Determinants of Overweight Among Government Employees in Gibe District, Hadiya Zone, Southern Ethiopia: A Case Control Study

Background: Overweight in Africa have been attributed to demographic and nutritional changes that have resulted from urbanization, sedentary lifestyles and increased caloric intake without much physical activities. This has put populations at an increased risk of chronic diseases. The purpose of the present study was to assess determinants of overweight among government employees. Methods: A case control study was conducted among government employees in Gibe district. A total of 206 employees (51 cases and 155controls) were selected by using simple random sampling technique. Cases were whose body mass index (BMI) greater than or equal to 25kg/m2, whereas, controls were whose body mass index greater than or equal to 18.5kg/m2 and less than 25kg/m2. Structured questionnaire was used to collect data on risk factors of overweight. Data entry and cleaning were done by using Epi-data version 3.1 and exported to SPSS version 20 for analysis. Bivariate and multivariable binary Logistic regression was used to identify predictors of overweight. Results: A total of 206 government employees were interviewed, male gender participants were 3.5 times more likely to develop overweight compared to females (AOR: 3.5; 95%CI: 1.044-11.957), old age group 45+ years responders were 6.3 times more likely to develop overweight compared to age group 20-34years (AOR: 6.3; 95%CI: 1.503-26.621) and employees who were low level physical activity 4.6 times more likely to develop overweight compared to those having high level physical activity(AOR: 4.6, 95%CI: 1.100-19.119). Conclusions and recommendation: Government employees of Gibe District had old age group, male gender and low physical activity that lead to increased risk of overweight. These are modifiable and nonmodifiable risk factors; therefore increased awareness about physical activity will be effective control measure.


Introduction
The human body needs energy to function and food is the source of this energy. The body stores excess energy mainly in the form of fats (1,2).
Among workers, health problems such as overweight appear to be associated with the type of work performed and with the working environment (1,2). Prevalence rates of overweight varying from 36.6% to 38.9% and of obesity varying from 12.7% to 17.0% have been observed in technical and administrative staff working at public institutions in Brazil (3).
In 2010, overweight and obesity were estimated to cause 3·4 million deaths, 3·9% of years of life lost, and 3·8% of disability-adjusted life-years (DALYs) worldwide (4). Africa is experiencing a shift from underweight to overweight along with rapid socioeconomic and nutritional transition particularly in their urban population. This transformation comes with increased access to energy-dense foods and less strenuous jobs resulting into many people having a positive energy balance and hence becoming overweight or obese (5,6).
A substantial decrease in physical activity and energy expenditure, coupled with an increase in energy intake has been reported as the main factors promoting overweight (7). The World Health Organization reported that the principal reason for this excess weight problem is an energy imbalance between calories consumed and calories expended. Increasing foods intake of high energy and decreasing in physical activity, due to increasing urbanization, changing modes of transportation and sedentary working environments account for this energy imbalance (8).
Perception of body-weight status plays an important role in weight management (9) and underestimation of body-weight can be a risk factor for obesity in some people (10). On the other hand, overestimation of the weight by underweight or normal weight subjects can be a risk factor for unhealthy weight control practices (11) and may cause eating disorders (12).
In western countries, about two thirds of adults in the United States and United Kingdom are overweight or obese and at least one quarter is obese (13,14). High prevalence of obesity was also found among employees at one of the South Africa diamond mine, prevalence was 45% and 32% among females and males respectively (15). In Addis Ababa, EDHS 2011 report showed that round 20% of women and 12.4% of men are overweight or obese (BMI ≥25 kg/m2) (16). Furthermore, there are reports of a more rapid rise in the prevalence of obesity in developing countries compared to developed countries (17).
According to WHO, in many developing countries, research and investment in health has been mainly devoted to infectious diseases, despite the growing need to address chronic diseases with more effort and resources (18). Deaths from infectious diseases, maternal and perinatal conditions and nutritional deficiencies combined are projected to decline by 3% over the next 10 years, while during the same time; deaths due to chronic diseases are projected to increase by 17%. As a result, it is estimated that of the projected 64 million deaths worldwide in 2015, 41 million (64%) will result from chronic diseases unless urgent action is taken (19).
Once associated only with high-income countries, overweight and obesity are now also prevalent in low and middle-income countries (20). It is currently estimated that as much as 20-50% of urban populations in Africa are classified as either overweight or obese and that by 2025, three quarters of the obese population worldwide will be in non-industrialized countries (21). WHO had declared overweight as one of the top ten health risks in the world and one of the top five in developed nations (22). This is therefore an area that needs attention in mitigating health related conditions resulting from long hours of sitting in the office, treating and preventing overweight among employees could result in health gains and possible cost reductions due to decreased health care use and absenteeism.

