| Literature DB >> 31993152 |
Abstract
Iron deficiency anemia (IDA) is a common micronutrient deficiency worldwide in infants. Iron deficiency anemia, during infancy, can have long-lasting detrimental effects on the immune and neural systems; the damage is irreversible. This study aimed to build a prediction model to predict the potential risk of IDA among infants. To collect relevant information for model building, we recruited 528 infants from Fenglin Community Health Service Center in Shanghai, China, and collected the information of infants and their parents by using a structured questionnaire. We also got the blood routine examination results of the infants. Then, we used a multilayer perceptron model (MLP) of the neural network model in IBM SPSS Modeler 18.0 to construct the prediction model. Of the 528 included infants, 80 (15.2%) of them had lower hemoglobin values (<110 g/L) and were finally diagnosed with IDA. Based on the accuracy of different models, the model with the highest accuracy rate (97.3%) was chosen, and all the preselected 26 variables were included in the model. After the modeling, the results indicated that the number of months of exclusive breastfeeding was the most important predictive variable, followed by the mother having anemia during pregnancy, and then the number of months of feeding the infant with iron-fortified rice flour. The model has good sensitivity (100%) and specificity (100%). By using this model, we can predict the potential risk of an infant having IDA and can take the initiative to prevent iron deficiency through the improvement of feeding methods.Entities:
Keywords: breastfeeding; complementary food; iron deficiency anemia; prediction model
Year: 2019 PMID: 31993152 PMCID: PMC6977486 DOI: 10.1002/fsn3.1301
Source DB: PubMed Journal: Food Sci Nutr ISSN: 2048-7177 Impact factor: 2.863
Demographic characteristics of the infants and parents in Shanghai, China (N = 528)
| Variables | Frequency | Percent (%) |
|---|---|---|
| Gender of the infants | ||
| Male | 271 | 51.3 |
| Female | 257 | 48.7 |
| Birth weight (g) of the infants | ||
| 2,500–3,000 | 84 | 15.9 |
| 3,000–3,500 | 232 | 43.9 |
| 3,500–4,000 | 172 | 32.6 |
| ≥4,000 | 40 | 7.6 |
| Gestational age of the infants | ||
| <37 weeks | 5 | 0.9 |
| 37 weeks | 41 | 7.8 |
| 38, 39, 40 weeks | 428 | 81.1 |
| 41 weeks | 45 | 8.5 |
| 42 weeks | 9 | 1.7 |
| Gravida | ||
| 1 | 328 | 62.1 |
| 2 | 149 | 28.2 |
| 3 | 38 | 7.2 |
| ≥4 | 13 | 2.5 |
| Para | ||
| 1 | 396 | 75.0 |
| 2 | 129 | 24.4 |
| 3 | 3 | 0.6 |
| Delivery mode of the infants | ||
| Cesarean section | 232 | 43.9 |
| Spontaneous delivery | 275 | 52.1 |
| Obstetric forceps | 21 | 4.0 |
| Feeding patterns of the infants | ||
| Exclusive breastfeeding | 239 | 45.3 |
| Mixed feeding | 266 | 50.4 |
| Artificial feeding | 23 | 4.4 |
| Age of complementary food (iron‐ fortified rice flour) added of the infants | ||
| 4 months old or below | 56 | 10.6 |
| 5 months old | 186 | 35.2 |
| 6 months old | 119 | 22.5 |
| Never | 167 | 31.6 |
| VitAD supplementation of the infants | ||
| Yes | 296 | 56.1 |
| No | 232 | 43.9 |
| VitD supplementation of the infants | ||
| Yes | 364 | 68.9 |
| No | 164 | 31.1 |
| Ca supplementation of the infants | ||
| Yes | 118 | 22.3 |
| No | 410 | 77.7 |
| Fe supplementation of the infants | ||
| Yes | 13 | 2.5 |
| No | 515 | 97.5 |
| Physical development evaluation of the infants | ||
| Moderate and severe malnutrition | 7 | 1.3 |
| Mild malnutrition | 13 | 2.5 |
| Normal | 329 | 62.3 |
| Overweight | 139 | 26.3 |
| Mild obesity | 38 | 7.2 |
| Moderate and severe obesity | 2 | 0.4 |
| Iron deficiency anemia of the infants | ||
| Yes | 80 | 15.2 |
| No | 448 | 84.8 |
| Childbearing age of the mother | ||
| <25 | 45 | 8.5 |
| 25–30 | 219 | 41.5 |
| 30–35 | 181 | 34.3 |
| 35–40 | 75 | 14.2 |
| ≥40 | 8 | 1.5 |
| The educational level of the mother | ||
| High school or below | 101 | 19.1 |
| 2‐year college degree | 105 | 19.9 |
| Bachelor degree | 248 | 47.0 |
| Master degree or above | 74 | 14.0 |
| The educational level of the father | ||
| High school or below | 99 | 18.8 |
| 2‐year college degree | 107 | 20.3 |
| Bachelor degree | 219 | 41.5 |
| Master degree or above | 103 | 19.5 |
| Parents are engaged in medical‐related career | ||
| Yes | 74 | 14.0 |
| No | 454 | 86.0 |
| Mother had anemia during pregnancy | ||
| Yes | 82 | 15.5 |
| No | 446 | 84.5 |
| Mother supplemented iron during pregnancy | ||
| Yes | 416 | 78.8 |
| No | 112 | 21.2 |
| Mother's BMI > 24 during pregnancy | ||
| Yes | 20 | 3.8 |
| No | 508 | 96.2 |
| Mother's BMI < 18.5 during pregnancy | ||
| Yes | 13 | 2.5 |
| No | 515 | 97.5 |
| Gestational hypertension | ||
| Yes | 17 | 3.2 |
| No | 511 | 96.8 |
| Gestational diabetes | ||
| Yes | 45 | 8.5 |
| No | 483 | 91.5 |
| Thyroid disease during pregnancy | ||
| Yes | 18 | 3.4 |
| No | 510 | 96.6 |
| Mother had passive smoking during pregnancy | ||
| Yes | 49 | 9.3 |
| No | 479 | 90.7 |
| Parents' annual income (RMB) | ||
| <50,000 | 38 | 7.2 |
| 50,000–100,000 | 102 | 19.3 |
| 100,000–200,000 | 154 | 29.2 |
| 200,000–500,000 | 186 | 35.2 |
| ≥500,000 | 48 | 9.0 |
None of the mothers had active smoking during pregnancy.
Accuracy rate of different models for predicting iron deficiency anemia among infants in Shanghai, China, 2015–2017 (N = 528)
| Model | Accuracy rate | Predictive variable | Size | Record |
|---|---|---|---|---|
| 1 | 97.3% | 26 | 307 | 528 |
| 2 | 83.3% | 26 | 422 | 528 |
| 3 | 80.5% | 26 | 422 | 528 |
| 4 | 84.5% | 26 | 542 | 528 |
| 5 | 86.6% | 26 | 415 | 528 |
| 6 | 78.4% | 26 | 297 | 528 |
| 7 | 76.5% | 26 | 422 | 528 |
| 8 | 82.6% | 26 | 356 | 528 |
| 9 | 81.3% | 26 | 466 | 528 |
| 10 | 84.1% | 26 | 415 | 528 |
Figure 1The ranking of different predictive variables in the final model in Shanghai, China, 2015–2017 (N = 528)
Figure 2ROC curve of the selected prediction model for predicting iron deficiency anemia among infants in Shanghai, China, 2015–2017 (N = 528)
Figure 3Gain curve of the selected prediction model for predicting iron deficiency anemia among infants in Shanghai, China, 2015–2017 (N = 528)