Evaluating 10-year Changes in Relative Risk of Smear Positive Tuberculosis in Iran Using Spatial Modelling: 2010-2019

2021 ◽  
Vol 20 (3) ◽  
pp. 246-255
Author(s):  
Azar Babaahmadi ◽  
◽  
Soraya Moradi ◽  
Elham Maraghi ◽  
Shima Younespour ◽  
...  
Keyword(s):  
Relative Risk ◽  
High Risk ◽  
Ecological Study ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Spatial Modelling ◽  
Data Set ◽  
Control Programs ◽  
Log Normal ◽  
Sistan And Baluchestan

Background and Objectives According to the importance of preventing tuberculosis, it is necessary to identify areas with a high relative risk. Subjects and Methods This is an ecological study. To estimate the relative risk of SPTB (smear-positive tuberculosis), the number of SPTB cases and at-risk population for each province was extracted from the data set of the Tuberculosis and Leprosy Department of the Ministry of Health. Relative risk (RR) estimation was obtained using Log-Normal and BYM models. Deviance information criterion was used to compare the performance of the models. Analyses were done in WinBUGS1.4.3 and ArcGIS10.8 software. Results The highest relative risk was seen in 2010 for Sistan and Baluchestan Province as RR = 4.02 with (95%CI: 3.73-4.32) and the lowest for Chaharmahal and Bakhtiari Province with RR= 0.22 [95%CI: 0.13-0.35). The highest relative risk in Sistan and Baluchestan Province in 2020 was RR= 3.77 (95%CI: 3.45-4.01), and the lowest relative risk was in Kohgiluyeh and Boyer Ahmad Province with RR= 0.21 (95% CI: 0.10-0.36). Conclusion The risk of tuberculosis was generally high in provinces bordering countries with high rates of tuberculosis and provinces with humid climates. The movement of populations from high-risk provinces and high-burden countries can be one of the main challenges in controlling tuberculosis. However, the pattern of risk reduction in provinces bordering high-risk countries shows relatively good progress in TB control programs and reminds us of the need for detailed studies on the pattern of increase in other provinces

scholarly journals High-risk areas of leprosy in Brazil between 2001-2015

2020 ◽  
Vol 73 (3) ◽  
Author(s):  
Rayssa Nogueira Rodrigues ◽  
Heloisy Alves de Medeiros Leano ◽  
Isabela de Caux Bueno ◽  
Kleane Maria da Fonseca Azevedo Araújo ◽  
Francisco Carlos Félix Lana
Keyword(s):  
High Risk ◽  
Detection Rate ◽  
Ecological Study ◽  
Spatial Clustering ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Control Programs ◽  
Cluster Area ◽  
Risk Areas ◽  
Spatial Scan

ABSTRACT Objectives: to identify high-risk areas of leprosy in Brazil from 2001 to 2015. Methods: this is an ecological study of spatial analysis based on Brazilian municipalities. Spatial scan statistics were used to identify spatial clustering and measure the relative risk from the annual detection rate of new cases of leprosy. By criterion based on the Gini index, only secondary clusters were considered. Results: spatial scan statistics detected 26 clusters, in which the detection rate was 59.19 cases per 100 thousand inhabitants, while in the remainder of the country it was 11.76. Large part of the cluster area is located in the Legal Amazon. These groups included only 21.34% of the total population, but 60.40% of the new cases of the disease. Conclusions: Leprosy remains concentrated in some areas, showing the need for control programs to intensify actions in these municipalities.

scholarly journals Bayesian analysis of additive epistasis arising from new mutations in mice

2014 ◽  
Vol 96 ◽  
Author(s):  
JOAQUIM CASELLAS ◽  
DANIEL GIANOLA ◽  
JUAN F. MEDRANO
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Inbred Mice ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Inbred Strains ◽  
Data Set ◽  
Biological Relevance ◽  
Interaction Terms ◽  
New Mutations

SummaryThe continuous uploading of polygenic additive mutational variability has been reported in several studies in laboratory species with an inbred genetic background. These studies have focused on the direct contribution of new mutations without considering the possibility of epistatic effects derived from the interaction of new mutations with pre-existing polymorphisms. In this work we focused on this main topic and analysed the statistical and biological relevance of the epistatic variance for 9 week body weight in two populations of inbred mice. We developed a new linear mixed model parameterization where founder-related additive genetic variability, additive mutational variability and the interaction terms between both sources of variation were accounted for under a Bayesian design and without requiring the inversion of a matrix of epistatic genetic covariances. The analyses focused on a six-generations data set from C57BL/6J mice (n = 3736) and a five-generations data set from C57BL/6Jhg/hg mice (n = 2843). The deviance information criterion (DIC) clearly favoured the model accounting for epistatic variability with reductions larger than 50 DIC units in both populations. Modal estimates for founder related, mutational and epistatic heritabilities were 0·068, 0·011 and 0·095 in C57BL/6J and 0·060, 0·010 and 0·113 in C57BL/6Jhg/hg, ruling out any doubt about the biological relevance of epistasis originating from new mutations in mice. These results contribute new insights on the relevance of epistasis in the genetic architecture of mammals and serve as an important component of an additional source of genetic heterogeneity for inbred strains of laboratory mice.

