Obesity among non-pregnant women in the 36 States and federal capital territory of Nigeria; sub-national variation of a national epidemic

doi:10.21203/rs.3.rs-3753428/v1

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Obesity among non-pregnant women in the 36 States and federal capital territory of Nigeria; sub-national variation of a national epidemic

https://doi.org/10.21203/rs.3.rs-3753428/v1

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Background: Obesity has been viewed as a pandemic, with women of reproductive age disproportionately affected compared to men. While studies have reported National prevalence rates in Nigeria, data on the sub-national variation is scarce. This is important because majority of policies/interventions in Nigeria, are developed/implemented at the sub-national level. This study therefore aimed to investigate the sub-national distribution and variation of obesity among non-pregnant women of reproductive age in Nigeria.

Methodology: This was a community-based cross-sectional study, which used secondary data from the Nigeria Demographic and Health Survey (NDHS) 2018. A total of 13,180 non-pregnant women of reproductive age (15 – 49 years) were selected from the 36 states and federal capital territory using a two-stage cluster sampling technique. Obesity was determined using the World Health Organization reference values for adolescents (less than 20 years) and others that were older. Sub-national variation was determined with binary logistic regression analysis using unadjusted and adjusted rates, and adjustment was made for individual, household and community level factors.

Results: The mean age of respondents was 29.5 ± 9.7 years and there was mostly an even distribution of respondents across the States. The National prevalence of obesity among non-pregnant women of reproductive age in this study was 28.5%. Anambra (54.6%) and Sokoto (6.9%) States had the highest and lowest prevalence rates respectively, with a range of 47.7%. The burden of obesity was higher among the southern states, compared to the northern states generally. After adjusting for all the commonly reported risk factors, about two-thirds of the states (24) still had a statistically significant variation in the prevalence of obesity, such that Akwa Ibom (OR: 4.5; p<0.001; 95% CI: 2.2, 9.0) and Anambra States (OR: 4.4; p<0.001; 95% CI: 2.2, 8,7) had nearly 5 times higher odds of obesity compared to the reference State (Sokoto).

Conclusion: Sub-national variations exist in the prevalence of obesity among non-pregnant women of reproductive age in Nigeria, even after adjusting for commonly reported predictors. There is therefore a need to identify state-level determinants of obesity, with a view to designing state-specific interventions to curb obesity among women of reproductive age in Nigeria.

Obesity

non-pregnant women

reproductive age

state

sub-national

variation

Obesity is a complex, multifactorial disease arising from a dysfunctional energy balance reinforced by an interplay of biological, social, cultural and economic factors.[1–3] According to the World Health Organization (WHO), the disease has been viewed as a pandemic, affecting not less than 650 million adults in 2016,[4] with more than half occurring among women.[5] In sub-Saharan Africa, despite persistent challenges with underweight, the obesity prevalence rose by 13% from the year 2000 to 2016, affecting an estimated 416 million people.[6, 7] This phenomenon has led to a double burden of malnutrition in the sub region.[8, 9]

Women of reproductive age more than men, are disproportionately affected by obesity due to biological peculiarities with the female sex,[6] poor physical activity,[6, 10] lower socioeconomic status,[11] and sociocultural beliefs which promote weight gain.[12] These patterns, further reinforced by demographic and nutritional transitions,[6, 11, 13] have led to a rising prevalence in many developing countries with attendant implications throughout the life course of women. Maternal obesity has been significantly associated with increased risk of diabetes, hypertension and cancers.[4] Furthermore, obesity predisposes pregnant women to increased risks of prolonged labour, preterm delivery, stillbirths and other direct/indirect pregnancy complications that ultimately may contribute to maternal death.[14, 15] Additionally, babies born to obese mothers are also at an increased risk of obesity.[4]

Majority of the existing studies on obesity among women in Nigeria have been scattered studies, but a few have been nationally representative giving the national perspectives on the prevalence and determinants of obesity among women in Nigeria. [16–18] However, most of the studies have been silent on the sub-national variations in the distribution of obesity in Nigeria, especially among women. Having the sub-national perspective is important because the national prevalence may not be a true reflection of the reality existing in various parts of the country. Nigeria is a very diverse Nation, with diversity across ethnic, cultural, religious, socio-economic, sub-national and regional lines.[19] Understanding the various contexts will help to design effective interventions that may help to address the rising prevalence of obesity among women in Nigeria.

