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Background Air quality health index (AQHI) is derived from exposure-response coefficients calculated from air pollution and morbidity/mortality time series, which helps to understand the overall short-term health impacts of air pollution. Objective To study the effects of common air pollutants on respiratory diseases in Urumqi and to develop an AQHI for the risk of respiratory diseases in the city. Methods The daily outpatient volume data of respiratory diseases from The First Affiliated Hospital of Xinjiang Medical University, meteorological data (daily mean temperature and daily mean relative humidity), and air pollutants [fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon dioxide (CO), and ozone (O3)] in Urumqi City, Xinjiang, China were collected from January 1, 2017 to December 31, 2021. A distributed lag nonlinear model based on quasi-Poisson distribution was constructed by time-stratified case crossover design. Adopting zero concentration of air pollutants as reference, the exposure-response coefficient (β value) was used to quantify the impact of included air pollutants on the risk of seeking medical treatment for respiratory diseases, and the AQHI was established. The association of between AQHI and the incidence of respiratory diseases and between air quality index (AQI) and the incidence of respiratory diseases was compared to evaluate the prediction effect of AQHI. Results Each 10 µg·m−3 increase in PM10, SO2, NO2, and O3 concentrations presented the highest excess risk of seeking outpatient services at 3 d cumulative lag (Lag03) and 2d cumulative lag (Lag02), with increased risks of morbidity of 0.687% (95%CI: 0.101%, 1.276%), 17.609% (95%CI: 3.253%, 33.961%), 13.344% (95%CI: 8.619%, 18.275%), and 4.921% (95%CI: 1.401%, 8.502%), respectively. There was no statistically significant PM2.5 or CO lag effect. An AQHI was constructed based on a model containing PM10, SO2, NO2, and O3, and the results showed that the excess risk of respiratory disease consultation for the whole population, different genders, ages, or seasons for each inter-quartile range increase in the AQHI was higher than the corresponding value of AQI. Conclusion PM10, SO2, NO2, and O3 impact the number of outpatient visits for respiratory diseases in Urumqi, and the constructed AQHI for the risk of respiratory diseases in Urumqi outperforms the AQI in predicting the effect of air pollution on respiratory health.
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Background Air pollution is a major public health concern. Air Quality Health Index (AQHI) is a very important air quality risk communication tool. However, AQHI is usually constructed by single-pollutant model, which has obvious disadvantages. Objective To construct an AQHI based on the joint effects of multiple air pollutants (J-AQHI), and to provide a scientific tool for health risk warning and risk communication of air pollution. Methods Data on non-accidental deaths in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces from January 1, 2013 to December 31, 2018 were obtained from the corresponding provincial disease surveillance points systems (DSPS), including date of death, age, gender, and cause of death. Daily meteorological (temperature and relative humidity) and air pollution data (SO2, NO2, CO, PM2.5, PM10, and maximum 8 h O3 concentrations) at the same period were respectively derived from China Meteorological Data Sharing Service System and National Urban Air Quality Real-time Publishing Platform. Lasso regression was first applied to select air pollutants, then a time-stratified case-crossover design was applied. Each case was matched to 3 or 4 control days which were selected on the same days of the week in the same calendar month. Then a distributed lag nonlinear model (DLNM) was used to estimate the exposure-response relationship between selected air pollutants and mortality, which was used to construct the AQHI. Finally, AQHI was classified into four levels according to the air pollutant guidance limit values from World Health Organization Global Air Quality Guidelines (AQG 2021), and the excess risks (ERs) were calculated to compare the AQHI based on single-pollutant model and the J-AQHI based on multi-pollutant model. Results PM2.5, NO2, SO2, and O3 were selected by Lasso regression to establish DLNM model. The ERs for an interquartile range (IQR) increase and 95% confidence intervals (CI) for PM2.5, NO2, SO2 and O3 were 0.71% (0.34%–1.09%), 2.46% (1.78%–3.15%), 1.25% (0.9%–1.6%), and 0.27% (−0.11%–0.65%) respectively. The distribution of J-AQHI was right-skewed, and it was divided into four levels, with ranges of 0-1 for low risk, 2-3 for moderate risk, 4-5 for high health risk, and ≥6 for severe risk, and the corresponding proportions were 11.25%, 64.61%, 19.33%, and 4.81%, respectively. The ER (95%CI) of mortality risk increased by 3.61% (2.93–4.29) for each IQR increase of the multi-pollutant based J-AQHI , while it was 3.39% (2.68–4.11) for the single-pollutant based AQHI . Conclusion The J-AQHI generated by multi-pollutant model demonstrates the actual exposure health risk of air pollution in the population and provides new ideas for further improvement of AQHI calculation methods.
