A Study on the Determinants of Arrest Rate

2020 ◽  
Vol 29 (1) ◽  
pp. 203-230
Author(s):  
Sang Ho Kim
Keyword(s):  
1993 ◽  
Vol 2 (4) ◽  
pp. 295-315 ◽  
Author(s):  
Hill M. Walker ◽  
Steve Stieber ◽  
Elizabeth Ramsey ◽  
Robert O'Neill

2017 ◽  
Vol 30 (5) ◽  
pp. 748-764 ◽  
Author(s):  
Glenn D. Walters

The purpose of this study was to determine whether criminal history risk moderates the effect of probation on future reoffending. A sample of 327 participants from the 1997 National Longitudinal Survey of Youth (NLSY97) who had been on probation were compared with 327 propensity score matched members of the NLSY97 who had been arrested but not placed on probation. Probation and arrest data analyzed between 1999 and 2008 failed to support the presence of an overall effect for probation. When the sample was divided into higher criminal history risk (one or more prior arrests) and lower criminal history risk (no prior arrests), however, probation was found to reduce recidivism in the lower criminal history risk group but not in the higher criminal history risk group. Accordingly, probation appeared to have a small but significant ameliorative effect on future offending in lower criminal history risk offenders.


Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Ari Moskowitz ◽  
Katherine Berg ◽  
Michael N Cocchi ◽  
Anne V Grossestreuer ◽  
Lakshman Balaji ◽  
...  

Background: Although patients in the ICU are closely monitored, some ICU cardiac arrest events may be preventable. In this study we sought to reduce the rate of ICU cardiac arrests. Methods: This was a prospective study of a novel clinical trigger and response tool deployed in the ICUs of a single, tertiary academic medical center. An interrupted time series approach was used to assess the impact of the tool on ICU cardiac arrests. Results: Forty-three patients experienced an ICU cardiac arrest in the pre-intervention epoch (6.79 arrests per 1000 discharges) and 59 patients experienced an ICU cardiac arrest in the intervention epoch (7.91 arrests per 1000 discharges). In the intervention epoch, the clinical trigger and response tool was activated 106 times over a 1-year period, most commonly due to unexpected new or worsening hypotension. There was no step change in arrest-rate (2.24 arrests/1000 patients, 95%CI -1.82, 6.28, p=0.28) or slope change (-0.02 slope of arrest rate, 95%CI -0.14, 0.11, p=0.79) comparing the pre-intervention and intervention time epochs (see Figure). Cardiac arrests occurring in the pre-intervention epoch were more likely to be deemed ‘potentially preventable’ than those in the intervention epoch (25.6% prior to the intervention vs. 12.3% during the intervention, OR 0.58, 95%CI 0.20, 0.88, p<0.01). Conclusions: A trigger-and-response tool did not reduce the incidence of ICU cardiac arrest. Arrests occurring after introduction of the tool were less likely to be rated as ‘potentially preventable.’


2002 ◽  
Vol 30 (Supplement) ◽  
pp. A74
Author(s):  
Erfan Hussain ◽  
Ali Mojaverian ◽  
Mary Nelson ◽  
Mitchel C Jacobs ◽  
Dana Lustbader ◽  
...  

10.2196/13147 ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. e13147 ◽  
Author(s):  
Alistair Connell ◽  
Rosalind Raine ◽  
Peter Martin ◽  
Estela Capelas Barbosa ◽  
Stephen Morris ◽  
...  

Background The development of acute kidney injury (AKI) in hospitalized patients is associated with adverse outcomes and increased health care costs. Simple automated e-alerts indicating its presence do not appear to improve outcomes, perhaps because of a lack of explicitly defined integration with a clinical response. Objective We sought to test this hypothesis by evaluating the impact of a digitally enabled intervention on clinical outcomes and health care costs associated with AKI in hospitalized patients. Methods We developed a care pathway comprising automated AKI detection, mobile clinician notification, in-app triage, and a protocolized specialist clinical response. We evaluated its impact by comparing data from pre- and postimplementation phases (May 2016 to January 2017 and May to September 2017, respectively) at the intervention site and another site not receiving the intervention. Clinical outcomes were analyzed using segmented regression analysis. The primary outcome was recovery of renal function to ≤120% of baseline by hospital discharge. Secondary clinical outcomes were mortality within 30 days of alert, progression of AKI stage, transfer to renal/intensive care units, hospital re-admission within 30 days of discharge, dependence on renal replacement therapy 30 days after discharge, and hospital-wide cardiac arrest rate. Time taken for specialist review of AKI alerts was measured. Impact on health care costs as defined by Patient-Level Information and Costing System data was evaluated using difference-in-differences (DID) analysis. Results The median time to AKI alert review by a specialist was 14.0 min (interquartile range 1.0-60.0 min). There was no impact on the primary outcome (estimated odds ratio [OR] 1.00, 95% CI 0.58-1.71; P=.99). Although the hospital-wide cardiac arrest rate fell significantly at the intervention site (OR 0.55, 95% CI 0.38-0.76; P<.001), DID analysis with the comparator site was not significant (OR 1.13, 95% CI 0.63-1.99; P=.69). There was no impact on other secondary clinical outcomes. Mean health care costs per patient were reduced by £2123 (95% CI −£4024 to −£222; P=.03), not including costs of providing the technology. Conclusions The digitally enabled clinical intervention to detect and treat AKI in hospitalized patients reduced health care costs and possibly reduced cardiac arrest rates. Its impact on other clinical outcomes and identification of the active components of the pathway requires clarification through evaluation across multiple sites.


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