Non-linear associations between meteorological factors, ambient air pollutants and major mosquito-borne diseases in Thailand
Copyright: © 2023 Tewari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
BACKGROUND: Transmission intensity for mosquito-borne diseases are highly heterogenous and multi-factorial. Understanding risk factors associated to disease transmission allow the optimization of vector control. This study sets out to understand and compare the combined anthropogenic and environmental risk factors of four major mosquito-borne diseases, dengue, malaria, chikungunya and Japanese encephalitis in Thailand.
METHODS: An integrated analysis of mosquito-borne diseases, meteorological and ambient air pollutants of 76 provinces of Thailand was conducted over 2003-2021. We explored the use of generalized linear models and generalized additive models to consider both linear and non-linear associations between meteorological factors, ambient air pollutants and mosquito-borne disease incidence. Different assumptions on spatio-temporal dependence and nonlinearity were considered through province-specific and panel models, as well as different spline functions. Disease-specific model evidence was assessed to select best-fit models for epidemiological inference downstream.
RESULTS: Analyses indicated several findings which can be generally applied to all diseases explored: (1) higher AH above mean values was positively associated with disease case counts (2) higher total precipitation above mean values was positively associated with disease case counts (3) extremely high temperatures were negatively associated with disease case counts (4) higher SO2 and PM2.5 surface concentrations were negatively associated with disease case counts. However, the relationships between disease and RH, non-extreme temperatures and CO surface concentration were more mixed, with directions of associations changing across the different diseases considered.
CONCLUSIONS: This study found protective and enhancing effects of meteorological and ambient air pollutant factors on mosquito-borne diseases burdens in Thailand. Further studies should employ these factors to understand and predict risk factors associated with mosquito-borne disease transmission.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
PLoS neglected tropical diseases - 17(2023), 12 vom: 27. Dez., Seite e0011763 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tewari, Pranav [VerfasserIn] |
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| Anmerkungen: |
Date Completed 29.12.2023 Date Revised 29.12.2023 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pntd.0011763 |
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| 520 | |a Copyright: © 2023 Tewari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | ||
| 520 | |a BACKGROUND: Transmission intensity for mosquito-borne diseases are highly heterogenous and multi-factorial. Understanding risk factors associated to disease transmission allow the optimization of vector control. This study sets out to understand and compare the combined anthropogenic and environmental risk factors of four major mosquito-borne diseases, dengue, malaria, chikungunya and Japanese encephalitis in Thailand | ||
| 520 | |a METHODS: An integrated analysis of mosquito-borne diseases, meteorological and ambient air pollutants of 76 provinces of Thailand was conducted over 2003-2021. We explored the use of generalized linear models and generalized additive models to consider both linear and non-linear associations between meteorological factors, ambient air pollutants and mosquito-borne disease incidence. Different assumptions on spatio-temporal dependence and nonlinearity were considered through province-specific and panel models, as well as different spline functions. Disease-specific model evidence was assessed to select best-fit models for epidemiological inference downstream | ||
| 520 | |a RESULTS: Analyses indicated several findings which can be generally applied to all diseases explored: (1) higher AH above mean values was positively associated with disease case counts (2) higher total precipitation above mean values was positively associated with disease case counts (3) extremely high temperatures were negatively associated with disease case counts (4) higher SO2 and PM2.5 surface concentrations were negatively associated with disease case counts. However, the relationships between disease and RH, non-extreme temperatures and CO surface concentration were more mixed, with directions of associations changing across the different diseases considered | ||
| 520 | |a CONCLUSIONS: This study found protective and enhancing effects of meteorological and ambient air pollutant factors on mosquito-borne diseases burdens in Thailand. Further studies should employ these factors to understand and predict risk factors associated with mosquito-borne disease transmission | ||
| 650 | 4 | |a Journal Article | |
| 650 | 7 | |a Air Pollutants |2 NLM | |
| 650 | 7 | |a Environmental Pollutants |2 NLM | |
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| 700 | 1 | |a Gan, Gregory |e verfasserin |4 aut | |
| 700 | 1 | |a Janhavi, A |e verfasserin |4 aut | |
| 700 | 1 | |a Choo, Esther Li Wen |e verfasserin |4 aut | |
| 700 | 1 | |a Koo, Joel Ruihan |e verfasserin |4 aut | |
| 700 | 1 | |a Dickens, Borame Lee |e verfasserin |4 aut | |
| 700 | 1 | |a Lim, Jue Tao |e verfasserin |4 aut | |
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