The Impact of Opioid Prescribing Limits on Drug Usage in South Carolina: A Novel Geospatial and Time Series Data Analysis

The opioid crisis in the United States has had devastating effects on communities across the country, leading many states to pass legislation that limits the prescription of opioid medications in an effort to reduce the number of overdose deaths. This study investigates the impact of South Carolina's prescription limit law (S.C. Code Ann. 44-53-360), which aims to reduce opioid overdose deaths, on opioid prescription rates. The study utilizes South Carolina Reporting and Identification Prescription Tracking System (SCRIPTS) data and proposes a distance classification system to group records based on proximity and evaluates prescription volumes in each distance class. Prescription volumes were found to be highest in classes with pharmacies located further away from the patient. An Interrupted Time Series (ITS) model is utilized to assess the policy impact, with benzodiazepine prescriptions as a control group. The ITS models indicate an overall decrease in prescription volume, but with varying impacts across the different distance classes. While the policy effectively reduced opioid prescription volumes overall, an unintended consequence was observed as prescription volume increased in areas where prescribers were located at far distances from patients, highlighting the limitations of state-level policies on doctors. These findings contribute to the understanding of the effects of prescription limit laws on opioid prescription rates and the importance of considering location and distance in policy design and implementation.

Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic

This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies.

Non-pharmaceutical interventions and urban vehicle mobility in Seoul during the COVID-19 pandemic

Non-pharmaceutical interventions to control human mobility are important in preventing COVID-19 transmission. These interventions must also help effectively control the urban mobility of vehicles, which can be a safer travel mode during the pandemic, at any time and place. However, few studies have identified the effectiveness of vehicle mobility in terms of time and place. This study demonstrates the effectiveness of non-pharmaceutical interventions at both local and national levels on intra- and inter-urban vehicle mobility by time of day in Seoul, South Korea, by applying the autoregressive integrated moving average with exogenous variables. The study found that social distancing measures at the national level were effective for intra-urban vehicle mobility, especially at night-time, but not for inter-urban mobility. Information provision with emergency text messages by cell phone was effective in reducing vehicle mobility in daytime and night-time, but not during morning peak hours. At the local level, both restrictions on late-night transit operations and stricter social distancing measures were mostly significant in reducing night-time mobility only in intra-urban areas. The study also indicates when (what time of the day), where (which area within the city), and which combination strategy could be more effective in containing urban vehicle mobility. This study recommends that restrictions on human mobility should also be extended to vehicle mobility, especially in inter-urban areas and during morning peak hours, by systematically designing diverse non-pharmaceutical interventions.

A potential tool for predicting epidemic trends and outbreaks of scrub typhus based on Internet search big data analysis in Yunnan Province, China

Introduction

Scrub typhus, caused by Orientia tsutsugamushi, is a neglected tropical disease. The southern part of China is considered an important epidemic and conserved area of scrub typhus. Although a surveillance system has been established, the surveillance of scrub typhus is typically delayed or incomplete and cannot predict trends in morbidity. Internet search data intuitively expose the public's attention to certain diseases when used in the public health area, thus reflecting the prevalence of the diseases.

Methods

In this study, based on the Internet search big data and historical scrub typhus incidence data in Yunnan Province of China, the autoregressive integrated moving average (ARIMA) model and ARIMA with external variables (ARIMAX) model were constructed and compared to predict the scrub typhus incidence.

Results

The results showed that the ARIMAX model produced a better outcome than the ARIMA model evaluated by various indexes and comparisons with the actual data.

Conclusions

The study demonstrates that Internet search big data can enhance the traditional surveillance system in monitoring and predicting the prevalence of scrub typhus and provides a potential tool for monitoring epidemic trends of scrub typhus and early warning of its outbreaks.

