Changes in antibiotic usage and susceptibility in nosocomial Enterobacteriaceae and Pseudomonas isolates following the introduction of ertapenem to hospital formulary

2011 ◽  
Vol 140 (1) ◽  
pp. 115-125 ◽  
Author(s):  
C. J. GRABER ◽  
C. HUTCHINGS ◽  
F. DONG ◽  
W. LEE ◽  
J. K. CHUNG ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Interrupted Time Series ◽  
Antibiotic Usage ◽  
Cross Resistance ◽  
Hospital Formulary ◽  
Time Series Analyses ◽  
The Mean ◽  
The Impact ◽  
Change In Mean

SUMMARYThere is concern that widespread usage of ertapenem may promote cross-resistance to other carbapenems. To analyse the impact that adding ertapenem to our hospital formulary had on usage of other broad-spectrum agents and on susceptibilities of nosocomial Enterobacteriaceae and Pseudomonas isolates, we performed interrupted time-series analyses to determine the change in linear trend in antibiotic usage and change in mean proportion and linear trend of susceptibility pre- (March 2004–June 2005) and post- (July 2005–December 2008) ertapenem introduction. Usage of piperacillin-tazobactam (P=0·0013) and ampicillin-sulbactam (P=0·035) declined post-ertapenem introduction. For Enterobacteriaceae, the mean proportion susceptible to ciprofloxacin (P=0·016) and piperacillin-tazobactam (P=0·038) increased, while the linear trend in susceptibility significantly increased for cefepime (P=0·012) but declined for ceftriaxone (P=0·0032). For Pseudomonas, the mean proportion susceptible to cefepime (P=0·011) and piperacillin-tazobactam (P=0·028) increased, as did the linear trend in susceptibility to ciprofloxacin (P=0·028). Notably, no significant changes in carbapenem susceptibility were observed.

scholarly journals Interrupted time series analyses to assess the impact of alcohol control policy on socioeconomic inequalities in mortality in Lithuania: a study protocol

2021 ◽  
Author(s):  
Jakob Manthey ◽  
Domantas Jasilionis ◽  
Huan Jiang ◽  
Olga Mesceriakova-Veliuliene ◽  
Janina Petkeviciene ◽  
...  
Keyword(s):  
Time Series ◽  
Research Ethics ◽  
Interrupted Time Series ◽  
Mortality Rates ◽  
Attributable Mortality ◽  
Population Census ◽  
Combining Data ◽  
Time Series Analyses ◽  
Inequalities In Mortality ◽  
The Impact

Introduction Alcohol use is a major risk factor for mortality. Previous studies suggest that the alcohol-attributable mortality burden is higher in lower socioeconomic strata. This project will test the hypothesis, that the 2017 increase of alcohol excise taxes for beer and wine, which was linked to lower all-cause mortality rates in previous analyses, will reduce socioeconomic mortality inequalities. Methods and analysis Data on all causes of deaths will be obtained from Statistics Lithuania. Record linkage will be implemented using personal identifiers combining data from 1) the 2011 whole-population census, 2) death records between March 1, 2011 (census date) and December 31, 2019, and 3) emigration records, for individuals aged 30 to 70 years. The analyses will be performed separately for all-cause and for alcohol-attributable deaths. Monthly age-standardized mortality rates will be calculated by sex, education, and three measures of socioeconomic status. Inequalities in mortality will be assessed using absolute and relative indicators between low and high SES groups. We will perform interrupted time series analyses, and test the impact of the 2017 rise in alcohol excise taxation using generalized additive mixed models. In these models, we will control for secular trends for economic development. Ethics and dissemination This work is part of project grant 1R01AA028224-01 by the National Institute on Alcohol Abuse and Alcoholism. It has been granted research ethics approval 050/2020 by CAMH Research Ethics Board on April 17, 2020, renewed on March 30, 2021.

scholarly journals Initial impact of COVID-19’s stay-at-home order on motor vehicle traffic and crash patterns in Connecticut: an interrupted time series analysis

