https://orcid.org/0000-0001-6628-8180
Figures
Abstract
Background
Obesity and overweight are known public health problems that affect populations across the world. These conditions have been associated with a wide range of chronic diseases including type 2 diabetes mellitus, cardiovascular disease, and cancers. In Ethiopia, the literature regarding the burden of central (abdominal) obesity is scarce. This study aimed to fill this gap by assessing the prevalence and risk factors associated with central obesity among adults in Ethiopia.
Methods
From May to July 2021, a community-based cross-sectional survey was conducted on a sample of 694 adults aged ≥18 years in administrative towns of Bale zone, Southeast Ethiopia. Multi-stage sampling followed by systematic random sampling was employed to identify study participants. Waist and hip circumferences were measured using standard protocols. The World Health Organization STEPS wise tool was used to assess risk factors associated with central obesity. Bi-variable and multi-variable binary logistic regression were used to identify factors associated with central obesity. Adjusted odds ratios (AOR) and their corresponding 95% confidence intervals (CI) have been reported to estimate the strength of associations.
Results
The overall prevalence of central obesity using waist circumference was 39.01% [(95% CI: 35.36–42.76; 15.44% for men and 53.12% for women)]. Multi-variable binary logistic regression analysis revealed that female sex (AOR = 12.93, 95% CI: 6.74–24.79), Age groups: 30–39 years old (AOR = 2.8, 95% CI: 1.59–4.94), 40–49 years (AOR = 7.66, 95% CI: 3.87–15.15), 50–59 years (AOR = 4.65, 95% CI: 2.19–9.89), ≥60 years (AOR = 12.67, 95% CI: 5.46–29.39), occupational status like: housewives (AOR = 5.21, 95% CI: 1.85–14.62), self-employed workers (AOR = 4.63, 95% CI: 1.62–13.24), government/private/non-government employees (AOR = 4.68, 95% CI: 1.47–14.88), and skipping breakfast (AOR = 0.46, 95% CI: 0.23–0.9) were significantly associated with central obesity.
Citation: Tekalegn Y, Solomon D, Sahiledengle B, Assefa T, Negash W, Tahir A, et al. (2022) Prevalence of central obesity and its associated risk factors among adults in Southeast Ethiopia: A community-based cross-sectional study. PLoS ONE 17(8): e0265107. https://doi.org/10.1371/journal.pone.0265107
Editor: Sabine Rohrmann, University of Zurich, SWITZERLAND
Received: February 22, 2022; Accepted: July 21, 2022; Published: August 5, 2022
Copyright: © 2022 Tekalegn et al. 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.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: This study was funded by Madda Walabu University, grant number: N/A The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The World Health Organization (WHO) defines obesity as an abnormal or excess fat accumulation that may impair health [1]. Obesity has been associated with the imbalance between intake and energy expenditure, a condition that may be caused by either non-modifiable or modifiable risk factors [2]. This caloric imbalance creates an excess accumulation of energy, which in turn is stored in the body resulting in excess body weight. Although genetic factors are not modifiable, obesity can result from a complex interaction between environmental, socio-economic, and/or personal behaviors. Addressing the modifiable factors play as one of the critical strategies in preventing obesity [3]. Globally, the prevalence of overweight and obesity increased by 27.5% for adults and 47.1% for children between 1980 and 2013. The number of individuals with overweight and obesity increased from 857 million in 1980 to 2.1 billion in 2013 [4]. If this secular trend continues persistently, 38% of the world’s adult population will be overweight and another 20% will be obese by 2030 [5].
Worldwide, a high body mass index (BMI) has been reported as responsible for four million deaths and 120 million disability-adjusted life years [6]. Several studies have further documented obesity as the risk factor for many non-communicable diseases (NCDs) and chronic health conditions including hypertension, high lipid concentrations, type 2 diabetes, coronary heart disease, stroke, and certain cancers [7–22]. Central (abdominal) obesity, measured in waist circumference, waist to hip ratio, and waist to height ratios, is highly linked with increased risk of morbidity and mortality and is considered to be superior to BMI in predicting cardiovascular disease and mortality risks [23–25].
Many low and middle-income countries (LMICs), including Ethiopia, currently face a double burden of malnutrition [26]. While LMICs are dealing with problems of infectious diseases and undernutrition, they also experience a rapid increase in non-communicable disease risk factors including overweight and obesity [1, 27–29]. The emergence of the Coronavirus Disease (COVID-19) pandemic has further challenged health systems, economies, and populations across the globe, with LMICs being severely affected [30, 31]. Evidence suggests that NCDs are dramatically increasing in Ethiopia and it was estimated that NCDs were responsible for 711 deaths per 100,000 population in 2015 [32]. Cardiovascular diseases, cancer, diabetes, and mental disorders were reported to be responsible for 30% of the total disease burden in Ethiopia as measured in age-standardized disability-adjusted life years (DALYs) rates in 2017 [33].
