Abstract
Being overweight and obesity are emerging public health issues in sub-Saharan Africa. Currently, there is limited knowledge on the temporal trend of the effect of socioeconomic factors and air quality on being overweight or obesity. Using data from the Ugandan Demographic and Health Survey and NASA’s Socioeconomic Data and Applications Center (SEDAC), we examined the spatio-temporal effect of individual and contextual factors on overweight and obesity among women in Uganda using cross-sectional data on 15,655 women in Uganda. We employed multilevel mixed-effect analysis and Bayesian hierarchical spatial models to examine the effect of individual socioeconomic status, contextual socioeconomic factors and air quality on women’s risk of being overweight or obese as well as investigate spatial heterogeneity in the association. The prevalence of overweight/obesity for the study periods were 17.23% (2000/2001), 15.36% (2006), 19.36% (2011) and 21.93% (2016). The result from the multilevel analysis shows change in the directions of the association between individual factors (educational status and household wealth) and overweight or obese over the years. Women with secondary education were 1.514 times (p = 0.002) more likely to be overweight or obese in the 2000/2001 group but 0.655 times (p = 0.007) less likely to be overweight or obese in the 2016 group. It also reveals temporal consistency in the effect of the air pollutant PM2.5 on overweight or obese. The spatial models reveal spatial heterogeneity in the association between district-level factors and the proportion of overweight or obese women. The findings suggest improving women’s socioeconomic status and air quality could reduce the rising obesity epidemic in Ugandan women.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The Ugandan survey data is available via the Demographic and Health Survey program data portal (https://dhsprogram.com/Data/). Interested parties have to register on the portal for the data. We do not have permission to share this data. The aggregate data used for the Bayesian spatial and spatio-temporal hierarchical modelling are available upon request. PM2.5 dataset is available on NASA’s Socioeconomic Data and Applications Center (SEDAC) (https://sedac.ciesin.columbia.edu/data/sets/browse). Again, interested parties have to register to access the data and the authors do not have permission to share the data.
Code availability
The code for the research is available upon request.
References
Abrahams, Z., McHiza, Z., & Steyn, N. P. (2011). Diet and mortality rates in Sub-Saharan Africa: Stages in the nutrition transition. BMC Public Health. https://doi.org/10.1186/1471-2458-11-801
Agyemang, C., Boatemaa, S., Frempong, G. A., & de-Graft Aikins, A. (2015). Obesity in sub-Saharan Africa. In R. S. Ahema (Ed.), Metabolic syndrome: A comprehensive textbook (pp. 41–53). Springer. https://doi.org/10.1007/978-3-319-12125-3_5-1
Ahmed, K. Y., Rwabilimbo, A. G., Abrha, S., Page, A., Arora, A., Tadese, F., et al. (2020). Factors associated with underweight, overweight, and obesity in reproductive age Tanzanian women. PLoS ONE, 15(8), e0237720. https://doi.org/10.1371/journal.pone.0237720
Amoah, A. G. (2003). Sociodemographic variations in obesity among Ghanaian adults. Public Health Nutrition, 6(8), 751–757. https://doi.org/10.1079/phn2003506
Aryeetey, E. (2015). The informal economy, economic growth, and poverty in sub-Saharan Africa. In A. McKay & E. Thorbecke (Eds.), Economic Growth and Poverty Reduction in Sub-Saharan Africa: Current and Emerging Issues (pp. 583–605). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198728450.003.0006
Astell-Burt, T., Feng, X., & Kolt, G. S. (2013). Mental health benefits of neighbourhood green space are stronger among physically active adults in middle-to-older age: Evidence from 260,061 Australians. Preventive Medicine, 57(5), 601–606. https://doi.org/10.1016/j.ypmed.2013.08.017
Auma, C. I., Pradeilles, R., Blake, M. K., & Holdsworth, M. (2019). What can dietary patterns tell us about the nutrition transition and environmental sustainability of diets in Uganda? Nutrients, 11(2), 342. https://doi.org/10.3390/nu11020342
Auma, C. I., Pradeilles, R., Blake, M. K., Musoke, D., & Holdsworth, M. (2020). Factors influencing dietary practices in a transitioning food environment: A cross-sectional exploration of four dietary typologies among rural and urban Ugandan women using Photovoice. Nutrition Journal, 19(1), 1–15. https://doi.org/10.1186/s12937-020-00634-9
Bakka, H., Fuglstad, G., Riebler, A., Bolin, D., Krainski, E., Simpson, D., & Lindgren, F. (2018). Spatial modelling with R-INLA: A review. WIREs Computational Statistics, 10, e1443.
