Time lag between immigration and tuberculosis rates in immigrants in the Netherlands: a time-series analysis

2017 ◽  
Vol 21 (5) ◽  
pp. 486-492 ◽  
Author(s):  
C. van Aart ◽  
H. Boshuizen ◽  
A. Dekkers ◽  
H. Korthals Altes
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Time Series Analysis ◽  
Time Lag ◽  
Series Analysis

scholarly journals A Time-Series Analysis of Contextual-Level Effects on Hate Crime in The Netherlands

Social Forces ◽  
2020 ◽  
Author(s):  
Laura Jacobs ◽  
Joost van Spanje
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Time Series Analysis ◽  
Hate Crime ◽  
Hate Speech ◽  
Contextual Factors ◽  
Hate Crimes ◽  
Party Leader ◽  
Terrorist Attacks ◽  
Series Analysis

Abstract Nowadays, registered hate crimes are on the rise in many Western societies. What explains temporal variation in the incidence of hate crimes? Combining insights from the grievance model and the opportunity model, we study the role of three types of contextual factors: security (terrorism), media (news about terrorism and immigration), and political factors (speech by anti-immigration actors, hate speech prosecution, and high-profile anti-immigration victories). We apply time-series analysis to our original dataset of registered hate crimes in the Netherlands, 2015–2017 (N = 7,219). Findings indicate that terrorist attacks, (both print and online) news on refugees, immigration, and terrorism boost nonviolent hate crime. Similarly, news of the hate speech prosecution of Freedom Party leader Geert Wilders increases nonviolent crime as well. Tentative evidence points to a contagion effect of speech by anti-immigration actors. With regard to violent hate crime, only terrorist attacks had an effect. This effect was modest and only found in one of our models. Hence, the grievance and the opportunities model each partially explain nonviolent hate crime, although the security and media context seem most influential. Our findings help to identify the contextual factors contributing to a climate for hate and suggest that perceived threats play a key role.

Projecting utilization of hospital in-patient days in The Netherlands: A time-series analysis

1995 ◽  
Vol 12 (3-4) ◽  
pp. 185-202 ◽  
Author(s):  
Maarten J. Postma ◽  
Dirk Ruwaard ◽  
Hans (J.) C. Jager ◽  
Arnold L. M. Dekkers
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Time Series Analysis ◽  
Series Analysis

Outdoor Air Temperature and Mortality in the Netherlands: A Time-Series Analysis

1993 ◽  
Vol 137 (3) ◽  
pp. 331-341 ◽  
Author(s):  
Anton E. Kunst ◽  
Casper W. N. Looman ◽  
Johan P. Mackenbach
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Time Series Analysis ◽  
Air Temperature ◽  
Outdoor Air ◽  
Series Analysis

scholarly journals Effect of reimbursement restriction policy on the use of benzodiazepines in the Netherlands: an interrupted time series analysis

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e029148
Author(s):  
Lennart Jan Stoker ◽  
Eibert Roelof Heerdink ◽  
Richard Janssen ◽  
Toine C G Egberts
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Restriction Policy ◽  
Initial Effect ◽  
Benzodiazepine Use ◽  
Chronic Use

ObjectivesUse of benzodiazepines has health risks. Reimbursement was restricted in the Netherlands from January 2009 onwards with the goal to reduce chronic use and healthcare expenditures. The aim of this study is to assess the initial and long-term effects of this policy on benzodiazepine use.DesignInterrupted time series analysis, segmented regression models, Kaplan-Meier survival analysis and Cox proportional hazards analysis.SettingA 10% random sample of benzodiazepine dispensings by outpatient pharmacies between January 2002 and August 2015 were obtained from the PHARMO database. This database covered a catchment area representing about 3.6 million residents in 2015.Participants2 500 800 benzodiazepine prescriptions from 128 603 patients were included.InterventionReimbursement restriction policy from January 2009 onwards.Outcome measuresChanges in: the volume of dispensed prescriptions and doses, the incidence, prevalence of incidental, regular and chronic use and discontinuation rates of benzodiazepines.ResultsThe volume of dispensed prescriptions and doses decreased by 12.5% (95% CI 9.0% to 15.9%) and 15.1% (95% CI 11.4% to 17.3%) respectively in January 2009 compared with December 2008. A clear initial effect on the overall incidence (−14.7%; 95% CI −19.8% to 9.6%) and the prevalence of incidental (−17.8%; 95% CI −23.9% to 11.7%), regular (−20.0%; 95% CI −26.1% to 13.9%) and chronic (−16.0%; 95% CI −23.1% to 8.9%) use was observed. A statistically significant reduction in the monthly trend per 1000 medication users was observed for the overall incidence (−0.017; 95% CI −0.031 to 0.003) and the prevalence of incidental (−3.624; 95% CI −4.996 to 2.252) but not for regular (−0.304; 95% CI −1.204 to 0.596) and chronic (0.136; 95% CI −0.858 to 1.130) use. Patients who started treatment before policy had a slightly higher probability of discontinuation (HR=1.013; 95% CI 1.004 to 1.022).ConclusionsThe reimbursement policy had a significant initial effect on the volume, incidence and prevalence of benzodiazepine use. In addition, there is a statistically significant reduction in the monthly trend of overall incidence and of the prevalence of incidental use. No statistically significant reduction in the monthly trend of chronic use, the main purpose of the reimbursement restriction, could be demonstrated.

scholarly journals An analytical process of spatial autocorrelation functions based on Moran’s index

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249589
Author(s):  
Yanguang Chen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spatial Autocorrelation ◽  
Time Lag ◽  
Analytical Framework ◽  
Geographical Information ◽  
Scaling Analysis ◽  
Autocorrelation Functions ◽  
Series Analysis ◽  
Displacement Parameter

A number of spatial statistic measurements such as Moran’s I and Geary’s C can be used for spatial autocorrelation analysis. Spatial autocorrelation modeling proceeded from the 1-dimension autocorrelation of time series analysis, with time lag replaced by spatial weights so that the autocorrelation functions degenerated to autocorrelation coefficients. This paper develops 2-dimensional spatial autocorrelation functions based on the Moran index using the relative staircase function as a weight function to yield a spatial weight matrix with a displacement parameter. The displacement bears analogy with the time lag in time series analysis. Based on the spatial displacement parameter, two types of spatial autocorrelation functions are constructed for 2-dimensional spatial analysis. Then the partial spatial autocorrelation functions are derived by using the Yule-Walker recursive equation. The spatial autocorrelation functions are generalized to the autocorrelation functions based on Geary’s coefficient and Getis’ index. As an example, the new analytical framework was applied to the spatial autocorrelation modeling of Chinese cities. A conclusion can be reached that it is an effective method to build an autocorrelation function based on the relative step function. The spatial autocorrelation functions can be employed to reveal deep geographical information and perform spatial dynamic analysis, and lay the foundation for the scaling analysis of spatial correlation.

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