In Ethiopia, epidemiological studies regarding the prevalence, distribution, and determinants of obesity are meager. More specifically, few studies have assessed the prevalence and risk factors of central (abdominal) obesity [34–39], a superior predictor of NCDs. The prevalence reported in the aforementioned previous studies ranges from 15.5% in the northern part of Ethiopia [36] to 37.4% in southwest Ethiopia [39]. Similarly, risk factors identified by those studies vary from district to district. As a result of this diversity, it is necessary to study the prevalence and context-based risk factors in different settings which will have local and national implications for prevention efforts / public health campaigns to address obesity and other chronic diseases. To the best of our knowledge, there is no published evidence on the magnitude and risk factors associated with central obesity in Southeast Ethiopia. The current study aims to assess the prevalence of central (abdominal) obesity and its associated risk factors in the administrative towns of Bale Zone, Southeast Ethiopia.
Methods and materials
Study design, setting, and subjects
From May to July 2021, a community-based cross-sectional study was conducted to assess the prevalence of central (abdominal) obesity among adults (≥18 years) in the administrative towns of Bale zone, Southeast Ethiopia. All adults residing in the study area for at least 6 months were eligible for inclusion. However, potential participants were excluded if they had: psychiatric problems, hearing impairments, body deformities (kyphosis and scoliosis), other debilitations, and/ or handicaps. Pregnant women were also ineligible for inclusion. Bale zone, one of the Zonal administrative units in the Southeastern part of Oromia regional state (an area of 43,690.56 km2), is located between 50 22’– 80 08’ latitude north and between 380 41’- 400 44’ longitude east. According to the Central Statistical Agency (CSA), the Bale zone had a total population of 1,840,746, including 932,224 men and 908,522 women in 2017. Of the total inhabitants in Bale Zone, 269,139 (14.62%) were urban dwellers and 1,571,605 (85.38%) were rural residents [40]. The zone comprises 18 districts and a total of 297,081 households (with an average of 4.72 persons per household). There are two administrative towns in the zone namely, Robe and Goba towns. Furthermore, each administrative town is divided into Kebeles (smaller administrative units). Kebeles are further subdivided into smaller clusters known as ‘gots’. Robe and Goba towns have 36 and 24 gots respectively. According to the 2021 administrative report, the population of Robe and Goba towns comprised 73152 and 52785 people respectively.
Sample size determination and sampling procedures
Based on the total population in the zone, the study sample size of 700 individuals was calculated using OpenEpi (Version 3, an open-source calculator) and considering the following parameters: 95% level of confidence, 4% margin of error, 24.4% reported prevalence of abdominal obesity by the previous study in Dilla town, South Ethiopia [38], design effect of 1.5, and non-response rate of 5%. A multi-stage stratified sampling and, systematic random sampling was employed to select the study participants. Initially, the study population sampling was stratified into the two (Robe and Goba) towns, followed by stratification into randomly selected clusters (gots). One-third of clusters were selected from each town, and proportionate to their number, twelve and eight gots were selected from Robe and Goba respectively. Furthermore, households in the sampled clusters were selected using systematic sampling techniques and one adult per sampled household was selected using the lottery method.
Data collection and measurement procedures
Interviewer-administered, structured questionnaires were used to collect data on socio-demographic and behavioural characteristics followed by physical measurements of weight, height, waist, and hip circumferences. A standard questionnaire was adapted from the WHO STEPS-wise questionnaire for chronic disease risk factor surveillance [41]. The English version of the questionnaire was translated into Afan Oromo and Amharic, the local languages spoken in the study area. After the translation, the questionnaire was -translated back to English to check the consistency. Weight was measured using an electronic digital weighing scale (Healthgenie HD-221) by putting the scale on a firm flat surface. Before taking weight, participants were asked to take off footwear, heavy clothes, and empty their pockets of heavy items. Participants’ height measurements were taken in a standing position by a portable height measuring board placed on a firm surface against the wall. With participants facing the data collector, feet placed together, and eyes leveled at the ears, readings were taken in centimeters (to the nearest 0.1 centimeters). With the participants’ arms relaxed at the sides, the waist circumferences were measured by constant tension tape at the end of a normal expiration; at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest (hip bone). Waist measurements were read at the level of the tape to the nearest 0.1 centimeters while ensuring the tape was comfortably tight enough not to cause compression of the skin. Hip circumferences were measured by constant tension tape with the arms relaxed at the sides at the maximum circumference over the buttocks. Hip circumferences were measured and read at the level of the tape to the nearest 0.1 centimeters. Further details on the study’s physical measurement protocols were consistent with the WHO STEPS-wise instrument guideline [42].
