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

The basic definitions of obstructive sleep apnea (OSA), its epidemiology, it's clinical features and complications, and the morbidity and mortality of OSA are discussed. Included in this treatise is a discussion of the various symptomatic and polysomnographic phenotypes of COPD that may enable better treatment and impact mortality in persons with OSA. The goal of this article is to serve as a reference for life and disability insurance company medical directors and underwriters when underwriting an applicant with probable or diagnosed sleep apnea. It is well-referenced (133 ref.) allowing for more in-depth investigation of any aspect of sleep apnea being queried.

Objective.—: To demonstrate a method which is being used to apportion between risk factors for occupationally related disease and compensate individuals with multiple risk factors. The application to individuals will be demonstrated for varicose veins.

Background.—: The National Insurance Institute (NII) is tasked with compensating work related injuries and illness in Israel. Population attributable fraction (PAF) has been utilized in order to estimate the amount of disease that can potentially be eliminated in a population through the elimination of individual risk factors. PAF is based on relative risks and the prevalence of these risks.

Methods.—: A review of the medical literature consisting of epidemiological studies of varicose veins and its multiple risk factors was conducted, with special attention to prolonged occupational standing. Summary, weighted, relative risks were calculated for eight different risk factors. The proposed formula then allowed for apportioning among those risk factors in the individual.

Results.—: The findings of the current study indicate that prolonged standing may be associated with the presence of varicose veins, however in light of the multiple other risk factors associated, its overall contribution is generally minor.

Conclusion.—: Apportionment among multiple risk factors for varicose veins can be accomplished mathematically in individuals. This application is being applied successfully for other diseases as well.

Moral hazard is well known to life insurance underwriters and medical directors to increase the risk of adverse consequences to insured individuals. The underwriting investigation of proposed insureds at time of policy issue is done to ensure no likely moral hazard exists. However, not all situations involving moral hazard may be identified at time of underwriting and policy issue, and may only be identified at time of claim. Three cases that were underwritten for life expectancies in legal matters are described here as examples of moral hazard identified at time of severe injury and/or death. All three of these cases involved a woman who manipulated her male partner into situations that increased the man's risk of severe injury and/or death to the woman's financial benefit. Such "black widows" made a great deal of effort over an extensive period of time to ensure that the moral hazard set up for their male partners resulted in a substantial financial windfall through litigation. The moral hazard set up by a black widow thus can be considered by the life insurance industry as sufficiently anti-selective and speculative to deny a claim at any time after policy issue.

As the Covid-19 pandemic continues into its 4th year, reports of long-term morbidity and mortality are now attracting attention. Recent studies suggest that Covid-19 survivors are at increased risk of common illnesses, such as myocardial infarction, diabetes mellitus and autoimmune disorders. Mortality may also be increased. This article will review the evidence that supports some of these observations and provide an opinion about their validity and their relevance to insured cohorts.

Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditions continue to evolve. Instances provided in this treatise include using a simple one-view PA chest radiograph to screen for Type 2 Diabetes Mellitus (T2DM), congestive heart failure, valvular heart disease, and to assess mortality in asymptomatic persons with respiratory diseases. This technology incorporates hundreds of thousands of CXRs into a convoluted neural network and is generally named AI CXR. As an example, the AUROC (Area Under Receiving Operator Characteristic) of screening for T2DM was 0.84, with sensitivity and specificities that exceed those of the United States Preventative Services Task Force (USPSTF) guidelines for screening with HBA1c or blood glucose studies. The AUROC's for diagnosing ejection fractions less than 40% was 0.92, and for detecting valvular heart diseases was 0.87. The potential implications for underwriting life and disability policies may be significant. A companion article in the Journal of Insurance Medicine addresses this same technology using a simple 12-lead ECG, generally named AI ECGs.

The life insurance industry is transitioning towards precision underwriting driven by increased data availability and access to advanced analytical tools. Effectively utilizing diverse data sources in life insurance underwriting presents an opportunity for medical directors to fully leverage their skillset in this evolving environment. By navigating these changes, balancing the value of data against its limitations, and fostering collaborative approaches to enhance risk assessment and underwriting processes, medical directors can maintain a pivotal role in the life insurance companies of tomorrow.

Context.—: As the Covid-19 pandemic continues into its 4th year, reports of long-term morbidity and mortality are now attracting attention. Recent studies suggest that Covid-19 survivors are at increased risk of common illnesses, such as myocardial infarction, diabetes mellitus and autoimmune disorders. Mortality may also be increased. This article will review the evidence that supports some of these observations and provide an opinion about their validity and their relevance to insured cohorts.

Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outcomes. This article aims to provide a basic understanding of AI enabled ECG technology. Specific conditions and findings will be discussed, followed by reviewing associated terminology and methodology. In the appendix, definitions of AUC versus accuracy are explained. The application of deep learning models enables detecting diseases from normal electrocardiograms at accuracy not previously achieved by technology or human experts. Results with AI enabled ECG are encouraging as they considerably exceeded current screening models for specific conditions (i.e., atrial fibrillation, left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy). This could potentially lead to a revitalization of the utilization of the ECG in the insurance domain. While we are embracing the findings with this rapidly evolving technology, but cautious optimism is still necessary at this point.