Dataset Information

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS).

ABSTRACT:

Background

Several studies have shown the health effects of air pollutants, especially in China, North American and Western European countries. But longitudinal cohort studies focused on health effects of long-term air pollution exposure are still limited in Southeast Asian countries where sources of air pollution, weather conditions, and demographic characteristics are different. The present study examined the association between long-term exposure to air pollution and self-reported morbidities in participants of the Thai cohort study (TCS) in Bangkok metropolitan region (BMR), Thailand.

Methods

This longitudinal cohort study was conducted for 9 years from 2005 to 2013. Self-reported morbidities in this study included high blood pressure, high blood cholesterol, and diabetes. Air pollution data were obtained from the Thai government Pollution Control Department (PCD). Particles with diameters ?10??m (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO) exposures were estimated with ordinary kriging method using 22 background and 7 traffic monitoring stations in BMR during 2005-2013. Long-term exposure periods to air pollution for each subject was averaged as the same period of person-time. Cox proportional hazards models were used to examine the association between long-term air pollution exposure with self-reported high blood pressure, high blood cholesterol, diabetes. Results of self-reported morbidity were presented as hazard ratios (HRs) per interquartile range (IQR) increase in PM₁₀, O₃, NO₂, SO₂, and CO.

Results

After controlling for potential confounders, we found that an IQR increase in PM₁₀ was significantly associated with self-reported high blood pressure (HR?=?1.13, 95% CI: 1.04, 1.23) and high blood cholesterol (HR?=?1.07, 95%CI: 1.02, 1.12), but not with diabetes (HR?=?1.05, 95%CI: 0.91, 1.21). SO₂ was also positively associated with self-reported high blood pressure (HR?=?1.22, 95%CI: 1.08, 1.38), high blood cholesterol (HR?=?1.20, 95%CI: 1.11, 1.30), and diabetes (HR?=?1.21, 95%CI: 0.92, 1.60). Moreover, we observed a positive association between CO and self-reported high blood pressure (HR?=?1.07, 95%CI: 1.00, 1.15), but not for other diseases. However, self-reported morbidities were not associated with O₃ and NO₂.

Conclusions

Long-term exposure to air pollution, especially for PM₁₀ and SO₂ was associated with self-reported high blood pressure, high blood cholesterol, and diabetes in subjects of TCS. Our study supports that exposure to air pollution increases cardiovascular disease risk factors for younger population.

SUBMITTER: Paoin K

PROVIDER: S-EPMC7768181 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS).

Paoin Kanawat K Ueda Kayo K Ingviya Thammasin T Buya Suhaimee S Phosri Arthit A Seposo Xerxes Tesoro XT Seubsman Sam-Ang SA Kelly Matthew M Sleigh Adrian A Honda Akiko A Takano Hirohisa H

Environmental research 20201014

<h4>Background</h4>Several studies have shown the health effects of air pollutants, especially in China, North American and Western European countries. But longitudinal cohort studies focused on health effects of long-term air pollution exposure are still limited in Southeast Asian countries where sources of air pollution, weather conditions, and demographic characteristics are different. The present study examined the association between long-term exposure to air pollution and self-reported mor ...[more]

PMID: 33068582

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Dataset Information

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS).

Background

Methods

Results

Conclusions

Publications

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS).

Similar Datasets

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