A survey of ambulatory-treated asthma and correlation with weather and air pollution conditions within Taiwan during 2001-2010

A life course approach to asthma and wheezing among young children caused by ozone: A prospective birth cohort in northern China

BACKGROUND

Given differences in vulnerability of children in early life, a life course approach to asthma and wheezing (AW) in young children caused by ozone (O₃) is not fully understood.

METHODS

We conducted a birth cohort in Jinan, China from 2018 to 2021 to elucidate the onset model of childhood AW due to O₃ exposure. An inverse distance weighted model was used for individual exposure assessment. The time-dependent Cox proportional-hazard model and logistic model were used to investigate the effects of O₃ exposure on AW. Principal component analysis, interaction analysis, and distributed lag model were used to analyze the life course approach.

RESULTS

The cumulative incidence rate for AW among 6501 children aged 2 was 1.4%. A high level of O₃ was related to AW (HR: 2.10, 95% CI: 1.31, 3.37). Only O₃ exposure after birth was associated with AW, with an OR of 1.82 (1.08, 3.12), after adjusting for the effect before birth. Furthermore, adjusting for other air pollutants, the HR for the individual effect of high O₃ exposure on AW was 2.44 (1.53, 3.89). Interestingly, P values for interactions for O₃ and the principal components of other pollutants, as well as the characteristic variable of open windows were less than 0.1. Moreover, an increase in the IQR of O₃ exposure at the 31st to 37th weeks before birth and the 1st to 105th weeks after birth was associated with an increase in the HRs for AW.

CONCLUSIONS

High-level of O₃ exposure after birth could lead to AW among young children. Importantly, the AW onset model may include the risk factors accumulation and the sensitive period model. Specifically, there are two sensitive windows in early life, and the correlated insults between the high level of O₃ and other pollutants as well as open windows in the asthma-inducing effect.

Predicting daily emergency department visits using machine learning could increase accuracy

OBJECTIVE

Administrators and clinicians alike have attempted to predict emergency department visits for many years. The ability to predict or "forecast" ED visit volume can allow for more efficient resource allocation, including up-staffing or down-staffing, changing OR schedules, and predicting the need for significant resources. The goal of this study is to examine combinations of variables via machine learning to increase prediction accuracy and determine the factors that are most predictive of overall ED visits. As compared to a simple univariate time series model, we hypothesize that machine learning models will predict St. Joseph Mercy Ann Arbor's patient visit load for the emergency department (ED) with higher accuracy than a simple univariate time series model.

METHODS

Univariate time series models for daily ED visits, including ARIMA, Exponential Smoothing (ETS), and Facebook Inc.'s prophet algorithm were estimated as a baseline comparison. Machine learning models, including random forests and gradient boosted machines (GBM), were trained using data from 2017 to 2018. After final models were created, they were applied to the 2019 data to determine how well these models predicted actual ED patient volumes in data not utilized during the model fitting process. The accuracy of the machine learning and time series models were assessed based on out-of-sample predictive accuracy, compared using root mean squared error (RMSE).

RESULTS

Using root mean squared error (RMSE) to assess out-of-sample predictive accuracy of the models, the results showed that the random forest model was the most accurate at predicting daily ED visits in the 2019 test set, followed by the GBM model. These performed only slightly better than the simple exponential smoothing model predictions. The ARIMA model performed poorly in comparison. The day of the week (likely capturing differences between weekdays and weekends) was found to be the most important predictor of patient volumes. Weather-related features such as maximum temperature and SFC pressure appeared to capture some of the seasonality trends related to changes in patient volumes.

CONCLUSIONS

Machine learning models perform better at predicting daily patient volumes as compared to simple univariate time series models, though not by a substantial amount. Further research can help confirm these limited initial results. Gathering more training data and additional feature engineering could also be beneficial to training the models and potentially improving predictive accuracy.

Long-term effect of intermediate particulate matter (PM1-2.5) on incident asthma among middle-aged and elderly adults: A national population-based longitudinal study

BACKGROUND

There is insufficient evidence about the long-term effects of intermediate particulate matter (PM_1-2.5) on asthma development in adults aged 45 years and above. This study aimed to investigate the relationship between long-term exposure to PM_1-2.5 and the incidence of asthma in adults aged 45 years and above.

