Nutrition, diet, and academic performance among school-age children in urban low-income settings: a case-control study in Addis Ababa, Ethiopia

Abstract

Objective

To examine the effects of nutrition, dietary practices, and other related factors on academic performance and IQ among children in Addis Ababa, Ethiopia.

Methods

A case-control study was conducted among 309 children aged 6–14 years attending public and private schools in low-income districts of Addis Ababa from March to August 2023. Binary and multinomial logistic regression models were used to estimate crude (COR) and adjusted odds ratios (AOR) with 95% confidence intervals.

Results

Hand washing (AOR = 3.7; 95% CI: 1.4, 9.8), access to toilets (AOR = 3.37; 95% CI: 1.25, 9.09), and effective teaching (AOR = 2.94; 95% CI: 1.04, 8.33) good academic performance. Stunting (AOR = 0.16; 95% CI: 0.04, 0.59), underweight (AOR = 0.25; 95% CI: 0.09, 0.70), and overweight (AOR = 0.04; 95% CI: 0.01, 0.14) were associated with poor academic performance. Low meal frequency (AOR = 2.65; 95% CI: 1.06, 6.67) and teachers with BA/BSc degrees (AOR = 6.97; 95% CI: 1.14, 42.68) predicted lower IQ.

Conclusion

Many factors, especially nutrition and diet, strongly influence academic and cognitive performance; targeted school interventions improve outcomes.

Highlights

• •Good hygiene, diet, and toilet access improve academic performance and intelligence.
• •Supportive school environments and good teaching boost learning and cognition.
• •Stunting, low weight, and poor school conditions reduce academic performance.
• •Improving school-based nutrition, hygiene, and teaching supports health and learning.

1. Introduction

Nutrition and dietary factors play crucial roles in a child's health and development, especially in low-income areas where food insecurity and malnutrition are widespread. Extensive research has demonstrated that nutrition and diet strongly impact the academic performance and Intelligence Quotient (IQ) levels of school-age children (Nakamura et al., 2024). The earlier study emphasizes how important a healthy diet is for both academic success and cognitive growth (Ayalew et al., 2020). Iron, iodine, and vitamin B12 deficiencies have been demonstrated to affect learning outcomes and cognitive function (Awuchi et al., 2020). Additionally, research suggests that meal frequency, diversity and quality are linked to variations in academic achievement among students. For example, regular consumption of nutrient-rich diets, including a variety of healthy foods, has been associated with better cognitive performance and academic outcomes in children (Mou et al., 2023).

Furthermore, extensive research has been conducted to investigate the correlation between nutrition and IQ levels. Extensive research has examined the relationship between nutrition and IQ, consistently demonstrating that dietary patterns strongly influence cognitive ability (Bassuoni et al., 2021). Longitudinal evidence shows that children who receive adequate nutrition during critical periods of brain development achieve higher IQ scores and perform better academically than those with poor dietary intake (Tomaszewski et al., 2022). In addition, deficiencies in essential micronutrients, such as zinc and omega-3 fatty acids, have been found to be associated with cognitive impairments and lower IQ levels in children (Abdulkadir et al., 2024). These findings emphasize the crucial role of nutrition in optimizing cognitive function and educational outcomes, particularly among vulnerable populations in low-income urban settings.

While existing studies provide insights, they often lack specificity on the challenges faced by urban low-income children. Socioeconomic constraints, food access, and sanitation may worsen the impact of nutritional deficiencies on academic performance (Yeboah et al., 2024). Research gaps exist in the exploration of schools' role and teaching quality in linking nutrition to academics (Bosede et al., 2025). Therefore, we aimed to assess how nutritional, dietary and other related factors impact academic performance and IQ among school-age children in urban low-income settings in Ethiopia.

2. Materials and methods

2.1. Study design and population

A case-control study was conducted from March to August 2023 in low-income areas of Kolfe Keraniyo and Nifas Silk Lafto subcities in Addis Ababa, Ethiopia (Fig. 1). The source population comprised all school-aged children (6–14 years) enrolled in selected schools within the study districts.

Fig. 1.

