Skip to main content
Download PDF

Prevalence of abdominal obesity and associated risk factors among women civil servants in Addis Ababa, Ethiopia, 2021: an institution-based study

BMC Nutrition volume 8, Article number: 119 (2022) Cite this article

Abstract

Background

Abdominal obesity increases the risk of cardio-metabolic diseases, disability, and poor quality of life, as well as health-care costs. It is a component of the metabolic syndrome, along with hypertension, diabetes, and dyslipidemia. The goal of this study was to determine the prevalence of abdominal obesity and associated risk factors among female civil servants in Addis Ababa, Ethiopia in 2021.

Methods

An institution-based cross-sectional study was undertaken from March31st to April 15th, 2021.A multi-stage sampling technique was employed to select 478 study participants. Data was entered into EpiData version 3.1 and then exported to SPSS version 21 for analysis. A descriptive data analysis was used to present the distribution of study variables. Bivariable and multivariable analyses were used to assess the relationship between independent variables and abdominal obesity at 95% CI. The level of statistical significance was declared at a p-value less than 0.05.

Result

The prevalence of abdominal obesity defined by waist circumference was found to be29.5% (95% CI: 25.39-33.6%) and 32.8% (95% CI: 28.57%-37.03%) by waist hip ratio, respectively. Age group 29-37 years [AOR= 2.451, 95% CI: (1.199-5.013)], age group 38-46 years [AOR=3.807, 95% CI: (1.328-10.914)], age group 47-55 years [AOR=6.489, 95% CI: (1.367-30.805)], being married [AOR= 4.762, 95% CI: (2.321-9.721)],consumption of meat >=5 per week[ AOR= 4.764, 95% CI: (1.939-11.711)], having lunch daily[AOR= 0.388, 95% CI:(0.166-0.910)] and snack consumption [AOR=4.163, 95% CI:(1.503-11.534)] were significantly associated with abdominal obesity.

Conclusion

The prevalence of abdominal obesity as measured by waist circumference and waist hip ratio was found to be moderate and high, respectively. Age, being married, high consumption of meat, and having lunch daily were identified as associated with abdominal obesity. Healthy diet health education and nutrition intervention should be considered, with a focus on married, meat-consuming, and older age-group female civil servants.

Peer Review reports

Background

Obesity is characterized as irregular or excessive fat accumulation that may affect health as a disorder of energy metabolism [1]. It is a significant public health concern in both developed and developing countries, affecting people of all ages and genders and related to a variety of chronic diseases such as type 2 diabetes, hypertension, coronary artery disease, and cancer [2,3,4]. According to 2016 data from the WHO, more than 1.9 billion adults aged 18 and older were overweight worldwide. Over 650 million of them were obese [5].

Abdominal obesity is especially linked to an increased risk of mortality, morbidity, disability, poor quality of life, and health care costs [6,7,8,9]. The prevalence of elevated waist circumference(WC) in adults has been demonstrated to be an independent risk factor for metabolic syndrome, like hypertension, diabetes mellitus, and cardiovascular diseases [10,11,12]. In adults, studies have reported a higher association between waist circumference and these anomalies than with body mass index (BMI) [13, 14].

Overweight, general obesity, abdominal obesity (AO), visceral fat obesity (VFO), and other forms of obesity are frequently classified using body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), skin fold thickness, and bio-impedance. BMI and waist circumference(WC) are the most commonly used measures [15]. Abdominal fat assessment is the best predictor of visceral fat, which is significantly linked with most metabolic abnormalities [16].

Waist circumference (WC) based indices have been described to assess body shape and fat distribution, especially abdominal visceral adiposity. The gold standard methods include computed tomography and magnetic resonance imaging [17, 18]. However, in a clinical or epidemiological setting, WC is the most often available measurement to determine visceral fat.

Despite the fact that studies on central obesity in Africa are few, recent research has revealed an unprecedented rise in central obesity prevalence [3]. According to WHO estimates, 1.2% of men and 6.0% of women in Ethiopia were overweight or obese in 2014 [19]. Between 1997 and 2016, the combined prevalence in the country grew considerably, from2.6 to 6.9% in females and from 0.6 to 1.9% in males [19].

In Ethiopia, a study conducted in 2015 reported that in urban areas, the prevalence of overweight and obesity was 12.1% and 2.8 percent, respectively [6]. In another recent study conducted in Woldia and urban areas of Northwest Ethiopia, the prevalence of central obesity among women was found to be 27.9% and 86.9%, respectively [20, 21].

From the data of the 2018 WHO, the non-communicable disease (NCD) country profiles of Ethiopia, it was estimated that the deaths from CVDs, cancers, diabetes, and other NCDs were 16%, 7%, 2%, and 12%, respectively, and the total estimated deaths from NCDs was 39% of all deaths [22].

According to the Ethiopian demographic and health survey (EDHS) 2016,the proportion of women who were overweight or obese had increased from 3% in 2000 to 8% in 2016, and the proportion of men who were obese was found to be 3% [23]. Even though there are no well documented national data and studies on central obesity, there are a few studies done on central obesity in different parts of Ethiopia, which revealed that the prevalence of central obesity is currently increasing. For instance, according to a study done in Dilla, Gonder, Dire dawa, and urban areas of Northwest Ethiopia, the prevalence of central obesity was found to be 24.4%, 33.6%, 46.6%, and 37.6%, respectively [21, 24,25,26]. Therefore, the aim of this study was to accurately assess the prevalence and risk factors associated with abdominal obesity in a volunteer sample of women employed in 2021 as civil servants in Addis Ababa, Ethiopia.

Methods

Study design, study area, and study period

An institution-based cross-sectional study design was conducted to assess the prevalence and associated risk factors of abdominal obesity among civil servant women in Addis Ababa city from March 31st to April 15th, 2021. In Addis Ababa city administration, the capital of Ethiopia, there are 11 sub-city and 117 district-level administration offices. Chartered in 1886, it has the status of both a state and a city. There are 13 public and 22 private hospitals, as well as 96 health centers in the capital.

Study participants

Women working as civil servants in Addis Ababa city administration in different districts and willing to participate in the study were included. Women who have deformities around their hip and abdominal areas and who were temporary employees were excluded.

Sample size determination

A single population proportion formula was used to calculate sample size considering 95% confidence level, 5% margin of error, and prevalence of central obesity from a study done in Dilla, Ethiopia at 27.3% [24]. Adding a 5% non-respondent rate and a 1.5% of design effect the final sample size was 478.

Sampling technique and procedure

In this study, multi-stage random sampling was used. First, the three sub-cities were selected from 10 sub-cities by using a simple random sampling technique, which covered 30% of the total sub-cities of Addis Ababa. Kirkos, Yeka, and Bole sub-cities were selected as primary sampling units using simple random sampling. After selecting three sub-cities, three woredas were selected from each sub-city by simple random sampling as a secondary sampling unit. Then samples were allocated to each selected woreda proportionally based on their total number of civil servants. The list of civil servant workers from the selected districts was used as a sampling frame to select the participants of the study, by a simple random sampling technique, from each woreda (district).

Data collection tools and procedures

An interviewer-administered structured questionnaire was adapted from WHO-stepwise for chronic non-communicable disease, having components of socio-demographic information; dietary intakes; physical activity; health risky behavior questions; and anthropometric measurement were made.

Participants were interviewed for their socio-demographic information, dietary intakes, physical activity, and health-risky behaviors. Anthropometric measurements were taken by trained professional nurses at the end of the interview. The food consumption habits of the participants were investigated by a semi-quantitative food frequency questionnaire (FFQ) by FAO and a food frequency questionnaire (FFQ) modified from the WHO-step-wise approach consisting of foods commonly consumed by the study population. Study subjects were asked to report their frequency of consumption and the number of times they consumed weekly [27, 28].

The global physical activity questionnaire (GPAQ) developed by WHO for physical activity surveillance was used to assess the physical activity pattern among selected individuals in three domains, including activity at work, travel to and from places, recreational activities, and sedentary behavior, through face-to-face interviews of the respondents in the study area. The level of total physical activity of study participants was classified as physically active (> 600 EM) or physically inactive (<600 EM) using the standard WHO total physical activity calculation guide [29].

Waist circumference was measured at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest, using a stretch resistant tape that provides a constant 100 g tension. The hip circumference measurement was taken around the widest portion of the buttocks. The waist to hip ratio was calculated by dividing the waist circumference by the hip circumference. The study participants were instructed to wear light clothing and stand with their feet close together, arms at their sides, and body weight evenly distributed for both measurements. The subjects were asked to relax, and the measurements were taken at the end of a normal expiration.

The measurements were repeated twice, and when they were within 1 cm, the average was calculated. Otherwise, the two measurements were repeated. Measurement was taken before meals or three hours after meals. The measurement was also taken at the end of a gentle expiration, after taking a deep inhalation with the tape snug but not compressing the skin [30].

Data quality control

Data quality was closely assured throughout the process, from tool development through to result analysis. First, the questionnaire was prepared in English and translated to the local language, Amharic. To maintain the tool's consistency, the Amharic text was then translated back into English. The research instrument which was used to measure abdominal obesity and associated risk factors was properly calibrated by defining each concept and assessing for content validity, in which the instrument items are adapted from the WHO-step-wise approach questionnaire found online by Google search and other standard questionnaires from FAO. To assess whether the instrument covered all dimensions of the construct, relevant literature and experts in the field were properly consulted. On the other hand, to maximize the quality of the data, data collectors were selected carefully based on their educational status. The training was also given on the nature and purpose of the research and the objective of the study.

Data processing and analysis

After the data was collected, the collected data was checked for completeness and consistency manually. The data was edited, coded, and entered on to a statistical software package, Epi Data Version 3.1, and then was exported to IBM the Statistical Package for the Social Sciences Version 20 for analysis. Descriptive analysis was primarily used to show the distribution data from the research variables and to summarize it. Bivariate and multi-variable analyses were used to assess any relationship between each independent variable (socio-demographic characteristics, behavioral factors, dietary factors, and physical activity) and the outcome variable (abdominal obesity). Crude and adjusted odds ratios were used to ascertain any associations between the dependent and independent variables, while significance will be determined using 95% confidence intervals. Independent variables were found to be significant with a p-value less than 0.25 at the bi-variate level and were included in a multi-variable analysis. Finally, the results were presented in the frequency distribution table, chart, and graphs.

Operational definitions

Abdominal obesity

Abdominally obese: WC>88 cm [30].

Normal: WC<=88 cm [30].

Abdominally obese: WHR>=0.85 [30].

Normal: WHR< 0.85 [30].

Civil servant women: are women employed by the Addis Ababa city government administration who are working at Woreda level.

Woreda (District): is the third (lowest) level of the administrative division of Addis Ababa city administration, which was previously called “kebele.”

Sub-city: is the middle level of the administrative division of Addis Ababa city administration, which is called “Kifleketema” locally.

Total physical activity metabolic equivalents (MET)-minutes/week= the sum of the total metabolic equivalents (MET) minutes of activity computed for each setting.

WHO total physical activity recommendations; total physical activity MET minutes per week is greater than 600 [29].

Total physical activity does not meeting WHO recommendations: Total physical activity MET minutes per week is < 600 [29].

Results

Socio-demographic characteristics

From a total of 478 study participants, a complete set of information was obtained from 451 working civil servants, which gave a response rate of 94.4%.The mean and the standard deviation of the respondent age were 30.11 (±6.86) years, of which 55% of the respondents were between the age groups of 20-28 years. Among the study participants, 48.3% and 48.6% were single and married, respectively. More than half (68.1%) of the study participants were degree holders. Only 6% of the study participants had a master's degree or higher.

Of the participants’ Ethiopian Orthodox Tewahedo (81.6%) religion followers were the highest, followed by Protestants (12%) and Muslims (4.4%). The mean (SD) of the respondent’s salary was 5459.92(±2021.16) Ethiopian birr. 23.1% of the respondents got a monthly salary of less than 3934 Ethiopian birr. The mean family size of the respondents was 3.57, of which 56.8% of the respondents had a family size of four or above (Table 1).

Table 1 Demographic and socioeconomic characteristics of civil servant women in Addis Ababa city, Ethiopian, 2021 (n = 451)
Full size table

Factors influencing food consumption

According to the data obtained from food frequency, of the total respondents, 44.6% consumed fruit three or less times per month and 41.7% consumed it within a week. 53.2% of study participants consumed vegetables1-4 times per week. Regarding the consumption of bread and cereals, 41.5% were consumed daily and 58.5% were consumed less frequently. According to this data, cereals were the most common food group among respondents. More than half of the respondents (61.2%, 58.1%, 53.7, 69.2%, and 66.5%) consumed meat, legumes, milk products, fast food, and sweetened beverages three or less times a month, respectively (Table 2).

Table 2 Food consumption frequency among civil servant women in Addis Ababa city, Ethiopia, 2021 (n=451)
Full size table

Factors influencing dietary habits

Of the total respondents, 88.9% had three or more meals per day, and only 11.1% had fewer than three meals per day. Nearly three-fourths (70.1%)of the study participantsreported that they didn't consume breakfast on a daily basis. Only 29.9% of the respondentssaid theyconsumed breakfast on a daily basis.The majority of the respondents (88% and 86.7%) consumed lunch and dinner on a daily basis, respectively. More than two thirds (63.6%) of respondents commonly used seed oil (sunflower) for household food preparation,followed bypalm oil(28.4%) and butter(8%).Of the total study participants, 80.1% of the respondentsreported that they ate meals prepared at home on a daily basis (Table 3).

Table 3 Dietary habit among civil servant women in Addis Ababa city, Ethiopia 2021 (n=451)
Full size table

Behavioral factors

Of the total study participants, 98.7% were non-smokers. Nearly three-fourths (73.2) of the respondents had never consumed alcohol. About 96.2% of the respondents never chewed khat. Study participants who met the WHO recommendation fortotal physical activity level were 5.8%, and the rest (38.8%) didn’t meet the WHO recommendation. And the remaining 55.4% reported no participation in any physical activity (Table 4).

Table 4 Behavioral factor of civil servant women in Addis Ababa city, Ethiopia, 2021 (n=451)
Full size table

Prevalence of abdominal obesity

The prevalence of abdominal obesity among civil servant women working in Addis Ababa determined by waist circumference(WC) and waist-hip ratio (WHR) was 29.5%(95% CI:25.39%-33.61%) and 32.8% (95%CI:28.57%-37.03%) respectively. The prevalence of abdominal obesity was highest among the age groups of 27–38 by both waist circumference (12.4%) and waist-hip ratio (13.3%), respectively. The prevalence was lowest among the age group 47–55years, by both WC (2.3%) and WHR (1.3%) (Table 5, figs 1 and 2).

