Abstract 076: Longitudinal Trajectories And Predictors Of County-level Cardiovascular Mortality In The United States (1980-2014)

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Shreya Rao ◽  
Amy E Hughes ◽  
Colby Ayers ◽  
Sandeep R Das ◽  
Ethan A Halm ◽  
...  
Keyword(s):  
Violent Crime ◽  
High Mortality ◽  
Multinomial Logistic Regression ◽  
The United States ◽  
Crime Rates ◽  
Social Characteristics ◽  
Mortality Data ◽  
County Level ◽  
Obesity And Diabetes ◽  
Health Factors

Introduction: CV mortality has declined over 4 decades in the U.S. However, whether declines have been uniformly experienced across U.S. counties, and predictors of CV mortality trajectory are not known. Methods: County-level mortality data from 1980-2014 was obtained from the National Center for Health Statistics. We used a ClustMix approach to identify 3 distinct county phenogroups based on mortality trajectory. Adjusted multinomial logistic regression models were constructed to evaluate the associations between county-level characteristics (demographic, social, and health status) and CV mortality trajectory-based phenogroups. Results: Among 3,133 counties, there were parallel declines in CV mortality in all groups (Fig.1A). High-mortality counties were located in the South and parts of the Ohio and Mississippi River valleys (Fig. 1B). County phenogroups varied significantly in social characteristics such as non-white proportion (low vs. high mortality: 12% vs. 27%), high-school education (11% vs. 20%), and violent crime rates (.01 vs. 0.3/100 population). Disparities in health factors were also observed with higher rates of smoking, obesity, and diabetes in the high (vs. low) mortality groups. A substantial collinearity was observed between social and health factors. In adjusted analysis, social, environmental, and health characteristics explained 56% variance in the county-level CV mortality trajectory. Education status (OR [95% CI]=12.4 [9.4-16.3]), violent crime rates (OR [95% CI] =1.6 [1.3-1.9]), and smoking (OR [95% CI] = 3.9 [3.1- 4.9]) were the strongest predictors of high mortality trajectory phenogroup membership (ref: low mortality). Conclusions: Despite a decline in CV mortality, disparities at the county-level have persisted over the past 4 decades largely driven by differences in social characteristics and smoking prevalence. This highlights the need for multi-domain interventions focusing on safety, education and public health to improve county-level disparities in CV health.

Club convergence of crime rates in the United States of America

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kolawole Ogundari
Keyword(s):  
Violent Crime ◽  
Property Crime ◽  
The United States ◽  
Crime Rates ◽  
Community Level ◽  
Club Convergence ◽  
Content Type ◽  
Level Data ◽  
Convergence Club ◽  
Over Time

Purpose The cyclical behavior of US crime rates reflects the dynamics of crime in the country. This paper aims to investigate the US's club convergence of crime rates to provide insights into whether the crime rates increased or decreased over time. The paper also analyzes the factors influencing the probability of states converging to a particular convergence club of crime. Design/methodology/approach The analysis is based on balanced panel data from all 50 states and the district of Columbia on violent and property crime rates covering 1976–2019. This yields a cross-state panel of 2,244 observations with 55 time periods and 51 groups. In addition, the author used a club clustering procedure to investigate the convergence hypothesis in the study. Findings The empirical results support population convergence of violent crime rates. However, the evidence that supports population convergence of property crime rates in the study is not found. Further analysis using the club clustering procedure shows that property crime rates converge into three clubs. The existence of club convergence in property crime rates means that the variation in the property crime rates tends to narrow among the states within each of the clubs identified in the study. Analysis based on an ordered probit model identifies economic, geographic and human capital factors that significantly drive the state's convergence club membership. Practical implications The central policy insight from these results is that crime rates grow slowly over time, as evident by the convergence of violent crime and club convergence of property crime in the study. Moreover, the existence of club convergence of property crime is an indication that policies to mitigate property crime might need to target states within each club. This includes the efforts to use state rather than national crime-fighting policies. Social implications As crimes are committed at the local level, this study's primary limitation is the lack of community-level data on crime and other factors considered. Analysis based on community-level data might provide a better representation of crime dynamics. However, the author hopes to consider this as less aggregated data are available to use in future research. Originality/value The paper provides new insights into the convergence of crime rates using the club convergence procedure in the USA. This is considered an improvement to the methods used in the previous studies.

