Significant cognitive delay among 3- to 4-year old children in low- and middle-income countries: prevalence estimates and potential impact of preventative interventions
Bibliographic record
Abstract
Background: We sought to: (i) estimate the prevalence of significant cognitive delay (a marked delay in the development of general cognitive functioning) among nationally representative samples of young children in middle- and low-income countries; (ii) estimate the total number of children under 5 years of age with significant cognitive delay living in low- and middle-income countries; and (iii) estimate the potential impact of five preventative interventions. Methods: Secondary analysis of data collected in Rounds 4 and 5 of UNICEF's Multiple Cluster Indicators Surveys in 51 countries involving 163 293 3- to 4-year-old children. Adjusted population-attributable fractions were used to estimate the potential impact of five interventions based on Sustainable Development Goals (SDGs). Results: The prevalence of significant cognitive delay in 3- to 4-year-old children in middle- and low-income countries was 10.1% (95% confidence interval 9.7-10.4%). Prevalence was strongly inversely related to country economic wealth. The estimated total number of children under 5 with significant cognitive delay living in low- and middle-income countries was just under 55 million. This number could be reduced by over 60% if three separate SDGs were achieved; every mother had secondary-level education, every household had access to improved water and sanitation, and every child had an acceptable level of home stimulation. Conclusions: Our results provide additional evidence in support of a range of specific preventative interventions in early childhood to reduce the loss of developmental potential among children in low- and middle-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".