Modifying the Interagency Emergency Health Kit to include treatment for non-communicable diseases in natural disasters and complex emergencies
Bibliographic record
Abstract
The Interagency Emergency Health Kit (IEHK) provides a standard package of medicines and simple medical devices for aid agencies to use in emergencies such as disasters and armed conflicts. Despite the increasing burden of non-communicable diseases (NCDs) in such settings, the IEHK includes few drugs and devices for management of NCDs. Using published data to model the population burden of acute and chronic presentations of NCDs in emergency-prone regions, we estimated the quantity of medications and devices that should be included in the IEHK. NCDs considered were cardiovascular diseases, diabetes, hypertension and chronic respiratory disease. In scenario 1 (the primary scenario), we assumed that resources in the IEHK would only include those needed to manage acute life-threatening conditions. In scenario 2, we included resources required to manage both acute and chronic presentations of NCDs. Drugs and devices that might be required included amlodipine, aspirin, atenolol, beclomethasone, dextrose 50%, enalapril, furosemide, glibenclamide, glyceryl trinitrate, heparin, hydralazine, hydrochlorothiazide, insulin, metformin, prednisone, salbutamol and simvastatin. For scenario 1, the number of units required ranged from 12 (phials of hydralazine) to ∼15 000 (tablets of enalapril). Space and weight requirements were modest and total cost for all drugs and devices was approximately US$2078. As expected, resources required for scenario 2 were much greater. Space and cost requirements increased proportionately: estimated total cost of scenario 2 was $22 208. The resources required to treat acute NCD presentations appear modest, and their inclusion in the IEHK seems feasible.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".