Nutritional research in sub-Saharan Africa has primarily focused on under-nutrition. However, there is evidence of an ongoing nutritional transition in these settings. This study aimed to examine the geographic variation of overweight and obesity prevalence at the state-level among women in Nigeria, while accounting for individual-level risk factors.
The analysis was based on the 2008 Nigerian Demographic and Health Survey (NDHS), including 27,967 women aged 15–49 years. Individual data were collected on socio-demographics, but were aggregated to the country's states. We used a Bayesian geo-additive mixed model to map the geographic distribution of overweight and obesity at the state-level, accounting for individual-level risk factors.
The overall prevalence of combined overweight and obesity (body mass index ≥25) was 20.9%. In multivariate Bayesian geo-additive models, higher education [odds ratio (OR) & 95% Credible Region (CR): 1.68 (1.38, 2.00)], higher wealth index [3.45 (2.98, 4.05)], living in urban settings [1.24 (1.14, 1.36)] and increasing age were all significantly associated with a higher prevalence of overweight/obesity. There was also a striking variation in overweight/obesity prevalence across ethnic groups and state of residence, the highest being in Cross River State, in south-eastern Nigeria [2.32 (1.62, 3.40)], the lowest in Osun State in south-western Nigeria [0.48 (0.36, 0.61)].
This study suggests distinct geographic patterns in the combined prevalence of overweight and obesity among Nigerian women, as well as the role of demographic, socio-economic and environmental factors in the ongoing nutritional transition in these settings.
Citation: Kandala N-B, Stranges S (2014) Geographic Variation of Overweight and Obesity among Women in Nigeria: A Case for Nutritional Transition in Sub-Saharan Africa. PLoS ONE 9(6): e101103. doi:10.1371/journal.pone.0101103
Editor: Lawrence Kazembe, Chancellor College, University of Malawi, Malawi
Received: February 19, 2014; Accepted: May 27, 2014; Published: June 30, 2014
Copyright: © 2014 Kandala and Stranges. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing interests: The authors have declared that no competing interests exist.
Nutritional research in sub-Saharan Africa has primarily focused on under-nutrition, particularly among vulnerable population subgroups such as women and children. However, while sub-Saharan Africa is still one of the poorest regions in the world with extremely high rates of malnutrition, there is suggestive evidence of an ongoing nutritional transition –. Data from several developing countries suggest that rising urbanisation and improvements in developmental indicators lead to concurrent under- and over-nutrition in the population –.
Furthermore, in these settings the nutritional transition often takes place in an environment which has traditionally favoured ‘plumpness’ as a symbol of sexual beauty, socioeconomic status and social standing –. When preference for large body shapes exists, a direct relationship between socioeconomic status and obesity is likely to be observed at the beginning of the transition, especially in women, because higher socioeconomic groups have the financial capacity to buy extra food and achieve their desire to look healthy and strong .
Although the growing number of obese individuals has received attention in many developing countries, sub-Saharan Africa is still lacking research into this subject partially due to the persisting high proportion of the population classified as underweight. Nonetheless, the ongoing nutritional transition in these settings has been mostly linked to the rapid process of urbanisation and westernisation –. In 1995, 35% of the African population lived in urban areas, whereas 54% of the population is projected to be urban by 2030, with estimates ranging from 43% in East Africa to 67% in southern Africa .
Furthermore, in the last decades the number of deaths from non-communicable diseases in developing countries has already exceeded those observed in developed countries –. Moreover, obesity is associated with an increased risk for many of these chronic conditions including heart disease, hypertension, arthritis and diabetes mellitus –.
The ongoing nutritional transition in Africa is likely to pose a major public health challenge with a significant proportion of adults becoming overweight, whilst large segments of the population still face risk of morbidity and mortality related to under-nutrition .
Understanding the underlying ecological and socioeconomic roots of both extremes of the nutritional status is vital to designing successful interventions and targeting limited resources to the dual problem of under- and overweight in African settings.
The aim of this study was to examine the geographic variation of overweight/obesity at the state-level among women in Nigeria, using the 2008 Nigerian Demographic and Health Survey (NDHS) data . We also examined a wide range of socioeconomic, individual and household factors which are likely to affect the ongoing nutritional transition in these settings.
