| Peer-Reviewed

Statistical Modeling for Findings the Determinants of Neonates’ Very Low Birth Weight

Received: 20 November 2018     Accepted: 8 December 2018     Published: 14 January 2019
Views:       Downloads:
Abstract

Aim of the epidemiological research is to identify a causal relationship between the risk factors and the disease. The present study aims to identify the determinates of neonates’ very low birth weight which have significant effects on their birth weight using generalized additive probabilistic modeling. In this present study very low birth weight (BWT) is the response variable with heterogeneity and non-normality in nature which can be modeled through either by the Log-normal or by the gamma models. A well-known method is joint modeling of mean and variance (JGLM) to handle this heterogeneity and non-normality but this study introduced the most advanced regression techniques namely generalized additive model (GAM). Materials and Methods: The present article is based on the secondary data on 174 very low birth weighted neonates along with 26 explanatory factors/ variables. The very low birth weight of 174 neonates is heterogeneous, positive, and gamma distributed. Therefore, generalized additive model with gamma distribution and log link function has been introduced to analyze this very low birth weight. The Native American neonates has the smaller birth weight (BWT) than the other racial neonates namely black, white or oriental (P-value = 0.009). The BWT is smaller for those neonates who were outsiders from Duke (P-value = 0.05) and it also decreases to the female births (P- value=0.09). BWT is higher for the pneumothorax infants than non-pneumothorax with P-value <0.001. It is smaller for those infants for whom oxygen supply had been introduced in between 30 days of his/her birth (P-value=0.002). The neonates who were not been survived they also have the smaller birth weight than the alive neonates (P-value= 0.07). Besides these factors, lowest pH in first 4 days of neonates’ life (P-value=0.09), Apgar score at one minute (P-value=0.03) and the interaction effects of lowest pH and Apgar score (0.03) are the significant variables (continuous cofactors) for neonates’ very low birth weight. Hospital stays in number of days, platelet counts and gestational age in weeks are also highly significant factors and these are entered in the GAM model as a non –parametric smoothing term (P-value<0.001). The birth weight of each new born baby is identified as heterogeneous and gamma distributed (possible). Most of the present findings, especially the Apgar score in one minute, occurrence of pneumothorax in neonates, oxygen supply given to new born baby within 30 days of life and smoothing term determinants namely number of days hospital stay, platelet counts and gestational age in weeks (non-parametric smoothing terms) are significant factors for neonate’s birth weight and are completely new in the literature.

Published in American Journal of Clinical and Experimental Medicine (Volume 6, Issue 6)
DOI 10.11648/j.ajcem.20180606.11
Page(s) 125-135
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), 2019. Published by Science Publishing Group

Keywords

Very Low Birth Weight, Generalized Additive Model, Gamma Distribution, Non-constant Variance

