scholarly journals Impact of COVID-19 Pandemic on Population-Level Interest in Skincare: Evidence from a Google Trends

2021 ◽  
pp. 147-160
Keyword(s):  
Time Series ◽  
Public Interest ◽  
Moving Average ◽  
Arima Model ◽  
Population Level ◽  
Interrupted Time Series ◽  
Limited Information ◽  
Interrupted Time Series Analysis ◽  
Search Volume ◽  
The World

COVID-19 pandemic created an unprecedented disruption of daily life including the pattern of skincare in healthcare settings by issuing stay-at-home orders around the world. There has been limited information about trends of skincare-related public interest during CVOID-19 and whether any substantial disruption in population-level behavior. The objective of this study is to evaluate the change in skincare-related population interest around the world during the COVID-19 pandemic time. Weekly RSV data were extracted worldwide and in 25 counties between August 1, 2016, and August 31, 2020. Interrupted time-series analysis was conducted as the quasi-experimental approach to evaluate the longitudinal effects of COVID-19 skincare-related search queries. For each country, autoregressive integrated moving average (ARIMA) model relative search volume (RSV) time series and then testing multiple periods simultaneously to examine the magnitude of the interruption. Multivariate linear regression was used to estimate the correlation between the relative changes in RSV with COVID-19 confirmed cases/ per million population and lockdown measures. Of 25 included countries in our study, 17 showed significantly increased (p<0.01) RSVs during the lockdown period compared with ARIMA forecasted data. The highest percentage of increments occurs in May and June in most countries. There was also a significant correlation between lockdown measures and the number of COVID-19 cases with relative changes in population interests for skincare. Understanding the trend and changes in skincare public interest during COVID-19 may assist health authorities to promote accessible educational information and preventive initiatives regarding skin problems.

scholarly journals Assessment of the Impact of COVID-19 pandemic on population level interest in Skincare: Evidence from a google trends-based Infodemiology study

2020 ◽  
Author(s):  
Hasan Symum ◽  
Md. F. Islam ◽  
Habsa K. Hiya ◽  
Kh M. Ali Sagor
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Population Level ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Search Volume ◽  
Quasi Experimental ◽  
Relative Search Volume ◽  
The Impact

AbstractBackgroundCOVID-19 pandemic created an unprecedented disruption of daily life including the pattern of skin related treatments in healthcare settings by issuing stay-at-home orders and newly coronaphobia around the world.ObjectiveThis study aimed to evaluate whether there are any significant changes in population interest for skincare during the COVID-19 pandemic.MethodsFor the skincare, weekly RSV data were extracted for worldwide and 23 counties between August 1, 2016, and August 31, 2020. Interrupted time-series analysis was conducted as the quasi-experimental approach to evaluate the longitudinal effects of COVID-19 skincare related search queries. For each country, autoregressive integrated moving average (ARIMA) model relative search volume (RSV) time series and then testing multiple periods simultaneously to examine the magnitude of the interruption. Multivariate linear regression was used to estimate the correlation between countries’ relative changes in RSV with COVID-19 confirmed cases/ per 10000 patients and lockdown measures.ResultsOut of 23 included countries in our study, 17 showed significantly increased (p<0.01) RSVs during the lockdown period compared with the ARIMA forecasted data. The highest percentage of increments occurs in May and June 2020 in most countries. There was also a significant correlation between lockdown measures and the number of COVID-19 cases with relatives changes in population interests for skincare.ConclusionUnderstanding the trend and changes in skincare public interest during COVID-19 may assist health authorities to promote accessible educational information and preventive initiatives regarding skin problems.

scholarly journals Did the UN convention on the rights of the child reduce child mortality around the world? An interrupted time series analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Christopher A. Tait ◽  
Abtin Parnia ◽  
Nishan Zewge-Abubaker ◽  
Wendy H. Wong ◽  
Heather Smith-Cannoy ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Child Mortality ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Rights Of The Child ◽  
Reduce Child Mortality ◽  
The World

scholarly journals Impact of campaign-style delivery of routine vaccines during Intensified Mission Indradhanush in India: a controlled interrupted time-series analysis

2020 ◽  
Author(s):  
Emma Clarke-Deelder ◽  
Christian Suharlim ◽  
Susmita Chatterjee ◽  
Logan Brenzel ◽  
Arindam Ray ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Immunization Coverage ◽  
Interrupted Time Series ◽  
Childhood Vaccination ◽  
World Health ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
The World ◽  
The Impact

