran of more than half-century in insurance medicine, Tony is a doyen of the field: his immense medical and insurance knowledge, work ethic, capabilities, and collegiality are well known, and will be missed. We wish him the very best in his life’s next journey.
Medical Team Updates
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Drug Watch: Additional Copy
Editor's note: These findings emphasize the need for nuanced risk assessments in insurance that consider the duration and timing of metabolic syndrome, leading to more accurate underwriting, better pricing, and targeted wellness programs.
Role of age and exposure duration in the association between metabolic syndrome and risk of incident dementia: a prospective cohort study
Oliver, A.J. et al. Single-cell integration reveals metaplasia in inflammatory gut diseases. Nature 635, 699–707 (2024). https://www.nature.com/articles/s41586-024-07571-1
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Publications relevant to insurance medicine appearing recently in research literature.
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As the healthcare industry embraces digital transformation, the integration of digital twin technology promises to unlock new avenues for innovation, cost optimization, and enhanced patient outcomes – and new opportunities for life and health insurers.
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Qureshi D. et al. The Lancet Healthy Longevity, Volume 5, Issue 12, 100652 https://www.thelancet.com/journals/lanhl/article/PIIS2666-7568(24)00185-5/fulltext
Lähteenvuo M. et al. JAMA Psychiatry. 2025;82(1):94-98. doi:10.1001/jamapsychiatry.2024.3599 https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2825650
In this comprehensive study, researchers integrated over 25 single-cell datasets to create the largest information resource of the human gut, encompassing samples from both healthy and diseased individuals. This Gut Cell Atlas, a significant component of the Human Cell Atlas project, aids in identifying changes associated with conditions such as ulcerative colitis and Crohn’s disease, thereby facilitating the discovery of new drug targets. Additionally, the study revealed that gut metaplastic cells are involved in inflammation. This invaluable resource is freely accessible to researchers worldwide, and its approach can be applied to other organs, significantly advancing our understanding of health and disease.
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This population-based study of over 20,000 individuals aged 50-79 years with 25 years of follow-up found that metabolic syndrome significantly increases the risk of developing dementia. The risk was particularly high for those with metabolic syndrome in mid-life (60-69 years) and for those with prolonged exposure to metabolic syndrome over 20 years. No significant association was found in late-life (70-79 years) or for those with newly developed metabolic syndrome. These findings highlight the importance of both the presence and duration of metabolic syndrome in assessing dementia risk and suggest critical periods for intervention.
In a Swedish nationwide register-based study, glucagon-like peptide-1 (GLP-1) agonists semaglutide and liraglutide were linked to a significantly reduced risk of alcohol use disorder (AUD) and substance use disorder (SUD) hospitalizations, as well as somatic hospitalizations. No significant changes in suicide attempt risk were observed, although semaglutide showed a potential decrease. These GLP-1 agonists performed better than traditional AUD medications (naltrexone, disulfiram, and acamprosate), but comparisons should be interpreted cautiously. The study suggests GLP-1 agonists might help treat various addictions due to their effects on craving and reward pathways. However, as an observational study, it shows only associations, not causality. Randomized clinical trials are needed to confirm these findings.
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A digital twin is a virtual representation constructed from real-time data of a physical entity, continuously updated with ongoing data to simulate and predict its behavior. Conceived in the 1960s, the digital twin concept is now gaining traction in the healthcare industry, offering a wealth of potential applications that could revolutionize patient care and streamline treatment delivery, which affects the insurance sector in many ways. In healthcare, digital twins can simulate patient-specific models, integrating data from medical records, genetic information, and real-time health monitoring devices. This allows for personalized treatment plans, predictive diagnostics, and improved patient outcomes. Furthermore, digital twins can facilitate clinical trials and drug development by providing a safe, controlled environment for experimentation. Together, these advances could potentially lead to more accurate predictive analytics and sharper risk stratification, allowing insurers to tailor coverage and pricing based on individual health profiles rather than demographic details. By creating detailed simulations of policyholders, digital twins can improve risk assessment and healthcare cost prediction, which leads to more accurate underwriting and policy pricing. Additionally, digital twins enable continuous monitoring of policyholders’ health, allowing insurers to offer dynamic and effective insurance models. This proactive approach can help promote a longer lifespan and a more robust “healthspan,” as well as facilitate early detection of health issues, potentially reducing the frequency and severity of claims.
