Application of Interrupted Time Series Modelling to Prime Motor Spirit Distribution in Nigeria

2017 ◽  
Vol 7 (8) ◽  
pp. 46
Author(s):  
Ette Harrison Etuk ◽  
Imo Udo Moffat ◽  
Azubuike Samuel Agbam
Keyword(s):  
Time Series ◽  
Oil Industry ◽  
Interrupted Time Series ◽  
Intervention Model ◽  
Post Intervention ◽  
Abrupt Jump ◽  
Time Series Modelling ◽  
And Control ◽  
The Impact ◽  
Time Plot

An inspection of the time-plot of monthly Prime Motor Spirit (PMS) distribution in Nigeria from 2009 to 2015 reveals an abrupt jump in January 2013 with the series continuing at that level till 2015. Clearly the trend of the series was interrupted in January 2013 and it is believed that this perturbation was due to the deregulation of the downstream sector of the crude oil industry.  A t-test comparison of the pre- and the post-intervention means is highly significant (p < 0.0001) indicating the impact of the intervention. A model of the ARIMA family was to be fitted to the pre-intervention data which were observed to have a downward trend and be non-stationary. Differencing once rendered it stationary. An adequate ARIMA(2,1,0) model was fitted to the original pre-intervention series. Post-intervention forecasts were obtained on the basis of this model. These forecasts were subtracted from their respective post-intervention counterpart observations. These differences were modelled to obtain the transfer function of the intervention. The resultant intervention model closely fits the post-intervention data and may be used to explain and control the situation.

The Impact of the Change in Payment System for German Psychiatric Clinics on Treatment Success: An Interrupted Time Series Analysis

2019 ◽  
Vol 82 (06) ◽  
pp. 559-567
Author(s):  
Christina Niedermeier ◽  
Andrea Barrera ◽  
Eva Esteban ◽  
Ivana Ivandic ◽  
Carla Sabariego
Keyword(s):  
Time Series ◽  
Negative Impact ◽  
Treatment Success ◽  
Interrupted Time Series ◽  
Sensitivity Analyses ◽  
Interrupted Time Series Analysis ◽  
Post Intervention ◽  
Intervention Point ◽  
The Impact ◽  
New System

Abstract Background In Germany a new reimbursement system for psychiatric clinics was proposed in 2009 based on the § 17d KHG Psych-Entgeltsystem. The system can be voluntary implemented by clinics since 2013 but therapists are frequently afraid it might affect treatment negatively. Objectives To evaluate whether the new system has a negative impact on treatment success by analysing routinely collected data in a Bavarian clinic. Material and methods Aggregated data of 1760 patients treated in the years 2007–2016 was analysed with segmented regression analysis of interrupted time series to assess the effects of the system on treatment success, operationalized with three outcome variables. A negative change in level after a lag period was hypothesized. The robustness of results was tested by sensitivity analyses. Results The percentage of patients with treatment success tends to increase after the new system but no significant change in level was observed. The sensitivity analyses corroborate results for 2 outcomes but when the intervention point was shifted, the positive change in level for the third outcome became significant. Conclusions Our initial hypothesis is not supported. However, the sensitivity analyses disclosed uncertainties and our study has limitations, such as a short observation time post intervention. Results are not generalizable as data of a single clinic was analysed. Nevertheless, we show the importance of collecting and analysing routine data to assess the impact of policy changes on patient outcomes.

scholarly journals The impact of China’s lockdown policy on the incidence of CoVID-19: An Interrupted time series analysis

2020 ◽  
Author(s):  
Mooketsi Molefi ◽  
John Tlhakanelo ◽  
Thabo Phologolo ◽  
Shimeles G. Hamda ◽  
Tiny Masupe ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Effective Means ◽  
Interrupted Time Series ◽  
Ordinary Least Squares ◽  
World Health ◽  
Least Squares Regression ◽  
Interrupted Time Series Analysis ◽  
Post Intervention ◽  
The Impact