Map of Ethiopia showing regional states, with a focus on Addis Ababa and the Kolfe Keraniyo and Nifas Silk Lafto sub-cities.

Cases were defined as children with poor academic performance (scores <80%) over two consecutive semesters, whereas controls were those with good academic performance (scores ≥80%) during the same period.

The sample size was determined using a double population proportion formula with 95% confidence level (Zα/2 = 1.96), 80% power (Zβ = 0.84), and a control-to-case ratio of 2:1 (Buric et al., 2024).

$$\mathit{sample\; size}\left(n\right)=\frac{r+1}{r}\frac{\left(P\ast \right)\left(1-P\ast \right){\left(Z_{\frac{\alpha }{2}}+Z_{\beta }\right)}^2}{{\left(p1-p2\right)}^2}$$ (1)

The proportions used were 58% for cases and 67.8% for controls based on previous studies (Mukti, 2025). After a 10% non-response adjustment, the final sample size was 315. With six non-responses, 309 participants were included (103 cases and 206 controls).

Cluster sampling was employed. Two subcities, ten districts, and ten schools (two public and eight private) were randomly selected. Eligible participants were children aged 6–14 years enrolled in the selected schools. Children who were not enrolled, living outside the selected areas, lacking parental consent, severely ill, or enrolled in special education programs were excluded.

Academic performance (GPA) and IQ were the primary outcomes. Academic performance was categorized as good or poor based on GPA. IQ was assessed using Raven's Standard Progressive Matrices and classified according to percentile rankings (Antoniou et al., 2022).

Ethical approval was obtained from the Institutional Review Board of Addis Ababa University (CNCSDO/515/15/2023). Written informed consent was obtained from parents or guardians. This research protocol adhered to the Ethical Principles for Medical Research Involving Human Subjects as outlined in the Helsinki Declaration amended in Fortaleza, Brazil, in October 2013. All study participants and their guardians were informed to read and sign a voluntary consent form.

2.2. Measures

2.2.1. Sociodemographic, nutritional and dietary factors

A semi-quantitative structured questionnaire was used to collect data on sociodemographic characteristics, socioeconomic status, and dietary practices. The tool was adapted from validated Ethiopian instruments and pretested on 10% of the target population (Zerga et al., 2022). Dietary habits were assessed by fruit and vegetable intake (>50% positive = good).

Dietary intake was assessed using two non-consecutive 24-hour recalls via the multiple-pass method. Caregivers reported food types, ingredients, and portion sizes with the aid of food photographs. Nutrient and energy intakes were analyzed using NutriSurvey software (https://www.nutrisurvey.de/) (NutriSurvey software for nutrition analysis, 2023).

Dietary diversity was evaluated based on six food groups defined in the Ethiopian Food-Based Dietary Guidelines: cereals/tubers; legumes/nuts; dairy; meat/fish/eggs; fruits/vegetables; and fats/oils (Ethiopian Public Health Institute (EPHI), 2022). Scores were classified as high (≥4 groups) or low (<4 groups) per national standards.

Meal frequency was categorized as adequate (≥3 meals/day) or low (<3 meals/day). Meal skipping was defined as skipping meals ≥2 times/day. Consumption of coffee or tea was used as an indicator of absorption inhibitors. Nutrition awareness of the children's parents/caregivers was assessed using two questions related to iron- and vitamin-rich foods. Responses were scored as ‘1’ (awareness) if ≥50% of the questions were answered correctly, and ‘0’ (lack of awareness/unknown) if <50% were answered correctly (FAO guidelines) (Food and Agriculture Organization of the United Nations, 2014). The questionnaire, adapted from validated Ethiopian tools, was pretested on 10% of the target population for clarity and cultural appropriateness (National Academies of Sciences E and Medicine, 2023). Data quality was assured through rigorous enumerator training and ongoing field supervision, following methods described previously (Fikadu et al., 2024). Dietary supplement intake was not assessed; reported nutrient intakes reflect foods and beverages only. Two nonconsecutive 24-hour recalls via the multiple-pass method were conducted by trained interviewers to improve accuracy (Belay, 2024).

