Adolescent Suicides and Social Disparities

2022 ◽  
Vol 7 (1) ◽  
pp. 0-0
Keyword(s):  
United States ◽  
Social Inequalities ◽  
Adolescent Suicide ◽  
State Level ◽  
The United States ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
High School Graduation Rates ◽  
Quantitative Evidence ◽  
Suicide Rates

Abstract: Suicides have been the second leading cause of deaths among adolescents in the United States in 2016. This paper aims to find qualitative and quantitative evidence of the relationship between socioeconomic inequalities and adolescent suicides. The suicide risk factors among all states are identified to form the pooled dynamic panel dataset from 1990 to 2016. To our knowledge, this paper is the first to find that social inequalities are significantly related to American adolescent suicides using the state-level dynamic panel data. Changes of unemployment rates have the consistent and significantly positive impacts on changes of adolescent suicides rates. Changes of Top 10% income index are uniformly positive to changes of adolescent suicide rates. Gini indices have inconsistently positive correspondence to adolescent suicide rates. Furthermore, high school graduation rates are insignificantly and negatively associated with adolescent suicide rates in the United States.

scholarly journals Economic disparities and suicides: The dynamic panel data analyses of 50 states in the United States

2021 ◽  
Vol 5 (1) ◽  
pp. 020-029
Author(s):  
Swan Bruce Q
Keyword(s):  
United States ◽  
Panel Data ◽  
Social Inequalities ◽  
State Level ◽  
The United States ◽  
Jel Classification ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Data Set ◽  
Effect Estimation

The economic inequalities associated with suicide risks among 50 states in the United States were identified in this paper to form the dynamic panel data set from 1981 to 2016. The effects of growing income inequalities on suicides in the Unites States were estimated using the Arellano–Bond method. This paper is the first to associate the social inequalities with suicides using the state-level dynamic panel data in America. It is found that the change of unemployment rates significantly and positively impact the changes of the overall suicides rates, female and male suicides rates. The changes of Top 10% income index are uniformly positive to the change of female, male and overall state-level suicide rates. The Gini index has positive correspondence within the overall and female groups, along with the insignificantly vague evidence within the male groups. The potential endogeneity problem inferring from the fixed effect estimation has been also investigated accordingly. JEL Classification: A13, A14, I18.

scholarly journals Absolutist Words From Search Volume Data Predict State-Level Suicide Rates in the United States

2020 ◽  
Vol 8 (4) ◽  
pp. 788-793
Author(s):  
Jais Adam-Troian ◽  
Thomas Arciszewski
Keyword(s):  
United States ◽  
Suicidal Ideation ◽  
Mixed Model ◽  
State Level ◽  
The United States ◽  
Psychological Research ◽  
Public Health Issue ◽  
Volume Data ◽  
Suicide Rates ◽  
Search Volume

Suicide continues to be a major public health issue, especially in the United States. It is a well-established fact that depression and suicidal ideation are risk factors for suicide. Drawing on recent research that shows that absolutist words (e.g., “completely,” “totally”) constitute linguistic markers of suicidal ideation, we created an online index of absolutist thinking (ATI) using search query data (i.e., Google Trends time series). Mixed-model analyses of age-adjusted suicide rates in the United States from 2004 to 2017 revealed that ATI is linked with suicides, β = 0.22, 95% CI = [0.12, 0.31], p < .001, and predicts suicides within 1 year, β = 0.16, 95% CI = [0.05, 0.28], p = .006, independently of state characteristics and historical trends. It is the first time that a collective measure of absolutist thinking is used to predict real-world suicide outcomes. Therefore, the present study paves the way for novel research avenues in clinical psychological research.

Big Five Personality Factors and Suicide Rates in the United States: A State-Level Analysis

2009 ◽  
Vol 109 (1) ◽  
pp. 208-212 ◽  
Author(s):  
Martin Voracek
Keyword(s):  
United States ◽  
Big Five ◽  
State Level ◽  
The United States ◽  
Personality Factors ◽  
Suicide Rates ◽  
Five Factors ◽  
Big Five Factors ◽  
Level Analysis ◽  
Cross National

Partly replicating findings from several cross-national studies (of Lester and of Voracek) on possible aggregate-level associations between personality and suicide prevalence, state-level analysis within the United States yielded significantly negative associations between the Big Five factor of Neuroticism and suicide rates. This effect was observed for historical as well as contemporary suicide rates of the total or the elderly population and was preserved with controls for the four other Big Five factors and measures of state wealth. Also conforming to cross-national findings, the Big Five factors of Agreeableness and Extraversion were negatively, albeit not reliably, associated with suicide rates.