Furthermore, majority of policies and interventions in Nigeria, including health-related issues are developed and/or implemented at the sub-national level. Nigeria being a federal republic has federating units (sub-nationals) that function independent of each other in most parts.[19] Investigating sub-national variation is therefore important for revenue allocation nationally, and the development/implementation of helpful interventions within the sub-national context. Additionally, most Non-Governmental Organizations (NGOs), programme partners and funding for women health/nutrition are domiciled and operate independently within the States (sub-nationals), hence state-specific data may be a better reference if there is significant sub-national variation in obesity prevalence among women. This study therefore aimed to investigate the sub-national distribution and variation of obesity among non-pregnant women of reproductive age in Nigeria.

The study was carried out in the federal republic of Nigeria, which is the most populous black Nation in the world. Nigeria is broadly divided into the northern and southern regions, with six geo-political regions/zones (i.e., three in the north, three in the south) namely Northwest, Northeast, Northcentral, Southwest, Southeast and Southsouth regions/zones. It has 36 states and federal capital territory (FCT) which is the capital, and the states are divided into a total of 774 local government areas (LGAs). The political/leadership structure of Nigeria is such that there is a central federal government headed by the president, then there are state governments at the sub-national level, headed by executive governors, and at the lowest level LGAs which are headed by LGA chairmen.

The study was a community-based cross-sectional study among non-pregnant women of reproductive age (15–49 years) in all the states and FCT in Nigeria. A total of 13,180 women were selected using a two-stage cluster sampling technique. The study used secondary data from the NDHS 2018, and more details about the methodology are contained in the report.[20]

The outcome variable for this study is obesity which was measured using the body mass index (BMI) for those 20 years and above, and the BMI-for-age reference values for those less than 20 years (adolescents). The BMI categories for those 20 years and above were defined as underweight (< 18.5), normal (18.5 to 24.9), overweight (25.0 to 29.9) and obesity (≥ 30.0). Similar categories were defined for those less than 20 years using their standard deviations (SD) or Z-scores against the WHO reference values, and these were defined as underweight (< -2SD), normal (-2SD to 1SD), overweight (> 1SD to 2SD) and obesity (> 2SD). Those who were overweight/obese in the 2 age groups were coded as 1 (obesity), while others were coded as 0 (Others).

The main explanatory variable is the states, which in all are 37 (including the FCT) and are as listed in Table 1 below. To investigate if there is a statistically significant sub-national variation, a wide range of common associated factors at individual, household and community levels were adjusted for, which are also listed in Table 1 below. Regions was removed because of multi-collinearity. Two community level variables, namely state educational level and state wealth levels, were derived. The state education level was derived by finding the proportion of women who had secondary education or higher in the states and finding the overall median value. The states were categorized into low or high level if they had below the median or ≥ median respectively. Similarly, proportion of women from the richer and richest households were derived, and the median calculated. States with proportion below the median or ≥ median were categorized as low or high state wealth level respectively.

Data were analyzed using STATA version 17 (Stata Corporation, College station, Texas, USA). Descriptive analysis of all the variables was done and presented in the Table 1 below. The distribution of obesity according to the states was done and represented graphically (Figs. 2 and 3). Figure 2 ranged the prevalence rates of obesity from the highest to lowest sub-nationally, while Fig. 3 depicted the burden of obesity in each of the states on the Nigerian Map. Table 2 showed the variation of obesity sub-nationally after varying levels and types of adjustments (five models) using binary logistics regression analysis. Model 0 showed the crude/unadjusted rates, while Models 1, 2 and 3 adjusted for individual, household and community level factors respectively. Model 4 is the full model that adjusted for all the explanatory variables together.

The overall (National) prevalence rate of obesity among non-pregnant women of reproductive age in Nigeria was 28.5%, (Fig. 1) but this ranged widely from 6.9% in Sokoto State to 54.6% in Anambra State (Range: 47.7%) (Fig. 2). Eighteen of the states had prevalence rates higher, while the remaining 19 states had prevalence rates lower than the National prevalence rate of 28.5%. (Fig. 2) The three states with the highest burdens were in the southern part of the country; Anambra State (54.6%), Lagos (51.0%) and Rivers (47.9%). On other hand, the three states with the lowest burdens, Sokoto (6.9%), Yobe (7.9%) and Jigawa (8.1%), all had prevalence rates less than 10%, and were all in the northern part of the country.(Figs. 2 and 3) In fact, with the exception of Ebonyi (20.8%) and Ondo (28.2%) states, all other 17 states with prevalence rates lower than the National average were all Northern States. (Figs. 2 and 3)