AbstractIntroductionThe world is not on track to achieve the goals for immunization coverage and equity described by the World Health Organization’s Global Vaccine Action Plan. In India, only 62% of children had received a full course of basic vaccines in 2016. We evaluated the Intensified Mission Indradhanush (IMI), a campaign-style intervention to increase routine immunization coverage and equity in India, implemented in 2017-2018.MethodsWe conducted a comparative interrupted time-series analysis using monthly district-level data on vaccine doses delivered, comparing districts participating and not participating in IMI. We estimated the impact of IMI on coverage and under-coverage (defined as the proportion of children who were unvaccinated) during the four-month implementation period and in subsequent months.FindingsDuring implementation, IMI increased delivery of thirteen infant vaccines by between 1.6% (95% CI: −6.4, 10.2%) and 13.8% (3.0%, 25.7%). We did not find evidence of a sustained effect during the 8 months after implementation ended. Over the 12 months from the beginning of implementation, IMI reduced under-coverage of childhood vaccination by between 3.9% (−6.9%, 13.7%) and 35.7% (−7.5%, 77.4%). The largest estimated effects were for the first doses of vaccines against diptheria-tetanus-pertussis and polio.InterpretationIMI had a substantial impact on infant immunization delivery during implementation, but this effect waned after implementation ended. Our findings suggest that campaign-style interventions can increase routine infant immunization coverage and reach formerly unreached children in the shorter term, but other approaches may be needed for sustained coverage improvements.FundingBill & Melinda Gates Foundation.

scholarly journals Changes in the dollar value of per capita alcohol, essential, and non-essential retail sales in Canada during COVID-19

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniel T. Myran ◽  
Brendan T. Smith ◽  
Nathan Cantor ◽  
Lennon Li ◽  
Sudipta Saha ◽  
...  
Keyword(s):  
Time Series ◽  
Alcohol Use ◽  
Population Level ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Retail Sales ◽  
Ongoing Research ◽  
Per Capita ◽  
Statistics Canada ◽  
Alcohol Sales

Abstract Background Multiple survey reports suggest that alcohol use has increased in Canada during the COVID-19 pandemic. However, less is known about how per capita alcohol sales, which predict population-level alcohol use, have changed and whether changes in alcohol sales differ from changes in sales of other products due to pandemic factors. Methods We obtained monthly retail sales data by industry from Statistics Canada, for the six largest provinces in Canada (containing 93% of the national population), between January 2010 and November 2020, representing time before and 9 months after the start of the pandemic in Canada. We used an interrupted time series analysis to estimate pandemic impacts on the dollar value of monthly per capita (per individuals 15+ years) alcohol, essential and non-essential retail sales. We adjusted our analyses for pre-pandemic sales trends, inflation, seasonality and changing population demographics over time. Results During the first 9 months of the pandemic, the values of per capita alcohol, essential and non-essential sales were, respectively, 13.2% higher, 3.6% higher and 13.1% lower than the average values during the same period in the prior 3 years. Interrupted time series models showed significant level change for the value of monthly per capita alcohol sales (+$4.86, 95% CIs: 2.88, 6.83), essential sales (−$59.80, 95% CIs: − 78.47, − 41.03) and non-essential sales (−$308.70, 95% CIs: − $326.60, − 290.79) during the pandemic. Alcohol sales were consistently elevated during the pandemic, and the pre- and post-pandemic slopes were comparable. In contrast, essential and non-essential retail sales declined in the early months of the pandemic before returning to regular spending levels. Conclusion During the first 9 months of the pandemic, per capita alcohol sales were moderately elevated in Canada. In contrast, non-essential sales were lower than prior years, driven by large decreases during the initial months of the pandemic. These findings suggest that the pandemic was associated with increased population-level alcohol consumption, which may lead to increased alcohol-related harms. Ongoing research is needed to examine how factors, including pandemic-related stressors and specific alcohol sales-related policies, may have influenced changes in alcohol use and harms.

scholarly journals Time-Series Forecasting and Analysis of COVID-19 Outbreak in Highly Populated Countries

2022 ◽  
Vol 13 (2) ◽  
pp. 1-17
Author(s):  
Arunkumar P. M. ◽  
Lakshmana Kumar Ramasamy ◽  
Amala Jayanthi M.
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Research Area ◽  
Time Series Forecasting ◽  
Disease Spread ◽  
Time Period ◽  
The World ◽  
Auto Regressive ◽  
Important Research Area

