Association of ambient air pollution with risk of hemorrhagic stroke: A time-stratified case crossover analysis of the Singapore stroke registry

Ho, Andrew Fu Wah; Lim, Mervyn Jun Rui; Zheng, Huili; Leow, Aloysius Sheng‐Ting; Tan, Benjamin Yong‐Qiang; Pek, Pin Pin; Raju, Yogeswari; Seow, Wei-Jie; Yeo, Tseng Tsai; Sharma, Vijay K.; Aik, Joel; Ong, Marcus Eng Hock

doi:10.1016/j.ijheh.2021.113908

Cited by 11 publications

(4 citation statements)

References 35 publications

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“…For the time series of HAs and environmental exposure, lag features were broadly considered in epidemiological studies and HAs predictions [27,28]. In our study, singleday lag features, namely historical values on day x (x ∈ {1, 2, 3, …, L}) before prediction, and cumulative lag features, including the moving average and standard deviation of historical values during the previous 1 to L days were extracted.…”

Section: Feature Extraction and Normalizationmentioning

confidence: 99%

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning

Qiu

2023

BMC Med Inform Decis Mak

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Background With the prevalence of cerebrovascular disease (CD) and the increasing strain on healthcare resources, forecasting the healthcare demands of cerebrovascular patients has significant implications for optimizing medical resources. Methods In this study, a stacking ensemble model comprised of four base learners (ridge regression, random forest, gradient boosting decision tree, and artificial neural network) and a meta learner (elastic net) was proposed for predicting the daily number of hospital admissions (HAs) for CD using the historical HAs data, air quality data, and meteorological data in Chengdu, China from 2015 to 2018. To solve the label imbalance problem, a re-weighting method based on label distribution smoothing was integrated into the meta learner. We trained the model using the data from 2015 to 2017 and evaluated its predictive ability using the data in 2018 based on four metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). In addition, the SHapley Additive exPlanations (SHAP) framework was applied to provide explanation for the prediction of our stacking model. Results Our proposed model outperformed all the base learners and long short-term memory (LSTM) on two datasets. Particularly, compared with the optimal results obtained by individual models, the MAE, RMSE, and MAPE of the stacking model decreased by 13.9%, 12.7%, and 5.8%, respectively, and the R2 improved by 6.8% on CD dataset. The model explanation demonstrated that environmental features played a role in further improving the model performance and identified that high temperature and high concentrations of gaseous air pollutants might strongly associate with an increased risk of CD. Conclusions Our stacking model considering environmental exposure is efficient in predicting daily HAs for CD and has practical value in early warning and healthcare resource allocation.

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Section: Feature Extraction and Normalizationmentioning

confidence: 99%

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning

Qiu

2023

BMC Med Inform Decis Mak

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“…The 2015 Global Burden of Disease Study analyzed the 25 years of data and found that air pollution significantly affects DALYs and mortality caused by ischemic heart disease, cerebrovascular disease, and chronic obstructive pulmonary disease [ 6 ]. In fact, growing evidence suggests that short-term exposure to air pollution increases the incidence and mortality of ICH [ 7 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of air pollution control policies on intracerebral hemorrhage mortality among residents in Tianjin, China

