Influence of air pollution on influenza-like illness in China: a nationwide time-series analysis

PM2.5 constituents and risk of influenza-like illness: A nationwide analysis in 289 Chinese cities

Discrepancies in fine particulate matter (PM2.5)-related influenza-like illness (ILI) risk have been widely observed in different studies in China, where the individual effect of PM2.5 constituents might be one of the important reasons. However, the associations between PM2.5 constituents and ILI risk in China have yet to be understood. We collected and aggregated weekly ILI cases in 289 Chinese cities during 2006-2019, and 47.8 million ILI cases were finally included in this study. Quasi-Poisson regression models and a random-effect meta-analysis were applied to estimate the impacts of PM2.5 and its constituents on ILI risk. Stratification analyses were also conducted by region, age group, season, and temperature and humidity quartiles. With per inter-quartile range increase in black carbon, ammonium, sulfate, PM2.5, nitrate and organic matter with a cumulative lag of 0-1 week, the overall ILI incidence would increase by 2.55 % (95 % CI: 1.71, 3.40), 2.32 % (1.33, 3.32), 2.19 % (1.29, 3.10), 2.19 % (1.25, 3.13), 2.15 % (1.08, 3.22) and 2.02 % (1.19, 2.85), respectively. The impacts tended to be much stronger in young- and middle-aged population, in North and East China, in winter, and in colder and drier conditions. PM2.5 and its major constituents all have significantly additive effects on ILI incidence. Specific preventive measures against individual constituent should be implemented for improving public health.

Effects and interaction of humidex and air pollution on influenza: A national analysis of 319 cities in mainland China

Influenza imposes a significant global health burden. This study investigates the effects of humidex and air pollution on influenza and their interactions, using multi-city surveillance data in China. Daily data on reported influenza cases, meteorological factors and air pollution from 319 cities in mainland China over the study period of 2014-2019 were collected. A two-stage analytical framework, comprising distributed lag non-linear model and multivariate meta-analysis, was employed to assess the associations between humidex, air pollution and influenza. Hierarchical and joint effect models were employed to examine their interaction. Nationally, an approximately L-shaped relationship between humidex and influenza was observed, with the highest relative risk (RR) of 2.603 (95 % confidence interval [CI]: 2.195-3.086). Per interquartile range increases in PM2.5, PM10, NO2, SO2, CO and O3 were associated with influenza risk increments of 0.035 (95 % CI: 0.010-0.061), 0.029 (95 % CI: 0.003-0.055), 0.191 (95 % CI: 0.152-0.231), 0.239 (95 % CI: 0.166-0.317), 0.038 (95 % CI: 0.001-0.076) and -0.171 (95 % CI: -0.238--0.099), respectively. A synergistic interaction effect was identified between low humidex and high air pollution as well as different air pollutants. Subgroup analyses indicated females and individuals aged 7-18 years old exhibited higher risks. Stronger effects were observed during winter season and in large cities. This study underscores the urgent need for tailored interventions to mitigate the health impacts in regions with concurrent low humidex and high air pollution.

Interactions for exposure to fine particulate matter and multiple time-period cold spells on influenza: A multi-center case-crossover study

With the increasing twin threats of climate change and air pollution, cumulating evidence has revealed that environmental exposure might promote influenza epidemic. However, it remains unclear whether the combination of air pollution and cold spells (CS) synergistically trigger influenza. Through a case-crossover study, we included 13,972 laboratory-diagnosed influenza patients in Hubei province, China, during 2011-2018. We then constructed the conditional logistic regression model to assess the adverse impact of overall-, daytime-, and nighttime-CS exposure, as well as PM2.5 pollution, on influenza, and further calculated their additive-scale interactions. Our results indicated that both PM2.5 and CS were associated with influenza. For an interquartile range increment of PM2.5 was associated with 8 % [odds ratio = 1.08, 95 % confidence interval (CI): 1.04-1.12] increased risk of influenza. The estimated risks of overall-, daytime-, and nighttime-CS exposure on influenza were 1.18 (95 %CI: 1.12-1.23), 1.16 (95 %CI: 1.08-1.25), and 1.28 (95 %CI: 1.19-1.38), respectively. Calculated population attributable fractions were ranged from 1.13 % to 4.42 %. Significant synergetic effects on influenza were observed for co-exposure to PM2.5, CS, indicated by relative excess risk due to interaction of 0.23 (overall-CS) and 0.26 (nighttime-CS). Exposure to both PM2.5 and CS, especially nighttime-CS, were associated with influenza, and PM2.5 and CS could interact synergistically trigger influenza epidemic.

Time series analysis of the impact of air pollutants on influenza-like illness in Changchun, China

BACKGROUND

Emerging evidence links air pollution to respiratory infections, yet systematic assessments in cold regions remain limited. This study evaluates the short-term effects of six major air pollutants on influenza-like illness (ILI) incidence in Changchun, Northeast China, with implications for air quality management and respiratory disease prevention.

