Epidemiology and Health

Original Article

Longitudinal standards for growth velocity of infants from birth to 4 years born in West Azerbaijan Province of northwest Iran: Parvin Ghaemmaghami, Seyyed Mohammad Taghi Ayatollahi, Vahid Alinejad, Elham Haem; Epidemiol Health. 2015;37:e2015029. Published online June 23, 2015; DOI: https://doi.org/10.4178/epih/e2015029

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Abstract

OBJECTIVES
Growth velocity is an important factor to monitor for appropriate child growth. This study presents the growth velocity of infants based on length, weight, and head circumference.

METHODS

The subjects of this study were 308 neonates (160 boys and 148 girls) born in West Azerbaijan Province of northwestern Iran who were followed from birth for 4 years. The weights and lengths of the subjects were recorded at birth, 1, 2, 4, 6, and 9 months, and 1, 1.5, 2, 3, and 4 years of age, while the head circumferences were measured just up to 1.5 years of age. In this study, the Lambda-Mu-Sigma (LMS) method using LMS Chartmaker Pro (Institute of Child Health, London, UK) was utilized to obtain growth velocity percentiles.

RESULTS

After obtaining growth velocity charts for weight, length, and head circumference (5th, 50th, and 95th percentiles), the researchers could deduce that there was a sharp decrease in the velocity growth charts from birth to 2 years of age but these charts remained relatively stable up to 4 years for both sexes. Growth velocities for the length and weight of boys in the present sample are slightly but not significantly greater than those in girls through the first months of infancy and there was no significant difference between girls and boys up to 4 years.

CONCLUSIONS

This paper provided the first local growth velocity standards of length, weight, and head circumference for infants by analyzing longitudinal measurements produced for West Azerbaijan Province, which should be updated periodically. It seems that there has been a significant difference between the growth velocity of infants in northwestern Iran and southern Iran within the past few years.

Summary

Citations

Citations to this article as recorded by

Growth Velocity and Nutritional Status in Children Exposed to Zika Virus during Pregnancy from Amazonas Cohort, Brazil
Lucíola de Fátima Albuquerque de Almeida Peixoto, Marília Rosa Abtibol-Bernardino, Cecilia Victoria Caraballo Guerra, Geruza Alfaia de Oliveira, Beatriz Caroline Soares Chaves, Cristina de Souza Rodrigues, Anny Beatriz Costa Antony de Andrade, Elijane de
Viruses.2023; 15(3): 662. CrossRef
Limitations of Weight Velocity Analysis by Commercial Computer Program Growth Analyser Viewer Edition
Martin J. C. van Gemert, Cornelis M. A. Bruijninckx, Ton G. van Leeuwen, H. A. Martino Neumann, Pieter J. J. Sauer
Annals of Biomedical Engineering.2019; 47(1): 297. CrossRef
Weight velocity equations with 14–448 days time separated weights should not be used for infants under 3 years of age
Martin J.C. van Gemert, Cornelis M.A. Bruijninckx, H.A. Martino Neumann, Pieter J.J. Sauer, D. Martijn de Bruin, Ton G. van Leeuwen
Medical Hypotheses.2019; 129: 109234. CrossRef
Growth indices of exclusively breastfed until 6 months age and formula-fed infants in southwest of Iran
Jan-mohamad Malekzadeh, Saiid Synaii, BehroozEbrahimzadeh Koor, Ghasem Falsafian, Mahmood-Reza Nakhaie
International Journal of Preventive Medicine.2019; 10(1): 207. CrossRef
Growth curves and their associated weight and height factors in children from birth to 4 years old in West Azerbaijan Province, northwest Iran
P. Ghaemmaghami, S.M.T. Ayatollahi, V. Alinejad, Z. Sharafi
Archives de Pédiatrie.2018; 25(6): 389. CrossRef

Note

Bias in Cancer Screening Evaluation.: Sang Heon Yoon, Hyeon Woo Yim, Won Chul Lee; Korean J Epidemiol. 2008;30(1):12-24. Published online June 30, 2008; DOI: https://doi.org/10.4178/kje.2008.30.1.12

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Abstract PDF

Abstract

Cancer screening evaluation will be a important issue in Korea in near future. Bias in cancer screening evaluation is well known by concept, but it is not applied yet using the data from screening program of Korea. So introducing the way how the bias is adjusted will be helpful. This review deals with the type, meaning, the way how the bias is adjusted, and examples. Especially, lead-time bias, length bias and self-selection bias were focused. Adjusting bias is one of the imperative step for epidemiologic analysis. Understanding the background concept and experience using the screening program data will be helpful for estimating the effectiveness of national screening program of Korea.

Summary

Citations

Citations to this article as recorded by

Evidence-based National Cancer Screening program of Korea
Won-Chul Lee
Journal of the Korean Medical Association.2011; 54(10): 1028. CrossRef

Original Article

A study on Statistical Method for Controlling the Effect of Intermediate Events: Application to the Control of the Healthy Worker Effect.: Chung Mo Nam, Jinheum Kim, Dae Ryong Kang, Yeon Soon Ahn, Hoo Yeon Lee, Dae Hee Lee; Korean J Epidemiol. 2002;24(1):7-16.

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Abstract PDF: Abstract
PURPOSE
The healthy worker effect is an important issue in occupational epidemiology. This study was conducted to propose a new method to test the relation between exposure and mortality in the presence of the healthy worker effect.
METHODS
In this study, the healthy worker hire effect was assumed to operate as a confounding variable of health status at the beginning of employment and healthy worker survival effect as a confounding and intermediate variable of employment status. In addition, the proposed method reflects the length bias sampling caused by changing of an employment status. Simulation studies were also carried out to compare the proposed method with Cox's time dependent covariates models .
RESULTS
The theoretical development of the healthy worker survival effect is based on the result that an observation with change of an employment status requires that the survival time without intermediate event exceeds the waiting time for the intermediate event. According to our simulation studies, both the proposed method and Cox's time dependent covariates model which includes the change of employment status as time dependent covariates seem to be satisfactory at 5% significance level. However, Cox's time dependent covariates models without or with the change of employment status as time fixed covariate are unsatisfactory. The proposed test is superior in power to tests based on Cox's model.
CONCLUSIONS
The healthy worker effect may not be controlled by classical Cox's proportional hazards models. The proposed method performed well in the presence of healthy worker effect in terms of level and power; Summary

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