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Abstract: This study looked at how cigarette smoking, alcohol consumption, obesity, and physical activity are associated with the prevalence and severity of arthritis among adults living in Delaware, U.S. through the analysis of survey data. We examined data from the 2009 Delaware Behavioral Risk Factor Surveillance System (BRFSS). Weighted percentages were calculated for the arthritis-related factors above by arthritis status and activity limitation due to arthritis/joint symptoms, and were analyzed using the Rao-Scott χ2 test. A multiple logistic regression analysis was performed to determine an odds ratio (OR) while adjusting for gender, age, race/ethnicity, and education. Adult Delawareans self-reporting arthritis were more likely to be former and current smokers than those without self-reported arthritis (p < 0.001; OR = 1.58 for former smokers vs. non-smokers; OR = 1.52 for current smokers vs. non-smokers). Moderate and heavy alcohol consumption was associated with lower severity of arthritis (p < 0.001; OR = 0.66 for moderate drinking vs. no drinking; OR = 0.50 for heavy drinking vs. no drinking). There was a significant relationship of obesity to both arthritis status (p < 0.001; OR = 2.13 for obesity vs. not overweight/obesity) and severity (p < 0.008; OR = 1.67 for obesity vs. not overweight/obesity). Furthermore, people having arthritis-related activity limitation were more likely to not meet the current physical activity recommendations (p = 0.013; OR = 1.46). It appears that smoking and obesity have a negative impact on the risk and severity of arthritis, whereas alcohol consumption and physical activity may be protective against arthritis. A proper analysis of survey data is essential to truly understand how human behavior impacts people’s health. Keywords: Rao-Scott χ2 test, logistic regression, Behavioral Risk Factor Surveillance System, cigarette smoking, alcohol consumption, obesity, physical activity, odds ratio.Download Full Article |
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Abstract: Cancer incidence and mortality trends in the Nordic countries show that most cancer types have been increasing for a long time, while a few show decreasing trends. The object of this study was to investigate melanoma mortality trends to see if there is a specific year for the trend breaks, possibly indicating a common causing factor affecting most of the population from the same time. The results clearly show that melanoma mortality started to increase exponentially by the time lived as an adult since 1955 and that the trends easily can be modeled and used for projection purpose. The findings are in support of earlier studies, suggesting reduced or temporarily disturbed DNA repair capacity due to a population-wide environmental change to be the main cause to increasing cancer rates in general, and increasing melanoma incidence and mortality in particular. Keywords: Melanoma, cancer, mortality, incidence, exponential, model, DNA repair.Download Full Article |
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Abstract: Diabetic nephropathy (DN) is a generic term referring to deleterious effect on renal structure and/or function caused by diabetes mellitus. World Health Organization estimates that diabetes affects more than 170 million people worldwide and this number may rise to 370 million by 2030. The rate of rise in Serum Creatinine (SrCr) is a well-accepted marker for the progression of Diabetic Nephropathy (DN). In this paper, survival functions of type 2 diabetic patients with renal complication are estimated. Firstly, most appropriate distribution for duration of diabetes is selected through minimum Akaike Information Criterion value, Gamma distribution is found to be an appropriate model. Secondly, the parameters estimates of the selected distribution are obtained by fitting a Generalized Linear Model (GLM), with duration of diabetes as the response variable and predictors as SrCr and number of successes (number of times SrCr values exceed its normal range (1.4 mg/dl)). These covariates are linked with the response variable using two different link functions namely log and reciprocal links. Using the estimates of parameters obtained from generalized linear regression analysis, survival functions for different durations under both the links are estimated. Further we compared the estimated survival functions under both the links with Kaplan Meier (KM) estimates graphically. Findings suggested that the Kaplan Meier estimate and Gamma distribution under both links provided a close estimate of survival functions. Median survival time is 16.3 years and 16.8 years obtained from KM method and Gamma GLM respectively. Keywords: Akaike Information Criterion, Gamma distribution, generalized linear models, Kaplan Meier method, log link, reciprocal link, serum creatinine, survival distributions.Download Full Article |
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Abstract: Background:The key feature of Bayesian methods is their lack of dependence on defaults necessary for classical statistics. Because of the high volume of simulation, Bayesian methods have a high degree of accuracy. They are efficient in data mining and analyzing large volumes of data, and can be upgraded by entering new data. Objective: We used Bayesian multidimensional scaling (MDS) to analyze the genetic relationships among 11 Iranian ethnic groups based on HLA class II data. Method: Allele frequencies of three HLA loci from 816 unrelated individuals belonging to 11 Iranian ethnic groups were analyzed by Bayesian MDS using R and WinBUGS software. Results: like the results of correspondence analysis as a prototype of classicalMDS analysis, the results of Bayesian MDS also showed Arabs from Famur, Balochis, Zoroastrians and Jews to be separate from other Iranian ethnic groups. Decreases stress in Bayesian MDS method compared to classicalmethod revealed the accuracy of Bayesian MDS for HLA data analyses. Conclusion:This study reports the first application of Bayesian multidimensional scaling to HLA data analysis with Nei’sDA genetic distances. Stress reduction in Bayesian MDScompared to classical MDS showed that the Bayesian approach can improve the accuracy of genetic data analysis. Keywords: Bayesian methods, Multidimensional scaling, Anthropological study, Immunogenetics, R and WinBUGS software.Download Full Article |
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Abstract: Time series data with excessive zeros frequently occur in medical and health studies. To analyze time series count data without excessive zeros, observation-driven Poisson regression models are commonly used in the literature. As handling excessive zeros in count data is not straightforward, observation-driven models are rarely used to analyze time series count data with excessive zeros. In this paper an observation-driven zero-inflated Poisson (ZIP) model for time series count data is proposed. This approach can accommodate an autoregressive serial dependence structure which commonly appears in time series. The estimation of the model parameters by using the quasi-likelihood estimating equation approach is discussed. To estimate the correlation parameters of the dependence structure, a moment approach is used. The proposed methodology is illustrated by applying it to a data set of daily emergency room visits due to bronchitis. Keywords: Autocorrelation structure, non-stationary, observation-driven model, quasi-likelihood, zero-inflated Poisson.Download Full Article |