Table 1 shows the descriptive analysis of the explanatory variables, with the mean age of respondents being 29.5 ± 9.7years. Table 2 shows the sub-national variation of Obesity among the respondents after adjusting for possible associated factors using logistics regression analysis. In the unadjusted model (Model 0), apart from Jigawa (OR: 1.2; p = 0.642; 95% CI: 0.6, 2.6) and Yobe States (OR: 1.2; p = 0.740; 95% CI: 0.5, 2.9) that had no statistically significant variation with the reference State (Sokoto), all other states varied significantly in their prevalence of obesity, with all of them having higher odds for obesity than the reference State (Sokoto). It was such that respondents from Anambra (OR: 16.3; p < 0.001; 95% CI: 8.8, 30.1), Lagos (OR: 14.1; p < 0.001; 95% CI: 8.0, 25.1) and Rivers (OR: 12.5; p < 0.001; 95% CI: 6.9, 22.6) States had 16 times, 14 times and 13 times higher odds of obesity compared to Sokoto State respectively.

After adjusting for individual characteristics (Model 1), all the states that had statistically significant variation at Model 0 retained the significant associations except for Borno (OR: 1.7; p = 0.112; 95% CI: 0.9, 3.4) and Ebonyi (OR: 1.5; p = 0.197; 95% CI: 0.8, 2.9) States. However, the odds for obesity in the three highest burden states of Anambra (OR: 2.2; p < 0.001; 95% CI: 3.8, 13.2), Lagos (OR: 6.5; p < 0.001; 95% CI: 3.7, 11.6) and Rivers (OR: 5.6; p < 0.001; 95% CI: 3.0, 10.3) had reduced to about 2-, 7- and 4-times higher odds compared to Sokoto State, with Lagos State having the highest odds.

When household characteristics were adjusted for (Model 2), all the states that had a statistically significant variation at Model 1 retained their significant association, including Ebonyi State. Anambra (OR: 7.4; p < 0.001; 95% CI: 3.9, 14.3) and Akwa Ibom States (OR: 7.0; p < 0.001; 95% CI: 3.5, 13.8) significantly had the highest odds for obesity (7) compared to the reference State. Furthermore, adjustment was made for community characteristics (Model 3), and the States that had statistically significant variation at Model 2 retained their significant variation. Anambra State (OR: 7.9; p < 0.001; 95% CI: 4.4, 14.1) retained the highest odds (8) of obesity compared the reference State. In the final model (Model 4), after all explanatory variables were adjusted for, about two-thirds of all the states (24 States) still had a statistically significant variation in their prevalence of obesity compared to the reference state, with Akwa Ibom (OR: 4.5; p < 0.001; 95% CI: 2.2, 9.0) and Anambra States (OR: 4.4; p < 0.001; 95% CI: 2.2, 8,7) having 5-, and 4-times higher odds for obesity compared to Sokoto State, respectively.

While some studies have reported National prevalence rates for obesity among non-pregnant women of reproductive age group in Nigeria, this study provides new information on sub-national distribution and variation of obesity among this important population. The findings of this study will inform the interventional approach(es) to address the rising epidemic of obesity among this critical population, both nationally and sub-nationally.

This study showed that sub-national variations exist in the prevalence of obesity among non-pregnant women of reproductive age in the different states of Nigeria, even after adjusting for a wide range of commonly reported individual, household and community level predictors. Such variations have been observed in studies carried out in the United States,[15] Bangladesh,[21, 22] Cambodia,[23] China,[24] and particularly in Africa.[11, 14, 25–27] The finding of this study is important because it highlights the role of the contextual unit or the environment in which a woman lives in the prevalence of obesity. Additionally, this underscores the fact that country-wide interventions may be grossly inadequate for obesity among women of reproductive age in Nigeria.

The pattern of sub-national variation in the prevalence of obesity among Nigerian women was such that the Southern States largely had a higher burden compared to the Northern States. Similar findings of regional variations, even after controlling for risk factors, have been reported in high,[15] middle,[24] and low income countries.[14, 21, 26] A plausible argument for the regional variation in Nigeria would have been the socio-economic inequality that has been reported between the generally poorer north and richer south.[20] However, the variation still existed after adjusting for the various indicators of socio-economic status including educational status, occupational status, wealth index and residence.

