Treatment-related cardiovascular events in patients with non-small cell lung cancer: Evidence from real-world data with a competing risks approach.

BACKGROUND: Understanding cancer treatment-related cardiovascular (CV) events is important for cancer care; however, comprehensive evaluation of CV events in patients with lung cancer is limited. This study aimed to assess the cumulative incidence and associated risks of various CV event types in patients with non-small cell lung cancer (NSCLC). METHODS: A total of 7868 individuals aged 40 years and older, recently diagnosed with NSCLC (2007-2018), were assessed with data obtained from the National Cancer Center, Korea. This study included nine types of CV events. A 2-year cumulative incidence function (CIF) of CV events was estimated, with death as a competing event. The associated risks were assessed by subdistribution hazard ratio (sHR) in the Fine-Gray competing risks model. RESULTS: CV events were observed in 7.8% of patients with NSCLC, with the most frequently observed types being atrial fibrillation and flutter (AF) (2.7%), venous thromboembolic disease (2.0%), and cerebrovascular disease (CeVD) (1.5%). Overall, all CV events were highest in the group treated with systemic therapy (CIF, 10.6%; 95% confidence interval [CI], 9.5%-11.8%), followed by those treated with surgery (CIF, 10.0%; 95% CI, 8.6%-11.6%); the incidence of AF (CIF, 5.7%; 95% CI, 4.6%-7.0%) was highest in patients treated with surgery. Individuals treated with systemic therapy were found to exhibit a higher CeVD risk than those treated with surgery (sHR, 4.12; 95% CI, 1.66-10.23). Among the patients who underwent surgery, those with lobectomy and pneumonectomy had a higher AF risk (vs. wedge resection/segmentectomy; sHR, 7.79; 95% CI, 1.87-32.42; sHR, 8.10; 95% CI, 1.60-40.89). CONCLUSIONS: These findings revealed treatment-related CV event risks in patients with NSCLC, which suggests that the risk of AF in surgery and CeVD in systemic therapy should be paid more attention to achieve a better prognosis and improve cancer survivorship outcomes. PLAIN LANGUAGE SUMMARY: Atrial fibrillation and flutter (AF) is the most common cardiovascular event, particularly at a high risk in patients with non-small cell lung cancer (NSCLC) undergoing surgery. Patients receiving surgery with poor performance status, diagnosed with regional stage, and undergoing lobectomy or pneumonectomy are at a high risk of AF. Systemic/radiotherapy is associated with cerebrovascular and ischemic heart disease in patients with NSCLC.

Seasonal influences on the efficacy of anti-programmed cell death (ligand) 1 inhibitors in lung cancer.

BACKGROUND: Seasonal variations in systemic immunity have been reported. This study aimed to evaluate whether seasonality affects the efficacy of anticancer immunotherapy. METHODS: A total of 604 patients with lung cancer receiving single anti-programmed cell death (ligand) 1 (anti-PD-[L]1) inhibitors from two prospective observational cohorts were screened. Primary outcomes were progression-free survival (PFS) and overall survival (OS). Patients were classified into two groups according to the season when the treatment started: winter (November-February) and other seasons (March-October). Kaplan-Meier analysis and Cox proportional hazards models were fitted to evaluate the impact of seasonality on survival. For validation, propensity score matching was performed. RESULTS: A total of 484 patients with advanced non-small cell lung cancer were included. In an unmatched population, multivariable analysis demonstrated that the winter group (n = 173) had a significantly lower risk of progression or death from immunotherapy than the other group (n = 311) (PFS: hazard ratio [HR], 0.77 [95% confidence interval (CI), 0.62-0.96]; p = .018; OS: HR, 0.77 [95% CI, 0.1-0.98]; p = .032). In a propensity score-matched population, the winter group (n = 162) showed significantly longer median PFS (2.8 months [95% CI, 1.9-4.1 months] vs. 2.0 months [95% CI, 1.4-2.7 months]; p = .009) than the other group (n = 162). The winter group's median OS was also significantly longer than that of the other group (13.4 months [95% CI, 10.2-18.0 months] vs. 8.0 months [95% CI, 3.6-8.7 months]; p = .012). The trend toward longer survival in the winter group continued in subgroup analyses. CONCLUSIONS: Starting an anti-PD-(L)1 inhibitor in winter was associated with better treatment outcomes in patients with lung cancer compared to other seasons.

