The effects of rain and drought on incidence of enteric disease in Pennsylvania (2010–2019)
Bibliographic record
Abstract
BACKGROUND: Campylobacter, nontyphoidal Salmonella, Cryptosporidium, and Giardia cause an estimated 1 million cases of domestically acquired waterborne diseases annually in the United States. Acute symptoms can include diarrhea and vomiting; however, these illnesses can result in longer term complications such as reactive arthritis, Guillan Barré syndrome and death, particularly in immunocompromised individuals. Precipitation and drought can plausibly increase the risk of enteric infections, but consensus in the literature is lacking. OBJECTIVES: To determine the effects of rain and drought on weekly counts of reportable enteric illness (due to Salmonella, Campylobacter, Giardia, or Cryptosporidium) in Pennsylvania, US between 2010 and 2019. METHODS: We obtained 10-years of data on confirmed illness from 66 Pennsylvania counties due to: Salmonella (9,924), Campylobacter (15,854), Giardia (4,537), and Cryptosporidium (4,017). A zero-inflated negative binomial model with random-intercept for county was used to assess the relationship between illnesses caused by these pathogens between 2010 and 2019, and weekly rain (cm) and Palmer Drought Severity Index (PDSI) values from the National Weather Service. The lag times that were tested, between illness and weather event, were chosen by calculating the cross correlation between the statewide average weekly rain and the statewide number of weekly cases. RESULTS: A positive association was found between rain and counts of campylobacteriosis, salmonellosis, and giardiasis. An increase in prior wetness (PDSI value) was associated with increased incidence rates of cryptosporidiosis and campylobacteriosis. The relationship between rain and PDSI and illness varied by organism type. DISCUSSION: Complex relationships exist between enteric disease and precipitation and prior environmental wetness. Our findings suggest that rainfall may be contributing to increased waterborne exposure. Further investigation is needed to explore these relationships with factors such as drinking water source, local geological conditions, presence of combined sewer overflows and agricultural activities, recreational water use and irrigation water sources to better elucidate important waterborne transmission pathways.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 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".