scholarly journals Use of Bayesian Markov Chain Monte Carlo Methods to Model Kuwait Medical Genetic Center Data: An Application to Down Syndrome and Mental Retardation

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 248
Author(s):  
Reem Aljarallah ◽  
Samer A Kharroubi
Keyword(s):  
Monte Carlo ◽  
Down Syndrome ◽  
Mental Retardation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Univariate Analysis ◽  
Deviance Information Criterion ◽  
Predictive Ability ◽  
Information Criterion ◽  
Medical Genetic

Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models.

scholarly journals Extreme geomagnetic activities: a statistical study

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Ryuho Kataoka
Keyword(s):  
Magnetic Storms ◽  
Magnetic Observatory ◽  
Statistical Distributions ◽  
Limited Data ◽  
Data Set ◽  
Sudden Commencements ◽  
The One ◽  
Log Normal ◽  
Geomagnetic Activities ◽  
Log Normal Distribution

Abstract Statistical distributions are investigated for magnetic storms, sudden commencements (SCs), and substorms to identify the possible amplitude of the one in 100-year and 1000-year events from a limited data set of less than 100 years. The lists of magnetic storms and SCs are provided from Kakioka Magnetic Observatory, while the lists of substorms are obtained from SuperMAG. It is found that majorities of events essentially follow the log-normal distribution, as expected from the random output from a complex system. However, it is uncertain that large-amplitude events follow the same log-normal distributions, and rather follow the power-law distributions. Based on the statistical distributions, the probable amplitudes of the 100-year (1000-year) events can be estimated for magnetic storms, SCs, and substorms as approximately 750 nT (1100 nT), 230 nT (450 nT), and 5000 nT (6200 nT), respectively. The possible origin to cause the statistical distributions is also discussed, consulting the other space weather phenomena such as solar flares, coronal mass ejections, and solar energetic particles.

scholarly journals RESTING HEART RATE MODERATES THE RELATIONSHIP BETWEEN NEUROPSYCHIATRIC SYMPTOMS, MCI, AND ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S641-S641
Author(s):  
Shanna L Burke
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Heart Rate ◽  
Relative Risk ◽  
Neuropsychiatric Symptoms ◽  
Cognitive Status ◽  
Resting Heart Rate ◽  
Data Set ◽  
Increased Risk ◽  
The Relationship

Abstract Little is known about how resting heart rate moderates the relationship between neuropsychiatric symptoms and cognitive status. This study examined the relative risk of NPS on increasingly severe cognitive statuses and examined the extent to which resting heart rate moderates this relationship. A secondary analysis of the National Alzheimer’s Coordinating Center Uniform Data Set was undertaken, using observations from participants with normal cognition at baseline (13,470). The relative risk of diagnosis with a more severe cognitive status at a future visit was examined using log-binomial regression for each neuropsychiatric symptom. The moderating effect of resting heart rate among those who are later diagnosed with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) was assessed. Delusions, hallucinations, agitation, depression, anxiety, elation, apathy, disinhibition, irritability, motor disturbance, nighttime behaviors, and appetite disturbance were all significantly associated (p<.001) with an increased risk of AD, and a reduced risk of MCI. Resting heart rate increased the risk of AD but reduced the relative risk of MCI. Depression significantly interacted with resting heart rate to increase the relative risk of MCI (RR: 1.07 (95% CI: 1.00-1.01), p<.001), but not AD. Neuropsychiatric symptoms increase the relative risk of AD but not MCI, which may mean that the deleterious effect of NPS is delayed until later and more severe stages of the disease course. Resting heart rate increases the relative risk of MCI among those with depression. Practitioners considering early intervention in neuropsychiatric symptomology may consider the downstream benefits of treatment considering the long-term effects of NPS.

SEMIPARAMETRIC CLUSTERING METHOD FOR MICROARRAY DATA ANALYSIS

2008 ◽  
Vol 06 (02) ◽  
pp. 261-282 ◽  
Author(s):  
AO YUAN ◽  
WENQING HE
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Optimal Number ◽  
Microarray Data Analysis ◽  
Parametric Methods ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Data Set

Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but not so when there is nonnegligible deviation between them. On the other hand, the nonparametric methods, which usually do not make distributional assumptions, are robust but pay the price for efficiency loss. In an attempt to utilize the known mixture form to increase efficiency, and to free assumptions about the unknown subdistributions to enhance robustness, we propose a semiparametric method for clustering. The proposed approach possesses the form of parametric mixture, with no assumptions to the subdistributions. The subdistributions are estimated nonparametrically, with constraints just being imposed on the modes. An expectation-maximization (EM) algorithm along with a classification step is invoked to cluster the data, and a modified Bayesian information criterion (BIC) is employed to guide the determination of the optimal number of clusters. Simulation studies are conducted to assess the performance and the robustness of the proposed method. The results show that the proposed method yields reasonable partition of the data. As an illustration, the proposed method is applied to a real microarray data set to cluster genes.