METHODS

ILI surveillance data from Changchun were extracted from "China Influenza Surveillance Network" and the ambient air quality monitoring data of the city were collected from 2017 to 2022. A generalized additive model (GAM) with quasi-Poisson regression analysis was employed to quantify pollutant-ILI associations, adjusting for meteorological factors and temporal trends.

RESULTS

Among 84,010 ILI cases, immediate exposure effects were observed: each 10 µg/m³ increase in PM2.5 (ER = 1.00%, 95% CI: 0.63-1.37%), PM10 (0.90%, 0.57-1.24%), and O3 (1.05%, 0.44-1.67%) significantly elevated ILI risks. Young and middle-aged individuals (25-59 years old) exhibited the highest susceptibility to five pollutants (PM2.5, PM10, SO2, O3, and CO), and age subgroups under 15 years old exhibited susceptibility to NO2. Post-COVID-19 outbreak showed amplified effects across all pollutants (p < 0.05 vs. pre-outbreak). The effects of PM2.5, PM10, SO2 and O3 on ILI cases were greater in the cold season (October to March) (p < 0.05).

CONCLUSIONS

PM2.5, PM10, and O3 exposure significantly increases ILI risks in Changchun, particularly among young/middle-aged populations during cold seasons and post-pandemic periods. These findings underscore the urgency for real-time air quality alerts and targeted protection strategies during high-risk periods to mitigate respiratory health burdens.

Short-term air pollution exposure and risk of respiratory pathogen infections: an 11-year case-crossover study in Guangzhou, China

BACKGROUND

Limited epidemiological evidence exists on the relationship between short-term exposure to air pollutants and respiratory pathogen infections. This study investigates the association between short-term air pollution exposure and respiratory pathogen infections in Guangzhou, southern China.

METHODS

A time-stratified case-crossover study design was applied. Data from 96,927 patients with suspected respiratory pathogen infections between 2013 and 2023 were collected. The daily air pollutant concentration is obtained from the local environmental monitoring station. Logistic regression was used to assess the effect of air pollutant exposure included in the equation on the risk of respiratory pathogen infection. Generalized additive models were used to analyze the relationship between pollutant exposure and hospital visits, adjusting for potential confounders such as temperature and precipitation. Sub-group analysis was performed to estimate the reliability of the correlations among the subgroups.

RESULTS

The logistic regression model shows that PM2.5, NO2 and CO are included in the variable equation. Single-pollutant models indicate that there is a significant association between short-term exposure to NO2 and CO and an increased risk of hospital visits for respiratory infections, especially on lag day 0, while PM2.5 shows a non-linear relationship. In the multi-pollutant model, for each unit increase in NO2, the risk of hospital visits increased by 11.66%, and for CO, the risk increased by 0.64%. Subgroup analysis showed the effects were more pronounced in minors (< 18 years), while no significant gender differences were observed. Additionally, CO and NO2 interacted with PM2.5, amplifying the risk of infection.

CONCLUSION

This large-scale epidemiological study demonstrates significant associations between short-term air pollutant exposure and respiratory infections, particularly highlighting the risks of NO2 and CO exposure. The findings underscore the critical need for strengthening air quality monitoring and protection strategies in rapidly urbanizing regions, with special attention to vulnerable populations such as minors. These results provide evidence-based support for enhancing environmental health policies in metropolitan areas to better protect public health through improved air quality standards and early warning systems.

Acute effects of air pollutants on influenza-like illness in Hangzhou, China

At present, with increasing awareness of the relationship between respiratory disease and air pollution, it is critical to assess the environmental risk factors for influenza. This study aimed to estimate the associations between ambient air pollution and the number of influenza-like illness (ILI) cases in Hangzhou, China, from 2015 to 2021. Weekly meteorological data, including average ambient temperature and average relative humidity, from December 29, 2014 to January 2, 2022 were collected from the Hangzhou Meteorological Service Center, and air pollutants, including nitrogen dioxide (NO2), sulfur dioxide (SO2), ground-level ozone (O3), particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM2.5), and PM with aerodynamic diameter ≤ 10 μm (PM10), were collected from National Ambient Air Quality Automatic Monitoring Stations in Hangzhou. The number of weekly ILI cases was collected from 15 influenza surveillance sentinel hospitals in Hangzhou. A generalized linear model (GLM) with quasi-Poisson regression was adopted to estimate the association between air pollution and ILI. After adjusting for the effects of average temperature, relative humidity, and seasonal and long-term trends, PM2.5, PM10, NO2, and SO2 were found to be significantly associated with the number of ILI cases, with relative risk (RR) values of 1.018 (95% CI 1.001-1.036), 1.016 (1.005-1.028), 1.063 (1.067-1.364), and 1.207 (1.067-1.364), respectively. In the two-pollutant model, putting PM2.5, PM10, NO2, or SO2 into the model separately with O3 produced results similar to those of the single-pollutant model. PM2.5, PM10, and NO2 have statistical significance in cold seasons, with the RR values of 1.020 (95% CI 1.001-1.038), 1.012 (95% CI 1.000-1.024), and 1.060 (95% CI 1.031-1.090), respectively. In summary, our study found that most air pollutants (PM10, PM2.5, NO2, and SO2) have a significant association with the risk of ILI cases in Hangzhou. These findings can serve as a reference for the formulation of effective protective measures.

