REPORTING OF PESTICIDE ILLNESS BY HEALTH CARE PROVIDERS TO WASHINGTON STATE’S PESTICIDE ILLNESS MONITORING SYSTEM
Bibliographic record
Abstract
ISEE-462 Introduction: Washington State Department of Health (DOH) investigates all suspected cases of pesticide-related-illness reported to the Pesticide Illness Monitoring System (PIMS). All cases reported to PIMS are investigated to determine whether they are pesticide-related. PIMS is a passive surveillance system and depends on cases being reported to it from multiple sources. Major reporting sources include: The Department of Labor and Industries, Washington Department of Agriculture, Washington State Poison Control, and Health Care Providers (HCPs). A passive surveillance system such as PIMS may only capture a fraction of the cases that are actually occurring. Cases who become sick after suffering a pesticide related illness and do not seek medical care are often difficult to capture. However, the percentage of cases that seek medical care and are actually reported to PIMS is unknown. HCPs, underreporting is thought to be likely since less than a quarter of cases reported to PIMS either directly or indirectly are reported by HCPs. Methods: To estimate the percent of pesticide related cases occurring that seek medical care, and are actually captured by PIMS, DOH requested information from records for a number of ICD-9 codes frequently associated with pesticide related illness for cases occurring between 1999-2001 from the major hospitals and clinics in an agricultural region in Central Washington State. The information requested records included: ICD-9 codes, name, age, gender, address, visit date, and insurance. These records were then matched to our PIMS database using a deterministic match. Results: We obtained approximately 100,000 records of unique patient visits. Of these records 169 had a pesticide related ICD-9 code, (Toxic effect of other substances, chiefly non-medicinal: 989.0-989.4, and Accidental poisoning by agricultural and horticultural chemical preparations: E863.0-E863.7). Fewer than half of these 169 records matched to our PIMS database. Discussion: Less than half of HCP visits that are coded with a pesticide related ICD-9 code were reported to our surveillance system over a three year period. This investigation suggests that underreporting of pesticide-related-illness is likely to occur. A passive surveillance system depends on reporting to capture cases. Although other sources may report cases, reporting may be delayed. Delayed reports are often difficult to classify as being pesticide related. HCPs need to be encouraged to report pesticide related illness. Obstacles to HCP reporting such as difficulties with identifying, treating, and coding possible pesticide related illnesses need to be explored.
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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 | 0.009 |
| 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".