Risk factors for colorectal cancer in a fecal immunochemical test-positive group: The National Health Insurance Service-National Health Screening Cohort.

BACKGROUND AND AIM: Colorectal cancer (CRC) was the fourth most common cancer in Republic of Korea in 2019. It has a gradually increasing mortality rate, indicating the importance of screening for CRC. Among the various CRC screening test, fecal immunochemical test (FIT) is a simple yet most commonly used. Neverthelss, there have been only few long-term studies on subjects with FIT-positive. Therefore, in this study, we aimed to investigate the risk factors for CRC in FIT-positive patients using the National Health Insurance Service Bigdata database. METHODS: Among 1 737 633 individuals with a FIT screening result for CRC in 2009, 101 143 (5.82%) were confirmed to be FIT positive. The CRC incidence over 10 years (up to 2018) of these participants was investigated using the National Cancer Registry. RESULTS: Out of the 101 143 FIT-positive participants, 4395 (4.35%) were diagnosed with CRC. The FIT-positive patients who underwent a second round of screening showed a 5-year cumulative CRC incidence of approximately 1.25%, whereas those who did not showed an incidence of approximately 3.75%. Among the FIT-positive patients, the CRC incidence in the non-compliance group for the second round of screening was 2.8 times higher than that in the compliance group. CONCLUSIONS: In FIT-positive participants, non-compliance with the second round of screening was identified as a major risk factor for CRC development. It is necessary to establish appropriate strategies for managing risk factors for CRC in FIT-positive patients to increase the rate of compliance with the second round of CRC screening.

Hyperthermic intraperitoneal chemotherapy for epithelial ovarian cancer: A meta-analysis.

BACKGROUND: The value of hyperthermic intraperitoneal chemotherapy (HIPEC) at the time of cytoreductive surgery (CRS) for epithelial ovarian cancer (EOC) is controversial and its use remains experimental in most national and international guidelines. We wished to systematically evaluate all available evidence. METHODS: A comprehensive review of data from MEDLINE, EMBASE, and Cochrane Library databases was conducted from the first report on HIPEC in EOC till April 3, 2022. Progression-free survival (PFS) and overall survival (OS) were compared between the HIPEC and control groups. This meta-analysis was registered with PROSPERO (CRD42021265810). RESULTS: Fifteen studies (10 case-control studies and 5 randomized controlled trials [RCTs]) were included in the present meta-analysis. Based on the time interval between the last systemic chemotherapy exposure and timing of CRS +/- HIPEC, all studies and patients' cohorts we classified into recent (<6 months; n = 9 studies/patients cohorts) and non-recent (≥6 months, n = 8 studies/patients cohorts) chemotherapy exposure groups. In the recent chemotherapy exposure group, HIPEC was associated with improvement of both PFS (HR, 0.585; 95% CI, 0.422-0.811) and OS (HR, 0.519; 95% CI, 0.346-0.777). On the contrary, in the non-recent chemotherapy exposure group, HIPEC failed to significantly affect PFS (HR, 1.037; 95% CI, 0.684-1.571) or OS (HR, 0.932; 95% CI, 0.607-1.430). Consistent results were observed in subsequent sensitivity analyses. CONCLUSION: Our present meta-analysis demonstrates that the value of HIPEC at CRS for EOC appears to depend on the timing of the last systemic chemotherapy exposure. Future trials are awaited to define the role of HIPEC in EOC.

Weighted least-squares regression with competing risks data.

The semiparametric accelerated failure time (AFT) model linearly relates the logarithm of the failure time to a set of covariates, while leaving the error distribution unspecified. This model has been widely investigated in survival literature due to its simple interpretation and relationship with linear models. However, there has been much less focus on developing AFT-type linear regression methods for analyzing competing risks data, in which patients can potentially experience one of multiple failure causes. In this article, we propose a simple least-squares (LS) linear regression model for a cause-specific subdistribution function, where the conventional LS equation is modified to account for data incompleteness under competing risks. The proposed estimators are shown to be consistent and asymptotically normal with consistent estimation of the variance-covariance matrix. We further extend the proposed methodology to risk prediction and analysis under clustered competing risks scenario. Simulation studies suggest that the proposed method provides rapid and valid statistical inferences and predictions. Application of our method to two oncology datasets demonstrate its utility in routine clinical data analysis.

