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      Analysis of “Stand Your Ground” Self-defense Laws and Statewide Rates of Homicides and Firearm Homicides

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      , PhD 1 , , , PhD 2 , , PhD 3 , 4 , , PhD 1
      JAMA Network Open
      American Medical Association

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          Key Points

          Question

          Are “stand your ground” (SYG) laws associated with increases in violent deaths, and does this vary by US state?

          Findings

          In this cohort study assessing 41 US states, SYG laws were associated with an 8% to 11% national increase in monthly rates of homicide and firearm homicide. State-level increases in homicide and firearm homicide rates reached 10% or higher for many Southern states, including Alabama, Florida, Georgia, and Louisiana.

          Meaning

          These findings suggest that SYG laws were associated with increased homicides each year and that the laws should be reconsidered to prevent unnecessary violent deaths.

          Abstract

          This cohort study evaluates the association of “stand your ground” laws with US homicide and firearm rates nationally and by state.

          Abstract

          Importance

          Most US states have amended self-defense laws to enhance legal immunities for individuals using deadly force in public. Despite concerns that “stand your ground” (SYG) laws unnecessarily encourage the use of deadly violence, their impact on violent deaths and how this varies across states and demographic groups remains unclear.

          Objective

          To evaluate the association of SYG laws with homicide and firearm homicide, nationally and by state, while considering variation by the race, age, and sex of individuals who died by homicide.

          Design, Setting, and Participants

          This cohort study used a controlled, multiple-baseline and -location interrupted time series design, using natural variation in the timings and locations of SYG laws to assess associations. Changes in homicide and firearm homicide were modeled using Poisson regression analyses within a generalized additive model framework. Analyses included all US states that enacted SYG laws between 2000 and 2016 and states that did not have SYG laws enacted during the full study period, 1999 to 2017. Data were analyzed from November 2019 to December 2020.

          Exposures

          SYG self-defense laws enacted by statute between January 1, 2000, to December 31, 2016.

          Main Outcomes and Measures

          The main outcomes were statewide monthly rates of homicide and firearm-related homicide (per 100 000 persons) from January 1, 1999, to December 31, 2017, grouped by characteristics (ie, race, age, sex) of individuals who died by homicide.

          Results

          Forty-one states were analyzed, including 23 states that enacted SYG laws during the study period and 18 states that did not have SYG laws, with 248 358 homicides (43.7% individuals aged 20-34 years; 77.9% men and 22.1% women), including 170 659 firearm homicides. SYG laws were associated with a mean national increase of 7.8% in monthly homicide rates (incidence rate ratio [IRR],1.08; 95% CI, 1.04-1.12; P < .001) and 8.0% in monthly firearm homicide rates (IRR, 1.08; 95% CI, 1.03-1.13; P = .002). SYG laws were not associated with changes in the negative controls of suicide (IRR, 0.99; 95% CI, 0.98-1.01) or firearm suicide (IRR, 1.00; 95% CI, 0.98-1.02). Increases in violent deaths varied across states, with the largest increases (16.2% to 33.5%) clustering in the South (eg, Alabama, Florida, Georgia, Louisiana). There were no differential associations of SYG laws by demographic group.

          Conclusions and Relevance

          These findings suggest that adoption of SYG laws across the US was associated with increases in violent deaths, deaths that could potentially have been avoided.

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          Most cited references31

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          Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017

          Summary Background Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries—Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings At the broadest grouping of causes of death (Level 1), non-communicable diseases (NCDs) comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5–74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 18·6% (17·9–19·6), and injuries 8·0% (7·7–8·2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22·7% (21·5–23·9), representing an additional 7·61 million (7·20–8·01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7·9% (7·0–8·8). The number of deaths for CMNN causes decreased by 22·2% (20·0–24·0) and the death rate by 31·8% (30·1–33·3). Total deaths from injuries increased by 2·3% (0·5–4·0) between 2007 and 2017, and the death rate from injuries decreased by 13·7% (12·2–15·1) to 57·9 deaths (55·9–59·2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000–289 000) globally in 2007 to 352 000 (334 000–363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118·0% (88·8–148·6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36·4% (32·2–40·6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33·6% (31·2–36·1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respiratory infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990—neonatal disorders, lower respiratory infections, and diarrhoeal diseases—were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. Funding Bill & Melinda Gates Foundation.
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            Developing and evaluating complex interventions: the new Medical Research Council guidance

            Evaluating complex interventions is complicated. The Medical Research Council's evaluation framework (2000) brought welcome clarity to the task. Now the council has updated its guidance
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              Interrupted time series regression for the evaluation of public health interventions: a tutorial

              Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                21 February 2022
                February 2022
                21 February 2022
                : 5
                : 2
                : e220077
                Affiliations
                [1 ]Department of Social Policy and Intervention, University of Oxford, Oxford, United Kingdom
                [2 ]Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia
                [3 ]Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [4 ]Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, United Kingdom
                Author notes
                Article Information
                Accepted for Publication: December 11, 2021.
                Published: February 21, 2022. doi:10.1001/jamanetworkopen.2022.0077
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Degli Esposti M et al. JAMA Network Open.
                Corresponding Author: Michelle Degli Esposti, PhD, Department of Social Policy and Intervention, University of Oxford, Barnett House, 32 Wellington Sq, Oxford OX1 2ER, United Kingdom ( mdesposti@ 123456gmail.com ).
                Author Contributions: Drs Degli Esposti and Humphreys had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Degli Esposti, Gasparrini, Humphreys.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Degli Esposti, Humphreys.
                Critical revision of the manuscript for important intellectual content: All authors.
                Statistical analysis: Degli Esposti, Gasparrini, Humphreys.
                Obtained funding: Gasparrini, Humphreys.
                Administrative, technical, or material support: Degli Esposti, Gasparrini, Humphreys.
                Supervision: Wiebe, Humphreys.
                Conflict of Interest Disclosures: None reported.
                Funding/Support: This work was funded by grant No. 18-38016 from the Joyce Foundation.
                Role of the Funder/Sponsor: The Joyce Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Disclaimer: The views expressed in this publication are those of the authors and not necessarily those of the Joyce Foundation.
                Additional Contributions: Jason Gravel, PhD (Temple University), provided technical assistance in data processing, and David Kirk, PhD (University of Oxford), assisted with data troubleshooting and providing insightful comments on the manuscript. Neither Drs Gravel nor Kirk received compensation for their contributions.
                Article
                zoi220006
                10.1001/jamanetworkopen.2022.0077
                8861849
                35188553
                0871fba3-4aca-4ff5-a79e-ae468380576b
                Copyright 2022 Degli Esposti M et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 26 September 2021
                : 11 December 2021
                Categories
                Research
                Original Investigation
                Online Only
                Public Health

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