Health View II
References: https://www.dw.com/en/how-australias-social-media-ban-could-affect-us/video-70972818 https://oxford.shorthandstories.com/social-media-digital-mental-health/index.html https://theconversation.com/australias-social-media-ban-for-kids-under-16-just-became-law-how-it-will-work-remains-a-mystery-244736 https://theconversation.com/the-government-has-introduced-laws-for-its-social-media-ban-but-key-details-are-still-missing-244272
To address these issues, authorities are studying ways to protect vulnerable people. In Australia, for example, a controversial law to ban social media use for children under 16 was introduced in November 2024. There remain more questions than answers as to whether the law will be effective in mitigating negative outcomes. As the digital age continues its rapid evolution, it is crucial to strike a balance between leveraging technology’s potential benefits and mitigating its negative impacts on mental health. A nuanced approach incorporating education, regulation, and the development of healthy digital habits is essential for promoting overall wellbeing.
The digital age has profoundly impacted mental health, producing both promising advances and significant challenges. On one hand, technology has democratized access to mental health resources and support networks. Online communities and forums provide platforms for individuals to share experiences and seek support, fostering a sense of belonging and reducing feelings of isolation. Additionally, mental health wellness apps and emerging digital therapeutics have made interventions more accessible, allowing users to manage stress, anxiety, and depression conveniently. In the UK, the National Institute for Health and Care Excellence (NICE) recommended digital tools to help children and young people with anxiety, some of which were adopted by the National Health Service (NHS). On the other hand, the digital age has introduced new stressors and challenges. Social media, while connecting people, often promotes a culture of comparison, leading to feelings of inadequacy and negatively impacting self-esteem for some people. The constant connectivity and bombardment of information can also contribute to feelings of anxiety and overwhelm. Social media use has been linked to increased rates of digital burnout and depression, particularly among young adults. Additionally, cyberbullying and online harassment bring potentially severe psychological consequences. One study found significant potential for harm from online behavior, particularly noting high levels of internet use and internet addiction and websites with self-harm or suicide-related content.
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ICLAM 2025 The International Committee for Insurance Medicine (ICLAM) will host the ICLAM 2025 Conference May 11-14, 2025 in Estoril, Portugal. This four-day conference, a leading industry event, will welcome expert speakers from around the world and draw a global audience. Information and registration can be found at www.iclam2025.org.
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Editor's note: These results highlight an innovative approach that may improve the identification of cardiac risk in applicants, supporting underwriting accuracy and allowing for more individualized risk assessment based on retrospective imaging data.
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Dr. Steve Woh Vice President, Global Medical Director
Health View:
In a landmark moment for digital healthcare, UK-based innovator Skin Analytics has secured EU regulatory approval for DERM, its autonomous artificial intelligence (AI) skin cancer detection platform. This makes it the first AI system globally authorized to make clinical decisions on skin cancer without human oversight. Using a high-resolution image captured with a dermatoscope, DERM electronically analyzes the lesion or mole, delivering a suspected diagnosis along with tailored recommendations for the most appropriate next steps in patient care. DERM has earned the prestigious Class III CE mark under the EU Medical Device Regulation (MDR), the highest level of regulatory scrutiny for medical devices. This milestone not only validates the system’s clinical robustness but also signals a new era in AI-driven diagnostics. With a 99.8% accuracy rate in ruling out skin cancer – surpassing the 98.9% benchmark set by dermatologists – DERM is poised to transform dermatological care. Its deployment has already demonstrated the ability to slash waiting times for skin cancer assessments from months to mere days. This is a critical improvement amid a global shortage of dermatologists, with Europe averaging just 30 specialists per one million people. As an example, the UK has seen a 170% surge in urgent skin cancer referrals within the past decade, with greater than 11% of patients waiting more than a month for evaluation. DERM has already assessed more than 110,000 cases across the UK, showcasing strong real-world performance and scalability. This regulatory green light paves the way for wider adoption across Europe and other CE-recognizing regions. More important, it sets a powerful precedent for responsible AI integration in clinical workflows, offering a scalable solution to workforce shortages and enabling earlier, more accessible cancer detection. https://skin-analytics.com/news/regulatory-certification/derm-class-iii-ce-mark/
AI Breakthrough: The first autonomous diagnostic tool
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RGA Study Explores the GenAI-Powered Revolution in Insurance Underwriting
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Underwriting Liver Function Tests Using a Data-Driven Approach
Smoke Signals: How insurers can uncover hidden tobacco use in the digital age
Please click the articles below to see some of RGA's recently published thought leadership on underwriting topics.
