Pediatric injuries and poisonings associated with detergent packets: results from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP), 2011–2023
Bibliographic record
Abstract
BACKGROUND: Detergent packets are common household products; however, they pose a risk of injuries and poisonings, especially among children. This study examined the epidemiological characteristics of pediatric injuries and poisonings related to all types of detergent packets in Canada using emergency department (ED) data from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) database. METHODS: The CHIRPP database was searched for ED visit records for injuries and poisonings related to all types of detergent packets between April 1, 2011 and October 12, 2023 (N = 2,021,814) using variable codes and narratives. Data for individuals aged 17 years and younger were analyzed descriptively. Temporal trends in the number of detergent packet-related injuries and poisonings per 100,000 CHIRPP cases were assessed using Joinpoint regression and annual percent change (APC). A proportion ratio and 95% confidence intervals (CI) were calculated to compare the proportion of detergent packet-related cases in CHIRPP during two 34-months periods, pre-COVID-19 pandemic and after the beginning of the pandemic. RESULTS: There were 904 detergent packet-related cases among children and youth aged 17 years and younger identified in CHIRPP between April 1, 2011 and October 12, 2023, representing 59.9 cases per 100,000 CHIRPP cases. The majority (86.5%) of cases were among children aged 4 years and younger. Poisonings (58.8%) and eye injuries (30.6%) were the most frequent primary diagnoses. Unintentional ingestion (56.9%) and squeezing/breaking a detergent packet (32.3%) were the most frequent exposure mechanisms. Sixty-five patients (7.2%) were admitted to hospital. The number of detergent packet-related cases per 100,000 CHIRPP cases increased by 5.0% (95% CI 0.8, 10.2) annually between 2012 and 2022. The number of detergent packet-related poisonings per 100,000 CHIRPP cases decreased by 15.3% (95% CI - 22.3, - 10.6) annually between 2015 and 2022, whereas eye injuries showed an average annual percent increase of 16.6% (95% CI 11.2, 23.0) between 2012 and 2022. The proportion of detergent packet-related cases in CHIRPP after the beginning of the pandemic (79.9/100,000 CHIRPP cases) was 1.43 (95% CI 1.20, 1.71) times greater than pre-pandemic (55.7/100,000 CHIRPP cases). CONCLUSIONS: Detergent packet-related injuries and poisonings are a persisting issue. Continued surveillance and prevention efforts are needed to reduce detergent packet-related injuries and poisonings in Canada, particularly among children and youth.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".