scholarly journals Opioid overdose events and child maltreatment indicators: Differential county-level associations

2020 ◽  
Vol 119 ◽  
pp. 105671
Author(s):  
Rebecca Rebbe ◽  
Asia S. Bishop ◽  
Jooree Ahn ◽  
Joseph A. Mienko
Keyword(s):  
Child Maltreatment ◽  
Opioid Overdose ◽  
County Level

scholarly journals Exploring the Association Between the “Big Five” Personality Traits and Fatal Opioid Overdose: County-Level Empirical Analysis (Preprint)

2020 ◽  
Author(s):  
Zhasmina Tacheva ◽  
Anton Ivanov
Keyword(s):  
United States ◽  
Big Five ◽  
Psychological Factors ◽  
The United States ◽  
Community Level ◽  
Opioid Overdose ◽  
County Level ◽  
Psychological Traits ◽  
Wide Range ◽  
The Relationship

BACKGROUND Opioid-related deaths constitute a problem of pandemic proportions in the United States, with no clear solution in sight. Although addressing addiction—the heart of this problem—ought to remain a priority for health practitioners, examining the community-level psychological factors with a known impact on health behaviors may provide valuable insights for attenuating this health crisis by curbing risky behaviors before they evolve into addiction. OBJECTIVE The goal of this study is twofold: to demonstrate the relationship between community-level psychological traits and fatal opioid overdose both theoretically and empirically, and to provide a blueprint for using social media data to glean these psychological factors in a real-time, reliable, and scalable manner. METHODS We collected annual panel data from Twitter for 2891 counties in the United States between 2014-2016 and used a novel data mining technique to obtain average county-level “Big Five” psychological trait scores. We then performed interval regression, using a control function to alleviate omitted variable bias, to empirically test the relationship between county-level psychological traits and the prevalence of fatal opioid overdoses in each county. RESULTS After controlling for a wide range of community-level biopsychosocial factors related to health outcomes, we found that three of the operationalizations of the five psychological traits examined at the community level in the study were significantly associated with fatal opioid overdoses: extraversion (β=.308, <i>P</i>&lt;.001), neuroticism (β=.248, <i>P</i>&lt;.001), and conscientiousness (β=.229, <i>P</i>&lt;.001). CONCLUSIONS Analyzing the psychological characteristics of a community can be a valuable tool in the local, state, and national fight against the opioid pandemic. Health providers and community health organizations can benefit from this research by evaluating the psychological profile of the communities they serve and assessing the projected risk of fatal opioid overdose based on the relationships our study predict when making decisions for the allocation of overdose-reversal medication and other vital resources.

scholarly journals Longitudinal Association of County-Level Economic Indicators and Child Maltreatment Incidents

2014 ◽  
Vol 18 (9) ◽  
pp. 2202-2208 ◽  
Author(s):  
Sarah Frioux ◽  
Joanne N. Wood ◽  
Oludolapo Fakeye ◽  
Xianqun Luan ◽  
Russell Localio ◽  
...  
Keyword(s):  
Child Maltreatment ◽  
Economic Indicators ◽  
County Level ◽  
Longitudinal Association

Assessing County-Level Vulnerability for Opioid Overdose and Rapid Spread of Human Immunodeficiency Virus and Hepatitis C Infection in South Dakota

2020 ◽  
Vol 222 (Supplement_5) ◽  
pp. S312-S321
Author(s):  
Chelsea A Wesner ◽  
Weiwei Zhang ◽  
Sandra Melstad ◽  
Elizabeth Ruen ◽  
Cassandra Deffenbaugh ◽  
...  
Keyword(s):  
Human Immunodeficiency Virus ◽  
Hepatitis C ◽  
South Dakota ◽  
Composite Index ◽  
Hcv Infection ◽  
Opioid Overdose ◽  
County Level ◽  
Hepatitis C Infection ◽  
Immunodeficiency Virus ◽  
Chronic Hcv