Underwriting is a profession requiring an extensive breadth and depth of risk-related knowledge, as well as the ability to apply that expertise to real-world situations. RGA’s Global Underwriting Philosophy and Education team is committed to providing our clients with up-to-date resources and information about the broad range of conditions and impairments underwriters might encounter.
Other recent Global Underwriting Manual updates provide guidance on mood disorders, specifically bipolar and feeding and eating disorders. These new guidelines are available via RGA Central.
Our newest online Global Underwriting Manual Resource Hub* features updated underwriting guidelines and information for alcohol consumption. Alcohol consumption is associated with an increased risk of several disorders, including alcohol dependence, liver cirrhosis, and other non-communicable diseases and mental health conditions. Elevated levels of alcohol use are correlated with increasing levels of morbidity and mortality. The revised guidelines provide enhanced risk stratification for accurate risk assessment.
Explore the Alcohol Consumption Resource Hub
An underwriting manual you can count on GUM by the numbers 2024: 695,000+ logins 1.3 million+ calculations 800,000+ Precision Calculator uses 3.2 million+ topic views
We look forward to providing you with news and updates regarding the Global Underwriting Manual in future issues of ReFlections. *GUM guidelines in North America can vary from those applied across all other markets. RGA clients in US and Canada, please click here to access your customized GUM resources.
By Brooke Butler, Director, Operations and Marketing, Global Underwriting Manual
Case ReView
Dr. Sheetal Salgaonkar, MBBS, MD, DBIM, FALU
Vice President and Global Medical Director
ssalgaonkar@rgare.com
A Case of Hemophilia
Case presentation: Applicant profile: A 44-year-old male, nonsmoker, working as a neurologist in a hospital setting Medical history: Known case of hemophilia Clinical tests: Routine investigations, including CBC, have been performed, which were all normal Underwriting decision: The underwriter requested copies of all medical consultations, follow-up records, and treatment details for further assessment. The client declared that he does not take any medications and leads a normal life, and that further medical reports were unavailable. The case was accepted based on this declaration with an EMR of +50. Claim details: The beneficiary submitted a death claim within one year for this life assured. The claim documents revealed that the life assured’s elder brother was also diagnosed with hemophilia. The life assured had a history of classical joint bleeds and excessive bleeds from cuts. He was admitted 12 months after his policy was initiated with a hemorrhagic stroke. His aPTT – activated partial thromboplastin time (sec) – during this admission was prolonged at 167 (normal is 38), and factor VIII assay (coagulation protein) was less than 1%. His cause of death was intracranial hemorrhage (ICH) and acute kidney failure.