Evaluation of weather parameter-based pre-harvest yield forecast models for wheat crop: a case study in Saurashtra region of Gujarat

Wheat is the important food grain and is cultivated in many Indian states: Punjab, Haryana, Uttar Pradesh, and Madhya Pradesh, which contributes to major crop production in India. In this study, popular statistical approach multiple linear regression (MLR) and time series approaches Time Delay Neural Network (TDNN) and ARIMAX models were envisaged for wheat yield forecast using weather parameters for a case study area, i.e., Junagarh district, western Gujarat region situated at the foot of Mount Girnar. Weather data corresponds to 19 weeks (42nd to 8th Standard Meteorological Week, SMW) during crop growing season was used for prediction of wheat yield using these statistical techniques and were evaluated for their predictive capability. Furthermore, trend analysis among weather parameters and crop yield was also carried out in this study using non-parametric Mann-Kendall test and Sen's slope method. Significant negative correlation was observed between wheat yield and some of the weekly weather variables, viz., maximum temperature (48, 49, 50, 51, 52, and 4th SMW), and total rainfall (50, 51, and 1st SMW) while positive correlation was observed with morning relative humidity (49 and 3rd SMW). Study indicated that forecast error varied from 1.80 to 10.28 in MLR, 0.79 to 7.79 in ARIMAX (2,2,2), - 3.09 to 10.18 in TDNN (4,5) during model training period (1985-2014). The MAPE value shows that the time series data predicted less than 5% of variation, whereas the conventional MLR technique indicated more than 7% variation. Both ARIMAX and TDNN approaches indicated better performance during model training periods, i.e., 1985-2014 and 1985-2015, while former performed well during the forecast periods 1985-2016 and 1985-2017. Overall, the study indicated that the ARIMAX approach can be used consistently for 4 years using the same model.

Association of quarterly prevalence of e-cigarette use with ever regular smoking among young adults in England: a time-series analysis between 2007 and 2018

AIMS

To assess how changes in the prevalence of e-cigarette use among young adults have been associated with changes in the uptake of smoking in England between 2007 and 2018.

DESIGN

Time-series analysis of population trends with autoregressive integrated moving average with exogeneous input (ARIMAX models).

SETTING

England.

PARTICIPANTS

Data were aggregated quarterly on young adults aged 16-24 years (n = 37 105) taking part in the Smoking Toolkit Study.

MEASURES

In the primary analysis, prevalence of e-cigarette use was used to predict prevalence of ever regular smoking among those aged 16-24. Sensitivity analyses stratified the sample into those aged 16-17 and 18-24. Bayes' factors and robustness regions were calculated for non-significant findings [effect size beta coefficient (B) = 3.1].

FINDINGS

There was evidence for no association between the prevalence of e-cigarette use and ever regular smoking among those aged 16-24 [B = -0.015, 95% confidence interval (CI) = -0.046 to 0.016; P = 0.341; Bayes factor (BF) = 0.002]. Evidence for no association was also found in the stratified analysis among those aged 16-17 (B = 0.070, 95% CI -0.014 to 0.155, P = 0.102; BF = 0.015) and 18-24 (B = -0.021, 95% CI -0.053 to 0.011; P = 0.205; BF = 0.003). These findings were able to rule out percentage point increases or decreases in ever regular smoking prevalence greater than 0.31% or less than -0.03% for 16-17-year-olds and 0.01 or -0.08% for 18-24-year-olds for every 1%-point increase in e-cigarette prevalence.

CONCLUSION

Prevalence of e-cigarette use among the youth population in England does not appear to be associated with substantial increases or decreases in the prevalence of smoking uptake. Small associations cannot be ruled out.

Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil

In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.

Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China

Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. Methods: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. Results: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. Conclusions: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.

Internet search data could Be used as novel indicator for assessing COVID-19 epidemic

The pandemic of the coronavirus disease (COVID-19) poses a huge challenge all countries, since no one is well prepared for it. To be better prepared for future pandemics, we evaluated association between the internet search data with reported COVID-19 cases to verify whether it could become an early indicator for emerging epidemic. After the keyword filtering and Index composition, we found that there were close correlations between Composite Index and suspected cases for COVID-19 (r = 0.921, P < 0.05). The Search Index was applied for the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model to quantify the relationship. Compared with the model based on surveillance data only, the ARIMAX model had smaller Akaike Information Criterion (AIC = 403.51) and the most accurate predictive values. Overall, the Internet search data could serve as a convenient indicator for predicting the epidemic and to monitor its trends.

