ISRN Public HealthVolume 2013 (2013), Article ID 238546, 7 pageshttp://dx.doi.org/10.1155/2013/238546

Research Article

Socioeconomic Factors Associated with Underweight and Stunting among Adolescents of Jimma Zone, South West Ethiopia: A Cross-Sectional Study

Huruy Assefa,¹ Tefera Belachew,² and Legesse Negash³

¹Department of Public Health, College of Health Sciences, Mekelle University, P.O.Box 1871, Mekelle, Ethiopia²Department of Population and Family Health, College of Public Health and Medical Sciences, Jimma University, P.O.Box 1104, Jimma, Ethiopia³Department of Statistics, College of Natural Sciences, Jimma University, P.O.Box 1104, Jimma, Ethiopia

Received 16 September 2013; Accepted 24 October 2013

Academic Editors: K. McLeroy and A. Rosano

Copyright © 2013 Huruy Assefa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Nutrition during adolescence plays an important role in the individual’s life. Socioeconomic status, age, sex, and mothers’ educational level are among the important determinant factors of nutritional status of adolescents. In this study, we test the competing hypotheses about the correlates of nutritional status among Ethiopian adolescents. Methods. We report a total of 2084 adolescents and univariate and multivariable linear regression were used to assess socioeconomic factors associated with underweight and stunting among adolescents. Results. Age, highest grade completed, job, and last attended in community school were positively associated with BMI for Age z-score and highest grade completed, household income, and job were positively associated with Height for Age z-score. However, male gender was negatively associated with BMI for Age z-score and male gender, last attended in community school, abdominal pain, and household size were negatively associated with Height for Age z-score. Conclusion. Age of the adolescents, gender, educational status, employment status, and type of last school attended are associated with underweight; gender, place of residence, household size, household income, educational status, employment status, type of last school attended, and abdominal pain are associated with stunting. The findings reflect socio-economic characteristics are associated with underweight and stunting.

1. Background of the study

The world population is believed to have reached over 6.6 billion [1]. Adolescence is a particularly unique period in life because it is a time of intense physical, psychosocial, and cognitive development. Nutrition during adolescence plays an important role in the individual’s life. Increased nutritional needs to adolescents gain up to 50% of their adult weight, more than 20% of their adult height, and 50% of their adult skeletal mass [2].

There are different factors that affect nutritional status of adolescents. Socioeconomic status, age, sex, and mothers’ educational level are among the important determinant factors of nutritional status of adolescents [3–6]. Studies have shown that adolescent women from low economic status households were most affected by malnutrition and the higher the level of education, the lower the proportion of undernourished adolescent women, and rural adolescent women are more likely to suffer from chronic energy deficiency than adolescent women in urban areas. [7, 8].

There are only a handful of studies on adolescent malnutrition in developing countries—they indicate that younger adolescents tend to be more undernourished than older adolescents, and, contrary to expectations, boys are almost twice as undernourished as girls. In addition, these few studies suggest that there are more undernourished adolescents in Sub-Saharan Africa and a higher prevalence in rural than in urban areas [9].

In Ethiopia, undernourishment among preschool-aged children has been well documented [10], but factors affecting the nutritional status of adolescents have not been studied in detail [11]. The available studies focused on factors affecting pregnancy outcomes rather than on problems associated with normal growth and development of adolescents [12, 13]. A recent study on the nutritional status of adolescent girls from rural communities of Tigray, Northern Ethiopia [5], has helped to close the research gap in the area, although it is still focused only on rural adolescent girls, on small geographic area and small population. This study is expected to play important role in identifying the socio-economic factors associated with underweight and stunting among adolescents of Jimma zone, south west Ethiopia. In this study, we test the competing hypothesis about the socio-economic factors associated with underweight and stunting among Ethiopian adolescents.

2. Methods

2.1. Study Sample

A community-based cross-sectional study was conducted from 2010 adolescents enrolled in the second round of the five-year longitudinal study of adolescents in Jimma zone, South west Ethiopia. A census was done to generate the list of all households which gave a sampling frame for random selection of 3,700 households from the total of 5,795 households in the list. A two-stage sampling plan was used to select the target sample of adolescents. Households were classified into urban (Jimma city), semiurban (Serbo, Dedo, and Yebbu towns), and six rural communities in the vicinity of the small towns. At the first stage, households were randomly sampled with the sample size in each kebele determined by the relative proportion of the study population in the kebele and the overall target sample size. In the second stage, one adolescent (a boy or a girl) was randomly selected from each household using a Kish Table [14]. Using this sampling strategy, a total of 1059 boys and 1025 girls were interviewed in round one. This paper reports on all the 2084 adolescents in the second round of the five-year longitudinal family survey of youth.