1. What is hemophilia? Hemophilia is a hereditary X-linked recessive bleeding disorder characterized by deficiency or functional absence of specific coagulation factors, primarily factor VIII (Hemophilia A) or factor IX (Hemophilia B). This deficiency impairs the intrinsic pathway of the coagulation cascade, resulting in defective thrombin generation and poor fibrin clot formation and leading to a bleeding diathesis. Hemophilia results from mutations in the F8 gene (Hemophilia A) or F9 gene (Hemophilia B), causing reduced or absent plasma levels of clotting factors VIII or IX, respectively. 2. What determines the severity of hemophilia? Clotting factor levels critically influence the prognosis of hemophilia by determining the severity of the disease and the associated bleeding risk. Lower clotting factor levels correlate with increased frequency and severity of bleeding, which directly impacts outcomes such as joint damage, disability, and life expectancy. Hemophilia severity is classified by the percentage of normal clotting factor activity in the blood. Severe: <1% normal factor activity, presenting with spontaneous hemorrhages often within joints and muscles Moderate: 1%–5% factor activity; bleeding mainly occurs after mild trauma or surgery Mild: 5%–40% factor activity; bleeding typically occurs only after significant injury or invasive procedures Intracranial hemorrhage (ICH) is relatively rare compared with other sites of bleeding, but it is one of the most dangerous and life-threatening events in individuals with hemophilia. ICH can occur in individuals of all ages, either spontaneously or after trauma. Severe factor deficiency is a risk consideration for ICH. 3. What are the diagnostic tests and treatment protocols? Most patients have affected family members, but a negative family history cannot be used to exclude the possibility of hemophilia occurring. Screening laboratory tests include prolonged activated partial thromboplastin time (aPTT) with normal prothrombin time (PT) and platelet counts. aPTT is a blood test that measures how long it takes for blood to clot. It specifically evaluates the function of the intrinsic and common pathways of the coagulation cascade. Specific factor assays to quantify factor VIII or IX levels must be conducted to confirm the diagnosis and classify the severity. Genetic testing may be employed for mutation identification, especially in familial cases. Replacement therapy with recombinant or plasma-derived factor VIII or IX concentrates remains the mainstay of treatment. 4. Case discussion In evaluating hemophilia cases, determining the factor activity level is essential for assessing disease severity. This should have been a mandatory component of this case assessment. The life assured misrepresented key medical information, resulting in partial disclosures. Had the severity of hemophilia and the history of recurrent excessive bleeding been accurately reported, the case would have warranted a different assessment and may have been declined. Key takeaways: Hemophilia is a hereditary X-linked recessive bleeding disorder. Most patients have affected family members, but a negative family history cannot be used to exclude the possibility of hemophilia. Factor activity level is a key determinant of both symptom severity and prognosis in individuals with hemophilia. Severe factor deficiency is a risk factor for ICH, which can be life threatening.
Key underwriting considerations
Please click the articles below to see some of RGA's published thought leadership on claims topics.
Claims Updates
Excellence in claims management demands deep technical expertise and a human touch. To support this, RGA delivers high-quality training and continuing education through our Pathfinder claims training program, available exclusively to RGA clients via the Global Claims Manual/Guide.
Total and Permanent Disability (TPD) – Get Started RGA’s new TPD Get Started modules are designed to elevate your approach to TPD claims management. These modules provide essential insights and practical skills to empower claims assessors to navigate the complexities of TPD claims with confidence and precision.
Tailored for busy claims professionals, the program offers self-paced learning with interactive elements and hands-on activities. Real-life examples and multimedia content help learners apply knowledge to practical scenarios, building a strong foundation for success in TPD case management.
Mental health claims training Following the launch of RGA’s Claims Mental Health Toolkit last year, we continue to expand our support for assessors managing mental health claims. Our latest module, “Essential Mental Health Literacy for Claims Assessors,” offers: Comprehensive mental health literacy Tools to combat stigma and bias Strategies for empathetic communication Guidance for interpreting clinical terminology in mental health reports
Start your journey to TPD claims excellence (available only to RGA clients)
By Jennie Calder Brown, Executive Director, Global Claims Philosophy and Education
Coming soon Mental health: Understanding delayed recovery This upcoming series of modules will support assessors with identifying and managing claims where recovery timelines exceed expectations. It includes practical strategies to transition claims from stagnation to active, progressive management. For assistance, or to learn more, please contact us.
The Fraud Fight’s New Frontier: Synthetic identities and an AI arms race
video
The Automated Advantage in Claims
Hot Topics in Disability Claims: Segmentation and ethical AI
The Longer Life Foundation (LLF) is a collaboration of more than a quarter century between RGA and Washington University School of Medicine in St. Louis. We are pleased to bring you our October 2025 newsletter, which discusses the foundation’s many recent activities. To find out more about LLF and the research it has funded to date, please visit www.longerlife.org or reach out to Dr. Preeti Dalawari at preeti.dalawari@rgare.com or Dr. Joesph Zhang at wzhang@rgare.com.
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