Statistical modeling in the optimization of breast cancer screening schedules

Although screening for breast cancer is broadly utilized in the United States, the optimal approach in terms of examination modality, frequency, and starting age is unclear. Furthermore, there is no consensus on the benefit from earlier, more frequent, or additional modes of screening for women with high-risk for breast cancer. This thesis explores methods for finding optimal screening strategies by modeling the costs and benefits of breast cancer screening programs through a simulation-based and a theoretical approach, which incorporate some ignored or unexplored components. For the general population, cost-effectiveness studies evaluating breast cancer screening have focused on mammography alone, and have not assessed the impact of its combination with clinical breast exam, a cheaper and more practical exam. Costs incurred beyond screening including costs for work-up, biopsy, and treatment have often been ignored. We account for these factors in a comprehensive microsimulation analysis by modeling the natural history of the disease including age-specific incidence, sojourn time, and exam sensitivity and specificity, and evaluate the cost-effectiveness of a set of screening programs on a simulated cohort of women. Similarly, we focus on a special case of high-risk women where we include screening programs that begin at an earlier age, have more frequent examinations, and include combinations of magnetic-resonance imaging, which has recently been recommended for women at high-risk for breast cancer. The most cost-effective approach depends on how much society is willing to pay to save a year of life. Finally, we take a theoretical approach which is motivated by the idea that other optimal screening programs may exist outside a set of predetermined strategies used in a simulation-based approach. While empirical studies give a better idea of which screening schedules are useful, a theoretical approach may uncover a true optimal schedule. We assume a nonstable disease model and account for costs of screening and dollar value of benefit in a utility function under two different frameworks to estimate the optimal number or ages of examinations given certain general assumptions. We show that a solution exists for these models and demonstrate in numerical studies that they will give reasonable results

Acceptance and expectance: Cultural norms for alcohol use in Denmark

Alcohol consumption levels in Denmark are high with the risk of increased morbidity and mortality in the population. It is suggested that people's views of “normal” use of alcohol must be the platform for formulating effective alcohol education and prevention strategies. However, little is known about the cultural norms for alcohol use. The aim of this article is to examine the perceptions of cultural norms for alcohol use in Denmark among different age groups and the similarities and differences between the groups, including examining how people construct and negotiate the cultural norms for drinking. Five focus group interviews were conducted with one group per the following age groups: 16–20; 21–34; 35–44; 45–64; and 65–82. These groups consisted of both men and women with five to six participants in each group (a total of 27). Thematic analysis was performed with the aim of developing themes that reflected the cultural norms for alcohol use. The unifying theme of this research was Danish people's acceptance and expectance of social drinking. Alcohol is widely accepted and associated with mutual expectations to drink, leading to identification of cultural influences and facilitation to drink. The social drinking context plays an important role in people's perceptions of the normality of drinking. This includes the selection of particular beverages, and regularly leads to consumption above the recommended levels for low risk to health. This calls for public health attention that promotes low risk drinking in the social context and aims to prevent and reduce serious alcohol-related harm and health problems across the population

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19