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      Urban–rural and geographic differences in overweight and obesity in four sub-Saharan African adult populations: a multi-country cross-sectional study

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          Abstract

          Background

          Overweight and obesity are on the rise in developing countries including sub-Saharan Africa. We undertook a four-country survey to show the collective burden of these health conditions as they occur currently in sub-Saharan Africa and to determine the differences between urban and rural populations and other socio-economic factors.

          Methods

          Participants were nurses in two hospitals in Nigeria (200), school teachers in South Africa (489) and Tanzania (229), and village residents in one peri-urban (297) and one rural location in Uganda (200) who completed a standardised questionnaire. Their height and weight were measured and body mass index calculated. Factor analysis procedure (Principal component) was used to generate a wealth index. Univariate and multivariate analyses with binary logistic regression models were conducted to examine the associations between potential correlates and the prevalence of overweight and obesity with 95 % confidence intervals.

          Results

          The prevalence of overweight and obese (combined) was 46 %, 48 %, 68 %, 75 % and 85 % in rural Uganda, peri-urban Uganda, Nigeria, Tanzania and South Africa (SA), respectively. Rural Uganda, Peri- urban Uganda, Nigeria, Tanzania and SA had obesity prevalence of 10 %, 14 %, 31 %, 40 % and 54 %, respectively ( p < 0.001). Overall, prevalence of overweight was 374 (31 %) and obesity, 414 (34 %). Female sex was a predictor of overweight and obesity (combined) in peri-urban Uganda [AOR = 8.01; 95 % CI: 4.02, 15.96) and obesity in rural Uganda [AOR = 11.22; 95%CI: 2.27, 55.40), peri-urban Uganda [AOR = 27.80; 95 % CI: 7.13, 108.41) and SA [AOR = 2.17; 95 % CI: 1.19, 4.00). Increasing age was a predictor of BMI > =25 kg/m 2 in Nigeria [Age > =45 - AOR = 9.11; 95 % CI: 1.72, 48.16] and SA [AOR = 6.22; 95 % CI: 2.75, 14.07], while marital status was predictor of BMI > =25 kg/m 2 only in peri-urban Uganda. [Married - AOR = 4.49; 95 % CI: 1.74, 11.57]. Those in Nigeria [AOR = 2.56; 95 % CI: 1.45, 4.53], SA [AOR = 4.97; 95 % CI: 3.18, 7.78], and Tanzania [AOR = 2.68; 95 % CI: 1.60, 4.49] were more likely to have BMI > =25 kg/m 2 compared with the rural and peri-urban sites.

          Conclusion

          The high prevalence of overweight and obesity in these sub-Saharan African countries and the differentials in prevalence and risk factors further highlights the need for urgent focused intervention to stem this trend, especially among women, professionals and urban dwellers.

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          Most cited references43

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          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.

          In 2010, overweight and obesity were estimated to cause 3·4 million deaths, 3·9% of years of life lost, and 3·8% of disability-adjusted life-years (DALYs) worldwide. The rise in obesity has led to widespread calls for regular monitoring of changes in overweight and obesity prevalence in all populations. Comparable, up-to-date information about levels and trends is essential to quantify population health effects and to prompt decision makers to prioritise action. We estimate the global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013. We systematically identified surveys, reports, and published studies (n=1769) that included data for height and weight, both through physical measurements and self-reports. We used mixed effects linear regression to correct for bias in self-reports. We obtained data for prevalence of obesity and overweight by age, sex, country, and year (n=19,244) with a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs). Worldwide, the proportion of adults with a body-mass index (BMI) of 25 kg/m(2) or greater increased between 1980 and 2013 from 28·8% (95% UI 28·4-29·3) to 36·9% (36·3-37·4) in men, and from 29·8% (29·3-30·2) to 38·0% (37·5-38·5) in women. Prevalence has increased substantially in children and adolescents in developed countries; 23·8% (22·9-24·7) of boys and 22·6% (21·7-23·6) of girls were overweight or obese in 2013. The prevalence of overweight and obesity has also increased in children and adolescents in developing countries, from 8·1% (7·7-8·6) to 12·9% (12·3-13·5) in 2013 for boys and from 8·4% (8·1-8·8) to 13·4% (13·0-13·9) in girls. In adults, estimated prevalence of obesity exceeded 50% in men in Tonga and in women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and Samoa. Since 2006, the increase in adult obesity in developed countries has slowed down. Because of the established health risks and substantial increases in prevalence, obesity has become a major global health challenge. Not only is obesity increasing, but no national success stories have been reported in the past 33 years. Urgent global action and leadership is needed to help countries to more effectively intervene. Bill & Melinda Gates Foundation. Copyright © 2014 Elsevier Ltd. All rights reserved.
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            A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

