Investigating the importance of social vulnerability in opioid-related mortality across the United States

• URL: http://arxiv.org/abs/2412.15218v2
• Date: Mon, 17 Feb 2025 16:54:24 GMT
• Title: Investigating the importance of social vulnerability in opioid-related mortality across the United States
• Authors: Andrew Deas, Adam Spannaus, Dakotah D. Maguire, Jodie Trafton, Anuj J. Kapadia, Vasileios Maroulas,
• Abstract summary: Our study examines the correlation between opioid-related mortality and thirteen components of the Social Vulnerability Index (SVI) Our findings highlight critical social factors strongly correlated with opioid-related mortality, emphasizing their potential roles in worsening the epidemic when their levels are high and mitigating it when their levels are low.
• Score: 1.061049003896232
• License:
• Abstract: The opioid crisis remains a critical public health challenge in the United States. Despite national efforts to reduce opioid prescribing rates by nearly 45\% between 2011 and 2021, opioid overdose deaths more than tripled during this same period. This alarming trend reflects a major shift in the crisis, with illegal opioids now driving the majority of overdose deaths instead of prescription opioids. Although much attention has been given to supply-side factors fueling this transition, the underlying socioeconomic conditions that perpetuate and exacerbate opioid misuse remain less understood. Moreover, the COVID-19 pandemic intensified the opioid crisis through widespread social isolation and record-high unemployment; consequently, understanding the socioeconomic drivers of this epidemic has become even more crucial in recent years. To address this need, our study examines the correlation between opioid-related mortality and thirteen components of the Social Vulnerability Index (SVI). Leveraging a nationwide county-level dataset spanning consecutive years from 2010 to 2022, this study integrates empirical insights from exploratory data analysis with feature importance metrics derived from machine learning models. Our findings highlight critical social factors strongly correlated with opioid-related mortality, emphasizing their potential roles in worsening the epidemic when their levels are high and mitigating it when their levels are low.

Related papers

• Mobility-GCN: a human mobility-based graph convolutional network for tracking and analyzing the spatial dynamics of the synthetic opioid crisis in the USA, 2013-2020 [0.0]
  opioids are the most common drugs involved in drug-involved overdose mortalities in the U.S. In 2018, about 70% of all drug overdose deaths involved opioids and 67% of all opioid-involved deaths were accounted for by synthetic opioids.
  arXiv  Detail & Related papers  (2024-09-16T02:37:51Z)
• Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts [26.161892748901252]
  We present a corpus of 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use. For every post, we annotate span-level explanations and crucially study their role both in annotation quality and model development.
  arXiv  Detail & Related papers  (2023-11-15T16:05:55Z)
• Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It [80.24494623756839]
  We present a framework for interpretable estimation of causal effects for critically ill patients. We apply this framework to the effect of seizures and other potentially harmful electrical events in the brain on outcomes.
  arXiv  Detail & Related papers  (2022-03-09T18:03:35Z)
• Deep learning for drug repurposing: methods, databases, and applications [54.08583498324774]
  Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing.
  arXiv  Detail & Related papers  (2022-02-08T09:42:08Z)
• Location inference on social media data for agile monitoring of public health crises: An application to opioid use and abuse during the Covid-19 pandemic [0.0]
  Covid-19 pandemic has intersected with the opioid epidemic to create a unique public health crisis. We use social media data to extract insights on the experiences of opioid users during the pandemic. We find that state government strategies that prioritized economic reopening over curtailing the spread of the virus created a markedly different environment and outcomes for opioid users.
  arXiv  Detail & Related papers  (2021-11-02T17:55:28Z)
• Understanding the factors driving the opioid epidemic using machine learning [10.021195517057462]
  U.S. has experienced an opioid epidemic with an unprecedented number of drugs overdose deaths. In this study we apply machine learning based techniques to identify opioid risks of neighborhoods in Delaware.
  arXiv  Detail & Related papers  (2021-08-16T18:08:56Z)
• Controlling the Outbreak of COVID-19: A Noncooperative Game Perspective [61.558752620308134]
  Isolation and social distancing seem to be effective preventive measures to control this pandemic. We propose a noncooperative game that can provide an incentive for maintaining social distancing to prevent the spread of COVID-19. Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation.
  arXiv  Detail & Related papers  (2020-07-27T04:28:32Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and Recovery [62.9980747784214]
  Substance Use Disorders involve the misuse of alcohol, opioids, marijuana, and methamphetamine. 1 in 12 U.S. adults have or have had a substance use disorder. National Institute on Drug Abuse estimates that SUDs cost the U.S. $520 billion annually.
  arXiv  Detail & Related papers  (2020-06-23T18:30:20Z)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.