There is increasing unease in the medical community about using the predictive value of the body measurement index (BMI) as a guide to an individual’s current and future health. Elevated BMIs are evaluated by life and disability underwriters as part of a comprehensive review, but other than verifying overweight status, the predictive value of BMI on future morbidity and mortality is questionable until the BMI is >35, and several studies have shown better mortality in the “overweight” BMI 25-29.9 category than in the “normal” BMI 18.5-24.9 category. The goal of this treatise is to evaluate other anthropomorphic measurements that might be better predictors of future health.
Benzodiazepines are a class of tranquilizers that, like prescription opiates, are commonly subject to abuse. Abusers of benzodiazepines commonly malinger to sustain access to these prescriptions from their health care providers by creating and enhancing medically necessary conditions requiring treatment with benzodiazepines. Such behavior may involve displays of unrelenting extreme anxiety that are resolved only by the administration of benzodiazepines. Typical substance abuse behaviors also include noncompliance with treatment, lack of self-care, and lack/failure of counseling. Three cases underwritten for life expectancies in legal matters provide insight as to what kinds of benzodiazepine abuse behaviors can arise, given the conditions and resources available. For life underwriters and medical directors, such benzodiazepine abuse behaviors can serve as an underwriting red flag at time of application. Signs and symptoms of additional substance abuse, such as alcohol and prescription opiates, are likely to be found among those who abuse benzodiazepines and can serve as underwriting red flags.
Interest in the concept of “biological age” has grown substantially in recent years. Longevity clinics and direct-to-consumer companies now promote biological age testing, often with the promise that targeted interventions can slow the aging process. This article examines the scientific validity of testing for biological age and evaluates its potential utility in life insurance underwriting.
Accurate and profitable risk selection requires evidence-based survival information. Medical Impairment Manual (MIM) ratings and life expectancies can be determined from illustrated survival and/or mortality curves in the medical literature. This article illustrates how to utilize these curves to calculate MIM ratings and life expectancy estimations.
Traditional cigarette smoking has declined markedly over recent decades due to increased health awareness, regulatory action, and public health campaigns. At the same time, alternative nicotine products, particularly e-cigarettes and oral nicotine pouches, have gained popularity, especially in the United States and Europe and among younger populations. While cigarettes remain the most widely used tobacco product globally, tobacco and nicotine consumption now spans a broad range of combustible and non-combustible products, with smokeless tobacco predominating in some regions. Definitions of a “smoker” vary significantly between clinical practice and insurance medicine. Clinical definitions are nuanced and health-focused, whereas insurance classifications are typically binary and time-bound, often defining a smoker as anyone who has used a nicotine-containing product within the past 12 months. The growing use of alternative nicotine products, alongside evolving cannabis legislation, has exposed limitations in traditional definitions when it comes to accurately capturing associated risk. The expansion of alternative nicotine products challenges traditional smoker classifications used in insurance underwriting. While combustible tobacco remains the highest risk exposure, growing evidence supports the existence of a heterogeneous medium risk group associated with nicotine, noncombustible products. Although generally lower risk than cigarettes, these products are not risk-free, particularly considering early cardiovascular and metabolic effects, dual or poly-use patterns, and growing uptake at younger ages. For insurers, this evolving landscape highlights the benefit of a more nuanced underwriting approach, including enhanced disclosure of product type and use patterns, improved verification strategies, and potential pricing differentiation that better aligns risk with exposure.
The soccer industry generates billions in revenue, largely from matchday sales, commercial activities, and television rights. A significant portion of club revenues are spent on player wages making their availability for games and training economically vital. Any period of injury-related absence functions as a form of temporary disability, creating significant financial exposure for teams, insurers, and reinsurers. Preventing injuries is crucial for soccer clubs to mitigate financial and sports risks associated with player unavailability. Transfermarkt® database has been utilized to develop an accurate method to estimate the incidence of injuries among male soccer players. To propose the most appropriate preventive measures, medical teams of soccer clubs should consider other risk factors than age, such as the history of injuries and the player’s position in the field. This new model offers a practical framework for insurers, reinsurers, and club medical teams to better quantify disability risk, refine prevention strategies, and improve the prediction of injury-related downtime.
Risk selection and underwriting are more than reviewing requirements and making a final decision. It is servicing our customers, the applicant, producer, and our company. Meeting the needs of all three assures the profitability of our company.
Objectives.—:To assess artificial intelligence adoption patterns, trust levels, and perceived utility among physicians conducting disability assessments for a national insurance system, with a focus on the paradox between widespread use and limited trust in artificial intelligence for medico-legal decisions. Background.—:Artificial intelligence is rapidly entering clinical workflows, yet its integration into insurance medicine-where physicians must translate complex clinical findings into legally defensible administrative decisions-remains unexplored. Physicians conducting disability assessments operate at the intersection of clinical judgment, administrative burden, and legal accountability, making them a critical population for evaluating artificial intelligence adoption and its epistemic limits. Methods.—:A cross-sectional survey was conducted in February 2026 among 428 physicians working in medical committees, appeals boards, and advisory roles at the Israeli National Insurance Institute (response rate 85.6%). The questionnaire assessed artificial intelligence usage frequency, trust in artificial intelligence-supported decisions (5-point scale), perceived risks, and preferred applications. Results.—:Artificial intelligence adoption was widespread (76% regular or occasional use; 27% daily use), yet trust remained low (mean 2.7 of 5). Daily users showed higher trust than non-daily users (odds ratio 1.81; 95% confidence interval 1.14-2.88). Administrative burden was the dominant adoption driver. Preferred applications included medical record summarization (85%), inconsistency detection (45%), and protocol drafting (35%). Most use was informal, relying on personal tools. Senior physicians expressed relational concerns; less experienced physicians emphasized technical risks. Conclusions.—:Insurance medicine physicians distinguish between instrumental trust for administrative efficiency and epistemic trust for legally accountable judgment. Artificial intelligence integration should emphasize administrative augmentation while preserving human interpretative authority.
This brief practical review explores the fundamentals of coronary artery calcium score testing, its role in cardiovascular risk stratification, and how it can empower underwriting CV risks with greater clarity, speed, and confidence.
Glucagon-like peptide-1 (GLP-1) receptor agonists have rapidly expanded beyond their initial role in diabetes management, demonstrating therapeutic potential across a range of non-diabetic conditions. Due to their potential to improve mortality and morbidity outcomes, GLP-1 drugs are becoming an increasingly salient topic for insurers, particularly in relation to mortality improvement, medical underwriting, and new product development. Our large-scale mortality study estimates that GLP-1 drugs may drive 0.2%-0.5% annual mortality improvement, realized over a 20-year period. Low adherence could limit long-term mortality benefits, and electronic health records will be vital for accurately assessing metabolic risk in underwriting. The mortality benefits identified for GLP-1 drugs present opportunities for carriers to transform how they assess metabolic risk in underwriting programs. However, variation in individual risk profiles, driven by poor adherence and inconsistent behavioral change, as well as other factors, underscores the need for nuance in program design and application.
Objective.— To validate Emerging Risk Factors Collaboration (ERFC) high-risk cardiometabolic life years lost (LYL) tables using Gompertz-Makeham calibrated to hazard ratios, offering a reproducible framework for insurance underwriting and medico-legal life expectancy projections. Methods.— ERFC LYL tables (91 cohorts; 689,300 participants; HR≥5) benchmarked against Irish Life Tables No. 17 via constant HR∼9.5 multiplier. Gompertz-Makeham generated risk-group survival curves; LYL as baseline-risk differential ages 40-85. Model performance via mean absolute error (MAE). Results.— Gompertz-Makeham reproduced ERFC high-risk LYL (males MAE 1.21 years; females 1.16 years across 40-85), preserving empirical S-curve without disease-specific adjustments (Table 2; Figures 1-3). Age 40 loses, 20-22 years; age 75, 5-6 years. Conclusions.— HR-calibrated Gompertz-Makeham provides aetiology-independent LYL estimates within 1 - 2 years mean absolute error (MAE), extending Strauss/Shavelle disability models to multimorbidity for actuarial risk assessment and forensic reports.
A traditional print journal is not ideal for the dissemination of computer code and related data. Without the actual code and data, reproducibility cannot be assessed, and there can be no validation of the premise of such an article. Fortunately, there are inexpensive modern ways to share both data and code while providing for explanatory text: GitHub and Google Colaboratory. This article introduces both and provides 3 different examples for the reader to experience.
Prostate cancer is a major cause of disease and mortality among older men (mean age 67.3 years old) in the United States and globally. Given the biological heterogeneity of this cancer in this short- and long-term United States retrospective population-based analysis, the focus is on incidence, mortality, and survival by age, race/ethnicity, stage, and severity in cohort entry time periods (1975-99 and 2000-22), and disease duration. This comparative cohort short- and long-term study is intended to provide age-adjusted epidemiologic and demographic survival and mortality data for convenient reference by all physicians, scientists, insurance underwriters, and others interested in cancer mortality follow-up.
The predictive value in determining a person’s insulin resistance (IR) is relevant for underwriters and medical directors of life and disability insurance companies as these measurements may screen for the future development of prediabetes and type 2 diabetes, metabolic dysfunction diseases, and cardiovascular disease morbidity and mortality. This treatise is a review of the importance of early recognition of IR by the routine measurements of the triglyceride-glucose (TyG) index and its variants. A review of PubMed for relevant articles reveals that most large studies have been done in Asian populations, suggesting that these measurements may not have gained sufficient attention in the U.S. and European disability and life insurance markets.