Also of concern is the wide sub-national variation in the prevalence of obesity among women of reproductive age in Nigeria, with a range of nearly 50% (Sokoto – 6.9% versus Anambra – 54.6%). About half of the women in Anambra, Lagos and Rivers States were obese, with prevalence rates nearly twice the national average, while less than 10% of women in Sokoto, Yobe and Jigawa States were obese, which is less than half of the national average. Furthermore, women in Anambra and Akwa Ibom States had about 500% higher odds for obesity compared to those in Sokoto State even after adjusting for commonly reported predictors. Marked sub-national variations like this have been similarly observed in both high income and low- and middle-income countries. In United States, there were marked disparities in the obesity prevalence with some cities having 3 to 6 times higher risks of pre-pregnancy and severe obesity compared to their counterparts.[15] In another study done in China, some of the regions had more than double the odds of obesity compared to others even after controlling for common risk factors.[24] In Mali, regional variations in the obesity risk among reproductive aged women was also seen, with one region in particular (Kidal) having ten times the odds (compared to reference state) after adjusting for risk factors.[26] Generally, that about a third of women of reproductive age in Nigeria are obese should be a thing of concern, but this study further shows that there is a big sub-national context to this problem in Nigeria. There is therefore a need to investigate and identify state-level determinants of obesity among women of reproductive age in each State in Nigeria. Furthermore, this finding underscores the need for state-specific interventions to curb obesity among women of reproductive age in Nigeria.

It should be noted that obesity among women in this study had statistically significant associations with age, educational status, religion, number of living children, wealth index and spouse’s educational status, even after adjusting for possible confounders. These findings corroborate the findings of previous studies among women of reproductive age in Nigeria.[11, 16, 21, 22, 25, 26] In the present study, the associations were such that women who were older, more educated, Christians, with more children, from richer households and who had more educated spouses had higher odds of being obese. This pattern of relationship has also been previously reported by studies among women of reproductive of age within and outside Nigeria.[11, 14, 16, 27] However, the finding of this study showed that these factors alone did not explain the sub-national variation of obesity among this category of women in Nigeria. There may be need to explore other methodological approaches including qualitative studies,[28–30] mixed methods,[31] and multi-level analysis,[15, 18, 22] to explain the sub-national variation in the prevalence of obesity among non-pregnant women of reproductive age in Nigeria.

A limitation of this study is its being a cross-sectional study, which means that causality cannot be determined.

The study concluded that sub-national variations exist in the prevalence of obesity among non-pregnant women of reproductive age in Nigeria, even after adjusting for commonly reported predictors. The sub-national variations were wide with a range of about 50% between the highest and lowest prevalence states, and there was a higher burden among the Southern States of the country. There is therefore a need to identify state-level determinants of obesity, with a view to designing state-specific interventions to curb obesity among women of reproductive age in Nigeria.

BMI Body mass index

CI Confidence interval

EA Enumeration Area

FCT Federal Capital Territory

HIC High Income Countries

LGA Local Government Areas

LMIC Low- and middle-income countries

NDHS Nigeria Demographic and Health Survey

NGO Non-Governmental Organizations

OR Odds ratio

PSU Primary Sampling Unit

SD Standard Deviation

WHO World Health Organization

Ethics approval and consent to participate

Ethical clearance had been previously obtained for the NDHS 2018. The present study used de-identified secondary data from the NDHS 2018, hence needed no additional ethical clearance

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are publicly available on the DHS Programme website on reasonable request. (https://dhsprogram.com/).

Competing interests

The authors declare no competing interest.

Funding

The authors received no funding for the present study

Authors' contributions

AAA and MAO conceived the research idea and topic. MAO and BOA wrote the background of the study, AAA and MAO wrote the methodology section. All authors were involved in data management. AAA analyzed the data, and together with BOA wrote the results and discussion sessions. All authors read and approved the final version of the manuscript.

Acknowledgement

The authors acknowledge and appreciate the DHS program, USAID and ICF Macro for granting permission and access to use the data employed in this study.

