Factors Associated with Low Birthweight in Low-and-Middle Income Countries in South Asia

Ngan Ngo, Jahar Bhowmik () and Raaj Kishore Biswas
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Ngan Ngo: Department of Health Science and Biostatistics, Swinburne University of Technology, Melbourne, VIC 3150, Australia
Jahar Bhowmik: Department of Health Science and Biostatistics, Swinburne University of Technology, Melbourne, VIC 3150, Australia
Raaj Kishore Biswas: Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia

IJERPH, 2022, vol. 19, issue 21, 1-9

Abstract: Child with Low Birth Weight (LBW) has a higher risk of infant mortality, learning difficulties in childhood due to stunted growth and impaired neurodevelopment, is more likely to develop heart diseases and diabetes in adulthood. This study aimed to evaluate the latest demographic and health surveys (DHSs) across multiple countries in South Asia to determine the factors associated with LBW among these countries. Latest available DHS data across Afghanistan (2015, n = 29,461), Bangladesh (2018, n = 20,127), Nepal (2016, n = 12,862), and Pakistan (2018, n = 15,068) were analysed. Complex survey adjusted generalized linear models were fitted to investigate the association of birth weight with sociodemographic and decision-making factors. Pakistan had the highest proportion of LBW at 18% followed by Afghanistan and Bangladesh at around 14% and Nepal had the lowest (13%). Children born in Pakistan were more likely to have LBW children than Afghanistan (AOR = 2.17, 95% CI = 1.49–3.14). Mothers living in rural areas (AOR = 0.77, 95% CI = 0.61–0.97), with highly educated partners and belonging to richer families were less susceptible to having child with LBW. To reduce 30% LBW in-line with the World Health Organisation’s 2025 goal, policymakers in SA should focus on women in urban areas with low-educated partners belonging to poor households to ease LBW burden.

Keywords: low birth weight; low-and-middle income; South Asia; multivariate binary logistic regression; complex survey (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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