Bissell, P., Peacock, M., Blackburn, J., & Smith, C. (2016). The discordant pleasures of everyday eating: Reflections on the social gradient in obesity under neo-liberalism. Social Science and Medicine, 159, 14–21. https://doi.org/10.1016/j.socscimed.2016.04.026
Cakmak, S., Dales, R., Leech, J., & Liu, L. (2011). The influence of air pollution on cardiovascular and pulmonary function and exercise capacity: Canadian Health Measures Survey (CHMS). Environmental Research, 111(8), 1309–1312. https://doi.org/10.1016/j.envres.2011.09.016
Chami, N., Preuss, M., Walker, R. W., Moscati, A., & Loos, R. J. F. (2020). The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK Biobank population. PLoS Medicine, 17(7), 1–20. https://doi.org/10.1371/journal.pmed.1003196
Cohen, A. K., Rai, M., Rehkopf, D. H., & Abrams, B. (2013). Educational attainment and obesity: A systematic review. Obesity Reviews, 14, 989–1005. https://doi.org/10.1111/obr.12062
Cornicelli, J. A. (2015). Gene-environment interactions in obesity. RSC Drug Discovery Series, 2015-Janua(45), 66–89. https://doi.org/10.1039/9781782622390-00066
Dai, H., Alsalhe, T. A., Chalghaf, N., Riccò, M., Bragazzi, N. L., & Wu, J. (2020). The global burden of disease attributable to high body mass index in 195 countries and territories, 1990–2017: An analysis of the Global Burden of Disease Study. PLoS Medicine, 17(7), e1003198. https://doi.org/10.1371/journal.pmed.1003198
Dinsa, G. D., Goryakin, Y., Fumagalli, E., & Suhrcke, M. (2012). Obesity and socioeconomic status in developing countries: A systematic review. Obesity Reviews, 13(11), 1067–1079. https://doi.org/10.1111/j.1467-789X.2012.01017.x
Doku, D. T., & Neupane, S. (2015). Double burden of malnutrition: Increasing overweight and obesity and stall underweight trends among Ghanaian women. BMC Public Health, 15(1), 1–9. https://doi.org/10.1186/s12889-015-2033-6
Drewnowski, A., Rehm, C. D., & Arterburn, D. (2014). The geographic distribution of obesity by census tract among 59 767 insured adults in King County, WA. International Journal of Obesity, 38(6), 833–839. https://doi.org/10.1038/ijo.2013.179
Ellaway, A., Benzeval, M., Green, M., Leyland, A., & MacIntyre, S. (2012). “Getting sicker quicker”: Does living in a more deprived neighbourhood mean your health deteriorates faster? Health and Place, 18(2), 132–137. https://doi.org/10.1016/j.healthplace.2011.08.005
Flowerdew, R., Manley, D. J., & Sabel, C. E. (2008). Neighbourhood effects on health: Does it matter where you draw the boundaries? Social Science and Medicine, 66(6), 1241–1255. https://doi.org/10.1016/j.socscimed.2007.11.042
Furlong, M. A., & Klimentidis, Y. C. (2020). Associations of air pollution with obesity and body fat percentage, and modification by polygenic risk score for BMI in the UK Biobank. Environmental Research, 185(March), 109364. https://doi.org/10.1016/j.envres.2020.109364
Hedeker, D. (2008). Multilevel models for ordinal and nominal variables. In J. de Leeuw & E. Meijer (Eds.), Handbook of multilevel analysis (1st ed., pp. 237–274). Springer. https://doi.org/10.1007/978-0-387-73186-5_6
Heft-Neal, S., Burney, J., Bendavid, E., & Burke, M. (2018). Robust relationship between air quality and infant mortality in Africa. Nature, 559(7713), 254–258. https://doi.org/10.1038/s41586-018-0263-3.
