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

This commentary article highlights the need for an insurance product for hospital-employed physicians that provides coverage against sham peer review and a complete defense against wrongful hospital allegations of incompetent, whistleblowing, or disruptive behavior.

As life insurance companies evaluate prospective health and wellness programs, one frequently used tool is the number needed to treat (NNT) calculation. It is helpful to identify what the NNT might be for individual components of the program as well as for the whole program when all components are combined.

Introduction: -Due to early detection and improved therapies, the prevalence of long-term breast cancer survivors is increasing. This has increased the need for more inclusive underwriting in individuals with a history of breast cancer. Herein, we developed a method using algorithm aiming facilitating the underwriting of multiple parameters in breast cancer survivors.

Methods: -Variables and data were extracted from the SEER database and analyzed using 4 different machine learning based algorithms (Logistic Regression, GA2M, Random Forest, and XGBoost) that were compared with Kaplan Meier survival estimates. The performances of these algorithms have been compared with multiple metrics (Log Loss, AUC, and SMR). In situ (non-invasive) and metastatic breast cancer were excluded from this analysis.

Results: -Parameters included the pathological subtype, pTNM staging (T: tumor size, N; number of nodes; M presence or absence of metastases), Scarff-Bloom-Richardson grading, the expression of estrogen and progesterone hormone receptors were selected to predict the individual outcome at any time point from diagnosis. While all models had identical performance in terms of statistical metrics (AUC, Log Loss, and SMR), the logistic regression was the one and only model that respects all business constraints and was intelligible for medical and underwriting users.

Conclusion: -This study provides insight to develop algorithms to set underwriter-friendly calculators for more accurate risk estimations that can be used to rationalize insurance pricing for breast cancer survivors. This study supports the development of a more inclusive underwriting based on models that can encompass the heterogeneity of several malignancies such as breast cancer.

Objectives: -To document the various laboratory and demographic/historical correlates of NT-proBNP levels in applicants for life insurance, and to explore the accuracy of a prediction model based on those variables.

Method: -NT-proBNP blood test results were obtained from 1.34 million insurance applicants between the age of 50 and 85 years, beginning in 2003. Exploratory data analysis was carried out to document correlations with other laboratory variables, sex, age, and the presence of relevant diseases. Further, predictive models were used to quantify the proportion of the variance of NT-proBNP, which can be explained by a combination of these other, easier to determine variables.

Results: -NT-proBNP shows the expected, negative correlation with estimated glomerular filtration rate (eGFR) is markedly higher in those with a history of heart disease and is somewhat higher in those with a history of hypertension. A strong, unexpected, negative correlation between NT-proBNP and albumin was discovered. Of the variables evaluated, a multivariate adaptive regression spline (MARS) model automated selection procedure selected 7 variables (age, sex, albumin, eGFR, BMI, systolic blood pressure, cholesterol, and history of heart disease). Variable importance evaluation determined that age, albumin and eGFR were the 3 most important continuous variables in the prediction of NT-proBNP levels. An ordinary least squares (OLS) model using these same variables achieved a R-squared of 24.7%.

Conclusion: -Expected ranges of NT-proBNP may vary substantially depending on the value of other variables in the prediction equation. Albumin is significantly negatively correlated with NT-proBNP levels. The reasons for this are unclear.

Breast cancer remains the most common non-cutaneous malignancy in women in both Europe and the United States and the second leading cause of cancer-related deaths. In this breast cancer mortality and survival study, a US retrospective population-based analysis of 656,501 microscopically confirmed breast cancer cases, 1975-2019, data is derived from the NCI Surveillance Epidemiology & End Results Program, SEER*Stat 8.4.0.1.

As the COVID-19 pandemic reaches the end of its third year, and as COVID-related mortality in North America wanes, long Covid and its disabling symptoms are attracting more attention. Some individuals report symptoms lasting more than 2 years, and a subset report continuing disability. This article will provide an update on long Covid, with a particular focus on disease prevalence, disability, symptom clustering and risk factors. It will also discuss the longer-term outlook for individuals with long Covid.

During the past 5 decades, there have been reports of increases in the incidence and mortality rates of non-Hodgkin lymphoma (NHL) in the United States and globally. The ability to address the epidemiologic diversity, prognosis and treatment of NHL depends on the use of an accurate and consistent classification system. Historically, uniform treatment for NHL has been hampered by the lack of a systematic taxonomy of non-Hodgkin lymphoma. Before 1982, there were 6 competing classification schemes with contending terminologies for NHL: the Rappaport, Lukes-Collins, Kiel, World Health Organization, British, and Dorfman systems without consensus as to which system is most satisfactory regarding clinical relevance, scientific accuracy and reproducibility and presenting a difficult task for abstractors of incidence information. In 1982, the National Cancer Institute sponsored a workshop1 that developed a working formulation designed to: 1) provide clinicians with prognostic information for the various types of NHLs, and 2) provide a common language that might be used to compare clinical trials from various treatment centers around the world. Studies imply that prognosis is dependent on tumor stage and histology rather than the primary localization per se.2 This study utilizes the National Cancer Institute PDQ adaptation of the World Health Organization's (WHO) updated REAL (Revised European American Lymphoma) classification3 of lymphoproliferative diseases, and the SEER*Stat 8.3.6 database (released Aug 8, 2019) for diagnosis years 1975-2016. In this article, we make use of 40 years of data to examine patterns of incidence, survival and mortality, and selected cell bio-behavioral characteristics of NHL in the United States.

Objective: -To update trends in incidence and prevalence in the United States of non-Hodgkin lymphoma, examine, compare and contrast short and long-term patterns of survival and mortality, and consider the outcome impacts of anatomic location of NHL nodal and extranodal subdivisions, utilizing selected ICD-O-3 histologic oncotypes stratified by age, sex, race/ethnicity, stage, cell behavioral morphology and histologic typology, cohort entry time-period and disease duration, employing the statistical database of the National Cancer Institute SEER*Stat 8.3.6 program for diagnosis years 1975-2016.4 Methods.- A retrospective, population-based cohort study using nationally representative data from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program to evaluate 384,651 NHL cases for diagnosis years 1975-2016 comparing multiple variables of age, sex, race, stage, cell behavioral morphology, cohort entry time-period, disease duration and histologic oncotype. Relative survival statistics were analyzed in two cohorts: 1975-1995 and 1996-2016. Survival statistics were derived from SEER*Stat Database: Incidence - SEER 9 Regs Research Data, November 2018 Submission (1975-20

Pituitary adenomas were recently reclassified as "neuroendocrine tumors," and are now considered to be cancers. The evolution and justification for this change are described. Critical illness policies, which currently provide coverage of pituitary adenomas under the "Benign Brain Tumor" provision must now be modified to reflect this new taxonomy. This change also prompts questions about the use of the words 'benign' and 'tumor' in critical illness policies.