Demographic characteristics of adolescents
Overall, there was non-significant higher proportion of female adolescents than males with 56.1% and 43.9% representation respectively (χ2, p>0.05). Adolescents aged 10-14 years had higher representation at 56.3% compared to 43.7% aged 15-19 years. There was no significant difference between gender distributions across the age cohorts (χ2, p=0.369). Majority (92.4%) of the adolescent females were neither pregnant nor lactating while 94.7% were not married. The married adolescents were more likely to be females than males (χ2, p<0.001) who were married to older youth with an average age of spouses at 29 years. Majority of the adolescents 99.2% were Christians (Table 1).
Table 1: Demographic Characteristics of adolescents
|
|
Female
N=275
|
Male
N=215
|
Total
N=490
|
Chi square
|
|
|
N
|
%
|
n
|
%
|
n
|
%
|
Age distribution
|
10-19 (N=490)
|
275
|
56.1%
|
215
|
43.9%
|
490
|
100%
|
χ=0.808
p=0.369
|
10-14
|
150
|
54.5%
|
126
|
58.6%
|
276
|
56.3%
|
15-19
|
125
|
45.5%
|
89
|
41.4%
|
214
|
43.7%
|
Physiological status (n=275)
|
Pregnant
|
8
|
2.9%
|
0
|
0%
|
8
|
2.9%
|
NA
|
Lactating
|
12
|
4.4%
|
0
|
0%
|
12
|
4.4%
|
Pregnant and lactating
|
1
|
0.4%
|
0
|
0%
|
1
|
0.4%
|
Marital status
|
Currently married/Cohabiting
|
22
|
7.3%
|
1
|
0.5%
|
23
|
4.3%
|
χ=15.522
p<0.001
|
Separated/Divorced
|
2
|
0.7%
|
1
|
0.5%
|
3
|
0.6%
|
Never married
|
251
|
91.3%
|
213
|
99.1%
|
464
|
94.7%
|
Religion
|
Christian
|
274
|
99.6%
|
212
|
98.6%
|
486
|
99.2%
|
χ=1.925
p=0.382
|
Muslim
|
0
|
0%
|
1
|
0.5%
|
1
|
0.2%
|
Traditional
|
1
|
0.4%
|
2
|
0.9%
|
3
|
0.6%
|
Education level of adolescents
Majority (91.2%) of the adolescents had ever been to school of which 21.9% attained less than primary school education while 21.9% completed primary education. Higher proportion of 41.8% was in primary school at the time of the study with the ages 10-14 years two times more likely to be in the primary school (OR, 2.014; CI, 1.244-3.262; P=0.004). There was no difference in gender distribution in both primary and secondary schools enrolment (χ2, p> 0.05). At the time of the survey most (61.1%) were not attending school with majority (87.5%) citing COVID 19 as a barrier, this was significantly associated with male adolescents (χ2, p=0.011). Due to COVID 19, schools were partially open with only grades 4 and 8 in school at the time of the survey. The females cited other reasons for not being in school such as Marriage (8.6%) pregnancy, 3.9% child care and 3.3% family labor (Table 2).