2020 ◽  
pp. injuryprev-2020-043945
Author(s):  
Mitchell L Doucette ◽  
Andrew Tucker ◽  
Marisa E Auguste ◽  
Amy Watkins ◽  
Christa Green ◽  
...  
Keyword(s):  
Time Series ◽  
Motor Vehicle ◽  
Interrupted Time Series ◽  
Data Repository ◽  
Interrupted Time Series Analysis ◽  
Crash Rates ◽  
Single Vehicle ◽  
The Mean ◽  
The Impact ◽  
At Home

IntroductionUnderstanding how the COVID-19 pandemic has impacted our health and safety is imperative. This study sought to examine the impact of COVID-19’s stay-at-home order on daily vehicle miles travelled (VMT) and MVCs in Connecticut.MethodsUsing an interrupted time series design, we analysed daily VMT and MVCs stratified by crash severity and number of vehicles involved from 1 January to 30 April 2017, 2018, 2019 and 2020. MVC data were collected from the Connecticut Crash Data Repository; daily VMT estimates were obtained from StreetLight Insight’s database. We used segmented Poisson regression models, controlling for daily temperature and daily precipitation.ResultsThe mean daily VMT significantly decreased 43% in the post stay-at-home period in 2020. While the mean daily counts of crashes decreased in 2020 after the stay-at-home order was enacted, several types of crash rates increased after accounting for the VMT reductions. Single vehicle crash rates significantly increased 2.29 times, and specifically single vehicle fatal crash rates significantly increased 4.10 times when comparing the pre-stay-at-home and post-stay-at-home periods.DiscussionDespite a decrease in the number of MVCs and VMT, the crash rate of single vehicles increased post stay-at-home order enactment in Connecticut after accounting for reductions in VMT.

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 Effects of the FDA Codeine Safety Investigation on Racial and Geographic Disparities in Opioid Prescribing after Pediatric Tonsillectomy and/or Adenoidectomy

2021 ◽  
Vol 8 ◽  
pp. 2333794X2098744
Author(s):  
Amy Lawrence ◽  
Jennifer N. Cooper ◽  
Katherine J. Deans ◽  
Peter C. Minneci ◽  
Sharon K. Wrona ◽  
...  
Keyword(s):  
Time Series ◽  
Interrupted Time Series ◽  
Black Box ◽  
Opioid Prescription ◽  
White Children ◽  
Geographic Disparities ◽  
Time Series Analyses ◽  
The Impact ◽  
Metropolitan Counties ◽  
The U.S

Objective. Our objective was to examine the impact of the U.S. FDA’s 2013 black box warning against codeine on codeine and other opioid prescription filling after pediatric tonsillectomy and/or adenoidectomy (T/A) overall and by child race and provider urbanity/rurality. Methods. Patients ≤ 18 who underwent T/A in 8/2011 to 8/2016 were identified in Ohio Medicaid claims. Interrupted time series analyses were used to evaluate the impact of the FDA warning on codeine or other opioid prescription filling post-T/A. Results. In August 2011, codeine prescription filling was lower among black than white children ( P < .001) and among children treated at institutions in metropolitan counties than less populous counties ( P < .001). The FDA warning was associated with a 24.0% drop in codeine prescription filling ( P < .001) and 5.5% increase in alternative opioid prescription filling ( P = .046). At conclusion, there remained geographic but no longer racial disparities in codeine prescribing. Conclusion. Codeine prescribing after pediatric T/A decreased after the FDA’s black box warning. However, geographic disparities in codeine prescribing remain.

scholarly journals Patterns of Suicide in the Context of COVID-19: Evidence From Three Australian States

2021 ◽  
Vol 12 ◽  
Author(s):  
Angela Clapperton ◽  
Matthew John Spittal ◽  
Jeremy Dwyer ◽  
Andrew Garrett ◽  
Kairi Kõlves ◽  
...  
Keyword(s):  
Linear Trend ◽  
Interrupted Time Series ◽  
Mitigation Measures ◽  
Contributing Factors ◽  
Time Data ◽  
Young Males ◽  
Time Series Analyses ◽  
Relationship Breakdown ◽  
Over Dispersion ◽  
The Impact

