Skip to main content
Download PDF

Factors associated with childhood overweight and obesity in Uganda: a national survey

BMC Public Health volume 21, Article number: 1494 (2021) Cite this article

Abstract

Background

Childhood obesity is an emerging public health problem globally. Although previously a problem of high-income countries, overweight and obesity is on the rise in low- and middle-income countries. This paper explores the factors associated with childhood obesity and overweight in Uganda using data from the Uganda Demographic and Health Survey (UDHS) of 2016.

Methods

We used Uganda Demographic and Health Survey (UDHS) 2016 data of 4338 children less than 5 years. Multistage stratified sampling was used to select study participants and data were collected using validated questionnaires. Overweight and obesity were combined as the primary outcome. Children whose BMI z score was over two were considered as overweight while those with a BMI z score greater than three were considered as obese. We used multivariable logistic regression to determine factors associated with obesity and overweight among children under 5 years of age in Uganda.

Results

The prevalence of overweight and obesity was 5.0% (217/4338) (95% CI: 4.3–5.6), with overweight at 3.9% (168/4338: 95% CI: 3.2–4.3) and obesity at 1.1% (49/4338: 95% CI: 0.8–1.5). Mother’s nutritional status, sex of the child, and child’s age were associated with childhood obesity and overweight. Boys were more likely to be overweight or obese (aOR = 1.81; 95% CI 1.24 to 2.64) compared to girls. Children who were younger (36 months and below) and those with mothers who were overweight or obese were more likely to have obesity or overweight compared to those aged 49–59 months and those with underweight mothers respectively. Children from the western region were more likely to be overweight or obese compared to those that were from the North.

Conclusion

The present study showed male sex, older age of the children, nutritional status of the mothers and region of residence were associated with obesity and overweight among children under 5 years of age.

Peer Review reports

Background

Sub-Sahara Africa is facing an epidemiological transition towards non-communicable diseases (NCDs) such as cardiovascular illnesses and diabetes, with about one third of current disability-adjusted life years (DALYs) attributable to NCDs [1]. The key drivers of the transition are yet to be described, however some documented risk factors include rising socio-economic status, urbanization, physical inactivity, limited and poor dietary choices, cultural misconceptions, and childhood obesity [1,2,3,4].

Childhood obesity is an emerging public health burden. Globally over 38 million children under the age of 5 years are affected [5,6,7]. Children are classified as overweight or obese if their weight-for-height is greater than two or three standard deviations above the age specific median as defined by the World Health Organization (WHO) [7, 8]. Previously, childhood obesity was thought to be a problem of high-income countries, but low- and middle-income countries are now registering higher proportions of overweight and obese children [7, 9]. In the last two decades, Africa has experienced a rise in overweight and obesity among children [7, 10].

Obesity arises from a complex interaction between behavioral, metabolic, environmental, and socioeconomic determinants ranging from changing food systems and reduced physical activity to indiscriminate marketing that promotes obesogenic foods [6, 11,12,13]. In addition, childhood obesity has been attributed to other risk factors such as high birth weight, maternal obesity, maternal smoking, consanguineous marriage, and poor breastfeeding practices [8, 14,15,16]. Similarly, the risk of overweight or obesity has been shown to be more prevalent among children attending affluent primary schools as compared to those in rural public schools [17, 18]. This could partly be explained by limited physical activity, excessive sugar consumption and uncensored food adverts that promote obesogenic diets for children in such schools [12, 15, 19].

Tackling childhood obesity is important because it is associated with unfavorable health and socioeconomic consequences. Excessive weight gain is linked to premature mortality and adverse short term and long-term effects on mental health. Furthermore, overweight and obese children have higher risk of early chronic diseases onset, such as diabetes, dyslipidemias, cardiovascular diseases and some cancers [6, 10, 20]. Additionally, they are reported to have lower educational attainment because of poor psychosocial wellbeing [6, 8], higher costs to health systems, and greater financial burden to households [6].

A plethora of studies from Africa have primarily focused on the study of undernutrition and not overweight or obesity among children. The few studies on obesity have mostly come from Nigeria and South Africa, and almost none from other countries in Sub-Sahara Africa [18]. In Uganda, there has been a steady increase in the prevalence of overweight and obesity in the preceding decade [21, 22]. The prevalence of overweight and obesity being highest in descending order, in the Central, Western, Eastern and Northern Regions, respectively [22]. Nevertheless, the distribution and determinants of obesity were not well articulated. Understanding the distribution and determinants of obesity or overweight is vital in designing public health interventions [10, 17]. Therefore, we aimed to determine the factors associated with childhood obesity and overweight in Uganda using data from the Uganda Demographic and Health Survey (UDHS) of 2016.

