Clyde B. Schechter

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.

Background. The Georgetown University-Albert Einstein College of Medicine breast cancer simulation model (Model GE) has evolved over time in structure and function to reflect advances in knowledge about breast cancer, improvements in early detection and treatment technology, and progress in computing resources. This article describes the model and provides examples of model applications. Methods. The model is a discrete events microsimulation of single-life histories of women from multiple birth cohorts. Events are simulated in the absence of screening and treatment, and interventions are then applied to assess their impact on population breast cancer trends. The model accommodates differences in natural history associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) biomarkers, as well as conventional breast cancer risk factors. The approach for simulating breast cancer natural history is phenomenological, relying on dates, stage, and age of clinical and screen detection for a tumor molecular subtype without explicitly modeling tumor growth. The inputs to the model are regularly updated to reflect current practice. Numerous technical modifications, including the use of object-oriented programming (C++), and more efficient algorithms, along with hardware advances, have increased program efficiency permitting simulations of large samples. Results. The model results consistently match key temporal trends in US breast cancer incidence and mortality. Conclusion. The model has been used in collaboration with other CISNET models to assess cancer control policies and will be applied to evaluate clinical trial design, recurrence risk, and polygenic risk-based screening.

Objectives: Favorable attitudes, emotions, personality characteristics, and self-rated health have been associated with successful aging in late life. However, less is known regarding these constructs and their relationships to mental health outcomes in the oldest old persons. This study examined cross-sectional relationships of these psychological factors to depressive symptoms in centenarians and near-centenarians. Methods: A selected sample of Ashkenazi Jewish older adults aged 98–107 (n = 54, 78% female) without significant cognitive impairment participated. Cognitive function was assessed by Mini-Mental Status Examination, positive attitude toward life and emotional expression by the Personality Outlook Profile Scale (POPS), self-rated health by participants' subjective rating of their present health, and depressive symptoms by the Geriatric Depression Scale. Results: Results demonstrated inverse associations of the positive attitude toward life domain of the POPS and self-rated health with participants' levels of depressive symptoms even after adjusting for the effects of history of medical illnesses, cognitive function, and demographic variables. Additionally, participants with high levels of care showed higher levels of depressive symptoms. Path analysis supported the partially mediating role of positive attitude toward life in the relationship between self-rated health and depressive symptoms. Conclusion: These findings emphasized the important roles of positive attitudes and emotions as well as self-rated health in mental health outcomes in the oldest old. Although, limited by its cross-sectional design, findings suggest these psychological factors may exert protective effects on mental health outcomes in advanced age.

The aim of this study is to determine whether access to fast food outlets and supermarkets is associated with overweight and obesity in New York City neigh

Background. Black women in the District of Columbia (DC) have the highest breast cancer mortality in the US. Local cancer control planners are interested in how to most efficiently reduce this mortality.Methods. An established simulation model was adapted to reflect the experiences of Black women in DC and estimate the past and future impact of changes in use of screening and adjuvant treatment.Results. The model estimates that the observed reduction in mortality that occurred from 1975 to 2007 attributable to screening, treatment, and both was 20.2%, 25.7%, and 41.0% respectively. The results suggest that, by 2020, breast cancer mortality among Black women in DC could be reduced by 6% more by initiating screening at age 40 versus age 50. Screening annually may also reduce mortality to a greater extent than biennially, albeit with a marked increase in false positive screening rates.Conclusion. This study demonstrates how modeling can provide data to assist local planners as they consider different cancer control policies based on their individual populations.