Social Patterning of Screening Uptake and the Impact of Facilitating Informed Choices: Psychological and Ethical Analyses

Screening for unsuspected disease has both possible benefits and harms for those who participate. Historically the benefits of participation have been emphasized to maximize uptake reflecting a public health approach to policy; currently policy is moving towards an informed choice approach involving giving information about both benefits and harms of participation. However, no research has been conducted to evaluate the impact on health of an informed choice policy. Using psychological models, the first aim of this study was to describe an explanatory framework for variation in screening uptake and to apply this framework to assess the impact of informed choices in screening. The second aim was to evaluate ethically that impact. Data from a general population survey (n = 300) of beliefs and attitudes towards participation in diabetes screening indicated that greater orientation to the present is associated with greater social deprivation and lower expectation of participation in screening. The results inform an explanatory framework of social patterning of screening in which greater orientation to the present focuses attention on the disadvantages of screening, which tend to be immediate, thereby reducing participation. This framework suggests that an informed choice policy, by increasing the salience of possible harms of screening, might reduce uptake of screening more in those who are more deprived and orientated to the present. This possibility gives rise to an apparent dilemma where an ethical decision must be made between greater choice and avoiding health inequality. Philosophical perspectives on choice and inequality are used to point to some of the complexities in assessing whether there really is such a dilemma and if so how it should be resolved. The paper concludes with a discussion of the ethics of paternalism

Bayesian metamodeling of complex biological systems across varying representations

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium