Mortality Risk of Low BMI in Life Insurance Applicants.

OBJECTIVES: -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. BACKGROUND: -It has been demonstrated that low BMI is associated with higher mortality risk than normal or near-normal BMI. METHODS: -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. RESULTS: -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. CONCLUSION: -Based on this study, low BMI is associated with elevated mortality in otherwise healthy applicants, but this association is dependent on age.

Correlates and Predictors of NT-proBNP in Life Insurance Applicants.

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.

Prevalence of Antibodies to COVID-19 Due to Infection or Vaccination in US Adults.

OBJECTIVE: -Determine the seroprevalence of SARS-CoV-2 infection and vaccination in a population applying for life insurance. SETTING: -This is a cross-sectional study of 2584 US life insurance applicants, to determine the seroprevalence of antibodies to COVID-19. This convenience sample was selected on two consecutive days April 25-26, 2022. RESULTS: -For COVID-19, 97.3% are seropositive, and 63.9% have antibodies to nucleocapsid protein, a marker of prior infection. An additional, 33.7% have been vaccinated with no serologic evidence of infection. METHODOLOGY: -Serum and urine samples from a nationwide group of insurance applicants for routine risk assessment were collected. The examination of applicants typically occurs, in their homes, their place of employment, or a clinic. The paramedic exam occurs 7-14 days after the insurance application. Before the exam, an office assistant calls the applicant and inquires if they have been in contact with a person with SARS-CoV-2, been ill within the last 2 weeks, felt sick, or recently had a fever. If the applicant answers yes, the exam is rescheduled. Before sample collection, the applicant reads and signs a consent form to release medical information and testing. Next, the examiner records the applicant's blood pressure, height, and weight. Then, a blood and a urine sample are collected and sent with the consent form to our laboratory via Federal Express. On April 25-26, 2022, we tested 2584 convenience samples from adult insurance applicants for the presence of antibodies to nucleocapsid and spike proteins from SARS-CoV-2. As a standard practice, we reported the client-specified test profile results to our life insurance carriers. In contrast, the COVID-19 test results were only available to the authors. Patient and Public Involvement.-There was no patient involvement in study design, reporting of results, or journal publication selection. There was patient consent to publish de-identified study results. No public involvement occurred in the creation or completion of the study. The authors thank the participants in this study for approving the use of their blood samples to further society's understanding of the SARS-CoV-19 pandemic. Ethics Review.-Western Institutional Review Board reviewed the study design and determined it to be exempt under the Common Rule and applicable guidance. Therefore, it is exempt under 45 CFR § 46.104(d)(4) from using de-identified study samples for epidemiologic investigation, WIRB Work Order #1-1324846-1. In addition, all test subjects had signed a consent allowing research of their blood and urine samples with the removal of personally identifiable information. RESULTS: -The combined seroprevalence for antibodies to nucleocapsid, a marker of prior infection, and antibodies to spike protein, an indicator of either previous infection or vaccination, was 97.3%. Higher infection rates occur in younger vs older age groups, with a non-statistical difference for vaccinated and acquired natural immunity. For the age group 16-84, the total estimated seroprevalence of COVID-19 in the US is 249 million cases. CONCLUSIONS: -The US population has widespread immune resistance to current variants of COVID-19 due to prior infection or vaccination. The infectivity of new variants and silent disease, independent of previous infection or vaccination, are the driving force behind the sporadic increase in clinical SARS-CoV-2 cases.

Prevalence of Antibodies to COVID-19 Due to Infection or Vaccination in US Adults.