2.2. Measurements

Structured adolescent level questionnaires were used to collect data. The questionnaires were interviewer-administered and translated into Amharic and Oromifa languages and checked for consistency by another person who speaks both Oromifa and English. The questionnaire focused on issues related to adolescents’ experiences of nutritional status, socio-economic, health, food insecurity, and anthropometric measurements of the adolescent. The interview was conducted in a private place by an interviewer of the same sex.

The interviewers received one week of intensive training prior to the pretest and an additional week of training was given with the final version of the questionnaire before the start of the actual interviews. Supervisors checked the data collection process and filled questionnaires daily to ensure accuracy of the data. The research team supervised the data collection team every week through meetings and checking of the filled questionnaires.

We measured height to the nearest 0.1 cm using a stadiometer (SECA, Hannover, Germany) and weight to the nearest 0.1 kg using digital scales (SECA). The level of stunting (height for age z-scores), which is an indicator of chronic malnutrition, and wasting (weight for age z-scores), which is another indicator of malnutrition, were calculated using WHO Athro-Plus software [15]. Thus, those below −2 standard deviations of the NCHS median reference for height-for-age and weight-for-height were defined as stunting and wasting, respectively.

Socio-economic variables like age, sex, place of residence, marital status, whether the adolescents have a child, highest grade completed, type of school completed, religion, whether the adolescents have a job, household income, household size, mother’s education status and father’s education status, health related variables like fever, cough, fast breathing, diarrhea, vomiting, unable to drink or eat, ulcer, Depression, sleep under insecticide-treated mosquito bed net, currently smoke tobacco, does any member of the household smoke, and food security variables were recorded. The questionnaire was tested on 200 adolescents (not included in the sample) selected from a community in Jimma city.

2.3. Data Analysis

The data were double-entered, checked for missing values and outliers using SPSS (SPSS Inc. version 16.1, Chicago, Illinois), and analyzed using SAS version 9.2. First, univariate linear regression analyses were conducted. To identify the predictors of underweight (BMI for Age z-score) and stunting (Height for Age z-score), multivariable linear regression analysis with stepwise variable selection and AIC criteria procedure was employed and variables that showed statistically significant () in the univariate analyses were entered to the multivariable linear regression.

Goodness-of-fit of the models was assessed using Akaike’s information criterion (AIC) and adjusted-R² and partial regression residual plots showed that all had linear relationship. Normality of the data was assessed using a Q-Q plot and there was no need for transformation; outliers and influential observations were not as such influential and then retained in the final model. Colinearity between predictor variables was checked using variance inflation factor (VIF); interaction and confounding variables were checked. All tests were two-sided and a was considered statistically significant. We present the results of the linear regression as parameter estimates (β), values, and 95% confidence intervals.

2.4. Ethical Consideration

Informed verbal consent was obtained both from the parents and each adolescent before the interview or measurement. The study obtained ethical clearance from the Ethical Review Board of Jimma University (Ethiopia). The head of the household was interviewed by using the household questionnaire.

We did not obtain written verbal consent, because most of the respondents did not write and read well. We first read the consent paper and explain the objective of the study in front of each respondent and after they agree on the objective of the study, they sign. The ethics committees also approved the consent procedure after we explain the procedures.

3. Results

Of 2084 adolescents included in the study (Table 1), 1951 and 1956 adolescents’ complete data were available for BMI for Age z-score and Height for Age z-score. Out of 1951 and 1956 adolescents covered in the study for BMI for Age z-score and Height for Age z-score, 1577 (80.83%) and 313 (16%) were found to be underweight and stunted, respectively.

Table 1: Sociodemographic characteristics of nutritional status of adolescents, Jimma zone, south west Ethiopia, 2011.

Variables
Category
        Prevalence (%)
Prevalence (%)

Underweight (HAZ < −2)
Total
value
Stunting (HAZ < −2)
Total number of adolescents
value

Place of residence
Urban
620 (83)
743

88 (12)
743

Semiurban
459 (84)
545
90 (16)
546

Rural
498 (75)
663
135 (20)
667

Marital status
Single
1571 (81)
1945
    0.232
313 (16)
1950
    0.284

Married
6 (100)
6
0 (0)
6

Sex
Female
839 (89)
941

103 (11)
942

Male
738 (73)
1010
210 (21)
1014

Ever had child
No
1568 (81)
1941
    0.460
312 (16)
1945
    0.794

Yes
9 (90)
10
1 (9)
11

Type of last school attended
Government
1511 (81)
1866
    0.680
291 (16)
1870
    0.003

