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

{"title":"Assessing the Pathophysiology, Morbidity, and Mortality of Obstructive Sleep Apnea.","authors":"R C Richie","doi":"10.17849/insm-51-3-1-20.2","DOIUrl":"10.17849/insm-51-3-1-20.2","url":null,"abstract":"<p><p>The basic definitions of obstructive sleep apnea (OSA), its epidemiology, its 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.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":" ","pages":"143-162"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548157","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-51-2-1-4.2","DOIUrl":"https://doi.org/10.17849/insm-51-2-1-4.2","url":null,"abstract":"<p><p>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":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141559959","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":"Through the Looking Glass Darkly: How May AI Models Influence Future Underwriting?","authors":"Rodney C Richie","doi":"10.17849/insm-51-2-59-63.1","DOIUrl":"https://doi.org/10.17849/insm-51-2-59-63.1","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 2","pages":"59-63"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297627","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":"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":"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":"https://doi.org/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":"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":"https://doi.org/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":"Fetal Alcohol Spectrum Disorder.","authors":"Rodney C Richie","doi":"10.17849/insm-51-2-55-58.1","DOIUrl":"https://doi.org/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":"Identification and Assessment of Undiagnosed Fetal Alcohol Spectrum Disorder: A Report of Three Cases.","authors":"Vera F Dolan","doi":"10.17849/insm-51-2-51-54.1","DOIUrl":"10.17849/insm-51-2-51-54.1","url":null,"abstract":"<p><p>Fetal alcohol spectrum disorder (FASD) and its associated physical and mental conditions is the most prevalent congenital impairment causing developmental and intellectual disability worldwide. Like alcohol abuse, FASD is typically undiagnosed by primary care providers. And like alcohol abuse, life underwriters and medical directors need to be aware of the signs, symptoms, and behaviors associated with FASD to accurately detect, identify, evaluate and assess the mortality risk. Three cases of suspected undiagnosed FASD that were underwritten for life expectancies in legal matters are discussed in this report. Not only were these patients' risks for excess mortality elevated due to their initial neurologic injury due to prenatal exposure to alcohol, but these cases demonstrate the importance of the stability and care needed to make them insurable. The following paper discusses the clinical and social settings at birth that may give underwriters and medical directors some clue to a potential case of the child having FASD and then to assess their statistical and lifestyle mortality risks.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"51 2","pages":"51-54"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297613","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}