Background: Persons with cancer commit suicide more frequently than those without, and males generally commit suicide more frequently than females. A population-based analysis of cancer patients was carried out here, comparing suicide risk between the genders, to elucidate the features specific to each gender. Patients and methods: A total of 1.3 million cancer cases from the Surveillance, Epidemiology, and End Results program were analyzed. Cox proportional hazards models were fitted to personal, tumor-related, and social variates. Results: A total of 265 female and 1307 male suicides were enumerated, reflecting 0.04% and 0.19% from each gender, and providing an overall hazard ratio for male suicide of 6.2 [95% confidence interval (CI) 5.4–7.1]. Females with colorectal (P = 0.01) and cervical (P < 0.0001) cancers showed decreased suicide rates. Males with head and neck cancers (P < 0.0001) and myeloma (P = 0.02) had increased rates, whereas rates were decreased in males with lung cancer (P = 0.01), liver (P = 0.01), brain tumors (P = 0.04), and leukemia (P = 0.007). The hazard ratio associated for male suicide with distant metastasis was 2.84 (95% CI 2.49–3.24); for married status, 0.46 (95% CI 0.39–0.54); and for African-American ancestry, 0.24 (95% CI 0.17–0.34)—comparable ratios were seen here for female suicides. In head and neck cancers, with both genders analyzed together, the suicide hazard was increased if surgery was contraindicated (3.0, 95% CI 1.3–6.8), but not if refused. Conclusions: The high-risk patient was male, with head and neck cancer or myeloma, advanced disease, little social or cultural support, and limited treatment options. Oncologists and allied health professionals should be aware of the potential for suicide in cancer patients and their associated risk factors.

BACKGROUND: Cancer survival is influenced by age, comorbidity, and type of cancer. A population‐based study was conducted to compare the interplay between age and mortality for different cancers.METHODS: This study analyzed 784,378 cases, comprising 22 of the commonest SEER cancers diagnosed between 1984 and 1993. Competing hazards and proportional hazard analyses for cancer‐specific and comorbid death were performed.RESULTS: Median follow‐up was up to 159 months, and the median age of diagnosis was 67 years. Cancer‐specific and comorbid deaths accumulated most within the first years of diagnosis. With the more biologically aggressive cancers, cancer deaths invariably exceeded comorbid deaths. For the remaining 70% of cancers, comorbidity remained the dominant mode of death. Deaths attributable to both cancer and comorbidity accumulated mostly after the seventh decade of life. Cancer site had a 3‐fold greater effect on overall survival than age at diagnosis and a 30‐fold effect with cancer‐specific survival; age at diagnosis had a 5‐fold greater effect on comorbid deaths than site.CONCLUSIONS: Both the age of the affected individual and the biology of the particular cancer have major influences on cancer survival and mode of death. Cancer is largely a disease of the elderly. Within affected individuals, fatalities attributable to cancer and comorbidity appeared inter‐related, with cancer‐specific deaths dominating for more lethal cancers and comorbid deaths dominating for the remaining majority. For these reasons, further improvements in overall survival may be best anticipated from better geriatric and general medical management as much as from better cancer management. Cancer 2008. © 2008 American Cancer Society.

Plants and animals of a given species tend to cluster within their habitats in accordance with a power function between their mean density and the variance. This relationship, Taylor's power law, has been variously explained by ecologists in terms of animal behavior, interspecies interactions, demographic effects, etc., all without consensus. Taylor's law also manifests within a wide range of other biological and physical processes, sometimes being referred to as fluctuation scaling and attributed to effects of the second law of thermodynamics. 1/f noise refers to power spectra that have an approximately inverse dependence on frequency. Like Taylor's law these spectra manifest from a wide range of biological and physical processes, without general agreement as to cause. One contemporary paradigm for 1/f noise has been based on the physics of self-organized criticality. We show here that Taylor's law (when derived from sequential data using the method of expanding bins) implies 1/f noise, and that both phenomena can be explained by a central limit-like effect that establishes the class of Tweedie exponential dispersion models as foci for this convergence. These Tweedie models are probabilistic models characterized by closure under additive and reproductive convolution as well as under scale transformation, and consequently manifest a variance to mean power function. We provide examples of Taylor's law, 1/f noise, and multifractality within the eigenvalue deviations of the Gaussian unitary and orthogonal ensembles, and show that these deviations conform to the Tweedie compound Poisson distribution. The Tweedie convergence theorem provides a unified mathematical explanation for the origin of Taylor's law and 1/f noise applicable to a wide range of biological, physical, and mathematical processes, as well as to multifractality.

A power function relationship observed between the variance and the mean of many types of biological and physical systems has generated much debate as to its origins. This Taylor's law (or fluctuation scaling) has been recently hypothesized to result from the second law of thermodynamics and the behavior of the density of states. This hypothesis is predicated on physical quantities like free energy and an external field; the correspondence of these quantities with biological systems, though, remains unproven. Questions can be posed as to the applicability of this hypothesis to the diversity of observed phenomena as well as the range of spatial and temporal scales observed with Taylor's law. We note that the cumulant generating functions derived from this thermodynamic model correspond to those derived over a quarter century earlier for a class of probabilistic models known as the Tweedie exponential dispersion models. These latter models are characterized by variance-to-mean power functions; their phenomenological basis rests with a central-limit-theorem-like property that causes many statistical systems to converge mathematically toward a Tweedie form. We review evaluations of the Tweedie Poisson-gamma model for Taylor's law and provide three further cases to test: the clustering of single nucleotide polymorphisms (SNPs) within the horse chromosome 1, the clustering of genes within human chromosome 8, and the Mertens function. This latter case is a number theoretic function for which a thermodynamic model cannot explain Taylor's law, but where Tweedie convergence remains applicable. The Tweedie models are applicable to diverse biological, physical, and mathematical phenomena that express power variance functions over a wide range of measurement scales; they provide a probabilistic description for Taylor's law that allows mechanistic insight into complex systems without the assumption of a thermodynamic mechanism.

Objective: To investigate the possible association between pelvic irradiation for rectal cancer and subsequent second primary cancers. Patients and Methods: A population-based analysis of 20,910 individuals with rectal cancer from the Surveillance, Epidemiology, and End Results registry, for whom follow-up times were at least 5 years, was performed. Kaplan-Meier estimates for the development of second cancers within irradiated and nonirradiated cohorts provided a comparison that accounted for censored data. Cox proportional hazards analyses were further conducted to compensate for patient and tumor-related factors. Results: A total of 656 (12%) and 2368 (16%) second primary cancers were enumerated from the irradiated and nonirradiated cohorts, respectively, with the proportion of second primary cancers within the irradiated cohort being significantly decreased (P < 0.001) on crude analysis. However, Kaplan-Meier and Cox analyses revealed no significant difference between the 2 cohorts when all second primary cancer sites were considered together (hazard ratio = 1.02; 95% confidence interval [CI], 0.92–1.12). Proportional hazards analysis for specific second primary sites revealed a decreased risk after pelvic irradiation for cancer of the prostate (hazard ratio = 0.63; 95% CI, 0.48–0.84), and an increased risk for cancers of the uterine corpus & cervix (hazard ratio = 2.5; 95% CI, 1.6–4.0). Conclusion: Second primary cancers after irradiation for rectal cancers appear relatively infrequent compared with the background incidence of spontaneous cancers, and should not factor into treatment decisions for this older population. We hypothesize that the incidence of second primary tumors within adjacent organs could represent a balance between the radiation-induction of tumors and the radiation-inhibition of spontaneously occurring tumors.