Table 5 Pervalence of abdominal obesity among civil servant women in Addis Ababa, Ethiopia, 2021 (n=451)
Full size table
Fig. 1
figure 1

Prevalence of abdominal obesity by waist circumference among women civil servant working in Addis Ababa by age group Ethiopia, 2021

Full size image
Fig. 2
figure 2

Prevalence of abdominal obesity by waist hip ratio among women civil servant working in Addis Ababa by age group Ethiopia, 2021

Full size image

The mean (SD) of waist circumference among civil servant women working in Addis Ababa was 79.4(11.28) cm. Both the mean waist circumference and waist hip ratio among civil servant women were slightly lower than the WHO cut-off points.

Associated Risk Factors for Abdominal Obesity by Waist Circumference

Multivariate analysis

In multi-viariable logistic regression analysis, variables with a p-value less than 0.25 from bi-variable analysis were included. Based on this criteria, age, marital status, consumption of meat, consumption of snacks, and having lunch daily were significantly associated with abdominal obesity at a p-value less than 0.05. 29–37-year-olds, 38–46-year-olds, and 47–55-year-olds were 2.451 [(AOR=2.451, 95% CI: (1.199-5.013), 3.807 (AOR=3.807, 95% CI: (1.328-10.914), and 6.489 (AOR=6.489, 95% CI: (1.367-30.805) times more likely to develop abdominal obesity than 20–28-year-olds, respectively.

Being married was 4.762 times more likely to develop abdominal obesity [AOR=4.762;95% CI: (2.321-9.771)] than unmarried women. Respondents who consumed meat more or equal to five times per week were found to be 4.764 more likely to develop abdominal obesity[AOR=4.764; 95% CI= (1.939-11.711)] as compared to those who consumed meat three or less times per month. Having lunch every day reduced the risk of developing abdominal obesity by 61.2% [AOR = 0.388, 95% CI: (0.166-0.910)]. Respondents who consumed snacks were 4.163 times more likely to be abdominally obese than those who did not consume snacks [AOR= 4.163; 95% CI:(1.503-11.534)] (Table 6).

Table 6 Multi logistic regression of factors associated with abdominal obesity by waist circumference among civil servant women in Addis Ababa, Ethiopia, 2021 (n=451)
Full size table

Factors associated with abdominal obesity by waist-to-hip ratio

Multivariate analysis

In a multivariate analysis for abdominal obesity measured by waist hip ratio, age, marital status (being married) and consumption of snacks were variables associated with abdominal obesity. Women who were married were 2.448 times more likely to develop abdominal obesity than those who were single. Similarly, the respondent age groups of 29–37 and 38–46 years were 2.637 [AOR = 2.637; 95% CI: (1.452–4.788)] and 5.439 [AOR = 5.439; 95% CI: (2.277–12.995)] times more likely to develop abdominal obesity than the age group of 20–28 years, respectively. The respondents who consumed snacks were 3.270 times more likely to develop abdominal obesity than those who didn’t consume snacks [AOR = 3.270; 95% CI: (1.437–7.442)] (Table 7).

Table 7 Multivariable logistic regression analysis of factors associated with abdominal obesity by waist hip ratio among civil servant women in Addis Ababa city, Ethiopia 2021 (n=451)
Full size table

Discussion

This study aimed to determine the prevalence and associated risk factors for abdominal obesity among civil servant women in Addis Ababa. The overall magnitude of abdominal obesity as defined by waist circumference and waist hip ratio among civil servant women was found to be 29.5% and 32.8%, respectively. This result was slightly greater than the studies done in Dilla based on WC (27.3%) [24] and Woldia Town based on both WC and WHR (24.3%, 27.9%) [20] and also higher than the study in Addis Ababa among working adultsbased on WC(19.6%) [31]. The possible explanation for the differencecould be the fact that a difference in place, setting, or time setting could influence, variation in WC and WHR cut off points,as well as may be for sociodemographic disparitiest. The prevalence of abdominal obesity in this study was high when compared to findings from Dilla and Woldia.This might be due to the lifestyle changes in Addis Ababa,the sedentary behavior of civil servants, and the nutrition transition adopted from western countries.

A study conducted in Ghana among female teachers revealed that the prevalence of abdominal obesity defined by WHR and WC was found to be 17.8% and 59% [32] respectively, which was not consistent with the current study prevalence of abdominal obesity defined by WHR (32.8%) and WC (29.5%).The difference in these results might be due to the study period and WHR and WC cut-off points. A study done in Ghana considered WC >80 cm, while this study considered WC >88cm.The study conducted in Ghana considered WHR > 0.85, but this study considered abdominal obesity > 0.85. The other possible explanation for the difference could be socio-demographic factors and dietary patterns.

The prevalence of abdominal obesity (85.9%) defined by WC in northwesturban areas of Ethiopia [21] among women was much higher when compared to the current study(29.5%).Contrariwise, the current finding was higher than in a study conducted in Nigeria among civil servants (23.1%) [33] and yet lower than the study done in Russia (44%) [34] among bank employees. The difference could be study period, WHO cut-off point variation, socioeconomic difference, socio-demographic factorsand dietary intake pattern or differences in MBI among population groups.