Nurul-Farehah S, Rohana AJ. Maternal obesity and its determinants: A neglected issue? Malaysian Fam Physician. 2020;15:34–42.
Amegbor PM, Yankey O, Davies M, Sabel CE. Individual and contextual predictors of overweight or obesity among women in Uganda: a spatio-temporal perspective. GeoJournal. 2022;87:3793–813.
Cavaco S, Eriksson T, Skalli A. Life cycle development of obesity and its determinants in six European countries. Econ Hum Biol. 2014;14:62–78.
World Health Organization (WHO). (2021) Obesity and overweight. In: Fact sheet. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Accessed 6 Sep 2021.
Bentham J, Di Cesare M, Bilano V, 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;390:2627–42.
Owobi O, Okonji O, Nzoputam C, Ekholuenetale M. Country-Level Variations in Overweight and Obesity among Reproductive-Aged Women in Sub-Saharan Countries. Women. 2022;2:313–25.
Jaacks LM, Vandevijvere S, Pan A, McGowan CJ, Wallace C, Imamura F, Mozaffarian D, Swinburn B, Ezzati M. The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol. 2019;7:231–40.
Hoffman D. The Double Burden of Malnutrition: Research Agenda for Reversing Global Trends. Ann Nutr Metab. 2019;75:149–52.
Akombi BJ, Chitekwe S, Sahle BW. (2019) Estimating the Double Burden of Malnutrition among Countries: A Meta-Analysis of Demographic and Health Surveys. 1–11.
Hillemeier MM, Weisman CS, Chuang C, Downs DS, McCall-Hosenfeld J, Camacho F. Transition to overweight or obesity among women of reproductive age. J Women’s Heal. 2011;20:703–10.
Ahmed KY, Rwabilimbo AG, Abrha S, et al. Factors associated with underweight, overweight, and obesity in reproductive age Tanzanian women. PLoS ONE. 2020;15:1–16.
Micklesfield LK, Lambert EV, Hume DJ, Chantler S, Pienaar PR, Puone T, Goedecke JH. (2013) Socio-culturnvironmental and behavioural determinants of obesity in black South African women. Cardiovasc J Afr 24.
Shrimpton R, Rokx C. (2012) The Double Burden of Malnutrition - a review of global evidences. Heal Nutr Popul Discuss Pap 1–59.
Ntenda PAM, Kazambwe JF. A multilevel analysis of overweight and obesity among non-pregnant women of reproductive age in Malawi: Evidence from the 2015-16 Malawi Demographic and Health Survey. Int Health. 2019;11:496–506.
Singh GK, Dibari JN, Lee H. Prevalence and Social and Built Environmental Determinants of Maternal Prepregnancy Obesity in 68 Major Metropolitan Cities of the United States, 2013–2016. J Environ Public Health. 2021. https://doi.org/10.1155/2021/6650956.
Badru O, Alabi TA. (2023) There is high prevalence of overnutrition among married and cohabiting women in Nigeria: Findings from the 2018 Nigeria Demographic and Health Survey. Qeios 1–18.
Tagbo S, Abebe D, Oguoma V. Overweight and obesity among non-pregnant women of reproductive age in Nigeria: findings from the 2008–2018 Nigerian Demographic and Health Survey. Public Health. 2021;198:348–57.
Akokuwebe ME, Idemudia ES. Multilevel analysis of urban–rural variations of body weights and individual-level factors among women of childbearing age in nigeria and South Africa: A cross-sectional survey. Int J Environ Res Public Health. 2022. https://doi.org/10.3390/ijerph19010125.
Ojo OV. (2016) Ethnic Diversity in Nigeria: A purview of mechanism for national ethnic diversity in Nigeria. In: Afro Asian J. Soc. Sci. https://www.onlineresearchjournals.com/aajoss/art/207.pdf. Accessed 11 Dec 2023.
National Population Commission (NPC) Nigeria and ICF. Nigeria Demographic and Health Survey 2018. NPC and ICF, Abuja, Nigeria and Rockville. Maryland, USA: NPC and ICF; 2019.
Kamal SMM. Individual- And community-level factors associated with underweight and overweight among women of reproductive age in Bangladesh: A multilevel analysis. J Biosoc Sci. 2021. https://doi.org/10.1017/S0021932021000195.
Ahammed B, Sarder MA, Kundu S, Keramat SA, Alam K. Multilevel exploration of individual- and community-level factors contributing to overweight and obesity among reproductive-aged women: a pooled analysis of Bangladesh Demographic and Health Survey, 2004–2018. Public Health Nutr. 2022;25:2074–83.
Um S, Yom A, Muir A, Sopheab J H. Practice and Factors associated with Overweight and Obesity among women of reproductive age in Cambodia: Analysis of Cambodia Demographic and Health Survey 2021–2022. PLOS Glob Public Heal. 2023;3:e0001768.
Song J, Zhang J, Fawzi W, Huang Y. Double burden of malnutrition among chinese women of reproductive age and their social determinants. Nutrients. 2020;12:1–12.
Asosega KA, Aidoo EN, Adebanji AO, Owusu-Dabo E. Examining the risk factors for overweight and obesity among women in Ghana: A multilevel perspective. Heliyon. 2023;9:e16207.
Seidu AA, Ahinkorah BO, Agbaglo E, Nyaaba AA. Overweight and obesity among women of reproductive age in Mali: What are the determinants? Int Health. 2021;13:428–35.
Yeshaw Y, Kebede SA, Liyew AM, Tesema GA, Agegnehu CD, Teshale AB, Alem AZ. Determinants of overweight/obesity among reproductive age group women in Ethiopia: Multilevel analysis of Ethiopian demographic and health survey. BMJ Open. 2020;10:1–7.
Timmermans YEG, Van De Kant KDG, Krumeich JSM, Zimmermann LJI, Dompeling E, Kramer BW, Maassen LLJ, Spaanderman MAE, Vreugdenhil ACE. Socio-ecological determinants of lifestyle behavior of women with overweight or obesity before, during and after pregnancy: Qualitative interview analysis in the Netherlands. BMC Pregnancy Childbirth. 2020;20:1–11.
Bell R, Smith C, Hale L, Kira G, Tumilty S. Understanding obesity in the context of an Indigenous population—A qualitative study. Obes Res Clin Pract. 2017;11:558–66.
Knight-Agarwal CR, Williams LT, Davis D, Davey R, Shepherd R, Downing A, Lawson K. The perspectives of obese women receiving antenatal care: A qualitative study of women’s experiences. Women Birth. 2016;29:189–95.
Knight-Agarwal C. (2016) A mixed methods study in exploring factors important in maternal obesity prevention. https://doi.org/https://doi.org/10.26191/z79e-f622.

