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Minority Adult Survivors of Childhood Cancer: A Comparison of Long-Term Outcomes, Health Care Utilization, and Health-Related Behaviors From the Childhood Cancer Survivor StudyCastellino, Sharon M., Casillas, Jacqueline, Hudson, Melissa M., Mertens, Ann C., Whitton, John, Brooks, Sandra L., Zeltzer, Lonnie K., Ablin, Arthur, Castleberry, Robert, Hobbie, Wendy, Kaste, Sue, Robison, Leslie L., Oeffinger, Kevin C. 20 September 2005 (has links)
PURPOSE: To determine the influence of race/ethnicity on outcomes in the Childhood Cancer Survivor Study (CCSS). PATIENTS AND METHODS: Of CCSS adult survivors in the United States, 443 (4.9%) were black, 503 (5.6%) were Hispanic and 7,821 (86.6%) were white. Mean age at interview, 26.9 years (range, 18 to 48 years); mean follow-up, 17.2 years (range, 8.7 to 28.4 years). Late mortality, second malignancy (SMN) rates, health care utilization, and health status and behaviors were assessed for blacks and Hispanics and compared with white survivors. RESULTS: Late mortality rate (6.5%) and 15-year cumulative incidence of SMN (3.5%) were similar across racial/ethnic groups. Minority survivors were more likely to have lower socioeconomic status (SES); final models were adjusted for income, education, and health insurance. Although overall health status was similar, black survivors were less likely to report adverse mental health (females: odds ratio [OR], 0.6; 95% CI, 0.4 to 0.9; males: OR, 0.5; 95% CI, 0.3 to 0.8). Differences in health care utilization and behaviors noted: Hispanic survivors were more likely to report a cancer center visit (females: OR, 1.5; 95% CI, 1.1 to 2.0; males: OR, 1.7; 95% CI, 1.2 to 2.3); black females were more likely (OR, 1.6; 95% CI, 1.1 to 2.4), and Hispanic females less likely to have a recent Pap smear (OR, 0.7; 95% CI, 0.5 to 1.0); black and Hispanic survivors were less likely to report smoking; black survivors were less likely to report problem drinking. CONCLUSION: Adjusted for SES, adverse outcomes in CCSS were not associated with minority status. Importantly, black survivors reported less risky behaviors and better preventive practices. Hispanic survivors had equitable access to cancer related care.
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Computational Bayesian techniques applied to cosmologyHee, Sonke January 2018 (has links)
This thesis presents work around 3 themes: dark energy, gravitational waves and Bayesian inference. Both dark energy and gravitational wave physics are not yet well constrained. They present interesting challenges for Bayesian inference, which attempts to quantify our knowledge of the universe given our astrophysical data. A dark energy equation of state reconstruction analysis finds that the data favours the vacuum dark energy equation of state $w {=} -1$ model. Deviations from vacuum dark energy are shown to favour the super-negative ‘phantom’ dark energy regime of $w {< } -1$, but at low statistical significance. The constraining power of various datasets is quantified, finding that data constraints peak around redshift $z = 0.2$ due to baryonic acoustic oscillation and supernovae data constraints, whilst cosmic microwave background radiation and Lyman-$\alpha$ forest constraints are less significant. Specific models with a conformal time symmetry in the Friedmann equation and with an additional dark energy component are tested and shown to be competitive to the vacuum dark energy model by Bayesian model selection analysis: that they are not ruled out is believed to be largely due to poor data quality for deciding between existing models. Recent detections of gravitational waves by the LIGO collaboration enable the first gravitational wave tests of general relativity. An existing test in the literature is used and sped up significantly by a novel method developed in this thesis. The test computes posterior odds ratios, and the new method is shown to compute these accurately and efficiently. Compared to computing evidences, the method presented provides an approximate 100 times reduction in the number of likelihood calculations required to compute evidences at a given accuracy. Further testing may identify a significant advance in Bayesian model selection using nested sampling, as the method is completely general and straightforward to implement. We note that efficiency gains are not guaranteed and may be problem specific: further research is needed.
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