Using syndromic surveillance to rapidly assess the impact of a June 2023 wildfire smoke event on respiratory-related emergency department visits, Massachusetts, United States
Bibliographic record
Abstract
Abstract A statewide air quality advisory was issued in Massachusetts for 6–7 June 2023 due to smoke originating from wildfires in Canada. Of particular concern was fine particulate matter, which has an aerodynamic diameter of ⩽2.5 μ m (PM 2.5 ) and has been linked to adverse respiratory outcomes. The objective of this study was to rapidly assess the impact of this wildfire smoke event on respiratory-related emergency department (ED) visits among Massachusetts residents. For exposure, daily air quality index (AQI) data from the US Environmental Protection Agency were used. Massachusetts counties, where for each day from 6 to 8 June 2023, the daily AQI was ⩾101 (i.e. unhealthy air quality), were considered exposed. For each exposed period, two unexposed reference periods where AQI < 101 (i.e. ‘good’ or ‘moderate’ air quality) were identified within the two weeks prior to the exposed period, with the same days of the week and in the same county. Data from the Massachusetts Department of Public Health’s syndromic surveillance system were used to examine daily counts of ED visits for asthma, air-quality-related respiratory illness, and all causes by county of residence, age group, race, and Hispanic/Latino ethnicity. For each outcome, the numbers of ED visits were compared between the exposed and reference periods. Overall, there were no large increases in ED visits for any conditions examined during this wildfire smoke event. However, residents who were aged 18–64 years, Hispanic/Latino or White experienced small but not statistically significant increases in asthma-related ED visits. These potential differences in the effect on asthma-related ED visits by age and race/ethnicity may be relevant for analyses of future events. This study provides an example of how real-time, publicly available exposure data can be used in conjunction with outcome data from syndromic surveillance to rapidly examine the impact of wildfires and other acute environmental events on health.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".