Comparative effectiveness of total population versus disease-specific neural network models in predicting medical costs

The objective of this research was to compare the accuracy of two types of neural networks in identifying individuals at risk for high medical costs for three chronic conditions. Two neural network models—a population model and three disease-specific models—were compared regarding effectiveness predicting high costs. Subjects included 33,908 health plan members with diabetes, 19,264 with asthma, and 2,605 with cardiac conditions. For model development/testing, only members with 24 months of continuous enrollment were included. Models were developed to predict probability of high costs in 2000 (top 15% of distribution) based on 1999 claims factors. After validation, models were applied to 2000 claims factors to predict probability of high 2001 costs. Each member received two scores—population model score applied to cohort and disease model score. Receiver Operating Characteristic (ROC) curves compared sensitivity, specificity, and total performance of population model and three disease models. Diabetes-specific model accuracy, C = 0.786 (95%CI = 0.779–0.794), was greater than that of population model applied to diabetic cohort, C = 0.767 (0.759–0.775). Asthma-specific model accuracy, C = 0.835 (0.825–0.844), was no different from that of population model applied to asthma cohort, C = 0.844 (0.835–0.853). Cardiac-specific model accuracy, C = 0.651 (0.620–0.683), was lower than that of population model applied to cardiac cohort, C = 0.726 (0.697–0.756). The population model predictive power, compared to the disease model predictive power, varied by disease; in general, the larger the cohort, the greater the advantage in predictive power of the disease model compared to the population model. Given these findings, disease management program staff should test multiple approaches before implementing predictive models. (Disease Management 2005;8:277–287

Coal dust alters β-naphthoflavone-induced aryl hydrocarbon receptor nuclear translocation in alveolar type II cells

<p>Abstract</p> <p>Background</p> <p>Many polycyclic aromatic hydrocarbons (PAHs) can cause DNA adducts and initiate carcinogenesis. Mixed exposures to coal dust (CD) and PAHs are common in occupational settings. In the CD and PAH-exposed lung, CD increases apoptosis and causes alveolar type II (AT-II) cell hyperplasia but reduces CYP1A1 induction. Inflammation, but not apoptosis, appears etiologically associated with reduced CYP1A1 induction in this mixed exposure model. Many AT-II cells in the CD-exposed lungs have no detectable CYP1A1 induction after PAH exposure. Although AT-II cells are a small subfraction of lung cells, they are believed to be a potential progenitor cell for some lung cancers. Because CYP1A1 is induced via ligand-mediated nuclear translocation of the aryl hydrocarbon receptor (AhR), we investigated the effect of CD on PAH-induced nuclear translocation of AhR in AT-II cells isolated from <it>in vivo</it>-exposed rats. Rats received CD or vehicle (saline) by intratracheal (IT) instillation. Three days before sacrifice, half of the rats in each group started daily intraperitoneal injections of the PAH, β-naphthoflavone (BNF).</p> <p>Results</p> <p>Fourteen days after IT CD exposure and 1 day after the last intraperitoneal BNF injection, AhR immunofluorescence indicated that proportional AhR nuclear expression and the percentage of cells with nuclear AhR were significantly increased in rats receiving IT saline and BNF injections compared to vehicle controls. However, in CD-exposed rats, BNF did not significantly alter the nuclear localization or cytosolic expression of AhR compared to rats receiving CD and oil.</p> <p>Conclusion</p> <p>Our findings suggest that during particle and PAH mixed exposures, CD alters the BNF-induced nuclear translocation of AhR in AT-II cells. This provides an explanation for the modification of CYP1A1 induction in these cells. Thus, this study suggests that mechanisms for reduced PAH-induced CYP1A1 activity in the CD exposed lung include not only the effects of inflammation on the lung as a whole, but also reduced PAH-associated nuclear translocation of AhR in an expanded population of AT-II cells.</p

Health Behaviors as Predictors of Medical Delay

Although at least 70% of the population experiences physical symptoms that could indicate illness at any given time, most people will not seek help for them. If a serious illness occurs, this delay in seeking help could seriously affect survival rates. While there are many barriers to seeking health care, there is evidence that both psychological factors, such as coping style, and situational variables, such as the nature of the symptoms, affect delay behavior. The present study investigated whether the practice of regular health behaviors affected participants\u27 willingness to seek help for various medical problems. One hundred forty nine introductory psychology students filled out a 2 part questionnaire that asked them to indicate: 1) how often they engaged in routine health behaviors (exercise, regular visits to health care resources): and 2) whether they would visit a physician promptly and follow medical advice if they suffered from a variety of illness. These included depression, back pain, strep throat, high blood pressure, allergies, and gastric ulcers. Exploratory factor analysis indicated that the health behaviors surveyed clustered into four factors that were named: 1) dietary; 2) dental health: 3) substance used; and 4) health care utilization. Regression analyses indicated that factor #4 predicted delay behavior, but only for strep throat, high blood pressure, and back pain. No factor predicted compliance with treatment. Results are consistent with a cognitive social theory model which would predict that complex behaviors indicative of self-regulation, such as visiting health care professionals, depend not only on the person suffering from symptoms, but also on the nature of the specific symptoms presented and the behavior expected (delay behavior vs. compliance with treatment)