Unsupervised learning methods for efficient geographic clustering and identification of disease disparities with applications to county-level colorectal cancer incidence in California.

Many public health policymaking questions involve data subsets representing application-specific attributes and geographic location. We develop and evaluate standard and tailored techniques for clustering via unsupervised learning (UL) algorithms on such amalgamated (dual-domain) data sets. The aim of the associated algorithms is to identify geographically efficient clusters that also maximize the number of statistically significant differences in disease incidence and demographic variables across top clusters. Two standard UL approaches, k means with k++ initialization (k++) and the standard self-organizing map (SSOM), are considered along with a new, tailored version of the SOM (TSOM). The TSOM algorithm involves optimization of a customized objective function with terms promoting individual geographic cluster cohesion while also maximizing the number of differences across clusters, and two hyper-parameters controlling the relative weighting of geographic and attribute subspaces in a non-Euclidean distance measure within the clustering problem. The performance of these three techniques (k++, SSOM, TSOM) is compared and evaluated in the context of a data set for colorectal cancer incidence in the state of California, at the level of individual counties. Clusters are visualized via chloropleth maps and ordered graphs are also used to illustrate disparities in disease incidence among four identity groups. While all three approaches performed well, the TSOM identified the largest number of disease and demographic disparities while also yielding more geographically efficient top clusters. Techniques presented in this study are relevant to applications including the delivery of health care resources and identifying disparities among identity groups, and to questions involving coordination between county- and state-level policymakers.

Estimating life expectancy adjusted by self-rated health status in the United States: national health interview survey linked to the mortality.

BACKGROUND: Life expectancy is increasingly incorporated in evidence-based screening and treatment guidelines to facilitate patient-centered clinical decision-making. However, life expectancy estimates from standard life tables do not account for health status, an important prognostic factor for premature death. This study aims to address this research gap and develop life tables incorporating the health status of adults in the United States. METHODS: Data from the National Health Interview Survey (1986-2004) linked to mortality follow-up through to 2006 (age ≥ 40, n = 729,531) were used to develop life tables. The impact of self-rated health (excellent, very good, good, fair, poor) on survival was quantified in 5-year age groups, incorporating complex survey design and weights. Life expectancies were estimated by extrapolating the modeled survival probabilities. RESULTS: Life expectancies incorporating health status differed substantially from standard US life tables and by health status. Poor self-rated health more significantly affected the survival of younger compared to older individuals, resulting in substantial decreases in life expectancy. At age 40 years, hazards of dying for white men who reported poor vs. excellent health was 8.5 (95% CI: 7.0,10.3) times greater, resulting in a 23-year difference in life expectancy (poor vs. excellent: 22 vs. 45), while at age 80 years, the hazards ratio was 2.4 (95% CI: 2.1, 2.8) and life expectancy difference was 5 years (5 vs. 10). Relative to the US general population, life expectancies of adults (age < 65) with poor health were approximately 5-15 years shorter. CONCLUSIONS: Considerable shortage in life expectancy due to poor self-rated health existed. The life table developed can be helpful by including a patient perspective on their health and be used in conjunction with other predictive models in clinical decision making, particularly for younger adults in poor health, for whom life tables including comorbid conditions are limited.

Binary genetic algorithm for optimal joinpoint detection: Application to cancer trend analysis.

The joinpoint regression model (JRM) is used to describe trend changes in many applications and relies on the detection of joinpoints (changepoints). However, the existing joinpoint detection methods, namely, the grid search (GS)-based methods, are computationally demanding, and hence, the maximum number of computable joinpoints is limited. Herein, we developed a genetic algorithm-based joinpoint (GAJP) model in which an explicitly decoupled computing procedure for optimization and regression is used to embed a binary genetic algorithm into the JRM for optimal joinpoint detection. The combinations of joinpoints were represented as binary chromosomes, and genetic operations were performed to determine the optimum solution by minimizing the fitness function, the Bayesian information criterion (BIC) and BIC3 . The accuracy and computational performance of the GAJP model were evaluated via intensive simulation studies and compared with those of the GS-based methods using BIC, BIC3 , and permutation test. The proposed method showed an outstanding computational efficiency in detecting multiple joinpoints. Finally, the suitability of the GAJP model for the analysis of cancer incidence trends was demonstrated by applying this model to data on the incidence of colorectal cancer in the United States from 1975 to 2016 from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Thus, the GAJP model was concluded to be practically feasible to detect multiple joinpoints up to the number of grids without requirement to preassign the number of joinpoints and be easily extendable to cancer trend analysis utilizing large datasets.