State- and County-Level Social Capital as Predictors of County-Level Suicide Rates in the United States: A Lagged Multilevel Study

2021 ◽  
pp. 003335492097655
Author(s):  
Saloni Dev ◽  
Daniel Kim
Keyword(s):  
United States ◽  
Social Capital ◽  
Black People ◽  
State Level ◽  
The United States ◽  
Suicide Mortality ◽  
County Level ◽  
Standard Deviation Increase ◽  
Suicide Rates ◽  
Prevalence Of Depression

From 1999 through 2017, age-adjusted suicide rates in the United States rose by 33% (from 10.5 to 14.0 per 100 000 population). Social capital, a key social determinant of health, could protect against suicide, but empirical evidence on this association is limited. Using multilevel data from the Centers for Disease Control and Prevention, we explored state- and county-level social capital as predictors of age-adjusted suicide rates pooled from 2010 through 2017 across 2112 US counties. In addition, we tested for causal mediation of these associations by state-level prevalence of depression. A 1-standard deviation increase in state-level social capital predicted lower county-level suicide mortality rates almost 2 decades later (0.87 fewer suicides per 100 000 population; P = .04). This association was present among non-Hispanic Black people and among men but not among non-Hispanic White people and women. We also found evidence of partial mediation by prevalence of depression. Our findings suggest that elevating state- and county-level social capital, such as through policy and local initiatives, may help to reverse the trend of rising suicide rates in the United States.

scholarly journals Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study (Preprint)

2020 ◽  
Author(s):  
James Francis Oehmke ◽  
Charles B Moss ◽  
Lauren Nadya Singh ◽  
Theresa Bristol Oehmke ◽  
Lori Ann Post
Keyword(s):  
Public Health ◽  
United States ◽  
State Government ◽  
Public Health Practice ◽  
Generalized Method Of Moments ◽  
The United States ◽  
Statistical Technique ◽  
Dynamic Panel Data ◽  
Transmission Model ◽  
Dynamic Panel

BACKGROUND The Great COVID-19 Shutdown aimed to eliminate or slow the spread of SARS-CoV-2, the virus that causes COVID-19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID-19 cases. Operationalization of “sustained decline” varies by state and county. Existing models of COVID-19 transmission rely on parameters such as case estimates or R<sub>0</sub> and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID-19 models use data that are subject to significant measurement error and contamination. OBJECTIVE This study will generate novel metrics of speed, acceleration, jerk, and 7-day lag in the speed of COVID-19 transmission using state government tallies of SARS-CoV-2 infections, including state-level dynamics of SARS-CoV-2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID-19 transmission, for use in combination with traditional surveillance tools. METHODS Dynamic panel data models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied. RESULTS The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 17-23 and August 24-30, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 24-30. This change represents an increase in the transmission model R value for that week and is consistent with a re-emergence of the pandemic. CONCLUSIONS Reopening the United States comes with three certainties: (1) the “social” end of the pandemic and reopening are going to occur before the “medical” end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily.

scholarly journals Dynamic Panel Surveillance of COVID-19 Transmission in the United States to Inform Health Policy: Observational Statistical Study

10.2196/21955 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e21955 ◽  
Author(s):  
James Francis Oehmke ◽  
Charles B Moss ◽  
Lauren Nadya Singh ◽  
Theresa Bristol Oehmke ◽  
Lori Ann Post
Keyword(s):  
Public Health ◽  
United States ◽  
State Government ◽  
Public Health Practice ◽  
Generalized Method Of Moments ◽  
The United States ◽  
Statistical Technique ◽  
Dynamic Panel Data ◽  
Transmission Model ◽  
Dynamic Panel

Background The Great COVID-19 Shutdown aimed to eliminate or slow the spread of SARS-CoV-2, the virus that causes COVID-19. The United States has no national policy, leaving states to independently implement public health guidelines that are predicated on a sustained decline in COVID-19 cases. Operationalization of “sustained decline” varies by state and county. Existing models of COVID-19 transmission rely on parameters such as case estimates or R0 and are dependent on intensive data collection efforts. Static statistical models do not capture all of the relevant dynamics required to measure sustained declines. Moreover, existing COVID-19 models use data that are subject to significant measurement error and contamination. Objective This study will generate novel metrics of speed, acceleration, jerk, and 7-day lag in the speed of COVID-19 transmission using state government tallies of SARS-CoV-2 infections, including state-level dynamics of SARS-CoV-2 infections. This study provides the prototype for a global surveillance system to inform public health practice, including novel standardized metrics of COVID-19 transmission, for use in combination with traditional surveillance tools. Methods Dynamic panel data models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique allows for the control of a variety of deficiencies in the existing data. Tests of the validity of the model and statistical techniques were applied. Results The statistical approach was validated based on the regression results, which determined recent changes in the pattern of infection. During the weeks of August 17-23 and August 24-30, 2020, there were substantial regional differences in the evolution of the US pandemic. Census regions 1 and 2 were relatively quiet with a small but significant persistence effect that remained relatively unchanged from the prior 2 weeks. Census region 3 was sensitive to the number of tests administered, with a high constant rate of cases. A weekly special analysis showed that these results were driven by states with a high number of positive test reports from universities. Census region 4 had a high constant number of cases and a significantly increased persistence effect during the week of August 24-30. This change represents an increase in the transmission model R value for that week and is consistent with a re-emergence of the pandemic. Conclusions Reopening the United States comes with three certainties: (1) the “social” end of the pandemic and reopening are going to occur before the “medical” end even while the pandemic is growing. We need improved standardized surveillance techniques to inform leaders when it is safe to open sections of the country; (2) varying public health policies and guidelines unnecessarily result in varying degrees of transmission and outbreaks; and (3) even those states most successful in containing the pandemic continue to see a small but constant stream of new cases daily.

