Benefits and Costs of Interventions to Improve Breast Cancer Outcomes in African American Women

Purpose Historically, African American women have experienced higher breast cancer mortality than white women, despite lower incidence. Our objective was to evaluate whether costs of increasing rates of screening or application of intensive treatment will be off-set by survival benefits for African American women. Methods We use a stochastic simulation model of the natural history of breast cancer to evaluate the incremental societal costs and benefits of status quo versus targeted biennial screening or treatment improvements among African Americans 40 years of age and older. Main outcome measures were number of mammograms, stage, all-cause mortality, and discounted costs per life year saved (LYS). Results At the current screening rate of 76%, there is little incremental benefit associated with further increasing screening, and the costs are high: $124,053 and$124,217 per LYS for lay health worker and patient reminder interventions, respectively, compared with the status quo. Using reminders would cost $51,537 per LYS if targeted to virtually unscreened women or$78,130 per LYS if targeted to women with a two-fold increase in baseline risk. If all patients received the most intensive treatment recommended, costs increase but deaths decrease, for a cost of $52,678 per LYS. Investments of up to$6,000 per breast cancer patient could be used to enhance treatment and still yield cost-effectiveness ratios of less than $75,000 per LYS. Conclusion Except in pockets of unscreened or high-risk women, further investments in interventions to increase screening are unlikely to be an efficient use of resources. Ensuring that African American women receive intensive treatment seems to be the most cost-effective approach to decreasing the disproportionate mortality experienced by this population

Lipoprotein Genotype and Conserved Pathway for Exceptional Longevity in Humans

Alteration of single genes involved in nutrient and lipoprotein metabolism increases longevity in several animal models. Because exceptional longevity in humans is familial, it is likely that polymorphisms in genes favorably influence certain phenotypes and increase the likelihood of exceptional longevity. A group of Ashkenazi Jewish centenarians ( n = 213), their offspring ( n = 216), and an age-matched Ashkenazi control group ( n = 258) were genotyped for 66 polymorphisms in 36 candidate genes related to cardiovascular disease (CVD). These genes were tested for association with serum lipoprotein levels and particle sizes, apolipoprotein A1, B, and C-3 levels and with outcomes of hypertension, insulin resistance, and mortality. The prevalence of homozygosity for the −641C allele in the APOC3 promoter (rs2542052) was higher in centenarians (25%) and their offspring (20%) than in controls (10%) ( p = 0.0001 and p = 0.001, respectively). This genotype was associated with significantly lower serum levels of APOC3 and a favorable pattern of lipoprotein levels and sizes. We found a lower prevalence of hypertension and greater insulin sensitivity in the −641C homozygotes, suggesting a protective effect against CVD and the metabolic syndrome. Finally, in a prospectively studied cohort, a significant survival advantage was demonstrated in those with the favorable −641C homozygote ( p < 0.0001). Homozygosity for the APOC3 −641C allele is associated with a favorable lipoprotein profile, cardiovascular health, insulin sensitivity, and longevity. Because modulation of lipoproteins is also seen in genetically altered longevity models, it may be a common pathway influencing lifespan from nematodes to humans

Speaking Stata: Seven steps for vexatious string variables

String variables that seemingly should be numeric require some care. The column provides a step-by-step guide explaining how to convert them or—as the case may merit—to leave them as they are. Dates in string form, identifiers and categorical variables, and pure numeric content trapped in string form need different actions. Practical advice on good and not so good technique is sprinkled throughout

Speaking Stata: How best to generate indicator or dummy variables

Indicator or dummy variables record whether some condition is true or false in each observation by a value of 1 or 0. Values may also be missing if truth or falsity is not known, and that fact should be flagged. Such indicators may be created on the fly by using factor-variable notation. tabulate also offers one method for automating the generation of indicators. In this column, we discuss in detail how otherwise to best generate such variables directly, with comments here and there on what not to do