scholarly journals Firearm laws and the network of firearm movement among US states

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sae Takada ◽  
Kristen R. Choi ◽  
Shaw Natsui ◽  
Altaf Saadi ◽  
Liza Buchbinder ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Rate Ratio ◽  
Negative Binomial ◽  
Incidence Rate Ratio ◽  
State Level ◽  
Negative Binomial Regression ◽  
Similar Model ◽  
Us States ◽  
Binomial Regression ◽  
The Relationship

Abstract Background The movement of firearm across state lines may decrease the effectiveness of state-level firearm laws. Yet, how state-level firearm policies affect cross-state movement have not yet been widely explored. This study aims to characterize the interstate movement of firearms and its relationship with state-level firearm policies. Methods We analyzed the network of interstate firearm movement using Bureau of Alcohol, Tobacco, Firearms, and Explosives firearm trace data (2010–2017). We constructed the network of firearm movement between 50 states. We used zero-inflated negative binomial regression to estimate the relationship between the number of a state’s firearm laws and number of states for which it was the source of 100 or more firearms, adjusting for state characteristics. We used a similar model to examine the relationship between firearm laws and the number of states for which a given state was the destination of 100 or more firearms. Results Over the 8-year period, states had an average of 26 (Standard Deviation [SD] 25.2) firearm laws. On average, a state was the source of 100 or more crime-related firearms for 2.2 (SD 2.7) states and was the destination of 100 or more crime-related firearms for 2.2 (SD 3.4) states. Greater number of firearm laws was associated with states being the source of 100 or more firearms to fewer states (Incidence Rate Ratio [IRR] 0.58 per SD, p < 0.001) and being the destination of 100 or more firearms from more states (IRR1.73 per SD, p < 0.001). Conclusions Restrictive state-level firearm policies are associated with less movement of firearms to other states, but with more movement of firearms from outside states. The effectiveness of state-level firearm-restricting laws is complicated by a network of interstate firearm movement.

scholarly journals Network analysis of firearm movement among US states

2020 ◽  
Author(s):  
Sae Takada ◽  
Kristen Choi ◽  
Shaw Natsui ◽  
Altaf Saadi ◽  
Liza Buchbinder ◽  
...  
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Similar Model ◽  
Cross Sectional ◽  
Us States ◽  
Binomial Regression ◽  
The Relationship

Abstract Background: The movement of firearm across state lines may decrease the effectiveness of state-level firearm laws. Yet how state-level firearm policies affect cross-state movement have not yet been widely explored. This study aims to characterize the interstate movement of firearms and its relationship with state-level firearm policies. Methods: Cross-sectional time series network analysis of interstate firearm movement using Bureau of Alcohol, Tobacco, Firearms, and Explosives firearm trace data (2010 -2017). We constructed the network of firearm movement between 50 states. We used zero-inflated negative binomial regression to estimate the relationship between the number of a state’s firearm laws and number of states for which it was the source of 100 or more firearms, adjusting for state characteristics. We used a similar model to examine the relationship between firearm laws and the number of states for which a given state was the destination of 100 or more firearms.Results: Over the 8-year period, states had an average of 26 (SD 25.2) firearm laws. On average, a state was the source of 100 or more crime-related firearms for 2.2 (SD 2.7) states and was the destination of 100 or more crime-related firearms for 2.2 (SD 3.4) states. Greater number of firearm laws was associated with states being the source of 100 or more firearms to fewer states (IRR0.67 per SD, p<0.001), higher odds of not being a source to any states (aOR1.56 per SD, p<0.001), and states being the destination of 100 or more firearms from more states (IRR1.83 per SD, p<0.001).Conclusions: Restrictive firearm policies are associated with less movement of firearms to other states, but with more movement of firearms from outside states. The effectiveness of state-level firearm-restricting laws is complicated by a network of interstate firearm movement.

scholarly journals Network analysis of firearm movement among US states

2020 ◽  
Author(s):  
Sae Takada ◽  
Kristen Choi ◽  
Shaw Natsui ◽  
Altaf Saadi ◽  
Liza Buchbinder ◽  
...  
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Similar Model ◽  
Cross Sectional ◽  
Us States ◽  
Binomial Regression ◽  
The Relationship

