Bivariate extreme value analysis of extreme temperature and mortality in Canada, 2000-2020.

Climate change increases the risk of illness through rising temperature, severe precipitation and worst air pollution. This paper investigates how monthly excess mortality rate is associated with the increasing frequency and severity of extreme temperature in Canada during 2000-2020. The extreme associations were compared among four age groups across five sub-blocks of Canada based on the datasets of monthly T90 and T10, the two most representative indices of severe weather monitoring measures developed by the actuarial associations in Canada and US. We utilize a combined seasonal Auto-regressive Integrated Moving Average (ARIMA) and bivariate Peaks-Over-Threshold (POT) method to investigate the extreme association via the extreme tail index χ and Pickands dependence function plots. It turns out that it is likely (more than 10%) to occur with excess mortality if there are unusual low temperature with extreme intensity (all χ > 0.1 except Northeast Atlantic (NEA), Northern Plains (NPL) and Northwest Pacific (NWP) for age group 0-44), while extreme frequent high temperature seems not to affect health significantly (all χ ≤ 0.001 except NWP). Particular attention should be paid to NWP and Central Arctic (CAR) since population health therein is highly associated with both extreme frequent high and low temperatures (both χ = 0.3182 for all age groups). The revealed extreme dependence is expected to help stakeholders avoid significant ramifications with targeted health protection strategies from unexpected consequences of extreme weather events. The novel extremal dependence methodology is promisingly applied in further studies of the interplay between extreme meteorological exposures, social-economic factors and health outcomes.

Short-term changes in mental health help-seeking behaviors following exposure to multiple social stressors and a natural disaster.

In 2020, unprecedented circumstances led to significant mental health consequences. Individuals faced mental health stressors that extended beyond the devastating impact of the COVID-19 pandemic, including widespread social unrest following the murder of George Floyd, an intense hurricane season in the Atlantic, and the politically divisive 2020 election. The objective of this analysis was to consider changes in help-seeking behavior following exposure to multiple social stressors and a natural disaster. Data from Crisis Text Line (CTL), a national text-based mental health crisis counseling service, was used to determine how help-seeking behavior changed in the wake of each event. Wilcoxon rank sum tests assessed changes in help-seeking behavior for each event in 2020 as compared to the same period in 2019. AutoRegressive Integrated Moving Average (ARIMA) models examined if changes in crisis conversation volumes following each event differed. Higher median conversation volumes noted for the COVID-19 pandemic (+1 to +5 conversations), Hurricane Laura (+1 to +7 conversations) and the 2020 Election (+1 to +26 conversations). ARIMA models show substantial increases in help-seeking behavior following the declaration of a national emergency for the COVID-19 pandemic (+4.3 to +38.2%) and following the 2020 election (+3 to +24.44%). Our analysis found that the mental health response following social stressors may be distinct from natural events, especially when natural disasters occur in the context of multiple social stressors. This analysis adds to the growing body of literature considering the mental health impact of exposure to multiple co-occurring societal stressors, like police violence and a global pandemic.

A class of statistical models for the motion of Daphnia over small time scales.

A common question in the aquatic sciences is that of how zooplankter movement can be modeled. It is well-established in the literature that there exists a randomness to this movement, but the question is how to characterize this randomness. The most common methods for doing this involve the random walk and correlated random walk (CRW) models. Here, we present a time series model that allows a better description the randomness in Daphnia motion when the amount of time that elapses between observations of their position is small. Our approach is adaptable to description of tracks of a multitude of animal species through re-estimation of model parameters. The model we propose uses information about how the animal moved during the previous two time intervals to explain how it moves currently. We demonstrate that the proposed model provides better predictive accuracy and fit than do the CRW and random walk models.

Dynamics of emotion trends in Canadian Twitter users during COVID-19 confinement in relation to caseloads: Artificial intelligence-based emotion detection approach.

Multiple waves of COVID-19 have significantly impacted the emotional well-being of all, but many were subject to additional risks associated with forced regulations. The objective of this research was to assess the immediate emotional impact, expressed by Canadian Twitter users, and to estimate the linear relationship, with the vicissitudes of COVID caseloads, using ARIMA time-series regression. We developed two Artificial Intelligence-based algorithms to extract tweets using 18 semantic terms related to social confinement and locked down and then geocoded them to tag Canadian provinces. Tweets (n = 64,732) were classified as positive, negative, and neutral sentiments using a word-based Emotion Lexicon. Our results indicated: that Tweeters were expressing a higher daily percentage of negative sentiments representing, negative anticipation (30.1%), fear (28.1%), and anger (25.3%), than positive sentiments comprising positive anticipation (43.7%), trust (41.4%), and joy (14.9%), and neutral sentiments with mostly no emotions, when hash-tagged social confinement and locked down. In most provinces, negative sentiments took on average two to three days after caseloads increase to emerge, whereas positive sentiments took a slightly longer period of six to seven days to submerge. As daily caseloads increase, negative sentiment percentage increases in Manitoba (by 68% for 100 caseloads increase) and Atlantic Canada (by 89% with 100 caseloads increase) in wave 1(with 30% variations explained), while other provinces showed resilience. The opposite was noted in the positive sentiments. The daily percentage of emotional expression variations explained by daily caseloads in wave one were 30% for negative, 42% for neutral, and 2.1% for positive indicating that the emotional impact is multifactorial. These provincial-level impact differences with varying latency periods should be considered when planning geographically targeted, time-sensitive, confinement-related psychological health promotion efforts. Artificial Intelligence-based Geo-coded sentiment analysis of Twitter data opens possibilities for targeted rapid emotion sentiment detection opportunities.

