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Topeka, KS, United States

Ho W.-H.,Kaohsiung Medical University | Lee K.-T.,Kaohsiung Medical University | Chen H.-Y.,Yuans Hospital | Ho T.-W.,Bureau of Health Promotion | Chiu H.-C.,Kaohsiung Medical University
PLoS ONE | Year: 2012

Background: A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods: The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions: The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection. © 2012 Ho et al. Source

Toblin R.L.,Centers for Disease Control and Prevention | Toblin R.L.,U.S. Army | MacK K.A.,Centers for Disease Control and Prevention | Perveen G.,Bureau of Health Promotion | Paulozzi L.J.,Centers for Disease Control and Prevention
Pain | Year: 2011

Chronic pain is a common reason for medical visits, but prevalence estimates vary between studies and have rarely included drug treatment data. This study aimed to examine characteristics of chronic pain and its relation to demographic and health factors, and factors associated with treatment of pain with opioid analgesics. A chronic pain module was added to the 2007 Kansas Behavioral Risk Factor Surveillance System (response rate = 61%). Data on prevalence, duration, frequency, and severity of chronic pain, demographics, and health were collected from a representative sample of 4090 adults 18 years and older by telephone. Logistic regression was used to examine the association of both chronic pain and opioid use with demographic and health factors. Chronic pain was reported by 26.0% of the participants and was associated with activity limitations (adjusted odds ratio [AOR] = 3.6, 95% confidence interval [95% CI] 2.8-4.5), arthritis (AOR = 3.3, 95% CI 2.6-4.0), poor mental health (AOR = 2.0, 95% CI 1.4-2.8), poor overall health (AOR = 1.9; 95% CI 1.5-2.5), and obesity (AOR = 1.6; 95% CI 1.2-2.0). Of the 33.4% of people with pain who use prescription pain medication, 45.7% took opioids, including 36.7% of those with mild pain. Chronic pain affects a quarter of adults in Kansas and is associated with poor health. Opioid analgesics are the mainstay of prescribed pharmacotherapy in this group, even among those reporting mild pain. Chronic pain affects 26.0% of adults in the state of Kansas, USA. Overall, 45.7% of people who take prescription drugs for chronic pain reported taking opioid analgesics. © 2011 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved. Source

Greenlund K.J.,Centers for Disease Control and Prevention | Keenan N.L.,Centers for Disease Control and Prevention | Clayton P.F.,Bureau of Health Promotion | Pandey D.K.,University of Illinois at Chicago | Hong Y.,Centers for Disease Control and Prevention
American Journal of Public Health | Year: 2012

Life expectancy at birth has increased from 74 years in 1980 to 78 years in 2006. Older adults (aged 65 years and older) are living longer with cardiovascular conditions, which are leading causes of death and disability and thus an important public health concern. We describe several major issues, including the impact of comorbidities, the role of cognitive health, prevention and intervention approaches, and opportunities for collaboration to strengthen the public health system. Prevention can be effective at any age, including for older adults. Public health models focusing on policy, systems, and environmental change approaches have the goal of providing social and physical environments and promoting healthy choices. Source

Glei D.A.,Georgetown University | Goldman N.,Princeton University | Lin Y.-H.,Bureau of Health Promotion | Weinstein M.,Georgetown University
Research on Aging | Year: 2011

Identifying how biological parameters change with age can provide insights into the physiological determinants of disease and, ultimately, death. Most prior studies of age-related change in biomarkers are based on cross-sectional data, small or selective samples, or a limited number of biomarkers. We use data from a nationally representative longitudinal sample of 639 Taiwanese aged 54 and older in 2000 to assess changes over a six-year period in a wide range of biomarkers. Markers that increased most with age were glycoslyated hemoglobin, interleukin-6, and norepinephrine. Markers that decreased most with age were diastolic blood pressure and creatinine clearance. For example, glycoslyated hemoglobin increased by 8% to 13%, depending on sex and age at baseline, over this six-year period. Several standard clinical risk factors exhibited little evidence of age-related change. Further research is needed to determine whether the observed variation between individuals in biomarker changes represents differences in underlying physiological function that are predictive of future health and survival. © The Author(s) 2011. Source

Wen S.-H.,Tzu Chi University | Lu Z.-S.,Bureau of Health Promotion
Journal of Human Genetics | Year: 2011

The number of tested marker becomes numerous in genetic association studies (GAS) and one major challenge is to derive the multiple testing threshold. Some approaches calculating an effective number (M eff) of tests in GAS were developed and have been shown to be promising. As yet, there have been no comparisons of their robustness to influencing factors. We evaluated the performance of three principal component analysis (PCA)-based M eff estimation formulas (M eff-C in Cheverud (2001), M eff-L in Li and Ji (2005), and M eff-G in Galwey (2009)). Four influencing factors including LD measurements, marker density, population samples and the total number of tested markers were considered. We validated them by the Bonferroni's method and the permutation test with 10 000 random shuffles based on three real data sets. For each factor, M eff-C yielded conservative threshold except with D′ coefficient, and M eff-G would be too liberal compared with the permutation test. Our results indicated that M eff-L based on r 2 coefficient achieve close approximation of the permutation threshold. As for a large number of markers, we recommended to use M eff-L with r 2 coefficient according to fixed-length separation, as well as fixed-number separation, to obtain accurate estimate of the multiple testing threshold and to save more computational time. © 2011 The Japan Society of Human Genetics All rights reserved. Source

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