American Journal of Theoretical and Applied Statistics

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Modeling Maternal Risk Factors Affecting Low Birth Weight Among Infants in Kenya

Received: 08 December 2016    Accepted: 20 December 2016    Published: 18 January 2017
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Abstract

The strong association of birth weight with infant mortality is the main focus of birth weight research, with the assumption that birth weight is a major determinant of infant survival. Studies on factors of low birth weight in Kenya have neglected the flexible approach of using smooth functions for some covariates in models. Such flexible approach reveals detailed relationship of covariates with the response. The study sought to investigate risk factors of low birth weight in Kenya by assuming a flexible approach for continuous covariates and geographical random effect. The study used semi parametric models to flexibly model the effects of selected covariates and spatial effects. Our spatial analysis is based on a flexible geo-additive model using the provinces as the geographic unit of analysis, which allows to separate smooth structured spatial effects from random effect. A Gaussian model for birth weight in grams and a binary logistic model for the binary outcome were fitted. Continuous covariates was modeled by the penalized (p) splines and spatial effects was smoothed by the two dimensional p-spline. The specific objectives of the study was to investigate factors of low birth weight in Kenya by taking into account the hierarchical nature of child birth weight factors using a Bayesian hierarchical model. The study used secondary data from Kenya 2014 demographic and health survey (KDHS) data. The study found that child birth order, mother weight and height are significant predictors of birth weight. Secondary education for mother, birth order categories 2-3 and 4-5, wealth index of richer family and mother height were significant predictors of child size at birth. The area associated with low birth weight was North Eastern and areas with increased risk to less than average size at birth were Central and Eastern. The study found support for the flexible modeling of some covariates that clearly have nonlinear influences. Nevertheless there was no strong support for inclusion of geographical spatial analysis. The spatial patterns and the maps generated could be used for targeting development efforts at a glance. These findings have important implications for targeting policy as well as the search for left-out variables that might account for these residual spatial patterns.

DOI 10.11648/j.ajtas.20170601.13
Published in American Journal of Theoretical and Applied Statistics (Volume 6, Issue 1, January 2017)
Page(s) 22-31
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), 2024. Published by Science Publishing Group

Keywords

Low Birth Weight, Geo-additive Models, Spatial Effect, Penalised Splines, Semi Parametric Bayesian Analysis

References
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[2] Back, W, (2010). Low Birth Weight. March of Dimes, Professionals and Researchers. Available at; http://www.marchofdimes.com/professionals/14332-1153.asp Accessed on February 2016.
[3] Besag J, Kooperberg C. (1995) On Conditional and Intrinsic autoregression. Biometrika., 82: 733–746. doi: 10.2307/2337341.
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[6] Kader M, Tripathi N (2013) Determinants of low birth weight in rural Bangladesh, Int J Reprod Contracept Obstet Gynecol. 2 (2): 130–134.
[7] Kenya Demographic and Health Survey 2008-09 (2009). Child Health; Weight and size at birth. Available http://www.measuresdhs.com/pubs/pdf/FR229/FR229.pdf.
[8] Kenya National Bureau of Statistics (KNBS) and ICF Macro (2010). Kenya Demographic and Health Survey 2008-09. Calverton, Maryland.
[9] Kneib T, Lang S, Brezger A.(2004) Bayesian semiparametric regression based on mixed model methodology: A tutorial. Munick. University of Munich.
[10] Mittendorf R, Herschel M, Williams MA, Hibbard JU, Moawad AH, Lee K(1994). Reducing the frequency of low birth weight in the United States. Obstet Gynecol vol. 83: 1056-105.
[11] Ngwira A, Stanley CC (2015) Determinants of Low Birth Weight in Malawi: Bayesian Geo-Additive Modelling. PLoS ONE 10 (6): e0130057. doi: 10.1371/journal. pone.0130057.
[12] Opondo C, Ntoburi S, Wagai J, Wafula J, Wassuna A, Were F, et.al(2009). Are hospitals prepared to support newborn survival?-an evaluation of eight first- level hospitals in Kenya. Tropical Medicine and International Health. 14 (10): 1165–72.
[13] Osei FB, Duker AA, Stern A. (2012)Bayesian structured additive regression modeling of epidemic cholera data: application to cholera. Medical Research Methodology. doi: 10.1186/1471-2288-12-118. pmid:22866662.
[14] Parker J D, Klebanoff M A (2009) Invited commentary: Crossing curves–it's time to focus on gestational age-specific mortality. Am J Epidemiol 169: 798–801. doi: 10.1093/aje/kwp025.
[15] Public Health Service (1998). Progress toward achieving the 1990 objectives for pregnancy and infant health. 37: 405–12.
[16] Pulver LS, Guest-Warnick G, Stoddard GJ, Byington CL, Young PC (2009) Weight for gestational age affects the mortality of late preterm infants. Pediatrics 123: e1072–e1077. doi: 10.1542/peds.2008-3288.
[17] Resolution WHA65.6. Comprehensive implementation plan on maternal, infant and young child nutrition. In: Sixty-fifth World Health Assembly Geneva, 21–26 May 2012. Resolutions and decisions, annexes. Geneva: World Health Organization; 2012: 12–13 (http://www.who.int/nutrition/topics/accessed 17 October 2015).
[18] Rode L, Hegaard HK, Kjaergaard H, Møller LF, Tabor A, Ottesen B.(2007). Association between maternal weight gain and birth weight. pubmed. 09 (6): 1309-15.
[19] Roth, J., & Hendrickson, J. (1998). The risk of teen mothers having low birth weight babies: Implications of recent medical research. Journal of School Health, 68 (7), 271.
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[21] United Nations Children’s Fund and World Health Organization, (2004) Low Birthweight: Country, regional and global estimates. UNICEF, New York.
[22] Wasunna A, Mohammed K (2002) Low birth weight babies: socio demographic and obstetric characteristics of adolescent mothers at Kenyatta National Hospital, Nairobi. East Afr Med J.79: 543-546.
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Author Information
  • Department of Statistics and Acturial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Acturial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Acturial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Acturial Sciences, Technical University, Nairobi, Kenya