The Demographic and Health Survey (DHS), funded by the United States Agency for International Development (USAID), is a well-established source of reliable population level data with a substantial focus on health. The objectives, organisation, sample design and questionnaires used in the DHS surveys are described elsewhere . This study was approved by the relevant institutional review board for DHS surveys. The authors thank Macro international for providing free access to the 2008 DHS datasets for Nigeria. Data were handled in an anonymized fashion.
A random sample of 34,596 women were identified to be eligible for the individual interview, anthropometric measurements of height and weight were collected for women aged 15–49 years, and risk factors for obesity were recorded, as were socio-demographic data and the state of residence. The body mass index (BMI), or the Quetelet index, was used to measure thinness and obesity. There were few participants with missing data for BMI, and other covariates; thus, data analysis on BMI was based on 27,967 women with a complete set of data .
Anthropometric measures were taken, including height, weight, and waist circumference; body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared and categorized as follows: underweight, BMI <18.5 kg/m2; normal weight, BMI 18.5–24.9 kg/m2 (reference category); overweight, BMI 25–29.9 kg/m2; and obese, BMI ≥30 kg/m2, according to WHO guidelines . For the present analyses, the outcome or dependent variable was combined prevalence of overweight and obesity, defined as BMI ≥25 kg/m2, as compared to normal weight, defined as BMI 18.5–24.9 kg/m2 (reference category). We choose the binary outcome instead of the continuous BMI values, due to interpretability reasons since with the binary outcome one can estimate the likelihood of overweight/obesity in a given state, while accounting for a number of potential covariates.
The main exposure variable investigated was the respondent's geographic location, i.e. the state of residence at the time of the survey (figure 1), in addition to various individual-level control variables such as socio-demographics known to be associated with nutritional status. The respondent and her partner's age at the time of survey were also included as an indicator of the birth cohort of the women. Other socio-demographic covariates were religion (catholic versus other Christian, Islam, traditionalist and other), wealth index (poorest versus poorer, middle, richer and richest), and education of the respondent and partner (no education vs. primary, secondary and higher education). Finally, environmental factors included place (locality) of residence (rural vs. urban) and state of residence of the women including her ethnicity (Ekoi versus Fulani, Hausa, Ibibio, Igala, Igbo, Ijaw/Izon, Kanuri/Beriberi, Tiv, Yoruba and others). It should be noted that Nigeria is currently divided into 36 states and Abuja, the federal capital territory, while it was previously divided into 30 states and Abuja (1991–1996), with the further addition of six states in 1996.
1, Akwa Ibom. 2, Anambra. 3, Bauchi. 4, Gombe. 5, Edo. 6, Benue. 7, Borno.-River. 9, Adamawa. 10, Imo. 11, Kaduna. 12, Kano. 13, Katsina. 14, Kwara. 15, Lagos. 16, Niger. 17, Ogun. 18, Ondo. 19, Ekiti. 20, Oyo. 21, Nassarawa. 22, Plateau. 23, Bayelsa. 24, Rivers. 25, Sokoto. 26, Zamfara. 27, Abia. 28, Delta. 29, Ebonyi. 30, Enugu. 31, Jigawa. 32, Kebbi. 33, Kogi. 34, Osun. 35, Taraba. 36, Yobe. 37, Abuja.
To account for spatial effects in the prevalence of combined overweight/obesity at the state level, in Nigeria, we applied a unified approach to account for possible nonlinear effects of continuous risk factors. This was achieved using a geo-additive semi-parametric mixed model. The model employed a fully Bayesian approach using Markov Chain Monte Carlo (MCMC) techniques for inference and model checking –. The response variable was defined as yi = 1 if overweight/obese and yi = 0 normal weight. The standard measure of effect was the posterior odds ratio (POR) and 95% credible region (CR).
The analysis was carried out using version 2.0.1 of the BayesX software package, which permits Bayesian inference based on Markov chain Monte Carlo (MCMC) simulation techniques . The statistical significance of apparent associations between potential risk factors and the prevalence of overweight/obesity was explored with chi-square (χ2) and Mann–Whitney U-tests, as appropriate. Adjusted marginal ORs of overweight/obesity risk across states were obtained from standard logistic regression models, with Yobe used as the reference category because of the lowest crude overweight/obesity prevalence. Multivariate Bayesian geo-additive regression models were used to evaluate the significance of the posterior OR determined for the fixed, non-linear effects and spatial effects.
