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

{"title":"Mortality Risk of Low BMI in Life Insurance Applicants.","authors":"Steven J Rigatti, Robert Stout","doi":"10.17849/insm-51-1-8-16.1","DOIUrl":"10.17849/insm-51-1-8-16.1","url":null,"abstract":"<p><strong>Objectives: </strong>-This study seeks to quantify the mortality effect of low levels of body mass index (BMI) on life insurance applicants who, based on their laboratory profile and other information, appear to be suitable for life insurance coverage.</p><p><strong>Background: </strong>-It has been demonstrated that low BMI is associated with higher mortality risk than normal or near-normal BMI.</p><p><strong>Methods: </strong>-Data were collected from over 4.7 million life insurance applicants with available BMI tested between 1995 and 2021, and vital status was assessed via the Social Security Death Master File. Cox models treating BMI as continuous and as a categorical variable were constructed, controlling for age, and split by sex after excluding those with laboratory or biometric test results, which were far enough outside the normal range to imply elevated mortality.</p><p><strong>Results: </strong>-Models treating BMI as a continuous variable and allowing an interaction term for age showed that low BMI was strongly associated with mortality at ages 50 and above in both sexes. In the categorical models, only the lowest category of BMI (below the 1st percentile) in men aged 40-60, the lowest 2 categories (below the 5th percentile) in women aged 40-60, and the lowest 3 categories (below the 10th percentile) in those aged 60-80 years, were significantly associated with elevated mortality. No elevated mortality was detected in those under age 40 with low BMI.</p><p><strong>Conclusion: </strong>-Based on this study, low BMI is associated with elevated mortality in otherwise healthy applicants, but this association is dependent on age.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 1","pages":"8-16"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141159011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How the Medical Director Should Use Data Sources.","authors":"Jean-Marc Fix","doi":"10.17849/insm-51-1-31-34.1","DOIUrl":"10.17849/insm-51-1-31-34.1","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 1","pages":"31-34"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Basics of Artificial Intelligence (AI) Modeling.","authors":"Rodney C Richie","doi":"10.17849/insm-51-1-35-40.1","DOIUrl":"10.17849/insm-51-1-35-40.1","url":null,"abstract":"<p><strong>Methodology: </strong>A key-word search of artificial intelligence, artificial intelligence in medicine, and artificial intelligence models was done in PubMed and Google Scholar yielded more than 100 articles that were reviewed for summation in this article.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 1","pages":"35-40"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Long-term Complications of Covid-19 Infection.","authors":"Timothy Meagher","doi":"10.17849/insm-51-2-111-115.1","DOIUrl":"10.17849/insm-51-2-111-115.1","url":null,"abstract":"<p><strong>Context.—: </strong>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.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 2","pages":"111-115"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cancer of the Nasal Cavity, Middle Ear and Accessory Sinuses - 15 Year Comparative Survival and Mortality Analysis by Age, Sex, Race, Stage, Grade, Cohort Entry Time-Period, Disease Duration and Topographic Primary Sites: A Systematic Review of 13,404 Cases for Diagnosis Years 2000-2017: (NCI SEER*Stat 8.3.8).","authors":"Anthony F Milano","doi":"10.17849/insm-51-2-77-91.1","DOIUrl":"10.17849/insm-51-2-77-91.1","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;.-Sinonasal malignancies are rare, aggressive, deadly and challenging tumors to diagnose and treat. Since 2000, age-adjusted incidence rates average less than 1 case per 100,000 per year, male and female combined, in the United States. For the entire cohort, 2000-2017, overall median age-onset was 62.6 years. Carcinoma constitutes over 90% of these upper respiratory cancers and most cases are advanced, more than 72% (regional or distant stage) when the diagnosis is made. Composite mortality at 5 years was 108 excess deaths/1000/year with a mortality ratio of 558%, and 41% of deaths occurred in this time frame. As a consequence, observed median survival was approximately 6 years with 5-year cumulative observed survival (P) and relative survival rates (SR) 53% and 60%. This mortality and survival update study follows the World Health Organization International Classification of Diseases for Oncology-3rd Edition (ICD-O-3)1 topographical identification, coding, labeling and listing of 13,404 patient-cases accessible for analysis in the United States National Cancer Institute's Surveillance, Epidemiology and End Results program (NCI SEER Research Data, 18 Registries), 2000-2017 located in 8 primary anatomical sites: C30.0-Nasal cavity, C30.1-Middle ear, C31.0-Maxillary sinus, C31.1-Ethmoid sinus, C31.2-Frontal sinus, C31.3-Sphenoid sinus, C31.8-Overlapping lesion of accessory sinuses, C31.9-Accessory sinus, NOS.