A “One-Stop” Screening Protocol for Haemoglobinopathy Traits and Iron Deficiency in Sri Lanka
Bibliographic record
Abstract
Introduction The high frequencies of carriers of severe haemoglobinopathies and of iron deficiency in Southeast Asia require reliable and affordable tests to improve on current screening procedures. Objectives We evaluate a “one stop” approach using the THALCON dichlorophenolindophenol (DCIP) and one-tube osmotic fragility (OF) tests and measurement of Zinc Protoporphyrin (ZPP) to detect and distinguish HbE and β-thalassaemia traits from iron deficiency. We compare findings with current screening practice in Sri Lanka that relies on the identification of low mean red cell volume and/or mean red cell haemoglobin for this purpose. Methods Between November 2017 and May 2018, we undertook a cross-sectional survey of secondary school students in Gampaha district, Sri Lanka. The THALCON–DCIP and OF tests were compared to capillary electrophoresis (CE), used as a gold standard to detect haemoglobinopathies. ZPP was measured in whole blood. Plasma ferritin and C-reactive protein (CRP) were measured in students with a raised ZPP concentration. Results We collected venous blood samples from 1324/1332 (99.4%) students. The median age of the students was 17 (IQR 16-18) years, all were Sinhalese and 814/1324 (61.5%) were female. CE identified 3 students with HbE trait and 26 students with β-thalassaemia trait. The THALCON–DCIP test was positive only in the 3 students with HbE (sensitivity 100%, 95% CI 29.2-100.0; specificity 100%, 95% CI 99.7-100.0). The THALCON–OF test identified all 26 students with β-thalassaemia trait (sensitivity=100%, 95% CI 86.8-100.0) and 287 students with a normal CE result (specificity = 77.9%; 95% CI 75.5-80.1). It was also positive in 2/3 (66.7%) students with HbE trait. Iron deficiency (ZPP >70μmol/mol heme) was present in 118/1240 (9.5%) students with a normal haemoglobin genotype, all of whom had plasma ferritin <15 ng/ml and CRP <5 mg/L. Conclusion This one–stop approach offers reliable and affordable population screening for both haemoglobinopathy traits and iron deficiency in resource-limited settings where these conditions are common and ensures that iron supplements are targeted only to those who require them. Further work is warranted to refine the OF test to reduce the number of false positive results.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".