From fear to facts: a multi-channel approach to information seeking amid influenza-like illness outbreaks

Background

During recurrent large-scale influenza-like illness (ILI) crises, the factors influencing the information-seeking intentions of Chinese individuals across multiple channels during crises remain underexplored.

Objective

Guided by the risk information seeking and processing (RISP) model, this study proposes a modified RISP model to comprehensively analyze information-seeking intentions through the lens of risk communication.

Methods

To empirically validate the proposed research model, we conducted an online cross-sectional survey with 2,604 Chinese citizens aged 18 years and older. Structural equation modeling (SEM) and ordinary least squares regression analysis were employed to analyze the survey data.

Results

Our findings revealed that during ILI crises, Chinese individuals experienced a spectrum of emotions; as perceived risk increased, negative emotions intensified while positive emotions decreased. Increased negative emotions correlated with a greater sense of information insufficiency, whereas heightened positive emotions correlated with a reduced perception of it. Consequently, Chinese individuals facing information deficiencies were more inclined to seek information from diverse sources, including interpersonal sources, traditional media, search engines, and social media. Moreover, statistical analysis indicated that stronger beliefs in channel complementary strengthened the relationship between information insufficiency and information-seeking intention across multiple channels (access to medical expertise belief, tailorability belief, convenience belief, anonymity belief).

Conclusion

This study outlines a pathway for advancing the RISP model and offers practical strategies for effective risk communication to mitigate risks and enhance public perception and behavior. It also discusses implications for health communication, promotion, and behavior change.

Interactive effects of air pollutants and viral exposure on daily influenza hospital visits in Mongolia

BACKGROUND

Air pollution is a well-documented public health hazard linked to various adverse health outcomes. While studies have shown associations between elevated levels of air pollutants and increased influenza incidence, there is a notable knowledge gap concerning the interactive effects of air pollution and viral exposure on respiratory viral infections.

OBJECTIVES

This study sought to examine the interactive effects of air pollution and viral exposure on influenza hospital visits in Ulaanbaatar, Mongolia.

METHODS

We conducted a time-series analysis linking daily hospital visits for influenza disease (defined as ICD10 diagnosis codes J11) with ambient concentrations of air pollutants (PM1, PM2.5, PM10, NO2, SO2, and O3) over a period of 7 years. Viral exposure for a specific geographical region was estimated based on influenza hospital visits within acute (previous day) and sub-acute (preceding 7 days) exposure windows. Covariates included long-term time trend, temperature, temperature variation, relative humidity, holiday, and raw coal ban policy. An over-dispersed generalized linear model (GLM) with a quasi-Poisson distribution was used to assess associations, exploring interactions and lag effects up to 3 days. Season-specific models and stratified analyses by sex and age were performed, with sensitivity analyses using multi-pollutant models.

RESULTS

A total of 16,364 influenza hospital visits were recorded, with significantly higher rates of visits during the winter season. All six pollutants amplified the effects of viral exposure on hospital visits in cold months, while only PM1, PM2.5, and O3 showed synergistic effects in warm months. Stronger synergistic effects were observed among children under 5 years old, particularly for O3.

CONCLUSIONS

Air pollution significantly amplified the adverse effects of viral exposure on influenza-hospital visits, particularly among young children and during high viral exposure periods. These findings underscore the need for employing protective measures against both air pollution and viral infections.

Short-term effects of ambient air pollution on influenza incidence in Chongqing, China: a time-series analysis

This study investigated the relationship between air pollution and influenza incidence in Chongqing from 2013 to 2022 using a generalized additive model (GAM), analyzing 199,712 cases. Subgroup analyses were conducted to investigate the impact of age, gender, season, and the COVID-19. Influenza incidence was positively associated with PM2.5, PM10, SO2, NO2 and CO, but negatively with O3. SO2 had the most effect. In single-day lag models, the largest percentage changes in influenza incidence at lag0 for each pollutant were: 2.930% for SO2, 1.552% for CO, -0.637% for O3, 0.516% for PM2.5, and 0.405% for PM10. NO2 showed the largest change at lag11 (1.376%). In multi-day lag models, changes peaked at lag011-014. Stratified analyses revealed children aged 0-14 years as particularly vulnerable during the cold season and COVID-19 period. The study demonstrates that short-term lags and cumulative effects of air pollution exposure increase influenza incidence, significant for establishing influenza response strategies.

Air Pollution and Influenza Incidence: Evidence from Highly Polluted Countries

Background

Air pollution has become a serious threat to public health. Epidemiological and clinical evidence in recent years has shown air pollutants are associated with respiratory diseases. We aimed to analyze the impact of environmental factors on influenza incidence by examining the most polluted countries in the world.