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;.-1) Utilize national population-based SEER registry data for 2000-2017 to update cancer survival and mortality outcomes for 8 ICD-O-3 topographically coded sinonasal primary sites. 2) Discern similarities and contrasts in NCI-SEER case characteristics. 3) Identify current risk pattern outcomes and shifts in United States citizens, 2000-2017.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;.-SEER Research Data, 18 Registries, Nov 2019 Sub (2000-2017)2,3 are used to examine the risk consequences of 13,404 patients diagnosed with sinonasal malignancies, 2000-2017, in this retrospective population-based study employing prognostic data stratified by topography, age, sex, race, stage, grade, 2 cohort entry time-periods (2000-06 & 2007-17), and disease-duration to 15 years. General methods and standard double decrement life table methodologies for displaying and converting SEER site-specific annual survival and mortality data to aggregate average annual data units in durational intervals of 0-1, 0-2, 1-2, 2-5, 0-5, 5-10, and 10-15 years are employed. The reader is referred to the \"Registrar Staging Assistant (SEER*RSA)\" for local-regional-distant Extent of Disease (EOD) sources used in the development of staging descriptions for the Nasal Cavity and Paranasal Sinuses (maxillary and ethmoid sinuses only) and Summary Stage 2018 Coding Manual v2.0 released September 1, 2020. Cancer staging & grading procedural explanations, statistical significance & 95% confidence levels4 are described in previous Jou","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 2","pages":"77-91"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG).","authors":"Emoke Posan, Rod Richie","doi":"10.17849/insm-51-2-64-76.1","DOIUrl":"10.17849/insm-51-2-64-76.1","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 2","pages":"64-76"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Predictions in Latin America.","authors":"Javier González Maciel, Elyssa Del Valle, Charles Lutz","doi":"10.17849/insm-51-1-17-24.1","DOIUrl":"10.17849/insm-51-1-17-24.1","url":null,"abstract":"","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 1","pages":"17-24"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Life and Disability Insurance for People with or at Risk of HIV: Aligning Policy with Evidence.","authors":"Benjamin Grobman, Michael J Silverberg, Julia L Marcus","doi":"10.17849/insm-51-1-25-28.1","DOIUrl":"10.17849/insm-51-1-25-28.1","url":null,"abstract":"<p><p>Antiretroviral medications have substantially improved life expectancy for people with HIV. These medications are also highly effective in preventing HIV acquisition in people who do not have HIV, a strategy known as HIV preexposure prophylaxis (PrEP). Despite these advances, some life and disability insurers continue to deny or limit coverage for people with HIV, and some have even refused to cover people who are using PrEP to protect themselves. These policies unfairly deny people with HIV, PrEP users, and their families the peace of mind and financial protection that can come with life and disability insurance coverage. This article summarizes the current evidence on HIV treatment and prevention, arguing that underwriting decisions by life and disability insurers should not be made based on HIV status or use of PrEP.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 1","pages":"25-28"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141159007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fetal Alcohol Spectrum Disorder.","authors":"Rodney C Richie","doi":"10.17849/insm-51-2-55-58.1","DOIUrl":"10.17849/insm-51-2-55-58.1","url":null,"abstract":"","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 2","pages":"55-58"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"JIM Reading List.","authors":"","doi":"10.17849/insm-51-2-116-123.1","DOIUrl":"https://doi.org/10.17849/insm-51-2-116-123.1","url":null,"abstract":"","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 2","pages":"116-123"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}