The prevalence of abdominal obesity by WC in Gaza Strip-Palestine was 82.2%, which was very high when compared to the current study of abdominal obesity by WC. The difference might be due to WHO cut-off pointsand socio-cultural and economic differences [35]. The study findings of Gaza Strip-Palestine considered WC >80 cm, but the current study considerswaist circumference(WC) greater than 88 cm. The other possible explanation for the difference might be the type of food consumed, socio-economic status, or age of the study participants; the current study considered age from 20 and the study in Palestine starts at greater or equal to 26 years.

Likewise, a study done in Panama among women showed the highest prevalence of abdominal obesity, which was reported by WC (97.9%), which was three times that of the current study [36]. This might be because Panamanian women consume beverages or sugar-rich foods and have socio-demographic characteristics leading to higher BMI. Sugary beverages or foods were statistically associated with abdominal obesity among Panamanian women. Similarly, the prevalence of abdominal obesity in studies conducted in Indonesia (68.3%) among adult female employees was higher than in the current study, but a study conducted in Iran (34.6%) among adult females was almost consistent with this study [37, 38].

This study revealed that, the odds of being abdominally obese by WC increased by age. The age groups of 29–37, 38–46, and 47–55 years were 2.553, 4.027, and 7.008 times more likely to develop abdominal obesity, respectively, as compared to the age group 20–29. This result was consistent with a study conducted in Nigeria among civil servants and in southern America [39, 40]. This might be due to sex hormone changes and a decrease in physical activity levels with ageing. The findings of this study also revealed that the age groups 29–37 and 38–46 years were significantly associated with AO defined by WHR, but the age group 47–55 years was not associated with AO, unlike that of WC. Thus, this finding was inconsistent with the finding from Ghana [32].

Marital status was one of the predictors of abdominal obesity among civil servant women in this study. Consistent with studies conducted in Greece, Nigeria, and Iran [41,42,43]. This can be explained by the fact that women after marriage may have less physical activity, changed dietary patterns, and experience pregnancy-induced social support. Married women have more social support than those who are not married. This marital support can lead to obesity through food, activity, and social values. Some people control their weight to attract mates, and once they get married, weight control may be less valued, so that diet/exercise behaviors for slimness may be neglected or they may not give attention to attractiveness once they have gotten married and experienced pregnancy, nursing and the stress of family life [43].

The findings of this study revealed that consumption of meat more than even once a week was associated with abdominal obesity by WC. The more meat consumed the greater were the odds of developing abdominal obesity . The OR for eating meat 1-4 times a week was 2.342 compared to those who ate meat three or fewer times a month. Likewise, using meat products morethan or equal to five times perweek increased the OR for abdominal obesity to 5.257. Because a possible explanation is that meat has high energy and high fat content that might be associated with a higher risk of being overweight, including general and central obesity [44]. This result was similar to studies done in the USA ,Woldia, and Hawassa [20, 45, 46].

Having lunch daily was significantly associated alower risk for abdominal obesity in this study. Theincidence of abdominal obesity was 61.2% lower in those who had lunch daily than only occasionally. Findings from China also showed that skipping lunch was positively associated with obesity in women [47]. In general, findings from various studies have confirmed that meals skipped are associated with overweight, obesity, and abdominal obesity [13, 48, 49]. It might be due to the decreased thermic effect of food after an irregular meal pattern when compared with individuals with a regular meal pattern. The reduced thermic effect with irregular meal frequency may lead to weight gain in the long term. On the other hand restrictive eating is being studied extensively as a weight loss and longetivity strategy [50].

This study revealed that those who consumed snacks were 4.163 and 3.270 times more likely to develop abdominal obesity as measured by WC and WHR respectively than those who didn’t consume snacks. Although, the relationship between snacking and obesity or abdominal obesity is unclear, some studies have suggested that consumption of energy-dense, high-sugar, high-fat snacks is a key factor in obesity [51]. Other studies failed to establish a relationship between snacking and obesity or abdominal obesity [52]. This may be because the type of snack eaten matters. Those who eat energy-dense and sugary snacks may be especially susceptible to abdominal obesity, as opposed to those who eat healthy snacks. This result is supported by studies conducted in association with South East Asian Nations countries and northeast Ethiopia [53, 54].

Limitation

This study could have some limitations most notably that height and weight were not measured which affect the results directly or indirectly. Some of the limitations emanate from the nature of the cross-sectional study since the outcome (abdominal obesity) and predictor variables relationships were temporal and examined at the same time, therefore no causal deduction can be made. The study did not include other measurements like skin fold thickness. The portion size of food consumed by respondents was not assessed. The type of snack they were practicing was not identified. On the other hand, there might be over and underestimations of food frequency and meal habits, alcohol consumption, physical exercises, and time spent sitting and reclining due to recall bias.