OBJECTIVE: -Determine the seroprevalence of SARS-CoV-2 infection and vaccination in a population applying for life insurance. SETTING: -This is a cross-sectional study of 2584 US life insurance applicants, to determine the seroprevalence of antibodies to COVID-19. This convenience sample was selected on two consecutive days April 25-26, 2022. RESULTS: -For COVID-19, 97.3% are seropositive, and 63.9% have antibodies to nucleocapsid protein, a marker of prior infection. An additional, 33.7% have been vaccinated with no serologic evidence of infection. METHODOLOGY: -Serum and urine samples from a nationwide group of insurance applicants for routine risk assessment were collected. The examination of applicants typically occurs, in their homes, their place of employment, or a clinic. The paramedic exam occurs 7-14 days after the insurance application. Before the exam, an office assistant calls the applicant and inquires if they have been in contact with a person with SARS-CoV-2, been ill within the last 2 weeks, felt sick, or recently had a fever. If the applicant answers yes, the exam is rescheduled. Before sample collection, the applicant reads and signs a consent form to release medical information and testing. Next, the examiner records the applicant's blood pressure, height, and weight. Then, a blood and a urine sample are collected and sent with the consent form to our laboratory via Federal Express. On April 25-26, 2022, we tested 2584 convenience samples from adult insurance applicants for the presence of antibodies to nucleocapsid and spike proteins from SARS-CoV-2. As a standard practice, we reported the client-specified test profile results to our life insurance carriers. In contrast, the COVID-19 test results were only available to the authors. Patient and Public Involvement.-There was no patient involvement in study design, reporting of results, or journal publication selection. There was patient consent to publish de-identified study results. No public involvement occurred in the creation or completion of the study. The authors thank the participants in this study for approving the use of their blood samples to further society's understanding of the SARS-CoV-19 pandemic. Ethics Review.-Western Institutional Review Board reviewed the study design and determined it to be exempt under the Common Rule and applicable guidance. Therefore, it is exempt under 45 CFR § 46.104(d)(4) from using de-identified study samples for epidemiologic investigation, WIRB Work Order #1-1324846-1. In addition, all test subjects had signed a consent allowing research of their blood and urine samples with the removal of personally identifiable information. RESULTS: -The combined seroprevalence for antibodies to nucleocapsid, a marker of prior infection, and antibodies to spike protein, an indicator of either previous infection or vaccination, was 97.3%. Higher infection rates occur in younger vs older age groups, with a non-statistical difference for vaccinated and acquired natural immunity. For the age group 16-84, the total estimated seroprevalence of COVID-19 in the US is 249 million cases. CONCLUSIONS: -The US population has widespread immune resistance to current variants of COVID-19 due to prior infection or vaccination. The infectivity of new variants and silent disease, independent of previous infection or vaccination, are the driving force behind the sporadic increase in clinical SARS-CoV-2 cases.

Association of Liver Function Tests with Mortality in an Insurance Applicant Population.

OBJECTIVES: -Determine the relationship between liver function test (LFT) results (GGT, alkaline phosphatase, AST, ALT and albumin) and all-cause mortality in life insurance applicants. METHOD: -By use of the Social Security Master Death File, mortality was examined in 15,272,955 insurance applicants for whom blood samples were submitted to the Clinical Reference Laboratory. There were 268,593 deaths observed in this study population, after an average follow-up time of 10.9 years. Results were stratified by sex and by age less/greater than 60, creating 4 groups. Liver function test values were grouped using percentiles of their distribution within these age/ sex groups - so as to update the results generated in prior publications. Additional models were fit using different exclusions and percentile groups within single year age groups. Also, LFTs were treated as continuous variables and included in Cox models with age and smoking status. RESULTS: -Using the risk of the middle 50% of the population by distribution as a reference, relative mortality observed for GGT and alkaline phosphatase was linear with a steep slope from very low to high values. AST showed a J-shaped association with mortality. ALT showed a low-magnitude inverse correlation with mortality. Albumin demonstrated a higher-magnitude inverse correlation with mortality, especially at values below the median. The overall risk associated with LFTs was durable over at least 10 years of follow-up. CONCLUSION: -Liver function tests show a strong and durable correlation to mortality in a large group of insurance applicants. The durability over time suggests that even older values of LFTs found in medical records could be of use in mortality risk prediction.

Activity Level as a Mortality Predictor in a Population Sample after Typical Underwriting Exclusions and Laboratory Scoring.