Methods and Materials Study area and period
Gibe District is found in Hadiya zone SNNPRs of Ethiopia. It is located 262Km away from Addis Ababa to southern part of Ethiopia, 226 km from regional city Hawassa to southwest. According to 2007 Ethiopia census projection, the District has total of 135,503 populations reside (23).The District has 22 kebeles. It has both Kolla (lowland), woinadega (midland) and Dega (highland) climatic zones. It has 23 total number of institutions, where a total number of government employees of the district is 1597of which 557(35%) are females. The study was conducted in Gibe District from, February to March, 2016. Study design: Institutional based case control study was conducted Population Sources population: All government employees working in Gibe District Study population: Cases: all government employees in selected institutions who were overweight Controls: all government employees in selected institutions who were normal body weight Cases definition: cases were those workers who had body mass index greater than or equal to 25kg/m 2 (BMI≥25kg/m 2 ) Controls definition: controls were those workers who had body mass index greater than or equal to 18.5kg/m 2 and lessthan25kg/m 2 .
Inclusion and Exclusion criteria: Inclusion criteria: Cases: we included all selected overweight workers Controls: we included all selected normal body weight workers Exclusion criteria: Cases/controls: we excluded pregnant female staffs and critically ill during data collection. Sample size determination and sampling techniques/procedures The sample size was determined by using EPI INFO version 7, software of two population proportion formula by assumption of 95% confidence interval, 80% power, 10% non-response, Ratio (normal: overweight) = 3:1, P1 = percent of controls exposed P2= percent of cases with exposure, Zα/2 =Standard normal variable at confidence level of 95 % (1.96) Was calculated on Epi-Info soft ware using factors associated with overweight by taking study findings from Sampling procedure: From twenty three institutions, thirteen institutions were selected by simple random sampling technique (lottery method).Census was done on selected institutions to know the number of cases and controls. During census 68 cases and 654 controls were identified. All cases and controls were registered on different register book before data collection. Finally, by using simple random sampling technique (lottery method) study subjects were selected.

Data collection procedures and Instrument
Data were collected using the structured questionnaire which was adapted from "WHO Stepwise approach to chronic disease risk factor surveillance" and from different literatures. Weight was measured using calibrated UNICEF Seca digital weighing scale (Germany) in kilogram (kg).
The measurements were taken once from each respondent and the records were made to the nearest 0.1 kg, without shoes and light clothes. Height was measured using Stadiometer (Seca, Germany)in centimeter (cm) in standing position on bare feet, with heels together, and buttocks and back touching the meter rule. The measurements were taken once from each respondent and the records were made to the nearest 0.1 cm. The questionnaire included socio demographic and economic characteristics such as age, sex, educational level , monthly salary and years of service, behavioral factors such as tobacco use, alcohol use, dietary habit and physical activity and risk perception.
The Questionnaire was first prepared in English and then translated into Amharic and again it was translated back to English to check for consistency. Three diploma nurse interviewers and one Bachelor of Science nurse supervisor was collected the data. Data collectors were trained for three days on questions included in the questionnaire, on interviewing techniques, purpose of the study, and importance of privacy, discipline and approach to the interviewees and confidentiality of the respondents. Before conducting the main study, pretest was carried out of the selected institutions which were not included in the main study. Based on the result, data collectors were reoriented and the questionnaires were modified as necessary.

Data quality control
To ensure the quality of data to be gathered from the study subjects, a range of mechanisms was employed to address major areas of bias introduction during the data collection process. First, the questionnaire was pre-tested on five present employees in out of selected institutions on similar settings and necessary modification was made based on the nature of gaps identified in the questionnaire. Data collectors were trained on how to gather the appropriate information, procedures of data collection techniques and the whole contents and subject matter of the questionnaire. A day today on site supervision by the researcher was carried out during the whole period of data collection. At the end of each day, the questionnaire was reviewed and cross checked for completeness and consistency by the investigator and corrective discussion was under taken with all the data collectors. Data were cleaned and edited after it was entered in to the software.

