scholarly journals Does the Implementation of Regulation Affect COVID-19 Transmissibility and Mortality? Lessons Learned from Nganjuk Regency

2021 ◽  
Vol 9 (2) ◽  
pp. 115
Author(s):  
Danik Iga Prasiska ◽  
Sangchul Yoon
Keyword(s):  
Large Scale ◽  
Task Force ◽  
Case Fatality ◽  
Interrupted Time Series ◽  
Lessons Learned ◽  
Preventive Strategy ◽  
Interrupted Time Series Analysis ◽  
Global Pandemic ◽  
Social Restriction ◽  
The Impact

Background: Coronavirus disease 2019 (COVID-19) as a global pandemic is ineluctable, transmission that originated from a foreign country became the local transmission in Indonesia. As several regional authorities implemented a large-scale social restriction policy to reduce the transmission of COVID-19, the Nganjuk Regency government chose to implement a different strategy with the implementation of Regent Regulation number 28 of 2020 about novel practice in the COVID-19 pandemic situation. Objective: This study aimed to analyze the impact of the implementation of the regulation on COVID-19 transmissibility and mortality at the Nganjuk Regency. Methods: Data were collected from the daily confirmed cases and death of COVID-19 made accessible for the public by the Nganjuk Regency Task Force for COVID-19 and Nganjuk Regency Health Office starting from March 30th to September 20th. Interrupted time series analysis was performed to estimate the impact of the implementation of regent regulation on COVID-19 transmission and mortality parameters. Result: The trend of new confirmed cases and deaths of COVID-19 in the Nganjuk Regency continued to fluctuate before and even after the implementation of regent regulation. It was found that there were reductions in case of fatality rates by -0.002 ± 0.003 (ρ 0.002) on CFR and -0.008 ± 0.008 (ρ 0.007) on eCFR after the regent regulation was implemented but there was no significant reduction on COVID-19 transmissibility parameter. Conclusion: Implementation of regent regulation in Nganjuk Regency significantly affected the reduction of case fatality rate but failed to slow down the COVID-19 transmissibility. Intensive community engagement to comply with the health preventive measures should be considered as an effective preventive strategy to reduce the transmission of COVID-19. 

scholarly journals Transformation of the Tanzania Medical Store Department through Global Fund support: lessons learned from the field

2020 ◽  
Author(s):  
Patrick Githendu ◽  
Linden Morrison ◽  
Rosemary Silaa ◽  
Sai Pothapregada ◽  
Sarah Asiimwe ◽  
...  
Keyword(s):  
Global Fund ◽  
Interrupted Time Series ◽  
Lessons Learned ◽  
Interrupted Time Series Analysis ◽  
Monthly Basis ◽  
Quantitative And Qualitative Research ◽  
Before And After ◽  
Multi Stakeholder ◽  
Health Commodities ◽  
The Impact

AbstractBackgroundThe Tanzania government sought support from The Global Fund to Fight AIDs, Tuberculosis and Malaria (Global Fund) to reform its Medical Stores Department (MSD), with the aim of improving performance. Our study aimed to assess the impact of the reforms and document the lessons learned.MethodsWe applied quantitative and qualitative research methods to assess the impact of the reforms. The quantitative part entailed a review of operational and financial data covering the period before and after the implementation of the reforms. We applied interrupted time series analysis to determine the change in average availability of essential health commodities at health zones. Qualitative data was collected through 41 key informant interviews. Participants were identified through stakeholder mapping, purposive and snowballing sampling techniques, and responses were analyzed through thematic content analysis.ResultsAvailability of essential health commodities increased significantly by 12.6% (95%CI, 9.6-15.6), after the reforms and continued to increase on a monthly basis by 0.2% (95%CI, 0.0-0.3) relative to the preintervention trend. Sales increased by 56.6% while the cost of goods sold increased by 88.6% between 2014/15 and 2017/18. Surplus income increased by 56.4% between 2014/15 and 2017/18, with reductions in rent and fuel expenditure. There was consensus among participants that the reforms, were instrumental in improving performance of MSD.ConclusionMany positive results were realized through the reforms at MSD. However, despite the progress, there were risks such as the increasing government receivable that could jeopardize the gains. Multi-stakeholder efforts are necessary, to sustain the progress and expand public health.

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 Impact of Mass Screening on the Number of Confirmed Cases, Recovered Cases, and Deaths Due to COVID-19 in Iran: An Interrupted Time Series Analysis

2020 ◽  
Vol 23 (11) ◽  
pp. 776-781
Author(s):  
Moslem Taheri Soodejani ◽  
Seyyed Mohammad Tabatabaei ◽  
Ali Dehghani ◽  
Willi McFarland ◽  
Hamid Sharifi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Mass Screening ◽  
Scale Up ◽  
Case Fatality ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Polymerase Chain ◽  
The Impact

