Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-25T14:22:05.436Z Has data issue: false hasContentIssue false
  • English
  • Français

MODELLING THE RISK FACTORS FOR BIRTH WEIGHT IN TWIN GESTATIONS: A QUANTILE REGRESSION APPROACH

Published online by Cambridge University Press:  27 February 2017

A. John Michael ,
Belavendra Antonisamy ,
S. Mahasampath Gowri  and
Ramasami Prakash
A. John Michael
Affiliation:
Department of Biostatistics, Christian Medical College, Vellore, India
Belavendra Antonisamy*
Affiliation:
Department of Biostatistics, Christian Medical College, Vellore, India
S. Mahasampath Gowri
Affiliation:
Department of Biostatistics, Christian Medical College, Vellore, India
Ramasami Prakash
Affiliation:
Department of Biostatistics, Christian Medical College, Vellore, India
*
1Corresponding author. Email: b.antonisamy@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

Birth weight is used as a proxy for the general health condition of newborns. Low birth weight leads to adverse events and its effects on child growth are both short- and long-term. Low birth weight babies are more common in twin gestations. The aim of this study was to assess the effects of maternal and socio-demographic risk factors at various quantiles of the birth weight distribution for twin gestations using quantile regression, a robust semi-parametric technique. Birth records of multiple pregnancies from between 1991 and 2005 were identified retrospectively from the birth registry of the Christian Medical College and hospitals in Vellore, India. A total of 1304 twin pregnancies were included in the analysis. Demographic and clinical characteristics of the mothers were analysed. The mean gestational age of the twins was 36 weeks with 51% having preterm labour. As expected, the examined risk factors showed different effects at different parts of the birth weight distribution. Gestational age, chroniocity, gravida and child’s sex had significant effects in all quantiles. Interestingly, mother’s age had no significant effect at any part of the birth weight distribution, but both maternal and paternal education had huge impacts in the lower quantiles (10th and 25th), which were underestimated by the ordinary least squares (OLS) estimates. The study shows that quantile regression is a useful method for risk factor analysis and the exploration of the differential effects of covariates on an outcome, and exposes how OLS estimates underestimate and overestimate the effects of risk factors at different parts of the birth weight distribution.