2.2.2. School environment and psychosocial factors

The school environment was assessed using principal component analysis (PCA) of Likert-scale items (1–5). Variables with communality >0.5 were retained, and factor scores were summed and classified as positive or negative based on eigenvalues. Hand-washing and toilet-facility access were assessed using two questions. If ≥ 60% of the responses were “1”, the facility was classified as “yes”; if < 60%, it was classified as “no” (International Food Policy Research Institute (IFPRI), 2025).

Teaching effectiveness was assessed using ten four-point Likert-scale items (1–4) on instructional methods. The grand mean score was calculated and dichotomized into poor (0) and good (1) effectiveness.

Student engagement was measured via the Engagement vs. Disaffection tool, which covers behavioral and emotional engagement and disaffection, with four-point Likert items (1 = Not at all true, 4 = Very true) (Assefa and Kumie, 2014). Engagement was classified as good when engagement items scored 3–4 and reverse-scored disaffection items scored 1–2. Factor analysis grouped the four dimensions into poor and good engagement.

2.2.3. Academic and cognitive measures

Raven's Standard Progressive Matrices were administered at the start of the first semester, and total IQ scores were calculated according to standardized guidelines using SPSS version 27 (IBM Corp., Armonk, NY, USA) (Skinner et al., 2009). The cumulative GPA for grades 1–8 over two semesters was collected, along with data on academic performance factors and baseline nutrition and dietary information.

2.3. Statistical analysis

After nutrient and energy intakes were calculated using NutriSurvey, values were adjusted using the nutrient density method in Microsoft Excel, and intake per 1000 kcal was expressed to enable comparison across participants. The final dataset was then exported to SPSS version 27 for further statistical analysis (IBM Corp., 2020). Binary and multinomial logistic regression estimated crude odds ratios (CORs) and adjusted odds ratios (AORs) with 95% confidence intervals. Model fit was assessed using the Hosmer–Lemeshow test (Surjanovic and Loughin, 2024). Statistical significance was declared at p < 0.025.

3. Results

3.1. Sociodemographic characteristics of teachers and students

Most teachers were 20–30 years old 165 (53.4%), male 166 (53.7%), held a BA/BS degree 188 (60.8%), and had <15 years of teaching experience 182 (58.9%) (Table 1).

Table 1.

Sociodemographic characteristics of teachers in the urban low-income setting of Addis Ababa, Ethiopia, 2023.

Variables	Categories	N (%)	95% CI (%)
Age of teachers	20–30 years	165 (53.4)	47.8, 59.0
	31–40 years	111 (35.9)	30.4, 40.2
	41–50 years	33 (10.7)	7.1, 14.3
Gender of teacher	Female	143 (46.3)	40.6, 51.9
	Male	166 (53.7)	48.1, 59.4
Educational status	Diploma	87 (28.2)	23.2, 33.2
	BA/BSc	188 (60.8)	54.9, 66.7
	MA/MSc	34 (11.0)	7.6, 15.3
Teaching experience	< 15 years	182 (58.9)	53.3, 64.4
	≥ 15 years	127 (41.1)	35.6, 46.7
Teaching effectiveness	Poor effectiveness	162 (52.4)	46.6, 58.2
	Good effectiveness	147 (47.6)	41.8, 53.4

N, number of participants; %, percentage; 95%CI, 95% confidence level.

More controls were aged 11–14 years (53.4%) than cases (43.7%). Good academic performance was observed in 66.7% of controls. Grades 1–4 were more common among cases (65.0%). Low dietary diversity and diarrhea were higher in cases, while knowledge and toilet access were higher in controls (Table 2).

Table 2.

Sociodemographic characteristics and health-related characteristics of case and control students in the urban low-income setting of Addis Ababa, Ethiopia, 2023.