Private
16 (94)
17
4 (24)
17

Community
4 (100)
4
0 (0)
4

Other
46 (72)
64
18 (28)
65

Religion
Muslim
918 (79)
1165
    0.008
191 (16)
1170
    0.681

Orthodox
562 (85)
664
104 (16)
664

Protestant
91 (81)
113
16 (14)
113

Other
6 (67)
9
2 (22)
9

Job
No
1117 (80)
1398
    0.097
234 (17)
1399
    0.166

Yes
460 (83)
553
79 (14)
557

Fathers’ education
No education
487 (80)
611
    0.086
188 (17)
1089
    0.018

Primary
633 (79)
800
102 (16)
624

Secondary
371 (84)
441
19 (9)
213

Mothers’ education
No education
860 (79)
1086
    0.127
112 (18)
614
    0.010

Primary
513 (82)
623
137 (17)
801

Secondary
178 (84)
212
53 (12)
441

Age in years (SD)
14.78 (1.34)
2083
0.001
14.78 (1.34)
2083

Highest grade completed (SD)
5.16 (2.66)
2084

5.16 (2.66)
2084

Household income (SD)
105.77 (188.13)
2084
0.686
105.77 (188.13)
2084
0.539

Household size (SD)
8.49 (3.42)
2084
0.021
8.49 (3.42)
2084
0.166

Most of the underweight adolescents (Table 1) were females (53.20%), reside in urban (39.32%), single (99.68%), muslim by religion (58.21%), no child (99.43%), attended last school in government (95.82%), no job (70.83%), adolescent father’s education had primary (42.45%), and adolescent mother’s education had no education (55.45%).

And most of the stunted adolescents (Table 1) were males (67%), reside in rural (43%), single (100%), muslim by religion (61%), no child (99.68%), attended last school in government (93%), no job (75%), adolescent father’s education had no education (61%), and adolescent mother’s education had primary (45%).

The mean (SD) age of the adolescents was 14.78 (1.34) and the mean (SD) highest grade completed, household income, and household size of the adolescents were 5.16 (2.66), 105.77 (188.13), and 8.49 (3.42), respectively, for both underweight and stunted adolescents.

Of the underweight adolescents (Table 2), fever (86%), cough (43%), vomiting (35%), unable to eat or drink (49%), and abdominal pain (31%) were the health problems in the study area. And of the 313 stunted adolescents, fever (85%), cough (49%), vomiting (35%), unable to eat or drink (45%), and abdominal pain (39%) were the health problems. Only 21% of the underweight and stunted adolescents did not secure their food in the study area.

Table 2: Health related and food insecurity characteristics of nutritional status of adolescents, Jimma zone, south west Ethiopia, 2011.

Variables
Category
        Prevalence (%)
Prevalence (%)

Underweight (HAZ < −2)
Total
value
Stunting (HAZ < −2)
Total
value

Diarrhea
No
1372 (87)
1696
    0.849
269 (86)
1701
    0.558

Yes
205 (13)
255
44 (14)
255

Fever
No
228 (14)
284
    0.799
48 (15)
286
    0.697

Yes
1349 (86)
1667
265 (85)
1670

Cough
No
903 (57)
1101
    0.130
161 (51)
1105
    0.049

Yes
674 (43)
850
152 (49)
851

Fast breathing
No
1191 (76)
1454
    0.038
239 (76)
1459
    0.433

Yes
386 (24)
497
74 (24)
497

Vomiting
No
1022 (65)
1259
    0.601
204 (65)
1264
    0.823

Yes
555 (35)
692
109 (35)
692

Could not eat or drink
No
804 (51)
965
    0.006
159 (51)
970
    0.641

Yes
773 (49)
986
154 (49)
986

Abdominal pain
No
1086 (69)
1327
    0.099
192 (61)
1332
    0.005

Yes
491 (31)
624
121 (39)
624

Genital discharge or ulcer
No
1562 (99)
1931
    0.506
310 (99)
1936
    0.902

Yes
15 (1)
20
3 (1)
20

Depression/extreme sadness/worry
No
1232 (78)
1529
    0.586
242 (77)
1534
    0.603

Yes
345 (22)
422
71 (23)
422

Sleeps under insecticide treated bed net
No
1376 (87)
1709
    0.347
282 (90)
1714
    0.148

Yes
201 (13)
242
31 (10)
242

Smoking
No
1571 (99.62)
1941
    0.093
311 (99)
1946
    0.730

Yes
6 (0.38)
10
2 (1)
10

Anyone in household smokes tobacco products
No
1377 (87)
1707
    0.630
276 (88)
1712
    0.703

Yes
200 (13)
244
37 (12)
244

Adolescent food insecurity
Secure
1247 (79)
1549
    0.472
247 (79)
1554
    0.799

Nonsecure
330 (21)
402
66 (21)
402

In bivariate linear regression models (Table 3), residence in semirural, age, highest grade completed, last attended in community school, household income, adolescent mothers with secondary education, and adolescent fathers with secondary education were positively associated with BMI for Age z-score, while residence in rural, male gender, and Household size were negatively associated with BMI for Age z-score.