Objectives.- To quantify the effect of physical activity on the mortality rates of healthy individuals in a population sample, after controlling for other sources of mortality risk. Background.- The widespread availability of activity monitors has spurred life insurance companies to consider incorporating such data into their underwriting practices. Studies have shown that sedentary lifestyles are associated with poor health outcomes and higher risks of death. The aim of this paper is to investigate how well certain measures of activity predict mortality when controlled for other known predictors of mortality including a multivariate laboratory based risk score. Methods.- Data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the years 1999 through 2014. Laboratory and biometric data were scored for mortality risk using a previously developed proprietary algorithm (CRL SmartScore). Data on activity were obtained from the NHANES questionnaires pertaining to activity. In a second analysis, data were obtained from pedometers worn for 1 week by NHANES participants (years 2003-2004, and 2005-2006 only). Before analysis, cases were selected based on commonly used life insurance underwriting criteria to remove from consideration those who have major health issues, which would ordinarily preclude an offer of life insurance. Results.-In fully-adjusted Cox model which included survey-based MET*hours per day as a 3-level categorical variable, the moderate and minimal levels of activity were associated with hazard ratios of 1.15 (95% CI: 1.04-1.28) and 1.38 (95% CI: 1.23-1.56), respectively, when compared to the highest level of activity. When treated as a continuous variable, the fully adjusted model the HR for MET*hours per day was 0.91 (95% CI: 0.87-0.95). In fully adjusted models using pedometer data, the percentage of wear time spent sedentary was associated with mortality (HR: 1.19, 95% CI: 1.09-1.31), while average counts per minute were negatively associated with mortality (HR: 0.82, CI: 0.75-0.90). Conclusions.-It is clear from these results that high proportions of sedentary time are associated with increased mortality, whether the sedentary time is quantified via questionnaire or pedometer. Because both laboratory scores and activity levels remain significant in Cox models where both are included, these factors are largely independent, indicating that they are measuring distinct influences on the risk of mortality.

Association of Carcinoembryonic Antigen with Mortality in an Insurance Applicant Population.

Objectives.- To quantify the mortality risks associated with elevated levels of carcinoembryonic antigen (CEA). Background.- Carcinoembryonic antigen is cell surface glycoprotein and has been associated with the presence of high grade or metastatic cancers of the colon as well as other malignant and non-malignant disease. Prior publications have demonstrated the utility of CEA levels in the determination of mortality risk in life insurance applicants. The aim of this paper is to further characterize this risk with a larger set of data containing additional person-years of follow-up, more outcomes, and additional variables potentially associated with occult malignancy. Methods.- By use of the Social Security Death Index, mortality was examined in 321,574 insurance applicants age 50 years and older, who submitted blood samples to Clinical Reference Laboratories for testing including CEA. Results were stratified by age group and by CEA level (<5 ng/mL, 5 to 9.9 ng/mL, 10+ ng/mL), though other thresholds were tested. Mortality comparisons were carried out using Cox models and tabular methods with the 2015 smoker-distinct Valuation Basic Tables as a comparator. Results.- Relative mortality is increased at CEA levels above 4.0 ng/mL in both smokers and non-smokers. This association is persistent in Cox models when albumin, BMI and cholesterol are included as covariates. The strongest association with mortality risk occurred in the first 3-4 durations. The 3-year cumulative mortality ratio when using the 2015 VBT as baseline was 6.51 when comparing the group with CEA levels of 10+ ng/mL, compared to those with levels below 5.0 ng/mL. Conclusion.- This study shows that CEA is strongly associated with the risk of early excess mortality in life insurance applicants, and this risk appears not to be mitigated by consideration of other markers thought to be associated with occult malignancy.

All-Cause Mortality for Life Insurance Applicants with the Presence of Bundle Branch Block.

