MétaCan
Menu
← Back to cohort
Record W4378575394 · doi:10.2196/47022

Associations Between Hourly Ambient Particulate Matter Air Pollution and Ambulance Emergency Calls: Time-Stratified Case-Crossover Study

2023· article· en· W4378575394 on OpenAlexvenueno aff
Qiang Zhou, Hanxu Shi, Rengyu Wu, Hong Zhu, Chongzhen Qin, Zhisheng Liang, Shengzhi Sun, Junfeng Zhao, Yasha Wang, Jie Huang, Yinzi Jin, Zhi‐Jie Zheng, Jingyan Li, Zhenyu Zhang

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaSanming Project of Medicine in Shenzhen
KeywordsEnvironmental scienceParticulatesAir pollutionConditional logistic regressionNames of the days of the weekEnvironmental healthOddsPollutantAir pollutantsMeteorologyLogistic regressionMedicinePopulationGeography

Abstract

fetched live from OpenAlex

Background Associations between short-term exposure to ambient particulate matter (PM) air pollutants and mortality or hospital admissions have been well-documented in previous studies. Less is known about the associations of hourly exposure to PM air pollutants with ambulance emergency calls (AECs) for all causes and specific causes by conducting a case-crossover study. In addition, different patterns of AECs may be attributed to different seasons and daytime or nighttime periods. Objective In this study, we quantified the risk of all-cause and cause-specific AECs associated with hourly PM air pollutants between January 1, 2013, and December 31, 2019, in Shenzhen, China. We also examined whether the observed associations of PM air pollutants with AECs for all causes differed across strata defined by sex, age, season, and the time of day. Methods We used ambulance emergency dispatch data and environmental data between January 1, 2013, and December 31, 2019, from the Shenzhen Ambulance Emergency Centre and the National Environmental Monitor Station to conduct a time-stratified case-crossover study to estimate the associations of air pollutants (ie, PM with an aerodynamic diameter less than 2.5 µm [PM2.5] or 10 µm [PM10]) with all-cause and cause-specific AECs. We generated a well-established, distributed lag nonlinear model for nonlinear concentration response and nonlinear lag-response functions. We used conditional logistic regression to estimate odds ratios with 95% CIs, adjusted for public holidays, season, the time of day, the day of the week, hourly temperature, and hourly humidity, to examine the association of all-cause and cause-specific AECs with hourly air pollutant concentrations. Results A total of 3,022,164 patients were identified during the study period in Shenzhen. Each IQR increase in PM2.5 (24.0 µg/m3) and PM10 (34.0 µg/m3) concentrations over 24 hours was associated with an increased risk of AECs (PM2.5: all-cause, 1.8%, 95% CI 0.8%-2.4%; PM10: all-cause, 2.0%, 95% CI 1.1%-2.9%). We observed a stronger association of all-cause AECs with PM2.5 and PM10 in the daytime than in the nighttime (PM2.5: daytime, 1.7%, 95% CI 0.5%-3.0%; nighttime, 1.4%, 95% CI 0.3%-2.6%; PM10: daytime, 2.1%, 95% CI 0.9%-3.4%; nighttime, 1.7%, 95% CI 0.6%-2.8%) and in the older group than in the younger group (PM2.5: 18-64 years, 1.4%, 95% CI 0.6%-2.1%; ≥65 years, 1.6%, 95% CI 0.6%-2.6%; PM10: 18-64 years, 1.8%, 95% CI 0.9%-2.6%; ≥65 years, 2.0%, 95% CI 1.1%-3.0%). Conclusions The risk of all-cause AECs increased consistently with increasing concentrations of PM air pollutants, showing a nearly linear relationship with no apparent thresholds. PM air pollution increase was associated with a higher risk of all-cause AECs and cardiovascular diseases–, respiratory diseases–, and reproductive illnesses–related AECs. The results of this study may be valuable to air pollution attributable to the distribution of emergency resources and consistent air pollution control.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.059
GPT teacher head0.345
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Public Health and Surveillance→Same topicAir Quality and Health Impacts→French-language works237,207→