Public Support for Sickle-Cell Disease Funding: Does Race Matter?

Despite the fact that sickle-cell disease (SCD) is perhaps the most “racialized” medical condition in the USA, very little is known about how “race” impacts public support for health policies related to the condition. We embedded an experiment within the 2011 Cooperative Congressional Election Study in order to assess perceptions about SCD among 1250 participants from diverse backgrounds and evaluate the extent to which these perceptions were associated with support for government spending on SCD-related benefits. We manipulated the racial phenotype of SCD advocates who requested additional government funding and asked participants to indicate how much the government should provide. Overall, participants expressed moderately positive attitudes about SCD, and there were no differences in funding support based on the race of the advocate. However, white participants supported less funding compared to nonwhite participants, even after adjusting for a number of demographic and attitudinal covariates. These findings suggest that a complex relationship between racial identification and implicit racism may shape public perceptions about SCD that negatively influences perceivers’ support for SCD-related policy.     

A Joint Modeling Approach for Longitudinal Outcomes and Non-ignorable Dropout under Population Heterogeneity in Mental Health Studies

The paper proposes a joint mixture model to model non-ignorable drop-out in longitudinal cohort studies of mental health outcomes. The model combines a (non)-linear growth curve model for the time-dependent outcomes and a discrete-time survival model for the drop-out with random effects shared by the two sub-models. The mixture part of the model takes into account population heterogeneity by accounting for latent subgroups of the shared effects that may lead to different patterns for the growth and the drop-out tendency. A simulation study shows that the joint mixture model provides greater precision in estimating the average slope and covariance matrix of random effects. We illustrate its benefits with data from a longitudinal cohort study that characterizes depression symptoms over time yet is hindered by non-trivial participant drop-out.KEYWORDS: Latent growth curve, MNAR drop-out, survival analysis, finite mixture model, mental health     

Regression models using the LINEX loss to predict lower bounds for the number of points for approximating planar contour shapes

Researchers in statistical shape analysis often analyze outlines of objects. Even though these contours are infinite-dimensional in theory, they must be discretized in practice. When discretizing, it is important to reduce the number of sampling points considerably to reduce computational costs, but to not use too few points so as to result in too much approximation error. Unfortunately, determining the minimum number of points needed to achieve sufficiently approximate the contours is computationally expensive. In this paper, we fit regression models to predict these lower bounds using characteristics of the contours that are computationally cheap as predictor variables. However, least squares regression is inadequate for this task because it treats overestimation and underestimation equally, but underestimation of lower bounds is far more serious. Instead, to fit the models, we use the LINEX loss function, which allows us to penalize underestimation at an exponential rate while penalizing overestimation only linearly. We present a novel approach to select the shape parameter of the loss function and tools for analyzing how well the model fits the data. Through validation methods, we show that the LINEX models work well for reducing the underestimation for the lower bounds.     

Conceptual frameworks and the philosophical foundations of general living systems theory

The continued expansion and fragmentation of biological disciplines impede education, communication, and efficient advance of biological research. Reversing these trends may require a unification of theories and concepts from all levels of biological organization. One form of this unification is the statement of generalizations that apply to all living systems. We explore the philosophical foundations of general theories of living systems by analyzing conceptual frameworks as they apply to biology. This analysis examines the relation of empirical observation, theories, and conceptual frameworks within the context of an individual scientist's conceptual continuum. Also presented are a small set of translevel generalizations that articulate our conceptual framework of living systems in terms of organismic system organization, the environmental system-organismic system dyad, system capacity, and system incorporation. A set of procedural rules are stated which suggest minimum criteria for the evaluation of the explanatory adequacy of biological theories. The relation of this work to other similar analyses and syntheses of biological knowledge is discussed.     

Preparing the school-age child for adoption.

The development of an adoptive relationship for children who have been in the limbo of foster care is a continuous process. It is unrealistic to expect these families to live happily ever after. Agencies must begin developing regular services to meet the special needs of the families being created. To a large extent the issues that will have to be worked through relate to the stages of preparation outlined in this paper. Each stage in the child's growth may activate unresolved issues around the grief at the biological parents' rejection, confusion about identity or difficulty in working through problems. Future adoptive home recruitment must take this into account by providing training and other supports to families considering adopting the older child.