Iodine Concentration Effects on Linear Growth of Children after Nutrition Behavior intervention, Central Highland of Ethiopia: A Cluster Randomized Community Trial
Bibliographic record
Abstract
Background: To improve the iodine status and growth of children was not documented in Ethiopia. This study aimed to determine the effects of nutrition behavior communication change (BCC) on improving iodine status and growth of children 6 to 59 months. Methods: A community cluster randomized trial with a single treatment arm was conducted from February 2018 to April 2020. “Kebeles” [lower administrative units] were randomly assigned to either the intervention or the control cluster. Mothers and their paired children were randomly selected from kebeles. Anthropometry data and urine samples were collected at baseline and end-line surveys. Percentile rank and Independent t-test were used to determine the difference between arms. Finally, Generalized Estimating Equation (GEE) is used to isolate independent predictors. Results: At baseline, 97.83 % (n = 812) mothers/ caregivers and paired children were enrolled for the trial study, but at the end-line, 88.05% (n =715) of children completed the intervention. Iodine deficiency prevalence was higher (11.82%, n = 96) at baseline and reduced to 6.15 % (n =44) at the end-line. The growth defect among children was 41 %( n = 332) at the baseline and declined to 28.67 %( n=205) at the end-line, while among interventions reduced by more than two times (39% to 12.81%). At the baseline, the median UIC among the intervention group was 106.0µg/L and increased to 207.190µg/L. The prevalence of iodine deficiency among intervention was 14.29% (n = 58) at the baseline and lowered to 3.45% (n=14) at the end-line, but a slight increment observed among control from 9.36% to 9.71% at end-line. The end-line median UIC was very high (210.56µg/L ± 150 compared to the baseline median UIC (107µg/L ± 8.66). Most (43.6%) of the intervention group found in the 4th and 5th percentile ranks factions of UIC by Height (Ht) mean differences. Being an intervention group increased Ht by 10.85cm (β =10.85, Std. E = 0.33). Likewise, for 1µg/l UIC change a 1cm (β = 1.0, p = <0.05) Ht change predicted at the end-line. Conclusions: Findings from this trial enhance nutrition behavior communication to improve the iodine status and growth of young children in the community. Longitudinal studies are needed to determine the level of iodine deficiency disorders in the community.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".