The Role of Cell Sterilization in Population Based Studies of Radiogenic Second Cancers Following Radiation Therapy

Advances in radiotherapy have generated increased interest in comparative studies of treatment techniques and their effectiveness. In this respect, pediatric patients are of specific interest because of their sensitivity to radiation induced second cancers. However, due to the rarity of childhood cancers and the long latency of second cancers, large sample sizes are unavailable for the epidemiological study of contemporary radiotherapy treatments. Additionally, when specific treatments are considered, such as proton therapy, sample sizes are further reduced due to the rareness of such treatments. We propose a method to improve statistical power in micro clinical trials. Specifically, we use a more biologically relevant quantity, cancer equivalent dose (DCE), to estimate risk instead of mean absorbed dose (DMA). Our objective was to demonstrate that when DCE is used fewer subjects are needed for clinical trials. Thus, we compared the impact of DCE vs. DMA on sample size in a virtual clinical trial that estimated risk for second cancer (SC) in the thyroid following craniospinal irradiation (CSI) of pediatric patients using protons vs. photons. Dose reconstruction, risk models, and statistical analysis were used to evaluate SC risk from therapeutic and stray radiation from CSI for 18 patients. Absorbed dose was calculated in two ways: with (1) traditional DMA and (2) with DCE. DCE and DMA values were used to estimate relative risk of SC incidence (RRCE and RRMA, respectively) after proton vs. photon CSI. Ratios of RR for proton vs. photon CSI (RRRCE and RRRMA) were then used in comparative estimations of sample size to determine the minimal number of patients needed to maintain 80% statistical power when using DCE vs. DMA. For all patients, we found that protons substantially reduced the risk of developing a second thyroid cancer when compared to photon therapy. Mean RRR values were 0.052±0.014 and 0.087±0.021 for RRRMA and RRRCE, respectively. However, we did not find that use of DCE reduced the number of patents needed for acceptable statistical power (i.e, 80%). In fact, when considerations were made for RRR values that met equipoise requirements and the need for descriptive statistics, the minimum number of patients needed for a micro-clinical trial increased from 17 using DMA to 37 using DCE. Subsequent analyses revealed that for our sample, the most influential factor in determining variations in sample size was the experimental standard deviation of estimates for RRR across the patient sample. Additionally, because the relative uncertainty in dose from proton CSI was so much larger (on the order of 2000 times larger) than the other uncertainty terms, it dominated the uncertainty in RRR. Thus, we found that use of corrections for cell sterilization, in the form of DCE, may be an important and underappreciated consideration in the design of clinical trials and radio-epidemiological studies. In addition, the accurate application of cell sterilization to thyroid dose was sensitive to variations in absorbed dose, especially for proton CSI, which may stem from errors in patient positioning, range calculation, and other aspects of treatment planning and delivery.

Sleep quality and quantity and associated factors among high school students from south Texas

Background. Insufficient and poor quality sleep among adolescents affects not only the cognitive functioning, but overall health of the individual. Existing research suggests that adolescents from varying ethnic groups exhibit differing sleep patterns. However, little research focuses on sleep patterns and associated factors (i.e. tobacco use, mental health indicators) among Hispanic youth. ^ Methods. The study population (n=2,536) included students in grades 9-12 who attended one of the three public high schools along the Texas-Mexico border in 2003. This was a cross sectional study using secondary data collected via a web-based, confidential, self-administered survey. Separate logistic regression models were estimated to identify factors associated with reduced (<9 hours/night) and poor quality sleep on average during weeknights. ^ Results. Of participants, 49.5% reported reduced sleep while 12.8% reported poor quality sleep. Factors significantly (p<0.05) associated with poor quality sleep were: often feeling stressed or anxious (OR=5.49), being born in Mexico (OR=0.65), using a computer/playing video games 15+ hours per week (OR=2.29), working (OR=1.37), being a current smoker (OR=2.16), and being a current alcohol user (OR=1.64). Factors significantly associated with reduced quantity of sleep were: often feeling stressed or anxious (OR=2.74), often having headaches/stomachaches (OR=1.77), being a current marijuana user (OR=1.70), being a current methamphetamine user (OR=4.92), and being a current alcohol user (OR=1.27). ^ Discussion. Previous research suggests that there are several factors that can influence sleep quality and quantity in adolescents. This paper discusses these factors (i.e. work, smoking, alcohol, etc.) found to be associated with poor sleep quality and reduced sleep quantity in the Hispanic adolescent population. A reduced quantity of sleep (81.20% of the participants) and a poor quality of sleep (12.80% of the participants) were also found in high school students from South Texas. ^

Minimum income for healthy living

Objectives: We sought to estimate the costs of implementing the current recommendations for healthy choices for a mother with two young children in Atlanta, Georgia. ^ Methods: Current recommendations for healthy choices promoted by the federal government or other credible source were compiled and operationalized into specific conditions or behaviors. The costs of implementing these choices in Atlanta were estimated by using internet searches of retailers/suppliers, phone interviews, and direct observation. The least expensive option was chosen when options were available. ^ Results: Recommendations for choosing a healthy neighborhood, home, school, child care, food, physical activity, and maintaining healthy relationships as well as access to health care were considered. Total costs for this family of three totaled $38,181. Housing, child care, and health insurance contributed to 78% of the total costs. ^ Conclusions: The minimum income needed to choose healthy choices falls short of current wages, and eligibility levels and benefits for income support. ^