BACKGROUND: There is growing evidence that the occurrence and severity of respiratory diseases in children are related to the concentration of air pollutants. Nonetheless, evidence regarding the association between short-term exposure to air pollution and outpatient visits for respiratory diseases in children remains limited. Outpatients cover a wide range of disease severity, including both severe and mild cases, some of which may need to be transferred to inpatient treatment. This study aimed to quantitatively evaluate the impact of short-term ambient air pollution exposure on outpatient visits for respiratory conditions in children. METHODS: This study employed data of the Second People's Hospital of Yichang from January 1, 2016 to December 31, 2023, to conduct a time series analysis. The DLNM approach was integrated with a generalized additive model to examine the daily outpatient visits of pediatric patients with respiratory illnesses in hospital, alongside air pollution data obtained from monitoring stations. Adjustments were made for long-term trends, meteorological variables, and other influencing factors. RESULTS: A nonlinear association was identified between PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO levels and the daily outpatient visits for respiratory diseases among children. All six pollutants exhibit a hysteresis impact, with varying durations ranging from 4 to 6 days. The risks associated with air pollutants differ across various categories of children's respiratory diseases; notably, O₃ and CO do not show statistical significance concerning the risk of chronic respiratory conditions. Furthermore, the results of infectious respiratory diseases were similar with those of respiratory diseases. CONCLUSIONS Our results indicated that short-term exposure to air pollutants may contribute to an increased incidence of outpatient visits for respiratory illnesses among children, and controlling air pollution is important to protect children's health.
Humans ; China/epidemiology* ; Air Pollutants/analysis* ; Respiratory Tract Diseases/chemically induced* ; Child ; Child, Preschool ; Environmental Exposure/adverse effects* ; Air Pollution/analysis* ; Infant ; Male ; Particulate Matter/adverse effects* ; Female ; Ambulatory Care/statistics & numerical data* ; Outpatients/statistics & numerical data* ; Adolescent ; Infant, Newborn

OBJECTIVE: To assess the short-term lag effects of climate and air pollution on hospital admissions for cardiovascular and respiratory diseases, and to develop deep learning-based models for daily hospital admission prediction. METHODS: A multi-city study was conducted in Tokyo's 23 wards, Osaka City, and Nagoya City. Random forest models were employed to assess the synergistic short-term lag effects (lag0, lag3, and lag7) of climate and air pollutants on hospitalization for five cardiovascular diseases (CVDs) and two respiratory diseases (RDs). Furthermore, we developed hybrid deep learning models that integrated an autoencoder (AE) with a Long Short-Term Memory network (AE+LSTM) to predict daily hospital admissions. RESULTS: On the day of exposure (lag0), air pollutants, particularly nitrogen oxides (NO x), exhibited the strongest influence on hospital admissions for CVD and RD, with pronounced effects observed for hypertension (I10-I15), ischemic heart disease (I20), arterial and capillary diseases (I70-I79), and lower respiratory infections (J20-J22 and J40-J47). At longer lags (lag3 and lag7), temperature and precipitation were more influential predictors. The AE+LSTM model outperformed the standard LSTM, improving the prediction accuracy by 32.4% for RD in Osaka and 20.94% for CVD in Nagoya. CONCLUSION Our findings reveal the dynamic, time-varying health risks associated with environmental exposure and demonstrate the utility of deep learnings in predicting short-term hospital admissions. This framework can inform early warning systems, enhance healthcare resource allocation, and support climate-adaptive public health strategies.
Humans ; Hospitalization/statistics & numerical data* ; Cardiovascular Diseases/epidemiology* ; Japan/epidemiology* ; Air Pollutants/analysis* ; Air Pollution/adverse effects* ; Cities/epidemiology* ; Climate ; Respiratory Tract Diseases/epidemiology* ; Deep Learning ; Male

