Ivermectin for COVID-19 in Peru: 14-fold reduction in nationwide excess deaths, p<0.002 for effect by state, then 13-fold increase after ivermectin use restricted
Bibliographic record
Abstract
Introduction. On May 8, 2020, Peru’s Ministry of Health approved ivermectin (IVM), a drug of Nobel Prize-honored distinction, for inpatient and outpatient treatment of COVID-19. As IVM treatments proceeded in that nation of 33 million residents, excess deaths decreased 14-fold over four months through December 1, 2020, consistent with clinical benefits of IVM for COVID-19 found in several RCTs. But after IVM use was sharply restricted under a new president, excess deaths then increased 13-fold. Methods. To evaluate possible IVM treatment effects suggested by these aggregate trends, excess deaths were analyzed by state for ages ≥ 60 in Peru’s 25 states. To identify potential confounding factors, Google mobility data, population densities, SARS-CoV-2 genetic variations and seropositivity rates were also examined.Results. The 25 states of Peru were grouped by extent of IVM distributions: maximal (mass IVM distributions through operation MOT, a broadside effort led by the army); medium (locally managed IVM distributions); and minimal (restrictive policies in one state, Lima). The mean reduction in excess deaths 30 days after peak deaths was 74% for the maximal IVM distribution group, 53% for the medium group and 25% for Lima. Reduction of excess deaths is correlated with extent of IVM distribution by state with a p value of 0.002 using the Kendall τb test.Conclusion. Mass treatments with IVM, a drug safely used in 3.7 billion doses worldwide since 1987, most likely caused the 14-fold reductions in excess deaths in Peru, prior to their 13-fold increase under reversed IVM policy. This strongly suggests that IVM treatments can likewise effectively complement immunizations to help eradicate COVID-19. The indicated biological mechanism of IVM, competitive binding with SARS-CoV-2 spike protein, is likely non-epitope specific, possibly yielding full efficacy against emerging viral mutant strains.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".