Methods

To analyze the relationship between environmental factors and influenza incidence in eighteen countries, we used a system generalized method of moments (GMM) using data from 2010 to 2020.

Results

The results suggest a positive effect of air pollution (PM2.5 and NO2) and population density on the incidence of influenza. While government health expenditures and education have a negative effect on influenza in the studied countries.

Conclusion

Our results confirmed the importance of environmental and social factors in the incidence of influenza. Furthermore, our results are interesting and informative for policymakers to design public health policies synchronized with other policies such as education, industrial, and environmental policies, for better management of influenza.

Temperature variability and influenza incidence in China: Effect modification by ambient fine particulate matter

This study aims to examine the association between temperature variabilit (TV) exposure and influenza incidence in China, and the modification effect of PM2.5 levels. Data on daily influenza cases, weather conditions, and PM2.5 concentrations were collected from 339 cities across mainland China from 2014 to 2019. TV was computed as the standard deviation of daily maximum and minimum temperatures for the current day and the previous several days (i.e., TV0-1 to TV0-7). A space-time-stratified case-crossover design with conditional Poisson regression was employed. Overall, each 1 °C increase in TV0-6 was linked to 3.3 % (95 % CI: 3.1 %, 3.5 %) rise in influenza incidence, potentially attributing 14.73 % (95 % CI: 14.08 %, 15.37 %) of cases to this exposure. PM2.5 concentration showed substantial modification effect on the association, such that the relative risk (RR) of influenza incidence grew from 1.027 (95 % CI: 1.025, 1.029) to 1.040 (95 % CI: 1.038, 1.042) as PM2.5 levels increased from 15 to 75 μg/m³ . Females and individuals over 65 years old were more susceptible to TV exposure and the PM2.5 modification. Stronger effects were observed during cold season and in North region. The findings highlight the integrating considerations of TV and PM2.5 exposures into public health measures for influenza prevention and control.

A comparative analysis of influenza and COVID-19: Environmental-ecological impacts, socioeconomic implications, and future challenges

In the last century, global pandemics have been primarily driven by respiratory infections, which consistently rank among the top 20 causes of death worldwide. The coronavirus disease 2019 (COVID-19) pandemic has underscored the intricate nature of managing multiple health crises simultaneously. In recent years, climate change has emerged as a major biosafety and population health challenge. Global warming and extreme weather events have intensified outbreaks of climate-sensitive infectious diseases, especially respiratory diseases. Influenza and COVID-19 have emerged as two of the most significant respiratory pandemics, each with unique epidemic characteristics and far-reaching consequences. Our comparative analysis reveals that while both diseases exhibit high transmission rates, COVID-19's longer incubation period and higher severity have led to more profound and prolonged socioeconomic disruptions than influenza. Both pandemics have highlighted the exacerbating effects of climate change, with extreme weather events intensifying the spread and impact of these diseases. The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and economies on an unprecedented scale, outstripping the strain caused by influenza outbreaks. Importantly, the COVID-19 pandemic has not only reshaped global public health strategies but also significantly impacted the epidemiology of influenza. Despite these differences and associations, both diseases underscore the urgent need for robust pandemic preparedness and adaptable public health strategies. This review delineates the overlaps and distinctions between influenza and COVID-19, offering insights into future challenges and the critical steps needed to enhance healthcare system resilience and improve global responses to pandemics.

Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China

BACKGROUND

Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work.

METHODS

In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013-2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values.

RESULTS

Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013-2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20-50 mm), relative humidity (70-78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19-22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O3 concentration reached 164 µg/m3. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only.

CONCLUSION

The extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health.

Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models

Background

Influenza is a respiratory infection that poses a significant health burden worldwide. Environmental indicators, such as air pollutants and meteorological factors, play a role in the onset and propagation of influenza. Accurate predictions of influenza incidence and understanding the factors influencing it are crucial for public health interventions. Our study aims to investigate the impact of various environmental indicators on influenza incidence and apply the ARIMAX model to integrate these exogenous variables to enhance the accuracy of influenza incidence predictions.

Method

Descriptive statistics and time series analysis were employed to illustrate changes in influenza incidence, air pollutants, and meteorological indicators. Cross correlation function (CCF) was used to evaluate the correlation between environmental indicators and the influenza incidence. We used ARIMA and ARIMAX models to perform predictive analysis of influenza incidence.

Results

From January 2014 to September 2023, a total of 21,573 cases of influenza were reported in Fuzhou, with a noticeable year-by-year increase in incidence. The peak of influenza typically occurred around January each year. The results of CCF analysis showed that all 10 environmental indicators had a significant impact on the incidence of influenza. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model exhibited the best prediction performance, as indicated by the lowest AIC, AICc, and BIC values, which were 529.740, 530.360, and 542.910, respectively. The model achieved a fitting RMSE of 2.999 and a predicting RMSE of 12.033.

