Anemia design effects in cluster surveys of women and young children in refugee settings
Bibliographic record
Abstract
BACKGROUND: Nutrition surveys in many refugee settings routinely estimate anemia prevalence in high-risk population groups. Given the lack of information on anemia design effects (DEFF) observed in surveys in these settings, the goal of this paper is to better understand the magnitude and distribution of DEFFs and intracluster correlation coefficients (ICCs) in order to inform future survey design. METHODS: Two-stage cluster surveys conducted during 2013-2016 were included if they measured hemoglobin in refugee children aged 6-59 months and/or non-pregnant women aged 15-49 years. Prevalence of anemia, anemia DEFFs and ICCs, mean cluster size, number of clusters, and total sample size were calculated per-survey for non-pregnant women and children. Non-parametric tests were used to assess differences and correlations of ICC and DEFF between women and children and inter-regional differences. RESULTS: Eighty-seven unique cluster surveys from nine countries were included in this analysis. More than 90% of all surveys had ICC values for anemia below 0.10. Median ICC for children was 0.032 (IQR: 0.015-0.048), not significantly different from that observed for non-pregnant women for whom the median was 0.024 (IQR: -0.002-0.055). DEFFs were significantly higher for children [1.54 (IQR: 1.21-1.82)] versus women [1.20 (IQR: 0.99-1.46)]. Regional differences in DEFFs and ICCs were observed. CONCLUSIONS: Both ICCs and DEFF were relatively small for both non-pregnant women and preschool children and fall in a narrow range. Differences in ICCs between women and children were non-significant, suggesting similar inter-cluster distributions of anemia; significant differences in DEFF were likely attributable to differing cluster sizes. Given regional differences in both ICCs and DEFFs, location-specific values are preferred. However, in the absence of other context-specific information, we suggest using DEFFs of 1.4-1.8 if mean cluster size is around 20, and DEFFs of 1.2-1.4 if mean cluster size is around 10.
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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 | 0.048 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".