Variables	Categories	Case	Control
		N (%) [95% CI]	N (%) [95% CI]
Age of students	6–10 years	58 (56.3) [46.7, 65.9]	96 (46.6) [39.8, 53.4]
	11–14 years	45 (43.7) [34.1, 53.3]	110 (53.4) [46.6, 60.2]
Gender	Male	37 (35.9) [26.6, 45.2]	94 (45.6) [39.0, 52.2]
	Female	66 (64.1) [54.8, 73.4]	112 (54.4) [47.8, 61.0]
School type	Public	58 (56.3) [46.7, 65.9]	114 (55.3) [48.5, 62.1]
	Private	45 (43.7) [34.1, 53.3]	92 (44.7) [37.9, 51.5]
Grade level	1–4	67 (65.0) [55.4, 74.6]	110 (53.4) [46.6, 60.2]
	5–8	36 (35.0) [25.4, 44.6]	96 (46.6) [39.8, 53.4]
Absorption inhibitor	No	39 (37.9) [28.4, 47.4]	107 (51.9) [45.1, 58.7]
	Yes	64 (62.1) [52.6, 71.6]	99 (48.1) [41.3, 54.9]
Dietary diversity score	<4	78 (75.7) [66.9, 84.5]	125 (60.7) [54.1, 67.3]
	≥4	25 (24.3) [15.5, 33.1]	81 (39.3) [32.7, 45.9]
Diarrhea incidence	No	33 (32.0) [22.5, 41.5]	103 (50.0) [43.3, 56.7]
	Yes	70 (68.0) [58.5, 77.5]	103 (50.0) [43.3, 56.7]
Dietary habit	Good	53 (51.5) [41.8, 61.2]	72 (35.0) [28.3, 41.7]
	Poor	50 (48.5) [38.8, 58.2]	134 (65.0) [58.3, 71.7]
Hand washing practice	No	68 (66.0) [56.6, 75.4]	96 (46.6) [39.8, 53.4]
	Yes	35 (34.0) [24.6, 43.4]	110 (53.4) [46.6, 60.2]
Meal frequency	<3 meals	62 (60.2) [50.7, 69.7]	91 (44.2) [37.5, 50.9]
	≥3 meals	41 (39.8) [30.3, 49.3]	115 (55.8) [49.1, 62.5]
Nutrition knowledge	Awareness	53 (51.5) [41.8, 61.2]	136 (66.0) [59.5, 72.5]
	Lack of awareness	50 (48.5) [38.8, 58.2]	70 (34.0) [27.5, 40.5]
Ravens IQ	Intellectually superior	1 (1.0) [0, 2.9]	10 (4.9) [1.9, 7.9]
	Definitely above average	9 (8.7) [3.2, 14.2]	82 (39.8) [33.0, 46.6]
	Intellectually average	13 (12.6) [6.2, 19.0]	91 (44.2) [37.5, 50.9]
	Definitely below average	47 (45.6) [36.0, 55.2]	12 (5.8) [2.7, 8.9]
	Intellectually deficit	33 (32.0) [23.0, 41.0]	11 (5.3) [2.5, 8.1]
School environment	Negative	71 (68.9) [59.7, 78.1]	102 (49.5) [42.7, 56.3]
	Positive	32 (31.1) [21.9, 40.3]	104 (50.5) [43.7, 57.3]
Skipping meal	Not skipped	56 (54.4) [44.8, 64.0]	78 (37.9) [31.2, 44.6]
	Skipped	47 (45.6) [36.0, 55.2]	128 (62.1) [55.4, 68.8]
Presence of toilet facility	No	75 (72.8) [63.6, 82.0]	98 (47.6) [40.8, 54.4]
	Yes	28 (27.2) [18.0, 36.4]	108 (52.4) [45.6, 59.2]
Student engagement	Poor	75 (72.8) [63.6, 82.0]	114 (55.3) [48.5, 62.1]
	Good	28 (27.2) [18.0, 36.4]	92 (44.7) [37.9, 51.5]
Nutritional status
Weight-for-Age	Underweight	41 (39.8) [30.3, 49.3]	70 (34.0) [27.5, 40.5]
	Normal	62 (60.2) [50.7, 69.7]	136 (66.0) [59.5, 72.5]
Height-for-Age	Stunted	35 (34.0) [24.8, 43.2]	40 (19.4) [13.9, 24.9]
	Normal	68 (66.0) [56.8, 75.2]	166 (80.6) [75.1, 86.1]
Weight-for-Height	Wasting	14 (13.6) [6.9, 20.3]	32 (15.5) [10.4, 20.6]
	Normal	89 (86.4) [79.7, 93.1]	174 (84.5) [79.4, 89.6]
BMI-for-Age	Overweight	47 (45.6) [36.0, 55.2]	11 (5.3) [2.5, 8.1]
	Normal	56 (54.4) [44.8, 64.0]	195 (94.7) [91.9, 97.5]

SE, school environment; Case, participant students who have <80% of the total average points; Control, participant students who have ≥80% of the total average points.