Type
Research Article
Information
Journal of Biosocial Science , Volume 49 , Issue 6 , November 2017 , pp. 744 - 756
Copyright
Copyright © Cambridge University Press, 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abrevaya, J. (2001) The effects of demographics and maternal behavior on the distribution of birth outcomes. Empirical Economics 26, 247–257.CrossRefGoogle Scholar
Ananth, C. V. & Preisser, J. S. (1999) Bivariate logistic regression: modelling the association of small for gestational age births in twin gestations. Statistics in Medicine 18, 2011–2023.3.0.CO;2-8>CrossRefGoogle ScholarPubMed
Ananth, C. V., Vintzileos, A. M., Shen-Schwarz, S., Smulian, J. C. & Lai, Y. L. (1998) Standards of birth weight in twin gestations stratified by placental chorionicity. Obstetrics & Gynecology 91, 917–924.Google ScholarPubMed
Belasco, E., Chidmi, B., Lyford, C. & Funtanilla, M. (2012) Using quantile regression to measure the differential impact of economic and demographic variables on obesity. Journal of Health Behavior and Public Health 2, 35–45.Google Scholar
Beyerlein, A., Fahrmeir, L., Mansmann, U. & Toschke, A. M. (2008) Alternative regression models to assess increase in childhood BMI. BMC Medical Research Methodology 8, 59.CrossRefGoogle ScholarPubMed
Beyerlein, A., Toschke, A. M., Schaffrath Rosario, A. & von Kries, R. (2011) Risk factors for obesity: further evidence for stronger effects on overweight children and adolescents compared to normal-weight subjects. PLoS One 6, e15739.CrossRefGoogle ScholarPubMed
Blickstein, I., Mincha, S., Goldman, R. D., Machin, G. A. & Keith, L. G. (2006) The Northwestern twin chorionicity study: testing the ‘placental crowding’ hypothesis. Journal of Perinatal Medicine 34, 158–161.CrossRefGoogle ScholarPubMed
Blickstein, I., Zalel, Y. & Weissman, A. (1995) Pregnancy order: a factor influencing birth weight in twin gestations. Journal of Reproductive Medicine 40, 443–446.Google Scholar
Buchinsky, M. (1998) Recent advances in quantile regression models: a practical guideline for empirical research. Journal of Human Resources 33, 88.CrossRefGoogle Scholar
Cade, B. S. & Noon, B. R. (2003) A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment 1, 412–420.CrossRefGoogle Scholar
Child Trend Data Bank (2014) Low and very low birthweight infants. Child Trends. URL: http://www.childtrends.org/?indicators=low-and-very-low-birthweight-infants. (accessed 9th September 2016).Google Scholar
Daniel-Spiegal, E., Weiner, E., Yarom, I., Doveh, E., Friedman, P., Cohen, A. & Shalev, E. (2013) Establishment of fetal biometric charts using quantile regression analysis. Journal of Ultrasound Medicine 2, 23–33.CrossRefGoogle Scholar
Eilers, P. H. C., Röder, E., Savelkoul, H. F. J. & van Wijk, R. G. (2012) Quantile regression for the statistical analysis of immunological data with many non-detects. BMC Immunology 13, 37.CrossRefGoogle Scholar
Eisner, V., Brazie, J. V., Pratt, M. W. & Hexter, A. C. (1979) The risk of low birth weight. American Journal of Public Health 69, 887–893.CrossRefGoogle Scholar
Ellerbe, C. N., Gebregziabher, M., Korte, J. E., Mauldin, J. & Hunt, K. J. (2013) Quantifying the impact of gestational diabetes mellitus, maternal weight and race on birth weight via quantile regression. PLoS One 8, e65017.CrossRefGoogle ScholarPubMed
Fenske, N., Fahrmeir, L., Rzehak, P. & Höhle, M. (2008) Detection of Risk Factors for Obesity in Early Childhood with Quantile Regression Methods for Longitudinal Data. URL: http://epub.ub.uni-muenchen.de/6260/ (accessed 19th September 2014).Google Scholar
Gielen, M., Lindsey, P. J., Derom, C., Smeets, H. J. M., Souren, N. Y., Paulussen, A. D. C. et al. (2008) Modeling genetic and environmental factors to increase heritability and ease the identification of candidate genes for birth weight: a twin study. Behavioral Genetics 38, 44–54.CrossRefGoogle ScholarPubMed
Grennert, L., Persson, P. H., Gennser, G. & Gullberg, B. (1980) Zygosity and intrauterine growth of twins. Obstetrics & Gynecology 55, 684–687.Google ScholarPubMed
Hack, K. E. A., Derks, J. B., de Visser, V. L., Elias, S. G. & Visser, G. H. A. (2006) The natural course of monochorionic and dichorionic twin pregnancies: a historical cohort. Twin Research and Human Genetics 9, 450–455.CrossRefGoogle ScholarPubMed
Hao, L. (2007) Quantile Regression. SAGE Publications Inc., Thousand Oaks, CA.CrossRefGoogle Scholar
Koenker, R. (2005) Quantile Regression. Cambridge University Press, Cambridge, New York.CrossRefGoogle Scholar
Koenker, R. & Bassett, G. (1978) Regression quantiles. Econometrica 46, 33.CrossRefGoogle Scholar
Koenker, R. & Hallock, K. F. (2001) Quantile regression. Journal of Economic Perspectives 15, 143–156.CrossRefGoogle Scholar
Le Cook, B. & Manning, W. G. (2013) Thinking beyond the mean: a practical guide for using quantile regression methods for health services research. Shanghai Archive of Psychiatry 25, 55–59.Google Scholar
Li, Y., Graubard, B. I. & Korn, E. L. (2010) Application of nonparametric quantile regression to body mass index percentile curves from survey data. Statistics in Medicine 29, 558–572.CrossRefGoogle ScholarPubMed
Loos, R. J. F., Derom, C., Derom, R. & Vlietinck, R. (2005) Determinants of birth weight and intrauterine growth in liveborn twins. Paediatric and Perinatal Epidemiology 19 (Supplement 1), 15–22.CrossRefGoogle ScholarPubMed
Mayr, A., Hothorn, T. & Fenske, N. (2012) Prediction intervals for future BMI values of individual children: a non-parametric approach by quantile boosting. BMC Medical Research and Methodology 12, 6.CrossRefGoogle ScholarPubMed
Min, S. J., Luke, B., Gillespie, B., Min, L., Newman, R. B., Mauldin, J. G. et al. (2000) Birth weight references for twins. American Journal of Obstetrics and Gynecology 182, 1250–1257.CrossRefGoogle ScholarPubMed
Onyiriuka, A. N. (2009) Birthweight data of live-born twins in Benin City, Nigeria. Sahel Medical Journal 11(4) doi: 10.4314/smj2.v11i4.12988.CrossRefGoogle Scholar
Onyiriuka, A. N. (2011a) Low birthweight infants in twin gestation. Current Pediatric Research 15(1), 37–41.Google Scholar
Onyiriuka, A. N. (2011b) Birthweight of full-term twin infants in relation to sex-pair. Genomic Medicine Biomarkers and Health Sciences 3(3–4), 123–127.CrossRefGoogle Scholar
Papageorghiou, A. T., Bakoulas, V., Sebire, N. J. & Nicolaides, K. H. (2008) Intrauterine growth in multiple pregnancies in relation to fetal number, chorionicity and gestational age. Ultrasound Obstetrics and Gynecology 32, 890–893.CrossRefGoogle ScholarPubMed
Silva, J. M. C. S. & Parente, P. M. D. C. (2013) Quantile regression with clustered data. Economics Discussion Paper No. 728. Department of Economics, University of Essex.Google Scholar
Terry, M. B., Wei, Y. & Esserman, D. (2007) Maternal, birth, and early-life influences on adult body size in women. American Journal of Epidemiology 166, 5–13.CrossRefGoogle ScholarPubMed
UNICEF (2013) Undernourishment in the womb can lead to diminished potential and predispose infants to early death. UNICEF Data: Monitoring the Situation of Children and Women. URL: http://data.unicef.org/nutrition/low-birthweight#sthash.BG4IvrwC.dpuf.Google Scholar
Verropoulou, G. & Basten, S. (2014) Very low, low and heavy weight births in Hong Kong SAR: how important is socioeconomic and migrant status? Journal of Biosocial Science 46, 316–331.CrossRefGoogle Scholar
Verropoulou, G. & Tsimbos, C. (2013) Modelling the effects of maternal socio-demographic characteristics on the preterm and term birth weight distributions in Greece using quantile regression. Journal of Biosocial Science 45, 375–390.CrossRefGoogle ScholarPubMed
Wei, Y., Pere, A., Koenker, R. & He, X. (2006) Quantile regression methods for reference growth charts. Statistics in Medicine 25, 1369–1382.CrossRefGoogle ScholarPubMed