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Background Cumulative risk index (CRI), as a commonly used approach to estimate the joint effects of multiple air pollutants on health, has been used by few studies to construct an air quality health index (AQHI). Objective To construct an AQHI based on the CRI of air pollution in Tianjin and evaluate the validity of the AQHI. Methods Daily data on air pollutants, meteorological factors, and non-accidental deaths during 2015–2019 in Tianjin were collected to create a time-series object. Descriptive statistical analyses were used to describe the characteristics of the data. To determine the best lag day and indicative pollutant, single-pollutant and two-pollutant generalized additive models were fitted to construct the exposure-response relationships between air pollutants and non-accidental deaths. After that we evaluated a CRI of air pollution using multi-pollutant models and constructed an AQHI and its classifications based on the CRI. Finally, we compared the exposure-response associations and coefficients of the AQHI and the conventional air quality index (AQI) with non-accidental deaths, and evaluated the health risk communication validity of the AQHI using generalized cross validation (GCV) values and R2 values. Results We selected lag1 as the best lag day and PM2.5, SO2, NO2 and O3 as the appropriate pollutants according to the unqualified rates of pollutants and significant statistical results. One μg·m−3 increase of PM2.5, SO2, NO2, and O3 was associated with −0.00002, 0.00079, 0.00015, and 0.00042 increase in effect size b of the non-accidental mortality, respectively. Based on these coefficients, we calculated the CRI and AQHI. According to a pre-determined classification scheme of the AQHI, the air quality of 63% study days was low risks and that of 34% study days was median risks. The associations of AQHI and AQI with non-accidental deaths in different populations were evaluated. The results showed that the excess risks of non-accidental deaths in total, female, and male populations for per interquartile range (IQR) increase in AQHI were higher than the corresponding values of AQI. The GCV values of the AQHI model (2.694, 1.819, and 1.938, respectively) were lower than those of the AQI model (2.747, 1.850, and 1.961, respectively), and the R2 values of the AQHI model (0.849, 0.780, and 0.820, respectively) were higher than those of the AQI model (0.846, 0.776, and 0.817, respectively). Conclusion Compared with AQI, the CRI-based AQHI may communicate the air pollution-related health risk to the public more effectively in Tianjin.
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Background Air quality health index (AQHI) has been widely used to quantify the health effects of multiple pollutants observed in population-based epidemiological studies, and can better reflect the widespread linear non-threshold between air pollution and health effects. Objective To explore an AQHI for pediatric respiratory diseases (AQHIr) in Shanghai and evaluate its feasibility. Methods The daily numbers of hospital outpatient visits for pediatric respiratory diseases from 2015 to 2019 were obtained from five general hospitals in Xuhui, Baoshan, Hongkou, Jinshan, and Chongming Districts of Shanghai. Monitoring data on air pollutants (PM2.5, PM10, SO2, NO2, and O3), air quality index (AQI), and meteorological variables (temperature, relative humidity, air pressure, and wind speed) were collected from five air quality monitoring sites nearest to selected hospitals. Time-series analysis using generalized additive model (GAM) was conducted to estimate the associations between respiratory-related pediatric outpatient visits and the concentrations of air pollutants. The sum of excess risk (ER) of hospital outpatient visits was used to construct AQHIr. To assess the predictive power of AQHIr, the associations of AQHIr and AQI with the number of pediatric respiratory outpatient visits in three hospitals in Xuhui, Hongkou, and Chongming districts were compared. Results Air pollutants had various effects on respiratory diseases outpatient visits. PM2.5, NO2, and O3 had most significant impacts on lag0 day and the associated ERs of hospital outpatient visits for each 10 μg·m−3 increase in pollutant concentration were 1.27% (95%CI: 0.88%-1.66%), 0.75% (95%CI: 0.40%-1.11%), and 0.36% (95%CI: 0.10%-0.62%), respectively. PM10 and SO2 had most significant impacts on lag3 day and the associated ERs of hospital outpatient visits for each 10 μg·m−3 increase in pollutant concentration were 0.81% (95%CI: 0.51%-1.12%) and 5.64% (95%CI: 3.37%-7.96%), respectively. There were significant effects of combinations of two pollutants among PM2.5, PM10, NO2, SO2, and O3 except for PM10+NO2, SO2+PM2.5, and SO2+NO2 (P<0.05). According to the results of single-pollutant and two-pollutant models, PM2.5, NO2, SO2, and O3 were selected to construct AQHIr. The comparison showed that for every interquartile range increase in AQHIr, the ER for pediatric outpatient visits was higher than that for the value corresponding to AQI. Conclusion Air pollutants in Shanghai have an impact on the number of pediatric respiratory outpatient visits. The AQHIr based on and outpatient visits for pediatric respiratory diseases can be a sensitive index to predict the effects of air pollution on children's respiratory health.