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Alleviation of PM2.5-associated Risk of Daily Influenza Hospitalization by COVID-19 Lockdown Measures: A Time-series Study in Northeastern Thailand
Benjawan Roudreo, Sitthichok Puangthongthub
J Prev Med Public Health. 2024;57(2):108-119.   Published online January 19, 2024
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AbstractAbstract AbstractSummary PDFSupplementary Material
Abrupt changes in air pollution levels associated with the coronavirus disease 2019 (COVID-19) outbreak present a unique opportunity to evaluate the effects of air pollution on influenza risk, at a time when emission sources were less active and personal hygiene practices were more rigorous.
This time-series study examined the relationship between influenza cases (n=22 874) and air pollutant concentrations from 2018 to 2021, comparing the timeframes before and during the COVID-19 pandemic in and around Thailand’s Khon Kaen province. Poisson generalized additive modeling was employed to estimate the relative risk of hospitalization for influenza associated with air pollutant levels.
Before the COVID-19 outbreak, both the average daily number of influenza hospitalizations and particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) concentration exceeded those later observed during the pandemic (p<0.001). In single-pollutant models, a 10 μg/m3 increase in PM2.5 before COVID-19 was significantly associated with increased influenza risk upon exposure to cumulative-day lags, specifically lags 0-5 and 0-6 (p<0.01). After adjustment for co-pollutants, PM2.5 demonstrated the strongest effects at lags 0 and 4, with elevated risk found across all cumulative-day lags (0-1, 0-2, 0-3, 0-4, 0-5, and 0-6) and significantly greater risk in the winter and summer at lag 0-5 (p<0.01). However, the PM2.5 level was not significantly associated with influenza risk during the COVID-19 outbreak.
Lockdown measures implemented during the COVID-19 pandemic could mitigate the risk of PM2.5-induced influenza. Effective regulatory actions in the context of COVID-19 may decrease PM2.5 emissions and improve hygiene practices, thereby reducing influenza hospitalizations.
Key Message
In the present research, both single- and multi-pollutant models indicated that the level of PM2.5 was significantly related to the daily number of influenza cases in Khon Kaen, Thailand, prior to the COVID-19 outbreak. Additionally, a significant risk difference was observed between the pre-outbreak and the pandemic periods due to the reduction in air pollutant concentrations because of lockdown measures to control the spread of COVID-19. These findings could be useful for developing environmental policies and strategies accordingly to mitigate respiratory health issues.
‘Pneumonia Weather’: Short-term Effects of Meteorological Factors on Emergency Room Visits Due to Pneumonia in Seoul, Korea
Sangho Sohn, Wonju Cho, Jin A Kim, Alaa Altaluoni, Kwan Hong, Byung Chul Chun
J Prev Med Public Health. 2019;52(2):82-91.   Published online February 11, 2019
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  • 18 Crossref
AbstractAbstract AbstractSummary PDF
Many studies have explored the relationship between short-term weather and its health effects (including pneumonia) based on mortality, although both morbidity and mortality pose a substantial burden. In this study, the authors aimed to describe the influence of meteorological factors on the number of emergency room (ER) visits due to pneumonia in Seoul, Korea.
Daily records of ER visits for pneumonia over a 6-year period (2009-2014) were collected from the National Emergency Department Information System. Corresponding meteorological data were obtained from the National Climate Data Service System. A generalized additive model was used to analyze the effects. The percent change in the relative risk of certain meteorological variables, including pneumonia temperature (defined as the change in average temperature from one day to the next), were estimated for specific age groups.
A total of 217 776 ER visits for pneumonia were identified. The additional risk associated with a 1°C increase in pneumonia temperature above the threshold of 6°C was 1.89 (95% confidence interval [CI], 1.37 to 2.61). Average temperature and diurnal temperature range, representing within-day temperature variance, showed protective effects of 0.07 (95% CI, 0.92 to 0.93) and 0.04 (95% CI, 0.94 to 0.98), respectively. However, in the elderly (65+ years), the effect of pneumonia temperature was inconclusive, and the directionality of the effects of average temperature and diurnal temperature range differed.
The term ‘pneumonia temperature’ is valid. Pneumonia temperature was associated with an increased risk of ER visits for pneumonia, while warm average temperatures and large diurnal temperature ranges showed protective effects.
Korean summary
본 연구에서는 기온 등 다양한 기상요인의 건강영향을 나타내는 표현 중 하나로 알려진 'pneumonia weather'가 역학적으로 가진 의미를 파악하고자 하였다. 이를 위해 국가응급진료정보망의 폐렴 진료기록과 기상자료개방포털 일기자료를 일반화가법모형을 이용해 분석하였다. 그 결과 pneumonia weather는 연속된 양일간 평균기온의 차이를 의미하며, 일정 수준 이상의 일간 기온차는 단기간 이후 폐렴으로 인한 응급실 내원 위험을 증가시킨다는 사실을 확인하였다.


Citations to this article as recorded by  
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Epidemiologic Methods and Study Designs for Investigating Adverse Health Effects of Ambient Air Pollution.
Jong Tae Lee, Ho Kim
Korean J Prev Med. 2001;34(2):119-126.
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AbstractAbstract PDF
Air pollution epidemiologic studies are intrinsically difficult because the expected effect size at general environmental levels is small, exposure and misclassification of exposure are common, and exposure is not selective to a specific pollutant. In this review paper, epidemiologic study designs and analytic methods are described, and two nationwide projects on air pollution epidemiology are introduced. This paper also demonstrates that possible confounding issues in time-series analysis can be resolved and the impact on the use of data from ambient monitoring stations may not be critical. In this paper we provide a basic understanding of the types of air pollution epidemiologic study designs that be subdivided by the mode of air pollution effects on human health (acute or chronic). With the improvements in the area of air pollution epidemiologic studies, we should emphasize that elaborate models and statistical techniques cannot compensate for inadequate study design or poor data collection.

JPMPH : Journal of Preventive Medicine and Public Health