Materials and methods

Study design and participants

UDHS 2016 was a nationally representative cross-sectional study conducted using validated questionnaires. UDHS is a periodical survey that is carried out every 5 years as part of the MEASURE DHS global survey and collects Information on demographic, health and nutrition indicators. The survey was conducted between June 2016 and December 2016 using stratified two-stage cluster sampling design that resulted in the random selection of a representative sample of 20,880 households [22, 23]. The households were randomly selected in two stages: clusters (or enumeration areas) were drawn in the first stage and then a count within each cluster led to a list of households from which was conducted a systematic sampling with equal probability [22]. A detailed explanation of the sampling process is available in the UDHS 2016 report [22]. A systematic random draw was conducted amongst the selected households to choose households whose women/ mothers’ and children’s anthropometric measurements (weight and height) were taken. Anthropometric measurements were done on a subsample of about one-third of households [22]. Weight was taken with an electronic SECA 878 flat scale while a Shorr Board® measuring board was used for height [22]. Children less than 24 months were measured lying down.

Our secondary analysis excluded children whose BMI z-score were missing or was recorded as “Flagged cases”. Flagged cases were defined as more than 5 SD above or below the standard population median (Z-scores) based on the WHO Child Growth Standards [24]. In the children’s dataset, a final weighted sample of 4338 was analyzed after excluding flagged cases and those with missing values. Written permission to access the whole UDHS database was obtained through DHS program website at the address https://dhsprogram.com/.

Variables

Dependent variables

The BMI z-scores based on WHO 2006 reference population were used to assess obesity and overweight [25]. Children whose BMI z score was over two were considered as overweight and those with a BMI z score greater than three were considered as obese [25].

Independent variables

Independent variables were categorized into children, parents’ and household characteristics that were chosen basing on previous studies [25,26,27] and availability in the UDHS data base.

Parental characteristics

Maternal nutritional status (underweight defined as body mass index (BMI) less than (<) 18.50 kg/m2, normal between 18.50 kg/m2 and 24.99 kg/m2 and overweight or obesity between 25.0 kg/m2 and above 30.0 kg/m2) [23, 28], mother’s level of education (no education, primary, secondary and tertiary), father’s level of education (no education, primary, secondary and tertiary), mother’s age (15–24, 25–34, 35–49), mother’s marital status (married and not married), mother’s working status (working and not working). Mother’s marital status was excluded from the multivariable model because data on father’s level of education was missing for children whose mothers were not married (separated, widowed and never been in a formal or informal relationship).

Household characteristics

Wealth index is a measure of relative household economic status and was calculated by DHS from information on household asset ownership using Principal Component Analysis (categorized into quintiles: richest, richer, middle poorer and poorest) [22], type of residence (urban and rural), number of household members (less than 5 and 5 and above), sex of household head (female and male) and region (North, East, West and Central).

Children’s characteristics

Age of the child in months (0–12, 13–24, 24–36, 37–48, 49–59), sex of the child (male and female) and stunting status (categorized as stunted and not stunted) defined as height-for-age Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population [22].

Statistical analysis

We used the SPSS analytic software version 25.0 Complex Samples package for this analysis. Weighted data was used to account for the unequal probability sampling in different strata. Frequency distributions were used to describe the background characteristics of the children. Pearson’s chi-squared tests were used to investigate the significant differences between childhood obesity and overweight and the explanatory variables. Bivariable logistic regression was also conducted and we present crude odds ratio (COR), 95% confidence interval (CI) and p-values. Variables included in our multivariable model were determined a priori during literature review [29]. All variables in the model were assessed for collinearity, which was considered present if the variables had a variance inflation factor (VIF) greater than 10. However, none of the factors had a VIF above 3. Sensitivity analyses were done excluding underweight children so that a comparison was made between overweight and obese children and normal weight children. We also conducted sensitivity analyses excluding children less than 2 years since some literature suggests that BMI is not an appropriate index for that age group.

