Association between population mean and distribution of deviance in demographic surveys from 65 countries: cross sectional study
Bibliographic record
Abstract
Abstract Objectives To examine whether conditions related to scarcity at the left side of the distribution (anaemia, severe chronic energy deficiency, and underweight) are as strongly related to population means as conditions of excess at the right side of the distribution (overweight and obesity). Design Observational study. Setting 65 countries, with nationally representative cross sectional data from 1994 to 2014 obtained from the Demographic Health Surveys. Participants Non-pregnant women aged 20-49. Sample of 65 countries and n=524 380 for analysis of BMI; sample of 44 countries and n=316 465 for analysis of haemoglobin. Main outcome measures The association between mean and prevalence of each category. For BMI, prevalence of severe chronic energy deficiency (SCED, BMI <16.0), underweight (BMI <18.5), overweight (BMI >25) and obese (BMI >30.) were measured; for haemoglobin, prevalence of anaemia (haemoglobin <12.0 g/dL) and severe anaemia (haemoglobin <8.0 g/dL) were examined. Results There was a strong association between mean BMI and prevalence of overweight (r2=0.98; r=0.99; β=8.3 (8.0 to 8.6)) and obesity (r2=0.93; r=0.97; β=4.2 (3.9 to 4.5)). For left sided conditions, a moderate to strong association was found between mean BMI and prevalence of underweight (r2=0.67; r=−0.82; β=−2.7 (−3.1 to −2.2)), and a weaker association for SCED (r2=0.38; r=−0.61; β=−0.32 (−0.43 to −0.22)). There was a moderate association between mean haemoglobin and prevalence of anaemia (r2=0.46; r=−0.68; β=−10.8 (−14.5 to −7.1)) and a weaker association with severe anaemia (r2=0.30; r=-0.55; β=−0.55 (−0.81 to −0.29)). Conclusions The associations between population means and prevalence of conditions of scarcity such as low BMI and anaemia were substantially weaker than the associations of mean BMI with conditions of excesses such as overweight and obesity.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".