Conclusion

This study provides insights into the impact of environmental indicators on influenza incidence in Fuzhou. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model could provide a scientific basis for formulating influenza control policies and public health interventions. Timely prediction of influenza incidence is essential for effective epidemic control strategies and minimizing disease transmission risks.

Seasonal patterns of influenza incidence and the influence of meteorological and air pollution factors in Thailand during 2009-2019

Influenza, an acute respiratory illness, remains a significant public health challenge, contributing substantially to morbidity and mortality worldwide. Its seasonal prevalence exhibits diversity across regions with distinct climates. This study aimed to explore the seasonal patterns of influenza and their correlation with meteorological and air pollution factors across six regions of Thailand. We conducted an analysis of monthly average temperature, relative humidity, precipitation, PM10, NO2, O3 concentrations, and influenza incidence data from 2009 to 2019 using wavelet analysis. Our findings reveal inconsistent biannual influenza prevalence patterns throughout the study period. The biannual pattern emerged during 2010-2012 across all regions but disappeared during 2013-2016. However, post-2016, the biannual cycles resurfaced, with peaks occurring during the rainy and winter seasons in most regions, except for the southern region. Wavelet coherence reveals that relative humidity can be the main influencing factor for influenza incidence over a one-year period in the northern, northeastern, central, Bangkok-metropolitan, and eastern regions, not in the southern region during 2010-2012 and 2016-2018. Similarly, precipitation can drive the influenza incidence at the same period for the northeastern, central, Bangkok-metropolitan, and eastern regions. PM10 concentration can influence influenza incidence over a half-year period in the northeastern, central, Bangkok-metropolitan, and eastern regions of Thailand during certain years. These results enhance our understanding of the temporal dynamics of influenza seasonality influenced by weather conditions and air pollution over the past 11 years. Such knowledge is invaluable for resource allocation in clinical settings and informing public health strategies, particularly in navigating Thailand's climatic complexities.

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

BACKGROUND

Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions.

METHOD

In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models.

RESULTS

The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models.

CONCLUSION

The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

The Effectiveness of National Expanded Program on Immunization With Hepatitis A Vaccines in the Chinese Mainland: Interrupted Time-Series Analysis

BACKGROUND

The high prevalence of hepatitis A delivered a blow to public health decades ago. The World Health Organization (WHO) set a goal to eliminate viral hepatitis including hepatitis A by 2030. In 2008, hepatitis A vaccines were integrated into the Expanded Program on Immunization (EPI) in China to alleviate the burden of hepatitis A, although the effectiveness of the EPI has not been well investigated.

OBJECTIVE

We aimed to evaluate the intervention effect at both provincial and national levels on the incidence of hepatitis A in the Chinese mainland from 2005 to 2019.

METHODS

Based on the monthly reported number of hepatitis A cases from 2005 to 2019 in each provincial-level administrative division, we adopted generalized additive models with an interrupted time-series design to estimate province-specific effects of the EPI on the incidence of hepatitis A among the target population (children aged 2-9 years) from 2005 to 2019. We then pooled province-specific effect estimates using random-effects meta-analyses. We also assessed the effect among the nontarget population and the whole population.

RESULTS

A total of 98,275 hepatitis A cases among children aged 2-9 years were reported in the Chinese mainland from 2005 to 2019, with an average annual incidence of 5.33 cases per 100,000 persons. Nationally, the EPI decreased the hepatitis A incidence by 80.77% (excess risk [ER] -80.77%, 95% CI -85.86% to -72.92%) during the study period, guarding an annual average of 28.52 (95% empirical CI [eCI] 27.37-29.00) cases per 100,000 persons among the target children against hepatitis A. Western China saw a more significant effect of the EPI on the decrease in the incidence of hepatitis A among the target children. A greater number of target children were protected from onset in Northwest and Southwest China, with an excess incidence rate of -129.72 (95% eCI -135.67 to -117.86) and -66.61 (95% eCI -67.63 to -64.22) cases per 100,000 persons on average, respectively. Intervention effects among nontarget (ER -32.88%, 95% CI -39.76% to -25.21%) and whole populations (ER -31.97%, 95% CI -39.61% to -23.37%) were relatively small.

CONCLUSIONS

The EPI has presented a lasting positive effect on the containment of hepatitis A in the target population in China. The EPI's effect on the target children also provided a degree of indirect protection for unvaccinated individuals. The continuous surveillance of hepatitis A and the maintenance of mass vaccination should shore up the accomplishment in the decline of hepatitis A incidence to ultimately achieve the goal set by the WHO.

