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Statistical Analysis of Factors Affecting the Weight of Babies at Birth

Received: 6 May 2017     Accepted: 25 May 2017     Published: 15 November 2017
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Abstract

This research work studied statistically those factors which determine the weight of a baby at birth. The data used in this research work was collected from the Prenatal and Postnatal Register of Ebonyi State University Teaching Hospital Abakaliki, Nigeria. The data covered all births recorded from January 2009 to December 2013. Factors which determine birthweight are numerous but for this work, variables of greater influence were considered which include: Mother’s age, Parity, Method of Delivery and the Sex of the baby. By Chi-square Test Statistics, it was observed that the birthweight of a baby depends on the sex of the baby with a calculated value 12.14 and a critical value 7.81 0.5 level of significance. Also the method of delivery of a baby also proved to be a significant factor affecting birthweight with a calculated 50.90 and a critical value 12.6 By Chi-Square Test also, the mother’s age and parity shown not to be significant factors affecting birthweight with Chi-Square values 1.90, 1.001 and critical values of 12.6 and 7.81 The Z–Test Statistic was also applied to test for the significance difference between the mean birth weigh of male and female babies and it yielded a calculated value 6.48 and a critical value 1.96 at 0.5 level of significance which indicates that there is a significant difference between the mean birthweights of sex of the babies. Also by Z–Test also, method of delivery proved to be a significant factor affecting birthweight with a calculated value 5.41 and a critical value 1.96. Time Series Analysis was also employed to obtain the seasonal variations between the sex of babies and it was observed that more female babies are born during the third quarter and more male babies are born during the fourth quarter of the year. Also by Least Square Method of Regression Analysis, it was predicted that in the year 2014, the total number of male and female birth will be 140 and also in the year 2015, the total number of male and female birth will be 141.

Published in Biomedical Statistics and Informatics (Volume 2, Issue 4)
DOI 10.11648/j.bsi.20170204.17
Page(s) 172-179
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Low Birthweight, Parity, Caesarian Section, Chi-Square, Trend Line, Prediction, Seasonal Variations

References
[1] World Health Organization (WHO) Expert Committee on Maternal and Child Health “Public Health Aspect of Low Birth Weights” Technical Report Series. No. 217, 1961.
[2] World Health Organization, International Statistical Classification of Diseases and Related Health Problems, Tenth Revi sion, World Health Organization, Geneva, Switzerland, 1992.
[3] Amosu A. M and Degun A. M ‘Impact of maternal nutrition on birth weight of babies’ Biomedical Research 2014; 25(1): 75-78 ISSN 0971-9032 http://www.biomedres.info
[4] Ravi Kumar Bhaskar, Krishna Kumar Deo, Uttam Neupane, Subhadra Chaudhary Bhaskar, Birendra Kumar Yadav, Hanoon P. Pokharel, and Paras Kumar Pokharel ‘A Case Control Study on Risk Factors Associated with Low Birth Weight Babies in Eastern Nepal’ International Journal of Pediatrics. Volume 2015 (2015), Article ID 807373, 7 pages http://dx.doi.org/10.1155/2015/807373
[5] Abubakari A, Kynast-Wolf G, Jahn A (2015) Maternal Determinants of Birth Weight in Northern Ghana. PLoS ONE 10(8): e0135641. https://doi.org/10.1371/journal.pone.
[6] Striessguth A.,“Fetal Alcohol Syndrom, A Guide for Families and Communities’’. Baltimore Publishing ISBN1-55766-283-5, 1997.
[7] Lamont RF and Jagged A. N, “Emerging drug therapies for preventing spontaneous preterm labor and preterm birth”. Expert Opine Investing Drugs 2007, Vol 16. Pg 337-345. PMIP1730, 2007.
[8] Abdulai Abubakari, Albrecht Jahn ‘Maternal Dietary Patterns and Practices and Birth Weight in Northern Ghana’ Pub lished: September 9, 2016 https://doi.org/10.1371/journal.pone.0162285
[9] Anders C. Erickson, Aleck Ostry, Hing Man Chan and Laura Arbour BMC Public Health BMC series – open, inclusive and trusted 201616: 585 DOI: 10.1186/s12889-016-3273-9
[10] Khader YS, Fa AN Q., “The periodontal diseases and the risk of preterm birth” 2005.
[11] Hold Chris and Mac Donald Anita, “Nutrition and child Health” Retrieved 04-03-2007.
[12] Shino, Mayo Clinic (2006): “Premature Birth”.
[13] Queenan John, “Management of High Risk Pregnancy” 2007.
[14] Dolby J. T., “Environmental effects on prenatal development soured of pediatric psychology”, Vol 3, Pg 105-109, 1978.
[15] Roberton NRC “Fetal growth, intrauterine growth retardation and small for gestational age babies”. In JM, Roberton NRC (eds) textbook of neonatology, Churchill Livingstone, London. Pg 389-398, 1999.
[16] Conde-Agudelo A., Diaz-Rossello, Belizan J. M., “Kangoroo care to reduce morbidity in low Birth weight” 2001.
[17] Diane M. Fraser and Margaret A. Cooper, “Myles Textbook for midwives” 14th Edition.
[18] Dole N., D. A. Savittz, M. J. Mcmahoa and P. Buekens, “Maternal Stress and Preterm Birth” 2003.
[19] OYEKA I. C. A, “An Introduction to Applied Statistical Methods in Sciences” third Edition 1992.
[20] University of Michigan Medical School: “Fetal Circulation and Changes at Birth” Retrieved 2007.
Cite This Article
  • APA Style