Data collection was conducted by six data collectors (three male-female pairs) with bachelor’s degree in health sciences (Nursing, Public Health, and Midwifery). Female and male data collectors respectively collected female and male participants’ data. Two supervisors with master’s degrees in public health oversaw the data collection process. Data collectors and supervisors were provided with a two-day intensive training on the objective of the study, administration protocols for the questionnaires, administration protocols for anthropometric measurements (weight, height, waist, and hip circumferences), and how to maintain confidentiality and privacy of the study participants. Assisted by the study supervisors, every day before leaving the households (where the data was collected), data collectors checked all the questionnaires for completeness.
Outcome variable
Derived from waist circumference measurements, central (abdominal) obesity was the dependent variable for the current study. As per the World Health Organization recommendations, waist-circumference >94 centimeters for males and >80 centimeters for females were categorized as central obesity [23]. Individuals with central obesity were coded as “1” and others were coded as “0”.
Independent variables
Independent variables included sociodemographic and behavioral variables. Sociodemographic variables included the town of residence (Robe or Goba); age (categorized into 18–29, 30–39, 40–49, 50–59, and ≥ 60 years); sex (male or female), marital status (categorized as never married, married/cohabiting, or divorced/separated/widowed); educational status (categorized as no formal education, secondary education, or diploma and above); occupational status (housewives, self-employed, government/non-government/private employee, student/unemployed, or Retired) and family size (categorized as ≤ 2, >2). The wealth index was computed using principal component analysis (PCA), household asset items, and other variables [43, 44]. We examined the assumptions of PCA including: correlation matrix for the variables containing 2 or more correlations ≥ 0.30, variables with measures of sampling adequacy less than 0.50 that must be removed (looking anti-image),the overall measures of sampling adequacy (KMO) ≥ 0.5) [45], Bartlett test of sphericity (p-value < 0.05), having commonality > 0.5, and not having the complex structure (correlation ≥ 0.40). Components that collectively explained more than 60% of the variance in the set of variables and eigenvalues > 1 [46, 47] were used to identify variables to be included in further analyses. Ultimately, the economic status of study subjects was categorized into tertiles as rich, medium, and poor. Behavioral variables included characteristics such as fruit and vegetable consumption. One medium-sized piece of apple, banana, or orange and/or a half cup of chopped or cooked fruit was considered to be one serving. One cup of raw green leafy vegetables and/or half a cup of other vegetables, cooked or chopped raw was considered to be one serving. Categories of fruit and/or vegetables consumption were described as less than five servings of fruit and/or vegetables per day, or five or more servings of fruit and/or vegetables per day. Other food-related variables included the number of days consuming fruits and/or vegetables, skipping breakfast (categorized as yes or no), and avoidance of eating foods prepared outside the home (categorized as yes or no). Levels of physical activities was derived by calculating the metabolic equivalent value (MET) minutes. We used MET-minutes to capture the intensity of physical activity. MET is the ratio of a person’s working metabolic rate relative to the resting metabolic rate. One MET was defined as the energy cost of sitting quietly and is equivalent to a caloric consumption of 1 kcal/kg/hour. It is estimated that, compared to sitting quietly, a person’s caloric consumption is four times as high when being moderately active, and eight times as high when being vigorously active. Therefore, for the calculation of the categorical indicator of the recommended amount of physical activity for health, the total time spent in physical activity during a typical week and the intensity of the physical activity were taken into account. Further details on the calculation of the MET values are consistent with the WHO STEPS-wise instrument guideline [42]. All domains of physical activity including work, transport, and recreation were used for the calculation of MET minutes. Study participants with an equivalent combination of moderate and vigorous-intensity physical activity achieving at least 600 MET-minutes were categorized as having a sufficient level of physical activity while others were categorized as having insufficient physical activity levels. Sedentary activities were measured by adding the total time spent sitting or reclining on a typical day. Current smokers of tobacco were categorized as yes or no and participants who consumed alcohol were categorized as never drank, consumed in the last 12 months, or consumed in the last 30 days. Measurements including fruit and/or vegetable consumption, level of physical activities, alcohol, and/or tobacco consumption were assessed and analyzed using World Health Organization (WHO) STEPS Surveillance tool recommendations. Detailed information has been reported elsewhere [42].