OBJECTIVE: This study investigated the association between household chemical use and respiratory disease (RD) in older Chinese adults. METHODS: The data were from the 2018 China Longitudinal Health and Longevity Survey (CLHLS) database, which included 12,866 participants aged ≥ 65 years. The prevalence of RD was based on self-reported medical history, and patients were divided into diseased and non-diseased groups. The frequency of household chemical usage was divided into four categories, and a total score for eight household chemical usage categories was constructed. Binary logistic regression was used to determine the relationship between the frequency of household chemical use and RD, and a restricted cubic spline was used to determine the dose-response association. RESULT: After adjusting for all covariates, regular use of repellents [odds ratios ( OR) = 1.28, 95% CI 1.06-1.55] and oil removers ( OR = 1.28, 95% CI 1.03-1.58) were associated with RD. There was a dose-response association between the total score of household chemicals usage and RD risk ( P non-linearity > 0.05, P for trend < 0.01). Using patients with the total score below 9 as a reference, the OR for patients with the total score ranging from 25 to 32 is 2.33 (95% CI 1.25-4.09). CONCLUSION Regular use of repellents and oil removers increased the risk of RD, and the dose-dependent relationship was also observed.
Humans ; China/epidemiology* ; Aged ; Male ; Female ; Respiratory Tract Diseases/chemically induced* ; Aged, 80 and over ; Household Products/adverse effects* ; Prevalence

Objective: To analyze the prevalence and the trend of the disease burden of chronic respiratory diseases and relevant risk factors in Jiangsu province from 1990 to 2019 and provide evidence for the prevention and treatment of chronic respiratory diseases. Methods: The data from the 2019 Global Burden of Disease Study (GBD2019) were used to calculate the prevalence rate, mortality rate and disability-adjusted life year (DALY) rate. Software Joinpoint was used to calculate the annual percent change (APC) and average annual percent change (AAPC) of the standardized prevalence rate, standardized mortality rate and standardized DALY rate. The population attributable fractions (PAF) were used to estimate the proportion of chronic respiratory disease caused by different risk factors. Results: In 1990 and 2019, the prevalence rates of chronic respiratory diseases were 4.83% and 5.45%. The mortality rates were 134.91/100 000 and 80.99/100 000 respectively, and the DALY rates were 2 678.52/100 000 and 1 534.31/100 000 respectively. From 1990 to 2019, the age-standardized prevalence rate, mortality rate and DALY rate in Jiangsu showed a significant downward trend (AAPC values were -0.90%, -5.28% and -4.70% respectively, P<0.05). Tobacco use was the leading cause of chronic respiratory diseases, followed by air pollution, occupational exposure, suboptimal temperature and high BMI. Compared with 1990, the proportion of DALYs of chronic respiratory diseases attributable to tobacco use and high BMI increased in 2019. Conclusion: The overall burden of chronic respiratory diseases in Jiangsu shows a downward trend. Prevention and health education should be focused on the population with a smoking history and high BMI. At the same time, environmental management, attention to suboptimal temperature and control of occupational exposure factors should also be adopted as important means to prevent and control chronic respiratory diseases.
Humans ; Global Burden of Disease ; Respiratory Tract Diseases/mortality* ; Risk Factors ; China/epidemiology* ; Prevalence

To analyze the association between exposure to air pollution and respiratory disease of primary school students in Chongqing City. Eight districts and counties were randomly selected based on the air pollution situation in Chongqing City. In each selected district and county, one primary school was randomly selected. A questionnaire survey was conducted on all primary school students in Grades 3-5 by the end of 2019. Air quality data from the nearest environmental monitoring sites were collected. A logistic regression model was used to analyze the impact of the living environment, lifestyle and air pollution on the respiratory disease of surveyed students. This study included 5 918 primary school students, with a prevalence rate of respiratory disease of 21.54%. The prevalence rates of boys and girls were 23.38% and 19.59%, respectively. The average Air quality index (AQI) of the surveyed school was 67, and the rates of exceeding standards of PM₁₀, PM_2.5, NO₂ and O₃ were 1.16%, 6.92%, 0.99% and 5.65%, respectively. The level of SO₂ and CO did not exceed the standard. After adjusting for relevant factors, logistic regression analysis showed that primary school students in areas with high exposure to air pollution (OR=2.52), using air pollution related-chemicals at home (OR=1.47), passive smoking (OR=1.27), and keeping pets at home (OR=1.18) had a higher risk of respiratory disease (all P<0.05). In addition, the average annual values of AQI (OR=1.18), PM₁₀ (OR=1.20), PM_2.5 (OR=1.35), and NO₂ (OR=1.11) increased the risk of respiratory diseases in primary school students (all P<0.05). In conclusion, the respiratory disease of primary school students in Chongqing City is related to the living environment, behavior habits and ambient air quality. The increased exposure concentration of PM₁₀, PM_2.5 and NO₂ in air pollutants can lead to an increased risk of respiratory disease among primary school students.
Female ; Humans ; Male ; Air Pollution/adverse effects* ; Nitrogen Dioxide ; Particulate Matter ; Respiratory Tract Diseases/epidemiology* ; Schools ; Students ; Child