Exploring the spatiotemporal relationship between influenza and air pollution in Fuzhou using spatiotemporal weighted regression model

Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts. VIF was used to test for collinearity among selected air pollutants. OLS regression, GWR, and STWR models were fitted to explore the potential spatiotemporal relationship between air pollution and influenza. The R2, the RSS and the AICc were used to evaluate and compare the models. In addition, the DTW and K-medoids algorithms were applied to cluster the county-level time-series coefficients. Compared with the OLS regression and GWR models, STWR model exhibits superior fit especially when the influenza outbreak changes rapidly and is able to more accurately capture the changes in different regions and time periods. We discovered that identical air pollutant factors may yield contrasting impacts on influenza within the same period in different areas of Fuzhou. NO2 and PM10 showed opposite impacts on influenza in the eastern and western areas of Fuzhou during all periods. Additionally, our investigation revealed that the relationship between air pollutant factors and influenza may exhibit temporal variations in certain regions. From 2013 to 2019, the influence coefficient of O3 on influenza epidemic intensity changed from negative to positive in the western region and from positive to negative in the eastern region. STWR model could be a useful method to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza in geospatial processes. The research findings emphasize the importance of considering spatiotemporal heterogeneity when studying the relationship between air pollution and influenza.

The effects of diurnal temperature range on mortality and emergency department presentations in Victoria state of Australia: A time-series analysis

State of Victoria, Australia (SVA) has a wide variation of diurnal temperatures (DTR). DTR has been reported to be associated with risk of mortality and morbidity. We examined the association between exposure to DTR and risk of all-cause mortality and emergency department (ED) presentations in the SVA. We obtained data on daily counts of deaths and ED presentations, and weather data from 1 st January 2000─2019. We applied a quasi-Poisson time-series regression analysis to examine the association between daily DTR exposures and risk of mortality and ED presentations. The analyses were queried by age, sex, seasons, ED presentations triages, and departure status. Risk of mortality and ED presentation increased by 0.33% (95% CI: 0.24%-0.43%), and 0.094% (95% CI: 0.077%-0.11%) in relation to one degree increase in the daily DTR. The association between DTR and ED presentations was stronger in children (0-15 years) (0.38% [95% CI: 0.34%-0.42%]) and the elderly (75+ years) (0.34% [95% CI: 0.29%-0.39%]). Resuscitation, which was consistently accounted for the highest vulnerability to DTR variation, increased by 0.79% (95% CI: 0.60%-0.99%). This study suggests that the risk of mortality and ED presentations associates with the increase of DTR. Children, the elderly, and their caregivers need to be made aware of the health risk posed by DTR.

Short-Term Exposure to Ambient Air Pollution and Influenza: A Multicity Study in China

BACKGROUND

Air pollution is a major risk factor for planetary health and has long been suspected of predisposing humans to respiratory diseases induced by pathogens like influenza viruses. However, epidemiological evidence remains elusive due to lack of longitudinal data from large cohorts.

OBJECTIVE

Our aim is to quantify the short-term association of influenza incidence with exposure to ambient air pollutants in Chinese cities.

METHODS

Based on air pollutant data and influenza surveillance data from 82 cities in China over a period of 5 years, we applied a two-stage time series analysis to assess the association of daily incidence of reported influenza cases with six common air pollutants [particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ), particulate matter with aerodynamic diameter ≤ 10 μ m (PM 10 ), NO 2 , SO 2 , CO, and O 3 ], while adjusting for potential confounders including temperature, relative humidity, seasonality, and holiday effects. We built a distributed lag Poisson model for one or multiple pollutants in each individual city in the first stage and conducted a meta-analysis to pool city-specific estimates in the second stage.

RESULTS

A total of 3,735,934 influenza cases were reported in 82 cities from 2015 to 2019, accounting for 72.71% of the overall case number reported in the mainland of China. The time series models for each pollutant alone showed that the daily incidence of reported influenza cases was positively associated with almost all air pollutants except for ozone. The most prominent short-term associations were found for SO 2 and NO 2 with cumulative risk ratios of 1.094 [95% confidence interval (CI): 1.054, 1.136] and 1.093 (95% CI: 1.067, 1.119), respectively, for each 10   μ g / m 3 increase in the concentration at each of the lags of 1-7 d. Only NO 2 showed a significant association with the daily incidence of influenza cases in the multipollutant model that adjusts all six air pollutants together. The impact of air pollutants on influenza was generally found to be greater in children, in subtropical cities, and during cold months.

DISCUSSION

Increased exposure to ambient air pollutants, particularly NO 2 , is associated with a higher risk of influenza-associated illness. Policies on reducing air pollution levels may help alleviate the disease burden due to influenza infection. https://doi.org/10.1289/EHP12146.

Associations between Short-Term Exposure to Ambient Air Pollution and Influenza: An Individual-Level Case-Crossover Study in Guangzhou, China

BACKGROUND

Influenza imposes a heavy burden on public health. Little is known, however, of the associations between detailed measures of exposure to ambient air pollution and influenza at an individual level.

OBJECTIVE

We examined individual-level associations between six criteria air pollutants and influenza using case-crossover design.