Effects of meteorological factors on the incidence of mumps and models for prediction, China

BACKGROUND

Mumps is an acute respiratory infectious disease with obvious regional and seasonal differences. Exploring the impact of climate factors on the incidence of mumps and predicting its incidence trend on this basis could effectively control the outbreak and epidemic of mumps.

METHODS

Considering the great differences of climate in the vast territory of China, this study divided the Chinese mainland into seven regions according to the administrative planning criteria, data of Mumps were collected from the China Disease Prevention and Control Information System, ARIMA model and ARIMAX model with meteorological factors were established to predict the incidence of mumps.

RESULTS

In this study, we found that precipitation, air pressure, temperature, and wind speed had an impact on the incidence of mumps in most regions of China and the incidence of mumps in the north and southwest China was more susceptible to climate factors. Considering meteorological factors, the average relative error of ARIMAX model was 10.87%, which was lower than ARIMA model (15.57%).

CONCLUSIONS

Meteorology factors were the important factors which can affect the incidence of mumps, ARIMAX model with meteorological factors could better simulate and predict the incidence of mumps in China, which has certain reference value for the prevention and control of mumps.

Association between changes in harm perceptions and e-cigarette use among current tobacco smokers in England: a time series analysis

BACKGROUND

There is a decreasing trend in the proportion of individuals who perceive e-cigarettes to be less harmful than conventional cigarettes across the UK, Europe and the US. It is important to assess whether this may influence the use of e-cigarettes. We aimed to estimate, using a time series approach, whether changes in harm perceptions among current tobacco smokers have been associated with changes in the prevalence of e-cigarette use in England, with and without stratification by age, sex and social grade.

METHODS

Respondents were from the Smoking Toolkit Study, which involves monthly cross-sectional household surveys of individuals aged 16+ years in England. Data were aggregated monthly on ~ 300 current tobacco smokers between 2014 and 2019. The outcome variable was the prevalence of e-cigarette use. The explanatory variable was the proportion of smokers who endorsed the belief that e-cigarettes are less harmful than combustible cigarettes. Covariates were cigarette (vs. non-cigarette combustible) current smoking prevalence, past-year quit attempt prevalence and national smoking mass media expenditure. Unadjusted and adjusted autoregressive integrated moving average with exogeneous variables (ARIMAX) models were fitted.

RESULTS

For every 1% decrease in the mean prevalence of current tobacco smokers who endorsed the belief that e-cigarettes are less harmful than combustible cigarettes, the mean prevalence of e-cigarette use decreased by 0.48% (βadj = 0.48, 95% CI = 0.25-0.71, p < .001). Marginal age and sex differences were observed, whereby significant associations were observed in older (but not in young) adults and in men (but not in women). No differences by social grade were detected.

CONCLUSIONS

Between 2014 and 2019 in England, at the population level, monthly changes in the prevalence of accurate harm perceptions among current tobacco smokers were strongly associated with changes in e-cigarette use.

Association of prevalence of electronic cigarette use with smoking cessation and cigarette consumption in England: a time-series analysis between 2006 and 2017

AIMS

To provide up-to-date estimates of how changes in the prevalence of electronic cigarette (e-cigarette) use in England have been associated with changes in smoking cessation activities and daily cigarette consumption among smokers in England.

DESIGN

Time-series analysis of population trends.

SETTING

England.

PARTICIPANTS

Participants came from the Smoking Toolkit Study, which involves repeated, cross-sectional household surveys of individuals aged 16 years and older in England. Data were aggregated on approximately 1200 past-year smokers each quarter (total n = 50 498) between 2007 and 2017.

MEASUREMENTS

Prevalence of e-cigarette use in current smokers was used to predict (a) prevalence of quit attempts among last-year smokers, (b) overall quit rates among last-year smokers and (c) mean cigarette consumption per day among current smokers. Prevalence of e-cigarette use during a quit attempt among last-year smokers was used to predict (a) quit success rate among last-year smokers and (b) overall quit rates among last-year smokers.

