Investigating the Association between Nutrient Intake and Food Insecurity among Children and Adolescents in Palestine Using Machine Learning Techniques
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source
2.2. Study Variables
2.3. Nutrient Intake
2.4. Machine Learning Methods
3. Results
3.1. Descriptive Analysis
3.2. QUEST Model Analysis of Nutritional and Sociodemographic Variables
3.3. QUEST Model Analysis of Nutritional Variables
3.4. Gini Coefficient Importance Analysis
4. Discussion
5. Strengths and Limitations
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Section | Items |
---|---|
Socioeconomic and Demographic Data | Gender, age, governorate, living area (south, middle, and north), locality (refugee camp, village, or city), education level, family size, father’s employment, mother’s employment, family income. |
Household Food Insecurity | Food quantity, food quality, food acceptability, and the certainty of obtaining food. |
Nutrition Status (Dietary Intake) | 24 h recall: grams intake, calories, protein, carbs, fiber, cholesterol, vitamin B1, vitamin B2, vitamin B3, vitamin B5, vitamin B6, choline, folate, vitamin B12, vitamin C, vitamin E, vitamin K1, calcium, chloride, magnesium, phosphorous, potassium, sodium, copper, fluoride, iron, manganese, zinc. |
Nutrition Status (Anthropometric Measures) | Height, weight, mid-upper arm circumference, waist circumference, body mass index (BMI). |
Nutrition-related Practices | Difficulty eating breakfast; difficulty eating three meals. |
Variable | Overall Food Security | ||
---|---|---|---|
Food Security n (%) | Food Insecurity n (%) | F (p-Value) | |
Total | 851 (81.8) | 189 (18.2) | |
Gender | |||
Male | 399 (78.5) | 109 (21.5) | 7.2 |
Female | 452 (85) | 80 (15) | |
Age (Year) | |||
5–8 | 263 (87.1) | 39 (12.9) | 4.0 * |
9–12 | 287 (79.3) | 75 (20.7) | |
13–18 | 301 (80.1) | 75 (19.9) | |
Living Area | |||
South | 339 (81.7) | 76 (18.3) | 0.290 |
Middle | 279 (80.9) | 66 (19.1) | |
North | 233 (83.2) | 47 (16.8) | |
Locality | |||
City | 464 (87.7) | 65 (12.3) | 68.1 ** |
Village | 327 (85.4) | 56 (14.6) | |
Camp | 60 (46.9) | 68 (53.1) | |
Family Income | |||
Low | 45 (39.5) | 69 (60.5) | 122.1 ** |
Moderate | 514 (81.3) | 118 (18.7) | |
High | 292 (99.3) | 2 (0.7) | |
Family Size | |||
2–4 | 213 (81.9) | 47 (18.1) | 9.3 ** |
5–6 | 390 (86.9) | 59 (13.1) | |
7+ | 248 (74.9) | 83 (25.1) | |
BMI | |||
Underweight | 374 (81.3) | 86 (18.7) | 0.303 |
Normal | 317 (81.3) | 73 (18.7) | |
Overweight | 115 (83.9) | 22 (16.1) | |
Obese | 45 (84.9) | 8 (15.1) |
Nutrient (Unit) | Nutrient Intake per RDA | Food Security Level | |||
---|---|---|---|---|---|
≥RDA n (%) | <RDA n (%) | Food-Secure Mean ± SD | Food-Insecure Mean ± SD | F (p-Value) | |