Hinney, A., Volckmar, A. L., & Antel, J. (2014). Genes and the hypothalamic control of metabolism in humans. Best Practice and Research: Clinical Endocrinology and Metabolism, 28(5), 635–647. https://doi.org/10.1016/j.beem.2014.04.007
Isabirye, N., Bukenya, J. N., Nakafeero, M., Ssekamatte, T., Guwatudde, D., & Fawzi, W. (2020). Dietary diversity and associated factors among adolescents in eastern Uganda: A cross-sectional study. BMC Public Health, 20(1), 4–11. https://doi.org/10.1186/s12889-020-08669-7
Jimenez, M. P., Wellenius, G. A., Subramanian, S. V., Buka, S., Eaton, C., Gilman, S. E., & Loucks, E. B. (2019). Longitudinal associations of neighborhood socioeconomic status with cardiovascular risk factors: A 46-year follow-up study. Social Science and Medicine, 241(September), 112574. https://doi.org/10.1016/j.socscimed.2019.112574
Kabwama, S. N., Kirunda, B., Mutungi, G., Wesonga, R., Bahendeka, S. K., & Guwatudde, D. (2018). Prevalence and correlates of abdominal obesity among adults in Uganda: Findings from a national cross-sectional, population based survey 2014. BMC Obesity, 5(1), 1–9. https://doi.org/10.1186/s40608-018-0217-1
Kershaw, K. N., Albrecht, S. S., & Carnethon, M. R. (2013). Racial and ethnic residential segregation, the neighborhood socioeconomic environment, and obesity among blacks and Mexican Americans. American Journal of Epidemiology, 177(4), 299–309. https://doi.org/10.1093/aje/kws372
Khera, A. V., Chaffin, M., Wade, K. H., Zahid, S., Brancale, J., Xia, R., et al. (2019). Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell, 177(3), 587-596.e9. https://doi.org/10.1016/j.cell.2019.03.028
Kim, J. S., Chen, Z., Alderete, T. L., Toledo-Corral, C., Lurmann, F., Berhane, K., & Gilliland, F. D. (2019). Associations of air pollution, obesity and cardiometabolic health in young adults: The Meta-AIR study. Environment International, 133(June), 105180. https://doi.org/10.1016/j.envint.2019.105180
Kirunda, B. E., Fadnes, L. T., Wamani, H., Van den Broeck, J., & Tylleskär, T. (2015). Population-based survey of overweight and obesity and the associated factors in peri-urban and rural Eastern Uganda. BMC Public Health, 15, 1168. https://doi.org/10.1186/s12889-015-2506-7
Lin, W. Y., Huang, C. C., Liu, Y. L., Tsai, S. J., & Kuo, P. H. (2019). Polygenic approaches to detect gene-environment interactions when external information is unavailable. Briefings in Bioinformatics, 20(6), 2236–2252. https://doi.org/10.1093/bib/bby086
Lindgren, F., & Rue, H. (2015). Bayesian spatial modelling with R-INLA. Journal of Statistical Software, 63(19), 1–25.
Lindgren, F., Rue, H., & Lindström, J. (2011). An explicit link between Gaussian fields and Gaussian Markov random fields: The stochastic partial differential equation approach. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 73(4), 423–498.
