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 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 Impact of Removal of Automatic 7-Day Stop Orders for Inpatient Antimicrobials

2020 ◽  
Vol 41 (S1) ◽  
pp. s264-s265
Author(s):  
Afia Adu-Gyamfi ◽  
Keith Hamilton ◽  
Leigh Cressman ◽  
Ebbing Lautenbach ◽  
Lauren Dutcher
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Antibiotic Use ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Treatment Interruptions ◽  
Days Of Therapy ◽  
Quasi Experimental ◽  
The Impact

Background: Automatic discontinuation of antimicrobial orders after a prespecified duration of therapy has been adopted as a strategy for reducing excess days of therapy (DOT) as part of antimicrobial stewardship efforts. Automatic stop orders have been shown to decrease antimicrobial DOT. However, inadvertent treatment interruptions may occur as a result, potentially contributing to adverse patient outcomes. To evaluate the effects of this practice, we examined the impact of the removal of an electronic 7-day ASO program on hospitalized patients. Methods: We performed a quasi-experimental study on inpatients in 3 acute-care academic hospitals. In the preintervention period (automatic stop orders present; January 1, 2016, to February 28, 2017), we had an electronic dashboard to identify and intervene on unintentionally missed doses. In the postintervention period (April 1, 2017, to March 31, 2018), the automatic stop orders were removed. We compared the primary outcome, DOT per 1,000 patient days (PD) per month, for patients in the automatic stop orders present and absent periods. The Wilcoxon rank-sum test was used to compare median monthly DOT/1,000 PD. Interrupted time series analysis (Prais-Winsten model) was used to compared trends in antibiotic DOT/1,000 PD and the immediate impact of the automatic stop order removal. Manual chart review on a subset of 300 patients, equally divided between the 2 periods, was performed to assess for unintentionally missed doses. Results: In the automatic stop order period, a monthly median of 644.5 antibiotic DOT/1,000 PD were administered, compared to 686.2 DOT/1,000 PD in the period without automatic stop orders (P < .001) (Fig. 1). Using interrupted time series analysis, there was a nonsignificant increase by 46.7 DOT/1,000 PD (95% CI, 40.8 to 134.3) in the month immediately following removal of automatic stop orders (P = .28) (Fig. 2). Even though the slope representing monthly change in DOT/1,000 PD increased in the period without automatic stop orders compared to the period with automatic stop orders, it was not statistically significant (P = .41). Manual chart abstraction revealed that in the period with automatic stop orders, 9 of 150 patients had 17 unintentionally missed days of therapy, whereas none (of 150 patients) in the period without automatic stop orders did. Conclusions: Following removal of the automatic stop orders, there was an overall increase in antibiotic use, although the change in monthly trend of antibiotic use was not significantly different. Even with a dashboard to identify missed doses, there was still a risk of unintentionally missed doses in the period with automatic stop orders. Therefore, this risk should be weighed against the modest difference in antibiotic utilization garnered from automatic stop orders.Funding: NoneDisclosures: None

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.

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.

scholarly journals Assessing The Impact of A Restrictive Opioid Prescribing Law in West Virginia

2020 ◽  
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 ◽  
Emergency Rooms ◽  
Opioid Prescribing ◽  
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 “day’s 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 statistically significant decrease in overall state opioid prescribing as well as 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 overall decrease of 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.

Assessing the Impact of Media Guidelines for Reporting on Suicides in Austria: Interrupted time Series Analysis

2007 ◽  
Vol 41 (5) ◽  
pp. 419-428 ◽  
Author(s):  
Thomas Niederkrotenthaler ◽  
Gernot Sonneck
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Interrupted Time Series ◽  
Public Health Issue ◽  
Level Shift ◽  
Interrupted Time Series Analysis ◽  
Quality Of Reporting ◽  
Media Reporting ◽  
Coverage Rates ◽  
The Impact

Objective: Media guidelines for reporting on suicides are a widely used means of preventing imitative suicides, but scientific accounts of their impact on suicide numbers are sparse. This report provides an evaluation of the Austrian guidelines that were introduced in 1987 as a natural experiment. Methods: The impact of the guidelines was tested by applying an autoregressive integrated moving average (ARIMA) model and a linear regression model. In addition to a nationwide evaluation, Austria was divided into three areas according to regional differences in coverage rates of the collaborating newspapers and the impact of the intervention was tested for each area separately. Main outcome measures were the overall annual suicide numbers, and the numbers of Viennese subway suicides that were exceptionally newsworthy for the mass media. In order to test intermediate impacts, also quantitative and qualitative changes in media reporting after the introduction of the guidelines were analysed. Results: There was some evidence of a nationwide impact of the guidelines, calculated as a significant reduction of 81 suicides (95% confidence interval: −149 to −13; t = −2.32, df = 54, p <0.024) annually. This effect was particularly due to a significant reduction in the area with the highest coverage rates of the collaborating newspapers. Viennese subway suicides showed a highly significant level shift (t = −4.44, df = 19, p <0.001) and a highly significant trend change (t = −4.20, df = 19, p <0.001) after the introduction of the guidelines. These effects corresponded to significant changes in the quality and quantity of media reporting. Conclusions: The present results clearly support the hypothesis that the media guidelines have had an impact on the quality of reporting as well as on suicidal behaviour in Austria, and stress the importance of collaborating with nationwide, but also with regional media to achieve efficacy. Further research is needed to provide an international insight into this public health issue.

