Growth Curves for Children Living with Sickle Cell Anemia in Kilifi County, Kenya, Do Not Follow Who Curves for Normal Children
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".