Into the Future

McDowall, David; McCleary, Richard; Bartos, Bradley J.

doi:10.1093/oso/9780190943943.003.0006

Cited by 14 publications

(22 citation statements)

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“…We used an interrupted time series design to compare monthly rates of homicide in Florida before and after the stand your ground law came into effect on October 1, 2005. Interrupted time series designs use data that are collected over time, usually recorded at regular intervals (eg, months) . These data are used to identify an underlying trend and when an intervention (eg, new law) occurs at a known time, postintervention trends can be examined for distinct changes from preexisting trends—thus serving as the counterfactual .…”

Section: Methodsmentioning

confidence: 99%

Evaluating the Impact of Florida’s “Stand Your Ground” Self-defense Law on Homicide and Suicide by Firearm

2017

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IMPORTANCEIn 2005, Florida amended its self-defense laws to provide legal immunity to individuals using lethal force in self-defense. The enactment of "stand your ground" laws in the United States has been controversial and their effect on rates of homicide and homicide by firearm is uncertain.OBJECTIVE To estimate the impact of Florida's stand your ground law on rates of homicide and homicide by firearm.DESIGN, SETTING, AND PARTICIPANTS Using an interrupted time series design, we analyzed monthly rates of homicide and homicide by firearm in Florida between 1999 and 2014. Data were collected from the Wide-ranging Online Data for Epidemiologic Research (WONDER) web portal at the Centers for Disease Control and Prevention. We used seasonally adjusted segmented Poisson regression models to assess whether the onset of the law was associated with changes in the underlying trends for homicide and homicide by firearm in Florida. We also assessed the association using comparison states without stand your ground laws (New York, New Jersey, Ohio, and Virginia) and control outcomes (all suicides and suicides by firearm in Florida). October 1, 2005, the effective date of the law, was used to define homicides before and after the change.MAIN OUTCOMES AND MEASURES Monthly rates of homicide, firearm-related homicide, suicide, and suicide by firearm in Florida and the 4 comparison states. RESULTSPrior to the stand your ground law, the mean monthly homicide rate in Florida was 0.49 deaths per 100 000 (mean monthly count, 81.93), and the rate of homicide by firearm was 0.29 deaths per 100 000 (mean monthly count, 49.06). Both rates had an underlying trend of 0.1% decrease per month. After accounting for underlying trends, these results estimate that after the law took effect there was an abrupt and sustained increase in the monthly homicide rate of 24.4% (relative risk [RR], 1.24; 95%CI, 1.16-1.33) and in the rate of homicide by firearm of 31.6% (RR, 1.32; 95% CI,. No evidence of change was found in the analyses of comparison states for either homicide (RR, 1.06; 95% CI, 0.98-1.13) or homicide by firearm (RR, 1.08; 95% CI, 0.99-1.17). Furthermore, no changes were observed in control outcomes such as suicide (RR, 0.99; 95% CI, 0.94-1.05) and suicide by firearm (RR, 0.98; 95% CI, 0.91-1.06) in Florida between 2005 and 2014. CONCLUSIONS AND RELEVANCEThe implementation of Florida's stand your ground self-defense law was associated with a significant increase in homicides and homicides by firearm but no change in rates of suicide or suicide by firearm.

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Section: Methodsmentioning

confidence: 99%

Evaluating the Impact of Florida’s “Stand Your Ground” Self-defense Law on Homicide and Suicide by Firearm

2017

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“…This indicates that possible unmeasured factors in the control group or the inclusion of inappropriate covariates could have a remarkably negative impact on the accuracy of intervention evaluation in the CITS model class, and an inappropriate choice of the control may reduce the validity of the design. [32] However, little research is available on the selection of an appropriate control for CITS design. Unlike randomized controlled trials, in which a control group is obtained through randomization, in CITS design, a control series is usually selected based on research experience.…”

Section: Discussionmentioning

confidence: 99%

Optimized ITS/CITS models for intervention evaluation considering the nonlinear impact of covariates