Abstract BackgroundPolicy changes are often necessary to contain the detrimental impact of epidemics such as the coronavirus disease (COVID-19). China imposed strict restrictions on movement on January 23rd, 2020.Interrupted time series methods were used to study the impact of the lockdown on the incidence of COVID-19. MethodsThe number of cases of COVID-19 reported daily from January 12thto March 30th, 2020 were extracted from the World Health Organization (WHO) COVID-19 dashboard ArcGIS® and matched to China’s projected population of 1 408 526 449 for 2020 in order to estimate daily incidences. Data were plotted to reflect daily incidences as data points in the series. A deferred interruption point of 6thFebruary was used to allow a 14-day period of diffusion. The magnitude of change and linear trend analyses were evaluated using the itsafunction with ordinary least-squares regression coefficients in Stata® yielding Newey-West standard errors.ResultsSeventy-eight (78) daily incidence points were used for the analysis, with 11(14.10%) before the intervention. There was a daily increase of 163 cases (β=1.16*10-07, p=0.00) in the pre-intervention period. Although there was no statistically significant drop in the number of cases reported daily in the immediate period following 6thFebruary 2020 when compared to the counterfactual (p=0.832), there was a 241 decrease (β=-1.71*10-07, p=0.00) in cases reported daily when comparing the pre-intervention and post-intervention periods. A deceleration of 78(47%) cases reported daily. ConclusionThe lockdown policy managed to significantly decrease the incidence of CoVID-19 in China. Lockdown provides an effective means of curtailing the incidence of COVID-19.

scholarly journals 74. Interrupted Time Series Analysis of the Impact of Fluoroquinolone Cascade Reporting

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S55-S55
Author(s):  
Matthew Nestler ◽  
John D Markley ◽  
Andrew Noda ◽  
Emily Godbout ◽  
Jihye Kim ◽  
...  
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Interrupted Time Series ◽  
Positive Change ◽  
Post Intervention ◽  
E Coli ◽  
Significant Positive Change ◽  
One Year ◽  
The One ◽  
The Impact

Abstract Background Cascade reporting is a form of selective reporting where antibiotic susceptibility results are revealed in a sequential order to optimize antibiotic use. On May 1, 2019, Virginia Commonwealth University Health implemented cascade reporting for ciprofloxacin and levofloxacin for E. coli from urine cultures. We hypothesize that suppressing fluoroquinolone (FQ) results for urine isolate E. coli susceptibility panels using cascade reporting led to a decrease in the overall rate of inpatient FQ use. Methods We compared inpatient FQ use (in days of therapy (DOT)/1000 patient days (PD)) for the one-year pre-cascade period (May 2018-April 2019) to the one-year post cascade period (May 2019-April 2020). Inpatient FQ use for May 2018-April 2020 was modeled as an interrupted time series (ITS) using ordinary least squares regression. The regression model followed the form of Y = B0 +B1T + B2 X + B3 XT with Y = (DOT/1,000 PD), T = time in months, X = cascade reporting represented with a binary digit, and XT= time since cascade reporting was implemented. Results were examined for autocorrelation and lag effects. Analysis conducted using Microsoft Excel and Python Statsmodel library v0.11.1. Results A segmented regression model was successfully fitted with R^2 = 0.73 (Figure 1). The pre-intervention slope (T), intervention change (X), and post-intervention slope (XT) were -3.9, -2.3, and 3.8 DOT respectively. A significant positive change in pre versus post intervention slope was detected (p = 0.01). Conclusion Results showed no significant change in FQ DOT/1000 PD when cascade reporting was implemented in May 2019. This may be due to empiric prescribing of FQs in the inpatient setting, due to the fact the rate of FQ use was already decreasing prior to cascade reporting adoption, or due to other factors. We detected a significant positive change in the slope of FQ from -4 to 4 DOT/1000 PD each month post-cascade reporting. Our hospital has had a decrease in FQ use over the past 8 years so this may be due to a ‘floor’ effect where the true minimum of necessary FQ use was reached; further investigation is warranted. We believe our data will be of interest to other Antimicrobial Stewardship Programs considering cascade reporting. Disclosures All Authors: No reported disclosures

scholarly journals LB-16. Association Between Universal Face Shield in a Quaternary Care Center and Reduction of SARS-COV2 Infections Among Healthcare Personnel and Hospitalized Patients

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S851-S851
Author(s):  
Vagesh Hemmige ◽  
Becky Winterer ◽  
Todd Lasco ◽  
Bradley Lembcke
Keyword(s):  
Time Series ◽  
Care Center ◽  
Interrupted Time Series ◽  
Hospitalized Patients ◽  
Healthcare Personnel ◽  
Segmented Regression ◽  
Intervention Period ◽  
Interrupted Time Series Analysis ◽  
Post Intervention ◽  
The Impact