Impact of comorbidity assessment methods to predict non-cancer mortality risk in cancer patients: a retrospective observational study using the National Health Insurance Service claims-based data in Korea.

BACKGROUND: Cancer patients' prognoses are complicated by comorbidities. Prognostic prediction models with inappropriate comorbidity adjustments yield biased survival estimates. However, an appropriate claims-based comorbidity risk assessment method remains unclear. This study aimed to compare methods used to capture comorbidities from claims data and predict non-cancer mortality risks among cancer patients. METHODS: Data were obtained from the National Health Insurance Service-National Sample Cohort database in Korea; 2979 cancer patients diagnosed in 2006 were considered. Claims-based Charlson Comorbidity Index was evaluated according to the various assessment methods: different periods in washout window, lookback, and claim types. The prevalence of comorbidities and associated non-cancer mortality risks were compared. The Cox proportional hazards models considering left-truncation were used to estimate the non-cancer mortality risks. RESULTS: The prevalence of peptic ulcer, the most common comorbidity, ranged from 1.5 to 31.0%, and the proportion of patients with ≥1 comorbidity ranged from 4.5 to 58.4%, depending on the assessment methods. Outpatient claims captured 96.9% of patients with chronic obstructive pulmonary disease; however, they captured only 65.2% of patients with myocardial infarction. The different assessment methods affected non-cancer mortality risks; for example, the hazard ratios for patients with moderate comorbidity (CCI 3-4) varied from 1.0 (95% CI: 0.6-1.6) to 5.0 (95% CI: 2.7-9.3). Inpatient claims resulted in relatively higher estimates reflective of disease severity. CONCLUSIONS: The prevalence of comorbidities and associated non-cancer mortality risks varied considerably by the assessment methods. Researchers should understand the complexity of comorbidity assessments in claims-based risk assessment and select an optimal approach.

Utility of the Patient-Reported Outcomes Measurement Information System (PROMIS) to measure primary health outcomes in cancer patients: a systematic review.

OBJECTIVE: To systematically review the literature on how the Patient-Reported Outcomes Measurement Information System (PROMIS) measure system is used to assess patient-reported outcomes (PROs) in cancer patients. METHODS: We conducted a systematic review following the PRISMA guidelines. Articles were identified through searches of PubMed, EMBASE, and additional manual review of the publications listed on the PROMIS website. We included studies measuring outcomes, including physical function, fatigue, pain, anxiety, and depression in cancer patients. Eligible articles included interventional and observational studies published in English between 2009 and 2019. RESULTS: A total of 1789 records were identified and screened by three reviewers, 118 articles were reviewed in full text, and 42 articles met the inclusion criteria. The majority of studies used the PROMIS measure system to prospectively assess longitudinal changes in PROs; the number of measurements ranges from 2 to 4 with the time points of follow-up set at 3, 6, and 12 months after the baseline assessment. Depression and fatigue were the most frequently measured outcomes. Fixed-length short forms with four items were the most common measure types. A transition toward utilizing a web- or smartphone-based electronic tool was observed to limit the burden of the conventional paper-based survey to collect and store PROs. CONCLUSION: The PROMIS measure system is increasingly popular to measure PROs in cancer patients with acceptance of its various short forms and electronic-based systems to administer data electronically. Findings from this review highlight various aspects of PROMIS and may help health professionals in their choice of PRO tools for optimizing care and support for cancer patients.

Cumulative incidence of chemotherapy-induced cardiotoxicity during a 2-year follow-up period in breast cancer patients.

