Bayesian Spatial Analysis of Risk Factors Affecting Low Birth Weight in Nigeria

Abstract

Low birth weight (LBW), defined by the World Health Organization as a birth weight of less than 2.5 kilograms, is a major public health concern with significant implications for neonatal morbidity, mortality, and long-term health outcomes. LBW prevalence is particularly high in developing countries, contributing to substantial healthcare challenges and socio-economic burdens. This study examines the determinants of LBW in Nigeria, focusing on socio-demographic, economic, and health-related factors. This cross-sectional study utilizes data from the 2018 Nigeria Demographic and Health Survey (NDHS). A stratified two-stage cluster sampling method was employed, and data were collected through structured interviews. The analysis included socio-demographic characteristics, economic status, health factors, and birth weights, which were classified into LBW and normal birth weight categories. Ethical approval was obtained, and informed consent ensured participant confidentiality. The analysis revealed significant associations between LBW and several factors. Higher maternal education levels were linked to lower odds of LBW. Religious affiliation also impacted LBW, with Muslim mothers having a lower likelihood of LBW compared to Christian mothers. Ethnicity influenced LBW outcomes, with Igbo mothers showing higher odds of LBW compared to Yoruba mothers. Economic stability and urban residency were associated with reduced LBW risk. Health factors such as maternal BMI and frequent antenatal visits were protective against LBW. Geographic disparities indicated higher risks in northern Nigeria. The study underscores the multifactorial nature of LBW, highlighting the importance of maternal education, socio-economic support, and healthcare access. Tailored interventions addressing ethnic and religious contexts, along with region-specific strategies, are essential. The Bayesian STAR model's superior performance suggests that spatial and non-parametric considerations provide deeper insights into LBW risk factors. Comprehensive, multifaceted strategies and policies are needed to address the determinants of LBW, focusing on vulnerable populations and regional disparities.