Objective.-To determine the all-cause mortality of life insurance applicants who have a bundle branch block. Background.-Bundle branch block is an electrocardiographic pattern that has variable prognostic implications. Research studies have shown that both left and right bundle branch block are associated with increased mortality among cases that have heart disease. In the general population and life insurance applicant population, the prevalence of bundle branch block is relatively low, and its effects on long-term prognosis are not as well established. Methodology.-Life insurance applicants with reported bundle branch block were extracted from data covering United States residents between October 2009 and October 2016. Information about these applicants was matched to the Social Security Death Master (SSDMF) file for deaths occurring from 2009 to 2012 and to another commercially available death source file (Other Death Source, ODS) for deaths occurring from 2009 to 2016 to determine vital status. Actual to expected (A/E) mortality ratios were calculated using the Society of Actuaries 2015 Valuation Basic Table (2015VBT), select and ultimate table (age last birthday). All expected bases were not smoker distinct. Confidence bands around these mortality ratios were calculated. The variables of interest were applicant age, gender, location of the bundle branch block, and the presence of cardiac or cardiovascular conditions. Results.-There were 258,529.85 person-years exposure for applicants with bundle branch block. Of the applicants, 57.2% had right bundle branch block. Of person-years exposure, 11.5% had a cardiac condition along with the bundle branch block, and 4.4% had an underlying cardiovascular condition. Female mortality ratios were higher than those for males, but due to the low number of deaths, this difference was not significant. Left bundle branch block mortality ratios (1.01) were 1.4 times higher than those with right (0.74). Those applicants with a cardiac condition along with their bundle branch block had between 1.6 to 1.8 times the mortality ratio depending on the bundle branch block location, and those with a cardiovascular condition had between 1.5 to 1.7 times the mortality ratio over those applicants with just bundle branch block alone. Conclusion.-The presence of bundle branch block in an insurance applicant may be associated with increased all-cause mortality. In this study, life insurance applicants overall had a mortality slightly lower than the expected mortality based on the 2015 VBT. However, applicants with bundle branch block and a cardiac or cardiovascular comorbid condition had a significantly higher mortality ratio.

All-Cause Mortality for Life Insurance Applicants with a Family History of Coronary Artery Disease Before 60.

OBJECTIVE: -To determine the all-cause mortality of life insurance applicants having a family history of coronary artery disease (CAD) before age 60. BACKGROUND: -Epidemiological studies have shown that a family history of premature CAD is an independent risk factor for CAD events. The strength of the association between family history and CAD is greatest with earlier age of presentation of CAD in the family member and when multiple family members are affected. Despite earlier insurance studies on this relationship, there is sparse current data on the association between family history of CAD and all-cause mortality in life insurance applicants. METHODOLOGY: -Life insurance applicants with reported family history of Coronary Artery Disease (CAD) were extracted from data covering United States residents between October 2009 and October 2016. Information about these applicants was matched to the Social Security Death Master (SSDMF) file for deaths occurring from 2009 to 2012 and to another commercially available death source file (Other Death Source, ODS) for deaths occurring from 2009 to 2016 to determine vital status. Actual to Expected (A/E) mortality ratios were calculated using the Society of Actuaries 2015 Valuation Basic Table (2015VBT), select and ultimate table (age last birthday). All expected bases were not smoker distinct. Confidence bands around these mortality ratios were calculated. The variables of interest were applicant age, gender, number of family members with CAD before age 60, and the presence of cardiac or cardiovascular conditions. RESULTS: -Overall, the mortality of applicants with family members with a history of CAD before age 60 was slightly lower than expected mortality based on the 2015 VBT. Applicants with a cardiac or cardiovascular comorbid condition had a significantly higher mortality ratio. For applicants aged 25-54 and 65-75 with cardiac comorbid conditions, the mortality ratio was 2 times that of those without a cardiac comorbid condition. For those aged 55-64 with cardiovascular comorbid conditions, the mortality ratio was 2.9 times that of those without a cardiovascular comorbid condition. Females had a slightly higher mortality ratio for all age groups, number of family members with CAD before age 60, and cardiovascular conditions. CONCLUSION: -A family history of CAD before the age of 60 in an insurance applicant may be associated with increased all-cause mortality. Overall in this study, life insurance applicants had a mortality slightly lower than the expected mortality based on the 2015 VBT. However, applicants with a positive family history and a cardiac or cardiovascular comorbid condition had a significantly higher mortality ratio.

Random Forest.

For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become increasingly common. Particularly in this era of "big data" and machine learning, survival analysis has become methodologically broader. This paper aims to explore one technique known as Random Forest. The Random Forest technique is a regression tree technique which uses bootstrap aggregation and randomization of predictors to achieve a high degree of predictive accuracy. The various input parameters of the random forest are explored. Colon cancer data (n = 66,807) from the SEER database is then used to construct both a Cox model and a random forest model to determine how well the models perform on the same data. Both models perform well, achieving a concordance error rate of approximately 18%.