PURPOSE: Cardiotoxicities are adverse effects often reported in chemotherapy-treated breast cancer patients. This study evaluated the potential risk factors and cumulative incidence of doxorubicin-induced cardiotoxicity in Korean breast cancer patients. METHODS: We retrospectively analyzed the data of 613 breast cancer patients who underwent a multigated acquisition (MUGA) scan or echocardiography prior to chemotherapy and at least one post-chemotherapy follow-up MUGA scan/echocardiography between 2007 and 2016 at National Cancer Center, Korea. The Cox proportional hazards models were used to evaluate cardiotoxicity risks. Competing risks analyses were performed to estimate cumulative incidence of cardiotoxicity. RESULTS: Risk factors associated with cardiotoxicity within 2 years of doxorubicin administration included age [adjusted hazard ratio (aHR) = 1.02, 95% confidence interval (CI) 1.00-1.04; p = 0.05], metastasis (aHR = 2.66; 95% CI 1.36-5.20; p < 0.01), and concomitant trastuzumab (aHR = 4.08; 95% CI 2.31-7.21; p < 0.01). The cumulative incidence of patients with cardiotoxicity was 6.1% at 2 years (without substantial change from about 9 months)and 20.2% at 2 years (without substantial change from about 15 months) after initiation of doxorubicin-containing therapy without and with trastuzumab, respectively. CONCLUSIONS: Susceptibility to chemotherapy-induced cardiotoxicity within 2 years of doxorubicin initiation in breast cancer patients was elevated with old age, metastasis, and concomitant trastuzumab. Regular imaging monitoring at least up to 9 months after doxorubicin initiation in patients treated without concomitant trastuzumab, and 15 months in patients treated with concomitant trastuzumab, is needed for early detection of chemotherapy-induced cardiotoxicity.

Association between viral hepatitis infection and Parkinson's disease: A population-based prospective study.

The association between hepatitis virus infection and Parkinson's disease remains controversial. To determine whether hepatitis B virus (HBV) and hepatitis C virus (HCV) infections are associated with an increased risk of Parkinson's disease in Korean aged ≥40 years, we completed a population-based prospective study including patients without infections and those with HBV, HCV and HBV/HCV infections from 2005 to 2015. We used the International Classification of Diseases 10th Revision to identify Parkinson's disease (G20) and chronic hepatitis C virus (B18.2) and chronic hepatitis B virus infections (B18.0 or B18.1). To identify Parkinson's disease risk, competing risk analysis adjusted for age, sex, comorbidities and death was performed. Overall, 1 010 317 patients (358 052, noninfection; 488 990, hepatitis B; 144 459 hepatitis C; and 18 680 hepatitis B/C) were included. The incidence density of Parkinson's disease per 10 000 person-years was highest in the hepatitis C group (8.0), followed by the hepatitis B/C (6.8) and hepatitis B (5.0) groups. Hypertension, ischaemic heart disease, epilepsy, stroke and depressive disorder increased the hazard of Parkinson's disease in all groups. The adjusted hazard ratios were 1.25 (95% confidence interval: 1.17-1.35), 1.39 (95% confidence interval: 1.27-1.52) and 1.46 (95% confidence interval: 1.14-1.85) in the HBV, HCV, and HBV/HCV groups, respectively. Our findings suggest that adult patient of 40 years and older with HBV and HCV infections should be monitored for signs of Parkinson's disease so that early intervention and accurate treatment can be provided for minimizing the development and consequences of Parkinson's disease.

Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions.

Linear models are typically used to analyze multivariate longitudinal data. With these models, estimating the covariance matrix is not easy because the covariance matrix should account for complex correlated structures: the correlation between responses at each time point, the correlation within separate responses over time, and the cross-correlation between different responses at different times. In addition, the estimated covariance matrix should satisfy the positive definiteness condition, and it may be heteroscedastic. However, in practice, the structure of the covariance matrix is assumed to be homoscedastic and highly parsimonious, such as exchangeable or autoregressive with order one. These assumptions are too strong and result in inefficient estimates of the effects of covariates. Several studies have been conducted to solve these restrictions using modified Cholesky decomposition (MCD) and linear covariance models. However, modeling the correlation between responses at each time point is not easy because there is no natural ordering of the responses. In this paper, we use MCD and hypersphere decomposition to model the complex correlation structures for multivariate longitudinal data. We observe that the estimated covariance matrix using the decompositions is positive-definite and can be heteroscedastic and that it is also interpretable. The proposed methods are illustrated using data from a nonalcoholic fatty liver disease study.