Suicide, age and marital status

1988 ◽  
Vol 18 (1) ◽  
pp. 121-128 ◽  
Author(s):  
Norman Kreitman
Keyword(s):  
Relative Risk ◽  
Marital Status ◽  
Data Set

SynopsisA new data set concerning suicide in relation to marital status for Scotland, 1973–83, is presented. The effects of age-standardization on marital status rates and of marital status standardization on age-specific rates are both elucidated. The difficulties of drawing conclusions from marital status rates for suicide are outlined. Nevertheless, the data suggest that the importance of the widowed state has been underestimated and that it appears that the relative risk for suicide associated with divorce has probably been decreasing among Scottish men over the study period.

PSXV-9 Transgenerational epigenetic variance for production and reproduction traits in maternal-line pigs

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 258-259
Author(s):  
Jason R Graham ◽  
Jay S Johnson ◽  
Andre C Araujo ◽  
Jeremy T Howard ◽  
Luiz F Brito
Keyword(s):  
Additive Genetic Variance ◽  
Phenotypic Expression ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Pedigree Information ◽  
Relationship Matrix ◽  
Maternal Line ◽  
Genetic Heritability ◽  
Reproduction Traits ◽  
Search Approach

Abstract Modeling epigenetic factors impacting phenotypic expression of economically important traits has become a hot-topic in the field of animal breeding due to the variability in genetic expression caused by environmental stressors (e.g., heat stress). This variability may be due, in part, to in-utero epigenomic remodeling, which has been reported to be passed from parent to offspring. We aimed to estimate transgenerational epigenetic variance for various production and reproduction traits measured in a maternal-line pig population, using a Bayesian approach. The phenotypes for production [n = 10,862; i.e., weaning weight (WW), birth weight (BW) and ultrasound-backfat thickness (BF)] and reproduction [n = 5,235, i.e., number of piglets born alive (NBA) and total number of piglets born (TB)] traits from a purebred Landrace population were provided by Smithfield Premium Genetics (NC, USA). The pedigree information traced back to 10 generations. Single-trait genetic analyses were performed using mixed models that included additive genetic, common environmental, and epigenetic random effects. The Gibbs sampler algorithm based on Markov chain Monte Carlo was used to estimate the variance components. The epigenetic relationship matrix was constructed using a recursive parameter (λ) related to the transmissibility coefficient of epigenetic markers. A grid search approach was used to define the optimal λ value (λ values ranged from 0.1 to 0.5, with an interval of 0.1). The optimal λ value was determined based on the deviance information criterion, and it was used to estimate the additive and epigenetic variances. For instance, based on preliminary results, the optimal λ value estimated for TB was 0.3 with an additive genetic variance of 0.94 (0.19 PSD) and epigenetic variance of 0.67 (0.18 PSD). The additive genetic heritability was 0.076 (0.015 PSD) and the estimated epigenetic heritability was 0.053 (0.015 PSD). This preliminary result suggests that epigenetics contribute to the non-Mendelian variability in pigs.

scholarly journals Parametric and Semiparametric Estimations of Bivariate Truncated Type I Generalized Logistic Models driven from Copulas

2017 ◽  
Vol 7 (1) ◽  
pp. 72 ◽  
Author(s):  
Lamya A Baharith
Keyword(s):  
Logistic Models ◽  
Real Data ◽  
Information Criterion ◽  
Logistic Distribution ◽  
Type I ◽  
Copula Functions ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Generalized Logistic Distribution ◽  
Weibull Models

Truncated type I generalized logistic distribution has been used in a variety of applications. In this article, a new bivariate truncated type I generalized logistic (BTTGL) distributional models driven from three different copula functions are introduced. A study of some properties is illustrated. Parametric and semiparametric methods are used to estimate the parameters of the BTTGL models. Maximum likelihood and inference function for margin estimates of the BTTGL parameters are compared with semiparametric estimates using real data set. Further, a comparison between BTTGL, bivariate generalized exponential and bivariate exponentiated Weibull models is conducted using Akaike information criterion and the maximized log-likelihood. Extensive Monte Carlo simulation study is carried out for different values of the parameters and different sample sizes to compare the performance of parametric and semiparametric estimators based on relative mean square error.

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