Data analysis procedures
The data were coded and entered into EpiData Version 3.1 and were cleaned, processed and analyzed using SPSS version 25 and STATA version 14. The study variables were described using mean, frequencies, proportions, and tables. The Chi-square test was used to check the statistical difference in the distribution of categorical independent variables between men and women. A two-sample Wilcoxon rank-sum (Mann-Whitney) test was used to check the statistical difference in the distribution of continuous independent variables between men and women. Both bi-variable and multi-variable binary logistic regression analyses were used to identify factors associated with the outcome variable. Variables having a p-value of less than 0.25 in the bi-variable binary logistic regression model were included in the multivariable binary logistic regression analysis model. The enter method was used to run the model. The logit of the dependent variable was checked for outliers and 7 outlying values (having standardized residual >2.58 at the level of α<0.01) were excluded from the analysis. Hosmer and Lemeshow’s goodness of fit model was checked and the data fitted the model well (p = 0.92). Multi-collinearity between independent variables was checked using the variance inflation factor (VIF), the mean VIF was 2.1 which is less than the recommended cut-off values [48]. Finally, adjusted odds ratios with 95% confidence intervals were used to estimate the strength of associations between the outcome variable and independent variables. All tests were two-tailed and statistical significances were declared at a p-value <0.05.
Ethical considerations
Ethical clearance and support letters to introduce the researchers and the study to the respective study areas were obtained from the ethical review committee of Madda Walabu University. Permission letters to conduct the survey were obtained from the respective authorities of the two towns (Robe and Goba). The methods were conducted following the tenets of the Helsinki declaration. To obtain oral informed consent an information sheet was read to all eligible study participants before data were collected. The privacy of the respondents was respected and data were de-identified before analysis and were reported in aggregate.
Inclusivity in global research
Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the Supporting Information (S1 Checklist).
Results
Sociodemographic characteristics of study participants
A total of 694 adults (259 men and 435 women) participated in this study with a response rate of 99.1%. Four hundred and eight (58.8%) and 286 (41.2%) study participants were from Robe and Goba towns, respectively. Participants’ age ranged from 18–95 years old. Men participants’ median age was 41 years old with an interquartile range (IQR) of 28–55 years. Women’s median age was 32 years with an IQR of 25–45 years (Table 1).
Behavioral characteristics of the study participants
More than half (58%) of the study participants consumed less than five servings of fruit and/or vegetable per day. The mean number of days when fruit was consumed per week was 2.3 days. Approximately 15% of the study participants reported skipping breakfast and another 15% avoided eating foods prepared outside the home. Nearly 29% of the study participants had an insufficient level of physical activity as per World Health Organization recommendations (Table 2).
Alcohol and tobacco use behavioural characteristics of the study participants
Only 7 (1%) participants indicated currently smoking tobacco products and 222 (32.0%) consumed alcohol in the past twelve months (Table 3).
Prevalence of central obesity
As described in Table 4, the overall prevalence of central obesity was 39.01% (95% CI: 35.36–42.76). Of the 259 men included in this study, 40 (15.44%, 95% CI: 11.26–20.43%) had waist circumference >94 centimeters. Of the 433 women included in the study, 230 (53.12%, 95% CI: 48.29–57.89%) had a waist circumference greater than 80 centimeters. Based on the waist-to-hip ratios, 204 (47.11%, 95% CI: 42.32, 51.93%) and 129 (49.80%, 95% CI: 43.55–56.06%) women and men had wait to hip ratios of greater than or equal to 0.85 and 0.90, respectively.
Factors associated with central (abdominal) obesity
Findings of the multivariable binary logistic regression analysis revealed that women had higher odds of central obesity compared to men (AOR: 12.93, 95% CI: 6.74–24.79). Age groups 30–39 (AOR: 2.80, 95% CI: 1.59–4.94), 40–49 (AOR: 7.66, 95% CI: 3.87–15.15), 50–59 (AOR: 4.65, 95% CI: 2.19–9.89), and ≥60 years (AOR: 12.67 95% CI: 5.46–29.39) had higher odds of central obesity compared to those below 30 years old. Likewise, Government/Non-governmental/Private employees (AOR: 4.68, 95% CI: 1.47–14.88), self-employed (AOR: 4.63, 95% CI: 1.62–13.24), and housewives (AOR: 5.21, 95% CI: 1.85–14.62) were more likely to be centrally obese compared to unemployed or students. Moreover, individuals who skipped breakfast were at a 54% reduced risk of central obesity compared to their counterparts (Table 5).