Abstract Background: The movement of firearm across state lines may decrease the effectiveness of state-level firearm laws. Yet how state-level firearm policies affect cross-state movement have not yet been widely explored. This study aims to characterize the interstate movement of firearms and its relationship with state-level firearm policies. Methods : Cross-sectional time series network analysis of interstate firearm movement using Bureau of Alcohol, Tobacco, Firearms, and Explosives firearm trace data (2010 -2017). We constructed the network of firearm movement between 50 states. We used zero-inflated negative binomial regression to estimate the relationship between the number of a state’s firearm laws and number of states for which it was the source of 100 or more firearms, adjusting for state characteristics. We used a similar model to examine the relationship between firearm laws and the number of states for which a given state was the destination of 100 or more firearms. Results : Over the 8-year period, states had an average of 26 (SD 25.2) firearm laws. On average, a state was the source of 100 or more crime-related firearms for 2.2 (SD 2.7) states and was the destination of 100 or more crime-related firearms for 2.2 (SD 3.4) states. Greater number of firearm laws was associated with states being the source of 100 or more firearms to fewer states (IRR0.67 per SD, p<0.001), higher odds of not being a source to any states (aOR1.56 per SD, p<0.001), and states being the destination of 100 or more firearms from more states (IRR1.83 per SD, p<0.001). Conclusions: Restrictive firearm policies are associated with less movement of firearms to other states, but with more movement of firearms from outside states. The effectiveness of state-level firearm-restricting laws is complicated by a network of interstate firearm movement.

scholarly journals Network analysis of firearm movement among US states

2020 ◽  
Author(s):  
Sae Takada ◽  
Kristen Choi ◽  
Shaw Natsui ◽  
Altaf Saadi ◽  
Liza Buchbinder ◽  
...  
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Similar Model ◽  
Cross Sectional ◽  
Us States ◽  
Binomial Regression ◽  
The Relationship

Abstract Background: The movement of firearm across state lines may decrease the effectiveness of state-level firearm laws. Yet how state-level firearm policies affect cross-state movement have not yet been widely explored. This study aims to characterize the interstate movement of firearms and its relationship with state-level firearm policies. Methods: Cross-sectional time series network analysis of interstate firearm movement using Bureau of Alcohol, Tobacco, Firearms, and Explosives firearm trace data (2010 -2017). We constructed the network of firearm movement between 50 states. We used zero-inflated negative binomial regression to estimate the relationship between the number of a state’s firearm laws and number of states for which it was the source of 100 or more firearms, adjusting for state characteristics. We used a similar model to examine the relationship between firearm laws and the number of states for which a given state was the destination of 100 or more firearms.Results: Over the 8-year period, states had an average of 26 (SD 25.2) firearm laws. On average, a state was the source of 100 or more crime-related firearms for 2.2 (SD 2.7) states and was the destination of 100 or more crime-related firearms for 2.2 (SD 3.4) states. Greater number of firearm laws was associated with states being the source of 100 or more firearms to fewer states (IRR0.67 per SD, p<0.001), higher odds of not being a source to any states (aOR1.56 per SD, p<0.001), and states being the destination of 100 or more firearms from more states (IRR1.83 per SD, p<0.001).Conclusions: Restrictive firearm policies are associated with less movement of firearms to other states, but with more movement of firearms from outside states. The effectiveness of state-level firearm-restricting laws is complicated by a network of interstate firearm movement.

scholarly journals NATO between Exclusivity and Inclusivity: Measuring NATO’s Partnerships

2021 ◽  
pp. 1-32
Author(s):  
Branislav Mičko
Keyword(s):  
Human Rights ◽  
Fixed Effects ◽  
International Institutions ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Original Dataset ◽  
Strategic Approach ◽  
Binomial Regression ◽  
The Given ◽  
The Relationship

Building on an original dataset, this article focuses on the interactions between NATO and its declared worldwide partners. It argues that the analysis of these interactions can reveal NATO’s strategic approach to partnerships, but it can also provide a tool for its classification as an organisation that is either exclusive – defined by the focus on defence of its members, or inclusive – emphasising the global protection of democracies and human rights. The relationship between types of interactions and NATO categorisation is estimated using an unconditional negative binomial regression with fixed effects as well as a within-between (hybrid) model. Furthermore, they are illustrated on two brief case studies of Sweden and Japan. The results of the study suggest that NATO engages primarily with countries that are powerful relative to their neighbourhood, even though they are not the most powerful among the partners. The given country’s level of democracy, integration into the international institutions, and stability, do not seem to play any overarching role here.