3.2. Factors associated with academic performance

Table 3 shows factors associated with good academic performance (GAP). Overweight (AOR = 0.04, 95% CI: 0.01, 0.14), underweight (AOR = 0.25, 95% CI: 0.09, 0.70), stunting (AOR = 0.16, 95% CI: 0.04, 0.59), low intelligence (AOR = 0.29, 95% CI: 0.02, 5.50), below-average intelligence (AOR = 0.01, 95% CI: 0.00, 0.20), and intake of absorption inhibitors (AOR = 0.32, 95% CI: 0.12, 0.87) were associated with lower odds of GAP. In contrast, a positive school environment (AOR = 3.70, 95% CI: 1.30, 10.60), effective teaching (AOR = 2.94, 95% CI: 1.04, 8.33), toilet access (AOR = 3.37, 95% CI: 1.25, 9.09), and hand washing (AOR = 3.70, 95% CI: 1.40, 9.80) increased the likelihood of GAP. Reliability testing of the instruments conducted on 10% of participants produced a Cronbach's alpha of 0.76, indicating acceptable internal consistency.

Table 3.

Factors associated with academic performance among school-aged children in the urban low-income setting of Addis Ababa, Ethiopia (2023).

Predicting variables	PAP (%) [95% CI]	GAP (%) [95% CI]	COR (95% CI)	AOR (95% CI)
Absorption inhibitor
No	39 (37.9) [28.8, 47.7]	107 (51.9) [44.4, 59.3]	1	1
Yes	64 (62.1) [52.3, 71.2]	99 (48.1) [40.7, 55.6]	0.56 (0.35, 0.91)	0.32 (0.12, 0.87)*
Hand washing practice
No	68 (66.0) [55.7, 75.3]	96 (46.6) [38.8, 54.4]	1	1
Yes	35 (34.0) [24.7, 44.3]	110 (53.4) [45.6, 61.2]	2.23 (1.40, 3.60)	3.7 (1.4, 9.8) *
Overweight
Normal	56 (54.4) [44.6, 63.9]	195 (94.7) [91.5, 97.0]	1	1
Overweight	47 (45.6) [36.1, 55.4]	11 (5.3) [3.0, 8.5]	0.07 (0.03, 0.14)	0.04 (0.01, 0.14) **
Raven's IQ
Intellectually superior	1 (1.0) [0, 5.4]	10 (4.9) [2.4, 8.8]	1	1
Definitely above average	9 (8.7) [4.0, 16.0]	82 (39.8) [32.9, 47.0]	0.91 (0.10, 7.96)	0.42 (0.02, 7.36)
Intellectually average	13 (12.6) [7.0, 21.0]	91 (44.2) [37.1, 51.5]	0.70 (0.08, 5.93)	0.29 (0.02, 5.50)
Definitely below average	47 (45.6) [36.1, 55.4]	12 (5.8) [3.1, 10.0]	0.03 (0.03, 0.22)	0.01 (0.00, 0.20) **
Intellectually deficit	33 (32.0) [23.7, 41.5]	11 (5.3) [2.8, 9.3]	0.03 (0.04, 0.29)	0.01 (0.00, 0.21) **
School Environment and Health
Negative SE	71 (68.9) [59.3, 77.5]	102 (49.5) [41.8, 57.3]	1	1
Positive SE	32 (31.1) [22.5, 40.7]	104 (50.5) [42.7, 58.2]	2.30 (1.40, 3.70)	3.70 (1.30, 10.60) *
Stunting
Normal	82 (79.6) [70.7, 86.8]	152 (73.8) [66.9, 80.0]	1	1
Stunting	21 (20.4) [13.2, 29.3]	54 (26.2) [20.0, 33.1]	0.47 (0.27, 0.80)	0.16 (0.04, 0.59) **
Teaching effectiveness
Poor effectiveness	64 (62.1) [52.3, 71.2]	98 (47.6) [39.9, 55.4]	1	1
Good effectiveness	39 (37.9) [28.8, 47.7]	108 (52.4) [44.6, 60.1]	1.80 (1.10, 2.90)	2.90 (1.10, 8.30) *
Presence of toilet facility
No	75 (72.8) [63.8, 80.8]	98 (47.6) [39.9, 55.4]	1	1
Yes	28 (27.2) [19.2, 36.2]	108 (52.4) [44.6, 60.1]	3 (1.80, 4.90)	3.40 (1.30, 9.10) *
Underweight
Normal	47 (45.6) [36.1, 55.4]	151 (73.3) [66.4, 79.5]	1	1
Underweight	56 (54.4) [44.6, 63.9]	55 (26.7) [20.5, 33.6]	0.31 (0.19, 0.50)	0.25 (0.09, 0.70) **
Wasting
Wasting	38 (36.9) [27.8, 46.6]	65 (63.1) [54.4, 71.2]	1	1
Normal	8 (3.9) [1.7, 7.5]	198 (96.1) [92.5, 98.3]	0.07 (0.03, 0.16)	0.05 (0.01, 0.24) **

