Disparities in Cause-Specific Cancer Survival by Census Tract Poverty Level in Idaho, U.S.

Objective. This population-based study compared cause-specific cancer survival by socioeconomic status using methods to more accurately assign cancer deaths to primary site. Methods. The current study analyzed Idaho data used in the Accuracy of Cancer Mortality Statistics Based on Death Certificates (ACM) study supplemented with additional information to measure cause-specific cancer survival by census tract poverty level. Results. The distribution of cases by primary site group differed significantly by poverty level (chi-square = 265.3, 100 df, p In the life table analyses, for 8 of 24 primary site groups investigated, and all sites combined, there was a significant gradient relating higher poverty with poorer survival. For all sites combined, the absolute difference in 5-year cause-specific survival rate was 13.6% between the lowest and highest poverty levels. Conclusions. This study shows striking disparities in cause-specific cancer survival related to the poverty level of the area a person resides in at the time of diagnosis

Estimation of net survival for cancer patients: Relative survival setting more robust to some assumption violations than cause-specific setting, a sensitivity analysis on empirical data.

Net survival is the survival that would be observed if the only possible underlying cause of death was the disease under study. It can be estimated with either cause-specific or relative survival data settings, if the informative censoring is properly considered. However, net survival estimators are prone to specific biases related to the data setting itself. We examined which data setting was the most robust against violation of key assumptions (erroneous cause of death and inappropriate life tables). We identified 4285 women in the Geneva Cancer Registry, diagnosed with breast, colorectal, lung cancer and melanoma between 1981 and 1991 and estimated net survival up to 20 years using cause-specific and relative survival settings. We used weights to tackle informative censoring in both settings and performed sensitivity analyses to evaluate the impact of misclassification of cause of death in the cause-specific setting or of using inappropriate life tables on net survival estimates in the relative survival setting. For all the four cancers, net survival was highest when using the cause-specific setting and the absolute difference between the two estimators increased with time since diagnosis. The sensitivity analysis showed that (i) the use of different life tables did not compromise net survival estimation in the relative survival setting, whereas (ii) a small level of misclassification for the cause of death led to a large change in the net survival estimate in the cause-specific setting. The relative survival setting was more robust to the above assumptions violations and is therefore recommended for estimation of net survival

Survival extrapolation in the presence of cause specific hazards.

Health economic evaluations require estimates of expected survival from patients receiving different interventions, often over a lifetime. However, data on the patients of interest are typically only available for a much shorter follow-up time, from randomised trials or cohorts. Previous work showed how to use general population mortality to improve extrapolations of the short-term data, assuming a constant additive or multiplicative effect on the hazards for all-cause mortality for study patients relative to the general population. A more plausible assumption may be a constant effect on the hazard for the specific cause of death targeted by the treatments. To address this problem, we use independent parametric survival models for cause-specific mortality among the general population. Because causes of death are unobserved for the patients of interest, a polyhazard model is used to express their all-cause mortality as a sum of latent cause-specific hazards. Assuming proportional cause-specific hazards between the general and study populations then allows us to extrapolate mortality of the patients of interest to the long term. A Bayesian framework is used to jointly model all sources of data. By simulation, we show that ignoring cause-specific hazards leads to biased estimates of mean survival when the proportion of deaths due to the cause of interest changes through time. The methods are applied to an evaluation of implantable cardioverter defibrillators for the prevention of sudden cardiac death among patients with cardiac arrhythmia. After accounting for cause-specific mortality, substantial differences are seen in estimates of life years gained from implantable cardioverter defibrillators

Cause-specific or relative survival setting to estimate population-based net survival from cancer? An empirical evaluation using women diagnosed with breast cancer in Geneva between 1981 and 1991 and followed for 20 years after diagnosis.

BACKGROUND: Both cause-specific and relative survival settings can be used to estimate net survival, the survival that would be observed if the only possible underlying cause of death was the disease under study. Both resulting net survival estimators are biased by informative censoring and prone to biases related to the data settings within which each is derived. We took into account informative censoring to derive theoretically unbiased estimators and examine which of the two data settings was the most robust against incorrect assumptions in the data. PATIENTS AND METHODS: We identified 2489 women in the Geneva Cancer Registry, diagnosed with breast cancer between 1981 and 1991, and estimated net survival up to 20-years using both cause-specific and relative survival settings, by tackling the informative censoring with weights. To understand the possible origins of differences between the survival estimates, we performed sensitivity analyses within each setting. We evaluated the impact of misclassification of cause of death and of using inappropriate life tables on survival estimates. RESULTS: Net survival was highest using the cause-specific setting, by 1% at one year and by up to around 11% twenty years after diagnosis. Differences between both sets of net survival estimates were eliminated after recoding between 15% and 20% of the non-specific deaths as breast cancer deaths. By contrast, a dramatic increase in the general population mortality rates was needed to see the survival estimates based on relative survival setting become closer to those derived from cause-specific setting. CONCLUSION: Net survival estimates derived using the cause-specific setting are very sensitive to misclassification of cause of death. Net survival estimates derived using the relative-survival setting were robust to large changes in expected mortality. The relative survival setting is recommended for estimation of long-term net survival among patients with breast cancer

Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC

The R package CFC performs cause-specific, competing-risk survival analysis by computing cumulative incidence functions from unadjusted, cause-specific survival functions. A high-level API in CFC enables end-to-end survival and competing-risk analysis, using a single-line function call, based on the parametric survival regression models in the survival package. A low-level API allows users to achieve more flexibility by supplying their custom survival functions, perhaps in a Bayesian setting. Utility methods for summarizing and plotting the output allow population-average cumulative incidence functions to be calculated, visualized and compared to unadjusted survival curves. Numerical and computational optimization strategies are employed for efficient and reliable computation of the coupled integrals involved. To address potential integrable singularities caused by infinite cause-specific hazards, particularly near time-from-index of zero, integrals are transformed to remove their dependency on hazard functions, making them solely functions of causespecific, unadjusted survival functions. This implicit variable transformation also provides for easier extensibility of CFC to handle custom survival models since it only requires the users to implement a maximum of one function per cause. The transformed integrals are numerically calculated using a generalization of Simpson's rule to handle the implicit change of variable from time to survival, while a generalized trapezoidal rule is used as reference for error calculation. An OpenMP-parallelized, efficient C++ implementation - using packages Rcpp and RcppArmadillo - makes the application of CFC in Bayesian settings practical, where a potentially large number of samples represent the posterior distribution of cause-specific survival functions

Seasonal Survival and Cause-Specific Mortality of Northern Bobwhites in Mississippi

Knowledge of northern bobwhite (Colinus virginianus) survival and rates at which specific mortality agents remove individuals from the population is important for implementation of science-based harvest and habitat management regimes. To better understand population response to habitat management, we monitored 194 radio-marked northern bobwhites in managed old-field habitats in eastcentral Mississippi, 1993 to 1996. Bobwhite populations increased during the first 3 years following initiation of disking and burning practices. During the 2nd year of bobwhite habitat management breeding season survival (0.509) was high relative to other southeastern populations. However, breeding season survival declined from the 2nd through the 5th year of management (1993, 0.509; 1994, 0.362; 1995, 0.338; 1996, 0.167; P \u3c 0.001). Declining seasonal survival was attributable to increasing mammalian mortality from 1993 to 1996 (P \u3c 0.01). Avian mortality rates were stochastic and differed among years (P = 0.04), while unknown mortality rates were similar (P = 0.13). Avian mortality evidently operated in a density-dependent fashion, whereas mammalian mortality continued to increase despite declining bobwhite population. Northern bobwhite cause-specific mortality rates among years differed by sex (P \u3c 0.01) and age (P \u3c 0.01). Indices of breeding season relative abundance declined with declining survival. We hypothesize that manipulations (bum, disk, bum/disk) which created habitat that met the seasonal requirements of breeding bobwhites and other early successional prey species, may have resulted in a functional and numerical response of mammalian predators

SURVIVAL AND CAUSE-SPECIFIC MORTALITY OF A SOUTHEASTERN KENTUCKY DEER POPULATION

White-tailed deer are one of the most sought after game species in Kentucky. While much of the Commonwealth boasts high deer populations, those in southeast Kentucky are viewed as relatively low compared to other regions, even after a decade of restrictive doe harvest and multiple years of population supplementation via translocation. We studied survival and cause specific mortality of a local population of deer near the Redbird District of the Daniel Boone National Forest in Clay and Leslie County, Kentucky from January 2014 - January 2017. We estimated female annual survival at 0.89 (CI: 0.88-0.87), with an overall 3-year survival of 0.69 (CI: 0.56-0.84). Deer vehicle collisions and poaching were the most frequent mortality causes and represented 13 of 18 (72%) of mortalities. Managers should consider all forms of mortality and their relative importance in wildlife population dynamics when making harvest decisions. We recommend longer-term studies similar to ours to better understand population trends and inform regional management of this species in Kentucky

Survival of patients with metastatic breast cancer: twenty-year data from two SEER registries