Counting Guns

2002 ◽  
Vol 26 (4) ◽  
pp. 699-708
Author(s):  
Gordon Wood ◽  
Robert Churchill ◽  
Edward Cook ◽  
James Lindgren ◽  
Wilbur Miller ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Social Science ◽  
Statistical Methods ◽  
Violent Crime ◽  
The United States ◽  
Real Income ◽  
Crime Rates ◽  
Gun Control ◽  
Gun Laws ◽  
The Impact

At the fall 2001 Social Science History Association convention in Chicago, the Crime and Justice network sponsored a forum on the history of gun ownership, gun use, and gun violence in the United States. Our purpose was to consider how social science historians might contribute nowand in the future to the public debate over gun control and gun rights. To date, we have had little impact on that debate. It has been dominated by mainstream social scientists and historians, especially scholars such as Gary Kleck, John Lott, and Michael Bellesiles, whose work, despite profound flaws, is politically congenial to either opponents or proponents of gun control. Kleck and Mark Gertz (1995), for instance, argue on the basis of their widely cited survey that gun owners prevent numerous crimes each year in theUnited States by using firearms to defend themselves and their property. If their survey respondents are to be believed, American gun owners shot 100,000 criminals in 1994 in selfdefense–a preposterous number (Cook and Ludwig 1996: 57–58; Cook and Moore 1999: 280–81). Lott (2000) claims on the basis of his statistical analysis of recent crime rates that laws allowing private individuals to carry concealed firearms deter murders, rapes, and robberies, because criminals are afraid to attack potentially armed victims. However, he biases his results by confining his analysis to the years between 1977 and 1992, when violent crime rates had peaked and varied little from year to year (ibid.: 44–45). He reports only regression models that support his thesis and neglects to mention that each of those models finds a positive relationship between violent crime and real income, and an inverse relationship between violent crime and unemployment (ibid.: 52–53)–implausible relationships that suggest the presence of multicollinearity, measurement error, or misspecification. Lott then misrepresents his results by claiming falsely that statistical methods can distinguish in a quasi-experimental way the impact of gun laws from the impact of other social, economic, and cultural forces (ibid.: 26, 34–35; Guterl 1996). Had Lott extended his study to the 1930s, the correlation between gun laws and declining homicide rates that dominates his statistical analysis would have disappeared. An unbiased study would include some consideration of alternative explanations and an acknowledgment of the explanatory limits of statistical methods.

scholarly journals Identification of county-level health factors associated with COVID-19 mortality in the United States

2020 ◽  
Vol 34 (6) ◽  
pp. 437 ◽  
Author(s):  
Wei Pan ◽  
Yasuo Miyazaki ◽  
Hideyo Tsumura ◽  
Emi Miyazaki ◽  
Wei Yang
Keyword(s):  
United States ◽  
The United States ◽  
County Level ◽  
Factors Associated ◽  
Health Factors

scholarly journals Concealed Handgun Licensing and Crime in Four States

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Charles D. Phillips ◽  
Obioma Nwaiwu ◽  
Szu-hsuan Lin ◽  
Rachel Edwards ◽  
Sara Imanpour ◽  
...  
Keyword(s):  
United States ◽  
Policy Issue ◽  
The United States ◽  
Crime Rates ◽  
State Legislators ◽  
County Level ◽  
Contextual Variables ◽  
Research Tradition ◽  
Level Data ◽  
Firearm Policy

Firearm policy in the United States has long been a serious policy issue. Much of the previous research on crime and firearms focused on the effects of states’ passage of concealed handgun licensing (CHL) legislation. Today, given the proliferation of CHL legislation and growing strength of the “pro-gun” movement, the primary policy focus has changed. State legislators now face issues concerning whether and how to increase access to CHLs. Because of this transformation, this research moves away from the research tradition focused on the effect of a legislative change allowing CHLs. Instead, we consider two issues more policy relevant in the current era: What are the dynamics behind CHL licensing? Do increases in concealed handgun licensing affect crime rates? Using county-level data, we found that the density of gun dealers and other contextual variables, rather than changing crime rates, had a significant effect on increases of the rates at which CHLs were issued. We also found no significant effect of CHL increases on changes in crime rates. This research suggests that the rate at which CHLs are issued and crime rates are independent of one another—crime does not drive CHLs; CHLs do not drive crime.

scholarly journals Predicting County-Level Overdose Death Rates Amid the Escalating Overdose Crisis in the United States: A Statistical Modeling Approach Predicting Deaths Through 2022

