City sanitation and socioeconomics predict rat zoonotic infection across diverse neighbourhoods
Bibliographic record
Abstract
Rat-associated zoonoses transmitted through faeces or urine are of particular concern for public health because environmental exposure in homes and businesses may be frequent and undetected. To identify times and locations with greater public health risks from rats, we investigated whether rat characteristics, environmental features, socioeconomic factors, or season could predict rat infection risk across diverse urban neighbourhoods. In partnership with a pest management company, we sampled rats in 13 community areas along an income gradient in Chicago, a large city where concern about rats has increased in recent years. We collected kidneys for Leptospira spp. testing and colon contents for aerobic bacteria such as Salmonella spp. and Escherichia coli. Of 202 sampled rats, 5% carried Leptospira spp. and 22% carried E. coli. Rats were significantly more likely to carry Leptospira spp. on blocks with more standing water complaints in higher-income neighbourhoods (OR = 6.74, 95% CI: 1.54-29.39). Rats were significantly more likely to carry E. coli on blocks with more food vendors (OR = 9.94, 2.27-43.50) particularly in low-income neighbourhoods (OR = 0.26, 0.09-0.82) and in the spring (OR = 15.96, 2.90-88.62). We detected a high diversity of E. coli serovars but none contained major virulence factors. These associations between environmental features related to sanitation and infection risk in rats support transmission through water for Leptospira spp. and faecal-oral transmission for E. coli. We also found opposing relationships between zoonotic infection risk and income for these two pathogens. Thus, our results highlight the importance of sanitation for predicting zoonotic disease risks and including diverse urban areas in pathogen surveillance to mitigate public health risks from rats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".