Energy (kcal) | 768 (73.8) | 272 (26.2) | 1584.4 ± 616.4 | 1465.8 ± 813 | 4.8 * |
Protein (g) | 816 (78.5) | 224 (21.5) | 58.7 ± 27.3 | 52.7 ± 34 | 47.2 ** |
Carb (g) | 842 (81) | 198 (19) | 216.1 ± 91.9 | 203.8 ± 115.7 | 8.3 * |
Fat (g) | 517 (49.7) | 523 (50.3) | 55.7 ± 27.3 | 51.2 ± 30.7 | 7.6 * |
Fiber (g) | 74 (7.1) | 966 (92.9) | 13.5 ± 7.8 | 13.8 ± 9.9 | 9 * |
Folate (mg) | 760 (73.1) | 280 (26.9) | 177.5 ± 115.4 | 151.4 ± 121 | 9.4 * |
Vit A (UI) | 745 (71.6) | 295 (28.4) | 134.2 ± 136 | 126 ± 125.2 | 12.3 ** |
VitB1 (mg) | 312 (30) | 728 (70) | 1.4 ± 2.1 | 1.6 ± 2.4 | 1.8 |
VitB2 (mg) | 529 (50.9) | 511 (49.1) | 2.6 ± 3.7 | 2.2 ± 4.8 | 19.3 ** |
VitB3 (mg) | 276 (26.5) | 764 (73.5) | 9.7 ± 7.7 | 8.1 ± 7.3 | 9.8 * |
VitB5 (mg) | 397 (38.2) | 643 (61.8) | 3.4 ± 2.1 | 3.3 ± 2.4 | 19.2 ** |
VitB6 (mg) | 743 (71.4) | 297 (28.6) | 2.4 ± 3.8 | 2.4 ± 4 | 12.4 ** |
VitB12 (mcg) | 288 (27.7) | 752 (72.3) | 2.3 ± 4.9 | 2.4 ± 5.5 | 5.7 * |
Vit C (mg) | 225 (21.6) | 815 (78.4) | 60.7 ± 62.4 | 43.7 ± 52.1 | 6.4 * |
Ca 1 (mg) | 31 (3) | 1009 (97) | 473 ± 289.5 | 401.4 ± 381 | 2.9 |
Mg 2 (mg) | 201 (19.3) | 839 (80.7) | 155.6 ± 81.5 | 163.5 ± 122.9 | 1.2 * |
Mn 3 (mg) | 636 (61.2) | 404 (38.8) | 2.1 ± 3.5 | 2.3 ± 3.7 | 16.7 ** |
P 4 (mg) | 235 (22.6) | 805 (77.4) | 691.7 ± 333.8 | 670.6 ± 485.5 | 9.2 * |
K 5 (mg) | 6 (0.6) | 1034 (99.4) | 1361.1 ± 673.2 | 1272 ± 908.2 | 27.8 ** |
Cu 6 (mg) | 445 (42.8) | 595 (57.2) | 2.1 ± 2.8 | 2.1 ± 3.3 | 12.8 ** |
Fe 7 (mg) | 383 (36.8) | 657 (63.2) | 9.6 ± 6 | 8.8 ± 5.7 | 3.3 |
Zn 8 (mcg) | 354 (34) | 686 (66) | 7.2 ± 5.5 | 6.3 ± 5.4 | 12.0 ** |
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Qasrawi, R.; Sgahir, S.; Nemer, M.; Halaikah, M.; Badrasawi, M.; Amro, M.; Vicuna Polo, S.; Abu Al-Halawa, D.; Mujahed, D.; Nasreddine, L.; et al. Investigating the Association between Nutrient Intake and Food Insecurity among Children and Adolescents in Palestine Using Machine Learning Techniques. Children 2024, 11, 625. https://doi.org/10.3390/children11060625
Qasrawi R, Sgahir S, Nemer M, Halaikah M, Badrasawi M, Amro M, Vicuna Polo S, Abu Al-Halawa D, Mujahed D, Nasreddine L, et al. Investigating the Association between Nutrient Intake and Food Insecurity among Children and Adolescents in Palestine Using Machine Learning Techniques. Children. 2024; 11(6):625. https://doi.org/10.3390/children11060625
Chicago/Turabian StyleQasrawi, Radwan, Sabri Sgahir, Maysaa Nemer, Mousa Halaikah, Manal Badrasawi, Malak Amro, Stephanny Vicuna Polo, Diala Abu Al-Halawa, Doa’a Mujahed, Lara Nasreddine, and et al. 2024. "Investigating the Association between Nutrient Intake and Food Insecurity among Children and Adolescents in Palestine Using Machine Learning Techniques" Children 11, no. 6: 625. https://doi.org/10.3390/children11060625