Mackenbach, J. P., Cavelaars, A. E. J. M., Kunst, A. E., Groenhof, F., Andersen, O., Borgan, J. K., et al. (2000). Socioeconomic inequalities in cardiovascular disease mortality. An international study. European Heart Journal, 21(14), 1141–1151. https://doi.org/10.1053/euhj.1999.1990
Magnusson, M., Sørensen, T. I. A., Olafsdottir, S., Lehtinen-Jacks, S., Holmen, T. L., Heitmann, B. L., & Lissner, L. (2014). Social inequalities in obesity persist in the Nordic region despite its relative affluence and equity. Current Obesity Reports, 3(1), 1–15. https://doi.org/10.1007/s13679-013-0087-2
Martins, T. G., Simpson, D., Lindgren, F., & Rue, H. (2013). Bayesian computing with INLA: New features. Computational Statistics and Data Analysis, 67, 68–83. https://doi.org/10.1016/j.csda.2013.04.014
McCormack, G. R., Blackstaffe, A., Nettel-Aguirre, A., Csizmadi, I., Sandalack, B., Uribe, F. A., et al. (2018). The independent associations betweenwalk score® and neighborhood socioeconomic status, waist circumference, waist-to-hip ratio and body mass index among urban adults. International Journal of Environmental Research and Public Health, 15(6), 1226. https://doi.org/10.3390/ijerph15061226
Micklesfield, L. K., Lambert, E. V., Hume, D. J., Chantler, S., Pienaar, P. R., Dickie, K., et al. (2013). Socio-cultural, environmental and behavioural determinants of obesity in black South African women. Cardiovascular Journal of Africa, 24(9/10), 369–375. https://doi.org/10.5830/CVJA-2013-069
Mitchell, R. J., Richardson, E. A., Shortt, N. K., & Pearce, J. R. (2015). Neighborhood environments and socioeconomic inequalities in mental well-being. American Journal of Preventive Medicine, 49(1), 80–84. https://doi.org/10.1016/j.amepre.2015.01.017
Mndala, L., & Kudale, A. (2019). Distribution and social determinants of overweight and obesity: A cross-sectional study of non-pregnant adult women from the Malawi Demographic and Health Survey (2015–2016). Epidemiology and Health, 41, 1–10. https://doi.org/10.4178/epih.e2019039
Moise, I. K., Kangmennaang, J., Halwiindi, H., Grigsby-Toussaint, D. S., & Fuller, D. O. (2019). Increase in obesity among women of reproductive age in Zambia, 2002–2014. Journal of Women’s Health, 28(12), 1679–1687. https://doi.org/10.1089/jwh.2018.7577
Monteiro, C. A., Conde, W. L., Lu, B., & Popkin, B. M. (2004). Obesity and inequities in health in the developing world. International Journal of Obesity, 28(9), 1181–1186. https://doi.org/10.1038/sj.ijo.0802716
NCD Risk Factor Collaboration (NCD-RisC). (2016). Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19.2 million participants. The Lancet, 387(10026), 1377–1396. https://doi.org/10.1016/S0140-6736(16)30054-X
Neupane, S., Prakash, K. C., & Doku, D. T. (2016). Overweight and obesity among women: Analysis of demographic and health survey data from 32 Sub-Saharan African Countries. BMC Public Health. https://doi.org/10.1186/s12889-016-2698-5
Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., et al. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: A systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 384(9945), 766–781. https://doi.org/10.1016/S0140-6736(14)60460-8
Nyberg, S. T., Batty, G. D., Pentti, J., Virtanen, M., Alfredsson, L., Fransson, E. I., et al. (2018). Obesity and loss of disease-free years owing to major non-communicable diseases: A multicohort study. The Lancet Public Health, 3(10), e490–e497. https://doi.org/10.1016/S2468-2667(18)30139-7
Ofori-Asenso, R., Agyeman, A. A., Laar, A., & Boateng, D. (2016). Overweight and obesity epidemic in Ghana—A systematic review and meta-analysis. BMC Public Health, 16(1), 1–8. https://doi.org/10.1186/s12889-016-3901-4
R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/.