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.

scholarly journals Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

The Impact of the Affordable Care Act on Burn Outcomes

2020 ◽  
Author(s):  
Justin E Dvorak ◽  
Erica L W Lester ◽  
Patrick J Maluso ◽  
Leah C Tatebe ◽  
Faran Bokhari
Keyword(s):  
Time Series ◽  
Low Socioeconomic Status ◽  
Affordable Care Act ◽  
Population Level ◽  
Interrupted Time Series ◽  
Home Health ◽  
Interrupted Time Series Analysis ◽  
Post Discharge ◽  
The Affordable Care Act ◽  
The Impact

Abstract Uninsured and low socioeconomic status patients who suffer burn injuries have disproportionately worse morbidity and mortality. The Affordable Care Act was signed into law with the goal of increasing access to insurance, with Medicaid expansion in January 2014 having the largest impact. To analyze the population-level impact of the Affordable Care Act on burn outcomes, and investigate its impact on identified at-risk subgroups, a retrospective time series of patients was created using data from the Healthcare Cost and Utilization Project National Inpatient Sample database between 2011 and 2016. An interrupted time series analysis was conducted to examine mortality, length of stay, and the probabilities of discharge home, home with home health, and to another facility before and after January 2014. There were no changes in burn mortality detected. There was a statistically significant reduction in the probability of being discharged home (−0.000967, P &lt; .01; 95% confidence interval [CI] −0.0015379 to −0.0003962) or discharged home with home health (−0.000709, P &lt; .01; 95% CI −0.00110 to 0.000317) after 2014. There was an increase in the probability of being discharged to another facility (0.00108, P = .01; 95% CI 0.000282–0.00188). While the enactment of the major provisions of the Affordable Care Act in 2014 was not associated with a change in mortality for burn patients, it was associated with more patients being discharged to a facility: This may represent a significant improvement in access to care and rehabilitation. Future studies will assess the societal and economic impact of improved access to post-discharge facilities and rehabilitation.

Time-series forecasting and analysis of COVID-19 outbreak in highly populated countries- A data-driven approach

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Research Area ◽  
Time Series Forecasting ◽  
Disease Spread ◽  
Time Period ◽  
The World ◽  
Data Driven Approach ◽  
Important Research Area

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.

scholarly journals Assessing the impact of a restrictive opioid prescribing law in West Virginia

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Cara L. Sedney ◽  
Maryam Khodaverdi ◽  
Robin Pollini ◽  
Patricia Dekeseredy ◽  
Nathan Wood ◽  
...  
Keyword(s):  
Time Series ◽  
West Virginia ◽  
Moving Average ◽  
Monitoring Program ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Opioid Prescribing ◽  
Small Change ◽  
The Impact ◽  
First Time

Abstract Background The Opioid Reduction Act (SB 273) took effect in West Virginia in June 2018. This legislation limited ongoing chronic opioid prescriptions to 30 days’ supply, and first-time opioid prescriptions to 7 days’ supply for surgeons and 3 days’ for emergency rooms and dentists. The purpose of this study was to determine the effect of this legislation on reducing opioid prescriptions in West Virginia, with the goal of informing future similar policy efforts. Methods Data were requested from the state Prescription Drug Monitoring Program (PDMP) including overall number of opioid prescriptions, number of first-time opioid prescriptions, average daily morphine milligram equivalents (MME) and prescription duration (expressed as “days’ supply”) given to adults during the 64 week time periods before and after legislation enactment. Statistical analysis was done utilizing an autoregressive integrated moving average (ARIMA) interrupted time series analysis to assess impact of both legislation announcement and enactment while controlling secular trends and considering autocorrelation trends. Benzodiazepine prescriptions were utilized as a control. Results Our analysis demonstrates a significant decrease in overall state opioid prescribing as well as a small change in average daily MME associated with the date of the legislation’s enactment when considering serial correlation in the time series and accounting for pre-intervention trends. There was no such association found with benzodiazepine prescriptions. Conclusion Results of the current study suggest that SB 273 was associated with an average 22.1% decrease of overall opioid prescriptions and a small change in average daily MME relative to the date of legislative implementation in West Virginia. There was, however, no association of the legislation on first-time opioid prescriptions or days’ supply of opioid medication, and all variables were trending downward prior to implementation of SB 273. The control demonstrated no relationship to the law.

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