Patterns of Wasting Among Pregnant and Lactating Women in Uganda, 2015-2018: Analysis of Nutrition Surveillance Data

Abstract

Background: Maternal nutrition is closely linked to the survival and development of children during the first 1000 days of life. Maternal wasting, a measure of malnutrition, is measured using the mid-upper arm circumference. However, in 2019, the rate and distribution of wasting among pregnant and lactating women was not known. We described annual trends and distribution of wasting among pregnant and lactating women (PLW), Uganda, 2015-2018, to inform programming on targeted nutritional interventions.Methods: We analyzed nutrition surveillance data from the District Health Information System for all PLW from 2015 to 2018. We used the World Health Organization standard thresholds to determine wasting among PLW by year and region, drawing choropleth maps to demonstrate the geographic distribution of wasting among PLW. We used logistic regression to assess wasting trends. Results: During 2015-2018, 268,636 PLW were wasted (prevalence=5.5%). Of the 15 regions of Uganda, Karamoja (prevalence=21%) and Lango (prevalence=17%) registered the highest prevalence while Toro (prevalence=2.7%) and Kigezi (prevalence=2.0%) registered the lowest prevalence. The national annual prevalence of wasting among PLW declined by 31% from 2015-2018 (OR=0.69, p<0.001). Regions in the north had increasing trends of wasting over the period [Lango (OR=1.6, p<0.001) and Acholi (OR=1.2, p<0.001)], as did regions in the east [(Bugisu (OR=3.4, p<0.001), Bukedi (OR=1.4, p<0.001), and Busoga (OR=1.3, p<0.001)]. The other 11 regions showed declines.Conclusion: The trend of wasting among PLW nationally declined during the study period. Lango and Acholi regions, both of which were experiencing a state of emergency during this period, had both high and rising rates of wasting, as did the Karamoja region, which experienced the highest wasting rates. We recommended that the Ministry of Health increases its focus on nutrition monitoring for PLW and conduct an analysis to clearly identify the factors underlying malnutrition specific for PLW in these regions.