Table 2: Education level of adolescents
|
|
Female
N=275
|
Male
N=215
|
Total
N=490
|
Chi square
|
|
|
N
|
%
|
n
|
%
|
n
|
%
|
|
Ever been to school
|
Yes
|
250
|
90.9%
|
197
|
91.6%
|
447
|
91.2%
|
χ=0.078
p=0.780
|
NO
|
25
|
9.1%
|
18
|
8.4%
|
43
|
8.8%
|
Education level
|
Less than primary school
|
58
|
23.2%
|
40
|
20.3%
|
98
|
21.9%
|
χ=5.359
p=0.616
|
Primary school
|
58
|
23.2%
|
40
|
20.3%
|
98
|
21.9%
|
Secondary school
|
11
|
4.4%
|
11
|
5.6%
|
22
|
4.9%
|
Vocational training
|
1
|
0.4%
|
1
|
0.5%
|
1
|
0.2%
|
College/pre-university/university
|
0
|
0%
|
1
|
0.5%
|
1
|
0.2%
|
Currently in primary school
|
98
|
39.2%
|
89
|
45.2%
|
187
|
41.8%
|
Currently in secondary school
|
23
|
9.2%
|
14
|
7.1%
|
37
|
8.3%
|
Currently in a vocational training
|
1
|
0.4%
|
2
|
1.0%
|
3
|
0.7%
|
Currently attending school
|
Yes
|
98
|
39.2%
|
76
|
38.6%
|
174
|
38.9%
|
χ=0.018
p=0.894
|
No
|
152
|
60.8%
|
121
|
61.4%
|
273
|
61.1%
|
Reason for NOT attending school
(N=273)
|
Chronic Sickness
|
0
|
0%
|
1
|
0.8%
|
1
|
0.4%
|
χ= 19.813
p=0.011*
|
Family labor responsibilities
|
5
|
3.3%
|
2
|
1.7%
|
7
|
2.6%
|
Working outside home
|
0
|
0%
|
2
|
1.7%
|
2
|
0.7%
|
Fees or costs
|
1
|
0.7%
|
1
|
0.5%
|
2
|
0.7%
|
Migrated/ displaced from school area
|
1
|
0.4%
|
0
|
0%
|
1
|
0.4%
|
Insecurity/ violence
|
1
|
0.7%
|
0
|
0%
|
1
|
0.4%
|
Married
|
13
|
8.6%
|
1
|
0.5%
|
14
|
5.1%
|
Pregnant/ taking care of her own child
|
6
|
3.9%
|
0
|
0%
|
6
|
2.2%
|
COVID 19 Pandemic
|
125
|
82.2%
|
114
|
94.2%
|
239
|
87.5%
|
*Significance at p<0.05
Distribution of adolescents in school by age indicated reduction in enrolment with advanced grades where, about a third (32.7%) were either in grade 4 or below, 28.7% in grade 5-6 and 24.1% in grade 7-8 with less than 15% in secondary school (Figure 4.1). There was higher representation of males than females at 26.3% and 22.4% respectively in 7th -8th grade despite non-significant differences (Figure 1). There was low enrolment of adolescents in 11th to 12th grade with a higher proportion of females (4%) than boys (1.3%).There was no difference in the distribution of adolescent by grades between males and females (χ2, p=0.591).
Underweight (BMI for age)
According to WHO growth reference standard11, underweight in adolescence is defined as BMI-for-age Z-score below –2, BMI-for-age Z-score below –3 as severe underweight, t as a BMI-for-age Z-score above 1 as overweigh, and obesity as Z-score > 2. of the.
In this study, overall, 22.7% of adolescents aged 10-19 years were underweight (GAM, <-2SD) while 7.9% had severe acute malnutrition (SAM, ≤-3SD). The younger adolescents (10-14) presented higher prevalence for underweight presenting GAM of 28.1% and SAM of 10% compared to the 15-19 years old with 15.7% and 4.8% (Global acute malnutrition (GAM) and severe acute malnutrition (SAM) respectively. The adolescents aged 10-14 years were two times more likely to be underweight compared to 15-19 year old (OR,2.101; CI,1.331-3.317; P=0.001). Generally, male adolescents presented higher prevalence for underweight compared to the female counterparts where males 10-19 years were 1.5 times more likely to be underweight compared to the female counterparts though not significant (OR, 1.472; CI, 0.959-2.260; p=0.077). Underweight was significantly associated with males aged 15-19 years (χ2, p=0.049).