BACKGROUND: Many researchers are interested to know if there are any improvements in recent treatment results for metastatic breast cancer in the community, especially for 10- or 15-year survival. METHODS: Between 1981 and 1985, 782 and 580 female patients with metastatic breast cancer were extracted respectively from the Connecticut and San Francisco-Oakland registries of the Surveillance, Epidemiology, and End Results (SEER) database. The lognormal statistical method to estimate survival was retrospectively validated since the 15-year cause-specific survival rates could be calculated using the standard life-table actuarial method. Estimated rates were compared to the actuarial data available in 2000. Between 1991 and 1995, further 752 and 632 female patients with metastatic breast cancer were extracted respectively from the Connecticut and San Francisco-Oakland registries. The data were analyzed to estimate the 15-year cause-specific survival rates before the year 2005. RESULTS: The 5-year period (1981–1985) was chosen, and patients were followed as a cohort for an additional 3 years. The estimated 15-year cause-specific survival rates were 7.1% (95% confidence interval, CI, 1.8–12.4) and 9.1% (95% CI, 3.8–14.4) by the lognormal model for the two registries of Connecticut and San Francisco-Oakland respectively. Since the SEER database provides follow-up information to the end of the year 2000, actuarial calculation can be performed to confirm (validate) the estimation. The Kaplan-Meier calculation for the 15-year cause-specific survival rates were 8.3% (95% CI, 5.8–10.8) and 7.0% (95% CI, 4.3–9.7) respectively. Using the 1991–1995 5-year period cohort and followed for an additional 3 years, the 15-year cause-specific survival rates were estimated to be 9.1% (95% CI, 3.8–14.4) and 14.7% (95% CI, 9.8–19.6) for the two registries of Connecticut and San Francisco-Oakland respectively. CONCLUSIONS: For the period 1981–1985, the 15-year cause-specific survival for the Connecticut and the San Francisco-Oakland registries were comparable. For the period 1991–1995, there was not much change in survival for the Connecticut registry patients, but there was an improvement in survival for the San Francisco-Oakland registry patients

Survival Probability in Patients with Sickle Cell Anemia Using the Competitive Risk Statistical Model.

The clinical picture of patients with sickle cell anemia (SCA) is associated with several complications some of which could be fatal. The objective of this study is to analyze the causes of death and the effect of sex and age on survival of Brazilian patients with SCA. Data of patients with SCA who were seen and followed at HEMORIO for 15 years were retrospectively collected and analyzed. Statistical modeling was performed using survival analysis in the presence of competing risks estimating the covariate effects on a sub-distribution hazard function. Eight models were implemented, one for each cause of death. The cause-specific cumulative incidence function was also estimated. Males were most vulnerable for death from chronic organ damage (p = 0.0005) while females were most vulnerable for infection (p=0.03). Age was significantly associated (p ≤ 0.05) with death due to acute chest syndrome (ACS), infection, and death during crisis. The lower survival was related to death from infection, followed by death due to ACS. The independent variables age and sex were significantly associated with ACS, infection, chronic organ damage and death during crisis. These data could help Brazilian authorities strengthen public policies to protect this vulnerable population

Ethnic Disparities in Cervical Cancer Survival Among Medicare Eligible Women in a Multiethnic Population

To determine predictors of cervical cancer survival by socioeconomic status (SES), urbanization, race/ethnicity, comorbid conditions, and treatment among elderly Medicare-eligible women whose conditions were diagnosed with cervical cancer in a multiethnic population. Methods: A total of 538 women with cervical cancer aged 65 years or older were identified from 1999 to 2001 from the Texas Cancer Registry and were linked with the state Medicare data and Texas Vital Records to determine survival times. All women had similar access to care through Medicare fee-for-services insurance. A composite measure of SES was created using census tract-level data as was urbanization. Treatment and comorbid conditions were available from the Medicare data. Cox proportional hazards modeling was used for all-cause and cervical cancer-specific survival analysis. Results: Increased age (P \u3c 0.0001) and advanced tumor stage (P \u3c 0.0001) were associated with poorer all-cause and cervical cancer-specific survival. Having a comorbid condition was associated with all-cause survival (P \u3c 0.01) but not cervical cancer-specific mortality. After adjusting for confounders, women receiving some form of treatment were almost half as likely to die with cervical cancer (adjusted hazard ratio = 0.68; 95% confidence interval, 0.52-0.89). After adjustment for all confounders, Hispanic women consistently had lower all-cause and cervical cancer-specific mortality rates relative to non-Hispanic white and non-Hispanic black women. Conclusions: Among women with similar health care coverage, Hispanic women had consistently lower all-cause and cervical cancer-specific mortality rates than other older women whose conditions were diagnosed with this disease in Texas. The presence of comorbid conditions and treatment were important predictors of survival, yet these factors do not explain the survival advantage for Hispanic women