Results

Of the 4338 children, 5.0% (217/4338) (95% CI: 4.3–5.6) were overweight or obese (overweight 3.9% (95% CI: 3.2–4.3) and obesity 1.1% (95% CI: 0.8–1.5). Mean age of the children and that of their mothers was 28.34 months (SD 17.20) and 28.80 years (SD 6.83) respectively. Boys were slightly more than girls. The highest number of children were those less than 1 year. Majority of the children resided in rural areas and belonged to households with five or more members. The mean weight, height, number of household members and BMI z-score were 11.5 kg (SD 3.57), 83.5 cm (SD 14.0), 6 members (SD 2.7) and 0.20 (SD 1.19) respectively. More detailed characteristics of study participants are shown in Table 1.

Table 1 Background characteristics of children under 5 years as per the 2016 UDHS
Full size table

Factors associated with childhood obesity or overweight

Region, BMI status of the mother and sex, stunting status, and age of the child were significant factors in the bivariable analysis. Results from multivariable logistic regression (Table 2) showed that mother’s nutritional status, sex of the child, and child’s age were associated with childhood obesity and overweight. The association with region (western) were imprecisely significant. Boys were more likely to be over nourished (aOR = 1.81; 95% CI 1.24 to 2.64) compared to girls. Children who were younger (36 months and below) and those with mothers who were overweight or obese were more likely to have obesity or overweight compared to those aged 49–59 months and those with underweight mothers respectively. The imprecise association with region showed that children from the western region (aOR = 1.65; 95% CI 0.98 to 2.80) were more likely to be overweight or obese compared to those from the Northern region.

Table 2 Association between different factors and obesity and overweight among under 5 children in Uganda
Full size table

Sensitivity analysis

After excluding underweight children from the analysis sample, the prevalence of overweight or obesity was 4.6% (138/3001) (95% CI: 3.7–5.2) with overweight at 3.7% (95% CI: 2.9–4.2) and obesity 0.9% (95% CI: 0.6–1.3). Only sex and age of the child were the significant factors. The association between sex of the child with being overweight or obese was stronger compared to the primary analysis with boys (AOR: 2.10 (95% CI: 1.32–3.34) being twice more likely to be overweight or obese compared to the girls. Younger children less than 12 months (AOR: 2.31 (95% CI: 1.03–5.18), 13–24 months (AOR: 4.15 (95% CI: 1.87–9.19) and 25–36 months (AOR: 3.37 (95% CI: 1.35–8.40) were more likely to be overweight or obese compared to the older children aged 49–59 months. After excluding children below 24 months, the prevalence of overweight or obesity was 4.0% (99/2487) (95% CI: 3.1–4.7) with overweight at 3.5% (95% CI: 2.7–4.1) and obesity 0.5% (95% CI: 0.3–0.9). Sex and age of the child were still the only factors significantly associated with overweight or obesity. Children less than 36 months (AOR: 3.44 (95% CI: 1,72–6.90) were more likely to be overweight or obese compared to their older counterparts (49–59 months). Boys (AOR: 2.30 (95% CI: 1.38–3.84) were more likely to be overweight or obese compared to girls.

Discussion

This study provides evidence on the factors associated with childhood obesity and overweight in Uganda from the UDHS conducted in 2016. We found that maternal overweight and obesity, age and sex of the child as well as region of residence were associated with the child’s overweight or obesity status. For instance, children of mother’s who were overweight were almost three (2.7) times more likely to be overweight or obese compared to those whose mothers were underweight.

The strong association between maternal obesity and childhood overweight/obesity has also been demonstrated elsewhere. Two previous studies showed similar findings with this study where the risk of childhood obesity was increased in cases of maternal overweight/obesity both before and during pregnancy, which also predisposed them to obesity in later life [30, 31]. This relationship can be attributed to various plausible mechanisms, mainly genetic and environmental factors, and their interactions. For instance, interaction between genetic factors associated with obesity and obesity-promoting environments enhances the effect of these genes leading to overall increase in body weight. Further, changes in environmental conditions that allow a pregnant woman easier access to energy-dense foods, would also alter the expression of genes related to body fatness [31]. With regard to lifestyle, sedentary behavior coupled with low levels of physical activity (which are exacerbated by poor food choices, favoring high calorie density) further increases their likelihood of obesity [32, 33].