METHODS

A cohort study based on the China Health and Retirement Longitudinal Study (CHARLS) database was conducted to investigate the long-term effects of PM_1-2.5 on self-reported asthma incidence in adults aged 45 years and above in China from 2011 to 2018. The PM concentrations were estimated using a high-resolution (1 km²) satellite-based spatiotemporal model. A covariate-adjusted generalized linear mixed model was used to analyze the relationship between long-term exposure to PM_1-2.5 and the incidence of asthma. Effect modifications and sensitivity analysis were conducted.

RESULTS

After a 7-year follow-up, 103 (1.61 %) of the 6400 participants developed asthma. Each 10 μg/m³ increment in the 1-, 2-, 3-, and 4-year moving average concentrations of PM_1-2.5 corresponded to a 1.82 [95 % confidence interval (CI):1.11-2.98], 1.95 (95 % CI: 1.24-3.07), 1.95 (95 % CI: 1.26-3.03) and 1.88 (95 % CI: 1.26-2.81) fold risk for incident asthma, respectively. A significant multiplicative interaction was observed between socioeconomic level and long-term exposure to PM_1-2.5. Stratified analysis showed that smokers and those with lower socioeconomic levels were at higher risk of incident asthma related to PM_1-2.5. Restricted cubic splines showed an increasing trend in asthma incidence with increasing PM_1-2.5. Sensitivity analyses showed that our model was robust.

CONCLUSION

Long-term exposure to PM_1-2.5 was positively associated with incident asthma in middle-aged and elderly individuals. Participants with a history of smoking and lower socioeconomic levels had a higher risk. More studies are warranted warrant to establish an accurate reference value of PM_1-2.5 to mitigate the growing asthma burden.

Lung function and air pollution exposure in adults with asthma in Beijing: a 2-year longitudinal panel study

The effect of air pollution on the lung function of adults with asthma remains unclear to date. This study followed 112 patients with asthma at 3-month intervals for 2 years. The pollutant exposure of the participants was estimated using the inverse distance weight method. The participants were divided into three groups according to their lung function level at every visit. A linear mixed-effect model was applied to predict the change in lung function with each unit change in pollution concentration. Exposure to carbon monoxide (CO) and particles less than 2.5 micrometers in diameter (PM_2.5) was negatively associated with large airway function in participants. In the severe group, exposure to chronic sulfur dioxide (SO2) was negatively associated with post-bronchodilator forced expiratory flow at 50%, between 25% and 75% of vital capacity % predicted (change of 95% CI per unit: -0.34 (-0.55, -0.12), -0.24 (-0.44, -0.03), respectively). In the mild group, the effect of SO₂ on the small airways was similar to that in the severe group, and it was negatively associated with large airway function. Exposure to CO and PM_2.5 was negatively associated with the large airway function of adults with asthma. The negative effects of SO₂ were more evident and widely observed in adults with severe and mild asthma than in adults with moderate asthma. Patients with asthma react differently to air pollutants as evidenced by their lung function levels.

Liver cancer prediction in a viral hepatitis cohort: A deep learning approach

Viral hepatitis is the primary cause of liver diseases, among which liver cancer is the leading cause of death from cancer. However, this cancer is often diagnosed in the later stages, which makes treatment difficult or even impossible. This study applied deep learning (DL) models for the early prediction of liver cancer in a hepatitis cohort. In this study, we surveyed 1 million random samples from the National Health Insurance Research Database (NHIRD) to analyze viral hepatitis patients from 2002 to 2010. Then, we used DL models to predict liver cancer cases based on the history of diseases of the hepatitis cohort. Our results revealed the annual prevalence of hepatitis in Taiwan increased from 2002 to 2010, with an average annual percentage change (AAPC) of 5.8% (95% CI: 4.2-7.4). However, young people (aged 16-30 years) exhibited a decreasing trend, with an AAPC of -5.6 (95% CI: -8.1 to -2.9). The results of applying DL models showed that the convolution neural network (CNN) model yielded the best performance in terms of predicting liver cancer cases, with an accuracy of 0.980 (AUC: 0.886). In conclusion, this study showed an increasing trend in the annual prevalence of hepatitis, but a decreasing trend in young people from 2002 to 2010 in Taiwan. The CNN model may be applied to predict liver cancer in a hepatitis cohort with high accuracy.