A Dynamic, Continuous Model for Age-Specific Anemia Classification in Children and Non-Pregnant Women.

BACKGROUND

Fixed hemoglobin cutoffs are commonly used to define anemia, yet hemoglobin concentration varies physiologically with age. Rigid thresholds may therefore misclassify individuals, particularly in early childhood. This study proposes a dynamic, continuous model based on empirical hemoglobin-age relationships to address limitations of fixed anemia cutoffs.

METHODS

This secondary data analysis used nationally representative data from the Lebanon Integrated Micronutrient, Anthropometry and Child Development Survey 2023 (LIMA-2023) survey conducted in Lebanon. Venous blood samples were collected, and complete blood counts were analyzed on the same day in a centralized laboratory. The analysis included 1448 children, 6 to 59 months of age and 4353 girls and non-pregnant women, 10 to 50 years of age. Polynomial regression was applied to hemoglobin distributions within a healthy reference population to derive continuous, age-specific thresholds for anemia severity (mild, moderate, or severe). Anemia prevalence under the dynamic model was compared with WHO 2024 fixed cutoffs. Misclassification patterns and age-related trends in hematological parameters (red blood cell count [RBC], mean corpuscular volume [MCV], mean corpuscular hemoglobin [MCH], mean corpuscular hemoglobin concentration [MCHC], soluble transferrin receptor [sTfR]) were explored.

RESULTS

The dynamic model identified a higher prevalence of anemia among children, 6 to 59 months of age, than WHO 2024 criteria, particularly for mild and severe anemia. Estimates in older children and women were largely comparable. The continuous model captured age-dependent physiological trends and reduced misclassification linked to fixed thresholds.

CONCLUSION

Fixed hemoglobin cutoffs may not adequately reflect age-related physiology. A dynamic, continuous model offers a refined alternative that improves anemia classification, especially in early childhood, and may inform more accurate epidemiological and public health responses.