This study demonstrated that boys had a statistically significant higher risk of being overweight or obese compared to girls, which is consistent with the findings of Beatrice J et al. [34] and Dubois et al. [35]. In these studies, the authors found that boys were more likely to be overweight or obese compared to girls. The differences between boys and girls could be explained by their genetic differences (boys generally tend to be bigger than girls), metabolism and environmental & behavioral factors partly explain this association.

Children aged 36 months and below were more likely to be overweight or obese compared to those aged between 49 and 59 months. A study conducted in Cameroon found a similar association [25] where children below 49 months of age had higher odds of being overweight or obese compared to their older counterparts. This association was stronger in younger children (less than 37 months) and was weakened in older children. Toddlers and preschoolers’ total diet and activity level play an important role in determining a child’s weight. As such, older children are more active compared to the younger ones, hence expending more energy.

Regional differences in the odds of being overweight or obese were imprecise in this study. However, some pertinent regional differences have been documented that may play a role in the observed differences. The western region of Uganda, for example, receives more rainfall annually, has higher crop yields hence better food security, and has the second highest GDP per capita [36, 37]. These factors may account for the increased risk of overweight and obesity among children. In contrast, Northern Uganda has some of the poorest and most food insecure sub-regions which could partly be attributed to the fact that the region experienced a long civil war which greatly affected agricultural production [23]. Furthermore, most people in the Northern part of Uganda are pastoralists, having a nomadic lifestyle which may negatively affect agricultural production of food crops and subsequent consumption, which substantially narrows their diet choices [23]. The decreased agricultural production have resulted in decreased food availability and access in this region.

Strengths

We used a nationally representative sample with data weighted to account for the unequal probability sampling in different strata, so our results are generalizable to all Ugandan children below the age of 5 years. Secondly, our study was adequately powered to study all these associations because of the large sample size from the UDHS.

Limitations

The lack of other measures of overweight and obesity, such as skin fold thickness measurements, measure of physical activity, analysis of nutritional characteristics such as dietary habits was major limitation. This data/information would have allowed us to assess the development of overweight and obesity. The cross-sectional design is limited by lack of temporality hence causality inferences could not be made. BMI adjusted for-age for children under 2-years is a contested measure because in this age group, BMI is based on Length other than height. BMI for age (calculated with length) in infancy has un unanswered questions however recent studies have found a high level of agreement between BMI for age and length for age and found BMI for age to be more appropriate [38].

Conclusion

This study established maternal nutritional status (BMI), region of residence, sex and age a child as significant determinants of childhood overweight/ obesity. Failure to observe any significant differences between wealth quintiles implies that strategies to improve child nutrition should be applied across all socioeconomic groups. Preventive interventions will also need to consider regional differences in outcomes and context. Efforts should be made to promote healthy weight in mothers across the spectrum of motherhood (prenatal, antenatal, and postnatal), as a healthy mother will likely have a healthy child.

Further studies including nutritional characteristics are needed to understand the association with child age and sex and will help in refining preventive strategies against childhood overweight and obesity in Uganda.

Availability of data and materials

Access to the DHS data sets is openly available upon requests made to MEASURE DHS on their website (https://www.dhsprogram.com/data/available-datasets.cfm).

Abbreviations

EA:

Enumeration area

AOR:

Adjusted Odds Ratio

CI:

Confidence Interval

COR:

Crude Odds Ratio

DHS:

Demographic Health Survey

UDHS:

Uganda Demographic Health Survey

OR:

Odds Ratio

SD:

Standard Deviation

WHO:

World Health Organization

BMI:

Body Mass Index

GDP:

Gross Domestic Product

SPSS:

Statistical Package for Social Science

USAID:

United States Agency for International Development

Kg:

Kilogram

Cm:

Centimeter

References

  1. Gouda HN, Charlson F, Sorsdahl K, Ahmadzada S, Ferrari AJ, Erskine H, et al. Burden of non-communicable diseases in sub-Saharan Africa, 1990-2017: results from the global burden of disease study 2017. Lancet Glob Health. 2019;7(10):e1375–87. https://doi.org/10.1016/S2214-109X(19)30374-2.