Renzaho, M. N. (2004). Fat, rich and beautiful: Changing socio-cultural paradigms associated with obesity risk, nutritional status and refugee children from sub-Saharan Africa. Health & Place, 10, 105–113. https://doi.org/10.1016/S1353-8292(03)00051-0
Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P., & Lindgren, F. K. (2017). Bayesian computing with INLA: A review. Annual Reviews of Statistics and Its Applications, 4, 395–421. http://arxiv.org/abs/1604.00860
Roberts, J. D., Voss, J. D., & Knight, B. (2014). The association of ambient air pollution and physical inactivity in the United States. PLoS ONE, 9(3), e90143. https://doi.org/10.1371/journal.pone.0090143
Rodríguez, G., & Elo, I. (2003). Intra-class correlation in random-effects models. The Stata Journal, 3(1), 32–46. https://doi.org/10.1177/1536867X0300300102
Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 71(2), 319–392. https://doi.org/10.1111/j.1467-9868.2008.00700.x
Steyn, N. P., & Mchiza, Z. J. (2014). Obesity and the nutrition transition in sub-Saharan Africa. Annals of the New York Academy of Sciences, 1311(1), 88–101. https://doi.org/10.1111/nyas.12433
Steyn, N. P., Parker, W., Nel, J. H., Ayah, R., & Mbithe, D. (2012). Urbanisation and the nutrition transition: A comparison of diet and weight status of South African and Kenyan women. Scandinavian Journal of Public Health, 40(3), 229–238. https://doi.org/10.1177/1403494812443605
Subramanian, S. V., Jones, K., & Duncan, C. (2003). Multilevel Methods for Public Health Research. In I. Kawachi & L. F. Berkman (Eds.), Neighborhoods and Health (pp. 583–605). Oxford University Press. https://doi.org/10.1093/acprof
Subramanian, S. V., Kim, D. J., & Kawachi, I. (2002). Social trust and self-rated health in US communities: A multilevel analysis. Journal of Urban Health, 79(4 SUPPL. 1), S21–S34. https://doi.org/10.1093/jurban/79.suppl_1.s21
Subramanian, S. V. (2010). Multilevel modeling. In M. M. Fischer & A. Getis (Eds.), Handbook of applied spatial analysis: Software tools, methods and applications (pp. 507–525). Berlin: Springer. https://doi.org/10.1007/978-3-642-03647-7
Taylor, B. V., Palmer, A., Simpson, S., Lucas, R., Simmons, R. D., Mason, D., et al. (2013). Assessing possible selection bias in a national voluntary MS longitudinal study in Australia. Multiple Sclerosis Journal, 19(12), 1627–1631. https://doi.org/10.1177/1352458513481511
Templin, T., Hashiguchi, T. C. O., Thomson, B., Dieleman, J., & Bendavid, E. (2019). The overweight and obesity transition from the wealthy to the poor in low- and middleincome countries: A survey of household data from 103 countries. PLoS Medicine, 16(11), 1–15. https://doi.org/10.1371/journal.pmed.1002968
Tennekes, M. (2018). tmap: Thematic maps in R. Journal of Statistical Software, 84(6), 1–39. https://doi.org/10.18637/jss.v084.i06
FAO, IFAD, UNICEF, WFP and WHO. (2019). The State of Food Security and Nutrition in the World 2019: Safeguarding against economic slowdowns and downturns. FAO, Rome.
Thomson, E. M. (2014). Neurobehavioral and metabolic impacts of inhaled pollutants. Endocrine Disruptors, 1(1), e27489. https://doi.org/10.4161/endo.27489
Turi, K. N., Christoph, M. J., & Grigsby-toussaint, D. S. (2013). Spatial distribution of underweight, overweight and obesity among women and children: Results from the 2011 Uganda demographic and health survey. International Journal of Environmental Research and Public Health, 10, 4967–4981. https://doi.org/10.3390/ijerph10104967
UBOS & ICF. (2016). Uganda demographic and health survey 2016. Kampala, Uganda and Rockville, Maryland, USA. www.DHSprogram.com.
Van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., et al. (2016). Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environmental Science & Technology. https://doi.org/10.1021/acs.est.5b05833.
Van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., et al. (2018). Global Annual PM2.5 Grids from MODIS, MISR and Sea WiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS.