All-Cause Mortality for Life Insurance Applicants with a History of Breast Cancer.

Breast cancer is the most commonly diagnosed cancer worldwide. Breast cancer is also the second leading cause of cancer death among women in the United States after lung cancer with over 40,000 breast cancer deaths occurring each year. The purpose of this research was to determine the all-cause mortality of applicants diagnosed with breast cancer currently or at some time in the past. Life insurance applicants with reported breast cancer were extracted from data covering United States residents between November 2007 and November 2014. Information about these applicants was matched to the Social Security Death Master (SSDMF) file for deaths occurring from 2007 to 2011 and to another commercially available death source file (Other Death Source, ODS) for deaths occurring from 2007 to 2014 to determine vital status. If there was a death from the other death source, then the SSDMF was searched to verify the death. The study had approximately 561,000 person-years of exposure. Actual-to-expected (A/E) mortality ratios were calculated using the Society of Actuaries 2008 Valuation Basic Table (2008VBT), select and ultimate table (age last birthday) and the 2010 US population as expected mortality ratios. Since the A/Es presented in this paper were known to be an underestimate due to the exclusion of the recent SSDMF deaths, comparative analysis of the mortality ratios was done. Since there was no smoking status information in this study, all expected bases were not smoker distinct. Overall, the 35-44 age group had 6.3 times the relative mortality ratio than those in the 65-75 age group. The relative mortality ratio for the 35-44 age group applicants, when cancer severity was accounted for in combination with 3 or more nodes of cancer involvement, was 29.3 times that when compared to those in the 65-75 age group having localized cancer, where no nodes are involved. The 35-44 age group applicants who were diagnosed with cancer within the last year had over 10-fold increase in relative mortality ratios compared to the 65-75 age group, who were over 10 years from diagnosis. Taking the severity of cancer along with time from diagnosis showed over a 12 times relative mortality ratio between the low rate of over 10 years from diagnosis and localized involvement to those diagnosed within the last year having 3 or more nodes with cancer. Applicant age, time since diagnosis and cancer severity were the most significant variables to predict the relative mortality ratios.

All-Cause Mortality for Life Insurance Applicants with a History of Prostate Cancer.

OBJECTIVE: - To determine the all-cause mortality of life insurance applicants diagnosed with prostate cancer currently or at some time in the past. BACKGROUND: - Prostate cancer is common and a frequent cause of cancer death. Both the frequency of prostate cancer in men and its propensity for causing premature mortality require insurance company medical directors and underwriters to have a good understanding of prostate cancer-related mortality trends, patterns, and outcomes in the insured population. METHODOLOGY: - Life insurance applicants with reported prostate cancer were extracted from data covering United States residents between November 2007 and November 2014. Information about these applicants was matched to the Social Security Death Master (SSDMF) file for deaths occurring from 2007 to 2011 and to another commercially available death source file (Other Death Source, ODS) for deaths occurring from 2007 to 2014 to determine vital status. Actual to Expected (A/E) mortality ratios were calculated using the Society of Actuaries 2015 Valuation Basic Table (2015VBT), select and ultimate table (age last birthday) and the 2013 US population as expected mortality ratios. All expected bases were not smoker distinct. RESULTS: - The study covered applicants between the ages of 45 and 75 and had approximately 405,000 person-years of exposure. Older aged applicants had a lower mortality ratio than those who were younger. Applicants 45 to 54 had the highest mortality ratios in the first year after diagnosis which steadily decreased in years 6 to 10 with an increase in the mortality ratio for those over 10 years from diagnosis. Relative mortality rate was close to unity for those with localized cancer across all age groups. The mortality ratio was 2 to 4 times greater for those with cancer in 1 positive node, and much greater with 3 positive nodes. For each time-from-diagnosis category, the relative mortality ratios compared to age were highest in the 45-54 age group. The A/E mortality ratios based on the 2015VBT were consistently 3 to 4 times that of the mortality ratios based on the 2013 US population. CONCLUSION: - The mortality patterns of insurance applicants with prostate cancer were similar to that observed in individuals with prostate cancer in the general population. Applicant age, time to diagnosis and cancer severity were the most significant variables to predict mortality.