METHODS

In this individual-level time-stratified case-crossover study, we linked influenza cases collected by the Guangzhou Center for Disease Control and Prevention from 1 January 2013 to 31 December 2019 with individual residence-level exposure to particulate matter (PM 2.5 and PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ) and carbon monoxide (CO). The exposures were estimated for the day of onset of influenza symptoms (lag 0), 1-7 d before the onset (lags 1-7), as well as an 8-d moving average (lag07), using a random forest model and linked to study participants' home addresses. Conditional logistic regression was developed to investigate the associations between short-term exposure to air pollution and influenza, adjusting for mean temperature, relative humidity, public holidays, population mobility, and community influenza susceptibility.

RESULTS

N = 108,479 eligible cases were identified in our study. Every 10 - μ g / m 3 increase in exposure to PM 2.5 , PM 10 , NO 2 , and CO and every 5 - μ g / m 3 increase in SO 2 over 8-d moving average (lag07) was associated with higher risk of influenza with a relative risk (RR) of 1.028 (95% CI: 1.018, 1.038), 1.041 (95% CI: 1.032, 1.049), 1.169 (95% CI: 1.151, 1.188), 1.004 (95% CI: 1.003, 1.006), and 1.134 (95% CI: 1.107, 1.163), respectively. There was a negative association between O 3 and influenza with a RR of 0.878 (95% CI: 0.866, 0.890).

CONCLUSIONS

Our findings suggest that short-term exposure to air pollution, except for O 3 , is associated with greater risk for influenza. Further studies are necessary to decipher underlying mechanisms and design preventive interventions and policies. https://doi.org/10.1289/EHP12145.

The associations of ambient fine particles with tuberculosis incidence and the modification effects of ambient temperature: A nationwide time-series study in China

Ambient fine particulate matter (PM2.5) is a major air pollutant that poses significant risks to human health. However, little is known about the association of PM2.5 with tuberculosis (TB) incidence, and whether temperature modifies the association.This study aimed to explore the association between ambient PM2.5 exposure and TB incidence in China and the modification effects of temperature. Weekly meteorological data, PM2.5 concentrations, and TB incidence numbers were collected for 22 cities across Mainland China, from 2011 to 2020. A quasi-Poisson regression with the distributed lag non-linear model was used to assess city-specific PM2.5-TB associations. A multivariate meta-regression model was then used to pool the city-specific effect estimates, at the national and regional levels. A J-shaped PM2.5-TB relationship was observed at the national level for China. Compared to those with minimum PM2.5-TB risk, people who were exposed to the highest PM2.5 concentrations had a 26 % (RR:1.26, 95 % confidence interval [CI]: 1.05, 1.52) higher risk for TB incidence. J-shaped PM2.5-TB associations were also observed for most sub-groups, however, no significant modifying effects were found. While a trend was observed between low temperatures and increased exposure-response associations, these results were not significant. Overall, approximately 20 % of TB cases in the 22 study cities, over the period 2011-2020, could be attributed to PM2.5 exposure. Strengthening the monitoring and emission control of PM2.5 could aid the prevention and control of TB incidence.

Mortality Risk and Burden From a Spectrum of Causes in Relation to Size-Fractionated Particulate Matters: Time Series Analysis

BACKGROUND

There is limited evidence regarding the adverse impact of particulate matters (PMs) on multiple body systems from both epidemiological and mechanistic studies. The association between size-fractionated PMs and mortality risk, as well as the burden of a whole spectrum of causes of death, remains poorly characterized.

OBJECTIVE

We aimed to examine the wide range of susceptible diseases affected by different sizes of PMs. We also assessed the association between PMs with an aerodynamic diameter less than 1 µm (PM1), 2.5 µm (PM2.5), and 10 µm (PM10) and deaths from 36 causes in Guangzhou, China.

METHODS

Daily data were obtained on cause-specific mortality, PMs, and meteorology from 2014 to 2016. A time-stratified case-crossover approach was applied to estimate the risk and burden of cause-specific mortality attributable to PMs after adjusting for potential confounding variables, such as long-term trend and seasonality, relative humidity, temperature, air pressure, and public holidays. Stratification analyses were further conducted to explore the potential modification effects of season and demographic characteristics (eg, gender and age). We also assessed the reduction in mortality achieved by meeting the new air quality guidelines set by the World Health Organization (WHO).

RESULTS

Positive and monotonic associations were generally observed between PMs and mortality. For every 10 μg/m3 increase in 4-day moving average concentrations of PM1, PM2.5, and PM10, the risk of all-cause mortality increased by 2.00% (95% CI 1.08%-2.92%), 1.54% (95% CI 0.93%-2.16%), and 1.38% (95% CI 0.95%-1.82%), respectively. Significant effects of size-fractionated PMs were observed for deaths attributed to nonaccidental causes, cardiovascular disease, respiratory disease, neoplasms, chronic rheumatic heart diseases, hypertensive diseases, cerebrovascular diseases, stroke, influenza, and pneumonia. If daily concentrations of PM1, PM2.5, and PM10 reached the WHO target levels of 10, 15, and 45 μg/m3, 7921 (95% empirical CI [eCI] 4454-11,206), 8303 (95% eCI 5063-11,248), and 8326 (95% eCI 5980-10690) deaths could be prevented, respectively. The effect estimates of PMs were relatively higher during hot months, among female individuals, and among those aged 85 years and older, although the differences between subgroups were not statistically significant.

