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      Length and weight growth trends for children less than two years old in Zanjan, Iran: Longitudinal modeling

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          Abstract

          Background: Growth failure in children less than five years old can lead to the serious complications such as increased mortality, learning difficulties or physical disability. The aim of this study was to investigate the nonorganic factors affecting the growth trend in less than two years children living in Zanjan, Iran.

          Methods: This longitudinal study was conducted on a sample of 3566 children less than two years old in Zanjan. Weight and length growth trends were recorded as ordinal variables and analyzed by longitudinal marginal model.

          Results: About 12% (n=289) and 8% (n=212) of children had at least one decline/stagnation in the weight and length growth curve, respectively. Based on the marginal model, the effect of the child’s age and residence area on the weight and length growth trends were statistically significant (p<0.05).

          Conclusion: Given the relatively high prevalence of growth failure among studied children less than two years old in rural areas of Zanjan, raising the awareness of parents in rural areas about feeding and nutritional behaviors of children seems an important issue. Additionally, healthcare providers should mostly focus on monitoring the growth of children older than 12 months.

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          Most cited references 22

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          WHO Child Growth Standards based on length/height, weight and age.

            (2006)
          To describe the methods used to construct the WHO Child Growth Standards based on length/height, weight and age, and to present resulting growth charts. The WHO Child Growth Standards were derived from an international sample of healthy breastfed infants and young children raised in environments that do not constrain growth. Rigorous methods of data collection and standardized procedures across study sites yielded very high-quality data. The generation of the standards followed methodical, state-of-the-art statistical methodologies. The Box-Cox power exponential (BCPE) method, with curve smoothing by cubic splines, was used to construct the curves. The BCPE accommodates various kinds of distributions, from normal to skewed or kurtotic, as necessary. A set of diagnostic tools was used to detect possible biases in estimated percentiles or z-score curves. There was wide variability in the degrees of freedom required for the cubic splines to achieve the best model. Except for length/height-for-age, which followed a normal distribution, all other standards needed to model skewness but not kurtosis. Length-for-age and height-for-age standards were constructed by fitting a unique model that reflected the 0.7-cm average difference between these two measurements. The concordance between smoothed percentile curves and empirical percentiles was excellent and free of bias. Percentiles and z-score curves for boys and girls aged 0-60 mo were generated for weight-for-age, length/height-for-age, weight-for-length/height (45 to 110 cm and 65 to 120 cm, respectively) and body mass index-for-age. The WHO Child Growth Standards depict normal growth under optimal environmental conditions and can be used to assess children everywhere, regardless of ethnicity, socio-economic status and type of feeding.
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            2000 CDC Growth Charts for the United States: methods and development.

            This report provides detailed information on how the 2000 Centers for Disease Control and Prevention (CDC) growth charts for the United States were developed, expanding upon the report that accompanied the initial release of the charts in 2000. The growth charts were developed with data from five national health examination surveys and limited supplemental data. Smoothed percentile curves were developed in two stages. In the first stage, selected empirical percentiles were smoothed with a variety of parametric and nonparametric procedures. In the second stage, parameters were created to obtain the final curves, additional percentiles and z-scores. The revised charts were evaluated using statistical and graphical measures. The 1977 National Center for Health Statistics (NCHS) growth charts were revised for infants (birth to 36 months) and older children (2 to 20 years). New body mass index-for-age (BMI-for-age) charts were created. Use of national data improved the transition from the infant charts to those for older children. The evaluation of the charts found no large or systematic differences between the smoothed percentiles and the empirical data. The 2000 CDC growth charts were developed with improved data and statistical procedures. Health care providers now have an instrument for growth screening that better represents the racial-ethnic diversity and combination of breast- and formula-feeding in the United States. It is recommended that these charts replace the 1977 NCHS charts when assessing the size and growth patterns of infants, children, and adolescents.
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              Models for longitudinal data: a generalized estimating equation approach.

              This article discusses extensions of generalized linear models for the analysis of longitudinal data. Two approaches are considered: subject-specific (SS) models in which heterogeneity in regression parameters is explicitly modelled; and population-averaged (PA) models in which the aggregate response for the population is the focus. We use a generalized estimating equation approach to fit both classes of models for discrete and continuous outcomes. When the subject-specific parameters are assumed to follow a Gaussian distribution, simple relationships between the PA and SS parameters are available. The methods are illustrated with an analysis of data on mother's smoking and children's respiratory disease.
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                Author and article information

                Journal
                Med J Islam Repub Iran
                Med J Islam Repub Iran
                MJIRI
                Med J Islam Repub Iran
                Medical Journal of the Islamic Republic of Iran
                Iran University of Medical Sciences
                1016-1430
                2251-6840
                2016
                23 May 2016
                : 30
                : 374
                Affiliations
                1 PhD Student of Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. nasim.vahabi@ 123456modares.ac.ir
                2 Professor of Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. kazem_an@ 123456modares.ac.ir
                3 PhD Student of Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. r.fallahvalamdehi@ 123456modares.ac.ir
                Author notes
                (Corresponding author) Professor of Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. kazem_an@ 123456modares.ac.ir
                4972070
                27493918
                © 2016 Iran University of Medical Sciences

                This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0), which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.

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                Tables: 5, References: 25, Pages: 8
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