References
[1] Kramer MS. Determinants of low birth weight: methodological assessment and meta-analysis. Bull World Health Organ 1987; 65 (5): 663-737.
[2] Rich-Edwards JW, Buka SL, Brennan RT, Earls F. Diverging associations of maternal age with low birth weight for black and white mothers. Int J Epidemiol 2003; 32: 83-90.
[3] Kumar P, Seshadri R. Neonatal morbidity and growth in very low birth-weight infants after multiple courses of Antenatal Steroids. J Perinatol 2005; 25: 698-702.
[4] Basu S, Rathore P, Bhatia BD. Predictors of mortality in very low birth weight neonates in India. Singapore Med J 2008; 49 (7): 556-560.
[5] Collins JW, David RJ. The different effect of traditional risk factors on infant birthweight among blacks and whites in Chicago. Am J Public Health 1990; 80: 679-681.
[6] David RJ, Collins JW. Differing birth weight among infants of U.S.-born blacks, African-born blacks, and U.S.-born whites. N Eng J Med 1997; 337: 209-214.
[7] Cole C, Binney G, Casey P, Fiascone J, Hagadorn J, Kim C, et al. Criteria for determining disability in infants and children: low birth weight. Evid Rep Technol Assess (Summ) 2002; 70: 1-7.
[8] Chase HC. International comparison of perinatal and infant mortality: the United States and six west European countries. Washington, DC: U.S. Department of Health, Education, and Welfare, Public Health Service; 1967.
[9] Chase HC. Infant mortality and weight at birth: 1960 United States birth cohort. Am J Public Health Nations Health 1969; 59: 1618-1628.
[10] Puffer PR, Serrano CV. Patterns of mortality in childhood: report of the Inter-American investigation of mortality in childhood. Washington, DC: Pan American Health Organization; 1973.
[11] Saugstad LF. Weight of all births and infant mortality. Journal of Epidemiological Community Health 1981; 35: 185-191.
[12] Badshah S, Mason L, McKelvie K, Payne R, Lisboa P. J. G. Risk factors for low Birth weight in the public-hospitals at Peshawar, NWFP-Pakistan, BMC Public Health, 2008; 8: 197.
[13] Roy S, Motghare DD, Ferreira AM, Vaz FS, Kulkarni MS, Maternal determinants of low birth weight at a tertiary care, The Journal of Family Welfare. 2009; 55: 79–83.
[14] Das RN, Devi RS, Kim J. Mothers’ Lifestyle Characteristics Impact on Her Neonates’ Low Birth Weight. International Journal of Women’s Health and Reproduction Sciences. 2014; 2 (4): 229–235.
[15] O’Shea M, Savitz DA, Hage ML, Feinstein K. A. Prenatal events and the risk of Sub ependymal/intraventricular haemorrhage in very low birth weight neonates. Paediatric and Perinutal Epidemwlogy 1992; 6: 352-362.
[16] McCullagh P, Nelder JA. Generalized linear models. London: Chapman & Hall; 1989.
[17] Myers RH, Montgomery DC, Vining GG. Generalized linear models with applications in engineering and the sciences. New York: John Wiley & Sons; 2002.
[18] Das RN, Lee Y. Log normal versus gamma models for analyzing data from quality- improvement experiments. Qual Eng 2009; 21 (1): 79-87.
[19] Das RN, Mukhopadhya BB. Affects of thyroid function and maternal urinary iodine on neonatal weights. J Neonatal Nurs 2009; 15: 204-211.
