Jamie L Romeiser1, Jake Labriola2, Jaymie R Meliker2. 1. Program in Public Health, Department of Family, Population, and Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA; Department of Anesthesiology, Stony Brook University, Stony Brook, NY 11794, USA. Electronic address: Jamie.Romeiser@stonybrookmedicine.edu. 2. Program in Public Health, Department of Family, Population, and Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA.
Abstract
OBJECTIVES: To examine the relationship between prescription opioid rates and prescription opioid overdose deaths using spatial cluster and regression analyses. METHODS: Publicly available county-level data were obtained from the New York State Health Department and the Centers for Disease Control and Prevention, 2013-2015. Kulldorff's spatial scan statistic was used to investigate spatial clustering of New York State opioid prescription overdose death rates, as well as opioid prescription rates. A Poisson regression was used to analyze opioid prescriptions as a predictor of mortality accounting for spatial autocorrelation in the residuals. RESULTS: We report 1440 overdose mortalities and 26.8 million opioid prescriptions throughout New York State in 2013-2015. Multiple significant clusters were found for both opioid prescription mortalities as well as prescriptions, although the locations of the elevated rates did not strongly overlap. Poisson regression showed a significant, small, negative relationship between prescriptions and opioid mortalities, wherein for every 10,000 prescriptions increased, the number of opioid mortalities decreased approximately 0.12%; therefore, essentially a null relationship. CONCLUSIONS: Simply reducing the number of prescriptions may not be effective in reducing prescription related mortality; although opioid prescription dosing information should be made available to engender a better evaluation of the epidemic. Geographical differences in opioid mortalities exist above and beyond what can be explained by prescription rate data; identifying these locations may help inform and guide public health interventions. Despite the recent reduction in opioid prescription rates, the overall population is still inundated with prescriptions.
OBJECTIVES: To examine the relationship between prescription opioid rates and prescription opioid overdose deaths using spatial cluster and regression analyses. METHODS: Publicly available county-level data were obtained from the New York State Health Department and the Centers for Disease Control and Prevention, 2013-2015. Kulldorff's spatial scan statistic was used to investigate spatial clustering of New York State opioid prescription overdose death rates, as well as opioid prescription rates. A Poisson regression was used to analyze opioid prescriptions as a predictor of mortality accounting for spatial autocorrelation in the residuals. RESULTS: We report 1440 overdose mortalities and 26.8 million opioid prescriptions throughout New York State in 2013-2015. Multiple significant clusters were found for both opioid prescription mortalities as well as prescriptions, although the locations of the elevated rates did not strongly overlap. Poisson regression showed a significant, small, negative relationship between prescriptions and opioid mortalities, wherein for every 10,000 prescriptions increased, the number of opioid mortalities decreased approximately 0.12%; therefore, essentially a null relationship. CONCLUSIONS: Simply reducing the number of prescriptions may not be effective in reducing prescription related mortality; although opioid prescription dosing information should be made available to engender a better evaluation of the epidemic. Geographical differences in opioid mortalities exist above and beyond what can be explained by prescription rate data; identifying these locations may help inform and guide public health interventions. Despite the recent reduction in opioid prescription rates, the overall population is still inundated with prescriptions.
Authors: A Salomone; R Bigiarini; J J Palamar; C McKnight; L Vinsick; E Amante; D Di Corcia; M Vincenti Journal: J Anal Toxicol Date: 2020-05-18 Impact factor: 3.367
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