Associations between anthropogenic factors, meteorological factors, and cause-specific emergency department admissions
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...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/180480 |
| _version_ | 1826179039641993216 |
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| author | Tewari, Pranav Xu, Baihui Pei, Ma Tan, Kelvin Bryan Abisheganaden, John Yim, Steve Hung Lam Dickens, Borame Lee Lim, Jue Tao |
| author2 | Lee Kong Chian School of Medicine (LKCMedicine) |
| author_facet | Lee Kong Chian School of Medicine (LKCMedicine) Tewari, Pranav Xu, Baihui Pei, Ma Tan, Kelvin Bryan Abisheganaden, John Yim, Steve Hung Lam Dickens, Borame Lee Lim, Jue Tao |
| author_sort | Tewari, Pranav |
| collection | NTU |
| description | 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 PM10) and respiratory infection-related ED admissions (IRR: 2.78, 95% CI: 1.69-4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28°C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM10 concentrations >60 μg/m3 were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered. |
| first_indexed | 2025-03-09T12:59:02Z |
| format | Journal Article |
| id | ntu-10356/180480 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T12:59:02Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1804802024-10-13T15:37:42Z Associations between anthropogenic factors, meteorological factors, and cause-specific emergency department admissions Tewari, Pranav Xu, Baihui Pei, Ma Tan, Kelvin Bryan Abisheganaden, John Yim, Steve Hung Lam Dickens, Borame Lee Lim, Jue Tao Lee Kong Chian School of Medicine (LKCMedicine) Asian School of the Environment Medicine, Health and Life Sciences Emergency department admissions Generalized additive models 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 PM10) and respiratory infection-related ED admissions (IRR: 2.78, 95% CI: 1.69-4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28°C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM10 concentrations >60 μg/m3 were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered. Ministry of Education (MOE) Nanyang Technological University Published version This research/project is supported by the Lee Kong Chian School of Medicine—Ministry of Education Start‐Up Grant, the Ministry of Education, Singapore, under its AcRF Tier 1 Thematic Call (RT4/22) and AcRF Tier 1 Seed Funding Grant (RS04/22). The funders had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. 2024-10-09T00:46:42Z 2024-10-09T00:46:42Z 2024 Journal Article Tewari, P., Xu, B., Pei, M., Tan, K. B., Abisheganaden, J., Yim, S. H. L., Dickens, B. L. & Lim, J. T. (2024). Associations between anthropogenic factors, meteorological factors, and cause-specific emergency department admissions. GeoHealth, 8(9), e2024GH001061-. https://dx.doi.org/10.1029/2024GH001061 2471-1403 https://hdl.handle.net/10356/180480 10.1029/2024GH001061 39238531 2-s2.0-85203250017 9 8 e2024GH001061 en NTU SUG RT4/22 RS04/22 GeoHealth © 2024 The Author(s). GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. application/pdf |
| spellingShingle | Medicine, Health and Life Sciences Emergency department admissions Generalized additive models Tewari, Pranav Xu, Baihui Pei, Ma Tan, Kelvin Bryan Abisheganaden, John Yim, Steve Hung Lam Dickens, Borame Lee Lim, Jue Tao Associations between anthropogenic factors, meteorological factors, and cause-specific emergency department admissions |
| title | Associations between anthropogenic factors, meteorological factors, and cause-specific emergency department admissions |
| title_full | Associations between anthropogenic factors, meteorological factors, and cause-specific emergency department admissions |
| title_fullStr | Associations between anthropogenic factors, meteorological factors, and cause-specific emergency department admissions |
| title_full_unstemmed | Associations between anthropogenic factors, meteorological factors, and cause-specific emergency department admissions |
| title_short | Associations between anthropogenic factors, meteorological factors, and cause-specific emergency department admissions |
| title_sort | associations between anthropogenic factors meteorological factors and cause specific emergency department admissions |
| topic | Medicine, Health and Life Sciences Emergency department admissions Generalized additive models |
| url | https://hdl.handle.net/10356/180480 |
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