scholarly journals Smokefree policy and medication dispensing for people in prison: interrupted time series analysis

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
A Leyland ◽  
E Tweed ◽  
T Byrne ◽  
P Conaglen ◽  
P Craig ◽  
...  
Keyword(s):  
Time Series ◽  
Prison Population ◽  
Moving Average ◽  
Unintended Consequences ◽  
Eye Drops ◽  
Natural Experiments ◽  
Interrupted Time Series Analysis ◽  
Objective Health ◽  
Smokefree Policy ◽  
Medication Dispensing

Abstract Background Previous evaluations of smokefree prison policies have suggested improvements in self-rated health and some smoking-related symptoms. No studies to date have investigated impacts on medication use as proxy measures of objective ill-health or as indicators of potential negative unintended consequences. These is limited evidence to date on these important outcomes. Methods We obtained from NHS National Services Scotland aggregate data on medication items dispensed in prisons, based on individual named patient medication records, and from the Scottish Prison Service data on the prison population, for the period Jan 2013-Nov 2019. Items of interest comprised those for smoking cessation (varenicline and buproprion); nicotine replacement; specific smoking-related health conditions (glyceryl trinitrate; inhaled bronchodilators and steroids; antibiotics; chloramphenicol eye drops; and proton pump inhibitors and H2 receptor antagonists), and potential unintended mental health consequences (anti-depressants). We also included a set of negative controls for which dispensing was not expected to be affected by the new smokefree policy (anticonvulsants, excluding pregabalin and gabapentin). Analyses were undertaken using AutoRegressive Integrated Moving Average (ARIMA) time series methods, with the dates of the policy's announcement and of implementation included as pre-specified breakpoints. Results The results of ARIMA modelling of medication dispensing are confidential until May 2020 due to their sensitivity and will be available to present at WCPH 2020. Conclusions The use of routinely available dispensing data as an indicator of objective health impacts and potential negative unintended consequences provides novel insights into the effectiveness of smokefree prison policies. Results will be of interest to international jurisdictions considering such policies and to those seeking to harness the potential of administrative data for natural experiments.

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 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.

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 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.

Do policy and training changes influence patterns of police use of force? An interrupted time-series analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alexis Rain Rockwell ◽  
Stephen A. Bishopp ◽  
Erin A. Orrick
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Use Of Force ◽  
Interrupted Time Series ◽  
Unintended Consequences ◽  
Interrupted Time Series Analysis ◽  
Police Use Of Force ◽  
Content Type ◽  
Series Analysis ◽  
And Training

PurposeThe current study examines the effect of changing a specific use-of-force policy coupled with de-escalation training implementation on patterns of police use of force.Design/methodology/approachAn interrupted time-series analysis was used to examine changes in police use-of-force incident records gathered from a large, southwestern US metropolitan police department from 2013 to 2017 based on a TASER policy change and de-escalation training implementation mid-2015.FindingsResults demonstrate that changes to use-of-force policy regarding one type of force (i.e. use of TASERs) coinciding with de-escalation training influence the prevalence of use-of-force incidents by increasing the reported police use-of-force incidents after the changes were implemented. This finding is somewhat consistent with prior literature but not always in the desired direction.Practical implicationsWhen police departments make adjustments to use-of-force policies and/or trainings, unintended consequences may occur. Police administrators should measure policy and training outcomes under an evidence-based policing paradigm prior to making those adjustments.Originality/valueThis study is the first to measure the effects of changing use-of-force policy and implementing de-escalation techniques in training on patterns of police use of force and shows that these changes can have a ripple effect across types of force used by police officers.

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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