FINDINGS

Overall quit rates increased by 0.054% [95% confidence interval (CI) = 0.032-0.076, P < 0.001] and 0.050% (95% CI = 0.031-0.069, P < 0.001) respectively for every 1% increase in the prevalence of e-cigarette use by smokers and e-cigarette use during a quit attempt. Quit success rates increased by 0.060% (95% CI = 0.043-0.078, P < 0.001) for every 1% increase in the prevalence of e-cigarette use during a quit attempt. No clear evidence was found for an association between e-cigarette use and either prevalence of quit attempt (BAdj = 0.011, 95% CI = -0.046 to 0.069, P = 0.698) or cigarette consumption (BAdj = 0.019, 95% CI = -0.043 to 0.082, P = 0.542).

CONCLUSION

Changes in prevalence of e-cigarette use in England have been positively associated with the overall quit rates and quit success rates but not clearly associated with the prevalence of quit attempts and mean cigarette consumption.

Population-level predictors of changes in success rates of smoking quit attempts in England: a time series analysis

AIMS

To quantify associations between the success of smoking quit attempts and factors that have varied throughout 2007-2018 at a population level.

DESIGN

time series analysis using Autoregressive Integrated Moving Average with Exogeneous Input (ARIMAX) modelling.

SETTING AND PARTICIPANTS

Data were aggregated from 54 847 past-year smokers taking part in the Smoking Toolkit Study which involves monthly repeated cross-sectional household surveys of individuals aged 16+ in England.

MEASUREMENTS

The input series were: (1) attempts at smoking reduction using (a) e-cigarettes and (b) nicotine replacement therapy (NRT); (2) use during a quit attempt of (a) e-cigarettes, (b) NRT over-the-counter, (c) medication on prescription and (d) face-to-face behavioural support; (3) use of roll-your-own tobacco; (4) prevalence of (a) smoking and (b) non-daily smoking; (5) tobacco control mass media expenditure; (6) expenditure on smoking; (7) smoker characteristics in the form of (a) high motivation to quit, (b) average age, (c) socio-economic status and (d) cigarette consumption; (8) implementation of tobacco control policies; and (9) quit attempt rate.

FINDINGS

The licensing of NRT for harm reduction was associated with a 0.641% [95% confidence interval (CI) = 0.073-1.209, P = 0.027] increase in the mean point prevalence of the success rate of quit attempts. For every 1% increase in the mean point prevalence of e-cigarette use and use of prescription medication during a quit attempt, the mean point prevalence of successful quit attempts increased by 0.106% (95% CI = 0.011-0.201, P = 0.029) and 0.143% (95% CI = 0.009-0.279, P = 0.038), respectively. For every 1% increase in the mean expenditure on tobacco control mass media, the mean point prevalence of successful quit attempts increased by 0.046% (95% CI = 0.001-0.092, P = 0.046). Other associations were not statistically significant.

CONCLUSION

In England between 2007 and 2018, licensing of nicotine replacement therapy for use in harm reduction, greater use of e-cigarettes and prescription medications during a quit attempt and higher expenditure on tobacco control mass media were all associated with higher success rates of quit attempts.

Understanding and using time series analyses in addiction research

Time series analyses are statistical methods used to assess trends in repeated measurements taken at regular intervals and their associations with other trends or events, taking account of the temporal structure of such data. Addiction research often involves assessing associations between trends in target variables (e.g. population cigarette smoking prevalence) and predictor variables (e.g. average price of a cigarette), known as a multiple time series design, or interventions or events (e.g. introduction of an indoor smoking ban), known as an interrupted time series design. There are many analytical tools available, each with its own strengths and limitations. This paper provides addiction researchers with an overview of many of the methods available (GLM, GLMM, GLS, GAMM, ARIMA, ARIMAX, VAR, SVAR, VECM) and guidance on when and how they should be used, sample size det ermination, reporting and interpretation. The aim is to provide increased clarity for researchers proposing to undertake these analyses concerning what is likely to be acceptable for publication in journals such as Addiction. Given the large number of choices that need to be made when setting up time series models, the guidance emphasizes the importance of pre-registering hypotheses and analysis plans before the analyses are undertaken.