Baseline characteristics of the study population are displayed in Table 1 for the overall sample (N = 27,967), and in Table 2 with participants split within the two categories of overweight/obese vs. normal-weight status (N = 24,614), after excluding underweight subjects (N = 3,353). Overall, mean age of participants was 29.3 (±7.1) and for their partners 40.9 (±12.8). The percentage of participants with no education was high (50.3%) and 41.4% among their partners. Mean BMI was 22.0 (±4.3); 26.6% of participants were living in an urban area, 56.7% were Muslim and 26.5% were in the poorest quintile of the wealth index. Prevalence values of underweight, normal-weight, overweight and obese were: 12.0%, 67.1%, 15.7% and 5.2%, respectively. The combined overweight/obesity prevalence was 20.9%, with variation across states ranging from 10.5% in Yobe to 50.2% in Lagos.
On average, overweight/obese respondents were older than their normal-weight counterparts, were more likely to be highly educated and have highly educated partners, more likely to be living in urban areas and more likely to be from other religions. In addition, overweight/obese women were more likely to be in the richest quintile of the wealth index, more likely to be from the Igbo ethnic group and living in Lagos State.
Associations were in the opposite directions when comparing underweight women with normal-weight subjects (Table S1). In fact, underweight respondents were younger than their normal-weight counterparts, were more likely to have no education and have uneducated partners, more likely to be living in rural settings and more likely to be either Muslim or traditionalist. In addition, underweight women were more likely to be in the poorest quintile of the wealth index, more likely to be from the Kanuri/Beriberi ethnic group and living in Yobe State, an agricultural state and one of the poorest regions in Nigeria.
Table 3 displays both marginal and posterior odds ratios of overweight/obese across the selected study characteristics. Results from both standard logistic regression and multivariate Bayesian geo-additive analyses (right-hand column) support the role of women's wealth, higher education and living in an urban setting as risk factors. Specifically, women with higher education [posterior odds ratio (POR) & 95% credible region (CR): 1.68 (1.38, 2.00)] from the richest wealth index [3.45 (2.98, 4.05)] were consistently associated with higher odds of being overweight/obese. Also women from the Igala ethnic group [7.47 (3.98, 12.3)] were consistently associated with higher odds of being overweight/obese. The associations of partner education and religion with overweight/obesity risk were not statistically significant. Moreover, there were nonlinear associations between women and partner's age and overweight/obesity status: the risk of overweight/obesity increased with age with a peak at around 45 years of age for women and 70 years of age for their partners and then a decrease thereafter (Figure 2) using a flexible nonlinear curve.
Shown is the posterior mean within 80% credible regions.
With regard to overweight/obesity status, in the marginal regression analyses there was a striking variation in overweight/obesity risk across states, the highest being in Cross River State [4.49 (2.80, 7.19)], Kebbi [3.32 (2.48, 4.44)] and Taraba [2.74 (2.06, 3.64)], the lowest in Osun [0.64 (0.44, 0.95)] and Benue [0.76 (0.51, 1.14)].
Figure 3 shows results for covariate-adjusted state overweight/obesity status spatial variation captured by the global total state effects. There was a clear pattern of states with higher risk of overweight/obesity, mostly the south-eastern states of Cross River, Akwa Ibom, Rivers and Bayelsa, including the eastern state of Taraba and the northern state of Kebbi, which were associated with higher prevalence of overweight/obesity, while states in the west and north were associated with a lower overweight/obesity prevalence. These spatial patterns confirm the observed marginal model findings shown in Table 3.
Shown are the posterior odds ratios (left) and corresponding posterior probabilities at 80% nominal level (right).
Specifically, the left-hand map shows estimated posterior total residual state odds of overweight/obesity for each state, ranging from a lower POR of 0.48 (0.36, 0.61) in Osun State to a higher POR of 2.32 (1.62, 3.40) in Cross River State, with red colour indicating the higher risk recorded and green colour denoting lower odds. The right-hand map shows the 95% posterior probability map of overweight/obesity, which indicates the statistical significance associated with the total excess risk. White indicates a negative spatial effect (associated with reduced risk of overweight/obesity), black a positive effect (an increased risk) and grey a non-significant effect. However, the total spatial residuals in Figure 3 show that much of the variation in overweight/obesity remains to be explained.
