Global Prevalence of Iron Deficiency Utilizing Differing Ferritin Cut-Offs - 15, 30, and 50 μg/L
Notice bibliographique
Résumé
Introduction: Ferritin is arguably the most accurate and most utilized test for iron deficiency. The World Health Organization (WHO) uses a ferritin cutoff of below 15 μg/L to diagnose iron deficiency in most individuals while a 2021 Cochrane review suggests 30. Our institution uses a lower limit of 50 in the non-inflamed patient, as this level has been shown to correlate with iron deficiency in women of reproductive age. Ferritin data from the WHO was utilized in this study to examine distribution of ferritin values across the world and thus estimate the prevalence of iron deficiency. Methods: Data was obtained from the Vitamin and Mineral Nutrition Information System from the WHO, and included datapoints from 2010-2022 from 36 countries. Each datapoint represented the weighted average of (where sample size was provided) median (preferred) or mean ferritins from the particular population (eg, adult males from Canada in 2011). If multiple years for the same country and category were present, the latest year was chosen. Human development index (HDI) data came from the United Nations. Data was categorized by sex, age, and pregnancy status, with age groups being roughly defined as 0-18 years (child) and greater than 18 years (adult). Data was analyzed using Welch's t-test, and the correlation coefficient. Results: Children: 37 ferritin datapoints representing at least 68,091 individuals. Male and female children had similar ferritins (33.4 male vs 31.3 μg/L female, P = 0.59) and HDI of contributing countries was also similar (0.705 vs 0.708, P = 0.94). No males and 5.3% of females had ferritins over 50; 66.7% of males and 52.6% of females had ferritins over 30; 88.9% of males and 89.5% of females had ferritins over 15. Adults: 56 ferritin datapoints representing at least 133,149 individuals. Males had significantly higher ferritins than females (117.0 vs 46.2, P = 0.001), though HDI was slightly higher for men (0.835 vs 0.732, P = 0.09). Pregnant women had lower ferritins than non-pregnant women (24.2 pregnant vs 34.2 non-pregnant, P = 0.02) with similar HDI (0.647 vs 0.612, P = 0.52). All adult males, 40.0% adult females, 16.7% non-pregnant females, and no pregnant females had ferritins over 50; 75% of adult females, 61.1% of non-pregnant females, and 10% of pregnant females had ferritins over 30; 100% of adult females, 100% of non-pregnant females, and 100% of pregnant females had ferritins over 15. In general, as HDI increased so did ferritin for all categories, though strength of the association varied by category, ranging from 0.20 in female children to 0.57 in male adults. Discussion: With a ferritin cut-off of 50, almost all children and pregnant women sampled had ferritins consistent with iron deficiency, with that number dropping to near zero when using a lower cut-off of 15. Thus, the majority of iron deficient individuals would be missed by using a lower, and to the best of our knowledge physiologically inappropriate, ferritin cut-off. As expected, adult men had significantly higher ferritins than adult women, likely due to obligate iron loss through menses for most women. Interestingly, while a country's HDI showed some correlation with ferritin values the strength was at most moderate, suggesting socioeconomic status does not play as large as expected role in iron deficiency. Being an acute phase reactant, ferritin levels may not correlate with iron deficiency in the inflamed state. Additional limitations include limited number of ferritin values, countries, and years. This study shows that with using a ferritin cut-off of 50 iron deficiency is widespread among children and women, particularly pregnant women. Use of a higher and more physiologically appropriate ferritin cut-off helps illustrate the magnitude of the problem of iron deficiency and the work that needs to be done to both better define and combat this treatable and preventable disease.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».