Consumer Decision-Making in the Health Insurance Marketplace

Objectives: Not much is known about consumer decision-making in the state Health Insurance Marketplaces established by the Affordable Care Act (ACA). This dissertation consists of three papers that explore this topic. In Paper 1, I explore how consumers value non-financial plan attributes in their choice of Marketplace plans. In Paper 2, I examine enrollee plan switching decisions in response to changes in the attributes of their chosen plans over time. In Paper 3, I simulate the expected effects of hypothetical minimum network adequacy and plan quality rating requirements on consumer welfare. Methods: The studies utilize discrete choice models on individual-level Marketplace enrollment data from California, Colorado, and Washington. Paper 1 uses conditional and mixed logit models of plan choice to estimate willingness-to-pay (WTP) amounts for key non-financial attributes, notably provider network size and plan quality ratings. Paper 2 uses logit models to explore consumer plan switching decisions as a function of changes in the attributes of chosen plans over time as well as choice set and household-level characteristics. Paper 3 applies the “log-sum” approach to Paper 1’s models to calculate changes in expected consumer welfare under different policy proposals. Results: In Paper 1, I find that consumers are very responsive to network size and plan quality in their choice of Marketplace plans. Individual enrollees exhibit an annual WTP of $200-$300 for a 10 percentage-point (25 percentile) increase in provider network size and a WTP of $1,200-$2,800 for a high quality plan relative to a low quality plan. In Paper 2, I find that changes in the premium, provider network size, and plan quality of chosen plans over time are significantly associated with the probability that enrollees switches plans in the subsequent enrollment period in the expected directions. In Paper 3, I find that minimum network adequacy restrictions may reduce expected consumer welfare, while the welfare effects of plan quality restrictions are more ambiguous. Policy Implications: Policymakers should take consumer responsiveness to provider network size and plan quality into account in their efforts to facilitate consumer decision-making in the Marketplaces. Given the finding that plan quality is highly valued, the implementation of quality ratings in other health exchange settings (such as the Federally Facilitated Marketplace) could be beneficial to enrollees. Moreover, consumer responsiveness to levels and changes in plan quality and network size could inform insurers' decisions to invest in these attributes to attract Marketplace enrollees. Policymakers should also carefully consider the unintended consequences, as well as the balance between plan benefits and affordability, when considering the implementation of requirements related to network adequacy and plan quality ratings

Dompep-a general method for predicting modular domain-mediated protein-protein interactions

Protein-protein interactions (PPIs) are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains. © 2011 Li et al

Statistical Evaluations of the Reproducibility and Reliability of 3-Tesla High Resolution Magnetization Transfer Brain Images: A Pilot Study on Healthy Subjects

Magnetization transfer imaging (MT) may have considerable promise for early detection and monitoring of subtle brain changes before they are apparent on conventional magnetic resonance images. At 3 Tesla (T), MT affords higher resolution and increased tissue contrast associated with macromolecules. The reliability and reproducibility of a new high-resolution MT strategy were assessed in brain images acquired from 9 healthy subjects. Repeated measures were taken for 12 brain regions of interest (ROIs): genu, splenium, and the left and right hemispheres of the hippocampus, caudate, putamen, thalamus, and cerebral white matter. Spearman's correlation coefficient, coefficient of variation, and intraclass correlation coefficient (ICC) were computed. Multivariate mixed-effects regression models were used to fit the mean ROI values and to test the significance of the effects due to region, subject, observer, time, and manual repetition. A sensitivity analysis of various model specifications and the corresponding ICCs was conducted. Our statistical methods may be generalized to many similar evaluative studies of the reliability and reproducibility of various imaging modalities

Designer lignins: harnessing the plasticity of lignification

Lignin is a complex polyphenolic constituent of plant secondary cell walls. Inspired largely by the recalcitrance of lignin to biomass processing, plant engineering efforts have routinely sought to alter lignin quantity, composition, and structure by exploiting the inherent plasticity of lignin biosynthesis. More recently, researchers are attempting to strategically design plants for increased degradability by incorporating monomers that lead to a lower degree of polymerisation, reduced hydrophobicity, fewer bonds to other cell wall constituents, or novel chemically labile linkages in the polymer backbone. In addition, the incorporation of value-added structures could help valorise lignin. Designer lignins may satisfy the biological requirement for lignification in plants while improving the overall efficiency of biomass utilisation

The Sediment Green-Blue Color Ratio as a Proxy for Biogenic Silica Productivity Along the Chilean Margin