Table 1

Distribution of Respondents according to the States and other explanatory variables
Variables	Frequency	Percentage
Sub-nationals Sokoto (R) Zamfara Katsina Jigawa Yobe Borno Adamawa Gombe Bauchi Kano Kaduna Kebbi Niger FCT Abuja Nasarawa Plateau Taraba Benue Kogi Kwara Oyo Osun Ekiti Ondo Edo Anambra Enugu Ebonyi Cross River Akwa Ibom Abia Imo Rivers Bayelsa Delta Lagos Ogun	212 374 686 384 390 457 258 218 371 671 729 332 426 106 216 260 264 447 193 230 554 341 186 233 163 471 305 317 218 318 222 369 603 106 284 928 339	1.6 2.8 5.2 2.9 3.0 3.5 1.9 1.7 2.8 5.1 5.5 2.5 3.2 0.8 1.6 1.9 2.0 3.4 1.5 1.8 4.2 2.6 1.4 1.8 1.2 3.6 2.3 2.4 1.7 2.4 1.7 2.8 4.6 0.8 2.2 7.0 2.6
Individual level Factors
Age groups in years 15–19 20–24 25–29 30–34 35–39 40–44 45–49 Mean (S..D): 29.5 ± 9.7	2,557 1,966 2,225 2,020 1,918 1,263 1,231	19.4 14.9 16.9 15.3 14.6 9.6 9.3
Highest level of Education No education Primary Secondary Higher	4,194 2,032 5,538 1,416	31.8 15.4 42.0 10.8
Religion Christian Islam Others	6,460 6,643 77	49.0 50.4 0.6
Marital status Never in a union Currently in a union Formerly in a union	3,296 9,142 743	25.0 69.4 5.6
Employment status (last 12 months) Not employed Employed	3,991 9,189	30.3 69.7
Number of living children No child 1–2 children 3–4 children 5 + children	3,527 3,373 3,045 3,235	26.8 25.6 23.1 24.5
Number of living children ever born No child 1–2 children 3–4 children 5 + children	3,446 3,020 2,680 4,035	26.1 23.0 20.3 30.6
Household level factors
Husband/partner’s education No education Primary Secondary Higher	2,810 1,357 3,361 1,478	31.2 15.1 37.3 16.4
Husband/partner’s employment (last 12 months) Not employed Employed	304 8,809	3.3 96.7
Number of usual household members 1–5 6–10 > 10	5,871 5,394 1,750	45.1 41.5 13.4
Household wealth index Poorest Poorer Middle Richer Richest	2,045 2,427 2,652 2,947 3,109	15.5 18.4 20.1 22.4 23.6
Water source Unimproved Improved	3,820 9,196	29.4 70.6
Community level factors
Place of residence Urban Rural	6,241 6,939	47.4 52.6
Community education level Low high	6,892 6,288	52.3 47.7
Community wealth level Low high	6,593 6,587	50.0 50.0

