Analysis of the spatial patterns of malnutrition among women in Nigeria with a Bayesian structured additive model
- 27 Downloads
Malnutrition among women has severe consequences on their health, households and national development. The different forms of malnutrition among adult individuals are severe thinness, underweight, overweight and obesity which are usually assessed using height and weight indices calculated as body mass index (BMI). In this study, the spatial distributions of the various forms of malnutrition measured at the upper and lower tails of the BMI were accessed among women of reproductive age in Nigeria after accounting for the influence of socio-economic factors. Bayesian quantile regression that permits for inference on conditional quantiles, yielding a complete description of the functional changes among covariates at different quantiles of the response variable was adopted. Markov random field and Bayesian P-splines were used as prior distributions for the spatial and nonlinear effects respectively, while computation was based on MCMC approach. Data were derived from the 2013 Nigeria Demographic and Health Survey. Findings indicate the existence of spatial structure in the various forms of malnutrition with neighbouring states sharing similar patterns and that the socio-economic variables exact dissimilar influence on the various forms of malnutrition. The study provides valuable insights for policy makers in the quest for halting all forms of malnutrition among Nigerian women.
KeywordsMalnutrition Spatial structure Nigeria Bayesian method Quantile regression
This work was supported with a grant from UNICEF’s Nigeria Country office but the design, analysis and interpretation of results were carried out by the authors without any role by UNICEF. We appreciate Measure DHS for granting access to the data and useful comments from Elisabeth Waldmann on Bayesian quantile regression.
Compliance with Ethical Standards
Conflict of interest
The authors have no conflict of interest to declare.
The data used in this study was obtained from The DHS Program (https://dhsprogram.com), who was the main partner that conducted the survey, after approval was given to our request.
- Ahmed, T., Hossain, M., & Sanin, K. I. (2011). Maternal obesity: Implications for pregnancy outcome and long-term risks-a link to maternal nutrition. International Journal of Gynecology and Obstetrics, 115(Suppl 1), S6–S10.Google Scholar
- Belitz, C., Brezger, A., Klein, N., Kneib, T., Lang, S., Umlauf, N. (2015). Bayesx—software for Bayesian inference in structured additive regression models, version 3.0.2. http://www.bayesx.org
- Duda, R. B., Darko, R., Saffah, J., Adanu, R. M., Anarfi, J. K., & Hill, A. G. (2007). Prevalence of obsesity among women in Accra, Ghana. African Journal of Health Sciences, 14(3), 154–159.Google Scholar
- Fahrmeir, L., & Kneib, T. (2011). Bayesian smoothing and regression for longitudinal, spatial and event history data. Oxford statistical science series. Oxford: Oxford University Press.Google Scholar
- Kamal, M., & Islam, M. A. (2010). Socio-economic correlates of malnutrition among married women in Bangladesh. Malasia Journal of Nutrition, 16(3), 349–359.Google Scholar
- Kandala, N. B., & Stranges, S. (2014). Geographic variation of overweight and obesity among women in Nigeria: A case for nutritional transition in sub-Saharan Africa. PLoS ONE, 9(e101), 103.Google Scholar
- Letamo, G., & Navaneetham, K. (2014). Prevalence and determinants of adult under-nutrition in Botswana. PLoS ONE, 9(e102), 675.Google Scholar
- NPC, & ICF International. (2014). Nigeria demographic and health survey 2013. National Population Commission [Nigeria] and ICF International, Abuja, Nigeria and Rockville, Maryland, USA.Google Scholar
- Samouda, H., Ruiz-Castell, M., Bocquet, V., Kuemmerle, A., Chioti, A., Dadoun, F., et al. (2018). Geographical variation of overweight, obesity and related risk factors: Findings from the European health examination survey in Luxembourg. PLoS ONE, 13(e0197), 021.Google Scholar
- UNICEF. (2009). Tracking progress on child and maternal nutrition: A survival and development priority. New York: United Nations Children Fund.Google Scholar
- WHO. (2017). Malnutrition: Fact sheet. Geneva: World Health Organization.Google Scholar