The reference category, 1; AOR, adjusted odd ratio; BA, Bachelor of art; BS, Bachelor of Science; CI, confidence interval; DBA, definitely below average; DAA, definitely above average; GAP, good academic performance; IA, intellectually average; ID, intellectually deficient; IQ, intelligence quotient; IS, intellectually superior; MA, Master of Art; MS, Master of Science; PAP, poor academic performance; * p value<0.05, ** p value<0.01.

3.3. Factors associated with IQ levels in school-aged children

Table 4 shows multinomial logistic regression results for factors associated with intelligence among school-aged children. Children with poor academic performance were less likely to be intellectually superior (AOR = 0.01, 95% CI: 0.01, 0.28), definitely above average (AOR = 0.04, 95% CI: 0.01, 0.16), or intellectually average (AOR = 0.06, 95% CI: 0.02, 0.21). Wasted children were less likely to be intellectually superior (AOR = 0.28, 95% CI: 0.08, 0.96). Consuming fewer than three meals per day (AOR = 2.65, 95% CI: 1.06, 6.67), having teachers with BA/BSc degrees (AOR = 6.97, 95% CI: 1.14, 42.68), poor teaching effectiveness (AOR = 2.95, 95% CI: 1.12, 7.83), and lack of nutrition awareness (AOR = 0.35, 95% CI: 0.13, 0.97) were associated with higher odds of definitely below-average intelligence.

Table 4.

Multinomial logistic regression analysis for factors associated with IQ levels of school-aged children in the urban low-income setting of Addis Ababa, Ethiopia, 2023.