    Article  PubMed  Google Scholar 

  2. Ngaruiya C, Hayward A, Post L, Mowafi H. Obesity as a form of malnutrition: over-nutrition on the Uganda “malnutrition” agenda. Pan Afr Med J. 2017;28:49.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Barr AL, Young EH, Smeeth L, Newton R, Seeley J, Ripullone K, et al. The need for an integrated approach for chronic disease research and care in Africa. Glob Health Epidemiol Genom. 2016;1:e19. https://doi.org/10.1017/gheg.2016.16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Nnko S, Bukenya D, Kavishe BB, Biraro S, Peck R, Kapiga S, Grosskurth H, Seeley J. Chronic diseases in North-West Tanzania and Southern Uganda public perceptions of terminologies, aetiologies, symptoms and preferred management. PLoS One. 2015;10(11)e0142194.

  5. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metab Clin Exp. 2019;92:6–10. https://doi.org/10.1016/j.metabol.2018.09.005.

    Article  CAS  PubMed  Google Scholar 

  6. Di Cesare M, Sorić M, Bovet P, Miranda JJ, Bhutta Z, Stevens GA, et al. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med. 2019;17(1):212. https://doi.org/10.1186/s12916-019-1449-8.

    Article  PubMed  PubMed Central  Google Scholar 

  7. World Health Organisation. Noncommunicable diseases: Childhood overweight and obesity. 2020. Available from: https://www.who.int/news-room/q-a-detail/noncommunicable-diseases-childhood-overweight-and-obesity.

    Google Scholar 

  8. Uğraş Dilmen A, Konşuk Ünlü H, Özcebe LH. Evaluation of being overweight/obese and related sociodemographic factors in 0-5 year age group in Turkey: Turkey demographic health survey 2013 advanced analysis. Turk J Med Sci. 2019;49(3):879–87. https://doi.org/10.3906/sag-1808-3.

    Article  PubMed  Google Scholar 

  9. Abarca-Gómez L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, Acuin C, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627–42.

    Article  Google Scholar 

  10. Adom T, Puoane T, De Villiers A, Kengne AP. Prevalence of obesity and overweight in African learners: a protocol for systematic review and meta-analysis. BMJ Open. 2017;7(1):e013538. https://doi.org/10.1136/bmjopen-2016-013538.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Weihrauch-Blüher S, Wiegand S. Risk factors and implications of childhood obesity. Curr Obes Rep. 2018;7(4):254–9. https://doi.org/10.1007/s13679-018-0320-0.

    Article  PubMed  Google Scholar 

  12. Sahoo K, Sahoo B, Choudhury AK, Sofi NY, Kumar R, Bhadoria AS. Childhood obesity: causes and consequences. J Family Med Prim Care. 2015;4(2):187–92. https://doi.org/10.4103/2249-4863.154628.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Arinda IK, Sserwanja Q, Nansubuga S, Mukunya D, Akampereza P. Factors associated with over-nutrition among men 15-54 years in Uganda: a National Survey. Nutr Metabol Insights. 2021;14:11786388211016833.

    Article  Google Scholar 

  14. Marshall NE, Lau B, Purnell JQ, Thornburg KL. Impact of maternal obesity and breastfeeding intention on lactation intensity and duration. Matern Child Nutr. 2019;15(2):e12732. https://doi.org/10.1111/mcn.12732.

    Article  PubMed  Google Scholar 

  15. Navti LK, Ferrari U, Tange E, Bechtold-Dalla Pozza S, Parhofer KG. Contribution of socioeconomic status, stature and birth weight to obesity in sub-Saharan Africa: cross-sectional data from primary school-age children in Cameroon. BMC Public Health. 2014;14(1):320. https://doi.org/10.1186/1471-2458-14-320.

    Article  PubMed  PubMed Central  Google Scholar 

  16. World Health Organization. Taking action on childhood obesity report. World Health Organization; 2018. p. 1–8. Available from: https://www.who.int/end-childhood-obesity/publications/taking-action-childhood-obesity-report/en/%0A.

  17. Tadesse Y, Derso T, Alene KA, Wassie MM. Prevalence and factors associated with overweight and obesity among private kindergarten school children in Bahirdar town, Northwest Ethiopia: cross-sectional study. BMC Res Notes. 2017;10(1):22. https://doi.org/10.1186/s13104-016-2308-8.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Fruhstorfer BH, Mousoulis C, Uthman OA, Robertson W. Socio-economic status and overweight or obesity among school-age children in sub-Saharan Africa - a systematic review. Clin Obes. 2016;6(1):19–32. https://doi.org/10.1111/cob.12130.