Effect of Antihypertensive Medications on Sepsis-Related Outcomes: A Population-Based Cohort Study.

OBJECTIVES: Although the effect of antihypertensive agents on sepsis has been studied, evidence for survival benefit was limited in the literature. We investigated differences in sepsis-related outcomes depending on the antihypertensive drugs given prior to sepsis in patients with hypertension. DESIGN: Population-based cohort study. SETTING: Sample cohort Database of the National Health Insurance Service from 2003 to 2013 in South Korea. PATIENTS: Patients over 30 years old who were diagnosed with sepsis after receiving hypertension treatment. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Primary outcomes, 30-day and 90-day mortality rates, were analyzed for differences among three representative antihypertensive medications: angiotensin- converting enzyme inhibitors or angiotensin II receptor blockers, calcium channel blockers, and thiazides. In total, 4,549 patients diagnosed with hypertension prior to hospitalization for sepsis were identified. The 30-day mortality was significantly higher among patients who did not receive any medications within 1 month before sepsis (36.8%) than among patients who did (32.0%; p < 0.001). The risk for 90-days mortality was significantly lower in prior angiotensin-converting enzyme inhibitors or angiotensin II receptor blocker users (reference) than in other drug users (odds ratio, 1.27; 95% CI, 1.07-1.52). There was no difference in the risk for 30-day and 90-day mortality depending on whether calcium channel blockers or thiazides were used. Use of calcium channel blockers was associated with a decreased risk for inotropic agent administration, compared with those of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (odds ratio, 1.23; 95% CI, 1.05-1.44) and thiazides (odds ratio, 1.33; 95% CI, 1.12-1.58). CONCLUSIONS: In patients with sepsis, lower mortality rate was associated with prior use of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers not with use of calcium channel blockers or thiazides. The requirement of inotropic agents was significantly lower in prior use of calcium channel blockers, although the survival benefits were not prominent.

Analyzing discrete competing risks data with partially overlapping or independent data sources and nonstandard sampling schemes, with application to cancer registries.

This paper demonstrates the flexibility of a general approach for the analysis of discrete time competing risks data that can accommodate complex data structures, different time scales for different causes, and nonstandard sampling schemes. The data may involve a single data source where all individuals contribute to analyses of both cause-specific hazard functions, overlapping datasets where some individuals contribute to the analysis of the cause-specific hazard function of only one cause while other individuals contribute to analyses of both cause-specific hazard functions, or separate data sources where each individual contributes to the analysis of the cause-specific hazard function of only a single cause. The approach is modularized into estimation and prediction. For the estimation step, the parameters and the variance-covariance matrix can be estimated using widely available software. The prediction step utilizes a generic program with plug-in estimates from the estimation step. The approach is illustrated with three prognostic models for stage IV male oral cancer using different data structures. The first model uses only men with stage IV oral cancer from population-based registry data. The second model strategically extends the cohort to improve the efficiency of the estimates. The third model improves the accuracy for those with a lower risk of other causes of death, by bringing in an independent data source collected under a complex sampling design with additional other-cause covariates. These analyses represent novel extensions of existing methodology, broadly applicable for the development of prognostic models capturing both the cancer and noncancer aspects of a patient's health.

Cancer treatment and survivorship statistics, 2012.

