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      Factors associated with undernutrition among 20 to 49 year old women in Uganda: a secondary analysis of the Uganda demographic health survey 2016

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          Abstract

          Background

          Women are at risk of undernutrition due to biological, socio-economic, and cultural factors. Undernourished women have higher risk of poor obstetric outcomes. We aimed to determine the prevalence and factors associated with undernutrition among women of reproductive age in Uganda.

          Methods

          We used Uganda Demographic and Health Survey (UDHS) 2016 data of 4640 women aged 20 to 49 years excluding pregnant and post-menopausal women. Multistage stratified sampling was used to select study participants and data were collected using validated questionnaires. We used multivariable logistic regression to determine factors associated with underweight and stunting among 20 to 49 year old women in Uganda.

          Results

          The prevalence of underweight and stunting were 6.9% (318/4640) and 1.3% (58/4640) respectively. Women who belonged to the poorest wealth quintile (Adjusted Odds Ratio (AOR) 3.60, 95% CI 1.85–7.00) were more likely to be underweight compared to those who belonged to the richest wealth quintile. Women residing in rural areas were less likely to be underweight (AOR 0.63, 95%CI 0.41–0.96) compared to women in urban areas. Women in Western (AOR 0.30, 95% CI 0.20–0.44), Eastern (AOR 0.42, 95% CI 0.28–0.63) and Central regions (AOR 0.42, 95% CI 0.25–0.72) were less likely to be underweight compared to those in the Northern region. Women belonging to Central (AOR 4.37, 95% CI 1.44–13.20) and Western (AOR 4.77, 95% CI 1.28–17.78) regions were more likely to be stunted compared to those in the Northern region.

          Conclusion

          The present study showed wealth index, place of residence and region to be associated with undernutrition among 20 to 49 year old women in Uganda. There is need to address socio-economic determinants of maternal undernutrition mainly poverty and regional inequalities.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12889-020-09775-2.

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          Most cited references35

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          Development of a WHO growth reference for school-aged children and adolescents.

          Mercedes de Onis, Adelheid W Onyango, Elaine Borghi … (2007)
          To construct growth curves for school-aged children and adolescents that accord with the WHO Child Growth Standards for preschool children and the body mass index (BMI) cut-offs for adults. Data from the 1977 National Center for Health Statistics (NCHS)/WHO growth reference (1-24 years) were merged with data from the under-fives growth standards' cross-sectional sample (18-71 months) to smooth the transition between the two samples. State-of-the-art statistical methods used to construct the WHO Child Growth Standards (0-5 years), i.e. the Box-Cox power exponential (BCPE) method with appropriate diagnostic tools for the selection of best models, were applied to this combined sample. The merged data sets resulted in a smooth transition at 5 years for height-for-age, weight-for-age and BMI-for-age. For BMI-for-age across all centiles the magnitude of the difference between the two curves at age 5 years is mostly 0.0 kg/m(2) to 0.1 kg/m(2). At 19 years, the new BMI values at +1 standard deviation (SD) are 25.4 kg/m(2) for boys and 25.0 kg/m(2) for girls. These values are equivalent to the overweight cut-off for adults (> or = 25.0 kg/m(2)). Similarly, the +2 SD value (29.7 kg/m(2) for both sexes) compares closely with the cut-off for obesity (> or = 30.0 kg/m(2)). The new curves are closely aligned with the WHO Child Growth Standards at 5 years, and the recommended adult cut-offs for overweight and obesity at 19 years. They fill the gap in growth curves and provide an appropriate reference for the 5 to 19 years age group.
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            Simulation study of confounder-selection strategies.