[20] Das RN. The role of Iodine in the thyroid status of mothers and their neonates. Thyroid Science 2011; 6 (2): 1-15.
[21] Das RN, Dihidar S, Verdugo R. Infant mortality in India: Evaluating Log-Gaussian and Gamma. Open Demography Journal 2011; 4: 34-41.
[22] Das RN, Park JS. Discrepancy in regression estimates between Log-normal and Gamma: Some case studies. J Appl Stat 2012; 39 (1): 97-111.
[23] Hosmer R, Lemeshow J. Applied logistic regression. 2nd ed. New York: John Wiley & Sons
[24] Box GEP, Cox DR. An analysis of transformations. J R Stat Soc Series B Stat Methodol 1964; 26: 211-252.
[25] Firth D. Multiplicative errors: log-normal or gamma? J R Stat Soc Series B Stat Methodol 1988; 50: 266-268.
[26] Nelder JA, Lee Y. Generalized linear models for the analysis of Taguchi-type experiments. Applied Stochastic Models and Data Analysis 1991; 7: 107-120.
[27] Cox DR, Reid N. Parameter orthogonality and approximate conditional inference. J R Stat Soc Series B Stat Methodol 1987; 49: 1-39.
[28] Lee Y, Nelder JA. Generalized linear models for the analysis of quality improvement experiments. Can J Statist 1998; 26: 95-105.
[29] Lee Y, Nelder JA. Robust design via generalized linear models. J Qual Tech 2003; 35: 2-12.
[30] Das RN, Mukherjee S. Joint Mean-Variance Overall Survival Time fitted Models from Stage III Non-Small Cell Lung Cancer. Epidemiology (Sunnyvale); 2017; 7: 296.
[31] Schimek MG. Estimation and inference in partially linear models with smoothing splines. Journal of Statistical Planning and Inference. 2000; 91 (2): 525-540.
[32] Hastie T, Tibshirani R. Generalized Additive Models, first ed., London: Chapman and Hall 1990.
[33] Mukherjee S, Kapoor S, Banerjee P. Diagnosis and Identification of Risk Factors for Heart Disease Patients Using Generalized Additive Model and Data Mining Techniques. J Cardiovasc Disease Res. 2017; 8 (4): 137-44.
[34] Wood SN. Generalized Additive Models: An Introduction with R. London: Chapman and Hall; 2006.
[35] Currie I, Durban M, Eilers P. Generalized linear array models with applications to Multi-dimensional smoothing. J R Stat Soc B. 2006; 68: 259–280.
[36] Green P, Silverman B. Nonparametric Regression and Generalized Linear Models. London: Chapman & Hall; 1994.
[37] Ruppert D. Selecting the number of knots and for penalized splines. J Comp Graph Stat. 2002; 11: 735-757.
[38] Eilers P, Marx B. Flexible smoothing with B-splines and penalties. StatSci 1996; 11: 89-121.
[39] S. Chatterjee., A. S. Hadi, Regression Analysis by Example, fifth ed. John Wiley & Sons, New Jersey; 2006.
[40] Hastie T, Tibshirani R. Generalized additive models for medical research. Statisti­cal Methods in Medical Research. 1995; 4: 187-196.
[41] Das RN, Mukherjee S, Sharma I. Alkaline Phosphatase Determinants of Liver Patients. JOP. Journal of the Pancreas. 2018; 19 (1): 18-23.
[42] Jr Collins J. W, David R. J, Handler A, Wall S, Andes S. Very Low Birth weight in African American Infants: The Role of Maternal Exposure to Interpersonal Racial Discrimination, American Journal of Public Health. 2004; 94 (12): 2132–2138.