The Experimental Verification of Direct-Write Silver Conductive Grid and ARIMA Time Series Analysis for Crack Propagation.

The paper presents experimental verification of customized resistive crack propagation sensors as an alternative method for other common structural health monitoring (SHM) techniques. Most of these are sensitive to changes in the sensor network configuration and a baseline dataset must be collected for the analysis of the structure condition. Sensors investigated within the paper are manufactured by the direct-write process with electrically conductive, silver-microparticle-filled paint to prepare a tailored measuring grid on an epoxy or polyurethane coating as a driving/insulating layer. This method is designed to enhance the functionality and usability compared to commercially available crack gauges. By using paint with conductive metal particles, the shape of the sensor measuring grid can be more easily adapted to the structure, while, in the previous approach, only a few grid-fixed sensors are available. A fatigue test on the compact tension (CT) specimen is presented and discussed to evaluate the ability of the developed sensors to detect and monitor fatigue cracks. Additionally, the ARIMA time series algorithm is developed both for monitoring and predicting crack growth, based on the acquired data. The proposed sensors' verification reveal their good performance to detect and monitor fatigue fractures with a relatively low measurement error and ARIMA estimated crack length compared with the crack opening displacement (COD) gauge.

Opioid-related emergencies in New York City after the Great Recession.

BACKGROUND: The rise in opioid-related mortality and opioid-related emergency department (ED) visits has stimulated research on whether broader economic declines, such as the Great Recession, affect opioid-related morbidity. We examine in New York City whether one measure of morbidity-opioid-related ED visits-responded acutely to the large negative "shock" of the Great Recession. METHODS: Data comprise outpatient "treat and release" opioid-related ED visits in New York City for the 72 months spanning January 2006 to December 2011, taken from the Statewide Emergency Department Database (n = 150,246). We modeled the monthly incidence of opioid-related ED visits using Autoregressive, Integrated, Moving Average (ARIMA) time-series methods to control for patterning in ED visits before examining its potential association with the economic shock of the Great Recession. RESULTS: New York City shows a mean of 1761 outpatient ED visits per month for opioid dependence and abuse. Unexpectedly large drops in employment coincide with fewer than expected opioid dependence and abuse ED visits in that same month. The result (coefficient = 0.046, 95% Confidence Interval [CI]: 0.002, 0.090) represents a 0.8% drop in overall incidence of opioid dependence and abuse ED visits during the Great Recession. We, however, observe no association between the Great Recession and ED visits for prescription opioid overdose or heroin overdose, or with inpatient ED visits for opioid dependence and abuse. CONCLUSIONS: Findings, if replicated, indicate distinct short-term reductions in opioid-related morbidity following the Great Recession. This result diverges from previous findings of increased opioid use following extended economic downturns.

Red tide at morning, tourists take warning? County-level economic effects of HABS on tourism dependent sectors.

A tourism dependent state such as Florida relies on its environment and climate to attract visitors and generate revenue. HABs can certainly have an impact on the coastal waters of the Gulf, but does this necessarily drive away tourist related activity? To determine not only if the impact of HABs is significant, but also at what magnitude, a time series econometric model was used to study effects of persistent and severe blooms on counties in Southwestern Florida, particularly Sarasota County, hit hardest by blooms in 2006 and 2018 that lasted multiple months. Lodging and restaurant sectors of the economy were found to have monthly losses of 15% and 1.75% respectively, during months when red tide was present. Neighboring counties unaffected by severe blooms did not experience significant losses to these sectors. These results support the intuition that effects of HABs reach far beyond the waters of the Gulf, and as red tide grows in frequency and severity, more economic loss could lie ahead.

Descriptive and predictive time series analysis of premature mortality from noncommunicable disease among Filipinos

INTRODUCTION: The problem of increasing mortality from noncommunicable disease (NCD) in the Philippines warrants an in-depth assessment of premature death rate in the country. This research aims to explore the temporal characteristics of mortality younger than 70 years old from the leading NCD among Filipinos from 2006 to 2012 and forecast premature mortality rates in 2013 to 2016. METHODS: Time series modeling and forecasting using the Box-Jenkins method was performed on secondary ecologic data extracted from the national mortality database maintained by the Philippine Statistics Authority-National Statistics Office.RESULTS: Premature death rate from cardiovascular diseases has been increasing steadily. Diabetes mellitus, which shows initially rising mortality among the 30-69-year-old age group, has been reversed in 2009. Trends of premature mortality from cancers and chronic lung diseases did not appear to change over time. NCD mortality rates in the 30-69-year-old age group are generally expected to plateau from 2013 onwards.CONCLUSION: This novel application of time series analysis on premature NCD mortality data drives both further scientific studies and formal programmatic evaluation by providing a better evidence-based picture of NCD burden in the country. 

Work-related accidents among the Iranian population: a time series analysis, 2000-2011.

BACKGROUND: Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world. OBJECTIVES: To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011. METHODS: In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box-Jenkins modeling to develop a time series model of the total number of accidents. RESULTS: There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE). CONCLUSIONS: The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection.