Journal of insurance medicine (New York, N.Y.)最新文献

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.

A common problem with mortality analyses on company or registry data is that the processing time on large datasets can be an impediment to the interactive process of the analysis. The following paper delineates an approach using the Polars dataframe library and the programming language Python to speed up the processing time considerably.

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.

A publication in the British Journal of Psychiatry reviews years of potential life lost (YPLL) and life expectancy in bipolar disorders.1 YPLL is useful in some insurance products but not in life insurance underwriting, where the magnitude of risk is derived from the mortality ratio and subsequent debits, eg, a mortality ratio of 200% is approximately 100 debits.2,3 A method to convert YPLL into mortality ratios and appropriate debits for medical risk underwriting of BPD is discussed.4.

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.

Cancers of the lung and bronchus are broad terms for a common, deadly, complex, heterogenous and histologically diverse group of respiratory malignancies that comprise the leading cause of global cancer incidence and mortality accounting for an estimated 2 million diagnoses and 1.8 million deaths, and occurs through a complicated multistage process that results from the combination of carcinogen exposure, the primary etiology of which is tobacco smoking and genetic susceptibilities. This pathologic scourge is the second most common cause of cancer in men and women (after prostate and breast cancer, respectively). The mean age of diagnosis by sex is 67 years old and by race in Whites-67, Blacks-64, Others (AI/AN-American Indian/Alaska Native, API-Asian/Pacific Islander)-66 years. In this short and long term retrospective population-based analysis of 588,785 microscopically confirmed lung and bronchus case mortality and survival study, data is derived from the National Cancer Institute (NCI) Surveillance, Epidemiology, End-Results programs, SEER*Stat software version 9.0.42 released October 19, 2025, and SEER Registry (Incidence - SEER Research Data, 8 Registries, Nov 2024 Submission (1975-2022) released April 2025. This comparative cohort entry time-period analysis is intended to provide age-adjusted epidemiologic, demographic short and long-term 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.

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 countries and 1 US state. It then attempts to establish whether MECD results are included in the legislation.