Exposure-lag-response associations between extreme environmental conditions and primary Sjögren's syndrome

Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease

BACKGROUND

According to the World Health Organization, foodborne disease is a significant public health issue. We will choose the best model to predict foodborne disease by comparison, to provide evidence for government policies to prevent foodborne illness.

METHODS

The foodborne disease monthly incidence data from June 2017 to April 2022 were obtained from the Chongqing Nan'an District Center for Disease Prevention and Control. Data from June 2017 to June 2021 were used to train the model, and the last 10 months of incidence were used for prediction and validation The incidence was fitted using the seasonal autoregressive integrated moving average (SARIMA) model, Holt-Winters model and Exponential Smoothing (ETS) model. Besides, we used MSE, MAE, RMSE to determine which model fits better.

RESULTS

During June 2017 to April 2022, the incidence of foodborne disease showed seasonal changes, the months with the highest incidence are June to November. The optimal model of SARIMA is SARIMA (1,0,0) (1,1,0)12. The MSE, MAE, RMSE of the Holt-Winters model are 8.78, 2.33 and 2.96 respectively, which less than those of the SARIMA and ETS model, and its prediction curve is closer to the true value. The optimal model has good predictive performance.

CONCLUSION

Based on the results, Holt-Winters model produces better prediction accuracy of the model.

Association between short-term exposure to extreme humidity and painful diabetic neuropathy: a case-crossover analysis

Painful diabetic neuropathy (PDN) is a common complication of diabetes mellitus, which reduces the quality of life. However, the association between PDN and environmental factors, especially ambient humidity, remains unclear. Therefore, this study investigated the impact of extreme humidity events on PDN. Data on PDN-related hospital admissions to two tertiary hospitals in Hefei, China (2014-2019) were obtained. A distributed lag non-linear model with a case-crossover design was used to quantitatively estimate the effects of ambient humidity on PDN, and the results were stratified by sex and age. The 1st, 10th, 90th, and 99th percentiles of relative humidity (RHU) were defined as extreme humidity, and the average relative humidity (74.94%) was set as the reference value. Non-linear exposure-response curves between the RHU and PDN cases were obtained. Extreme humidity (92%) had a significant effect on PDN with a relative risk (RR) of 1.13 (95% confidence interval (CI): 1.01-1.26) on a particular day, which increased with the RHU (RR: 1.21, 95% CI: 1.02-1.45 at 98% extreme humidity). Stratification analysis showed that women (RR: 1.38, 95% CI: 1.07-1.77) and patients aged < 65 years (RR: 1.26, 95% CI: 1.01-1.57) were highly susceptible to this effect on the same day. The results suggest that extreme humidity is a crucial trigger for PDN onset in diabetes patients. Furthermore, the effects vary with sex and age. This study provides detailed evidence of the adverse effects of extreme weather on diabetes patients.

Exposure to particulate pollutant increases the risk of hospitalizations for Sjögren's syndrome

Objective

Numerous researches have reported the role of air pollution in the development of autoimmune diseases. However, few have evaluated the relationship between inhalable particulate matter (PM) exposure and Sjögren's syndrome (SS). This study aimed to analyze the association between exposure to two particulate pollutants (PM_2.5, PM₁₀) and SS-related hospitalizations.

Methods

Daily data were obtained on PM_2.5 and PM₁₀, meteorological factors, and hospital hospitalizations for SS between 2016 and 2021. The daily data on PM_2.5 and PM₁₀, meteorological factors, and the number of SS hospitalizations were collected between 2016 and 2021. A distributed lag non-linear model and a generalized linear model were established to explore the association between PM_2.5 and PM₁₀ exposure and hospitalizations for SS. Stratified analyses were performed to explore possible gender-, age-, and season-related differences in PM_2.5 and PM₁₀ effects.

Results

Exposure to PM_2.5 was related to the evaluated risk of hospitalizations for SS (RR=1.015, 95% CI: 1.001-1.029, lag 3 day), similarly, PM₁₀ exposure had a statistically significant positive association with SS hospitalizations (RR =1.013, 95% CI: 1.001-1.026, lag 3 day). Stratified analyses found that exposure to PM_2.5 and PM₁₀ exhibited higher impact on SS-related hospitalizations in female patients and exposure to PM_2.5 was also associated with the higher risk of SS-related hospitalizations in patients aged ≥ 65 years. In addition, exposure to PM_2.5, PM₁₀ in colder season were more likely to increase SS-related hospitalizations.

Conclusion

Our findings suggested that exposure to PM_2.5 and PM₁₀ were significantly linked to an elevated risk of hospitalizations for SS.

Exposure to ambient gaseous pollutant and daily hospitalizations for Sjögren's syndrome in Hefei: A time-series study

OBJECTIVE

Increasing evidence suggested that gaseous pollutants were associated with the development of autoimmune diseases, while there were few studies on the association between gaseous pollutants and Sjögren's syndrome (SS). This study sought to assess the relationship between exposure to several gaseous pollutants and the hospitalizations for SS.

METHODS

The data regarding SS hospitalizations, gaseous pollutants, and meteorological factors in Hefei from 2016 to 2021 were collected. A distributed lag non-linear model combined with a generalized linear model were adopted to analyze the association between gaseous pollutants and SS hospitalizations, and stratified analyses were also conducted.

RESULTS

We detected significant associations between gaseous pollutants (NO2, SO2, O3, CO) and SS hospitalizations. Exposure to NO2 was linked with the elevated risk of hospitalizations for SS (RR=1.026, lag1 day). A positive correlation between CO exposure and hospitalizations for SS was found (RR=1.144, lag2 day). In contrast, exposure to SO2, O3 was respectively related to the decreased risk of hospitalizations for SS (SO2: RR=0.897, lag14 day; O3: RR=0.992, lag9 day). Stratified analyses found that female patients were more vulnerable to these gaseous pollutants. SS patients ≥ 65 years were more susceptible to NO2, CO exposure, and younger patients were more vulnerable to O3 exposure. In addition, exposure to O3, CO in cold season were more likely to affect hospitalizations for SS.

CONCLUSION

Our results demonstrated a significant association between exposure to NO2, CO and elevated risk of hospitalizations for SS, and SO2, O3 exposure might be linked to reduced risk of SS hospitalizations.

Comparison of SARIMA model and Holt-Winters model in predicting the incidence of Sjögren's syndrome

OBJECTIVE

To analyze the prevalence trend of Sjögren's syndrome in the Department of Immunology and Rheumatology of Nanjing Zhongda Hospital from January 2015 to December 2019, and compare the application of SARIMA model and Holt-Winters model in predicting the number of cases of Sjögren's syndrome.

METHODS

All of the data from the Department of Immunology and Rheumatology of Nanjing Zhongda Hospital were collected. The number of monthly cases from January 2015 to December 2019 was regarded as the training set, and it was used to establish the SARIMA model and Holt-Winters model. The number of monthly incidences from January 2020 to December 2020 was regarded as the test set, and it was used to check the model performance.

RESULTS

The optimal model of SARIMA is ARIMA (0,1,1) (2,1,1)₁₂ model, and the optimal model of Holt-Winters model is Holt-Winters addition model. It was found that the Holt-Winters addition model produced the smallest error.

CONCLUSION

Holt-Winters addition model produces better prediction accuracy of the model.