Conclusion

A relatively high 29.5% and 32.8% of the study participants were found to be centrally obese based on abdominal obesity defined by WC and WHR. Age groups of 29–37 years, 38–46 years, and 47–55 years, being married, meat consumption 1-4 days and >=5 days, having lunch daily, and snack consumption were the predictors of abdominal obesity based on WC in this study of women civil servant. Age groups of 29–37 years and 38–46 years, snack consumption, and being married were the predictors of abdominal obesity based on WHR in this study of women civil servant employees in Addis Ababa. Governmental and non-governmental organizations should provide special awareness campaigns regarding abdominal obesity for married and older age group civil servant women in collaboration with other stakeholders like the city-administration of women’s affairs and Addis Ababa city-administration health Bureau. Regular health education should be considered for female civil servants regarding the frequency of meat consumption and of unhealthy snacks.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Abbreviations

AO:

Abdominal Obesity

BMI:

Body Mass Index

CI:

Confidence Interval

CHO:

Carbohydrate

CVD:

cardiovascular disease

DDS:

Dietary Diversity Score

FVS:

Food Variety Scores

HEI:

Health Eating Index

METs:

Metabolic Equivalents

NCD:

Noncommunicable Diseases

GDP:

Gross Domestic Product

NHLBI:

National Heart, Lung, and Blood Institute

SPSS:

Statistical Package for Social Science

WHR:

Waist Hip Ratio

WHO:

World Health Organization

VAI:

Visceral Adiposity Index

WHtR:

Waist-to-Height Ratio

References

  1. World Health Organization. Obesity and overweight [Internet]. 2020. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.

  2. Golpour-Hamedani S, Rafie N, Pourmasoumi M, Saneei P, Safavi SM. The association between dietary diversity score and general and abdominal obesity in Iranian children and adolescents. BMC Endocr Disord. 2020;20(1):1–8.

    Article  Google Scholar 

  3. Omar SM, Taha Z, Hassan AA, Al-Wutayd O, Adam I. Prevalence and factors associated with overweight and central obesity among adults in the Eastern Sudan. PLoS One. 2020;15(4):e0232624.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wang K, Wang D, Pan L, Yu Y, Dong F, Li L, et al. Prevalence of obesity and related factors among Bouyei and Han peoples in Guizhou Province, Southwest China. PLoS One. 2015;10(6):e0129230.

    Article  PubMed  PubMed Central  Google Scholar 

  5. World Health Organization. Obesity and overweight [Internet]. 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.

  6. Yohannes M. Prevalence of overweight and obesity among office-based urban civil servants in southern nations, nationalities and peoples’ region Ethiopia. Ethiop Med J. 2019;57:133–41.

    Google Scholar 

  7. Cameron AJ, Magliano DJ, Shaw JE, Zimmet PZ, Carstensen B, Alberti KGM, et al. The influence of hip circumference on the relationship between abdominal obesity and mortality. Int J Epidemiol. 2012;41(2):484–94.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Nigatu YT, Reijneveld SA, de Jonge P, van Rossum E, Bültmann U. The combined effects of obesity, abdominal obesity and major depression/anxiety on health-related quality of life: the lifelines cohort study. PLoS One. 2016;11(2):e0148871.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Corona LP, da Silva Alexandre T, de Oliveira Duarte YA, Lebrão ML. Abdominal obesity as a risk factor for disability in Brazilian older adults. Public Health Nutr. 2017;20(6):1046–53.

    Article  PubMed  Google Scholar 

  10. Wang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr. 2005;81(3):555–63.

    Article  CAS  PubMed  Google Scholar 

  11. Maffeis C, Corciulo N, Livieri C, Rabbone I, Trifiro G, Falorni A, et al. Waist circumference as a predictor of cardiovascular and metabolic risk factors in obese girls. Eur J Clin Nutr. 2003;57(4):566–72.

    Article  CAS  PubMed  Google Scholar 

  12. De Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J. 2007;28(7):850–6.

    Article  PubMed  Google Scholar 

  13. Chew WF, Leong PP, Yap SF, Yasmin AM, Choo KB, Low GKK, et al. Risk factors associated with abdominal obesity in suburban adolescents from a Malaysian district. Singapore Med J. 2018;59(2):104.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Song X, Jousilahti P, Stehouwer C, Söderberg S, Onat A, Laatikainen T, et al. Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations. Eur J Clin Nutr. 2013;67(12):1298–302.

    Article  CAS  PubMed  Google Scholar 

  15. Baek Y, Park K, Lee S, Jang E. The prevalence of general and abdominal obesity according to sasang constitution in Korea. BMC Complement Altern Med. 2014;14(1):1–8.

    Article  Google Scholar 

  16. Martins-Silva T, Vaz JdS, Mola CLd, Assunção MCF, Tovo-Rodrigues L. Prevalence of obesity in rural and urban areas in Brazil: National Health Survey, 2013. Rev Bras Epidemiol. 2019;22:e190049.

    Article  PubMed  Google Scholar 

  17. Hu HH, Nayak KS, Goran MI. Assessment of abdominal adipose tissue and organ fat content by magnetic resonance imaging. Obes Rev. 2011;12(5):e504–15.