Table 3: Underweight in adolescents (BMI for Age)
BMI (WHO BMI-for-age in years and months)
|
Females
|
Males
|
Total
|
Chi square
P value
|
N
|
%
|
n
|
%
|
n
|
%
|
|
10-19 yrs
N=480
M=211
F=269
|
Normal
|
216
|
80.3%
|
155
|
73.5%
|
374
|
77.3%
|
χ=3.150
p=0.076
|
MAM (<-2and >-3SD)
|
35
|
13.0%
|
37
|
17.5%
|
72
|
14.9%
|
SAM(≤-3SD)
|
18
|
6.7%
|
19
|
9.0%
|
38
|
7.9%
|
GAM(<-2SD)
|
53
|
19.7%
|
56
|
26.5%
|
110
|
22.7%
|
10-14 yrs
N=270
M=124
F=146
|
Normal
|
107
|
73.3%
|
87
|
70.2%
|
194
|
71.9%
|
χ=1.120
p=0.571
|
MAM (<-2and >-3SD)
|
27
|
18.5%
|
22
|
17.7%
|
49
|
18.1%
|
SAM(≤-3SD)
|
12
|
8.2%
|
15
|
12.1%
|
27
|
10.0%
|
GAM(<-2SD)
|
39
|
26.7%
|
37
|
29.8%
|
76
|
28.1%
|
|
15-19 yrs
N=210
M=87
F=123
|
Normal
|
109
|
87.2%
|
68
|
78.2%
|
177
|
84.3%
|
χ=6.033
p=0.049*
|
MAM(<-2and >-3SD)
|
8
|
6.5%
|
15
|
17.2%
|
23
|
11.0%
|
SAM(≤-3SD)
|
6
|
4.9%
|
4
|
4.6%
|
10
|
4.8%
|
GAM(<-2SD)
|
14
|
11.4%
|
19
|
21.8%
|
33
|
15.7%
|
Dietary practices of adolescents
This study measured dietary practices of adolescents using 24-hour dietary diversity while observing the key nutrients to adolescents aged 10-19 years. Food groups consumed by adolescents were mainly starchy staples (97.6%), milk (80.8%) and dark green leafy vegetables (53.9%). The rest of the food groups were consumed by less than 50% of the adolescents with eggs being the least consumed (9.8%) despite its availability (Figure 2). Legumes, nuts and seeds intake was more associated to females (43.9%) than 27.6% males (χ2, p>0.001) and significant low likelihood among the males (OR, 0.489; CI, 0.333-0.718; p=<0.001). Similar observation was made in consumption of other fruits and vegetables with 41.9% females against 28.5% males (χ2, p=0.002). Despite low consumption of organ meat generally (12.7%), males were 1.6 times more likely to consume the same compared to females (OR, 1.646; CI, 0.963-2.813; p=0.068).
Dietary diversity: Figure 3 shows dietary diversity among adolescents. Among adolescents 10-19 years, the mean dietary diversity score (MDDS) was 3.77±1.40. Females had more diversified diets expressed with significantly higher MDDS (3.93±1.39) compared to their male counterparts that recorded 3.59±1.40 (t-test, p=0.007). Similar observation was made between males and female adolescents aged 15-19 years with MDDS at 3.36(±1.32) and 3.89(±1.46) respectively (t- test, p=0.007).
Individual Dietary Diversity Score (IDDS):
This was determined as proxy indicator for nutrient adequacy of adolescent diet. Among adolescents aged 10-19 years, 42.9% consumed less than 4 food groups in the preceding 24 hours that was more associated with male adolescents (59.9%) compared to 35.3% of the females (χ2, p<0.001) and this observation was similar within 10-14 and 15-19 years age cohorts with χ2, p=0.033 and χ2, p=0.022 respectively (Figure 4). Overall, the males were 2 times more likely to achieve IDDS below 4 food groups compared to the females (OR, 1.976; CI, 1.371-2.848; p<0.001).
Micronutrient intake by adolescents:
Figure 5 shows that on overall, dietary sources of vitamin A and calcium were consumed by 81.5% and 80.8% of adolescents respectively, mainly from high intake of milk by both males and females. Iron rich foods intake was at 73.5% mainly from plant sources that was more associated with adolescent females (79.0%) compared to 66.4% males (χ2, p=0.002). Vitamin C rich foods consumed by 68.2% mainly from cooked green leafy vegetable with higher association of consumption to females than males at 73.5% and 61.7% respectively (χ2, p=0.005). Low consumption of dietary sources of Zinc was registered at 36.7% and 39.4% from plant and animal sources respectively with higher association of plant sources to females (χ2, p< 0.001).