Objective: To analyze the prevalence and the trend of the disease burden of chronic respiratory diseases and relevant risk factors in Jiangsu province from 1990 to 2019 and provide evidence for the prevention and treatment of chronic respiratory diseases. Methods: The data from the 2019 Global Burden of Disease Study (GBD2019) were used to calculate the prevalence rate, mortality rate and disability-adjusted life year (DALY) rate. Software Joinpoint was used to calculate the annual percent change (APC) and average annual percent change (AAPC) of the standardized prevalence rate, standardized mortality rate and standardized DALY rate. The population attributable fractions (PAF) were used to estimate the proportion of chronic respiratory disease caused by different risk factors. Results: In 1990 and 2019, the prevalence rates of chronic respiratory diseases were 4.83% and 5.45%. The mortality rates were 134.91/100 000 and 80.99/100 000 respectively, and the DALY rates were 2 678.52/100 000 and 1 534.31/100 000 respectively. From 1990 to 2019, the age-standardized prevalence rate, mortality rate and DALY rate in Jiangsu showed a significant downward trend (AAPC values were -0.90%, -5.28% and -4.70% respectively, P<0.05). Tobacco use was the leading cause of chronic respiratory diseases, followed by air pollution, occupational exposure, suboptimal temperature and high BMI. Compared with 1990, the proportion of DALYs of chronic respiratory diseases attributable to tobacco use and high BMI increased in 2019. Conclusion: The overall burden of chronic respiratory diseases in Jiangsu shows a downward trend. Prevention and health education should be focused on the population with a smoking history and high BMI. At the same time, environmental management, attention to suboptimal temperature and control of occupational exposure factors should also be adopted as important means to prevent and control chronic respiratory diseases.
Humans ; Global Burden of Disease ; Respiratory Tract Diseases/mortality* ; Risk Factors ; China/epidemiology* ; Prevalence

To analyze the association between exposure to air pollution and respiratory disease of primary school students in Chongqing City. Eight districts and counties were randomly selected based on the air pollution situation in Chongqing City. In each selected district and county, one primary school was randomly selected. A questionnaire survey was conducted on all primary school students in Grades 3-5 by the end of 2019. Air quality data from the nearest environmental monitoring sites were collected. A logistic regression model was used to analyze the impact of the living environment, lifestyle and air pollution on the respiratory disease of surveyed students. This study included 5 918 primary school students, with a prevalence rate of respiratory disease of 21.54%. The prevalence rates of boys and girls were 23.38% and 19.59%, respectively. The average Air quality index (AQI) of the surveyed school was 67, and the rates of exceeding standards of PM₁₀, PM_2.5, NO₂ and O₃ were 1.16%, 6.92%, 0.99% and 5.65%, respectively. The level of SO₂ and CO did not exceed the standard. After adjusting for relevant factors, logistic regression analysis showed that primary school students in areas with high exposure to air pollution (OR=2.52), using air pollution related-chemicals at home (OR=1.47), passive smoking (OR=1.27), and keeping pets at home (OR=1.18) had a higher risk of respiratory disease (all P<0.05). In addition, the average annual values of AQI (OR=1.18), PM₁₀ (OR=1.20), PM_2.5 (OR=1.35), and NO₂ (OR=1.11) increased the risk of respiratory diseases in primary school students (all P<0.05). In conclusion, the respiratory disease of primary school students in Chongqing City is related to the living environment, behavior habits and ambient air quality. The increased exposure concentration of PM₁₀, PM_2.5 and NO₂ in air pollutants can lead to an increased risk of respiratory disease among primary school students.
Female ; Humans ; Male ; Air Pollution/adverse effects* ; Nitrogen Dioxide ; Particulate Matter ; Respiratory Tract Diseases/epidemiology* ; Schools ; Students ; Child

