American Journal of Theoretical and Applied Statistics
Volume 6, Issue 1, January 2017, Pages: 22-31
Received: Dec. 8, 2016;
Accepted: Dec. 20, 2016;
Published: Jan. 18, 2017
Views 3568 Downloads 125
Nancy Wairimu Gathimba, Department of Statistics and Acturial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Acturial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
George Kipruto Kiplagat, Department of Statistics and Acturial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Levi Mbugua, Department of Statistics and Acturial Sciences, Technical University, Nairobi, Kenya
Koima Kibiwott, Department of Statistics and Acturial Sciences, Kabarak University, Nakuru, Kenya
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.
Nancy Wairimu Gathimba,
George Kipruto Kiplagat,
Modeling Maternal Risk Factors Affecting Low Birth Weight Among Infants in Kenya, American Journal of Theoretical and Applied Statistics.
Vol. 6, No. 1,
2017, pp. 22-31.
Aras R Y (2013). Is maternal age risk factor for low birth weight ?Arch med Health Sci 1: 33-7.
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.
Besag J, Kooperberg C. (1995) On Conditional and Intrinsic autoregression. Biometrika., 82: 733–746. doi: 10.2307/2337341.
Copper RL, Goldenberg RL, Creasy RK, DuBard MB, Davis RO, (1993) A multicenter study of preterm birth weight and gestational age-specific neonatal mortality. 168: 78–84.
Fahrmeir L, Kneib T, Lang S.(2004). Penalized structured additive regression for space-time data: a Bayesian perspective. Statistica Sinica.; 14: 731–761.
Kader M, Tripathi N (2013) Determinants of low birth weight in rural Bangladesh, Int J Reprod Contracept Obstet Gynecol. 2 (2): 130–134.
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.
Kenya National Bureau of Statistics (KNBS) and ICF Macro (2010). Kenya Demographic and Health Survey 2008-09. Calverton, Maryland.
Kneib T, Lang S, Brezger A.(2004) Bayesian semiparametric regression based on mixed model methodology: A tutorial. Munick. University of Munich.
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.
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.
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.
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.
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.
Public Health Service (1998). Progress toward achieving the 1990 objectives for pregnancy and infant health. 37: 405–12.
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.
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).
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.
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.
Stewart PJ, Potter J, Dulberg C, Niday P, Nimrod C. Tawagi G.(1995) Change in smoking prevalence among pregnant women 1982-93. Can J Public Health vol. 86: 37-41.
United Nations Children’s Fund and World Health Organization, (2004) Low Birthweight: Country, regional and global estimates. UNICEF, New York.
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.
Were FN, Mukhwana BO, Musoke RN(2002). Neonatal survival of infants less than 2000g born at Kenyatta National Hospital. East African Medical Journal.;79 (2): 77–9.
World Health Organization (2004) Department of Reproductive Health and Research Avenue Appia 20, 1211 Geneva 27, Switzerland firstname.lastname@example.org www.who.int.
World Health Organization (2005): Low birth weight: country regional and global estimates. WHO publication.
World Health Organization (2014). Global targets 2025. To improve maternal, infant and young child nutrition (www.who.int/nutrition/topics/nutrition globaltargets2025/en/, accessed 17 October 2015).
World Health Organisation (2006) Neonatal and perinatal mortality: country, regional and global estimates. Available: http://whqlibdoc.who.int/publications/2006/9241563206 eng.pdf.