Discussion
The findings of the current study reveal that the overall prevalence of central (abdominal) obesity was 39.0% (95% CI: 35.36–42.76). In comparison to men, women had a higher prevalence of central obesity (53.1% vs 15.4%). The figures reported in this study are comparable with studies conducted in Gondar and Dabat towns, Northwest Ethiopia (37.6% & 33.6%) [37, 49], but higher than the studies conducted in Nekemte town, West Ethiopia (28.4%) [35], Woldia town, Northeast Ethiopia (15.5%) [36], and Dilla town South Ethiopia (24.4%) [38]. The possible variations in the obesity prevalence could be explained by the use of different cutoff values for waist circumferences [36], and the age distribution variation among the study participants [35, 38]. Furthermore, variation in the gender make-up of the samples might be a possible reason for the differences, given such large differences in prevalence found in this study. Though there are slight variations in the prevalence rates across the regions, the magnitude of central obesity in the study appeared to be high. A recent systematic review and meta-analysis in Ethiopia reported that the prevalence of overweight and obesity is increasing especially in urban settings [50]. The results of the present study confirm these findings and suggest gender as a particularly important factor to examine when estimating overall prevalence rates in Ethiopian regions.
In this study, women had 13 times higher odds of central obesity compared to men. This finding is corroborated by similar studies in different parts of Ethiopia [35–39]. Similarly, studies conducted by Jaacks et al., suggested that in countries with stage 1 obesity transition, the prevalence of obesity is higher among women compared to men [51]. According to a study conducted by the Global Burden of Diseases researchers, the trend of obesity and overweight is persistently higher among women than men in developing countries [4]. Further appropriately powered longitudinal studies within males and females in Ethiopia examining predictors of obesity/central obesity are warranted.
The current study revealed that the odds of central obesity tend to increase as age increases. Elsewhere, a longitudinal study conducted by Baum & Ruhm reported that body weight increased as age increased [52]. The positive association between age and abdominal obesity is supported by several other studies conducted elsewhere including in Ethiopia, China, Russian Federation [35–37, 39, 53–56]. This association might be explained partly by the gradual decline in physiologic activities and basal metabolic rate as age increases [57].
Employed adults and housewives had higher odds of central obesity compared to unemployed or students. This might be explained by the hypothesis that employed adults might have better access to foods than their counterparts. Previous studies in Ethiopia suggested that unemployed men are less likely to be obese compared to employed men [58]. This finding contradicts a study conducted in North Glasgow, UK, which reported that unemployed men and women were less likely to be centrally obese compared to full-time workers [59]. Additional longitudinal epidemiological studies exploring the role of employment status on the development of abdominal obesity in low-income countries are warranted. With regards to findings of the higher odds of central obesity among housewives, gender may play a role here as well. Furthermore, it is possible that that women who stay at home have food within easy reach. Moreover, frequency of meal intakes has been associated with an increase in total energy intake [60], hence increased opportunities to develop obesity.
Individuals who reported skipping their breakfast were 54% less likely to be centrally obese compared to their counterparts. This might be partly explained by the fact that the frequency of meals is associated with an increase in total energy intake [60]. However, this finding is in contrast with a systematic review and meta-analysis conducted by Ma et al., which reported that skipping breakfast resulted in a 31% increase in abdominal obesity [61]. In our study, breakfast skipping behavior was measured through self-reporting and the reason for skipping breakfast was not collected. Furthermore, meal frequency and portion size were not assessed, which could be a source of variation with existing evidence. Furthermore, this study was not topic-specific to assess the association between skipping breakfast and the risk of central obesity.
This study aimed to examine predictors of central (abdominal) obesity in a region with no prior evidence on the distribution and associated risk factors. Although the study used primary data on physical measurements like waist and hip circumferences and the WHO-STEPS wise tool for non-communicable disease risk factors surveillance with trained data collectors and supervisors, the findings must be interpreted in light of the following limitations. Firstly, due to the cross-sectional nature of the study, a cause-effect relationship cannot be established between the risk factors and obesity. Secondly, the study sample was too small for sex and age-specific reporting of the prevalence and risk factors. Differences in prevalence and risk factors by gender suggest the need for gender-specific stratification in larger, appropriately powered longitudinal studies. Lastly, data regarding dietary habits, physical activity, alcohol, and tobacco use were collected through self-reported behavior questionnaires, which might be affected by the recall and social desirability bias. Future studies could use more objective measurements, including third part reporting and wearables to assess these behaviors.
