Elinor R Schoenfeld1, George S Leibowitz2, Yu Wang3, Xin Chen3, Wei Hou4, Sina Rashidian3, Mary M Saltz5, Joel H Saltz6, Fusheng Wang7. 1. Department of Family, Population and Preventive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York; Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York. Electronic address: elinor.schoenfeld@stonybrook.edu. 2. School of Social Welfare, Stony Brook University, Stony Brook, New York. 3. Department of Computer Science, School of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York. 4. Department of Family, Population and Preventive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York. 5. Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York; Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York. 6. Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York. 7. Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York; Department of Computer Science, School of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York.
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.
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.
Authors: Xin Chen; Wei Hou; Sina Rashidian; Yu Wang; Xia Zhao; George Stuart Leibowitz; Richard N Rosenthal; Mary Saltz; Joel H Saltz; Elinor Randi Schoenfeld; Fusheng Wang Journal: Sci Rep Date: 2021-03-04 Impact factor: 4.379
Authors: Andres Hernandez; Adam J Branscum; Jingjing Li; Neil J MacKinnon; Ana L Hincapie; Diego F Cuadros Journal: Sci Rep Date: 2020-03-09 Impact factor: 4.379