Long-term effects of weather condition and air pollution on acute hemorrhagic conjunctivitis in China: A nationalwide surveillance study in China

Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model

This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

Nonlinear effects of humidex on risk of outpatient visit for allergic conjunctivitis among children and adolescents in Shanghai, China: A time series analysis

Background

Various epidemiological studies have focused on the adverse health outcomes of meteorological factors. However, there has been little research on the impact of humidex on allergic conjunctivitis, especially in child and adolescent populations. We aimed to explore the impact of humidex, a comprehensive index of relative humidity and temperature, on child and adolescent allergic conjunctivitis admissions.

Methods

Outpatient visit data for allergic conjunctivitis, meteorological factors and air pollutants in Shanghai for the 2017-2022 period were retrieved. For the purpose of analysing the nonlinear connection and lag impact between humidex and admissions for paediatric and adolescent allergic conjunctivitis, the distributed lag nonlinear model (DLNM) was fitted.

Results

A total of 147 090 cases were included in our cohort. We found a significantly nonlinear effect on humidex and allergic conjunctivitis. In the single-day lag pattern, the relative risks (RR) of allergic conjunctivitis were significant at lag 0 (RR = 1.08, 95% confidence interval (CI) = 1.05-1.11) to lag 2 (RR = 1.01, 95% CI = 1.00-1.01), lag 5 (RR = 1.01, 95% CI = 1.00-1.01) to lag 9 (RR = 1.01, 95% CI = 1.00-1.01), and lag 14 (RR = 1.02, 95% CI: 1.01-1.03). In the cumulative-lag day pattern, the RR of allergic conjunctivitis were significant at lag 0-0 (RR = 1.08, 95% CI = 1.05-1.11) to lag 0-14 (RR = 1.21, 95% CI = 1.13-1.28). We found that boys, children aged 7-17 years, and children in the warm season were more vulnerable to humidex. In addition, the highest attributable fraction (AF) and attributable number (AN) of humidex are at lag 0-14 (AF = 0.17, AN = 25 026).

Conclusions

Humidex exposure markedly increased the risk of allergic conjunctivitis, especially in highly high humidex. Appropriate public health management is needed for disease management and early intervention.

An update on the association between ambient short-term air pollution exposure and daily outpatient visits for conjunctivitis: a time-series study in Hangzhou, China

Air pollution is a major public health problem that can lead to conjunctivitis. This study aimed to explore the associations between air pollutants and outpatient visits for conjunctivitis in Hangzhou, China. This study collected data on 50,772 patients with conjunctivitis and the concentrations of six air pollutants from February 1, 2014, to August 31, 2018. A time series analysis using a generalized additive model (GAM) was conducted. We found that the risk of conjunctivitis was related to the air pollutants PM2.5, PM10, SO2, NO2, and O3, which had concentration hysteresis effects. The risk of conjunctivitis increased by 1.009 (95% confidence interval (CI): 1.003, 1.014), 1.011 (95% CI: 1.008, 1.015), 1.238 (95% CI: 1.186, 1.292), 1.028 (95% CI: 1.019, 1.038), and 1.013 (95% CI: 1.008, 1.017) for every 10 µg/m3 increase in PM2.5, PM10, SO2, NO2, and O3 concentrations, respectively. The lag effects of SO2 and NO2 were stronger than those of particulate matter. Females exposed to PM10, PM2.5, SO2, and O3 had a higher risk of conjunctivitis than males, while males exposed to NO2 had a nearly identical risk of conjunctivitis as females. People aged 19-59 were more likely to suffer from conjunctivitis. The risk of conjunctivitis caused by PM10, SO2, and O3 was highest in the transitional season, while the risk caused by NO2 was highest in the winter season. In conclusion, females and middle-aged adults were at higher risk of conjunctivitis. People were more susceptible to conjunctivitis during the transitional season. These findings highlight the importance of atmospheric pollution governance and reference for public health measures.

Density forecasting of conjunctivitis burden using high-dimensional environmental time series data

As one of the most common eye conditions being presented at clinics, acute conjunctivitis puts substantial strain on primary health resources. To reduce this public health burden, it is important to forecast and provide forward guidance to policymakers by estimating conjunctivitis trends, taking into account factors which influence transmission. Using a high-dimensional set of ambient air pollution and meteorological data, this study describes new approaches to point and probabilistic forecasting of conjunctivitis burden which can be readily translated to other infectious diseases. Over the period of 2012 - 2022, we show that simple models without environmental data provided better point forecasts but the more complex models which optimized predictive accuracy and combined multiple predictors demonstrated superior density forecast performance. These results were shown to be consistent over periods with and without structural breaks in transmission. Furthermore, ecological analysis using post-selection inference showed that increases in SO2, O3 surface concentration and total precipitation were associated to increased conjunctivitis attendance. The methods proposed can provide rich and informative forward guidance for outbreak preparedness and help guide healthcare resource planning in both stable periods of transmission and periods where structural breaks in data occur.