Conclusions
The burden of abdominal obesity is high in the administrative towns of Bale Zone, southeast Ethiopia. One out of every two women and one out of every five men were found to be centrally obese. Female sex, age, and being employed were significantly associated with central obesity, while skipping breakfast was a protective factor. Prevention strategies might be more effective when they occur as part of workplace health promotion programs. Public health campaigns and community health promotion programs directed at women and older adults may also be a valuable use of available resources in order to curb the increasing burden of central obesity and its impact. Further studies of gender-specific risk factors are also warranted to determine the specific associated risk factors and assist in improved tailoring of prevention programs in LMICs.
Supporting information
S1 Checklist. Inclusivity in global research checklist.
https://doi.org/10.1371/journal.pone.0265107.s001
(DOCX)
Acknowledgments
We are grateful to all data collectors, Robe and Goba Health Office and Health extension workers, kebele, and got administrators for their facilitation and cooperation to smoothly conduct this study.
References
- 1.
World Health Organization. Obesity and overweight 2021 [updated 9 June 2021]. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Accessed on December 17, 2021.
- 2. Kelishadi R, Hashemi Pour M, Sarraf‐Zadegan N, Sadry GH, Ansari R, Alikhassy H, et al. Obesity and associated modifiable environmental factors in Iranian adolescents: Isfahan Healthy Heart Program− heart health promotion from childhood. Pediatrics international. 2003;45(4):435–42. pmid:12911481
- 3. Hruby A, Hu FB. The Epidemiology of Obesity: A Big Picture. PharmacoEconomics. 2015;33(7):673–89. pmid:25471927
- 4. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. 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. Lancet. 2014;384(9945):766–81. pmid:24880830
- 5. Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. International journal of obesity (2005). 2008;32(9):1431–7. pmid:18607383
- 6. Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A, et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. The New England journal of medicine. 2017;377(1):13–27. pmid:28604169
- 7. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983;67(5):968–77. pmid:6219830
- 8. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of US adults. New England Journal of Medicine. 2003;348(17):1625–38. pmid:12711737
- 9. Molarius A, Seidell J. Selection of anthropometric indicators for classification of abdominal fatness—a critical review. International journal of obesity. 1998;22(8):719. pmid:9725630
- 10. Kragelund C, Omland T. A farewell to body-mass index? The Lancet. 2005;366(9497):1589–91. pmid:16271629
- 11. Sowers JR. Obesity as a cardiovascular risk factor. The American journal of medicine. 2003;115(8):37–41. pmid:14678864
- 12. Tabrizi JS, Sadeghi-Bazargani H, Farahbakhsh M, Nikniaz L, Nikniaz Z. Prevalence and associated factors of prehypertension and hypertension in Iranian Population: The Lifestyle Promotion Project (LPP). PloS one. 2016;11(10):e0165264. pmid:27783691
- 13. Larsson SC, Wolk A. Obesity and the risk of gallbladder cancer: a meta-analysis. British journal of cancer. 2007;96(9):1457–61. pmid:17375043
- 14. Larsson SC, Wolk A. Overweight, obesity and risk of liver cancer: a meta-analysis of cohort studies. British journal of cancer. 2007;97(7):1005–8. pmid:17700568
- 15. Larsson SC, Wolk A. Obesity and risk of non-Hodgkin’s lymphoma: a meta-analysis. International journal of cancer. 2007;121(7):1564–70. pmid:17443495
- 16. Larsson SC, Wolk A. Overweight and obesity and incidence of leukemia: a meta-analysis of cohort studies. International journal of cancer. 2008;122(6):1418–21. pmid:18027857
- 17. Ohlson L-O, Larsson B, Svärdsudd K, Welin L, Eriksson H, Wilhelmsen L, et al. The influence of body fat distribution on the incidence of diabetes mellitus: 13.5 years of follow-up of the participants in the study of men born in 1913. Diabetes. 1985;34(10):1055–8. pmid:4043554
- 18. Polednak AP. Estimating the number of U.S. incident cancers attributable to obesity and the impact on temporal trends in incidence rates for obesity-related cancers. Cancer detection and prevention. 2008;32(3):190–9. pmid:18790577
- 19. Olsen CM, Green AC, Whiteman DC, Sadeghi S, Kolahdooz F, Webb PM. Obesity and the risk of epithelial ovarian cancer: a systematic review and meta-analysis. European journal of cancer. 2007;43(4):690–709. pmid:17223544
- 20. Kodama S, Horikawa C, Fujihara K, Yoshizawa S, Yachi Y, Tanaka S, et al. Quantitative relationship between body weight gain in adulthood and incident type 2 diabetes: a meta-analysis. Obesity reviews: an official journal of the International Association for the Study of Obesity. 2014;15(3):202–14. pmid:24165305
- 21. Reilly JJ, Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. International journal of obesity (2005). 2011;35(7):891–8. pmid:20975725
- 22. Narayan KM, Boyle JP, Thompson TJ, Gregg EW, Williamson DF. Effect of BMI on lifetime risk for diabetes in the U.S. Diabetes Care. 2007;30(6):1562–6. pmid:17372155
- 23.
