Prevalence of type 1 diabetes mellitus in school children 6–18 years old in Diyarbakır, Southeastern Anatolian Region of Turkey

Abstract

This study aimed to model the performance indices of deep bed drying of rough rice using artificial neural networks (ANNs), compare the ANN approach to the multivariate regression method, and determine the sensitivity of the ANN model to the input variables. The effects of air temperature, air velocity, and air relative humidity on drying kinetics, product output rate (POR), evaporation rate (ER), and percentage of kernel cracking (KC) were investigated. To predict the dependent parameters, 3 well-known networks, namely the multilayer perceptron, generalized feed forward (GFF), and modular neural network, were examined. The GFF networks with the Levenberg-Marquardt learning algorithm, hyperbolic tangent activation function, and 4-15-1, 3-4-4-1, 3-7-1, and 3-11-1 topologies provided superior results, respectively, for predicting moisture content, POR, ER, and CK. The values of all of the drying indices predicted by the ANN were closer to the experimental data than linear and logarithmic regression models. The output variables were significantly affected by the dependent variables. However, air temperature and air relative humidity showed the maximum and the minimum influence on the network outputs, respectively.

First report on the nationwide incidence and prevalence of Type 1 diabetes among children in Turkey

“…The lower estimate compared with our data is likely to result from the method of case ascertainment (students treated with insulin reported to the Ministry of Education data), the population studied (local vs nationwide) and the different dates covered by the studies (2009 vs 2011–2013). The prevalence in the Diyarbakır region in 2012 was also lower (0.42/1000) using Ministry of Education estimates . The present study confirms the importance of using a national, population‐based registry and ascertaining cases using a method by which few cases will be missed (such as a mandatory reporting system).…”

“…Other European countries tend to have intermediate to high incidence rates, with a crude tendency to decrease from Western and Northern to Eastern and Southeastern Europe . By contrast, these rates are clearly higher than in Albania (3.9/100 000) and Bosnia and Herzegovina (3.5/100 000) , which are located beside the Adriatic Sea, relatively far from Turkey. It is also possible there may be under‐ascertainment of cases in these nations.…”

“…There are only two published studies that have previously reported the epidemiology of Type 1 diabetes among children in Turkey [9,10] (Table 4). Both were regional/local studies; one conducted in Istanbul and the other in Diyarbakır.…”

“…Published data on the incidence and prevalence of Type 1 diabetes among children in Turkey are scarce [9][10][11] and none of the previous studies were based on population-based registry data. Indeed, the lack of nationwide data on paediatric diabetes from Turkey was highlighted in the International Diabetes Federation 2011 Diabetes Atlas [12].…”

Incidence of Type 1 Diabetes in Children Aged Below 18 Years During 2013-2015 in Northwest Turkey

“…In this study we investigated the incidence of T1DM in children residing in the Northwestern region of Turkey, our results demonstrate that the incidence of T1DM here is intermediate (8.99/100,000) in the pediatric age group ( 4 ). In Turkey, there are only a few reports on the epidemiology of T1DM in children and most of them focused on children below 15 years of age ( 11 , 12 , 13 , 14 ). Our study is one of the few population-based reports presenting the incidence of T1DM among children in Turkey.…”