Yin

Wang

et al. 2023

BMC Public Health

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Background Exposure to air pollution is an important risk factor for intracerebral hemorrhage (ICH), which is a major cause of death worldwide. However, the relationship between ICH mortality and air quality improvement has been poorly studied. This study aims to evaluate the impact of the air pollution control policies in the Beijing-Tianjin-Hebei region on ICH mortality among Tianjin residents. Methods This study used an interrupted time series analysis. We fitted autoregressive integrated moving average (ARIMA) models to assess the changes in ICH deaths before and after the interventions of air pollution control policies based on the data of ICH deaths in Tianjin collected by the Tianjin Center for Disease Control and Prevention. Results Between 2009 and 2020, there were 63,944 ICH deaths in Tianjin, and there was an overall decreasing trend in ICH mortality. The intervention conducted in June 2014 resulted in a statistically significant (p = 0.03) long-term trend change, reducing the number of deaths from ICH by 0.69 (95% confidence interval [CI]: -1.30 to -0.07) per month. The intervention in October 2017 resulted in a statistically significant (p = 0.04) immediate decrease of 25.74 (95% CI: -50.62 to -0.85) deaths from ICH in that month. The intervention in December 2017 caused a statistically significant (p = 0.04) immediate reduction of 26.58 (95% CI: -52.02 to -1.14) deaths from ICH in that month. The intervention in March 2018 resulted in a statistically significant (p = 0.02) immediate decrease of 30.40 (95% CI: -56.41 to -4.40) deaths from ICH in that month. No significant differences were observed in the changes of male ICH mortality after any of the four interventions. However, female ICH deaths showed statistically significant long-term trend change after the intervention in June 2014 and immediate changes after the interventions in December 2017 and March 2018. Overall, the interventions prevented an estimated 5984.76 deaths due to ICH. Conclusion During the study period, some interventions of air pollution control policies were significantly associated with the reductions in the number of deaths from ICH among residents in Tianjin. ICH survivors and females were more sensitive to the protective effects of the interventions. Interventions for air pollution control can achieve public health gains in cities with high levels of air pollution.

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“…Up until now, the stroke remains at the top cause of death and disability in the world with over 12.2 million new strokes each year [3]. There are many causes of stroke, and recently the air pollution due to the carbon emission is often associated with the stroke [4], [5]. Pre-venting the stroke in every person is important.…”

Section: Introductionmentioning

confidence: 99%

Robot Ankle Foot Orthosis with Auto Flexion Mode for Foot Drop Training on Post-Stroke Patient in Indonesia

Adiputra¹,

Ubaidillah²,

Asfari³

et al. 2022

KINETIK

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Robot Ankle Foot Orthosis (AFO) has been proven to assist the gait impairment, such as the foot drop. However, development challenge is still remains, such as the trade-off between complexity, functionality and cost. High functionality resulted in high cost, bulky, and complex device. But affordability and simplicity may decrease functionality. Therefore, this research proposed a robot AFO, which has the necessary function of auto dorsi-plantarflexion so it can keep the affordability and simplicity. The robot AFO consists of structure, electronics part and algorithm. The structure is custom made according to the user’s anatomy. A brushless DC (BLDC) motor, Force-Sensing Resistor (FSR) and microcontroller builds the electronic parts. The BLDC motor actuates the flexion, while the FSR detects the gait phase to determine the action. Both are integrated by the microcontroller with the P control algorithm that commands the BLDC motor to generate necessary torque so it rotates in a constant speed. A functionality test has been carried out on the robot AFO, where the robot AFO perform a dorsi-plantarflexion continuously in three conditions, such as no load, 1 Kg load, and foot load. The robot AFO successfully performed a constant velocity rotation in both directions, in all conditions. In the case of 1 Kg load, the maximum angular speed is 0.7 rad/s dorsiflexion and -1.8 rad/s plantarflexion. The torque keeps increasing and decreasing from -0.3 Nm to 4 Nm to keep the angular velocity. The result shows that the robot AFO can perform the necessary function to assist the foot drop training. Functionality test on the gait detection has also been done where it shows that the robot AFO can detect the four gait phases accurately. The robot AFO has been tested and future study should test the robot on a real post-stroke patient to see the effect of the gait control in reality.

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Association of ambient air pollution with risk of hemorrhagic stroke: A time-stratified case crossover analysis of the Singapore stroke registry

Cited by 11 publications

References 35 publications

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning

Effects of air pollution control policies on intracerebral hemorrhage mortality among residents in Tianjin, China

Robot Ankle Foot Orthosis with Auto Flexion Mode for Foot Drop Training on Post-Stroke Patient in Indonesia

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