Long-term effects of meteorological factors and extreme weather on daily outpatient visits for conjunctivitis from 2013 to 2020: a time-series study in Urumqi, China

Conjunctivitis is a common multifactorial inflammatory ocular surface disease characterized by symptoms such as congestion, edema, and increased secretion of conjunctival tissue, and the potential effects of meteorological factors as well as extreme meteorological factors on conjunctivitis and their lagging effects have not been fully evaluated. We obtained the electronic case information of 59,731 outpatients with conjunctivitis from the Ophthalmology Department of the First Affiliated Hospital of Xinjiang Medical University (Urumqi, Xinjiang, China) for the period from January 1, 2013, to December 31, 2020. Meteorological data for daily mean temperature (°C), daily relative humidity (%), daily average wind speed (m/s), and atmospheric pressure (hPa) were obtained from the China Meteorological Data Sharing Service. The air pollutant data were obtained from 11 standard urban background fixed air quality monitors. A time-series analysis design and a quasi-Poisson generalized linear regression model combined with a distributed lagged nonlinear model (DLNM) were used to fit the effects of exposure to different meteorological factors and extreme weather on conjunctivitis outpatient visits. Subgroup analyses were performed on gender, age and season, and type of conjunctivitis. Univariate and multifactorial model results indicated that each 10-unit increase in mean temperature and relative humidity was associated with an increased risk of conjunctivitis outpatient visits, while each 10-unit increase in atmospheric pressure was associated with a decreased risk. The results of the extreme weather analysis suggested that extremely low levels of atmospheric pressure and relative humidity as well as extreme levels of temperature were associated with an increased risk of outpatient conjunctivitis visits, and extreme wind speeds were associated with a decreased risk. The results of the subgroup analysis suggested gender, age, and seasonal differences. We conducted the first large sample size time-series analysis in the large city furthest from the ocean in the world and confirmed for the first time that elevated mean temperature and extreme low levels of relative humidity in Urumqi were risk factors for local conjunctivitis outpatient visits, while elevated atmospheric pressure and extreme low levels of wind speed were protective factors, and there were lagged effects of temperature and atmospheric pressure. Multicenter studies with larger sample sizes are needed.

Epidemiological trends and sociodemographic factors associated with acute hemorrhagic conjunctivitis in mainland China from 2004 to 2018

BACKGROUND

Acute hemorrhagic conjunctivitis (AHC) is classified as a class C notifiable infectious disease in China and poses a great threat to public health. This study aimed to investigate the epidemiological trends and hotspots of AHC in mainland China. Sociodemographic factors that could contribute to early warning of AHC were further explored.

METHODS

Yearly and monthly incidences of acute hemorrhagic conjunctivitis by date and region from 2004 to 2018 were extracted from the Data Center of China Public Health Science. Joinpoint regression and spatial autocorrelation analysis were performed to explore the epidemiological trends and hotspots of AHC. A generalized linear model was then applied to explore the relationship between sociodemographic factors and AHC incidence.

RESULTS

The average annual AHC incidence was 3.58/100,000 in mainland China. The first-level spatial and temporal aggregation areas were distributed in Guangxi, Hainan, Guangdong, Guizhou, Hunan, Jiangxi, Fujian, Chongqing, Hubei, Anhui, and Zhejiang, with gathering times from 2010/1/1 to 2010/12/31 (RR = 20.13, LLR = 474,522.89, P < 0.01). After 2010, the AHC incidence was stable (APC = - 8.37, 95% CI: - 23.02-9.06). However, it was significantly increased in low- and middle-income provinces (AAPC = 10.65, 95% CI: 0.62-21.68, AAPC = 11.94, 95% CI: 0.62-24.53). The peak of AHC occurred during the August to October period. Children who age 0-3 years are identified as high-risk group with AHC incidence significantly increased (APC = 31.54, 95% CI: 0.27-72.56). Birth rate, population ages 0-14 (% of total population), passenger traffic, and urban population (% of total population) were positively associated with the AHC incidence, while per capita gross domestic product was negatively associated with the AHC incidence.

CONCLUSION

Overall, the AHC incidence was stable after 2010 in China, but it was significantly increased in low- and middle-income provinces. Regions with a high birth rate, population ages 0-14 (% of the total population), passenger traffic, urban population (% of the total population) and low per capita gross domestic product are at high risk of incidences of AHC. In the future, public health policy and resource priority for AHC in regions with these characteristics are necessary.