Modelling and forecasting Positive and Negative Syndrome Scale scores to achieve remission using time series analysis

OBJECTIVES

Schizophrenia is a chronic mental condition. The objective of this study is to apply time series modelling to Positive and Negative Syndrome Scale scores of outpatients with schizophrenia, observed at regular intervals of time, and hence forecast the number of visits required to reach remission.

METHODS

A retrospective data of outpatients diagnosed with chronic paranoid-type schizophrenia were extracted from the records of outpatient department of a tertiary hospital in New Delhi, India. Autoregressive integrated moving average (ARIMA) and ARIMAX models (ARIMA with explanatory variable as Clinical Global Impression Severity scale) are fitted to the data. The best fit models are employed to forecast the number of visits required to reach remission for the outpatients who did not achieve remission by the end of study. Prediction accuracy of the two models is compared using mean absolute percentage error and mean absolute deviation.

RESULTS

The ARIMA (1, 2, 1) and ARIMAX (1, 2, 1) models are identified to be suitable models after a series of statistical tests.

CONCLUSIONS

ARIMA and ARIMAX models are suitable to predict number of visits required to reach remission. Further, ARIMAX model performed better than ARIMA model.

Time series modeling of pneumonia admissions and its association with air pollution and climate variables in Chiang Mai Province, Thailand

This study aimed to predict the number of pneumonia cases in Chiang Mai Province. An autoregressive integrated moving average (ARIMA) was used in data fitting and to predict future pneumonia cases monthly. Total pneumonia cases of 67,583 were recorded in Chiang Mai during 2003-2014 that the monthly pattern of case was similar every year. Monthly pneumonia cases were increased during February and September, which are the periods of winter and rainy season in Thailand and decreased during April to July (the period of summer season to early rainy season). Using available data on 12 years of pneumonia cases, air pollution, and climate in Chiang Mai, the optimum ARIMA model was investigated based on several conditions. Seasonal change was included in the models due to statistically strong season conditions. Twelve ARIMA model (ARMODEL1-ARMODEL12) scenarios were investigated. Results showed that the most appropriate model was ARIMA (1,0,2)(2,0,0)[12] with PM10 (ARMODEL5) exhibiting the lowest AIC of - 38.29. The predicted number of monthly pneumonia cases by using ARMODEL5 during January to March 2013 was 727, 707, and 658 cases, while the real number was 804, 868, and 783 cases, respectively. This finding indicated that PM₁₀ held the most important role to predict monthly pneumonia cases in Chiang Mai, and the model was able to predict future pneumonia cases in Chiang Mai accurately.

Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California

Accurate and reliable reservoir inflow forecast is instrumental to the efficient operation of the hydroelectric power systems. It has been discovered that natural and anthropogenic aerosols have a great influence on meteorological variables such as temperature, snow water equivalent, and precipitation, which in turn impact the reservoir inflow. Therefore, it is imperative for us to quantify the impact of aerosols on reservoir inflow and to incorporate the aerosol models into future reservoir inflow forecasting models. In this paper, a comprehensive framework was developed to quantify the impact of aerosols on reservoir inflow by integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem) and a dynamic regression model. The statistical dynamic regression model produces forecasts for reservoir inflow based on the meteorological output variables from the WRF-Chem model. The case study was performed on the Florence Lake and Lake Thomas Alva Edison of the Big Creek Hydroelectric Project in the San Joaquin Region. The simulation results show that the presence of aerosols results in a significant reduction of annual reservoir inflow by 4-14%. In the summer, aerosols reduce precipitation, snow water equivalent, and snowmelt that leads to a reduction in inflow by 11-26%. In the spring, aerosols increase temperature and snowmelt which leads to an increase in inflow by 0.6-2%. Aerosols significantly reduce the amount of inflow in the summer when the marginal value of water is extremely high and slightly increase the inflow in the spring when the run-off risk is high. In summary, the presence of aerosols is detrimental to the optimal utilization of hydroelectric power systems.