In China, the level of ambient fine particulate matter (PM 2.5 ) pollution far exceeds the air quality standards recommended by the World Health Organization. Moreover, the health effects of PM 2.5 exposure have become a major public health issue. More than half of PM 2.5 -related excess deaths are caused by cardiopulmonary disease, which has become a major health risk associated with PM 2.5 pollution. In this review, we discussed the latest epidemiological advances relating to the health effects of PM 2.5 on cardiopulmonary diseases in China, including studies relating to the effects of PM 2.5 on mortality, morbidity, and risk factors for cardiovascular and respiratory diseases. These data provided important evidence to highlight the cardiopulmonary risk associated with PM 2.5 across the world. In the future, further studies need to be carried out to investigate the specific relationship between the constituents and sources of PM 2.5 and cardiopulmonary disease. These studies provided scientific evidence for precise reduction measurement of pollution sources and public health risks. It is also necessary to identify effective biomarkers and elucidate the biological mechanisms and pathways involved; this may help us to take steps to reduce PM 2.5 pollution and reduce the incidence of cardiopulmonary disease.
Humans ; Particulate Matter/analysis* ; Air Pollution/adverse effects* ; Risk Factors ; Respiratory Tract Diseases ; China/epidemiology* ; Environmental Exposure/adverse effects*

Objective: To describe the clinical characteristics of Mycoplasma pneumoniae infection and analyze the factors associated with co-infections with other pathogens in children, and provide evidence for improvement of community acquired pneumonia (CAP) prevention and control in children. Methods: Based on the surveillance of hospitalized acute respiratory infections cases conducted in Soochow University Affiliated Children's Hospital (SCH), the CAP cases aged <16 years hospitalized in SCH between 2018 and 2021 were screened. The pathogenic test results of the cases were obtained through the laboratory information system, and their basic information, underlying conditions, and clinical characteristics were collected using a standardized questionnaire. The differences in clinical characteristics between M. pneumoniae infection and bacterial or viral infection and the effect of the co-infection of M. pneumoniae with other pathogens on clinical severity in the cases were analyzed; logistic regression was used to analyze the factors associated with the co-infections with other pathogens. Results: A total of 8 274 hospitalized CAP cases met the inclusion criteria. Among them, 2 184 were positive for M. pneumoniae (26.4%). The M. pneumoniae positivity rate increased with age (P<0.001), and it was higher in girls (P<0.001) and in summer and autumn (P<0.001). There were statistically significant differences in the incidence of wheezing, shortness of breath, wheezing sounds and visible lamellar faint shadow on chest radiographs, as well as fever and hospitalization days among M. pneumoniae, bacterial, and viral infection cases (all P<0.05). In the cases aged <60 months years, co-infection cases had higher rates of wheezing, gurgling with sputum and stridor; and in the cases aged ≥60 months, co-infection cases had a higher rate of shortness of breath (all P<0.05). Multifactorial logistic regression analysis showed that being boys (aOR=1.38,95%CI:1.15-1.67), being aged <6 months (aOR=3.30,95%CI:2.25-4.89), 6-23 months (aOR=3.44,95%CI:2.63-4.51), 24-47 months (aOR=2.50,95%CI:1.90-3.30) and 48-71 months (aOR=1.77,95%CI:1.32-2.37), and history of respiratory infection within 3 months (aOR=1.28,95%CI:1.06-1.55) were factors associated with co-infections of M. pneumoniae with other pathogens. Conclusions: M. pneumoniae was the leading pathogen in children hospitalized due to CAP. M. pneumoniae infections could cause fever for longer days compared with bacterial or viral infections; M. pneumoniae was often co-detected with virus or bacteria. Being boys, being aged <72 months and history of respiratory infection within 3 months were associated factors for co-infections.
Bacteria ; Child ; Coinfection/epidemiology* ; Community-Acquired Infections/epidemiology* ; Dyspnea ; Female ; Humans ; Male ; Mycoplasma pneumoniae ; Pneumonia, Mycoplasma/microbiology* ; Respiratory Sounds ; Respiratory Tract Infections/epidemiology* ; Virus Diseases

BACKGROUND: Ambient temperature may contribute to seasonality of mortality; in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate. METHODS: Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its amplitude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics. RESULTS: The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27-1.30), 1.53 (95% CI: 1.50-1.55), and 1.46 (95% CI: 1.44-1.48), respectively; adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08-1.10), 1.10 (95% CI: 1.08-1.11), and 1.35 (95% CI: 1.32-1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54-1.42), 1.39% (95% CI: 0.82-1.97), and 0.13% (95% CI: - 1.24 to 1.48), respectively. CONCLUSION Seasonality of mortality is driven partly by temperature, and its amplitude may be decreasing under a warming climate.
Cardiovascular Diseases/mortality* ; Cause of Death ; Climate Change/mortality* ; Cold Temperature/adverse effects* ; Hot Temperature/adverse effects* ; Humans ; Japan/epidemiology* ; Mortality/trends* ; Regression Analysis ; Respiratory Tract Diseases/mortality* ; Seasons ; Time