Table 3: Predictors of BMI for age -scores in adolescents in Jimma zone, south west Ethiopia, 2011.

Variables
Univariate linear regression
Multivariable linear regression

95% CI
value

95% CI
value

Residence in semiurban
0.1283
0.0124
0.2443
0.0301
0.0820

0.2132
0.2204

Residence in rural

0.0000

0.0649
0.2363

Sex

0.0000

0.0000

Age in years
0.1131
0.0746
0.1517
0.0000
0.0594
0.0173
0.1015
0.0057

Highest grade completed
0.0685
0.0489
0.0881
0.0000
0.0554
0.0290
0.0817
0.0000

Private school
0.0278

0.4658
0.9009
0.1677

0.6016
0.4485

Community school
0.7070
0.0119
1.4020
0.0462
0.7912
0.0238
1.5585
0.0433

Other type of school

0.1029
0.1072
0.2767

0.8447
0.3396

Household income
0.0004
0.0001
0.0006
0.0072
0.0001

0.0004
0.6156

Job
0.1144

0.2298
0.0523
0.2121
0.0967
0.3275
0.0003

Abdominal pain

0.0350
0.1783
0.0073

0.1151
0.8950

Household size

0.0026

0.0070
0.2662

Mothers with primary education
0.0120

0.1240
0.8344

0.0813
0.4838

Mothers with secondary education
0.1706
0.0033
0.3379
0.0456

0.1733
0.7914

Fathers with no education

0.0433
0.2254
0.1205

0.2513
0.0710

Fathers with secondary education
0.1743
0.0492
0.2995
0.0063
0.0312

0.1767
0.6745

Source: Jimma Longitudinal Family of Youth; Round 2, 2006-2007.

CI: confidence interval.

After adjusting for all other variables in the multivariable linear regression model (Table 3), age, highest grade completed, job, and last attended in community school were positively associated with BMI for Age z-score. However, male gender was negatively associated with BMI for Age z-score. The effect of residence in semiurban, residence in rural, household income, household size, adolescent mothers with secondary education, and adolescent fathers with secondary education disappeared in the multivariable linear regression model.

3.1. Determinants of Nutrition Status of Adolescents in terms of Height for Age z-Scores

In bivariate linear regression models (Table 4), age, highest grade completed, household income, job, and adolescent fathers with secondary education were positively associated with BMI for Age z-score, while residence in rural, male gender, last school attended in community school, abdominal pain, and household size were negatively associated with the Height for Age z-score.

Table 4: Determinants of nutrition status of adolescents in terms of height for age -scores in Jimma zone, south west Ethiopia, 2011.

Variables
Univariate linear regression
Multivariable linear regression

95% CI
value

95% CI
value

Residence in semiurban
0.0041

0.1148
0.9417

0.0819
0.4645

Residence in rural

0.0001
0.1136

0.2768
0.1721

Sex

0.0036

0.0020

Age in years
0.0638
0.0268
0.1007
0.0007

0.0100
0.1350

Highest grade completed
0.0917
0.0732
0.1101
0.0000
0.1066
0.0803
0.1328
0.0000

Private school

0.0996
0.1355

0.3133
0.5897

Community school

0.0095

0.0019

Other type of school

0.0062
0.0527

0.5478
0.9988

Household income
0.0006
0.0003
0.0008
0.0000
0.0005
0.0002
0.0007
0.0014

Job
0.1258
0.0159
0.2356
0.0248
0.2198
0.1051
0.3345
0.0002

Abdominal pain

0.0075

0.0153

Household size

0.0006

0.0062

Mothers primary education

0.0422
0.2347

0.0296
0.1336

Mothers secondary education
0.2982
0.1389
0.4574
0.0002
0.0747

0.2741
0.4628

Fathers no education

0.0211
0.1144
0.0716

0.2018
0.2805

Fathers secondary education
0.1536
0.0337
0.2735
0.0121

0.0646
0.2770

Source: Jimma Longitudinal Family of Youth; Round 2, 2006-2007.