scholarly journals Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Andrea L. Schaffer ◽  
Timothy A. Dobbins ◽  
Sallie-Anne Pearson
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Moving Average ◽  
Interrupted Time Series ◽  
Health Interventions ◽  
Policy Intervention ◽  
Arima Models ◽  
Interrupted Time Series Analysis ◽  
Autoregressive Integrated Moving Average ◽  
The Impact

Abstract Background Interrupted time series analysis is increasingly used to evaluate the impact of large-scale health interventions. While segmented regression is a common approach, it is not always adequate, especially in the presence of seasonality and autocorrelation. An Autoregressive Integrated Moving Average (ARIMA) model is an alternative method that can accommodate these issues. Methods We describe the underlying theory behind ARIMA models and how they can be used to evaluate population-level interventions, such as the introduction of health policies. We discuss how to select the shape of the impact, the model selection process, transfer functions, checking model fit, and interpretation of findings. We also provide R and SAS code to replicate our results. Results We illustrate ARIMA modelling using the example of a policy intervention to reduce inappropriate prescribing. In January 2014, the Australian government eliminated prescription refills for the 25 mg tablet strength of quetiapine, an antipsychotic, to deter its prescribing for non-approved indications. We examine the impact of this policy intervention on dispensing of quetiapine using dispensing claims data. Conclusions ARIMA modelling is a useful tool to evaluate the impact of large-scale interventions when other approaches are not suitable, as it can account for underlying trends, autocorrelation and seasonality and allows for flexible modelling of different types of impacts.

scholarly journals The impact of COVID-19 on outpatient antibiotic prescriptions in Ontario, Canada; An interrupted time series analysis

2021 ◽  
Author(s):  
Taito Kitano ◽  
Kevin A Brown ◽  
Nick Daneman ◽  
Derek R MacFadden ◽  
Bradley J Langford ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Respiratory Infections ◽  
Population Level ◽  
Interrupted Time Series ◽  
Relative Reduction ◽  
Antibiotic Prescribing ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
The Impact

Abstract Background The COVID-19 pandemic has potentially impacted outpatient antibiotic prescribing. Investigating this impact may identify stewardship opportunities in the ongoing COVID-19 period and beyond. Methods We conducted an interrupted time series analysis on outpatient antibiotic prescriptions and antibiotic prescriptions/patient visits in Ontario, Canada between January 2017 and December 2020 to evaluate the impact of the COVID-19 pandemic on population-level antibiotic prescribing by prescriber’s specialty, patient demographics and conditions. Results In the evaluated COVID-19 period (March-December 2020), there was a 31.2% [95% CI: 27.0%–35.1%] relative reduction in total antibiotic prescriptions. Total outpatient antibiotic prescriptions decreased during the COVID-19 period by 37.1% [32.5%–41.3%] among family physicians, 30.7% [25.8%–35.2%] among sub-specialist physicians, 12.1% [4.4%–19.2%] among dentists and 25.7% [21.4%–29.8%] among other prescribers. Antibiotics indicated for respiratory infections decreased by 43.7% [38.4–48.6%]. Total patient visits and visits for respiratory infections decreased by 10.7% [5.4%–15.6%] and 49.9% [43.1%%–55.9%]). Total antibiotic prescriptions/1,000 visits decreased by 27.5% [21.5%–33.0%], while antibiotics indicated for respiratory infections/1,000 visits with respiratory infections only decreased by 6.8% [2.7%–10.8%]. Conclusion The reduction in outpatient antibiotic prescribing during the COVID-19 pandemic was driven by less antibiotic prescribing for respiratory indications and largely explained by decreased visits for respiratory infections.

scholarly journals Massive social protests amid the pandemic in selected Colombian cities: Did they increase COVID-19 cases?

2021 ◽  
Author(s):  
Jose Moreno-Montoya ◽  
Laura A Rodriguez Villamizar ◽  
Alvaro Javier Idrovo
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Interrupted Time Series ◽  
Series Data ◽  
Arima Models ◽  
Interrupted Time Series Analysis ◽  
Social Protests ◽  
Show Evidence ◽  
The Impact

Background. Since April 28, 2021, in Colombia there are social protests with numerous demonstrations in various cities. This occurs whereas the country faces the third wave of the COVID-19 pandemic. The aim of this study was to assess the effect of social protests on the number and trend of the confirmed COVID-19 cases in some selected Colombian cities where social protests had more intensity. Methods. We performed and interrupted time-series analysis (ITSA) and Autoregressive Integrated Moving Average (ARIMA) models, based on the confirmed COVID-19 cases in Colombia, between March 1 and May 15, 2021, for the cities of Bogota, Cali, Barranquilla, Medellin, and Bucaramanga. The ITSA models estimated the impact of social demonstrations on the number and trend of cases for each city by using Newey-West standard errors and ARIMA models assessed the overall pattern of the series and effect of the intervention. We considered May 2, 2021, as the intervention date for the analysis, five days after social demonstrations started in the country. Findings. During the study period the number of cases by city was 1,014,815 for Bogota, 192,320 for Cali, 175,269 for Barranquilla, 311,904 for Medellin, and 62,512 for Bucaramanga. Heterogeneous results were found among cities. Only for the cities of Cali and Barranquilla statistically significant changes in trend of the number of cases were obtained after the intervention: positive in the first city, negative in the second one. None ARIMA models show evidence of abrupt changes in the trend of the series for any city and intervention effect was only positive for Bucaramanga. Interpretation. The findings confer solid evidence that social protests had an heterogenous effect on the number and trend of COVID-19 cases. Divergent effects might be related to the epidemiologic time of the pandemic and the characteristics of the social protests. Assessing the effect of social protests within a pandemic is complex and there are several methodological limitations. Further analyses are required with longer time-series data.

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