The Cycle of Violence Revisited: Childhood Victimization, Resilience, and Future Violence

2016 ◽  
Vol 34 (6) ◽  
pp. 1261-1286 ◽  
Author(s):  
Kevin A. Wright ◽  
Jillian J. Turanovic ◽  
Eryn N. O’Neal ◽  
Stephanie J. Morse ◽  
Evan T. Booth
Keyword(s):  
Protective Factors ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Future Research ◽  
Cycle Of Violence ◽  
Childhood Victimization ◽  
Adult Health ◽  
Violent Offending ◽  
Binomial Regression ◽  
The Individual

The individual and social protective factors that help break the cycle of violence are examined. Specifically, this study investigates (a) the individual and social protective factors that reduce violent offending among previously victimized children, and (b) whether certain protective factors are more or less important depending on the type and frequency of childhood victimization experienced. Data on young adults from Wave III of the National Longitudinal Study of Adolescent to Adult Health are used ( N = 13,116). Negative binomial regression models are estimated to examine the protective factors that promote resiliency to violent offending among individuals who reported being physically and sexually victimized as children. Results indicate that a number of individual and social protective factors reduce violent offending in young adulthood. With a few exceptions, these factors are specific to the type, frequency, and comorbidity of abuse experienced. The results suggest a number of promising approaches to break the cycle of violence among previously victimized children. Future research should move beyond explaining the cycle of violence to examine how the cycle may be broken.

scholarly journals Is area deprivation associated with greater impacts of COVID-19 in care homes across England? A preliminary analysis of COVID-19 outbreaks and deaths

2021 ◽  
pp. jech-2020-215039 ◽  
Author(s):  
Anders Malthe Bach-Mortensen ◽  
Michelle Degli Esposti
Keyword(s):  
Negative Binomial ◽  
Care Home ◽  
Negative Binomial Regression ◽  
Population Characteristics ◽  
Care Homes ◽  
Future Research ◽  
Area Deprivation ◽  
Binomial Regression ◽  
Index Of Multiple Deprivation

IntroductionThe COVID-19 pandemic has disproportionately impacted care homes and vulnerable populations, exacerbating existing health inequalities. However, the role of area deprivation in shaping the impacts of COVID-19 in care homes is poorly understood. We examine whether area deprivation is linked to higher rates of COVID-19 outbreaks and deaths among care home residents across upper tier local authorities in England (n=149).MethodsWe constructed a novel dataset from publicly available data. Using negative binomial regression models, we analysed the associations between area deprivation (Income Deprivation Affecting Older People Index (IDAOPI) and Index of Multiple Deprivation (IMD) extent) as the exposure and COVID-19 outbreaks, COVID-19-related deaths and all-cause deaths among care home residents as three separate outcomes—adjusting for population characteristics (size, age composition, ethnicity).ResultsCOVID-19 outbreaks in care homes did not vary by area deprivation. However, COVID-19-related deaths were more common in the most deprived quartiles of IDAOPI (incidence rate ratio (IRR): 1.23, 95% CI 1.04 to 1.47) and IMD extent (IRR: 1.16, 95% CI 1.00 to 1.34), compared with the least deprived quartiles.DiscussionThese findings suggest that area deprivation is a key risk factor in COVID-19 deaths among care home residents. Future research should look to replicate these results when more complete data become available.

Armed burglary: a marker for extreme instrumental violence

2017 ◽  
Vol 7 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Matt DeLisi ◽  
Eric Beauregard ◽  
Hayden Mosley
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Criminal Careers ◽  
Content Type ◽  
Violent Offending ◽  
Binomial Regression ◽  
Material Gain ◽  
Sexually Abuse ◽  
Severe Type

Purpose Most burglaries are property offenses yet some offenders perpetrate burglary for the purpose of violent instrumental crimes. Sexual burglars are distinct from non-sexual burglars because the former seek to rape or sexually abuse victims within the homes they burgle whereas the latter seek theft and material gain. It is unclear to what degree burglars who are armed with firearms or knives represent a type of sexual burglar, or perhaps a more severe type of offender who enters homes not merely to rape a victim, but to perhaps murder them as well. The paper aims to discuss these issues. Design/methodology/approach Drawing on data from 790 felons in Florida, t-test and negative binomial regression models were used to compare armed burglars to offenders who were not convicted of armed burglary. Findings Compared to offenders not convicted of armed burglary, armed burglars were involved in significantly more instrumental crimes of violence including first-degree murder, kidnapping, armed rape, armed robbery and assault with intent to murder. Armed burglary may be a marker of extreme instrumental violent offending and warrants further study. Originality/value To the authors’ knowledge, this is among the first studies of armed burglary offenders and adds understanding to the heterogeneity of burglary offenders and their criminal careers.