All-Cause Mortality for Diabetics or Individuals with Hyperglycemia Applying for Life Insurance.

Diabetics and individuals with lab results consistent with a diagnosis of diabetes or hyperglycemia were extracted from data covering US residents who applied for life insurance between January 2007 and January 2014. Information about these applicants was matched to the Social Security Death Master File (SSDMF) and another commercially available death source file to determine vital status. Due to the inconsistencies of reporting within the death files, there were two cohorts of death cases, one including the imputed year of birth (full cohort of deaths), and the second where the date of birth was known (reduced cohort of deaths). The study had approximately 8.5 million person-years of exposure. Actual to expected (A/E) mortality ratios were calculated using the Society of Actuaries 2008 Valuation Basic Table (2008VBT) select table, age last birthday and the 2010 US population as expected mortality rates. With the 2008VBT as an expected basis, the overall A/E mortality ratio was 3.15 for the full cohort of deaths and 2.56 for the reduced cohort of deaths. Using the US population as the expected basis, the overall A/E mortality ratio was 0.98 for the full cohort of deaths and 0.79 for the reduced cohort. Since there was no smoking status information in this study, all expected bases were not smoker distinct. A/E mortality ratios varied by disease treatment category and were considerably higher in individuals using insulin. A/E mortality ratios decreased with increasing age and took on a J-shaped distribution with increasing BMI (Body Mass Index). The lowest mortality ratios were observed for overweight and obese individuals. The A/E mortality ratio based on the 2008VBT decreased with the increase in applicant duration, which was defined as the time since initial life insurance application.

The Long QT Syndrome: A Review and Mortality Analysis.

Objective .- To provide a recent review of literature pertinent to the assessment of life risk for individuals with a history of long QT syndrome (LQTS) and assess the mortality risk of a subset of these patients. Methods .- Standard comparative mortality techniques were employed to analyze relevant survival curves. Results .- Mortality risk in LQTS varies by age, sex, QTc interval length, genotype, and history of symptoms. A narrowly-defined subgroup of LQTS patients, those over age 20 with no history of syncope prior to age 20 and a QTc of less than 500ms, have an excess death rate of 0.5 - 2.0 per thousand per year. Conclusions .- Though the risk of sudden cardiac death in those with a history of long QT syndrome persists, there are subgroups of patients for whom the excess risk is lower than has been reported for this group of patients as a whole.

Response after treatment for advanced hepatitis C--a mortality abstract.

This article presents an analysis of a recently published study of the survival experience among a group of patients treated for hepatitis C with advanced hepatic fibrosis/cirrhosis, divided into groups based on whether or not sustained virological response was achieved. The purpose is to evaluate the magnitude of excess mortality relative to a comparison population. The potential errors inherent in generating mock age/sex distributions from limited published data are also highlighted.

Breast cancer micrometastasis--a mortality abstract.

This article presents an analysis of a survival study involving women with a history of breast cancer and either no nodal metastases, nodal micrometastases or nodal macrometastases, for the purpose of determining approximate life insurance ratings. In addition, a modification to the traditional method of determining the mortality rates of a comparison population is presented.

Issues in breast cancer with minimal nodal involvement.

An overview of the literature pertaining to the mortality risks posed by breast cancers with micrometastases or isolated tumor cells found in the lymph nodes is presented. Also addressed is the current debate about the appropriateness of omitting a completion axillary node dissection when minimal disease has been detected via sentinel node immunohistochemistry. A companion article will present an analysis of survival data generated by one particular study of women with microscopic nodal involvement vs those with macroscopic involvement and those with no nodal disease.

Life risk assessment in cancer: a guide to using the surveillance, epidemiology, and end results (SEER) program.

A guide to the use of SEER data to analyze cancer survival using the SEER*Stat software package is presented. Relative and cause-specific survival techniques are demonstrated via examples, and the strengths and weaknesses of these approaches are explored.

A simple method for computer-based survival curve measurement.

An easy-to-use method for generating tabular data from a survival curve is given. This technique measures a given survival curve in units of pixels, then converts the data to a table of cumulative survival (P) vs time utilizing software already present on most computer systems.