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

Authors: Huruy Assefa; Tefera Belachew; Legesse Negash
Journal: ISRN Public Health
 (2013)

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## 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.


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## Body

## 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 agez-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 Agez-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 (P<0.05) 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 P<0.05 was considered statistically significant. We present the results of the linear regression as parameter estimates (β), P 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.


## 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 agez-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 Agez-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 (P<0.05) 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 P<0.05 was considered statistically significant. We present the results of the linear regression as parameter estimates (β), P 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 (Table1), 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
P value
Stunting (HAZ < −2)
Total number of adolescents
P value
Place of residence
Urban
620 (83)
743
P
<
0.001
88 (12)
743
P
<
0.001
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
P
<
0.001
103 (11)
942
P
<
0.001
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
P
<
0.001
Highest grade completed (±SD)
5.16 (±2.66)
2084
P
<
0.001
5.16 (±2.66)
2084
P
<
0.001
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
0.166Most of the underweight adolescents (Table1) 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 (Table1) 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 (Table2), 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
P value
Stunting (HAZ < −2)
Total
P 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)
402In bivariate linear regression models (Table3), 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 ageZ-scores in adolescents in Jimma zone, south west Ethiopia, 2011.
Variables
Univariate linear regression
Multivariable linear regression
β
95% CI
P value
β
95% CI
P value
Residence in semiurban
0.1283
0.0124
0.2443
0.0301
0.0820
-
0.0492
0.2132
0.2204
Residence in rural
-
0.3132
-
0.4223
-
0.2041
0.0000
-
0.0990
-
0.2628
0.0649
0.2363
Sex
-
0.7330
-
0.8321
-
0.6340
0.0000
-
0.7419
-
0.8434
-
0.6404
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
-
0.4102
0.4658
0.9009
0.1677
-
0.2662
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
-
0.4743
-
1.0516
0.1029
0.1072
0.2767
-
0.2914
0.8447
0.3396
Household income
0.0004
0.0001
0.0006
0.0072
0.0001
-
0.0002
0.0004
0.6156
Job
0.1144
-
0.0011
0.2298
0.0523
0.2121
0.0967
0.3275
0.0003
Abdominal pain
-
0.0766
-
0.1883
0.0350
0.1783
0.0073
-
0.1006
0.1151
0.8950
Household size
-
0.0233
-
0.0384
-
0.0081
0.0026
-
0.0091
-
0.0253
0.0070
0.2662
Mothers with primary education
0.0120
-
0.1001
0.1240
0.8344
-
0.0452
-
0.1717
0.0813
0.4838
Mothers with secondary education
0.1706
0.0033
0.3379
0.0456
-
0.0270
-
0.2273
0.1733
0.7914
Fathers with no education
-
0.0702
-
0.1837
0.0433
0.2254
0.1205
-
0.0103
0.2513
0.0710
Fathers with secondary education
0.1743
0.0492
0.2995
0.0063
0.0312
-
0.1144
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 (Table3), 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 Agez-Scores

In bivariate linear regression models (Table4), 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 ageZ-scores in Jimma zone, south west Ethiopia, 2011.
Variables
Univariate linear regression
Multivariable linear regression
β
95% CI
P value
β
95% CI
P value
Residence in semiurban
0.0041
-
0.1065
0.1148
0.9417
-
0.0487
-
0.1794
0.0819
0.4645
Residence in rural
-
0.2136
-
0.3178
-
0.1093
0.0001
0.1136
-
0.0495
0.2768
0.1721
Sex
-
0.1473
-
0.2464
-
0.0482
0.0036
-
0.1595
-
0.2606
-
0.0585
0.0020
Age in years
0.0638
0.0268
0.1007
0.0007
-
0.0320
-
0.0739
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
-
0.3180
-
0.7355
0.0996
0.1355
-
0.1189
-
0.5510
0.3133
0.5897
Community school
-
0.8765
-
1.5391
-
0.2140
0.0095
-
1.2114
-
1.9757
-
0.4471
0.0019
Other type of school
-
0.5280
-
1.0622
0.0062
0.0527
-
0.0004
-
0.5486
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
-
0.1451
-
0.2514
-
0.0388
0.0075
-
0.1328
-
0.2401
-
0.0255
0.0153
Household size
-
0.0254
-
0.0398
-
0.0110
0.0006
-
0.0224
-
0.0384
-
0.0064
0.0062
Mothers primary education
-
0.0649
-
0.1720
0.0422
0.2347
-
0.0963
-
0.2222
0.0296
0.1336
Mothers secondary education
0.2982
0.1389
0.4574
0.0002
0.0747
-
0.1248
0.2741
0.4628
Fathers no education
-
0.0874
-
0.1959
0.0211
0.1144
0.0716
-
0.0585
0.2018
0.2805
Fathers secondary education
0.1536
0.0337
0.2735
0.0121
-
0.0804
-
0.2253
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 (Table4), 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.


## 3.1. Determinants of Nutrition Status of Adolescents in terms of Height for Agez-Scores

In bivariate linear regression models (Table4), 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 ageZ-scores in Jimma zone, south west Ethiopia, 2011.
Variables
Univariate linear regression
Multivariable linear regression
β
95% CI
P value
β
95% CI
P value
Residence in semiurban
0.0041
-
0.1065
0.1148
0.9417
-
0.0487
-
0.1794
0.0819
0.4645
Residence in rural
-
0.2136
-
0.3178
-
0.1093
0.0001
0.1136
-
0.0495
0.2768
0.1721
Sex
-
0.1473
-
0.2464
-
0.0482
0.0036
-
0.1595
-
0.2606
-
0.0585
0.0020
Age in years
0.0638
0.0268
0.1007
0.0007
-
0.0320
-
0.0739
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
-
0.3180
-
0.7355
0.0996
0.1355
-
0.1189
-
0.5510
0.3133
0.5897
Community school
-
0.8765
-
1.5391
-
0.2140
0.0095
-
1.2114
-
1.9757
-
0.4471
0.0019
Other type of school
-
0.5280
-
1.0622
0.0062
0.0527
-
0.0004
-
0.5486
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
-
0.1451
-
0.2514
-
0.0388
0.0075
-
0.1328
-
0.2401
-
0.0255
0.0153
Household size
-
0.0254
-
0.0398
-
0.0110
0.0006
-
0.0224
-
0.0384
-
0.0064
0.0062
Mothers primary education
-
0.0649
-
0.1720
0.0422
0.2347
-
0.0963
-
0.2222
0.0296
0.1336
Mothers secondary education
0.2982
0.1389
0.4574
0.0002
0.0747
-
0.1248
0.2741
0.4628
Fathers no education
-
0.0874
-
0.1959
0.0211
0.1144
0.0716
-
0.0585
0.2018
0.2805
Fathers secondary education
0.1536
0.0337
0.2735
0.0121
-
0.0804
-
0.2253
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 (Table4), 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 Agez-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 Agez-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 Agez-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 Agez-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 Agez-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 Agez-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 Agez-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.



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*Source: 238546-2013-12-10.xml*