Zhang

Yin

Pan

et al. 2023

Preprint

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There is a lack of approaches to evaluate the effectiveness of interventions when there are nonlinear impacts of covariates to the outcome series. Based on the classic framework of ITS/CITS segmented regression, while considering autocorrelation of time series, we adopted a nonlinear dynamic modeling strategy (Hammerstein) to measure the nonlinear effects of covariates, and proposed four optimized models: ITS-A, CITS-A, ITS-HA, and CITS-HA. To compare the accuracy and precision in estimating the long-term impact of an intervention between the optimized and classic segmented models, we constructed a sequence generator to simulate the outcome series with actual characteristics. The relative error with respect to the true value was the accuracy indicator, and the width of the 95% CI and the truth value coverage rate of the corresponding 95% CI are the precision indicator for model assessments. The relative error of impact evaluation in the four optimized models was 4.49 percentage points lower than that in the classic models, specifically ITS-A (14.34%) and ITS-HA (21.47%) relative to ITS (26.66%), CITS-A (16.57%), and CITS-HA (17.94%) relative to CITS (21.59%). The width of the 95% CI of point estimate of long-term impacts in the optimized models was 0.1261, which was expanded by 58.71% compared with 0.0875 for the classic model. However, the optimized models covered the true value in all test scenarios, whereas the coverage rates of the classic ITS and CITS models were 73.33% and 83.33%, respectively. The optimized models are useful tools as they can assess the long-term impact of interventions with additional considerations for the nonlinear effects of covariates and allow for modeling of time-series autocorrelation and lag of intervention effects.

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“…To investigate the effect of the e-consultation programme on (i) delay time in care, (ii) CV hospital admissions, and (iii) CV mortality 1-year after cardiology consultation, an ITS regression approach was performed. 12 Time elapsed from the beginning of the study (months), type of consultation (0, in-person consultation; 1, e-consultation), and interaction time × type of consultation were entered as predictors. Overdispersion was considered and controlled.…”

Section: Methodsmentioning

confidence: 99%

A clinician-to-clinician universal electronic consultation programme at the cardiology department of a Galician healthcare area improves healthcare accessibility and outcomes in elderly patients

Mazón‐Ramos

Cinza‐Sanjurjo

Garcia‐Vega

et al. 2023

European Heart Journal - Digital Health

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Aim We aimed to assess the longer-term results (accessibility, and hospital admissions and mortality) in elderly patients referred to a cardiology department (CD) from primary care using an e-consultation in outpatient care. Methods We included 9,963 patients >80 years from January 1, 2010, to December 31, 2019. Until 2012, all patients were attended in an in-person consultation (2010-2012). In 2013, we instituted an e-consult programme (2013-2019) for all primary care referrals to cardiologists that preceded patient’s in-person consultation when considered. We used an interrupted time series (ITS) regression approach to investigate the impact of the e-consultation on (1) cardiovascular hospital admissions and mortality. We also analyzed (2) total number and referral rate (population-adjusted referred rate) in both periods, and (3) the accessibility was measured as number of consultations and variation according to distance from municipality and reference hospital. Results During the e-consultation, the demand of care increased (12·8 ± 4·3% vs 25·5 ± 11·1% per 1,000 inhabitants, p < 0·001) and referrals from different areas were equalized. After the implementation of e-consultation, we observed that the increasing of hospital admissions and mortality were stabilised (incidence rate ratio [iRR]: 1·351 [95% CI, 0·787, 2·317], p = 0·874) and (iRR: 1·925 [95% CI: 0·889, 4·168], p = 0·096), respectively. The geographic variabilities in hospital admissions and mortality seen during the in-person consultation were stabilized after e-consultation implementation. Conclusions Implementation of a clinician-to-clinician e-consultation program in the outpatient care was associated with improved accessibility to cardiology healthcare in elderly patients. After e-consultations were implemented, the hospital admissions and mortality were stabilised.

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Into the Future

Cited by 14 publications

References 0 publications

Evaluating the Impact of Florida’s “Stand Your Ground” Self-defense Law on Homicide and Suicide by Firearm

Evaluating the Impact of Florida’s “Stand Your Ground” Self-defense Law on Homicide and Suicide by Firearm

Optimized ITS/CITS models for intervention evaluation considering the nonlinear impact of covariates

A clinician-to-clinician universal electronic consultation programme at the cardiology department of a Galician healthcare area improves healthcare accessibility and outcomes in elderly patients

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