Although there has been considerable progress in reducing cancer incidence in the United States, the number of cancer survivors continues to increase due to the aging and growth of the population and improvements in survival rates. As a result, it is increasingly important to understand the unique medical and psychosocial needs of survivors and be aware of resources that can assist patients, caregivers, and health care providers in navigating the various phases of cancer survivorship. To highlight the challenges and opportunities to serve these survivors, the American Cancer Society and the National Cancer Institute estimated the prevalence of cancer survivors on January 1, 2012 and January 1, 2022, by cancer site. Data from Surveillance, Epidemiology, and End Results (SEER) registries were used to describe median age and stage at diagnosis and survival; data from the National Cancer Data Base and the SEER-Medicare Database were used to describe patterns of cancer treatment. An estimated 13.7 million Americans with a history of cancer were alive on January 1, 2012, and by January 1, 2022, that number will increase to nearly 18 million. The 3 most prevalent cancers among males are prostate (43%), colorectal (9%), and melanoma of the skin (7%), and those among females are breast (41%), uterine corpus (8%), and colorectal (8%). This article summarizes common cancer treatments, survival rates, and posttreatment concerns and introduces the new National Cancer Survivorship Resource Center, which has engaged more than 100 volunteer survivorship experts nationwide to develop tools for cancer survivors, caregivers, health care professionals, advocates, and policy makers.

Increasing Trend in Hospital Deaths Consistent among Older Decedents in Korea: A Population-based Study Using Death Registration Database, 2001-2014.

BACKGROUND: With improvement in hospice palliative care services and long-term care, Republic of Korea (hereafter South Korea) has recorded significant changes in places of death (e.g., hospital, home), especially among older adults. Over the last few decades, the most common places of death in South Korea were hospitals. However, Koreans, especially older adults, reportedly prefer to receive terminal care and eventually die at home. This study was conducted to investigate trends in places of death among older Korean adults and factors associated therewith. METHODS: Data were obtained from the Korean Death Registration Database maintained by the National Statistical Office. Decedents who died after the age of 65 years from 2001 to 2014 were included in the analysis. For descriptive analysis, proportions of places of death were analyzed and were used to plot graphs for visualizing trends during 13-year period. Logistic regression model was used to evaluate factors associated with places of death (hospital versus home). RESULTS: Two million three hundred fifty eight thousand two hundred eleven older adult decedents were included in final analysis. Hospitals were the most common places of death (57.82%), followed by homes (32.12%). Dying at social welfare facilities was rare (2.61%). A gradual increase in hospital deaths (31.38% in 2001 to 75.30% in 2014) and a subsequent decrease in home deaths (60.44% to 15.95% over the same period) were noted. Hospital deaths were more likely for younger patients (ORs 1.28, 95% CI 1.27-1.29), females (ORs 1.28, 95% CI 1.27-1.29), and single/divorced or widowed individuals (ORs 1.77, 1.49 and 1.03 respectively). A higher education level and living in urban areas were strongly associated with a higher likelihood of dying in a hospital. CONCLUSION: Over the study period, there was a consistent increasing trend in hospital deaths in South Korea. Trends in place of death and factors associated therewith should be more intensely investigated and monitored. Resources and facilities should be increased to fulfill end-of-life care preferences and the needs of an increasingly older population in South Korea.

Alanine aminotransferase and gamma-glutamyl transferase have different dose-response relationships with risk of mortality by age.

BACKGROUND & AIMS: It remains unclear whether the respective dose-response relationships between serum alanine aminotransferase (ALT) and gamma-glutamyl transferase (GGT) levels and risk of mortality are consistent by age. METHODS: We used sampled cohort data from the National Health Insurance Corporation to conduct a retrospective cohort study. A total of 313 252 participants who received medical health check-ups from 2002 to 2008 were assessed for risk of death according to serum ALT and GGT levels over an average of 6 years. The hazard ratios (HRs) for mortality were analysed with Cox proportional hazard model. RESULTS: The crude mortality rate increased linearly with increasing serum ALT and GGT levels in adults aged <60 years. However, the all-cause mortality rate showed a J-shaped relationship with increasing serum ALT levels whereas all-cause mortality rate showed a linear relationship with increasing serum GGT levels in adults aged ≥60 years. The HR of death showed U-shaped relationships with increasing serum ALT levels in adults aged ≥60 years. On the contrary, the HR of death from any cause had a linear association with increasing serum GGT levels among all age groups. CONCLUSIONS: In this study, U-shaped relationship patterns were demonstrated between serum ALT levels and risk for all-cause mortality in adults aged ≥60 years while serum GGT levels showed a linear relationship with risk for all-cause death. Very low levels of serum ALT in elderly patients suggest that they are at high risk of mortality.

Annual Report to the Nation on the status of cancer, 1975-2010, featuring prevalence of comorbidity and impact on survival among persons with lung, colorectal, breast, or prostate cancer.