Aims: We aimed to determine whether there has been a change in the number of suicides occurring in three Australian states overall, and in age and sex subgroups, since the COVID-19 pandemic began, and to see if certain risk factors for suicide have become more prominent as likely underlying contributing factors for suicide.Method: Using real-time data from three state-based suicide registers, we ran multiple unadjusted and adjusted interrupted time series analyses to see if trends in monthly suicide counts changed after the pandemic began and whether there had been an increase in suicides where relationship breakdown, financial stressors, unemployment and homelessness were recorded.Results: Compared with the period before COVID-19, during the COVID-19 period there was no change in the number of suicides overall, or in any stratum-specific estimates except one. The exception was an increase in the number of young males who died by suicide in the COVID-19 period (adjusted RR 1.89 [95% CI 1.11–3.23]).The unadjusted analysis showed significant differences in suicide in the context of unemployment and relationship breakdown during the COVID-19 compared to the pre-COVID-19 period. Analysis showed an increase in the number of suicides occurring in the context of unemployment in the COVID-19 period (unadjusted RR 1.53 [95% CI 1.18–1.96]). In contrast, there was a decrease in the number of suicides occurring in the context of relationship breakdown in the COVID-19 period (unadjusted RR 0.82 [95% CI 0.67–0.99]). However, no significant changes were identified when the models were adjusted for possible over-dispersion, seasonality and non-linear trend.Conclusion: Although our analysis found no evidence of an overall increase in suicides after the pandemic began, the picture is complex. The identified increase in suicide in young men indicates that the impact of the pandemic is likely unevenly distributed across populations. The increase in suicides in the context of unemployment reinforces the vital need for mitigation measures during COVID-19, and for ongoing monitoring of suicide as the pandemic continues.

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 Did England's Teenage Pregnancy Strategy reduce pregnancy rates in England? A Natural Experiment study

2020 ◽  
Author(s):  
Andrew J Baxter ◽  
Ruth Dundas ◽  
Frank Popham ◽  
Peter Craig
Keyword(s):  
Time Series ◽  
Teenage Pregnancy ◽  
Interrupted Time Series ◽  
Pregnancy Rates ◽  
Control Population ◽  
Sensitivity Analyses ◽  
Synthetic Control ◽  
Birth Rates ◽  
Time Series Analyses ◽  
The Impact

Objective To re-evaluate the impact of England's Teenage Pregnancy Strategy (1999 to 2010) on pregnancy and birth rates. Hailed as a unique, nation-wide, comprehensive, evidence-based intervention, the strategy has been promoted as a reproducible model for other countries with high teenage pregnancy rates. Design Controlled interrupted time series and synthetic control analyses using routinely collected data on births and abortions in 16 countries. Setting The Strategy was published in July 1999 and implemented from 2000-2010, with increased investment in areas with higher rates of under-18 pregnancies from 2006 onwards. Participants Women aged under 20 living in England during the intervention period were considered to be the target population. Women in Scotland and Wales were the control population in our interrupted time series analyses. Women from European and English-speaking high-income countries were the control population in our synthetic control analyses. Main outcome measures The pregnancy rate among women aged under-18 was our primary outcome, as this was the target of the Strategy. We used under-18 births and under-20 pregnancies as secondary outcomes. Results In the controlled interrupted time series analyses, trends in rates of teenage pregnancy in England were similar to Scotland (0.08 fewer pregnancies per 1,000 women per year in England; -0.74 to 0.59) and Wales (0.14 more pregnancies per 1,000 women per year in England; -0.48 to 0.76). In synthetic control analyses, under-18 birth rates were very similar in England and the synthetic control. Under-20 pregnancy rates were marginally higher in England than in the synthetic control. Placebo testing and other sensitivity analyses supported the finding of no observable effect. Conclusion Although teenage pregnancies and births in England fell following implementation of the Teenage Pregnancy Strategy, comparisons with other countries suggest the strategy had little, if any, effect. The strategy should not be used as a model for future public health interventions in England or in other countries. The protocol for the analysis was published online at https://osf.io/tdbr8/

scholarly journals Understanding the Implementation of Hierarchical Medical System Policy: An Interrupted Time Series Analysis of a Pilot Scheme in China