Organization WH. Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8–11 December 2008. 2011.
- 24. Sahakyan KR, Somers VK, Rodriguez-Escudero JP, Hodge DO, Carter RE, Sochor O, et al. Normal-Weight Central Obesity: Implications for Total and Cardiovascular Mortality. Annals of internal medicine. 2015;163(11):827–35. pmid:26551006
- 25. Coutinho T, Goel K, Corrêa de Sá D, Carter RE, Hodge DO, Kragelund C, et al. Combining body mass index with measures of central obesity in the assessment of mortality in subjects with coronary disease: role of "normal weight central obesity". Journal of the American College of Cardiology. 2013;61(5):553–60. pmid:23369419
- 26. Eshete T, Kumera G, Bazezew Y, Marie T, Alemu S, Shiferaw K. The coexistence of maternal overweight or obesity and child stunting in low-income country: Further data analysis of the 2016 Ethiopia demographic health survey (EDHS). Scientific African. 2020;9:e00524.
- 27. Popkin BM, Corvalan C, Grummer-Strawn LM. Dynamics of the double burden of malnutrition and the changing nutrition reality. Lancet. 2020;395(10217):65–74. pmid:31852602
- 28. Boutayeb A. The double burden of communicable and non-communicable diseases in developing countries. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2006;100(3):191–9. pmid:16274715
- 29. Bollyky TJ, Templin T, Cohen M, Dieleman JL. Lower-Income Countries That Face The Most Rapid Shift In Noncommunicable Disease Burden Are Also The Least Prepared. Health affairs (Project Hope). 2017;36(11):1866–75. pmid:29137514
- 30.
Bong C-L, Brasher C, Chikumba E, McDougall R, Mellin-Olsen J, Enright A. The COVID-19 pandemic: effects on low-and middle-income countries. Anesthesia and analgesia. 2020.
- 31.
Tan MK. COVID-19 in an inequitable world: the last, the lost and the least. Oxford University Press; 2021. p. 493–6.
- 32. Misganaw A, Haregu TN, Deribe K, Tessema GA, Deribew A, Melaku YA, et al. National mortality burden due to communicable, non-communicable, and other diseases in Ethiopia, 1990–2015: findings from the Global Burden of Disease Study 2015. Population Health Metrics. 2017;15(1):29. pmid:28736507
- 33. Misganaw A, Melaku YA, Tessema GA, Deribew A, Deribe K, Abera SF, et al. National disability-adjusted life years (DALYs) for 257 diseases and injuries in Ethiopia, 1990–2015: findings from the global burden of disease study 2015. Population Health Metrics. 2017;15(1):28. pmid:28732542
- 34.
Janakiraman B, Abebe SM, Chala MB, Demissie SF. Central obesity, not BMI explains cardio-metabolic risks among university employees, Ethiopia–a cross-sectional study. 2019.
- 35. Biru B, Tamiru D, Taye A, Regassa Feyisa B. Central obesity and its predictors among adults in Nekemte town, West Ethiopia. SAGE open medicine. 2021;9:20503121211054988. pmid:34733515
- 36. Dagne S, Menber Y, Petrucka P, Wassihun Y. Prevalence and associated factors of abdominal obesity among the adult population in Woldia town, Northeast Ethiopia, 2020: Community-based cross-sectional study. PloS one. 2021;16(3):e0247960. pmid:33684157
- 37.
Derbew Molla M, Fekadu Wolde H. Magnitude of Central Obesity and its Associated Factors Among Adults in Urban Areas of Northwest Ethiopia. 2021.
- 38. Tesfaye TS, Zeleke TM, Alemu W, Argaw D, Bedane TK. Dietary diversity and physical activity as risk factors of abdominal obesity among adults in Dilla town, Ethiopia. PloS one. 2020;15(7):e0236671. pmid:32730320
- 39.
Yazew G, Abate A, Alemseged F, Tewelde T. Factors Associated with Overweight and Central Obesity in Adults at Gilgel Gibe Field Research Center, Southwest Ethiopia: A Secondary Analysis of Data. 2019.
- 40.
Ababa A. Federal Democratic Republic of Ethiopia central statistical agency population projection of Ethiopia for all regions at Wereda level from 2014–2017. Addis Ababa: Central Statistical Agency. 2014.
- 41.