The spatial effects of the populous Lagos State was greatly attenuated after multiple adjustments of the urban environment and other risk factors indicating that perhaps the higher number of obese/overweight women living in Lagos was inflated by the large number of women living in Lagos and the younger age structure of urban areas.
To our knowledge, this is the first and largest epidemiological study which examined the geographic variation, at the state-level, of combined prevalence of overweight and obesity in an adult population from sub-Saharan Africa. We used data from the 2008 Nigerian DHS on a large, nationwide sample of women across the country's states. Overall, the combined prevalence of overweight and obesity was 20.9%, with a striking variation across ethnic groups and states of residence, ranging from 10.5% in Yobe to 50.2% in Lagos. Findings also point to the crucial role of demographic, socio-economic and environmental factors driving the ongoing nutritional transition in these settings. In fact, we found that women in older age groups, with higher education and wealth index, and living in urban settings were all significantly associated with a higher prevalence of combined overweight and obesity.
These findings provide novel evidence to support the notion of a rapidly changing scenario in sub-Saharan Africa, in terms of nutrition and dietary patterns, with co-existence of a dual burden of under- and overnutrition, which is already posing a major public health challenge in these low-resource settings –. Several factors are likely to contribute to the ongoing nutritional transition in these countries, primarily the rapid process of urbanisation and westernisation, with over-reliance on energy-dense processed foods, decreased intakes of staple and low-calorie foods, physical inactivity and sedentary lifestyles –. The rapidly changing role of women in these societies, with their increasing involvement in the labour force especially in urban areas, might also contribute to the dramatic changes in dietary patterns and food supply occurring in these settings . However, we cannot disregard the role of cultural factors in this transition, because in developing societies ‘fatness’ has been often considered as a symbol of sexual beauty and social standing, particularly among women. In addition, in these settings a direct relationship between socioeconomic status and obesity has been observed, since higher socioeconomic groups are more likely to buy extra food and achieve their desire to look healthy and strong –.
Our estimates of overweight and obesity prevalence are generally in keeping with previous studies from Nigeria –, as well as with WHO reports on Sub-Saharan African countries . Specifically, in the latest published systematic review, which included only four good-quality communitywide studies in Nigeria, Chukwuonye et al found that the prevalence of overweight ranged from 20.3% to 35.1%, while the prevalence of obesity ranged from 8.1% to 22.2% . The four included studies comprised participants from both sexes and showed a consistently higher prevalence of obesity among women, which is in line with the notion that the ongoing nutritional transition in sub-Saharan Africa, and possibly in other developing countries, may affect primarily this population subgroup due to a range of demographic, cultural, socio-economic and environmental factors –.
These observations have been further corroborated by a recent cross-sectional study on the prevalence of abdominal obesity in Abia State, south-eastern Nigeria, among 2,807 individuals of both sexes . In this study, abdominal obesity was defined as a waist circumference of 102 cm or more in men and 88 cm or more in women. While the prevalence of general obesity (BMI≥30) was twice as high in women than men (14.37% vs. 7.73%, respectively), there was a much wider gap between the two sexes in the prevalence of abdominal obesity, being 39.2% among women and only 3.2% in men. Again, biological and experimental evidence on potential mechanisms that may explain this remarkable sex difference in body fat distribution in the Sub-Saharan African context is extremely limited; therefore any such discussion is highly speculative. However, it is believed that, in these societies, men still engage in more strenuous physical activities than women . An additional plausible explanation may reside in the fact that women in developing countries commonly undergo multiple pregnancies, which is a potential risk factor for female obesity and central adiposity .
It should be noted that in our study the prevalence estimates of both overweight (15.7%) and obesity (5.2) were somewhat lower than those reported in some previous Nigerian surveys –. However, the mean age of participants in our sample was 29.3 (age range: 15–49), whereas previous studies were generally based on samples of middle-aged individuals, among whom the prevalence of both overweight and obesity is expected to be higher. In fact, in our sample of women the risk of overweight/obesity increased with age with a peak at around 45 years of age.
Unlike previous surveys from Nigeria –, this is the first study that attempted to estimate the geographic variation, at the state-level, of combined prevalence of overweight and obesity in an adult population from sub-Saharan Africa, independent of individual-level risk factors. Our findings point to a striking variation in overweight/obesity prevalence across states of residence. Specifically, in unadjusted analyses there was a clear pattern of higher prevalence of overweight/obesity in those states with higher degrees of urbanization, namely Lagos State (50.2%), which covers the most populous area in Nigeria and one of the fastest growing cities in the world . On the other hand, the prevalence of overweight/obesity was the lowest in Yobe State (10.5%), in Northern Nigeria, which mainly covers an agricultural area. However, in multivariate Bayesian geo-additive regression models, there was evidence of a slightly different scenario. Specifically, there was a clear pattern of states with higher risk of overweight/obesity in the south-eastern states of Cross River, Akwa Ibom, Rivers, Bayelsa, and Taraba, whereas states in the west and north were generally associated with lower overweight/obesity prevalence (except for the northern state of Kebbi). Importantly, the elevated unadjusted prevalence of overweight/obesity of the populous Lagos State was greatly attenuated after multiple adjustments for the urban environment and other risk factors, suggesting that the unadjusted estimate was possibly inflated by the large number of women living in Lagos and the younger age structure of population in urban settings.
Potential explanations for the observed geographic variation in the prevalence of overweight/obesity across the country's states are highly speculative at the present time, given the lack of published literature or policy reports in this specific area of research. This geographic variation could well be due to differences in lifestyles, level of urbanisation and westernisation, wealth distribution, access to food supply and increasing presence of fast food chains and outlets in these states –. For example, Cross River State and its capital Calabar are now among the leading tourism sites of Nigeria, where the large presence of international visitors is likely to influence local lifestyles and dietary patterns, with a gradual process of westernisation, penetration of the fast food sector, increasing intake of energy-dense processed foods, and related health consequences, such as obesity .
There are some limitations in the present study that deserve discussion. First, the cross-sectional nature of the present study does not allow for establishing temporality and causality of the observed associations. However, our exposure variables were primarily demographic and socioeconomic factors, whereby the potential of reverse causation should be minimized. Second, the analysis was based on a nationally representative sample of Nigerian women; therefore, the generalizability and applicability of these findings to male populations or other Sub-Saharan African countries warrants further investigations. Finally, there was limited or lack of information for variables such as dietary habits, physical activity, and biomarker data, which are relevant to metabolism and overweight/obesity aetiology. Therefore, we cannot rule out the possibility that our findings might have been somewhat biased by the lack of these important confounders.
In conclusion, this is the first and largest study that examined the geographic variation of combined prevalence of overweight and obesity in an adult population from sub-Saharan Africa. We found several consistent associations between socio-demographic variables and prevalence of overweight/obesity in a nationwide sample of Nigerian women from the 2008 DHS. The geographic analysis showed distinct patterns in the prevalence of overweight/obesity across the country's states, pointing to the potential influence of demographic, cultural, socio-economic and environmental factors, as well as to an increasing level of urbanisation and westernisation, which are all driving the ongoing nutritional transition in these settings. Importantly, policy makers and public health practitioners can use this geographic information on overweight/obesity mapping for planning purposes, educational and nutritional programs on lifestyles and dietary patterns, but also in the decision making process for the allocation of public resources to the most affected areas of the population, especially in these low-income settings .
Baseline characteristics of the study population by underweight status (NDHS 2008).
The authors thank Macro International for providing free access to the 2008 DHS datasets for Nigeria.
Ngianga-Bakwin Kandala is also supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care West Midlands at University Hospitals Birmingham NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
Conceived and designed the experiments: N-BK SS. Performed the experiments: N-BK SS. Analyzed the data: N-BK. Contributed reagents/materials/analysis tools: N-BK SS. Wrote the paper: N-BK SS. Reviewed and edited the manuscript: N-BK SS.
- 1. Loaiza E (1997) Maternal Nutritional Status. DHS Comparative Studies No 24, Calverton, Maryland, Macro International Inc.
- 2. Popkin BM (1993) Nutrition patterns and transitions. Pop Dev Rev 19: 138–157.
- 3. Popkin BM (1998) The nutrition transition and its health implications in lower-income countries. Public Health Nutr 1: 5–21. doi: 10.1079/phn19980004
- 4. Vorster HH, Wissing MP, Venter CS, Kruger HS, Kruger A, et al. (2000) The impact of urbanization on physical physiological and mental health of Africans in the North West Province of South Africa: the THUSA study. S Afr J Sci 96: 505–13.
- 5. Griffiths PL, Bentley ME (2001) The nutrition transition is underway in India. J Nutr 131: 2692–2700.
- 6. Martorell R, Khan LK, Hughes ML, Grummer-Strawn LM (2000) Obesity in women from developing countries. Eur J Clin Nutr 54: 247–252. doi: 10.1038/sj.ejcn.1600931
- 7. Popkin BM (2003) The nutrition transition in the developing world. Dev Policy Rev 21: 581–597. doi: 10.1111/j.1467-8659.2003.00225.x
- 8. Brown PJ, Konner M (1987) An anthropological perspective on obesity. Ann N Y Acad Sci 499: 29–46. doi: 10.1111/j.1749-6632.1987.tb36195.x
- 9. Sobal J, Stunkard AJ (1989) Socioeconomic status and obesity: a review of the literature. Psychol Bull 105: 260–75. doi: 10.1037/0033-2909.105.2.260
- 10. United Nations Population Division, Department of Economic and Social Affairs (1999) World Urbanization Prospects: 1999 Revision. New York, United Nations Population Division, Department of Economic and Social Affairs.
- 11. Salomon JA, Murray CJL (2002) The epidemiologic transition revisited: compositional models for causes of death by age and sex. Popul Dev Rev 28: 205–28. doi: 10.1111/j.1728-4457.2002.00205.x
- 12. Mathers C, Loncar D (2006) Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 3: 2011–2030. doi: 10.1371/journal.pmed.0030442
- 13. World Health Organization (2000) Obesity: prevention and managing the global epidemic. Report of a WHO Consultation. WHO technical report series 894..
- 14. Prentice AM (2006) The emerging epidemic of obesity in developing countries. Int J Epidemiol 35: 93–99. doi: 10.1093/ije/dyi272
- 15. Amuna P, Zotor FB (2008) Epidemiological and nutrition transition in developing countries: impact on human health and development. Proc Nutr Soc 67: 82–90. doi: 10.1017/s0029665108006058
- 16. Food and Agriculture Organization (FAO) of the United Nations (2006) The double burden of malnutrition. Case studies from six developing countries. FAO food and nutrition paper 84 , Rome.
- 17. National Population Commission (NPC) [Nigeria], ICF Macro (2009) Nigeria Demographic and Health Survey 2008. Abuja, Nigeria: National Population Commission and ICF Macro.
- 18. WHO (1995) World Health Organization (WHO) Expert Committee on Physical Status. The use and Interpretation of Anthropometry. Report of a World Health Organization Expert Committee. Geneva: WHO Technical Support, Series 854..
- 19. Fahrmeir L, Lang S (2001) Bayesian Inference for Generalized Additive Mixed Models Based on Markov Random Field Priors. Applied Statistics (JRSS C) 50: 201–220. doi: 10.1111/1467-9876.00229
- 20. Brezger A, Kneib T, Lang S (2005) BayesX Software for Bayesian Inference based on Markov Chain Monte Carlo simulation techniques. J Statist Software 14: 11.
- 21. Kandala NB, Gebrenegus G (2006) A Geo-additive Bayesian Discrete-time Survival Model and its Application to Spatial Analysis of Childhood Mortality in Malawi. Quality & Quantity 40: 935–957. doi: 10.1007/s11135-005-3268-6
- 22. Brezger A, Kneib T, Lang S (2012) BayesX Software for Bayesian Inference in Structured Additive Regression Models Version 2.0.1. Available: http://www.stat.uni-muenchen.de/~bayesx/manual/methodology_manual.pdf. Accessed 2014 Jun 5.
- 23. Vorster HH, Venter CS, Wissing MP, Margetts BM (2005) The nutrition and health transition in the North West Province of South Africa: a review of the THUSA (Transition and Health during Urbanisation of South Africans) study. Public Health Nutr 8: 480–490. doi: 10.1079/phn2005784
- 24. Kimani-Murage EW, Kahn K, Pettifor JM, Tollman SM, Dunger DB, et al. (2010) The prevalence of stunting, overweight and obesity, and metabolic disease risk in rural South African children. BMC Public Health 10: 158. doi: 10.1186/1471-2458-10-158
- 25. Collinson MA (2010) Striving against adversity: the dynamics of migration, health and poverty in rural South Africa. Glob Health Action 3 :doi:10.3402/gha.v3i0.5080.