2021 ◽  
Author(s):  
Huajie Hu ◽  
Ruilin Wang ◽  
Huangqianyu Li ◽  
Sheng Han ◽  
Peng Shen ◽  
...  
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Healthcare System ◽  
Betweenness Centrality ◽  
Interrupted Time Series ◽  
Medical System ◽  
Patient Encounter ◽  
The Mean ◽  
The Impact ◽  
Hierarchical Medical System

Abstract Background The Chinese healthcare system faces a dilemma between its hospital-centric approach to healthcare delivery and a rapidly aging population that requires strong primary care. To improve system efficiency and continuity of care, the Hierarchical Medical System (HMS) policy package was implemented in 2015 in Zhejiang province, China. This paper investigated the impact of HMS on the local healthcare system.MethodsWe conducted a repeated cross-sectional study with quarterly data collected between 2010 and 2018 from Yinzhou district, Ningbo. The data was analyzed with an interrupted time series (ITS) design to assess the impact of HMS on the changes of three outcome variables: primary care physicians (PCPs) patient encounter ratio (i.e., the mean quarterly number of patient encounters of PCPs divided by that of all other physicians), PCP degree ratio (i.e., the mean degree of PCPs divided by that of all other physicians), PCP betweenness centrality ratio (i.e., the mean betweenness centrality of PCPs divided by that of all other physicians). Results272,267 patients visited doctors for hypertension between 2010 and 2018. Compared to the counterfactual in the fourth quarter of 2018, the PCP patient encounter ratio rose by 42.7% (95%CI: 27.1—58.2, p<0.001), the PCP degree ratio increased by 23.6% (95%CI: 8.6—38.5, p<0.01), and the PCP betweenness centrality ratio grew by 129.4% (95%CI: 87.1—171.7, p<0.001).ConclusionsThe HMS policy can incentivize patients to visit primary care facilities and enhance the centrality of PCPs within their professional network. Local policymakers should sustain HMS policy efforts to obtain long-term and large-scale benefits.

scholarly journals The impact of an additional year of education on myopia: an interrupted time series in the UK Biobank.

2021 ◽  
Author(s):  
Benjamin Woolf ◽  
Riaz Aziz
Keyword(s):  
Time Series ◽  
Instrumental Variables ◽  
Refractive Error ◽  
Interrupted Time Series ◽  
Quasi Experimental ◽  
Time Series Analyses ◽  
One Year ◽  
The Uk ◽  
The Impact ◽  
Segmented Regression Model

Abstract Introduction: In the past decade, the minimal school leaving age has been raised twice. Previous studies have found evidence for a link between this type of policy and myopia. We aim to use the 1972 raising of school leaving age to estimate the effect of the raising of school leaving age in 2013 and 2015. Methods: We use a segmented regression model to conduct an instrumental time series analyses of the effect of years of education on myopia using the 1972 raising of school leaving age. To recover the effect of a one-year change, we use the effect of the change on years of education and reflective error in an instrumental variables analysis. Results: We found evidence for a 0.60 (SE = 0.10) increase in years of education and, after adjusting for probability of having missing data and sex, a -0.14d (SE = 0.03) for refractive error. Instrumental variables analyse implies a -0.24 d/year (SE = 0.05) change in refractive error for each additional year in education. Conclusion: Our results triangulate the findings of pervious quasi-experimental methods on the effect of years of education on myopia and imply that each raising of school leaving age in the 2010s should be expected to a lead to -0.07 d/yr change in refractive error in the UK population.

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