Lung cancer is the most common cause of cancer-related mortality worldwide. With the introduction of low-dose computed tomography (LDCT), detection of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are increasingly being found in young, never-smoking females in Asia. There are survival studies suggesting that these early cancers, with resection, have no recurrence in 5 and even 10 years and therefore might be considered cured, allowing for favorable underwriting consideration for life insurance. However, other studies have shown incidences of secondary primary lung cancers (SPLCs) occurring within 10 years after surgical resections of AIS and MIA tumors, but with their clinical course and response to treatment appear to be better than original primary lung cancers, potentially still allowing for insurance with rating. The goal of this article is to review the evidence both for and against insuring these populations of lung cancer patients.
The role of artificial intelligence (AI) in biologic discovery, and in the practice of medicine is rapidly increasing. AI-assisted analyses of large databases are leading to impressive biologic discoveries and AI-diagnostics are beginning to change clinical practice. With time, AI-generated content will become a substantive component of electronic health records. By extension, more risk-relevant information will be available to life insurers. Further, as AI-assisted biomedical discovery provides new foundational knowledge, clinical medicine will benefit, and human mortality should improve. This article explains why AI will become indispensable to healthcare, describes its current role, and predicts the expanded role it will have in the future. It also outlines the abundance of barriers to its implementation. Finally, it describes the relevance of this evolution to medical risk selection, on which it will have a considerable impact.
Background.—:Cardiovascular risk estimation for life insurance underwriting relies on risk estimation from conventional metrics: age, sex, smoking status, body mass index, systolic and diastolic blood pressure, total and high-density lipoprotein cholesterol and stress electrocardiogram. Coronary artery calcium (CAC) scoring via CT is a validated predictor of cardiovascular risk but remains costly, invasive, and unsuitable for large-scale underwriting. A novel artificial intelligence (AI) model, RetiCAC, predicts CAC scores from retinal photographs, offering a non-invasive and scalable alternative. Objective.—:To assess the potential role of RetiCAC in life insurance underwriting for improved cardiovascular risk stratification and pricing accuracy. Methods.—:This review draws on evidence from The Lancet Digital Health study of RetiCAC and evaluates its accuracy to and prognostic value compared with traditional CAC scoring. Potential underwriting applications were considered, including risk stratification, replacement of costly diagnostics, predictive augmentation, improvement in customer experience and integration with dynamic underwriting models and wellness programs. Results.—:RetiCAC demonstrated incremental predictive prognostic value, particularly in borderline and intermediate-risk groups, and showed comparable performance to CT-derived CAC scoring in external cohorts. For insurers, RetiCAC could enable scalable, non-invasive cardiovascular risk assessment, refine mortality predictions, and improve classification of substandard applicants. Its digital nature supports remote underwriting models and wellness integration. Conclusion.—:RetiCAC has potential as a non-invasive adjunct to traditional underwriting, enhancing cardiovascular risk prediction while reducing reliance on invasive testing. Broader adoption will require further validation, regulatory approval, and ethical safeguards, but integration could provide insurers with competitive advantages and align risk assessment with preventive health strategies.
There was a steady decrease in cardiovascular disease (CVD ischemic heart disease and stroke) mortality from 1960 to 2020, but since then, this decline has reversed. There have been over 228,000 excess CVD deaths through 2022, 1 undoubtedly partially due to the COVID-19 pandemic, but the mortality rate continues to rise (arguably due to the rising epidemic of obesity and diabetes). CVD remains the leading cause of death in developed countries, accounting for over 30% of deaths, and risk estimation is a cornerstone approach to guiding CVD prevention in clinical medicine. Data from the CDC reveal that 36% of US adults have no CVD risk factors, 35% have 1, and 29% have 2 or more risk factors. The age-adjusted percentage of adults with 2 or more CVD risk factors has increased between 2013-2014 to August 2021-August 2023, especially in older age groups. 2 Assessing the risk for CVD mortality is essential for the disability and life insurance industry required to assess that risk at a single point in time (at the issuance of an insurance policy). Evaluating this risk requires careful attention to modifiable and non-modifiable factors, including hypertension and other co-morbidities, abnormal lipid profiles, and lifestyle inequalities. The goal of this treatise is to evaluate the various CVD calculators, but also to review other risk factors that may not be routinely sought in estimating CVD risk. The importance of apolipoproteinB (apoB) and lipoprotein A (LpA) as better risk predictors than just elevated LDL levels will be emphasized, and evidence of systemic inflammation and insulin resistance will be proposed as essential early indicators of future cardiovascular disease.
Multi-cancer early detection (MCED) tests are increasingly popular. Are these tests "genetic," and if so, can insurers use them in the risk assessment process? This article reviews definitions of genetic tests. It then reviews the motivation for limiting insurers' access to genetic tests and examines the wording in the legislation in 3 English speaking jurisdictions. It then attempts to establish whether MECD results are included in the legislation.