CONCLUSIONS

We observed positive and monotonical exposure-response curves between PMs and deaths from several diseases. The effect of PM1 was stronger on mortality than that of PM2.5 and PM10. A substantial number of premature deaths could be preventable by adhering to the WHO's new guidelines for PMs. Our findings highlight the importance of a size-based strategy in controlling PMs and managing their health impact.

Lagging effects and prediction of pollutants and their interaction modifiers on influenza in northeastern China

BACKGROUND

Previous studies have typically explored the daily lagged relations between influenza and meteorology, but few have explored seasonally the monthly lagged relationship, interaction and multiple prediction between influenza and pollution. Our specific objectives are to evaluate the lagged and interaction effects of pollution factors and construct models for estimating influenza incidence in a hierarchical manner.

METHODS

Our researchers collect influenza case data from 2005 to 2018 with meteorological and contaminative factors in Northeast China. We develop a generalized additive model with up to 6 months of maximum lag to analyze the impact of pollution factors on influenza cases and their interaction effects. We employ LASSO regression to identify the most significant environmental factors and conduct multiple complex regression analysis. In addition, quantile regression is taken to model the relation between influenza morbidity and specific percentiles (or quantiles) of meteorological factors.

RESULTS

The influenza epidemic in Northeast China has shown an upward trend year by year. The excessive incidence of influenza in Northeast China may be attributed to the suspected primary air pollutant, NO2, which has been observed to have overall low levels during January, March, and June. The Age 15-24 group shows an increase in the relative risk of influenza with an increase in PM2.5 concentration, with a lag of 0-6 months (ERR 1.08, 95% CI 0.10-2.07). In the quantitative analysis of the interaction model, PM10 at the level of 100-120 μg/m3, PM2.5 at the level of 60-80 μg/m3, and NO2 at the level of 60 μg/m3 or more have the greatest effect on the onset of influenza. The GPR model behaves better among prediction models.

CONCLUSIONS

Exposure to the air pollutant NO2 is associated with an increased risk of influenza with a cumulative lag effect. Prioritizing winter and spring pollution monitoring and influenza prediction modeling should be our focus.

The association between ambient pollutants and influenza transmissibility: A nationwide study involving 30 provinces in China

Background

The impact of exposure to ambient pollutants on influenza transmissibility is poorly understood. We aim to examine the associations of six ambient pollutants with influenza transmissibility in China and assess the effect of the depletion of susceptibles.

Methods

Provincial-level surveillance data on weekly influenza-like illness (ILI) incidence and viral activity were utilized to estimate the instantaneous reproduction number (R_t) using spline functions. Log-linear regression and the distributed lag non-linear model (DLNM) were employed to investigate the effects of ambient pollutants-ozone (O₃), particulate matter ≤2.5 μm (PM_2.5), particulate matter ≤10 μm (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and carbon monoxide (CO)-on influenza transmissibility across 30 Chinese provinces from 2014 to 2019. Additionally, the potential effects of the depletion of susceptibles and regional characteristics were explored.

Results

There is a significantly positive correlation between influenza transmissibility and five distinct ambient pollutants: PM_2.5, PM₁₀, SO₂, CO, and NO₂. On average, these ambient pollutants explained percentages of the variance in R_t: 0.8%, 0.8%, 1.9%, 1.3%, and 1.4%, respectively. Conversely, O₃ was found to be negatively associated with R_t, explaining 1.5% of the variance in R_t. When controlling for the effect of susceptibles depletion, the effects of all pollutants were more pronounced. The effects of PM_2.5, PM₁₀, CO, and SO₂ were higher in the eastern and southern regions.

Conclusions

Most ambient pollutants may potentially contribute to the facilitation of human-to-human influenza virus transmission in China. This observed association was maintained even after adjusting for variation in the susceptible population.

The impact of ambient air pollution on an influenza model with partial immunity and vaccination

In this paper, we investigate the effects of ambient air pollution (AAP) on the spread of influenza in an AAP-dependent dynamic influenza model. The value of this study lies in two aspects. Mathematically, we establish the threshold dynamics in the term of the basic reproduction number $ \mathcal{R}_0 $: If $ \mathcal{R}_0 < 1 $, the disease will go to extinction, while if $ \mathcal{R}_0 > 1 $, the disease will persist. Epidemiologically, based on the statistical data in Huaian, China, we find that, in order to control the prevalence of influenza, we must increase the vaccination rate, the recovery rate and the depletion rate, and decrease the rate of the vaccine wearing off, the uptake coefficient, the effect coefficient of AAP on transmission rate and the baseline rate. To put it simply, we must change our traveling plan and stay at home to reduce the contact rate or increase the close-contact distance and wear protective masks to reduce the influence of the AAP on the influenza transmission.