Growth Curves for Children Living with Sickle Cell Anemia in Kilifi County, Kenya, Do Not Follow Who Curves for Normal Children
Notice bibliographique
Résumé
Introduction: Several prospective clinical trials have documented the safety, feasibility, and benefits of hydroxyurea treatment for children with sickle cell anemia (SCA) living in sub-Saharan Africa, with significant increases in hemoglobin and fetal hemoglobin, along with significant reductions in the rate of sickle-related vaso-occlusive events, transfusions, malaria, and death. Whether hydroxyurea can help improve growth in children with SCA in Africa has yet to be determined. The World Health Organization (WHO) has published growth curves for children based on data from multiple populations around the world, but those norms include few African children and none with chronic diseases. We therefore analyzed height and weight data from a large cohort of SCA children living in the Kilifi area of coastal Kenya, to generate reference growth curves and then compare them to WHO growth norms and other published datasets. Methods: Serial paired height and weight data collected between 2003 and 2022 were available for analysis, although the majority of measurements (98%) were collected between 2003 and 2014, predating the availability of hydroxyurea for the treatment of children with SCA within the region. A total of 11,039 potential observations were available on 1,688 unique children with SCA between 0.5 and 19.0 years of age, with a median of 9 paired measurements per child (IQR 2-18). Height and weight data were then processed to include only a single paired value in either 6-month or 12-month age categories, to reduce within-child correlations. The generalized additive models for location scale and shape (GAMLSS) method were used first to fit growth curves, and then to generate Z-scores for age, height, and body mass index (BMI) for age, and weight for height. Goodness of fit was assessed by comparing observed and predicted percentages in centile categories. Results: The 6-month data set included 6,095 paired growth measurements (47.2% females) and the 12-month data set included 3,574 paired measurements (47.1% females). Both data sets generated growth curves with excellent fit for both males and females over percentiles ranging from 3-97%, with observed and predicted percentages typically differing by <0.3%. There were no practical differences between the growth curves generated using the 6-month and the 12-month datasets. Compared to the corresponding WHO growth centiles at age 18 years, the 5th, 50th, and 95th centiles for the Kilifi growth curves had substantially lower height (average ~6cm difference for females, ~16cm for males) and BMI (average ~4 kg/m2 difference for females, ~5 kg/m2 for males). The Kilifi SCA growth curve centiles were also slightly lower at most ages than centiles in reference growth curves published previously for children with SCA living in Jamaica (Thomas et al, 2000) and the United States (Wolf et al, 2015). Conclusion: We have developed new reference growth curves specific for children living with SCA in Africa based on data from a large cohort from Kilifi County, Kenya. These growth curves are substantially different from established WHO norms and will serve as a valuable reference data set for analyzing the growth response to treatment with hydroxyurea. We recommend that Africa-specific growth curves be used for all growth analyses of children living with SCA in sub-Saharan Africa.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».