Evidence map›Paper›PMID 39238531›Full record

ArticleGeoHealth2024

Associations Between Anthropogenic Factors, Meteorological Factors, and Cause-Specific Emergency Department Admissions.

Pranav Tewari, Baihui Xu, Ma Pei, Kelvin Bryan Tan, John Abisheganaden, Steve Hung-Lam Yim, Borame Lee Dickens, Jue Tao Lim

Abstract read
In one paragraph

Article in GeoHealth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Pranav TewariLee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore.ORCID https://orcid.org/0009-0002-2608-7207
Baihui XuLee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore.
Ma PeiSaw Swee Hock School of Public Health National University of Singapore Singapore Singapore.
Kelvin Bryan TanMinistry of Health Singapore Singapore.
John AbisheganadenTan Tock Seng Hospital Singapore Singapore.
Steve Hung-Lam YimAsian School of the Environment Nanyang Technological University Singapore Singapore.
Borame Lee DickensSaw Swee Hock School of Public Health National University of Singapore Singapore Singapore.
Jue Tao LimLee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unpredictable emergency department (ED) admissions challenge healthcare systems, causing resource allocation inefficiencies. This study analyses associations between air pollutants, meteorological factors, and 2,655,861 cause-specific ED admissions from 2014 to 2018 across 12 categories. Generalized additive models were used to assess non-linear associations for each exposure, yielding Incidence Rate Ratios (IRR), while the population attributable fraction (PAF) calculated each exposure's contribution to cause-specific ED admissions. IRRs revealed increased risks of ED admissions for respiratory infections (IRR: 1.06, 95% CI: 1.01-1.11) and infectious and parasitic diseases (IRR: 1.09, 95% CI: 1.03-1.15) during increased rainfall (13.21-16.97 mm). Wind speeds >12.73 km/hr corresponded to increased risks of ED admissions for respiratory infections (IRR: 1.12, 95% CI: 1.03-1.21) and oral diseases (IRR: 1.58, 95% CI: 1.31-1.91). Higher concentrations of air pollutants were associated with elevated risks of cardiovascular disease (IRR: 1.16, 95% CI: 1.05-1.27 for PM

Indexed as

emergency department admissionsgeneralized additive modelsnon‐linear associationspopulation attributable fractionSingapore

Identifiers

PMID39238531
PMCPMC11375029

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.