	Intellectually superior (Ravan's IQ > 90th percentile)
Predicting variables		ID (%)	IS (%)	AOR (95%CI)	P value
Academic performance
	PAP	33 (75.00)	1 (9.10)	0.01 (0.01, 0.28)	0.005
	GAP	11 (25.00)	10 (90.90)	1
	Definitely above average (Ravan's IQ > 75th percentile)
Academic performance		ID (%)	IS (%)
	PAP	9 (9.90)	33 (75.00)	0.04 (0.01, 0.16)	<0.001
	GAP	82 (90.10)	11 (25.00)	1
	Intellectually average (Ravan's IQ > 50th percentile)
Academic performance		ID (%)	IA (%)
	PAP	33 (75.00)	1 (9.10)	0.06 (0.02, 0.21)	<0.001
	GAP	11 (25.00)	10 (90.90)	1
Wasting
	Wasting	17 (38.60)	2 (18.20)	0.28 (0.08, 0.96)	0.043
	Normal	27 (61.40)	9 (81.80)	1
	Definitely below average (Ravan's IQ between 25th and 50th percentile)
Educational status		ID (%)	DBA (%)
	Diploma	9 (20.50)	18 (30.50)	5.58 (0.78, 39.75)	0.09
	BA/BS	29 (65.90)	39 (66.10)	6.97 (1.14, 42.68)	0.04
	MA/MS	6 (13.60)	2 (3.40)	1
Meal frequency
	< 3 meal times	20 (45.50)	39 (66.10)	2.65 (1.06, 6.67)	0.04
	≥ 3 meal times	24 (54.50)	20 (33.90)	1
Nutrition information
	Awarred	28 (63.60)	27 (45.80)	0.35 (0.13, 0.97)	0.04
	Lack of awareness	16 (36.40)	32 (54.20)	1
Teaching effectiveness
	Poor effective	20 (45.50)	43 (72.90)	2.95 (1.12, 7.83)	0.03
	Good effective	24 (54.50)	16 (27.10)	1

The reference category, 1; AOR, adjusted odd ratio; BA, Bachelor of Art; BSc, Bachelor of Science; CI, confidence interval; DBA, definitely below average; DAA, definitely above average; GAP, good academic performance; IA, intellectually average; ID, intellectually deficient; IQ, intelligent quotient; IS, intellectually superior; MA, Master of Art; MSc, Master of Science; PAP, poor academic performance.

4. Discussion

This study assessed the impact of nutritional, dietary, and other related factors on the academic performance of school-aged children living in low-income settings in Addis Ababa. We found that 66.7% of the children achieved good academic performance (GAP), scoring above 80%, whereas only 5.3% of these children were classified as intellectually deficit. This finding is consistent with previous studies showing a positive association between Raven's IQ scores and academic performance (Akubuilo et al., 2020).

Furthermore, 53.4% of the students who practiced hand washing achieved GAP, highlighting the positive association between hygiene and academic outcomes. This finding is consistent with previous studies reporting that proper hand washing reduces infection rates and absenteeism among schoolchildren (Hoyle et al., 2023). This implies that poor hygiene can have the opposite effect and greater implications in the long run, where student productivity and education may suffer, resulting in worsened school grades.

Compared with 94.7% of their normal-weight peers, only 5.3% of overweight students achieved GAP, indicating a significantly lower likelihood of academic success among overweight students. This finding aligns with a previous study by Emon HH, et al., who reported that students with higher BMIs were more likely to have lower final grades (Emon et al., 2024). Furthermore, students with intellectually deficient IQ levels had a 0.99% probability of achieving GAP compared with intellectually superior students.

Additionally, students learning in a positive school environment were significantly more likely to achieve GAP (50.5%), and the association remained strong after adjustment (AOR = 3.70), indicating that a supportive school environment substantially enhances academic success, which is consistent with findings from Malaysia (Asare et al., 2024).

The findings revealed that stunted and underweight students had 26.2% (95% CI: 20.0, 33.1) and 26.7% (95% CI: 20.5, 33.6), respectively, probabilities of achieving good academic performance (GAP), whereas their normal-nutrition counterparts had higher rates—73.8% (95% CI: 66.9, 80.0) for non-stunted and 73.3% (95% CI: 66.4, 79.5) for normal-weight students—indicating a negative impact of poor nutritional status on academic outcomes, consistent with studies in Southwest Ethiopia (Abebe et al., 2017).

After adjusting for confounders, students taught by effective teachers were 2.9 times more likely to achieve GAP than those taught by poor effective teachers, highlighting the positive impact of teaching effectiveness. Our results are in line with those of a previous study that revealed that some aspects of teacher quality in mathematics, such as monitoring, communication skills, teacher knowledge, teamwork, and providing constructive feedback, affect students' success in mathematics (Asare et al., 2024).

Moreover, students in schools with proper toilet facilities had a 52.4% probability of achieving good academic performance (GAP), compared with 47.6% in schools without such facilities, indicating that access to adequate sanitation is associated with better academic outcomes, as supported by a study in Ghana (Akanzum and Pienaah, 2023). This suggests that providing access to adequate toilet facilities is an important part of improving student academic performance. Schools should allocate resources to ensure that toilet facilities are accessible and properly maintained. Additionally, toilet access should be prioritized in educational policy and programming.

