Objectives: Determine the age and gender distribution of children who experience diabetes mellitus (DM) under
the age of 15 years and the presence of some associated factors that might be a predisposing factor for the
disease including obesity.
Methodology: A cross-sectional study was conducted at diabetic clinic in Children Welfare Teaching Hospital
in Baghdad City during 2006. The study sample included diabetic children less than 15 years of age. Data were
taken from the patients' record and by direct interview with the patients' parents. Information included
demographic data, as well as past history of the patient and his/her family relative to diabetes and other immune
diseases.
Results: Data analysis showed that there was an equal distribution of patients among the three age groups (I-5,
6-10-, and 11-15 years) in a rate of 33% for each. Females had higher incidence rate (660/o) than males. Onset of
the disease was mostly at age group I-3 years (32%) followed by age group 4-6 years (30%). IIistory of viral
illness was present in 24% of patients and a positive family history of DM was found in 23% of them, and a
family history of autoimmune disease was positive in 13% of cases. Body weight at the first visit to the clinic
was mainly below the 50th percentile (42% within the 25th percentile) and only 4% of patients had a body
weight within the 75th percentile.
Recommendations: Health education about the disease and provision of health centers those are easily
accessible for early diagnosis and treatment and uninerrupted insulin supply.
Nonlinear time series analysis is one of the most complex problems ; especially the nonlinear autoregressive with exogenous variable (NARX) .Then ; the problem of model identification and the correct orders determination considered the most important problem in the analysis of time series . In this paper , we proposed splines estimation method for model identification , then we used three criterions for the correct orders determination. Where ; proposed method used to estimate the additive splines for model identification , And the rank determination depends on the additive property to avoid the problem of curse dimensionally . The proposed method is one of the nonparametric methods , and the simulation results give a
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