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

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.

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

10.1101/2020.05.01.20087288 ◽

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.

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

Das Gesundheitswesen ◽

10.1055/a-0828-7504 ◽

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.

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

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa515.1913 ◽

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 < 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< 0.001) and change in the post-intervention slope on the log odds scale (p< 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< 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

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The Effect of an Italian Nationwide Mandatory Visibility Aids Law for Cyclists

10.31234/osf.io/8r3jf ◽

2019 ◽

Author(s):

Gabriele Prati

Keyword(s):

Safety Data ◽

Interrupted Time Series ◽

Ordinary Least Squares ◽

Contributory Factor ◽

Least Squares Regression ◽

Road Accidents ◽

Interrupted Time Series Analysis ◽

Bicycle Safety ◽

The Impact ◽

Italian National Institute

There is evidence that the conspicuity of cyclists is a contributory factor in some bicycle–motorized vehicle collisions and the use of visibility aids increases visibility and improves drivers’ responses in detection and recognition. To date, no study has evaluated the impact of a legislation imposing bicycling visibility aids on bicycle safety. The aim of the present study is to investigate whether a compulsory bicycling visibility aids law affects bicycle safety. Data on the monthly number of vehicles (including bicycles) involved in road accidents during the period 2001–2015 were obtained from the Italian National Institute of Statistics. Data were analyzed through an interrupted time-series analysis using an ordinary least-squares regression-based approach. Results revealed that the implementation of a legislation imposing bicycling visibility aids did not influence the number of bicycles involved in road accidents as well as its proportion in the total vehicles involved in road accidents. The introduction of the legislation did not produce immediate effects, nor did it have any effects over time.

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

10.1101/2021.02.19.21251770 ◽

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.

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

JAC-Antimicrobial Resistance ◽

10.1093/jacamr/dlaa076 ◽

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 > 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.

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

Pharmacy ◽

10.3390/pharmacy7030115 ◽

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.

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Hospital performance and payment: impact of integrating pay-for-performance on healthcare effectiveness in Lebanon

Wellcome Open Research ◽

10.12688/wellcomeopenres.15810.2 ◽

2020 ◽

Vol 5 ◽

pp. 95

Author(s):

Jade Khalife ◽

Walid Ammar ◽

Maria Emmelin ◽

Fadi El-Jardali ◽

Bjorn Ekman

Keyword(s):

Pay For Performance ◽

Routine Data ◽

Unknown Origin ◽

Ordinary Least Squares ◽

Hospital Performance ◽

Least Squares Regression ◽

Analysis Model ◽

Interrupted Time Series Analysis ◽

Post Intervention ◽

The Impact

Background: In 2014 the Lebanese Ministry of Public Health integrated pay-for-performance into setting hospital reimbursement tiers, to provide hospitalization service coverage for the majority of the Lebanese population. This policy was intended to improve effectiveness by decreasing unnecessary hospitalizations, and improve fairness by including risk-adjustment in setting hospital performance scores. Methods: We applied a systematic approach to assess the impact of the new policy on hospital performance. The main impact measure was a national casemix index, calculated across 2011-2016 using medical discharge and surgical procedure codes. A single-group interrupted time series analysis model with Newey ordinary least squares regression was estimated, including adjustment for seasonality, and stratified by case type. Code-level analysis was used to attribute and explain changes in casemix index due to specific diagnoses and procedures. Results: Our final model included 1,353,025 cases across 146 hospitals with a post-intervention lag-time of two months and seasonality adjustment. Among medical cases the intervention resulted in a positive casemix index trend of 0.11% per month (coefficient 0.002, CI 0.001-0.003), and a level increase of 2.25% (coefficient 0.022, CI 0.005-0.039). Trend changes were attributed to decreased cases of diarrhea and gastroenteritis, abdominal and pelvic pain, essential hypertension and fever of unknown origin. A shift from medium to short-stay cases for specific diagnoses was also detected. Level changes were attributed to improved coding practices, particularly for breast cancer, leukemia and chemotherapy. No impact on surgical casemix index was found. Conclusions: The 2014 policy resulted in increased healthcare effectiveness, by increasing the casemix index of hospitals contracted by the Ministry. This increase was mainly attributed to decreased unnecessary hospitalizations and was accompanied by improved medical discharge coding practices. Integration of pay-for-performance within a healthcare system may contribute to improving effectiveness. Effective hospital regulation can be achieved through systematic collection and analysis of routine data.

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