    Article  CAS  PubMed  Google Scholar 

  19. Meko LNM, Slabber-Stretch M, Walsh CM, Kruger SH, Nel M. School environment, socioeconomic status and weight of children in Bloemfontein, South Africa. Afr J Prim Health Care Fam Med. 2015;7(1):751.

    Article  PubMed Central  Google Scholar 

  20. Chedjou-Nono E, Sap S, Choukem S-P, Ngosso Tetanye I, Nebongo D, Koki Ndombo O. Cardiometabolic profile of obese children in a sub-Saharan African setting: a cross-sectional study. BMC Pediatr. 2017;17(1):129. https://doi.org/10.1186/s12887-017-0880-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kirunda BE, Fadnes LT, Wamani H, Van den Broeck J, Tylleskär T. Population-based survey of overweight and obesity and the associated factors in peri-urban and rural eastern Uganda. BMC Public Health. 2015;15(1):1168. https://doi.org/10.1186/s12889-015-2506-7.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Uganda Bureau of Statistics - UBOS, ICF. Uganda Demographic and Health Survey 2016. Kampala: UBOS and ICF; 2018.

    Google Scholar 

  23. Sserwanja Q, Mukunya D, Habumugisha T, Mutisya LM, Tuke R, Olal E. Factors associated with undernutrition among 20 to 49 year old women in Uganda: a secondary analysis of the Uganda demographic health survey 2016. BMC Public Health. 2020;20(1):1644. https://doi.org/10.1186/s12889-020-09775-2.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zambia Statistics Agency - ZSA, Ministry of Health - MOH, University Teaching Hospital Virology Laboratory - UTH-VL, ICF. Zambia Demographic and Health Survey 2018. Lusaka: ZSA, MOH, UTH-VL and ICF; 2020.

    Google Scholar 

  25. Tchoubi S, Sobngwi-Tambekou J, Noubiap JJ, Asangbeh SL, Nkoum BA, Sobngwi E. Prevalence and risk factors of overweight and obesity among children aged 6-59 months in Cameroon: a multistage, stratified cluster sampling Nationwide survey. PLoS One. 2015;10(12):e0143215. https://doi.org/10.1371/journal.pone.0143215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Muthuri SK, Francis CE, Wachira LJ, Leblanc AG, Sampson M, Onywera VO, et al. Evidence of an overweight/obesity transition among school-aged children and youth in sub-Saharan Africa: a systematic review. PLoS One. 2014;9(3):e92846. https://doi.org/10.1371/journal.pone.0092846.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Danquah FI, Ansu-Mensah M, Bawontuo V, Yeboah M, Udoh RH, Tahiru M, et al. Risk factors and morbidities associated with childhood obesity in sub-Saharan Africa: a systematic scoping review. BMC Nutr. 2020;6(1):37. https://doi.org/10.1186/s40795-020-00364-5.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Sserwanja Q, Kawuki J. Prevalence of underweight and associated factors among lactating women in Ethiopia: a mini-review. J Adv Med Med Res. 2020;32(8):1–9. https://doi.org/10.9734/jammr/2020/v32i830459.

    Article  Google Scholar 

  29. Lederer DJ, Bell SC. Control of confounding and reporting of results in causal inference studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals. Ann Am Thorac Soc. 2019;16(1):22–8.

    Article  PubMed  Google Scholar 

  30. Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, et al. Maternalandchild undernutritionandoverweightinlow-incomeandmiddle-incomecountries. Lancet. 2013;382(9890):427–51. https://doi.org/10.1016/S0140-6736(13)60937-XPMID:23746772.

    Article  PubMed  Google Scholar 

  31. Modi N, Murgasova D, Ruager-Martin R, Thomas EL, Hyde MJ, Gale C, et al. The influence of maternal body mass index on infant adiposity and hepatic lipid content. Pediatr Res. 2011;70(3):287–91. https://doi.org/10.1203/PDR.0b013e318225f9b1.

    Article  PubMed  Google Scholar 

  32. Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M. Obesity and socioeconomic status in developing countries: a systematic review. Obes Rev. 2012;13(11):1067–79. https://doi.org/10.1111/j.1467-789X.2012.01017.x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Saunders T. Potential contributors to the Canadian pediatric obesity epidemic. ISRN Pediatr. 2011;2011:917684.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Jouret B, Ahluwalia N, Cristini C, Dupuy M, Nègre-Pages L, Grandjean H, et al. Factors associated with overweight in preschool-age children in southwestern France. Am J Clin Nutr. 2007;85(6):1643–9. https://doi.org/10.1093/ajcn/85.6.1643.