Table 2

Sub-national variation of Obesity among non-pregnant women of reproductive age in Nigeria after controlling for known risk factors using logistics regression analysis
	^aModel 0			^bModel 1			^cModel 2			^dModel 3			^eModel 4
	OR	p	95% CI	OR	p	95% CI	OR	p	95% CI	OR	p	95% CI	OR	p	95% CI
Sub-nationals
Sokoto (R)
Zamfara	2.1	0.019	1.1, 4.1	2.3	0.009	1.2, 4.2	2.6	0.005	1.3, 5.0	1.9	0.028	1.1, 3.4	2.8	0.002	1.5, 5.4
Katsina	3.0	0.001	1.6, 5.6	2.8	0.001	1.5, 5.3	2.7	0.003	1.4, 5.2	2.8	0.001	1.5, 5.1	2.8	0.002	1.5, 5.3
Jigawa	1.2	0.642	0.6, 2.6	1.1	0.887	0.5, 2.2	1.1	0.807	0.5, 2.5	1.2	0.604	0.6, 2.6	1.1	0.820	0.5, 2.5
Yobe	1.2	0.740	0.5, 2.9	1.1	0.870	0.4, 2.6	1.2	0.631	0.5, 3.1	1.2	0.705	0.5, 2.9	1.1	0.804	0.5, 2.8
Borno	2.1	0.029	1.1, 4.1	1.7	0.112	0.9, 3.4	1.5	0.248	0.7, 3.1	1.5	0.219	0.8, 2.7	1.3	0.494	0.6. 2.6
Adamawa	3.6	< 0.001	2.0, 6.7	2.3	0.009	1.2, 4.3	3.4	< 0.001	1.8, 6.3	2.9	< 0.001	1.6, 5.3	2.5	0.005	1.3, 4.8
Gombe	2.8	0.002	1.5, 5.4	2.1	0.014	1.2, 3.9	2.3	0.010	1.2, 4.2	2.6	0.001	1.5, 4.6	1.8	0.053	1.0, 3.3
Bauchi	2.4	0.010	1.2, 4.5	2.1	0.017	1.2, 4.0	2.2	0.021	1.1, 4.4	2.3	0.006	1.3, 4.3	2.4	0.009	1.3, 4.7
Kano	2.7	0.002	1.4, 5.2	2.2	0.012	1.2, 4.02	2.0	0.038	1.0, 3.7	2.3	0.007	1.3, 4.1	1.8	0.065	1.0, 3.5
Kaduna	4.5	< 0.001	2.5, 8.3	3.2	< 0.001	1.8, 5.6	2.9	0.001	1.6, 5.5	3.6	< 0.001	2.1, 6.3	2.5	0.004	1.4, 4.5
Kebbi	2.3	0.012	1.2, 4.3	2.2	0.017	1.2, 4.2	2.6	0.005	1.3, 5.1	2.2	0.011	1.2, 4.1	2.5	0.009	1.3, 5.1
Niger	3.9	< 0.001	2.2, 7.2	3.4	< 0.001	1.9, 6.0	2.9	0.001	1.6, 5.5	3.4	< 0.001	1.9, 5.9	2.9	0.001	1.6, 5.3
FCT Abuja	8.7	< 0.001	4.5,16.8	5.0	< 0.001	2.7, 9.4	3.8	< 0.001	1.9, 7.9	4.9	< 0.001	2.7, 8.8	2.7	0.007	1.3, 5.6
Nasarawa	5.6	< 0.001	3.1,10.1	3.4	< 0.001	1.8, 6.3	2.9	0.001	1.6, 5.4	3.8	< 0.001	2.2, 6.7	2.2	0.026	1.1, 3.9
Plateau	5.5	< 0.001	2.9,10.3	2.9	0.001	1.5, 5.5	4.4	< 0.001	2.3, 8.4	4.4	< 0.001	2.4, 8.0	2.6	0.005	1.3, 5.1
Taraba	4.2	< 0.001	2.3,7.7	3.0	< 0.001	1.6, 5.3	4.1	< 0.001	2.1, 7.8	3.9	< 0.001	2.2, 6.9	3.3	< 0.001	1.8, 6.3
Benue	4.0	< 0.001	2.2,7.4	2.1	0.015	1.2, 3.9	3.3	< 0.001	1.7, 6.4	3.6	< 0.001	2.0, 6.2	2.2	0.027	1.1, 4.3
Kogi	5.1	< 0.001	2.7, 9.7	2.8	0.001	1.5, 5.1	3.5	< 0.001	1.7, 7.0	3.4	< 0.001	1.9, 6.2	2.2	0.027	1.1, 4.5
Kwara	4.9	< 0.001	2.6, 9.3	3.1	< 0.001	1.7, 5.6	3.5	< 0.001	1.8, 6.8	3.3	< 0.001	1.8, 5.9	2.4	0.010	1.2, 4.7
Oyo	6.1	< 0.001	3.4,11.2	2.6	0.001	1.4, 4.7	2.7	0.002	1.4, 5.1	3.2	< 0.001	1.8, 5.8	1.7	0.104	1.0, 3.2
Osun	5.4	< 0.001	2.9,10.2	2.5	0.005	1.3, 4.7	3.4	< 0.001	1.8, 6.7	3.1	< 0.001	1.7, 5.6	2.1	0.034	1.1, 4.1
Ekiti	6.1	< 0.001	3.4,11.1	2.4	0.004	1.3, 4.4	3.4	< 0.001	1.7, 6.6	3.2	< 0.001	1.8, 5.7	1.8	0.098	1.0, 3.6
