Cost-Effectiveness of the Wellness Incentives and Navigation (WIN) Program.

OBJECTIVES: Promoting patient involvement in managing co-occurring physical and mental health conditions is increasingly recognized as critical to improving outcomes and controlling costs in this growing chronically ill population. The main objective of this study was to conduct an economic evaluation of the Wellness Incentives and Navigation (WIN) intervention as part of a longitudinal randomized pragmatic clinical trial for chronically ill Texas Medicaid enrollees with co-occurring physical and mental health conditions. METHODS: The WIN intervention used a personal navigator, motivational interviewing, and a flexible wellness expense account to increase patient activation, that is, the patient's knowledge, skills, and confidence in managing their self-care and co-occurring physical and mental health conditions. Regression models were fit to both participant-level quality-adjusted life years (QALYs) and total costs of care (including the intervention) controlling for demographics, health status, poverty, Medicaid managed care plan, intervention group, and baseline health utility and costs. Incremental costs and QALYs were calculated based on the difference in predicted costs and QALYs under intervention versus usual care and were used to calculate the incremental cost-effectiveness ratios (ICERs). Confidence intervals were calculated using Fieller's method, and sensitivity analyses were performed. RESULTS: The mean ICER for the intervention compared with usual care was $12 511 (95% CI $8971-$16 842), with a sizable majority of participants (70%) having ICERs below $40 000. The WIN intervention also produced higher QALY increases for participants who were sicker at baseline compared to those who were healthier at baseline. CONCLUSION: The WIN intervention shows considerable promise as a cost-effective intervention in this challenging chronically ill population.

The Wellness Incentive and Navigation intervention improved health-related quality of life among Medicaid enrollees: A randomized pragmatic clinical trial.

OBJECTIVE: To examine whether the Wellness Incentive and Navigation (WIN) intervention can improve health-related quality of life (HRQOL) among Medicaid enrollees with co-occurring physical and behavioral health conditions. DATA SOURCES: Annual telephone survey data from 2013 to 2016, linked with claims data. STUDY DESIGN: We recruited 1259 participants from the Texas STAR + PLUS managed care program and randomized them into an intervention group that received flexible wellness accounts and navigator services or a control group that received standard care. We conducted 4 waves of telephone surveys to collect data on HRQOL, patient activation, and other participant demographic and clinical characteristics. DATA COLLECTION/EXTRACTION METHODS: The 3M Clinical Risk Grouping Software was used to extract variables from claims data and group participants based on disease severity. PRINCIPAL FINDINGS: Our results showed that the WIN intervention was effective in increasing patient activation and HRQOL among Medicaid enrollees with co-occurring physical and behavioral health conditions. Furthermore, we found that this intervention effect on HRQOL was partially mediated by patient activation. CONCLUSIONS: Providing navigator support with wellness account is effective in improving HRQOL among Medicaid enrollees. The pragmatic nature of the trial maximizes the chance of successfully implementing it in state Medicaid programs.

Looking against the light: how perception of translucency depends on lighting direction.

Translucency is an important aspect of material appearance. To some extent, humans are able to estimate translucency in a consistent way across different shapes and lighting conditions, i.e., to exhibit translucency constancy. However, Fleming and Bülthoff (2005) have shown that that there can be large failures of constancy, with lighting direction playing an important role. In this paper, we explore the interaction of shape, illumination, and degree of translucency constancy more deeply by including in our analysis the variations in translucent appearance that are induced by the shape of the scattering phase function. This is an aspect of translucency that has been largely neglected. We used appearance matching to measure how perceived translucency depends on both lighting and phase function. The stimuli were rendered scenes that contained a figurine and the lighting direction was represented by spherical harmonic basis function. Observers adjusted the density of a figurine under one lighting condition to match the material property of a target figurine under another lighting condition. Across the trials, we varied both the lighting direction and the phase function of the target. The phase functions were sampled from a 2D space proposed by Gkioulekas et al. (2013) to span an important range of translucent appearance. We find the degree of translucency constancy depends strongly on the phase function's location in the same 2D space, suggesting that the space captures useful information about different types of translucency. We also find that the geometry of an object is important. We compare the case of a torus, which has a simple smooth shape, with that of the figurine, which has more complex geometric features. The complex shape shows a greater range of apparent translucencies and a higher degree of constancy failure. In summary, humans show significant failures of translucency constancy across changes in lighting direction, but the effect depends both on the shape complexity and the translucency phase function.

Heterogeneous Subsurface Scattering Using the Finite Element Method.

Materials with visually important heterogeneous subsurface scattering, including marble, skin, leaves, and minerals are common in the real world. However, general, accurate, and efficient rendering of these materials is an open problem. In this paper, we describe a finite element (FE) solution of the heterogeneous diffusion equation (DE) that solves this problem. Our algorithm is the first to use the FE method to solve the difficult problem of heterogeneous subsurface rendering. To create our algorithm, we make two contributions. First, we correct previous work and derive an accurate and complete heterogeneous diffusion formulation with two key elements: the diffusive source boundary condition (DSBC)-an accurate model of the reduced intensity (RI) source-and its associated render query function. Second, we solve this formulation accurately and efficiently using the FE method. With these contributions, we can render subsurface scattering with a simple four step algorithm. To demonstrate that our algorithm is simultaneously general, accurate, and efficient, we test its performance on a series of difficult scenes. For a wide range of materials and geometry, it produces, in minutes, images that match path traced references, that required hours.

Accurate direct illumination using iterative adaptive sampling.

This paper introduces a new multipass algorithm for efficiently computing direct illumination in scenes with many lights and complex occlusion. Images are first divided into 8 x 8 pixel blocks and for each point to be shaded within a block, a probability density function (PDF) is constructed over the lights and sampled to estimate illumination using a small number of shadow rays. Information from these samples is then aggregated at both the pixel and block level and used to optimize the PDFs for the next pass. Over multiple passes the PDFs and pixel estimates are updated until convergence. Using aggregation and feedback progressively improves the sampling and automatically exploits both visibility and spatial coherence. We also use novel extensions for efficient antialiasing. Our adaptive multipass approach computes accurate direct illumination eight times faster than prior approaches in tests on several complex scenes.