[43] Alexander G. R, Tompkins M. E, Altekruse J. M, Hornung C. A. Racial differences in the relation of birth weight and gestational age to neonatal mortality. Public Health Reports. 1985; 100 (5): 539–547.
[44] Stevenson D, Verter J, Fanaroff A, Oh W, Ehrenkranz R, Shankaran S, Donovan E, Wright L, Lemons J, Tyson J, Korones S, Bauer C, Stoll B, Papile L, Sex differences in outcomes of very low birthweight infants: the newborn male disadvantage, Archives of disease in childhood. Fetal and neonatal edition. 2000; 83 (3): 182–185.
[45] Magee BD, Role of multiple births in very low birth weight and infant mortality, Journal of Reproductive Medicine 2004; 49 (10): 812-816.
[46] Impact of multiple births on low birth weight--Massachusetts, 1989-1996. Centers for Disease Control and Prevention (CDC), MMWR Morbidity and Mortality Weekly Report. 1999; 48 (14): 289-292.
[47] Aly H, Massaro A, Acun C, Ozen M. Pneumothorax in the newborn: clinical presentation, risk factors and outcomes, The Journal of Maternal-Fetal & Neonatal Medicine. 2014; 27 (4): 402-416.
[48] Terzic S, Heljic S, Panic J, Sadikovic M, Maksic H. Pneumothorax in premature infants with respiratory distress syndrome: focus on risk factors. Journal of Pediatric and Neonatal Individualized Medicine. 2016; 5 (1): e050124.
[49] Tin W, Gupta S. Optimum oxygen therapy in preterm babies. Archives of disease in childhood Fetal and neonatal edition. 2007 Mar; 92 (2): F143–F147.
[50] Cummings JJ, Polin RA, COMMITTEE ON FETUS AND NEWBORN. Oxygen Targeting in Extremely Low Birth Weight Infants. Pediatrics. 2016; 138 (2): e20161576.
[51] Rahi E, Baneshi M. R, Mirkamandar E, Maghsoudi S. H, Rastegari A. A Comparison between APGAR Scores and Birth Weight in Infants of Addicted and Non-Addicted Mothers, Addiction & Health 2011; 3 (1): 61-67.
[52] Hegyi T, Carbone T, Anwar M, Ostfeld B, Hiatt M, Koons A, Pinto-Martin J, Paneth N. The Apgar Score and Its Components in the Preterm Infant. Pediatrics, 1998; 101 (1): 77-81.
[53] Behnke M, Carter RL, Hardt NS, Eyler FD, Cruz AC, Resnick MB. The relationship of Apgar scores, gestational age, and birth weight to survival of low-birth weight infants. American journal of perinatology 1987; 4 (2): 121-4.
[54] Gera T, Ramji S. Early Predictors of Mortality in Very Low Birth Weight Neonates, Indian Pediatrics 2001; 38: 596-602.
[55] Wilejto M, Steele M, Jadavji T. Dropping platelet counts in the neonatal intensive care unit – an unsuspected cause for thrombocytopenia in a neonate. Pediatric Child Health. 2011; 16 (9): 557–558.
[56] Lesiński J. Relationship between length of gestation, birth weight and certain other factors: A statistical study. Bulletin of the World Health Organization 1962; 26 (2): 183–191.
[57] Ozdemir O, Tunay Z. O, Acar D. E, Erol M. K, Sener E, Acar U. The relationship of birth weight, gestational age, and postmenstrual age with ocular biometry parameters in premature infants. Arquivos Brasileiros De Oftalmologiavol. 2015; 78 (3): 146-149.
Cite This Article
  • APA Style

