Comparison of alternative risk adjustment measures for predictive modeling: high risk patient case finding using Taiwan's National Health Insurance claims

<p>Abstract</p> <p>Background</p> <p>Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.</p> <p>Methods</p> <p>A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.</p> <p>Results</p> <p>Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.</p> <p>Conclusions</p> <p>Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.</p

Evaluation of quality of life according to asthma control and asthma severity in children and adolescents

ABSTRACT OBJECTIVE: To evaluate quality of life according to the level of asthma control and degree of asthma severity in children and adolescents. METHODS: We selected children and adolescents with asthma (7-17 years of age) from the Pediatric Pulmonology Outpatient Clinic of the State University of Campinas Hospital de Clínicas, located in the city of Campinas, Brazil. Asthma control and asthma severity were assessed by the Asthma Control Test and by the questionnaire based on the Global Initiative for Asthma, respectively. The patients also completed the Paediatric Asthma Quality of Life Questionnaire (PAQLQ), validated for use in Brazil, in order to evaluate their quality of life. RESULTS: The mean age of the patients was 11.22 ± 2.91 years, with a median of 11.20 (7.00-17.60) years. We selected 100 patients, of whom 27, 33, and 40 were classified as having controlled asthma (CA), partially controlled asthma (PCA), and uncontrolled asthma (UA), respectively. As for asthma severity, 34, 19, and 47 were classified as having mild asthma (MiA), moderate asthma (MoA), and severe asthma (SA), respectively. The CA and the PCA groups, when compared with the NCA group, showed higher values for the overall PAQLQ score and all PAQLQ domains (activity limitation, symptoms, and emotional function; p < 0.001 for all). The MiA group showed higher scores for all of the PAQLQ components than did the MoA and SA groups. CONCLUSIONS: Quality of life appears to be directly related to asthma control and asthma severity in children and adolescents, being better when asthma is well controlled and asthma severity is lower

Key features of the seed germination response to high temperatures

The responses of seed germination rates (GRs) and final germination percentages to soil temperature follow trends similar to those for many plant processes. There is an optimum temperature for seed germination (Tₒ), which is defined as the soil temperature at which the highest germination percentage is achieved by a seed population in the shortest possible period of time (Mayer & Poljakoff-Mayber, 1975). At both sub-optimal ( Tₒ) temperatures, the reductions in the GR and germination percentage scale with the amount by which the soil temperature is less than or greater than Tₒ, respectively, until threshold temperatures (base temperature Tb and ceiling temperature Tc) are reached at which germination is completely prevented (Fig. 1). These responses of seed germination to temperature and their underlying physiology have been thoroughly described in the literature and are very familiar to seed scientists. Specific aspects, such as thermoinhibition, when seeds fail to germinate at high soil temperatures, are of great importance for commercial crops and have been investigated for many crop species. However, there are two key features of germination commonly observed within seed germination studies that are nonetheless seldom discussed in the literature. First, Tₒ is often not a specific temperature, but rather a range of temperatures seen as a broad, curvilinear peak in the plot of GR vs temperature (GR–T). Second, the GR–T response varies between the seed percentiles in the population, where the order in which seeds germinate is specified by the seed percentile (G), such that G = 1 is the first percentile of seeds to germinate, G = 2 is the second percentile to germinate, and so on, up to G = 100, which represents complete germination of a seed population. Fast-germinating seeds (the lowest percentiles) often have higher values for Tₒ and Tc, compared with slower germinating seeds (the higher percentiles), which reach their Tₒ and Tc at lower temperatures. From an extensive literature review of GR–T responses, we identify the extent to which these two key features are shown by the data. We discuss two common mathematical models that have been used to describe the GR response to temperature, and which model more accurately describes the important features of GR–T. Lastly, we discuss how this more accurate model aligns with our physiological and ecological understanding of the seed germination process

Modeling growth response to soil water availability simulated by HYDRUS for a mature triploid Populus tomentosa plantation located on the North China Plain

To establish the methodological basis for developing optimal irrigation strategies for increasing the productivity of triploid Populus tomentosa plantations using modelling methods, the accuracy of HYDRUS models for simulating one-dimensional (HYDRUS-1D) soil water dynamics under rainfed natural conditions (NC), and two-dimensional soil water dynamics (HYDRUS (2D/3D)) under subsurface drip irrigated (SDI) conditions was evaluated using field data. The relationship between tree growth and soil water availability (rθ) at different depths, which has not been thoroughly investigated in poplar plantations, was also examined. In general, the average soil water content (θ) in different soil layers predicted by both HYDRUS models and the θ within the two-dimensional domain around drippers predicted by HYDRUS (2D/3D) agreed well with the observed values. Under both treatments, the rθ increased with depth and was most variable in the surface 30 cm soil. The amount of variation in basal area at breast height (ABH) growth explained by rθ in various soil layers ranged widely, suggesting that soil water at different soil depths made different contributions to the variation in growth. The proportion of variation in ABH growth explained by average rθ was highest (R² = 0.709) in the 0-30 cm layer, and decreased with increasing integrated depth of the root-zone. Tree growth was unconstrained when the rθ of the 0-30 cm layer was above 0.7. Based on these results, it can be concluded that HYDRUS-1D and HYDRUS (2D/3D) can be used as tools to accurately simulate long-term soil water dynamics in P. tomentosa plantations, at least in sites with similar characteristics to ours. HYDRUS modeling can be used to assess the impacts of rθ on productivity of mature P. tomentosa plantations. This study also shows that monitoring soil moisture of the surface soil provides a robust means for predicting tree growth of P. tomentosa plantations at sites with similar soil to ours