Ondo	5.3	< 0.001	2.8,10.0	2.3	0.011	1.2, 4.2	3.2	0.001	1.6, 6.2	3.1	< 0.001	1.7, 5.7	1.7	0.139	1.0, 3.3
Edo	8.7	< 0.001	4.5,17.3	3.5	< 0.001	1.8, 7.0	3.7	< 0.001	1.8, 7.7	5.4	< 0.001	2.8,10.4	2.0	0.081	0.9, 4.2
Anambra	16.3	< 0.001	8.8,30.1	2.2	< 0.001	3.8,13.2	7.4	< 0.001	3.9,14.3	7.9	< 0.001	4.4,14.1	4.4	< 0.001	2.2, 8.7
Enugu	7.9	< 0.001	4.4,14.2	3.6	< 0.001	2.0, 6.5	5.1	< 0.001	2.7, 9.7	4.5	< 0.001	2.6, 7.8	2.6	0.005	1.4, 5.1
Ebonyi	3.6	< 0.001	1.9, 6.7	1.5	0.197	0.8, 2.9	2.8	0.003	1.4, 5.5	2.7	0.001	1.5, 4.8	1.5	0.283	0.7, 3.0
Cross River	7.0	< 0.001	3.9,12.7	3.1	< 0.001	1.7, 5.7	4.4	< 0.001	2.2, 8.9	4.7	< 0.001	2.7, 8.2	2.3	0.024	1.1, 4.9
Akwa Ibom	10.2	< 0.001	5.6,18.6	4.9	< 0.001	2.7, 8.9	7.0	< 0.001	3.5,13.8	6.5	< 0.001	3.7,11.6	4.5	< 0.001	2.2, 9.0
Abia	7.8	< 0.001	4.3,41.2	2.9	< 0.001	1.6, 5.4	3.3	< 0.001	1.7, 6.5	4.1	< 0.001	2.3, 7.2	1.7	0.132	0.9, 3.3
Imo	11.4	< 0.001	6.3,20.5	4.1	< 0.001	2.3, 7.6	6.3	< 0.001	3.4,11.8	5.7	< 0.001	3.3, 10.0	3.1	< 0.001	1.7, 5.9
Rivers	12.5	< 0.001	6.9,22.6	5.6	< 0.001	3.0,10.3	5.7	< 0.001	3.0,10.6	6.5	< 0.001	3.7,11.4	3.1	0.001	1.6, 6.0
Bayelsa	7.7	< 0.001	4.1,14.5	3.9	< 0.001	2.0, 7.6	4.1	< 0.001	2.1, 8.0	5.3	< 0.001	2.9,10.0	2.9	0.005	1.4, 6.0
Delta	11.0	< 0.001	6.1,20.1	4.9	< 0.001	2.7, 9.0	5.7	< 0.001	3.0,10.9	5.9	< 0.001	3.3,10.3	3.1	0.001	1.6, 6.0
Lagos	14.1	< 0.001	8.0,25,1	6.5	< 0.001	3.7,11.6	5.6	< 0.001	2.9,10.8	6.4	< 0.001	3.7,11.0	3.2	< 0.001	1.7, 6.2
Ogun	7.4	< 0.001	4.0,13.7	3.1	< 0.001	1.7, 5.8	2.8	0.004	1.4, 5.6	4.2	< 0.001	2.3, 7.5	1.8	0.125	0.9, 3.6

R- reference variable; * Statistically significant; CI – confidence interval; ꭓ²– chi-squared test; p – p-value

^aModel 0 is the empty model showing crude/unadjusted rates;^bModel 1 adjusted for individual characteristics, which include age, educational status, religion, marital status, employment status, number of living children and number of children ever born; ^cModel 2 adjusted for the household characteristics, which are husband/partner’s education, husband/partner’s employment status, number of usual household members, household wealth index and water source; ^dModel 3 adjusted for community characteristics, which are the residence, community educational level and community wealth level; ^eModel 4 is the full model that adjusted for all the explanatory variables.

No competing interests reported.

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Obesity among non-pregnant women in the 36 States and federal capital territory of Nigeria; sub-national variation of a national epidemic

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