BACKGROUND: The American Cancer Society (ACS), the Centers for Disease Control and Prevention (CDC), the National Cancer Institute (NCI), and the North American Association of Central Cancer Registries (NAACCR) collaborate annually to provide updates on cancer incidence and death rates and trends in these outcomes for the United States. This year's report includes the prevalence of comorbidity at the time of first cancer diagnosis among patients with lung, colorectal, breast, or prostate cancer and survival among cancer patients based on comorbidity level. METHODS: Data on cancer incidence were obtained from the NCI, the CDC, and the NAACCR; and data on mortality were obtained from the CDC. Long-term (1975/1992-2010) and short-term (2001-2010) trends in age-adjusted incidence and death rates for all cancers combined and for the leading cancers among men and women were examined by joinpoint analysis. Through linkage with Medicare claims, the prevalence of comorbidity among cancer patients who were diagnosed between 1992 through 2005 residing in 11 Surveillance, Epidemiology, and End Results (SEER) areas were estimated and compared with the prevalence in a 5% random sample of cancer-free Medicare beneficiaries. Among cancer patients, survival and the probabilities of dying of their cancer and of other causes by comorbidity level, age, and stage were calculated. RESULTS: Death rates continued to decline for all cancers combined for men and women of all major racial and ethnic groups and for most major cancer sites; rates for both sexes combined decreased by 1.5% per year from 2001 through 2010. Overall incidence rates decreased in men and stabilized in women. The prevalence of comorbidity was similar among cancer-free Medicare beneficiaries (31.8%), breast cancer patients (32.2%), and prostate cancer patients (30.5%); highest among lung cancer patients (52.9%); and intermediate among colorectal cancer patients (40.7%). Among all cancer patients and especially for patients diagnosed with local and regional disease, age and comorbidity level were important influences on the probability of dying of other causes and, consequently, on overall survival. For patients diagnosed with distant disease, the probability of dying of cancer was much higher than the probability of dying of other causes, and age and comorbidity had a smaller effect on overall survival. CONCLUSIONS: Cancer death rates in the United States continue to decline. Estimates of survival that include the probability of dying of cancer and other causes stratified by comorbidity level, age, and stage can provide important information to facilitate treatment decisions.

Comorbidity-adjusted life expectancy: a new tool to inform recommendations for optimal screening strategies.

BACKGROUND: Many guidelines recommend considering health status and life expectancy when making cancer screening decisions for elderly persons. OBJECTIVE: To estimate life expectancy for elderly persons without a history of cancer, taking into account comorbid conditions. DESIGN: Population-based cohort study. SETTING: A 5% sample of Medicare beneficiaries in selected geographic areas, including their claims and vital status information. PARTICIPANTS: Medicare beneficiaries aged 66 years or older between 1992 and 2005 without a history of cancer (n = 407 749). MEASUREMENTS: Medicare claims were used to identify comorbid conditions included in the Charlson index. Survival probabilities were estimated by comorbidity group (no, low/medium, and high) and for the 3 most prevalent conditions (diabetes, chronic obstructive pulmonary disease, and congestive heart failure) by using the Cox proportional hazards model. Comorbidity-adjusted life expectancy was calculated based on comparisons of survival models with U.S. life tables. Survival probabilities from the U.S. life tables providing the most similar survival experience to the cohort of interest were used. RESULTS: Persons with higher levels of comorbidity had shorter life expectancies, whereas those with no comorbid conditions, including very elderly persons, had favorable life expectancies relative to an average person of the same chronological age. The estimated life expectancy at age 75 years was approximately 3 years longer for persons with no comorbid conditions and approximately 3 years shorter for those with high comorbidity relative to the average U.S. population. LIMITATIONS: The cohort was limited to Medicare fee-for-service beneficiaries aged 66 years or older living in selected geographic areas. Data from the Surveillance, Epidemiology, and End Results cancer registry and Medicare claims lack information on functional status and severity of comorbidity, which might influence life expectancy in elderly persons. CONCLUSION: Life expectancy varies considerably by comorbidity status in elderly persons. Comorbidity-adjusted life expectancy may help physicians tailor recommendations for stopping or continuing cancer screening for individual patients.