    Obikee Adaku Caroline, Obiora-Ilouno Happiness Onyebuchi, Okoli Cecilia Nchedo. (2017). Statistical Analysis of Factors Affecting the Weight of Babies at Birth. Biomedical Statistics and Informatics, 2(4), 172-179. https://doi.org/10.11648/j.bsi.20170204.17

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    ACS Style

    Obikee Adaku Caroline; Obiora-Ilouno Happiness Onyebuchi; Okoli Cecilia Nchedo. Statistical Analysis of Factors Affecting the Weight of Babies at Birth. Biomed. Stat. Inform. 2017, 2(4), 172-179. doi: 10.11648/j.bsi.20170204.17

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    AMA Style

    Obikee Adaku Caroline, Obiora-Ilouno Happiness Onyebuchi, Okoli Cecilia Nchedo. Statistical Analysis of Factors Affecting the Weight of Babies at Birth. Biomed Stat Inform. 2017;2(4):172-179. doi: 10.11648/j.bsi.20170204.17

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  • @article{10.11648/j.bsi.20170204.17,
      author = {Obikee Adaku Caroline and Obiora-Ilouno Happiness Onyebuchi and Okoli Cecilia Nchedo},
      title = {Statistical Analysis of Factors Affecting the Weight of Babies at Birth},
      journal = {Biomedical Statistics and Informatics},
      volume = {2},
      number = {4},
      pages = {172-179},
      doi = {10.11648/j.bsi.20170204.17},
      url = {https://doi.org/10.11648/j.bsi.20170204.17},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170204.17},
      abstract = {This research work studied statistically those factors which determine the weight of a baby at birth. The data used in this research work was collected from the Prenatal and Postnatal Register of Ebonyi State University Teaching Hospital Abakaliki, Nigeria. The data covered all births recorded from January 2009 to December 2013. Factors which determine birthweight are numerous but for this work, variables of greater influence were considered which include: Mother’s age, Parity, Method of Delivery and the Sex of the baby. By Chi-square Test Statistics, it was observed that the birthweight of a baby depends on the sex of the baby with a calculated value 12.14 and a critical value 7.81 0.5 level of significance. Also the method of delivery of a baby also proved to be a significant factor affecting birthweight with a calculated 50.90 and a critical value 12.6 By Chi-Square Test also, the mother’s age and parity shown not to be significant factors affecting birthweight with Chi-Square values 1.90, 1.001 and critical values of 12.6 and 7.81 The Z–Test Statistic was also applied to test for the significance difference between the mean birth weigh of male and female babies and it yielded a calculated value 6.48 and a critical value 1.96 at 0.5 level of significance which indicates that there is a significant difference between the mean birthweights of sex of the babies. Also by Z–Test also, method of delivery proved to be a significant factor affecting birthweight with a calculated value 5.41 and a critical value 1.96. Time Series Analysis was also employed to obtain the seasonal variations between the sex of babies and it was observed that more female babies are born during the third quarter and more male babies are born during the fourth quarter of the year. Also by Least Square Method of Regression Analysis, it was predicted that in the year 2014, the total number of male and female birth will be 140 and also in the year 2015, the total number of male and female birth will be 141.},
     year = {2017}
    }
    

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Author Information
  • Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

  • Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

  • Department of Statistics, Faculty of Physical Sciences, Chukwuemeka Odumegwu Ojukwu University, Uli, Nigeria

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