Sediment cores recently collected from the Chilean Margin during D/V JOIDES Resolution Expedition 379T (JR100) document variability in shipboard-generated records of the green/blue (G/B) ratio. These changes show a strong coherence with benthic foraminiferal δ18O, Antarctic ice core records, and sediment lithology (e.g., higher diatom abundances in greener sediment intervals), suggesting a climate-related control on the G/B. Here, we test the utility of G/B as a proxy for diatom productivity at Sites J1002 and J1007 by calibrating G/B to measured biogenic opal. Strong exponential correlations between measured opal% and the G/B were found at both sites. We use the empirical regressions to generate high-resolution records of opal contents (opal%) on the Chilean Margin. Higher productivity tends to result in more reducing sedimentary conditions. Redox-sensitive sedimentary U/Th generally co-varies with the reconstructed opal% at both sites, supporting the association between sediment color, sedimentary U/Th, and productivity. Lastly, we calculated opal mass accumulation rate (MAR) at Site J1007 over the last ∼150,000 years. The G/B-derived opal MAR record from Site J1007 largely tracks existing records derived from traditional wet-alkaline digestion from the south and eastern equatorial Pacific (EEP) Ocean, with a common opal flux peak at ∼50 ka suggesting that increased diatom productivity in the EEP was likely driven by enhanced nutrient supply from the Southern Ocean rather than dust inputs as previously suggested. Collectively, our results identify the G/B ratio as a useful tool with the potential to generate reliable, high-resolution paleoceanographic records that circumvent the traditionally laborious methodology.publishedVersio

Deep submarine infiltration of altered geothermal groundwater on the south Chilean Margin

Submarine groundwater discharge is increasingly recognized as an important component of the oceanic geochemical budget, but knowledge of the distribution of this phenomenon is limited. To date, reports of meteoric inputs to marine sediments are typically limited to shallow shelf and coastal environments, whereas contributions of freshwater along deeper sections of tectonically active margins have generally been attributed to silicate diagenesis, mineral dehydration, or methane hydrate dissociation. Here, using geochemical fingerprinting of pore water data from Site J1003 recovered from the Chilean Margin during D/V JOIDES Resolution Expedition 379 T, we show that substantial offshore freshening reflects deep and focused contributions of meteorically modified geothermal groundwater, which is likely sourced from a reservoir ~2.8 km deep in the Aysén region of Patagonia and infiltrated marine sediments during or shortly after the last glacial period. Emplacement of fossil groundwaters reflects an apparently ubiquitous phenomenon in margin sediments globally, but our results now identify an unappreciated locus of deep submarine groundwater discharge along active margins with potential implications for coastal biogeochemical processes and tectonic instability.publishedVersio

DomPep—A General Method for Predicting Modular Domain-Mediated Protein-Protein Interactions

Protein-protein interactions (PPIs) are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains

Molecular Biomarkers of Vascular Dysfunction in Obstructive Sleep Apnea

Untreated and long-lasting obstructive sleep apnea (OSA) may lead to important vascular abnormalities, including endothelial cell (EC) dysfunction, hypertension, and atherosclerosis. We observed a correlation between microcirculatory reactivity and endothelium-dependent release of nitric oxide in OSA patients. Therefore, we hypothesized that OSA affects (micro)vasculature and we aimed to identify vascular gene targets of OSA that could possibly serve as reliable biomarkers of severity of the disease and possibly of vascular risk. Using quantitative RT-PCR, we evaluated gene expression in skin biopsies of OSA patients, mouse aortas from animals exposed to 4-week intermittent hypoxia (IH; rapid oscillations in oxygen desaturation and reoxygenation), and human dermal microvascular (HMVEC) and coronary artery endothelial cells (HCAEC) cultured under IH. We demonstrate a significant upregulation of endothelial nitric oxide synthase (eNOS), tumor necrosis factor-alpha-induced protein 3 (TNFAIP3; A20), hypoxia-inducible factor 1 alpha (HIF-1α?? and vascular endothelial growth factor (VEGF) expression in skin biopsies obtained from OSA patients with severe nocturnal hypoxemia (nadir saturated oxygen levels [SaO2]<75%) compared to mildly hypoxemic OSA patients (SaO2 75%–90%) and a significant upregulation of vascular cell adhesion molecule 1 (VCAM-1) expression compared to control subjects. Gene expression profile in aortas of mice exposed to IH demonstrated a significant upregulation of eNOS and VEGF. In an in vitro model of OSA, IH increased expression of A20 and decreased eNOS and HIF-1α expression in HMVEC, while increased A20, VCAM-1 and HIF-1αexpression in HCAEC, indicating that EC in culture originating from distinct vascular beds respond differently to IH stress. We conclude that gene expression profiles in skin of OSA patients may correlate with disease severity and, if validated by further studies, could possibly predict vascular risk in OSA patients

Genome modeling system: A knowledge management platform for genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms