Associations between rat infestations and mental health vary by gender, race, and income in Chicago
Bibliographic record
Abstract
Abstract Rats are an understudied stressor for people in urban environments around the world but the effects may not be distributed equally among residents. In this study, we examined associations between residential rat sightings and mental health in Chicago, where rat complaints are the highest of any American city. We examined how this relationship varied by frequency of rat sightings, race, ethnicity, income, home ownership, and gender and explored potential psychosocial pathways (e.g. feelings about the home) between rat sightings and mental distress. We conducted a randomized household survey along an income gradient in 2021 and asked about depressive symptoms in the past week (i.e. Center for Epidemiologic Studies Depression scale), frequency of rat sightings in/around the home, perceptions of rats, neighborhood conditions, and socio-demographic characteristics. We used logistic regression to assess relationships among these variables for our entire sample and for specific demographics using stratified models. Respondents (n = 589; 409 complete cases) who saw rats in/around the home daily/almost daily had 5.5 times higher odds of reporting high depressive symptoms relative to respondents who saw rats less frequently after accounting for socio-demographics and neighborhood conditions. This relationship was significant for men and respondents with lower incomes or race or ethnicity other than white. Our results show that rat infestations should be considered a threat to mental health among urban residents. Increased mental health support for residents living in rat-infested housing may improve public health in cities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".