scholarly journals Modelling the Safety Performance of Roadways for Specific Collision Types

2021 ◽  
Author(s):  
Tony Chiu
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Constant Factor ◽  
Safety Performance ◽  
Annual Average Daily Traffic ◽  
Collision Type ◽  
Binomial Regression ◽  
Safety Performance Functions ◽  
The Individual ◽  
Screening Approaches

Safety performance functions (SPFs) are rarely developed for specific accident types because this can be very lengthy especially when relevant data are unavailable. Because of this constraint, a factor is applied along with the SPF for all accident types to estimate the safety for specific accidents types. This factor is the proportion of the individual collision type in the entire population of all accidents. However, there is no reason to believe that this factor is a constant which is independent of Annual Average Daily Traffic (AADT). Accordingly, a constant factor and the proportion model are applied to the SPF for all accident types combined to estimate the SPF for specific accident types on both rural road segments and Two-Way Stop-Controlled (TWSC) intersections. The validity of these factors are tested using the state-of-the-art network screening approaches. Furthermore, a detailed investigation on Property Damaage Only (PDO) estimates is carried out on certain aspects of safety performance functions, using negative binomial regression. PDO estimates are then evaluated based on three different approaches.

scholarly journals Modelling the Safety Performance of Roadways for Specific Collision Types

2021 ◽  
Author(s):  
Tony Chiu
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Constant Factor ◽  
Safety Performance ◽  
Annual Average Daily Traffic ◽  
Collision Type ◽  
Binomial Regression ◽  
Safety Performance Functions ◽  
The Individual ◽  
Screening Approaches

Safety performance functions (SPFs) are rarely developed for specific accident types because this can be very lengthy especially when relevant data are unavailable. Because of this constraint, a factor is applied along with the SPF for all accident types to estimate the safety for specific accidents types. This factor is the proportion of the individual collision type in the entire population of all accidents. However, there is no reason to believe that this factor is a constant which is independent of Annual Average Daily Traffic (AADT). Accordingly, a constant factor and the proportion model are applied to the SPF for all accident types combined to estimate the SPF for specific accident types on both rural road segments and Two-Way Stop-Controlled (TWSC) intersections. The validity of these factors are tested using the state-of-the-art network screening approaches. Furthermore, a detailed investigation on Property Damaage Only (PDO) estimates is carried out on certain aspects of safety performance functions, using negative binomial regression. PDO estimates are then evaluated based on three different approaches.

Hate Groups and Muslim Population Changes in the Fifty States: Does the Presence of Muslims Encourage Hate Group Formation?

2018 ◽  
Vol 25 (2) ◽  
pp. 317-332
Author(s):  
Daniel Hummel
Keyword(s):  
Negative Binomial ◽  
Group Formation ◽  
Negative Binomial Regression ◽  
The United States ◽  
Future Research ◽  
Group Status ◽  
Hate Groups ◽  
Muslim Population ◽  
Binomial Regression ◽  
Positive Correlations

The size of the Muslim population in the United States is growing, while the number of hate groups also continues to increase across the states. Based on the social dominance theory and group threat theory, there may be a link between these two dynamics as social dominators become concerned about their group status, i.e. white Christian males. This potential relationship is explored in this article. Although there were significant positive correlations between the number of hate groups and the size of the Muslim population, a panelled negative binomial regression with a number of relevant control variables found that there was not a significant relationship between these variables at the 0.05 probability level. The article further explores these findings and future research in this area.

Juvenile Institutional Misconduct: Examining the Role of Educational Attainment and Academic Achievement

2020 ◽  
Vol 66 (5) ◽  
pp. 663-686
Author(s):  
Ronda Shepherd Engstrom ◽  
Daniel Scott
Keyword(s):  
High School ◽  
Academic Achievement ◽  
Juvenile Offenders ◽  
Educational Attainment ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Future Research ◽  
Institutional Misconduct ◽  
Binomial Regression ◽  
Educational Variables

Identifying risk factors of institutional misconduct among juvenile offenders is important in curbing costs and consequences of these infractions. Research shows low educational attainment and poor academic achievement are consistently related to delinquency and crime. Yet, no research has examined whether these educational variables are associated with institutional misconduct among juveniles. Official records and interviews yielded information on the educational characteristics and institutional misconduct of 349 juvenile prisoners in a California facility. Negative binomial regression analysis was employed to examine hypothesized relationships. Results indicate that graduating high school significantly predicts institutional misconduct. Implications for juvenile corrections policies, including the importance of assisting juvenile detainees in obtaining their high school diploma, and directions for future research are provided.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

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