Bonita R, Winkelmann R, Douglas KA, de Courten M. The WHO Stepwise approach to surveillance (STEPS) of non-communicable disease risk factors. Global behavioral risk factor surveillance: Springer; 2003. p. 9–22.
- 42.
Organization WH. The WHO STEPwise approach to noncommunicable disease risk factor surveillance. Geneva: World Health Organization. 2005.
- 43.
CSA I. Central Statistical Agency (CSA)[Ethiopia] and ICF. Ethiopia Demographic and Health Survey 2016. Addis Ababa, Ethiopia, and Rockville, Maryland, USA: CSA and ICF; 2016. 2017.
- 44.
Rutstein SO. Steps to constructing the new DHS Wealth Index. Rockville, MD: ICF International. 2015.
- 45.
Katchova A. Principal Component Analysis-Econometrics Academy. 2017.
- 46. Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer. Front Public Health. 2018;6:149. pmid:29942800
- 47.
DeVellis RF. Scale development: Theory and applications (Vol. 26). Sage publications Los Angeles; 2016.
- 48. Kim JH. Multicollinearity and misleading statistical results. Korean journal of anesthesiology. 2019;72(6):558.
- 49. Janakiraman B, Abebe SM, Chala MB, Demissie SF. Epidemiology of general, central obesity and associated cardio-metabolic risks among University Employees, Ethiopia: a cross-sectional study. Diabetes, metabolic syndrome and obesity: targets and Therapy. 2020;13:343. pmid:32104031
- 50. Kassie AM, Abate BB, Kassaw MW. Prevalence of overweight/obesity among the adult population in Ethiopia: a systematic review and meta-analysis. BMJ open. 2020;10(8):e039200. pmid:32764091
- 51. Jaacks LM, Vandevijvere S, Pan A, McGowan CJ, Wallace C, Imamura F, et al. The obesity transition: stages of the global epidemic. The lancet Diabetes & endocrinology. 2019;7(3):231–40. pmid:30704950
- 52. Baum CL, Ruhm CJ. Age, socioeconomic status and obesity growth. Journal of health economics. 2009;28(3):635–48. pmid:19261343
- 53. Wang H, Wang J, Liu M-M, Wang D, Liu Y-Q, Zhao Y, et al. Epidemiology of general obesity, abdominal obesity and related risk factors in urban adults from 33 communities of northeast china: the CHPSNE study. BMC Public Health. 2012;12(1):967. pmid:23146089
- 54. Munyogwa MJ, Mtumwa AH. The Prevalence of Abdominal Obesity and Its Correlates among the Adults in Dodoma Region, Tanzania: A Community-Based Cross-Sectional Study. Advances in medicine. 2018;2018:6123156. pmid:30417022
- 55. Malik SK, Kouame J, Gbane M, Coulibaly M, Ake MD, Ake O. Prevalence of abdominal obesity and its correlates among adults in a peri-urban population of West Africa. AIMS public health. 2019;6(3):334–44. pmid:31637282
- 56. Zhernakova YV, Zheleznova EA, Chazova IE, Oshchepkova EV, Dolgusheva YA, Yarovaya EB, et al. The prevalence of abdominal obesity and the association with socioeconomic status in Regions of the Russian Federation, the results of the epidemiological study—ESSE-RF. Terapevticheskii arkhiv. 2018;90(10):14–22. pmid:30701790
- 57. Henry CJ. Mechanisms of changes in basal metabolism during ageing. European journal of clinical nutrition. 2000;54 Suppl 3:S77–91. pmid:11041079
- 58. Tekalegn Y, Engida ZT, Sahiledengle B, Rogers HL, Seyoum K, Woldeyohannes D, et al. Individual and community-level determinants of overweight and obesity among urban men: Further analysis of the Ethiopian demographic and health survey. PloS one. 2021;16(11):e0259412. pmid:34735510
- 59. Chen R, Tunstall-Pedoe H. Socioeconomic deprivation and waist circumference in men and women: The Scottish MONICA surveys 1989–1995. European Journal of Epidemiology. 2005;20(2):141–7. pmid:15792280
- 60. Sievert K, Hussain SM, Page MJ, Wang Y, Hughes HJ, Malek M, et al. Effect of breakfast on weight and energy intake: systematic review and meta-analysis of randomised controlled trials. BMJ (Clinical research ed). 2019;364:l42. www.icmje.org/coi_disclosure.pdf pmid:30700403
- 61.
Ma X, Chen Q, Pu Y, Guo M, Jiang Z, Huang W, et al. Skipping breakfast is associated with overweight and obesity: A systematic review and meta-analysis. (1871-403X (Print)).