            George Maldonado, Sander Greenland (1993)
            In the absence of prior knowledge about population relations, investigators frequently employ a strategy that uses the data to help them decide whether to adjust for a variable. The authors compared the performance of several such strategies for fitting multiplicative Poisson regression models to cohort data: 1) the "change-in-estimate" strategy, in which a variable is controlled if the adjusted and unadjusted estimates differ by some important amount; 2) the "significance-test-of-the-covariate" strategy, in which a variable is controlled if its coefficient is significantly different from zero at some predetermined significance level; 3) the "significance-test-of-the-difference" strategy, which tests the difference between the adjusted and unadjusted exposure coefficients; 4) the "equivalence-test-of-the-difference" strategy, which significance-tests the equivalence of the adjusted and unadjusted exposure coefficients; and 5) a hybrid strategy that takes a weighted average of adjusted and unadjusted estimates. Data were generated from 8,100 population structures at each of several sample sizes. The performance of the different strategies was evaluated by computing bias, mean squared error, and coverage rates of confidence intervals. At least one variation of each strategy that was examined performed acceptably. The change-in-estimate and equivalence-test-of-the-difference strategies performed best when the cut-point for deciding whether crude and adjusted estimates differed by an important amount was set to a low value (10%). The significance test strategies performed best when the alpha level was set to much higher than conventional levels (0.20).
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              The impact of confounder selection criteria on effect estimation.

              R Mickey, S Greenland (1988)
              Much controversy exists regarding proper methods for the selection of variables in confounder control. Many authors condemn any use of significance testing, some encourage such testing, and other propose a mixed approach. This paper presents the results of a Monte Carlo simulation of several confounder selection criteria, including change-in-estimate and collapsibility test criteria. The methods are compared with respect to their impact on inferences regarding the study factor's effect, as measured by test size and power, bias, mean-squared error, and confidence interval coverage rates. In situations in which the best decision (of whether or not to adjust) is not always obvious, the change-in-estimate criterion tends to be superior, though significance testing methods can perform acceptably if their significance levels are set much higher than conventional levels (to values of 0.20 or more).
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                Author and article information

                Contributors
                Quraish Sserwanja:
                ORCID: http://orcid.org/0000-0003-0576-4627
                qura661@gmail.com , q.sserwanja@cuamm.org
                David Mukunya: zebdaevid@gmail.com
                Theogene Habumugisha: habumutheos2020@gmail.com
                Linet M. Mutisya: lin4one@gmail.com
                Robert Tuke: roberttukei2014@gmail.com
                Emmanuel Olal: e.olal@yahoo.com
                Journal
                Journal ID (nlm-ta): BMC Public Health
                Journal ID (iso-abbrev): BMC Public Health
                Title: BMC Public Health
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1471-2458
                Publication date (Electronic): 3 November 2020
                Publication date PMC-release: 3 November 2020
                Publication date Collection: 2020
                Volume: 20
                Electronic Location Identifier: 1644
                Affiliations
                [1 ]Monitoring and Evaluation Department, Doctors with Africa, Juba, South Sudan
                [2 ]GRID grid.489163.1, Department of Research, , Sanyu Africa Research Institute, ; Mbale, Uganda
                [3 ]GRID grid.7914.b, ISNI 0000 0004 1936 7443, Centre for International Health, , University of Bergen, ; Bergen, Norway
                [4 ]GRID grid.8993.b, ISNI 0000 0004 1936 9457, Department of Women and Children’s Health, , Uppsala University, ; Uppsala, Sweden
                [5 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Psychiatry, , Michigan State University, ; East Lansing, USA
                [6 ]Yotkom Medical Centre, Kitgum, Uganda
                Author information
                http://orcid.org/0000-0003-0576-4627
                Article
                Publisher ID: 9775
                DOI: 10.1186/s12889-020-09775-2
                PMC ID: 7607648
                PubMed ID: 33143673
                SO-VID: 080a71d0-c97b-4c2b-8635-1bdb17f35e1f
                Copyright © © The Author(s) 2020
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 13 April 2020
                Date accepted : 26 October 2020
                Categories
                Subject: Research Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2020

                ScienceOpen disciplines: Public health
                Keywords: under nutrition,prevalence,stunting,underweight,socio-economic,women and uganda
                Data availability:
                ScienceOpen disciplines: Public health
                Keywords: under nutrition, prevalence, stunting, underweight, socio-economic, women and uganda

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