- 26. Abubakari AR, Bhopal RS (2008) Systematic review on the prevalence of diabetes, overweight/obesity and physical inactivity in Ghanaians and Nigerians. Public Health 2008 122: 173–182. doi: 10.1016/j.puhe.2007.06.012
- 27. Abubakari AR, Lauder W, Agyemang C, Jones M, Kirk A, et al. (2008) Prevalence and time trends in obesity among adult West African populations: a meta-analysis. Obes Rev 9: 297–311. doi: 10.1111/j.1467-789x.2007.00462.x
- 28. Desalu OO, Salami AK, Oluboyo PO, Olarinoye JK (2008) Prevalence and socio-demographic determinants of obesity among adults in an urban Nigerian population. Sahel Medical Journal 11: 61–64. doi: 10.4314/smj2.v11i2.12970
- 29. Adedoyin RA, Mbada CE, Balogun MO, Adebayo RA, Martins T, et al. (2009) Obesity prevalence in adult residents of Ile-Ife, Nigeria. Nig Q J Hosp Med 19: 100–105. doi: 10.4314/nqjhm.v19i1.50211
- 30. Amira CO, Sokunbi DOB, Dolapo D, Sokunbi A (2011) Prevalence of obesity, overweight and proteinuria in an urban community in South West Nigeria. Nigerian Medical Journal 52: 110–113.
- 31. Oyeyemi AL, Adegoke BO, Oyeyemi AY, Deforche B, De Bourdeaudhuij I, et al. (2012) Environmental factors associated with overweight among adults in Nigeria. Int J Behav Nutr Phys Act 9: 32. doi: 10.1186/1479-5868-9-32
- 32. Chukwuonye II, Chuku A, John C, Ohagwu KA, Imoh ME, et al. (2013) Prevalence of overweight and obesity in adult Nigerians – a systematic review. Diabetes Metab Syndr Obes 6: 43–47. doi: 10.2147/dmso.s38626
- 33. Chukwuonye II, Chuku A, Onyeonoro UU, Okpechi IG, Madukwe OO, et al. (2013) Prevalence of abdominal obesity in Abia State, Nigeria: results of a population-based house-to-house survey. Diabetes Metab Syndr Obes 6: 285–291. doi: 10.2147/dmso.s43545
- 34. World Health Organization Regional Committee for Africa (2005) Cardiovascular diseases in the African region: current situation and perspectives. Fifty-filth session. Maputo, Mozambique, 22–26 August 2005. Provisional Agenda Item 8.6. Available: http://apps.who.int/iris/bitstream/10665/1871/1/AFR%20RC55-12.pdf. Accessed 2014 Jun 5.
- 35. Chigbu C, Aja L (2011) Obesity in pregnancy in southeast Nigeria. Ann Med Health Sci Res 1: 135–140.
- 36. Udo RK (1975) Migration and urbanization in Nigeria. Population growth and socioeconomic change in West Africa. Columbia University Press, Columbia, pages: 298–307.
- 37. Kennedy G, Nantel G, Shetty P (2004) Food and Agriculture Organization of the United Nations (2004) Globalization of food systems in developing countries: impact on food security and nutrition. FAO Food Nutr Pap 83: 1–300.
- 38. Raschke V, Cheema B (2008) Colonisation, the New World Order, and the eradication of traditional food habits in East Africa: historical perspective on the nutrition transition. Public Health Nutr 11: 662–674. doi: 10.1017/s1368980007001140
- 39. Olotu OA, Awoseila F (2011) Reinventing business growth through franchising in developing economies: A study of the Nigerian fast food sector. Int J Mark Studies 3: 162–170. doi: 10.5539/ijms.v3n1p162
- 40. Kandala NB, Tigbe W, Manda SOM, Stranges S (2013) Geographic variation of hypertension in Sub-Saharan Africa: a case study of South-Africa. Am J Hypertens 26: 382–391. doi: 10.1093/ajh/hps063