  18. Shuster A, Patlas M, Pinthus J, Mourtzakis M. The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. Br J Radiol. 2012;85(1009):1–10.

  19. World Health Organization. Overweight and obesity [Internet]. 2015. Available from: http://www.who.int/gho/ncd/risk_factors/overweight/en/.

  20. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Molla MD, Wolde HF, Atnafu A. Magnitude of Central Obesity and its Associated Factors Among Adults in Urban Areas of Northwest Ethiopia. Diabetes Metab Syndr Obes. 2020;13:4169.

    Article  PubMed  PubMed Central  Google Scholar 

  22. World Health Organization. Noncommunicable diseases country profiles. 2018.

    Google Scholar 

  23. Central Statistical Agency (CSA)[Ethiopia] and ICF. Ethiopia demographic and health survey. Ethiopia and Calverton, Maryland, USA: Addis Ababa; 2016.

    Google Scholar 

  24. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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 Metab Syndr Obes. 2020;13:343.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mengesha MM, Ayele BH, Beyene AS, Roba HS. Clustering of Elevated Blood Pressure, Elevated Blood Glucose, and Abdominal Obesity Among Adults in Dire Dawa: A Community-Based Cross-Sectional Study. Diabetes Metab Syndr Obes. 2020;13:2013.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Assessment FD. A resource guide to method selection and application in low resource settings. FAO: Rome, Italy. 2018:152.

  28. Krebs-Smith SM, Smiciklas-Wright H, Guthrie HA, Krebs-Smith J. The effects of variety in food choices on dietary quality. J Am Diet Assoc. 1987;87(7):897–903.

    Article  CAS  PubMed  Google Scholar 

  29. World Health Organization. Global physical activity questionnaire (GPAQ) analysis guide. Geneva; 2012.

  30. World Health Organization. Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008. 2011.

  31. Tran A, Gelaye B, Girma B, Lemma S, Berhane Y, Bekele T, et al. Prevalence of metabolic syndrome among working adults in Ethiopia. International journal of hypertension. 2011;2011.

  32. Pobee RA, Owusu W, Plahar W. The prevalence of obesity among female teachers of child-bearing age in Ghana. African journal of food, agriculture, nutrition and development. 2013;13(3).

  33. Ajani SR, Susan HJA, Oluwaseun A. Gender differences in factors associated with overweight and obesity among civil servants in Lagos, Nigeria. Int J Nutr Metab. 2015;7(6):66–73.

    Article  Google Scholar 

  34. Prevalence of Metabolic Syndrome Components in a Population of Bank Employees from St. Petersburg, Russia. Metabolic Syndrome and Related Disorders. 2011;9(5):337-43.

  35. El Kishawi RR, Soo KL, Abed YA, Muda WAMW. Prevalence and predictors of overweight and obesity among women in the Gaza strip-Palestine: a cross-sectional study. BMC Public Health. 2020;20(1):1–8.

    Article  Google Scholar 

  36. Mc Donald A, Bradshaw RA, Fontes F, Mendoza EA, Motta JA, Cumbrera A, et al. Prevalence of obesity in panama: some risk factors and associated diseases. BMC Public Health. 2015;15:1075.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Handayani M, Putri AN, Yani IE, Hasniyati R, Sidiq R. Central Obesity Incidence in Adult Women. Int J Med Sci Clin Invent. 2020;7:5117–24.

    Article  Google Scholar 

  38. Mohammadi-Nasrabadi M, Sadeghi R, Rahimiforushani A, Mohammadi-Nasrabadi F, Shojaeizadeh D, Montazeri A. Socioeconomic determinants of excess weight and central obesity among Iranian women: Application of information, motivation, and behavioral skills model. Journal of education and health promotion. 2019;8.

  39. Olawuyi AT, Adeoye IA. The prevalence and associated factors of non-communicable disease risk factors among civil servants in Ibadan, Nigeria. PLoS One. 2018;13(9):e0203587.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Lanas F, Bazzano L, Rubinstein A, Calandrelli M, Chen C-S, Elorriaga N, et al. Prevalence, distributions and determinants of obesity and central obesity in the Southern Cone of America. PLoS One. 2016;11(10):e0163727.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Tzotzas T, Vlahavas G, Papadopoulou SK, Kapantais E, Kaklamanou D, Hassapidou M. Marital status and educational level associated to obesity in Greek adults: data from the National Epidemiological Survey. BMC Public Health. 2010;10(1):1–8.

    Article  Google Scholar 

  42. Aladeniyi I, Adeniyi OV, Fawole O, Adeolu M, Ter Goon D, Ajayi AI, et al. Pattern and correlates of obesity among public service workers in Ondo State, Nigeria: a cross-sectional study. S Afr Fam Pract. 2017;59(6):195–200.

    Article  Google Scholar 

  43. Janghorbani M, Amini M, Rezvanian H, GOUYA MM, DELAVARI AR, Alikhani S, et al. Association of body mass index and abdominal obesity with marital status in adults. 2008.

  44. You W, Henneberg M. Meat consumption providing a surplus energy in modern diet contributes to obesity prevalence: an ecological analysis. BMC Nutr. 2016;2(1):1–11.