Wei, Y., Zhang, J., Li, Z., Gow, A., Chung, K. F., Hu, M., et al. (2016). Chronic exposure to air pollution particles increases the risk of obesity and metabolic syndrome: Findings from a natural experiment in Beijing. The FASEB Journal, 30(6), 2115–2122. https://doi.org/10.1096/fj.201500142
Wheeler, B. W., Lovell, R., Higgins, S. L., White, M. P., Alcock, I., Osborne, N. J., et al. (2015). Beyond greenspace: An ecological study of population general health and indicators of natural environment type and quality. International Journal of Health Geographics, 14(1), 17.
Wojcicki, J. M. (2014). The double burden household in sub-Saharan Africa: Maternal overweight and obesity and childhood undernutrition from the year 2000: Results from World Health Organization Data (WHO) and Demographic Health Surveys (DHS). BMC Public Health, 14, 1124. https://doi.org/10.1186/1471-2458-14-1124
Wood, L., Hooper, P., Foster, S., & Bull, F. (2017). Public green spaces and positive mental health—Investigating the relationship between access, quantity and types of parks and mental wellbeing. Health and Place, 48(November 2016), 63–71. https://doi.org/10.1016/j.healthplace.2017.09.002
World Bank. (2016). The Uganda Poverty Assessment Report 2016—Farms, cities and good fortune: assessing poverty reduction in Uganda from 2006 to 2013. Washington, D.C. www.worldbankgroup.org.
World Health Organization. (1995). Physical status: the use of and interpretation of anthropometry, report of a WHO expert committee. Geneva, Switzerland. https://apps.who.int/iris/handle/10665/37003.
World Health Organization. (2018a). Noncommunicable diseases country profiles 2018. Geneva.
World Health Organization. (2018b). World health statistics 2018: Monitoring health for the SDGs, sustainable development goals. Geneva. https://doi.org/10.22201/fq.18708404e.2004.3.66178
World Health Organization. (2018c). Atlas of African Health Statistics 2018: Universal health coverage and the Sustainable Development Goals in the WHO African Region. Brazzaville. http://www.afro.who.int/en/clusters-a-programmes/ard/african-health-observatory-a-knowledge-management/features/4008-atlas-of-african-health-statistics-2014-health-situation-analysis-of-the-african-region.html.
Yaya, S., & Ghose, B. (2019). Trend in overweight and obesity among women of reproductive age in Uganda: 1995–2016. Obesity Science & Practice, 5(4), 312–323. https://doi.org/10.1002/osp4.351
Acknowledgements
Prince M. Amegbor, Megan Davies and Clive E. Sabel acknowledge support from BERTHA—the Danish Big Data Centre for Environment and Health funded by the Novo Nordisk Foundation Challenge Programme (grant NNF17OC0027864). We are also grateful to the NASA Socioeconomic Data and Applications Center (SEDAC), the USAID DHS programme, and Uganda Bureau of Statistics for the data used in this study.
Funding
There was no funding for this research.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest in carrying out this research.
Ethical approval
The data for this study was obtained from the Demographic and Health Survey (DHS) platform. Procedures and questionnaires for standard DHS surveys have been reviewed and approved by the ICF Institutional Review Board (IRB). Additionally, country-specific DHS survey protocols are reviewed by the ICF IRB and typically by an IRB in the host country. ICF IRB ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the protection of human subjects (45 CFR 46), while the host country IRB ensures that the survey complies with laws and norms of the nation. No further ethics approval was needed for this study.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
10708_2021_10466_MOESM3_ESM.tiff
Figure S3. Proportional distribution of overweight or obese women in Uganda by districts and UDHS period. Generated with R software using the tmap and tmaptools packages (TIFF 6225 KB)
Rights and permissions
About this article
Cite this article
Amegbor, P.M., Yankey, O., Davies, M. et al. Individual and contextual predictors of overweight or obesity among women in Uganda: a spatio-temporal perspective. GeoJournal 87, 3793–3813 (2022). https://doi.org/10.1007/s10708-021-10466-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10708-021-10466-7