Data Processing and Analysis
Data were coded, entered, cleaned by using Epi-data version 3.1 and exported to SPSS version 20.0 for analysis. Frequencies and cross tabulations were used to summarized descriptive statistics. Tables used for data presentation. The association between outcome variable and explanatory variables were assessed first bivariate analysis. In Bivariate logistic regression, the variables with P-value <0.25 was candidates to multivariable Logistic regression. Then the multivariable logistic regression model was performed to determine the effect of various factors on the outcome variable and to control confounding. The model fitness for variables was assessed by Hosmer-Lemeshow Goodness of fit test. Multicollinearity among independently associated variables was checked by Multicollinearity diagnostic test VIF but it was not detected. The results were presented using adjusted Odds ratios with ninety five percent confidence intervals. Statistical significance declared at P-value < 0.05.

Ethical considerations
Prior to data collection appropriate ethical clearance was obtained from the ethical clearance committee College of Health Sciences of the Jimma University. Formal letter of permission was produced from administrative bodies of the zone to the study district. Letter of cooperation from each institution administrators was obtained. Finally verbal consent was requested from every study participant included in the study during data collection time after explaining the objectives of the study. Confidentiality assured for the information provided.
Operational definitions Physical activity: We collected data on the type of physical activity. We divided into three categories namely activities as a part of individual work, travel to and from places (walk/cycling) and recreational activities. Both work related and recreational activities were classified as vigorous intensity and moderate intensity activities. 'Vigorous intensity activity' was one which causes large increases in breathing or heart rate and was carried out for at least 10 minutes continuously. 'Moderate intensity activity' was one which causes small increases in breathing or heart rate and was carried out for at least 10 minutes continuously. Recreational (leisure) activities: We collected data about time spent at sports and fitness activities. Level of physical activity was computed using metabolic equivalent based on the time spent for work, transportation and recreation. It provides 3 level physical activities. Low level physical activity was a combination of walking, moderate or vigorous physical activities achieving a <600 MET-minutes/week. Moderate level physical activity was a combination of walking, moderate or vigorous activities achieving 600-3000 MET-minutes/week. High level physical activity was a combination of walking, moderate or vigorous activities achieving ≥ 3000 MET-minutes/week. Time spending on sitting: We collected data about sitting or reclining at work, at home, getting to and from places, or with friends including time spent( sitting at a desk sitting with friends, traveling in a car, bus, reading, playing card or watching television), but not included time spent sleeping. Body weight status: BWS are categorized as normal weight (BMI greater or equal to 18.5kg/m 2 and less than 25kg/m 2 ) or overweight (BMI greater or equal to 25kg/m 2 ).
Smoking/Tobacco use: Current smoker were one who was smoking tobacco at the time of data collection. Daily smokers were one who was smoking any tobacco products daily. Alcohol Consumption: Ever consumers were one who was drinking any alcohol product. Consumption of fruits and vegetables: We collected data on the frequency of consumption of fruits and vegetables in a week. We considered a person to be at risk if the consumption of fruits and vegetables was less than 5 days/week Risk perception: From total of six dichotomized questions, 1 for correct, 0 for incorrect answer about body weight, whose answer medium score and above were taken as having low risk perception and below medium score, were taken as having high risk perception. Typical week: means a week when a person has eaten fruit or vegetable and not an average over a period.

Results
Socio-demographic and economic characteristics of the respondents A total of 206(51 cases and 155 controls) government employees were interviewed. Of all study participants 24(47.1%) of cases and 97(62.6%) of controls were found in the age group 20-34 years and with ranges from 20-58 years. The median age of study participants were 34 years (SD ± 8.25) and 31years (SD ±6.78) of cases and controls, respectively. With regarding to marital status, 31(60.8%) of cases and 69 (44.5%) of controls were married. Majority of the participants,(82.2%) of cases and (92.9%) of controls were protestants in religion.

Risk perception and behavioral characteristics of the respondents
Of all study participants, 26(51.0%) of cases and 65(41.9%) of controls were high risk perception about their body weight. Among the study participants, 11(21.6%) of cases and 2(1.3%) of controls were currently smoker any tobacco products. From the study participants, 6(11.8%) of cases and16 (10.3%) of controls were ever smoking in the past. Of all the study participants, 20 (39.2%) of cases and 43 (27.7%) of controls were exposed to tobacco smoking at home or workplace. Of all study participants, 18(35.3%) of cases and 42(27.9%) of controls were ever consumed an alcoholic drink. In terms of frequency of alcohol consumption in the 12 months preceding the study, 3(16.7%) of cases and 7(16.7%) of controls were consumed daily, whereas, 7(38.9%) of cases and 14(33.3%) of controls were consumed 1-4 days a week and 5(27.8%) of cases and 10(23.8%) of controls were consumed 1-3 days a week.
The intake of fruits and vegetables among the study participants, majority of the study participants (88.2%) of cases and (69.7%) of controls were received less than five days usually ate fruit in a typical week. Almost all of study participants, (97.9%) of cases and ( 94.8%) of controls were received less than five servings of fruit per day the ate on one of those days in a week. Of all study of participants, 46(90.2%) of cases and 121(78.1%) of controls were received less than five days ate vegetables in a typical week. Most of study participants, (88.9%) of cases and (78.3%) of controls were received less than five servings of vegetables ate on one of those days in a week. Commonly used oil or fat for the preparation of meals as reported by the study participants, 22 Independently Determinants associated with overweight among government employees.
The Variables which has independently significant association with overweight among government employees were age, gender and level of physical activity. (Table 5)

Discussion
After handling other confounding factors age, gender and level of physical activity were found to be determinant factors for risk of overweight.
Findings from this study show that, the odds of overweight increased about 3.5 times comparing employees who were men to those who were females(AOR: 3.5, 95%CI: 1.044-11.957). This finding is in line with study conducted in Turkey (25) and in Nigeria (26) showed male gender of employees significant association with increasing risk of overweight. In contrast study conducted in Ghana (24) and South Africa (27) showed that male gender was inversely associated with overweight. This is could be due to biological bases, frequency of eating food prepared outside the home and social pressure to pay greater attention to their weight.
In the current study, age was one of the risk factor that predisposes an individual to development of overweight. Employees who were age greater than or equal to 45 years old 6.3 times more likely to develop overweight compared to those had age group 20-34 years old (AOR: 6.3, 95%CI: 1.503-26.621).This finding is supported study done in Brazil (28), Nigeria (26), Turkey (25) and Ghana (24) showed age significant association with increasing risk of overweight. This is could be due to advancing age results in slowing of the metabolic processes and reduced physical activity.
Low level physical activity was one of the most important risk factors for overweight identified in this study. Accordingly, Employees who were low level of physical activity 4.6 times more likely to develop overweight in comparison to participants who were high level physical activity (AOR: 4.6, 95%CI: 1.100-19.119).This finding is in agreement with the study done in Ghana (24), in Turkey (25) and in Ghana (29) showed physical activity significant associations with increasing level of physical activity to decreases risk of develop overweight. This is could be due to lack of willingness to do the physical exercise, no time for physical exercise and low inter-sectoral collaboration in promoting physical exercise. Strength of this study is an original study in this particular study area that paves a way and expected to generate base line information for the one who needs to conduct another assessment. Limitation of this study is risk of recall bias, especially, in behavioral risk factors.

Conclusion and Recommendation
Conclusion: Government employees of Gibe District had old age group, male gender and low physical activity that lead to increased risk of overweight. These are modifiable and non-modifiable risk factors; therefore increased awareness about physical activity will be effective control measure.

Recommendations:
Based on the findings obtained from this study, a number of recommendations are made under the following: For employers: Older age employees should be frequently get screened for overweight and need to be informed of results so they can improve their life style. All segments of study population practicing low level physical activities should be motivated to exercise physical activities of varying intensity based on their capacity and health status. Awareness creation interventions aimed at reducing overweight should especially target the men and older employees. The perceptions of the employees about body size and shape should be improved to build positive attitudes that ultimately encourage exercise of physical activities through different awareness-creation mechanisms For sectors: Recreational/sports-related physical activities should be promoted and opportunities should be created and facilitated by different sectors to enable the employee's practice of sports-related physical activities.
Promote the health benefits of physical activities including work, transport and leisure time related activities of various intensities in the general so that the risk of developing overweight will be markedly reduced.

Further Research
Assessment of knowledge, attitude and practices towards risk factors of overweight should be conducted.