Abstract Background SARS-COV2 transmission to healthcare personnel (HCP) and hospitalized patients is a significant challenge. Our hospital is a quaternary healthcare system with more than 500 beds and 8,000 HCP. Between April 1 and April 17, 2020, we instituted several infection prevention strategies to limit transmission of SARS-COV2 including universal masking of HCP and patients, surveillance testing every two weeks for high-risk HCP and every week for cluster units, and surveillance testing for all patients on admission and prior to invasive procedures. On July 6, 2020, we implemented universal face shield for all healthcare personnel upon entry to facility. The aim of this study is to assess the impact of face shield policy on SARS-COV2 infection among HCP and hospitalized patients. Figure 1- Interrupted time series Methods The preintervention period (April 17, 2020-July 5, 2020) included implementation of universal face masks and surveillance testing of HCP and patients. The intervention period (July 6, 2020-July 26, 2020) included the addition of face shield to all HCP (for patient encounters and staff-to-staff encounters). We used interrupted time series analysis with segmented regression to examine the effect of our intervention on the difference in proportion of HCP positive for SARS-COV2 (using logistic regression) and HAI (using Poisson regression). We defined significance as p values &lt; 0.05. Results Of 4731 HCP tested, 192 tested positive for SARS-COV2 (4.1%). In the preintervention period, the weekly positivity rate among HCP increased from 0% to 12.9%. During the intervention period, the weekly positivity rate among HCP decreased to 2.3%, with segmented regression showing a change in predicted proportion positive in week 13 (18.0% to 3.7%, p&lt; 0.001) and change in the post-intervention slope on the log odds scale (p&lt; 0.001). A total of 14 HAI cases were identified. In the preintervention period, HAI cases increased from 0 to 5. During the intervention period, HAI cases decreased to 0. There was a change between pre-intervention and post-intervention slope on the log scale was significant (p&lt; 0.01). Conclusion Our study showed that the universal use of face shield was associated with significant reduction in SARS-COV2 infection among HCP and hospitalized patients. Disclosures All Authors: No reported disclosures

scholarly journals RITS: A Toolbox for Assessing Complex Interventions via Interrupted Time Series Models

2021 ◽  
Author(s):  
Maricela Francis Cruz ◽  
Marco A. Pinto-Orellana ◽  
Daniel L. Gillen ◽  
Hernando Ombao
Keyword(s):  
Time Series ◽  
Change Point ◽  
Complex Interventions ◽  
Interrupted Time Series ◽  
Correlation Structure ◽  
Experimental Methodology ◽  
Post Intervention ◽  
Correlation Structures ◽  
The Mean ◽  
The Impact

Abstract Background: Various interacting and interdependent components comprise complex interventions. These components create difficulty in assessing the true impact of interventions designed to improve patient-centered outcomes. Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. While ITS designs are aptly situated for studying the impacts of large-scale public health policies, existing ITS software implement rigid ITS methodology that often assume the pre- and post-intervention phases are fully differentiated (by a known change-point or set of time points) and do not allow for changes in both the mean functions and correlation structure. Results: This article describes the Robust Interrupted Time Series (RITS) toolbox, a stand-alone user-friendly application researchers can use to implement flexible ITS models that estimate the lagged effect of an intervention on an outcome, level and trend changes, and post-intervention changes in the correlation structure, for single and multiple ITS. The RITS toolbox incorporates a formal test for the existence of a change in the outcome and estimates a change-point over a set of possible change-points defined by the researcher. In settings with multiple ITS, RITS provides a global over-all units change-point and allows for unit-specific changes in the mean functions and correlation structures. Conclusions: The RITS toolbox is the first piece of software that allows researchers to use flexible ITS models that test for the existence of a change-point, estimate the change-point (if estimation is desired), and allow for changes in both the mean functions and correlation structures at the change point. RITS does not require any knowledge of a statistical (or otherwise) programming language, is freely available to the community, and may be downloaded and used on a local machine to ensure data protection.

scholarly journals Assessing the impact of SIGN 136 on opioid prescribing rates in Scotland: An interrupted time series analysis

2021 ◽  
Author(s):  
Harry L. Hébert ◽  
Daniel R. Morales ◽  
Nicola Torrance ◽  
Blair H. Smith ◽  
Lesley A. Colvin
Keyword(s):  
Chronic Pain ◽  
Time Series ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Relative Reduction ◽  
Interrupted Time Series Analysis ◽  
Post Intervention ◽  
Opioid Prescribing ◽  
Prescribing Trends ◽  
The Impact