Background: Mass screening for the coronavirus disease 2019 (COVID-19) began in Iran on March 23, 2020, with the purpose of improving early detection of patients for their own health and to prevent onward transmission to others. In this study, we evaluated the impact of the change towards mass screening on new cases reported, cases recovered, and deaths due to COVID-19. Methods: This study analyzed the daily reports on the number of new cases confirmed by polymerase chain reaction (PCR) testing, cases recovered, and deaths due to COVID-19 provided to the Ministry of Health and Medical Education of Iran. Changes in trends on these outcomes were evaluated using interrupted time series analysis. Results: From February 19 to May 6, 2020, a total of 519544 COVID-19 tests were done and 101650 diagnoses were made (case/ test ratio 19.6%). For the same period, 6418 deaths due to COVID-19 were reported (case fatality ratio 6.3%). The number of cases detected increased significantly over the period of scale-up of mass screening (P=0.003), as did the number of recovered cases (P=0.001). The number of deaths due to COVID-19 did not change before versus after mass screening. Conclusion: Following the scale-up of mass screening for COVID-19 in Iran, the rate of new cases detected and reported recovered accelerated significantly. Mass screening is likely to have detected many mild and asymptomatic cases that were infectious. Our data support the role that mass screening, coupled with isolation and contract tracing, can have in slowing the COVID-19 epidemic.

scholarly journals Does admission prevalence change after reconfiguration of inpatient services? An interrupted time series analysis of the impact of reconfiguration in five centres

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Joanne Martin ◽  
Edwin Amalraj Raja ◽  
Steve Turner
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Emergency Admission ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Emergency Admissions ◽  
Series Analysis ◽  
Inpatient Services ◽  
The Impact ◽  
Admission Prevalence

Abstract Background Service reconfiguration of inpatient services in a hospital includes complete and partial closure of all emergency inpatient facilities. The “natural experiment” of service reconfiguration may give insight into drivers for emergency admissions to hospital. This study addressed the question does the prevalence of emergency admission to hospital for children change after reconfiguration of inpatient services? Methods There were five service reconfigurations in Scottish hospitals between 2004 and 2018 where emergency admissions to one “reconfigured” hospital were halted (permanently or temporarily) and directed to a second “adjacent” hospital. The number of emergency admissions (standardised to /1000 children in the regional population) per month to the “reconfigured” and “adjacent” hospitals was obtained for five years prior to reconfiguration and up to five years afterwards. An interrupted time series analysis considered the association between reconfiguration and admissions across pairs comprised of “reconfigured” and “adjacent” hospitals, with adjustment for seasonality and an overall rising trend in admissions. Results Of the five episodes of reconfiguration, two were immediate closure, two involved closure only to overnight admissions and one with overnight closure for a period and then closure. In “reconfigured” hospitals there was an average fall of 117 admissions/month [95% CI 78, 156] in the year after reconfiguration compared to the year before, and in “adjacent” hospitals admissions rose by 82/month [32, 131]. Across paired reconfigured and adjacent hospitals, in the months post reconfiguration, the overall number of admissions to one hospital pair slowed, in another pair admissions accelerated, and admission prevalence was unchanged in three pairs. After reconfiguration in one hospital, there was a rise in admissions to a third hospital which was closer than the named “adjacent” hospital. Conclusions There are diverse outcomes for the number of emergency admissions post reconfiguration of inpatient facilities. Factors including resources placed in the community after local reconfiguration, distance to the “adjacent” hospital and local deprivation may be important drivers for admission pathways after reconfiguration. Policy makers considering reconfiguration might consider a number of factors which may be important determinants of admissions post reconfiguration.

Recession and risk of suicide in Denmark during the 2009 global financial crisis: an ecological register-based study

2021 ◽  
pp. 140349482110132
Author(s):  
Agnieszka Konieczna ◽  
Sarah Grube Jakobsen ◽  
Christina Petrea Larsen ◽  
Erik Christiansen
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Time Series Analysis ◽  
Suicide Rate ◽  
Global Financial Crisis ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Prevention Programmes ◽  
The Impact

Aim: The aim of this study is to analyse the potential impact from the financial crisis (onset in 2009) on suicide rates in Denmark. The hypothesis is that the global financial crisis raised unemployment which leads to raising the suicide rate in Denmark and that the impact is most prominent in men. Method: This study used an ecological study design, including register data from 2001 until 2016 on unemployment, suicide, gender and calendar time which was analysed using Poisson regression models and interrupted time series analysis. Results: The correlation between unemployment and suicide rates was positive in the period and statistically significant for all, but at a moderate level. A dichotomised version of time (calendar year) showed a significant reduction in the suicide rate for women (incidence rate ratio 0.87, P=0.002). Interrupted time series analysis showed a significant decreasing trend for the overall suicide rate and for men in the pre-recession period, which in both cases stagnated after the onset of recession in 2009. The difference between the genders’ suicide rate changed significantly at the onset of recession, as the rate for men increased and the rate for women decreased. Discussion: The Danish social welfare model might have prevented social disintegration and suicide among unemployed, and suicide prevention programmes might have prevented deaths among unemployed and mentally ill individuals. Conclusions: We found some indications for gender-specific differences from the impact of the financial crises on the suicide rate. We recommend that men should be specifically targeted for appropriate prevention programmes during periods of economic downturn.

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

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