Abstract Background Key indicators of vulnerability for the syndemic of opioid overdose, human immunodeficiency virus (HIV), and hepatitis C virus (HCV) due to injection drug use (IDU) in rural reservation and frontier counties are unknown. We examined county-level vulnerability for this syndemic in South Dakota. Methods Informed by prior methodology from the Centers for Disease Control and Prevention, we used acute and chronic HCV infections among persons aged ≤40 years as a proxy measure of IDU. Twenty-nine county-level indicators potentially associated with HCV infection rates were identified. Using these indicators, we examined relationships through bivariate and multivariate analysis and calculated a composite index score to identify the most vulnerable counties (top 20%) to this syndemic. Results Of the most vulnerable counties, 69% are reservation counties and 62% are rural. The county-level HCV infection rate is 4 times higher in minority counties than nonminority counties, and almost all significant indicators of opioid-related vulnerability in our analysis are structural and potentially modifiable through public health interventions and policies. Conclusions Our assessment gives context to the magnitude of this syndemic in rural reservation and frontier counties and should inform the strategic allocation of prevention and intervention services.

scholarly journals Predicting Opioid Overdose Crude Rates with Text-Based Twitter Features (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13787-13788
Author(s):  
Nupoor Gandhi ◽  
Alex Morales ◽  
Sally Man-Pui Chan ◽  
Dolores Albarracin ◽  
ChengXiang Zhai
Keyword(s):  
Public Health ◽  
Social Media ◽  
Time Signal ◽  
Opioid Overdose ◽  
Semantic Features ◽  
County Level ◽  
Social Media Data ◽  
Public Health Monitoring ◽  
Health Monitoring Systems ◽  
Media Data

Drug use reporting is often a bottleneck for modern public health surveillance; social media data provides a real-time signal which allows for tracking and monitoring opioid overdoses. In this work we focus on text-based feature construction for the prediction task of opioid overdose rates at the county level. More specifically, using a Twitter dataset with over 3.4 billion tweets, we explore semantic features, such as topic features, to show that social media could be a good indicator for forecasting opioid overdose crude rates in public health monitoring systems. Specifically, combining topic and TF-IDF features in conjunction with demographic features can predict opioid overdose rates at the county level.

scholarly journals Exploring the Association Between the “Big Five” Personality Traits and Fatal Opioid Overdose: County-Level Empirical Analysis

10.2196/24939 ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. e24939
Author(s):  
Zhasmina Tacheva ◽  
Anton Ivanov
Keyword(s):  
United States ◽  
Big Five ◽  
Psychological Factors ◽  
The United States ◽  
Community Level ◽  
Opioid Overdose ◽  
County Level ◽  
Psychological Traits ◽  
Wide Range ◽  
The Relationship

Background Opioid-related deaths constitute a problem of pandemic proportions in the United States, with no clear solution in sight. Although addressing addiction—the heart of this problem—ought to remain a priority for health practitioners, examining the community-level psychological factors with a known impact on health behaviors may provide valuable insights for attenuating this health crisis by curbing risky behaviors before they evolve into addiction. Objective The goal of this study is twofold: to demonstrate the relationship between community-level psychological traits and fatal opioid overdose both theoretically and empirically, and to provide a blueprint for using social media data to glean these psychological factors in a real-time, reliable, and scalable manner. Methods We collected annual panel data from Twitter for 2891 counties in the United States between 2014-2016 and used a novel data mining technique to obtain average county-level “Big Five” psychological trait scores. We then performed interval regression, using a control function to alleviate omitted variable bias, to empirically test the relationship between county-level psychological traits and the prevalence of fatal opioid overdoses in each county. Results After controlling for a wide range of community-level biopsychosocial factors related to health outcomes, we found that three of the operationalizations of the five psychological traits examined at the community level in the study were significantly associated with fatal opioid overdoses: extraversion (β=.308, P<.001), neuroticism (β=.248, P<.001), and conscientiousness (β=.229, P<.001). Conclusions Analyzing the psychological characteristics of a community can be a valuable tool in the local, state, and national fight against the opioid pandemic. Health providers and community health organizations can benefit from this research by evaluating the psychological profile of the communities they serve and assessing the projected risk of fatal opioid overdose based on the relationships our study predict when making decisions for the allocation of overdose-reversal medication and other vital resources.

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