The probabilities of students with poor academic performance (PAP) falling into the intellectually superior, definitely above average, and intellectually average categories were approximately 1%, 9%, and 13%, respectively, which aligns with studies reporting an association between lower IQ levels and poor academic achievement (Akubuilo et al., 2020).

Our study findings also highlight an interesting association between wasting status and intellectual superiority among children, with approximately 11.8% of wasted children classified as intellectually superior. The result of our study aligns with a study conducted in Nigeria, which also reported a similar association between wasting and intellectual outcomes (Chakraborty and Ghosh, 2020). The observed relationship underscores the significant impact of malnutrition on cognitive development (Zerga et al., 2022).

Our study findings revealed that, compared to students with MSc-level teachers, those taught by BA/BS-level teachers had a higher probability (∼57%) of being classified in the below-average IQ category, highlighting that multiple factors influence cognitive development in low-income settings. In support of this observation, a previous study emphasized the critical role of teacher student relationships in effective classroom management and teaching effectiveness (Thornberg et al., 2020). These results imply that the educational backgrounds of teachers may have an impact on how they interact with students and how they teach, which may then have an effect on the cognitive outcomes of the students.

The findings of this study also revealed that the probability of children eating fewer than three meals a day being in the definitely below average IQ category is approximately 72.6%. Numerous studies have provided valuable insights into this finding. For example, Liu et al. reported that a six-year longitudinal study in which regular breakfast habits were maintained was linked to higher IQ levels (Liu et al., 2021). Another study by Khadem et al. reported a clear and significant positive correlation between children's IQ and their nutritional habits in both sexes, indicating that children with better dietary habits tend to exhibit higher IQ levels (Khadem et al., 2024).

Our study also demonstrated that children whose parents had high levels of nutritional information were more likely to be classified as intellectually superior, with approximately 49% falling into this category. Our findings are consistent with Dahlberg, who reported that interactions between educators and children, as well as the skills imparted by adults, can have lasting effects on children's cognitive and mental development (Dahlberg et al., 2023). While our study focused on school-aged children, this supports the idea that teacher and parental inputs play a key role in cognitive outcomes.

Several limitations should be considered when interpreting these findings. First, the case-control design allows identification of associations but does not establish causality. Second, some measures relied on self-reported data, which may introduce recall and social desirability bias. Finally, the study was conducted in urban low-income settings of Addis Ababa, which may limit generalizability to rural or higher-income populations.

5. Conclusion

This study examined the effects of nutritional, dietary, and other related factors on academic performance and cognitive ability among school-aged children in low-income urban areas. Positive influences included hand washing, supportive school environments, effective teaching, and access to proper toilet facilities, whereas stunting, underweight status, and inadequate nutrition were linked to lower academic achievement and IQ. Strategies that improve hygiene, promote healthy environments, and enhance teaching quality can boost educational outcomes. The findings highlight the complex interplay of academic, nutritional, and environmental factors, emphasizing the need for comprehensive approaches to support children's development and well-being.

Recommendations for future research

• 1.Conduct longitudinal studies to clarify causal links between nutrition, dietary practices, and academic/cognitive performance in school-age children.
• 2.Evaluate school- and community-based interventions targeting nutrition, hygiene, and teaching quality to improve educational outcomes in low-income settings.
• 3.Investigate specific nutrient intake and dietary patterns, along with socioeconomic and psychosocial factors, to better understand their impact on learning and cognition.
• 4.Compare urban and rural low-income populations to identify context-specific strategies for enhancing child nutrition and academic achievement.

CRediT authorship contribution statement

Yimer Mihretie Adugna: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Abebe Ayelign: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. Tadesse Alemu Zerfu: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization.

Consent for publication

Not applicable.

Funding source

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

The datasets generated and analyzed during the present study may be made available from the corresponding author upon reasonable request and with appropriate institutional approval.