Metallophosphide poisoning, a rising public health problem in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Metallophosphide poisoning, mainly due to aluminum phosphide and zinc phosphide, is a growing public health problem in developing countries associated with a high mortality rate, including in Ethiopia, where it is used a fumigant for stored grains and agricultural commodities. Ethiopia lacks a well-organized poison control center, making it difficult to obtain primary data on metallophosphide poisoning cases and outcomes. This systematic review and meta-analysis aim to determine the pooled prevalence and mortality rate from metallophosphide poisoning in Ethiopia. METHODS: As of August 2024, PUBMED, EMBASE, SCOPUS, and GOOGLE SCHOLAR were inclusively searched. Two independent reviewers extracted the data. Quality was assessed using the Modified Newcastle-Ottawa Scale adapted for cross-sectional studies. A random effects model was used to obtain the pooled estimate of the prevalence of and mortality rate from metallophosphide poisoning. RESULTS: = 96.6%, p < 0.0001). In the teen-included studies for the pooled mortality analysis, the sample size was 677 and the pooled mortality rate was 37% (95 % CI: 0.22, 0.55, I2 = 87.8%, P < 0.0001). CONCLUSION: We found a high pooled prevalence of metallophosphide poisoning in Ethiopia. This highlights the urgent need for regulatory actions to restrict the sales and distribution of these substances. This is supported by international experiences from similar low-resource settings. We recommend safer alternatives to control insects and rodents, such as mechanical rodent controls and integrated pest management. Public awareness creation and enhancing local management protocols to reduce the burden and improve the outcome of metallophosphide poisoning.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.025 | 0.003 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.001 | 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 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".