    Google Scholar 

  45. Wang Y, Beydoun MA. Meat consumption is associated with obesity and central obesity among US adults. Int J Obes. 2009;33(6):621–8.

    Article  CAS  Google Scholar 

  46. Darebo T, Mesfin A, Gebremedhin S. Prevalence and factors associated with overweight and obesity among adults in Hawassa city, southern Ethiopia: a community based cross-sectional study. BMC Obes. 2019;6(1):1–10.

    Article  Google Scholar 

  47. Hu C, Zhang M, Zhang X, Zhao Z, Huang Z, Li C, et al. Relationship between eating behavior and obesity among Chinese adults. Zhonghua liu xing bing xue za zhi= Zhonghua liuxingbingxue zazhi. 2020;41(8):1296–302.

    CAS  PubMed  Google Scholar 

  48. Yamamoto R, Tomi R, Shinzawa M, Yoshimura R, Ozaki S, Nakanishi K, et al. Associations of skipping breakfast, lunch, and dinner with weight gain and overweight/obesity in university students: a retrospective cohort study. Nutrients. 2021;13(1):271.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Huang C, Hu H, Fan Y, Liao Y, Tsai P. Associations of breakfast skipping with obesity and health-related quality of life: evidence from a national survey in Taiwan. Int J Obes. 2010;34(4):720–5.

    Article  Google Scholar 

  50. Farshchi H, Taylor M, Macdonald IA. Decreased thermic effect of food after an irregular compared with a regular meal pattern in healthy lean women. Int J Obes. 2004;28(5):653–60.

    Article  CAS  Google Scholar 

  51. Bo S, De Carli L, Venco E, Fanzola I, Maiandi M, De Michieli F, et al. Impact of snacking pattern on overweight and obesity risk in a cohort of 11-to 13-year-old adolescents. J Pediatr Gastroenterol Nutr. 2014;59(4):465–71.

    Article  PubMed  Google Scholar 

  52. Faghih S, Mohebpour R, Eskandari L. Assessment of the Correlation between BMI, Waist Circumference, and the Snacking Pattern and Dairy Consumption among Female Student Residents of Shiraz University Dormitories. Women’s Health Bull. 2014;1(1):1–4.

    Article  Google Scholar 

  53. Peltzer K, Pengpid S. The association of dietary behaviors and physical activity levels with general and central obesity among ASEAN university students. AIMS Public Health. 2017;4(3):301.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Dagne S, Gelaw YA, Abebe Z, Wassie MM. Factors associated with overweight and obesity among adults in northeast Ethiopia: a cross-sectional study. Diabetes Metab Syndr Obes. 2019;12:391.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors thank those who help us in providing data regarding civil servant women in each Woreda. Our gratitude goes to health professionals who contributed to this study by participating as supervisor and data collector as well as civil servant women of city government of Addis Ababa who participated in the study to their respective contribution to the study.

Funding

The research was not funded by any organization.

Author information

Authors and Affiliations

  1. Department of Nutrition Menelik II Medical and Health Sciences College, Kotebe Metropolitan University, Addis Ababa, Ethiopia

    Solomon Gebretsadik Bereka & Ayele Worku Demisse

  2. Department of Public health, College of Health Sciences, Arsi University, Assela, Ethiopia

    Solomon Gebretsadik Bereka

  3. Department of Public Health Menelik II Medical and Health Sciences College, Kotebe Metropolitan University, Addis Ababa, Ethiopia

    Genanew Kassie Getahun

Authors
  1. Solomon Gebretsadik Bereka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayele Worku Demisse
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Genanew Kassie Getahun
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AW: Conceptualization and development of the proposal, data collection and draft data analysis. SG: data analysis, editing, and develop the manuscript. GK: read and approved the final manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Solomon Gebretsadik Bereka.

Ethics declarations

Ethics approval and consent to participate

The study was carried out according to the guidelines and regulations laid down in the declaration of Helsinki. Before starting the data collection process, Kotebe Metropolitan University Menelik II Medical and Health Sciences College, Institutional Review Board (IRB) secured ethical clearance, the clear description of the study title, procedure and duration, possible risks and benefits of the study was explained for each study participants. In addition to this, letter of permission and ethical clearance was obtained from Addis Ababa Health Bureau prior to actual data collection.

Then informed written and signed consent was taken from each study participants. Confidentiality of information was collected from each study participant was not disclosed. They were informed that they have full right to withdraw from the study at any time if they face any difficulties. The data was collected under covid-19 prevention protocols.

Consent for publication

Not applicable.

Competing interests

The authors declared that no competing interests exist.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bereka, S.G., Demisse, A.W. & Getahun, G.K. Prevalence of abdominal obesity and associated risk factors among women civil servants in Addis Ababa, Ethiopia, 2021: an institution-based study. BMC Nutr 8, 119 (2022). https://doi.org/10.1186/s40795-022-00613-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40795-022-00613-9

Keywords

BMC Nutrition

ISSN: 2055-0928

Contact us