#Geographic, Temporal, and #Sociodemographic #Differences in #Opioid #Poisoning (Am J Prev Med., abstract)

[Source: American Journal of Preventive Medicine, full page: (LINK). Abstract, edited.]

Geographic, Temporal, and Sociodemographic Differences in Opioid Poisoning

Elinor R. Schoenfeld, PhD1,2, George S. Leibowitz, PhD, LICSW3, Yu Wang, BE4, Xin Chen, PhD4, Wei Hou, PhD1, Sina Rashidian, BS4, Mary M. Saltz, MD2,5, Joel H. Saltz, MD, PhD2, Fusheng Wang, PhD2,4

Open Access / DOI: https://doi.org/10.1016/j.amepre.2019.03.020

 

Abstract

Introduction

Not enough is known about the epidemiology of opioid poisoning to tailor interventions to help address the growing opioid crisis in the U.S. The objective of this study is to expand the current understanding of opioid poisoning through the use of data analytics to evaluate geographic, temporal, and sociodemographic differences of opioid poisoning– related hospital visits in a region of New York State with high opioid poisoning rates.

Methods

This retrospective cohort study utilized patient-level New York State all-payer hospital data (2010–2016) combined with Census data to evaluate geographic, patient, and community factors for 9,714 Long Island residents with an opioid poisoning–related inpatient or outpatient hospital facility discharge. Temporal, 7-year opioid poisoning rates and trends were evaluated, and geographic maps were generated. Overall, significance tests and tests for linear trend were based upon logistic regression. Analyses were completed between 2017 and 2018.

Results

Since 2010, Long Island and New York State opioid poisoning hospital visit rates have increased 2.5- to 2.7-fold (p<0.001). Opioid poisoning hospital visit rates decreased for men, white patients, and self-payers (p<0.001) and increased for Medicare payers (p<0.001). Communities with high opioid poisoning rates had lower median home values, higher percentages of high school graduates, were younger, and more often white patients (p<0.01). Maps displayed geographic patterns of communities with high opioid poisoning rates overall and by age group.

Conclusions

Findings highlight the changing demographics of the opioid poisoning epidemic and utility of data analytics tools to identify regions and patient populations to focus interventions. These population identification techniques can be applied in other communities and interventions.

Keywords: Opioids; Illicit drugs; Society; USA.

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gimi69

I am an Italian blogger, active since 2005 with main focus on emerging infectious diseases such as avian influenza, SARS, antibiotics resistance, and many other global Health issues. Other fields of interest are: climate change, global warming, geological and biological sciences. My activity consists mainly in collection and analysis of news, public services updates, confronting sources and making decision about what are the 'signals' of an impending crisis (an outbreak, for example). When a signal is detected, I follow traces during the entire course of an event. I started in 2005 my blog ''A TIME'S MEMORY'', now with more than 40,000 posts and 3 millions of web interactions. Subsequently I added an Italian Language blog, then discontinued because of very low traffic and interest. I contributed for seven years to a public forum (FluTrackers.com) in the midst of the Ebola epidemic in West Africa in 2014, I left the site to continue alone my data tracking job.

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