scholarly journals Dynamic Panel Data Modeling and Surveillance of COVID-19 in Metropolitan Areas in the United States: Longitudinal Trend Analysis

10.2196/26081 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e26081
Author(s):  
Theresa B Oehmke ◽  
Lori A Post ◽  
Charles B Moss ◽  
Tariq Z Issa ◽  
Michael J Boctor ◽  
...  
Keyword(s):  
United States ◽  
Panel Data ◽  
Data Model ◽  
Metropolitan Areas ◽  
The United States ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Dynamic Panel Data Model ◽  
Extreme Behavior ◽  
Second Wave

Background The COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence. Objective The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level. Methods Using a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week. Conclusions Three of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

Can increases in Twitter posts predict increases in cumulative incidence of COVID-19 in the United States? Evidence that social media can inform epidemic surveillance. (Preprint)

2020 ◽  
Author(s):  
Ruoyan Sun ◽  
Henna Budhwani
Keyword(s):  
Public Health ◽  
United States ◽  
Cumulative Incidence ◽  
Hot Spots ◽  
Adult Population ◽  
State Level ◽  
The United States ◽  
Education Attainment ◽  
Public Health Systems ◽  
Per Capita

BACKGROUND Though public health systems are responding rapidly to the COVID-19 pandemic, outcomes from publicly available, crowd-sourced big data may assist in helping to identify hot spots, prioritize equipment allocation and staffing, while also informing health policy related to “shelter in place” and social distancing recommendations. OBJECTIVE To assess if the rising state-level prevalence of COVID-19 related posts on Twitter (tweets) is predictive of state-level cumulative COVID-19 incidence after controlling for socio-economic characteristics. METHODS We identified extracted COVID-19 related tweets from January 21st to March 7th (2020) across all 50 states (N = 7,427,057). Tweets were combined with state-level characteristics and confirmed COVID-19 cases to determine the association between public commentary and cumulative incidence. RESULTS The cumulative incidence of COVID-19 cases varied significantly across states. Ratio of tweet increase (p=0.03), number of physicians per 1,000 population (p=0.01), education attainment (p=0.006), income per capita (p = 0.002), and percentage of adult population (p=0.003) were positively associated with cumulative incidence. Ratio of tweet increase was significantly associated with the logarithmic of cumulative incidence (p=0.06) with a coefficient of 0.26. CONCLUSIONS An increase in the prevalence of state-level tweets was predictive of an increase in COVID-19 diagnoses, providing evidence that Twitter can be a valuable surveillance tool for public health.

Dissent in the Atlantic World, 1787–1830

2018 ◽  
Author(s):  
Katherine Carté Engel
Keyword(s):  
United States ◽  
African Americans ◽  
First Amendment ◽  
Church And State ◽  
State Level ◽  
The United States ◽  
American Population ◽  
Simple Fact ◽  
General Role

The very term ‘Dissenter’ became problematic in the United States, following the passing of the First Amendment. The formal separation of Church and state embodied in the First Amendment was followed by the ending of state-level tax support for churches. None of the states established after 1792 had formal religious establishments. Baptists, Congregationalists, Presbyterians, and Methodists accounted for the majority of the American population both at the beginning and end of this period, but this simple fact masks an important compositional shift. While the denominations of Old Dissent declined relatively, Methodism grew quickly, representing a third of the population by 1850. Dissenters thus faced several different challenges. Primary among these were how to understand the idea of ‘denomination’ and also the more general role of institutional religion in a post-establishment society. Concerns about missions, and the positions of women and African Americans are best understood within this context.

Sign in / Sign up

Export Citation Format

Share Document