Intervention time series analysis of crime rates: The case of sentence reform in Virginia

Vujić, Sunčica; Commandeur, Jacques J.F.; Koopman, Siem Jan

doi:10.1016/j.econmod.2016.02.017

Cited by 18 publications

(12 citation statements)

References 48 publications

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“…Time-series analyses have been used previously in the literature to assess the effect of different sentencing policy reforms in the United States (Stolzenberg and D'Alessio 1994;1997;D'Alessio and Stolzenberg 1995;Marvell 1995;Marvell and Moody 1996;Nicholson-Crotty 2004;Chen 2008;Vujić et al 2016). The use of time-series analysis, as opposed to simpler before and after comparisons, is key to establishing whether any observed changes following the implementation of sentencing guidelines can be attributed to them, rather than an ongoing prior trend.…”

Section: Introductionmentioning

confidence: 99%

Have The England and Wales Guidelines Affected Sentencing Severity? An Empirical Analysis Using a Scale of Severity and Time-Series Analyses

Pina-Sánchez

Gosling

Chung

et al. 2019

The British Journal of Criminology

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Sentence severity has increased in England and Wales in recent years. The causes of the increase remain unclear. One possible explanation relates to the introduction of sentencing guidelines, which seem to coincide in time with the increase in sentence severity. To date, investigations of this hypothesis have been limited to simple exploratory analyses or to specific offences. We use a new scale of sentence severity-developed using Thurstone scaling and the participation of 21 magistrates-and time-series modelling to explore whether a causal effect can be attributed to seven different guidelines. We corroborate the existence of an increase in sentence severity; however, we do not find conclusive evidence pointing at the guidelines having caused it.

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Section: Introductionmentioning

confidence: 99%

Have The England and Wales Guidelines Affected Sentencing Severity? An Empirical Analysis Using a Scale of Severity and Time-Series Analyses

Pina-Sánchez

Gosling

Chung

et al. 2019

The British Journal of Criminology

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“…Although the traditional Poisson regression analysis could quantify rates ratios, comparing pre-law monthly concussion-related medical encounter rates to post-law rates, and the polynomial curve could describe the trend of yearly rates, these two traditional methods are limited by their ability to describe patterns of rate changes or forecast future trends of interest. ARIMA time series intervention analysis, on the other hand, has emerged as a standard statistical method to assess the impact of an intervention (i.e., a planned policy change) over time or in time series forecasting [ 19 , 30 , 31 ]. ARIMA time series intervention analysis has several advantages over traditional statistical methods (i.e., Poisson regression, a polynomial curve).…”

Section: Discussionmentioning

confidence: 99%

Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis

Huang

Sullivan

Yang

2020

BMC Health Serv Res

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Background Existing studies analyzing the impact of state concussion laws have found an increase in concussion-related medical encounters post-law, in some instances, such increases were observed during the pre-law period due to a potential “spillover” effect. This study assessed the effects of Ohio’s concussion law, while accounting for such a “spillover” effect, on the trends in monthly rates of concussion-related medical encounters in Medicaid insured children using autoregressive integrated moving average (ARIMA) analysis. Methods We analyzed claim data obtained from the Partners For Kids database, a pediatric accountable care organization in Ohio. Concussion-related medical encounters for Medicaid-insured children (ages 0–18 years) treated between April 1, 2008 to December 31, 2016 were selected and analyzed. We assessed pre- and post-law trends in concussion-related medical encounters using an ARIMA intervention model. We also used traditional regression methods to validate the study results. Results A total of 16,943 concussion-related medical encounters sustained by 15,545 unique patients were included. Monthly rates of concussion-related medical encounters significantly increased from 4.64 per 10,000 member months during the pre-law period to 6.69 per 10,000 member months in the post-law period (P < 0.0001). Three upward breaks in the monthly rates of concussion-related medical encounters were observed between 2009 and 2016, with two breaks observed during the pre-law period. Specifically, the increased breakpoint observed in July 2011 (P = 0.0186) was significantly associated with an estimated 7.3% increase (95% CI: 1.1–13.7) in the rate of concussion-related medical encounters. This finding was confirmed in the Poisson regression and curve fitting models. Furthermore, a seasonal trend in concussion-related medical encounters was observed with the highest rates in September and October of each year. Conclusions Two of the three upward breaks identified in the monthly rate of concussion-related medical encounters occurred before the enactment of Ohio’s concussion law, suggesting a potential “spillover” effect. Further research is needed to confirm such an effect in children with other types of medical insurance.

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“…Although the traditional Poisson regression analysis could quantify rates ratio, comparing pre-law monthly concussion-related medical encounter rates to post-law rates, and the polynomial curve could describe the trend of yearly rates, these two traditional methods are limited by their ability to describe patterns of rate changes or forecast future trends of interest. ARIMA time series intervention analysis, on the other hand, has emerged as a standard statistical method to assess the impact of an intervention (i.e., a planned policy change) over time or in time series forecasting [19,30,31]. ARIMA time series intervention analysis has several advantages over traditional statistical methods (i.e., Poisson regression, a polynomial curve).…”

Section: Discussionmentioning

confidence: 99%

Analyzing the Impact of a State Concussion Law using an Autoregressive Integrated Moving Average Intervention Analysis

Huang

Sullivan

Yang

2020

Preprint

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Background: Existing studies analyzing the impact of state concussion laws have found an increase in concussion-related medical encounters post-law, in some instances, such increases were observed during the pre-law period due to a potential “spillover” effect. This study assessed the effects of Ohio’s concussion law, while accounting for such a “spillover” effect, on the trends in monthly rates of concussion-related medical encounters in Medicaid insured children using autoregressive integrated moving average (ARIMA) analysis.Methods: We analyzed claim data obtained from the Partners For Kids database, a pediatric accountable care organization in Ohio. Concussion-related medical encounters for Medicaid-insured children (ages 0-18 years) treated between April 1, 2008 to December 31, 2016 were selected and analyzed. We assessed pre- and post-law trends in concussion-related medical encounters using an ARIMA intervention model. We also used traditional regression methods to validate the study results.Results: A total of 16,943 concussion-related medical encounters sustained by 15,545 unique patients were included. Monthly rates of concussion-related medical encounters significantly increased from 4.64 per 10,000 member months during the pre-law period to 6.69 per 10,000 member months in the post-law period (P < 0.0001). Three upward breaks in the monthly rates of concussion-related medical encounters were observed between 2009 and 2016, with two breaks observed during the pre-law period. Specifically, the increased breakpoint observed in July 2011 (P = 0.0186) was significantly associated with an estimated 7.3% increase (95% CI: 1.1-13.7) in the rate of concussion-related medical encounters. This finding was confirmed in the Poisson regression and curve fitting models. Furthermore, a seasonal trend in concussion-related medical encounters was observed with the highest rates in September and October of each year. Conclusions: Two of the three upward breaks identified in the monthly rate of concussion-related medical encounters occurred before the enactment of Ohio’s concussion law, suggesting a potential “spillover” effect. Further research is needed to confirm such an effect in children with other types of medical insurance.

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Intervention time series analysis of crime rates: The case of sentence reform in Virginia

Cited by 18 publications

References 48 publications

Have The England and Wales Guidelines Affected Sentencing Severity? An Empirical Analysis Using a Scale of Severity and Time-Series Analyses

Have The England and Wales Guidelines Affected Sentencing Severity? An Empirical Analysis Using a Scale of Severity and Time-Series Analyses

Analyzing the impact of a state concussion law using an autoregressive integrated moving average intervention analysis

Analyzing the Impact of a State Concussion Law using an Autoregressive Integrated Moving Average Intervention Analysis

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