Background: Covid-19 was first reported in Wuhan, China in Dec 2019. Since then, it has been transmitted rapidly in China and the rest of the world. While Covid-19 transmission rate has been declining in China, it is increasing exponentially in Europe and America. Although there are numerous studies examining Covid-19 infection, including an archived paper looking into the meteorological effect, the role of outdoor air pollution has yet to be explored rigorously. It has been shown that air pollution will weaken the immune system, and increase the rate of respiratory virus infection. We postulate that outdoor air pollution concentrations will have a negative effect on Covid-19 infections in China, whilst lockdowns, characterized by strong social distancing and home isolation measures, will help to moderate such negative effect. Methods: We will collect the number of daily confirmed Covid-19 cases in 31 provincial capital cities in China during the period of 1 Dec 2019 to 20 Mar 2020 (from a popular Chinese online platform which aggregates all cases reported by the Chinese national/provincial health authorities). We will also collect daily air pollution and meteorology data at the city-level (from the Chinese National Environmental Monitoring Center and the US National Climatic Data Center), daily inter-city migration flows and intra-city movements (from Baidu). City-level demographics including age distribution and gender, education, and median household income can be obtained from the statistical yearbooks. City-level co-morbidity indicators including rates of chronic disease and co-infection can be obtained from related research articles. A regression model is developed to model the relationship between the infection rate of Covid-19 (number of confirmed cases/population at the city level) and outdoor air pollution at the city level, after taking into account confounding factors such as meteorology, inter- and intra-city movements, demographics, and co-morbidity and co-infection rates. In particular, we shall study how air pollution affects infection rates across different cities, including Wuhan. Our model will also study air pollution would affect infection rates in Wuhan before and after the lockdown. Expected findings: We expect there be a correlation between Covid-19 infection rate and outdoor air pollution. We also expect that reduced intra-city movement after the lockdowns in Wuhan and the rest of China will play an important role in reducing the infection rate. Interpretation: Infection rate is growing exponentially in major cities worldwide. We expect Covid-19 infection rate is related to the air pollution concentration, and is strongly dependent on inter- and intra-city movements. To reduce the infection rate, the international community may deploy effective air pollution reduction plans and social distancing policies.
This is a report of the RGC-TBRS funded observational pilot study which examines the effects of personal exposures to three types of air pollutants, namely, PM1.0, PM2.5, and PM10, on personal health condition and perception of young asthmatics (aged 12 – 15) in Hong Kong. This is the first study to investigate the relationship between PM1.0 and FEV1 and FVC of young asthmatics in Hong Kong, based on personal exposures obtained from portable sensors. Our preliminary results show that a higher level of PM1.0, PM2.5 and PM10 would deteriorate the health conditions of young asthmatics in HK. All correlations between particulates and lung functions are significant and negative, including PM1.0 exposure vs. FEV1 (R2=12%; p=0.023), PM1.0 exposure vs. FVC (R2=15%; p=0.010), PM2.5 exposure vs. FEV1 (R2=13%; p=0.019), PM2.5 exposure vs. FVC (R2=16%; p=0.008), PM10 exposure vs. FEV1 (R2=14%; p=0.012), and PM10 exposure vs. FVC (R2=18%; p=0.005). Moreover, after accounting for covariates, including age, gender, body mass index (BMI), temperature, and relative humidity, we found a significant relationship between PM1.0 exposure vs. FVC (Coefficient=-0.1224; p=0.032), PM2.5 exposure vs. FVC (Coefficient=-0.1177; p=0.021), PM10 exposure vs. FEV1 (Coefficient=-0.0703; p=0.019), and PM10 exposure vs. FVC (Coefficient=-0.1204; p=0.006). Further, using the pilot study data, we have performed a power analysis to estimate the sample size for our follow-up main study. Based on the primary null hypothesis that personal PM exposure would not change the FEV1 and FVC of young asthmatics in HK, the lowest sample size that gives 80% power at a 5% significance level is 107. Hence, the sample size (or the total number of participated asthma subjects) expected for the follow-up longitudinal clinical study should be 125 (after adjusting for the non-compliance and withdrawal of subjects). Our pilot study has demonstrated the feasibility of research into the effects of personal air pollutant exposure on health condition and health perception. Our follow-up study will address the challenges identified in the pilot study, based on the proposed follow-up actions for subject engagement, data collection, and data analysis.
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