  • Department of Statistics and Acturial Sciences, Kabarak University, Nakuru, Kenya

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    Nancy Wairimu Gathimba, Anthony Wanjoya, George Kipruto Kiplagat, Levi Mbugua, Koima Kibiwott. (2017). Modeling Maternal Risk Factors Affecting Low Birth Weight Among Infants in Kenya. American Journal of Theoretical and Applied Statistics, 6(1), 22-31. https://doi.org/10.11648/j.ajtas.20170601.13

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    Nancy Wairimu Gathimba; Anthony Wanjoya; George Kipruto Kiplagat; Levi Mbugua; Koima Kibiwott. Modeling Maternal Risk Factors Affecting Low Birth Weight Among Infants in Kenya. Am. J. Theor. Appl. Stat. 2017, 6(1), 22-31. doi: 10.11648/j.ajtas.20170601.13

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    AMA Style

    Nancy Wairimu Gathimba, Anthony Wanjoya, George Kipruto Kiplagat, Levi Mbugua, Koima Kibiwott. Modeling Maternal Risk Factors Affecting Low Birth Weight Among Infants in Kenya. Am J Theor Appl Stat. 2017;6(1):22-31. doi: 10.11648/j.ajtas.20170601.13

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  • @article{10.11648/j.ajtas.20170601.13,
      author = {Nancy Wairimu Gathimba and Anthony Wanjoya and George Kipruto Kiplagat and Levi Mbugua and Koima Kibiwott},
      title = {Modeling Maternal Risk Factors Affecting Low Birth Weight Among Infants in Kenya},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {6},
      number = {1},
      pages = {22-31},
      doi = {10.11648/j.ajtas.20170601.13},
      url = {https://doi.org/10.11648/j.ajtas.20170601.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20170601.13},
      abstract = {The strong association of birth weight with infant mortality is the main focus of birth weight research, with the assumption that birth weight is a major determinant of infant survival. Studies on factors of low birth weight in Kenya have neglected the flexible approach of using smooth functions for some covariates in models. Such flexible approach reveals detailed relationship of covariates with the response. The study sought to investigate risk factors of low birth weight in Kenya by assuming a flexible approach for continuous covariates and geographical random effect. The study used semi parametric models to flexibly model the effects of selected covariates and spatial effects. Our spatial analysis is based on a flexible geo-additive model using the provinces as the geographic unit of analysis, which allows to separate smooth structured spatial effects from random effect. A Gaussian model for birth weight in grams and a binary logistic model for the binary outcome were fitted. Continuous covariates was modeled by the penalized (p) splines and spatial effects was smoothed by the two dimensional p-spline. The specific objectives of the study was to investigate factors of low birth weight in Kenya by taking into account the hierarchical nature of child birth weight factors using a Bayesian hierarchical model. The study used secondary data from Kenya 2014 demographic and health survey (KDHS) data. The study found that child birth order, mother weight and height are significant predictors of birth weight. Secondary education for mother, birth order categories 2-3 and 4-5, wealth index of richer family and mother height were significant predictors of child size at birth. The area associated with low birth weight was North Eastern and areas with increased risk to less than average size at birth were Central and Eastern. The study found support for the flexible modeling of some covariates that clearly have nonlinear influences. Nevertheless there was no strong support for inclusion of geographical spatial analysis. The spatial patterns and the maps generated could be used for targeting development efforts at a glance. These findings have important implications for targeting policy as well as the search for left-out variables that might account for these residual spatial patterns.},
     year = {2017}
    }
    

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    AB  - The strong association of birth weight with infant mortality is the main focus of birth weight research, with the assumption that birth weight is a major determinant of infant survival. Studies on factors of low birth weight in Kenya have neglected the flexible approach of using smooth functions for some covariates in models. Such flexible approach reveals detailed relationship of covariates with the response. The study sought to investigate risk factors of low birth weight in Kenya by assuming a flexible approach for continuous covariates and geographical random effect. The study used semi parametric models to flexibly model the effects of selected covariates and spatial effects. Our spatial analysis is based on a flexible geo-additive model using the provinces as the geographic unit of analysis, which allows to separate smooth structured spatial effects from random effect. A Gaussian model for birth weight in grams and a binary logistic model for the binary outcome were fitted. Continuous covariates was modeled by the penalized (p) splines and spatial effects was smoothed by the two dimensional p-spline. The specific objectives of the study was to investigate factors of low birth weight in Kenya by taking into account the hierarchical nature of child birth weight factors using a Bayesian hierarchical model. The study used secondary data from Kenya 2014 demographic and health survey (KDHS) data. The study found that child birth order, mother weight and height are significant predictors of birth weight. Secondary education for mother, birth order categories 2-3 and 4-5, wealth index of richer family and mother height were significant predictors of child size at birth. The area associated with low birth weight was North Eastern and areas with increased risk to less than average size at birth were Central and Eastern. The study found support for the flexible modeling of some covariates that clearly have nonlinear influences. Nevertheless there was no strong support for inclusion of geographical spatial analysis. The spatial patterns and the maps generated could be used for targeting development efforts at a glance. These findings have important implications for targeting policy as well as the search for left-out variables that might account for these residual spatial patterns.
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