    Sabyasachi Mukherjee. (2019). Statistical Modeling for Findings the Determinants of Neonates’ Very Low Birth Weight. American Journal of Clinical and Experimental Medicine, 6(6), 125-135. https://doi.org/10.11648/j.ajcem.20180606.11

    Copy | Download

    ACS Style

    Sabyasachi Mukherjee. Statistical Modeling for Findings the Determinants of Neonates’ Very Low Birth Weight. Am. J. Clin. Exp. Med. 2019, 6(6), 125-135. doi: 10.11648/j.ajcem.20180606.11

    Copy | Download

    AMA Style

    Sabyasachi Mukherjee. Statistical Modeling for Findings the Determinants of Neonates’ Very Low Birth Weight. Am J Clin Exp Med. 2019;6(6):125-135. doi: 10.11648/j.ajcem.20180606.11

    Copy | Download

  • @article{10.11648/j.ajcem.20180606.11,
      author = {Sabyasachi Mukherjee},
      title = {Statistical Modeling for Findings the Determinants of Neonates’ Very Low Birth Weight},
      journal = {American Journal of Clinical and Experimental Medicine},
      volume = {6},
      number = {6},
      pages = {125-135},
      doi = {10.11648/j.ajcem.20180606.11},
      url = {https://doi.org/10.11648/j.ajcem.20180606.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcem.20180606.11},
      abstract = {Aim of the epidemiological research is to identify a causal relationship between the risk factors and the disease. The present study aims to identify the determinates of neonates’ very low birth weight which have significant effects on their birth weight using generalized additive probabilistic modeling. In this present study very low birth weight (BWT) is the response variable with heterogeneity and non-normality in nature which can be modeled through either by the Log-normal or by the gamma models. A well-known method is joint modeling of mean and variance (JGLM) to handle this heterogeneity and non-normality but this study introduced the most advanced regression techniques namely generalized additive model (GAM). Materials and Methods: The present article is based on the secondary data on 174 very low birth weighted neonates along with 26 explanatory factors/ variables. The very low birth weight of 174 neonates is heterogeneous, positive, and gamma distributed. Therefore, generalized additive model with gamma distribution and log link function has been introduced to analyze this very low birth weight. The Native American neonates has the smaller birth weight (BWT) than the other racial neonates namely black, white or oriental (P-value = 0.009). The BWT is smaller for those neonates who were outsiders from Duke (P-value = 0.05) and it also decreases to the female births (P- value=0.09). BWT is higher for the pneumothorax infants than non-pneumothorax with P-value <0.001. It is smaller for those infants for whom oxygen supply had been introduced in between 30 days of his/her birth (P-value=0.002). The neonates who were not been survived they also have the smaller birth weight than the alive neonates (P-value= 0.07). Besides these factors, lowest pH in first 4 days of neonates’ life (P-value=0.09), Apgar score at one minute (P-value=0.03) and the interaction effects of lowest pH and Apgar score (0.03) are the significant variables (continuous cofactors) for neonates’ very low birth weight. Hospital stays in number of days, platelet counts and gestational age in weeks are also highly significant factors and these are entered in the GAM model as a non –parametric smoothing term (P-value<0.001). The birth weight of each new born baby is identified as heterogeneous and gamma distributed (possible). Most of the present findings, especially the Apgar score in one minute, occurrence of pneumothorax in neonates, oxygen supply given to new born baby within 30 days of life and smoothing term determinants namely number of days hospital stay, platelet counts and gestational age in weeks (non-parametric smoothing terms) are significant factors for neonate’s birth weight and are completely new in the literature.},
     year = {2019}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Statistical Modeling for Findings the Determinants of Neonates’ Very Low Birth Weight
    AU  - Sabyasachi Mukherjee
    Y1  - 2019/01/14
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajcem.20180606.11
    DO  - 10.11648/j.ajcem.20180606.11
    T2  - American Journal of Clinical and Experimental Medicine
    JF  - American Journal of Clinical and Experimental Medicine
    JO  - American Journal of Clinical and Experimental Medicine
    SP  - 125
    EP  - 135
    PB  - Science Publishing Group
    SN  - 2330-8133
    UR  - https://doi.org/10.11648/j.ajcem.20180606.11
    AB  - Aim of the epidemiological research is to identify a causal relationship between the risk factors and the disease. The present study aims to identify the determinates of neonates’ very low birth weight which have significant effects on their birth weight using generalized additive probabilistic modeling. In this present study very low birth weight (BWT) is the response variable with heterogeneity and non-normality in nature which can be modeled through either by the Log-normal or by the gamma models. A well-known method is joint modeling of mean and variance (JGLM) to handle this heterogeneity and non-normality but this study introduced the most advanced regression techniques namely generalized additive model (GAM). Materials and Methods: The present article is based on the secondary data on 174 very low birth weighted neonates along with 26 explanatory factors/ variables. The very low birth weight of 174 neonates is heterogeneous, positive, and gamma distributed. Therefore, generalized additive model with gamma distribution and log link function has been introduced to analyze this very low birth weight. The Native American neonates has the smaller birth weight (BWT) than the other racial neonates namely black, white or oriental (P-value = 0.009). The BWT is smaller for those neonates who were outsiders from Duke (P-value = 0.05) and it also decreases to the female births (P- value=0.09). BWT is higher for the pneumothorax infants than non-pneumothorax with P-value <0.001. It is smaller for those infants for whom oxygen supply had been introduced in between 30 days of his/her birth (P-value=0.002). The neonates who were not been survived they also have the smaller birth weight than the alive neonates (P-value= 0.07). Besides these factors, lowest pH in first 4 days of neonates’ life (P-value=0.09), Apgar score at one minute (P-value=0.03) and the interaction effects of lowest pH and Apgar score (0.03) are the significant variables (continuous cofactors) for neonates’ very low birth weight. Hospital stays in number of days, platelet counts and gestational age in weeks are also highly significant factors and these are entered in the GAM model as a non –parametric smoothing term (P-value<0.001). The birth weight of each new born baby is identified as heterogeneous and gamma distributed (possible). Most of the present findings, especially the Apgar score in one minute, occurrence of pneumothorax in neonates, oxygen supply given to new born baby within 30 days of life and smoothing term determinants namely number of days hospital stay, platelet counts and gestational age in weeks (non-parametric smoothing terms) are significant factors for neonate’s birth weight and are completely new in the literature.
    VL  - 6
    IS  - 6
    ER  - 

    Copy | Download

Author Information
  • Department of Mathematics, NSHM Knowledge Campus, Durgapur, India

  • Sections