    Article  CAS  PubMed  Google Scholar 

  35. Dubois L, Girard M. Early determinants of overweight at 4.5 years in a population-based longitudinal study. Int J Obes. 2006;30(4):610–7.

    Article  CAS  Google Scholar 

  36. Moyer JD, Rafa M, Sutton P, Wang X. Estimating district GDP in Uganda. In: Invited Research Paper for USAID. Denver: The Frederick S. Pardee Center for International Futures, University of Denver; 2017. https://pardee.du.edu/estimating-district-gdp-uganda.

    Google Scholar 

  37. Turi KN, Christoph MJ, Grigsby-Toussaint DS. Spatial distribution of underweight, overweight and obesity among women and children: results from the 2011 Uganda demographic and health survey. Int J Environ Res Public Health. 2013;10(10):4967–81. https://doi.org/10.3390/ijerph10104967.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Furlong KR, Anderson LN, Kang H, Lebovic G, Parkin PC, Maguire JL, et al. BMI-for-age and weight-for-length in children 0 to 2 years. Pediatrics. 2016;138(1). https://doi.org/10.1542/peds.2015-3809.

  39. Nantale G, Tumwesigye NM, Kiwanuka N, Kajjura R. Prevalence and factors associated with food insecurity among women aged 18-49 years in Kampala slums Uganda; a mixed methods study. J Food Secur. 2017;5(4):120–8. https://doi.org/10.12691/jfs-5-4-2.

    Article  Google Scholar 

  40. Okot-Okumu J, Oosterveer P. Providing Sanitation for the Urban Poor in Uganda. In: van Vliet B, Spaargaren G, Oosterveer P, editors. Social Perspectives on the Sanitation Challenge. Dordrecht: Springer Netherlands; 2010. p. 49–66.

    Chapter  Google Scholar 

  41. Namy S, Carlson C, Norcini Pala A, Faris D, Knight L, Allen E, et al. Gender, violence and resilience among Ugandan adolescents. Child Abuse Negl. 2017;70:303–14. https://doi.org/10.1016/j.chiabu.2017.06.015.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the MEASURE DHS program for availing us with the data.

Funding

No funding was obtained for this study.

Author information

Author notes

  1. Quraish Sserwanja and Linet M. Mutisya are co-first authors.

Authors and Affiliations

  1. Programs Department, GOAL, Arkaweet Block 65 House No, 227, Khartoum, Sudan

    Quraish Sserwanja

  2. Maternal and Child Health Project, Swedish Organization for Global Health, Mayuge, Uganda

    Linet M. Mutisya

  3. Yotkom Medical Centre, Kitgum, Uganda

    Emmanuel Olal

  4. Department of Obstetrics and Gynaecology, Busitema University, Mbale, Uganda

    Milton W. Musaba

  5. Department of Community and Public Health, Busitema University, Mbale, Uganda

    David Mukunya

  6. Sanyu Africa Research Institute, Mbale, Uganda

    David Mukunya

Authors
  1. Quraish Sserwanja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Linet M. Mutisya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emmanuel Olal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Milton W. Musaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Mukunya
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QS conceived the idea, drafted the manuscript, performed analysis and interpreted the results. LMM participated in drafting and reviewing the first version of the manuscript, interpretation of results and drafted the subsequent versions of the manuscript. DM and MWM participated in the design of the study and helped in results interpretation and writing. EO participated in drafting and reviewing the first version of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Quraish Sserwanja.

Ethics declarations

Ethics approval and consent to participate

High international ethical standards are ensured for MEASURE DHS surveys as ethical approval from the country is obtained from a national ethical review board and local authorities before implementing the survey [39, 40] and well-informed verbal consent is sought from the respondents prior to data collection [40, 41]. This data set was obtained from the MEASURE DHS website (https://www.dhsprogram.com/data/available-datasets.cfm) after getting their permission and no formal ethical clearance was obtained since we conducted secondary analysis of publicly available data.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This 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.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sserwanja, Q., Mutisya, L.M., Olal, E. et al. Factors associated with childhood overweight and obesity in Uganda: a national survey. BMC Public Health 21, 1494 (2021). https://doi.org/10.1186/s12889-021-11567-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-021-11567-1

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us