            The Lancet, 380(9859), 2224-2260
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              Estimating wealth effects without expenditure data--or tears: an application to educational enrollments in states of India.

              Using data from India, we estimate the relationship between household wealth and children's school enrollment. We proxy wealth by constructing a linear index from asset ownership indicators, using principal-components analysis to derive weights. In Indian data this index is robust to the assets included, and produces internally coherent results. State-level results correspond well to independent data on per capita output and poverty. To validate the method and to show that the asset index predicts enrollments as accurately as expenditures, or more so, we use data sets from Indonesia, Pakistan, and Nepal that contain information on both expenditures and assets. The results show large, variable wealth gaps in children's enrollment across Indian states. On average a "rich" child is 31 percentage points more likely to be enrolled than a "poor" child, but this gap varies from only 4.6 percentage points in Kerala to 38.2 in Uttar Pradesh and 42.6 in Bihar.
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                Author and article information

                Contributors
                ikeajayi2003@yahoo.com
                cadebamo@yahoo.com
                hadami@hsph.harvard.edu
                sdalal@hsph.harvard.edu
                mbd976@mail.harvard.edu
                fbaj@yahoo.com
                dguwatudde@gmail.com
                maudala@yahoo.com
                eron.jm@hotmail.com
                fschiwanga@yahoo.com
                jvolmink@sun.ac.za
                rkalyes@yahoo.com
                carienl@sun.ac.za
                reid1001@gmail.com
                ddockery@hsph.harvard.edu
                hemenway@hsph.harvard.edu
                stdls@hsph.harvard.edu
                Michelle.Holmes@channing.harvard.edu
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                28 October 2016
                28 October 2016
                2016
                : 16
                : 1126
                Affiliations
                [1 ]Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
                [2 ]Institute of Human Virology, Abuja, Nigeria
                [3 ]School of Medicine Greenbaum Cancer Center and Institute of Human Virology, University of Maryland, Baltimore, MD USA
                [4 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
                [5 ]Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA USA
                [6 ]Department of Community Health, Mbarara University of Science and Technology, Mbarara, Uganda
                [7 ]Department of Epidemiology & Biostatistics, Makerere School of Public Health, Kampala, Uganda
                [8 ]Department of Physiology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
                [9 ]Department of Internal Medicine, Muhimbili National Hospital, Dar es Salaam, Tanzania
                [10 ]The South African Cochrane Centre, South African Medical Research Council, Cape Town, South Africa
                [11 ]Centre for Evidence-based Health Care, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
                [12 ]Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA USA
                [13 ]Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA USA
                [14 ]Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
                Article
                3789
                10.1186/s12889-016-3789-z
                5084330
                27793143
                f8140944-0f38-47b8-8a99-d3c4fb11605c
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                History
                : 26 February 2016
                : 20 October 2016
                Funding
                Funded by: Dean’s office of the Harvard T. H. Chan School of Public Health
                Funded by: The Harvard T. H. School of Public Health Department of Nutrition
                Funded by: Karolinska Institutet Distinguished Professor Award
                Award ID: Dnr: 2368/10_221
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Public health
                prevalence of obesity and overweight,risk factors for over-nutrition,sub-saharan africa,south africa,nigeria,tanzania,uganda

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