AbstractBackgroundOpioids are used to treat patients with chronic pain, but their long-term use is associated with harms. In December 2013, SIGN 136 was published, providing a comprehensive evidence-based guideline for the assessment and management of chronic pain in ScotlandAimsThis study aimed to examine the impact of SIGN 136 on opioid prescribing trends and costs across the whole of Scotland.MethodsOpioid prescribing data and average cost per item were obtained from Public Health Scotland. An interrupted time series analysis examined the effects of SIGN 136 publication on the number of items prescribed per 1,000 population per quarter for 29 opioids (or opioid-containing combinations) from 2005 to 2019 inclusive. Exploratory analysis was conducted in NHS Tayside and NHS Fife combined and then up-scaled to all 14 NHS Scotland health boards. A similar approach was also used to assess the effect of SIGN 136 on estimated gross ingredient costs per quarter.ResultsAt six years post-intervention there was a relative reduction in opioid prescribing of 18.8% (95% CI: 16.0-21.7) across Scotland. There was also a relative reduction of 22.8% (95%: 14.9-30.1) in gross ingredient cost nationally. Opioid prescribing increased significantly pre-intervention across all 14 NHS Scotland health boards (2.19 items per 1000 population per quarter), followed by a non-significant change in level and a significant negative change in trend post-intervention (−2.69 items per 1000 population per quarter). Similar findings were observed locally in NHS Tayside and NHS Fife.ConclusionsThe publication of SIGN 136 coincided with a statistically significant reduction in opioid prescribing rates in Scotland and suggests that changes in clinical policy are having a positive effect on prescribing practices in primary care. These prescribing trends appear to be in contrast to the UK as a whole.

scholarly journals The impact of stringent prescription-only antimicrobial sale regulation (Schedule H1) in India: an interrupted time series analysis, 2008–18

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Habib Hasan Farooqui ◽  
Sakthivel Selvaraj ◽  
Aashna Mehta ◽  
Manu Raj Mathur
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Private Sector ◽  
Positive Impact ◽  
Interrupted Time Series ◽  
Control Group ◽  
Interrupted Time Series Analysis ◽  
Post Intervention ◽  
Series Analysis ◽  
The Impact

Abstract Objectives To assess the impact of Schedule H1 regulation notified and implemented in 2014 under the amended rules of the Drugs and Cosmetics Act (DCA), 1940 on the sale of antimicrobials in the private sector in India. Methods The dataset was obtained from the Indian pharmaceutical sales database, PharmaTrac. The outcome measure was the sales volume of antimicrobials in standard units (SUs). A quasi-experimental research design—interrupted time series analysis—was used to detect the impact of the intervention. Results We observed a substantial rise in antimicrobial consumption during 2008–18 in the private sector in India, both for antimicrobials regulated under Schedule H1 as well as outside the regulation. Key results suggested that post-intervention there was an immediate reduction (level change) in use of Schedule H1 antimicrobials by 10% (P = 0.007), followed by a sustained decline (trend change) in utilization by 9% (P &gt; 0.000) compared with the pre-intervention trend. Segregated analysis on different antimicrobial classes suggests a sharp drop (level changes) and sustained decline (trend changes) in utilization post-intervention compared with the pre-intervention trend. Our findings remained robust on carrying out sensitivity analysis with the oral anti-diabetics market as a control. Post-intervention, the average monthly difference between antimicrobials under Schedule H1 and the control group witnessed an immediate increase of 16.3% (P = 0.10) followed by a sustained reduction of 0.5% (P = 0.13) compared with the pre-intervention scenario. Conclusions Though the regulation had a positive impact in terms of reducing sales of antimicrobials notified under the regulation, optimizing the effectiveness of such stand-alone policies will be limited unless accompanied by a broader set of interventions.

scholarly journals Impact of the 2016 Policy Change on the Delivery of MedsCheck Services in Ontario: An Interrupted Time-Series Analysis

Pharmacy ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 115
Author(s):  
Shakeri ◽  
Dolovich ◽  
MacCallum ◽  
Gamble ◽  
Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Policy Change ◽  
Community Pharmacists ◽  
Professional Community ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Post Intervention ◽  
Term Care ◽  
The Government ◽  
The Impact

MedsCheck (MC) is an annual medication review service delivered by community pharmacists and funded by the government of Ontario since 2007 for residents taking three or more medications for chronic conditions. In 2010, MC was expanded to include patients with diabetes (MCD), home-bound patients (MCH), and residents of long-term care homes (MCLTC). The Ontario government introduced an abrupt policy change effective 1 October 2016 that added several components to all MC services, especially those completed in the community. We used an interrupted time series design to examine the impact of the policy change (24 months pre- and post-intervention) on the monthly number of MedsCheck services delivered. Immediate declines in all services were identified, especially in the community (47%–64% drop MC, 71%–83% drop MCD, 55% drop MCH, and 9%–14% drop MCLTC). Gradual increases were seen over 24 months post-policy change, yet remained 21%–76% lower than predicted for MedsCheck services delivered in the community, especially for MCD. In contrast, MCLTC services were similar or exceeded predicted values by September 2018 (from 5.1% decrease to 3.5% increase). A more effective implementation of health policy changes is needed to ensure the feasibility and sustainability of professional community pharmacy services.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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