CI: confidence interval.

After adjusting for all other variables in the multivariable linear regression model (Table 4), highest grade completed, household income, and job were positively associated with Height for Age z-score, while male gender, last attended in community school, abdominal pain, and household size were negatively associated with Height for Age z-score. The effect of adolescents’ residence in rural, age of the adolescent, adolescent mothers with secondary education, and adolescents fathers with secondary education disappeared in the multivariable linear regression model.

4. Discussion

Our results showed that age was positively associated with BMI for Age z-score. An association of age with BMI for Age z-score has previously been reported [5, 8]. The risk of underweight was, on average, significantly higher for younger adolescents than older adolescents.

Studies have suggested a positive association between BMI for Age z-score and educational status and employment [7, 8, 12, 16, 17]. Our study found that highest grade completed was positively associated with BMI for Age z-score and Height for Age z-score. They indicated that adolescents who receive even a minimal education are generally more aware than those who have no education of how to utilize available resources for the improvement of their own nutritional status and that of their families. Education may enable them to make independent decisions, to be accepted by other household members, and to have greater access to household resources that are important to nutritional status [16, 17].

Our study found that job was positively associated with BMI for Age z-score and Height for Age z-score. The risk of underweight and stunted was significantly high, on average, for unemployed adolescents than employed adolescents in Jimma zone. This finding is consistent with other studies [18]. They indicated that unemployment is a significant factor for chronic energy deficiency in these adolescents as compared with employed adolescents. Our study also found that adolescents who attended their last school in community school was positively associated with BMI for Age z-score.

Studies have demonstrated the connection between Height for Age z-score and household income [6–8]. Our study also indicated that household income was positively associated with Height for Age z-score.

We found that male gender was negatively associated with BMI for Age z-score and Height for Age z-score, as previously reported by others [4]. The risk of underweight was, on average, significantly higher for adolescent males than adolescent females. This may be because of the fact that biological, behavioral, and sociocultural mechanisms have been proposed for the gender differences in morbidity and mortality. Biologically, female subjects have an advantage for better health and longer survival because of the role of sex hormones in modulating lipid levels and increasing immune response. In addition, the difference in morbidity and mortality between boys and girls is further related to individual lifestyle, the use of health care, and health and illness behaviors and practices. For example, adolescent boys are more likely to smoke and have higher propensities of taking greater risks that expose them to injury.

The result also showed that residence in rural was negatively associated with the Height for Age z-score which is consistent with reports of studies in Ethiopia and Sub-Saharan Africa [8, 9, 18, 19]. The observed urban-rural difference could be an indication of low access and use of health services in the rural areas as compared with urban areas. In general, people living in cities have better health and lower death rates than rural residents, even though the urban poor often live in unsanitary and crowded conditions. Compared with rural residents, urban residents have better access to medical services and are more easily reached by immunization and educational campaign.

We found also that household size was negatively associated with the Height for Age z-score, as previously reported by others [3, 6]. In addition, abdominal pain and last school attended in community were negatively associated with the Height for Age z-score.

We acknowledge a number of limitations in our study. As the study involved adolescents who are at the different stages of academic status, we used the highest grade completed as a measure of educational attainment that can serve across all age groups. However, there are other measures of educational attainment that were not captured. The fact that we did not have data from the school records regarding the academic performances is also a limitation of our analysis.

5. Conclusion

In conclusion, the study revealed that age of the adolescents, gender, educational status, employment status, and type of last school attended were associated with underweight; gender, place of residence, household size, household income, educational status, employment status, type of last school attended, and abdominal pain were associated with stunted. Thus, underweight and stunting are the reflection of socioeconomic development demanding combination of different types of policies and programs for its solution. To reduce and prevent underweight and stunting, strategies need to be involved and more targeted interventions. Promoting gender equality through effective behavior change communications needs to be considered. Further research will help to understand and identify the most effective strategies for reducing adolescent malnutrition in the study area.

Abbreviations

BMI:
Body mass index.

Conflict of Interests

The authors declare that they have no conflict of interests.

Authors’ Contribution

The authors’ responsibilities were as follows. Huruy Assefa, Tefera Belachew, and Legesse Negash designed and supervised the study and assisted in the analysis and interpretation of the data. All authors critically reviewed the paper. Huruy Assefa, the corresponding author, did the analysis and drafted the paper and had the responsibility to submit it for publication.

Acknowledgments

The Jimma Longitudinal Family Survey of Youth was funded by the Packard Foundation, Campton Foundation, the National Institute of Health, and the National Science Foundation. The authors are extremely grateful to research team members and Professor Tefera Belachew.

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