Imported cases and minimum temperature drive dengue transmission in Guangzhou, China: evidence from ARIMAX model

Dengue is the fastest spreading mosquito-transmitted disease in the world. In China, Guangzhou City is believed to be the most important epicenter of dengue outbreaks although the transmission patterns are still poorly understood. We developed an autoregressive integrated moving average model incorporating external regressors to examine the association between the monthly number of locally acquired dengue infections and imported cases, mosquito densities, temperature and precipitation in Guangzhou. In multivariate analysis, imported cases and minimum temperature (both at lag 0) were both associated with the number of locally acquired infections (P < 0.05). This multivariate model performed best, featuring the lowest fitting root mean squared error (RMSE) (0.7520), AIC (393.7854) and test RMSE (0.6445), as well as the best effect in model validation for testing outbreak with a sensitivity of 1.0000, a specificity of 0.7368 and a consistency rate of 0.7917. Our findings suggest that imported cases and minimum temperature are two key determinants of dengue local transmission in Guangzhou. The modelling method can be used to predict dengue transmission in non-endemic countries and to inform dengue prevention and control strategies.

Association between smoking and alcohol-related behaviours: a time-series analysis of population trends in England

AIMS

This paper estimates how far monthly changes in prevalence of cigarette smoking, motivation to quit and attempts to stop smoking have been associated with changes in prevalence of high-risk drinking, and motivation and attempts to reduce alcohol consumption in England.

DESIGN

Data were used from the Alcohol and Smoking Toolkit Studies between April 2014 and June 2016. These involve monthly household face-to-face surveys of representative samples of ~1700 adults in England.

MEASUREMENTS

Autoregressive Integrated Moving Average with Exogeneous Input (ARIMAX) modelling was used to assess the association over time between monthly prevalence of (a) smoking and high-risk drinking; (b) high motivation to quit smoking and high motivation to reduce alcohol consumption; and (c) attempts to quit smoking and attempts to reduce alcohol consumption.

FINDINGS

Mean smoking prevalence over the study period was 18.6% and high-risk drinking prevalence was 13.0%. A decrease of 1% of the series mean smoking prevalence was associated with a reduction of 0.185% of the mean prevalence of high-risk drinking 2 months later [95% confidence interval (CI) = 0.033 to 0.337, P = 0.017]. A statistically significant association was not found between prevalence of high motivation to quit smoking and high motivation to reduce alcohol consumption (β = 0.324, 95% CI = -0.371 to 1.019, P = 0.360) or prevalence of attempts to quit smoking and attempts to reduce alcohol consumption (β = -0.026, 95% CI = -1.348 to 1.296, P = 0.969).

CONCLUSION

Between 2014 and 2016, monthly changes in prevalence of smoking in England were associated positively with prevalence of high-risk drinking. There was no significant association between motivation to stop and motivation to reduce alcohol consumption, or attempts to quit smoking and attempts to reduce alcohol consumption.

The threat of Asian dust storms on asthma patients: a population-based study in Taiwan

This study explores the relationship between Asian dust storms (ADSs), asthma hospital admissions and average medical cost discharge. We adopt the hospitalisation data from the Taiwan National Health Insurance research database covering the period from 2000 to 2009. The autoregressive integrated moving average with exogenous variables (ARIMAX) analyses were performed to explore the relationship between ADS and asthma hospital admissions, adjusting for temperature, air pollutants and season dummy. The results show that ADS events do generate a critical influence upon the occurrences of asthma on post-ADS events from days 1 through 3, with an average of 17-20 more hospitalised admissions, and have stronger effects on preschool children, middle-aged people and the elderly. From the perspective of medical expenses, the cost of hospitalised admissions for asthma substantially rises daily, on average, by NT$634,698 to NT$787,407 during ADS event days. This study suggests that government should establish a forecast and alert system and release warnings about dust storms, so that the individuals predisposed to asthma can take precautionary measures to reduce their outdoor exposure. Consequently, personal risk and medical expenditure could be reduced significantly, especially for preschool children, middle-aged people and the elderly with asthma.