2022 ◽  
Author(s):  
Charles Marks ◽  
Daniela Abramowitz ◽  
Christl A. Donnelly ◽  
Daniel Ciccarone ◽  
Natasha Martin ◽  
...  
Keyword(s):  
Negative Binomial ◽  
The United States ◽  
Similar Efficacy ◽  
Original Model ◽  
Mortality Data ◽  
County Level ◽  
Online Application ◽  
Death Rates ◽  
Overdose Death ◽  
The Us

Aims. U.S. overdose (OD) deaths continue to escalate but are characterized by geographic and temporal heterogeneity. We previously validated a predictive statistical model to predict county-level OD mortality nationally from 2013 to 2018. Herein, we aimed to: 1) validate our model’s performance at predicting county-level OD mortality in 2019 and 2020; 2) modify and validate our model to predict OD mortality in 2022.Methods. We evaluated our mixed effects negative binomial model’s performance at predicting county-level OD mortality in 2019 and 2020. Further, we modified our model which originally used data from the year X to predict OD deaths in the year X+1 to instead predict deaths in year X+3. We validated this modification for the years 2017 through 2019 and generated future-oriented predictions for 2022. Finally, to leverage available, albeit incomplete, 2020 OD mortality data, we also modified and validated our model to predict OD deaths in year X+2 and generated an alternative set of predictions for 2022.Results. Our original model continued to perform with similar efficacy in 2019 and 2020, remaining superior to a benchmark approach. Our modified X+3 model performed with similar efficacy as our original model, and we present predictions for 2022, including identification of counties most likely to experience highest OD mortality rates. There was a high correlation (Spearman’s ρ = 0.93) between the rank ordering of counties for our 2022 predictions using our X+3 and X+2 models. However, the X+3 model (which did not account for OD escalation during COVID) predicted only 62,000 deaths nationwide for 2022, whereas the X+2 model predicted over 87,000.Conclusion. We have predicted county-level overdose death rates for 2022 across the US. These predictions, made publicly available in our online application, can be used to identify counties at highest risk of high OD mortality and support evidence-based OD prevention planning.

Geographic Variation in the Black-Violence Stereotype

2018 ◽  
Vol 10 (3) ◽  
pp. 287-294 ◽  
Author(s):  
David J. Johnson ◽  
William J. Chopik
Keyword(s):  
Violent Crime ◽  
Black People ◽  
Violent Behavior ◽  
State Level ◽  
The United States ◽  
Crime Rates ◽  
Racial Groups ◽  
White People ◽  
Implicit Stereotypes ◽  
Implicit And Explicit

The stereotype that Blacks are violent is pervasive in the United States. Yet little research has examined whether this stereotype is linked to violent behavior from members of different racial groups. We examined how state-level violent crime rates among White and Black Americans predicted the strength of the Black-violence stereotype using a sample of 348,111 individuals from the Project Implicit website. State-level implicit and explicit stereotypes were predicted by crime rates. States where Black people committed higher rates of violent crime showed a stronger Black-violence stereotype, whereas states where White people committed higher rates of violent crime showed a weaker Black-violence stereotype. These patterns were stronger for explicit stereotypes than implicit stereotypes. We discuss the implications of these findings for the development and maintenance of stereotypes.

scholarly journals Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Sadiya S. Khan ◽  
Amy E. Krefman ◽  
Megan E. McCabe ◽  
Lucia C. Petito ◽  
Xiaoyun Yang ◽  
...  
Keyword(s):  
United States ◽  
Rural Areas ◽  
Urban Areas ◽  
Latent Class ◽  
Communicable Disease ◽  
Local Level ◽  
Healthcare Access ◽  
The United States ◽  
County Level ◽  
Health Factors

Abstract Background Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. Methods We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. Results Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. Conclusions County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

scholarly journals Modeling County-Level Spatio-Temporal Mortality Rates Using Dynamic Linear Models

Risks ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 117
Author(s):  
Zoe Gibbs ◽  
Chris Groendyke ◽  
Brian Hartman ◽  
Robert Richardson
Keyword(s):  
United States ◽  
Linear Models ◽  
Temporal Trends ◽  
The United States ◽  
Mortality Rates ◽  
Mortality Data ◽  
County Level ◽  
Spatio Temporal ◽  
Highly Correlated ◽  
Mortality Improvement

The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated.

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