scholarly journals The Relationship between Near-Repeat Street Robbery and the Environment: Evidence from Malmö, Sweden

2020 ◽  
Vol 9 (4) ◽  
pp. 188
Author(s):  
Markus Rasmusson ◽  
Marco Helbich
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Socioeconomic Indicators ◽  
Law Enforcement Agencies ◽  
Environmental Characteristics ◽  
Binomial Regression ◽  
Using Data ◽  
Near Repeat ◽  
The Relationship ◽  
Crime Event

Near-repeat crime refers to a pattern whereby one crime event is soon followed by a similar crime event at a nearby location. Existing research on near-repeat crime patterns is inconclusive about where near-repeat patterns emerge and which physical and social factors influence them. The present research addressed this gap by examining the relationship between initiator events (i.e., the first event in a near-repeat pattern) and environmental characteristics to estimate where near-repeat patterns are most likely to emerge. A two-step analysis was undertaken using data on street robberies reported in Malmö, Sweden, for the years 2006–15. After determining near-repeat patterns, we assessed the correlations between initiator events and criminogenic places and socioeconomic indicators using a negative binomial regression at a street segment level. Our results show that both criminogenic places and socioeconomic indicators have a significant influence on the spatial variation of initiator events, suggesting that environmental characteristics can be used to explain the emergence of near-repeat patterns. Law enforcement agencies can utilize the findings in efforts to prevent further street robberies from occurring.

scholarly journals Retracted: Analysis of the Strength of Legal Firearms Restrictions for Perpetrators of Domestic Violence and Their Associations With Intimate Partner Homicide

2017 ◽  
Vol 187 (7) ◽  
pp. 1449-1455 ◽  
Author(s):  
April M Zeoli ◽  
Alexander McCourt ◽  
Shani Buggs ◽  
Shannon Frattaroli ◽  
David Lilley ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Fixed Effects ◽  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
Intimate Partner ◽  
Restraining Order ◽  
Level Data ◽  
Binomial Regression ◽  
Firearms Restrictions

Abstract In this research, we estimate the association of firearm restrictions for domestic violence offenders with intimate partner homicides (IPHs) on the basis of the strength of the policies. We posit that the association of firearm laws with IPHs depends on the following characteristics of the laws: 1) breadth of coverage of high-risk individuals and situations restricted; 2) power to compel firearm surrender or removal from persons prohibited from having firearms; and 3) systems of accountability that prevent those prohibited from doing so from obtaining guns. We conducted a quantitative policy evaluation using annual state-level data from 1980 through 2013 for 45 US states. Based on the results of a series of robust, negative binomial regression models with state fixed effects, domestic violence restraining order firearm-prohibition laws are associated with 10% reductions in IPH. Statistically significant protective associations were evident only when restraining order prohibitions covered dating partners (−11%) and ex parte orders (−12%). Laws prohibiting access to those convicted of nonspecific violent misdemeanors were associated with a 24% reduction in IPH rates; there was no association when prohibitions were limited to domestic violence. Permit-to-purchase laws were associated with 10% reductions in IPHs. These findings should inform policymakers considering laws to maximize protections against IPH.

scholarly journals Prevalence of SARS-CoV-2 infection among people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic

2021 ◽  
Author(s):  
Linh Luong ◽  
Michaela Beder ◽  
Rosane Nisenbaum ◽  
Aaron Orkin ◽  
Jonathan Wong ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Infection Rates ◽  
Individual Site ◽  
Site Characteristics ◽  
Increased Risk ◽  
Binomial Regression ◽  
Great Heterogeneity ◽  
The Relationship ◽  
Over Time

Background: People experiencing homelessness are at increased risk of SARS-CoV-2 infection. This study reports the point prevalence of SARS-CoV-2 infection during testing conducted at sites serving people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic. We also explored the association between site characteristics and prevalence rates. Methods: The study included individuals who were staying at shelters, encampments, COVID-19 physical distancing sites, and drop-in and respite sites and completed outreach-based testing for SARS-CoV-2 during the period April 17 to July 31, 2020. We examined test positivity rates over time and compared them to rates in the general population of Toronto. Negative binomial regression was used to examine the relationship between each shelter-level characteristic and SARS-CoV-2 positivity rates. We also compared the rates across 3 time periods (T1: April 17-April 25; T2: April 26-May 23; T3: May 24-June 25). Results: The overall prevalence of SARS-CoV-2 infection was 8.5% (394/4657). Site-specific rates showed great heterogeneity with infection rates ranging from 0% to 70.6%. Compared to T1, positivity rates were 0.21 times lower (95% CI: 0.06, 0.75) during T2 and 0.14 times lower (95% CI: 0.043, 0.44) during T3. Most cases were detected during outbreak testing (384/394 [97.5%]) rather than active case finding. Interpretation: During the first wave of the pandemic, rates of SARS-CoV-2 infection at sites for people experiencing homelessness in Toronto varied significantly over time. The observation of lower rates at certain sites may be attributable to overall time trends, expansion of outreach-based testing to include sites without known outbreaks and/or individual site characteristics.

The relationship between citations, downloads and alternative metrics in rheumatology publications: a bibliometric study

Rheumatology ◽  
2019 ◽  
Vol 59 (2) ◽  
pp. 277-280 ◽  
Author(s):  
Winnie M Y Chen ◽  
Marwan Bukhari ◽  
Francesca Cockshull ◽  
James Galloway
Keyword(s):  
Negative Binomial ◽  
Positive Association ◽  
Negative Binomial Regression ◽  
Significant Positive Association ◽  
H Index ◽  
Binomial Regression ◽  
Number Of Citations ◽  
The Impact ◽  
The Relationship ◽  
Alternative Metrics

Abstract Objective Scientific journals and authors are frequently judged on ‘impact’. Commonly used traditional metrics are the Impact Factor and H-index. However, both take several years to formulate and have many limitations. Recently, Altmetric—a metric that measures impact in a non-traditional way—has gained popularity. This project aims to describe the relationships between subject matter, citations, downloads and Altmetric within rheumatology. Methods Data from publications in Rheumatology were used. Articles published from 2010 to 2015 were reviewed. Data were analysed using Stata 14.2 (StataCorp, College Station, TX, USA). Correlation between citations, downloads and Altmetric were quantified using linear regression, comparing across disease topics. Relationship between downloads and months since publications were described using negative binomial regression, clustering on individual articles. Results A total of 1460 Basic Science and Clinical Science articles were identified, with the number of citations, downloads and Altmetric scores. There were no correlations between disease topic and downloads (R2 = 0.016, P = 0.03), citations (R2 = 0.011, P = 0.29) or Altmetric (R2 = 0.025, P = 0.02). A statistically significant positive association was seen between the number of citations and downloads (R2 = 0.29, P &lt; 0.001). No correlations were seen between Altmetric and downloads (R2 = 0.028, P &lt; 0.001) or citations (R2 = 0.004, P = 0.445). Conclusion Disease area did not correlate with any of the metrics compared. Correlations were apparent with clear links between downloads and citations. Altmetric identified different articles as high impact compared with citation or download metrics. In conclusion: tweeting about your research does not appear to influence citations.

scholarly journals The Code of the Street Fights Back! Significant Associations with Arrest, Delinquency, and Violence Withstand Psychological Confounds

2020 ◽  
Vol 17 (7) ◽  
pp. 2432
Author(s):  
Kyle A. Burgason ◽  
Matt DeLisi ◽  
Mark H. Heirigs ◽  
Abdi Kusow ◽  
Jacob H. Erickson ◽  
...  
Keyword(s):  
African American ◽  
Antisocial Behavior ◽  
Juvenile Delinquents ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Code Of The Street ◽  
Binomial Regression ◽  
The Relationship ◽  
Practical Implications

Since Anderson’s now classic, Code of the Street: Decency, Violence, and the Moral Life of the Inner City, an increasing number of researchers have found a significant association between the code of the street and antisocial behavior. Less researched, however, is the relationship between the code of the street and cognate psychological factors. Building on the hypothesis that the code of the street is simply a reflection of elements of the population who exhibit antisocial traits, our aim in this study is to empirically test whether the observed association between the code of the street and antisocial behavior can withstand psychological confounds among a sample of institutionalized juvenile delinquents. Negative binomial regression models show that the code of the street remained a significant predictor of antisocial behavior despite the specification of psychopathy and temperamental traits and other controls. Moreover, as theorized, differential effects were found for African American delinquents compared to non-African American delinquents. We discuss theoretical and practical implications.

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