Use of laboratory data for illicit drug use surveillance and identification of socioeconomic risk factors

被引:1
|
作者
Azimi, Vahid [1 ]
Jackups, Ronald, Jr. [1 ]
Farnsworth, Christopher W. [1 ]
Budelier, Melissa M. [1 ,2 ,3 ]
机构
[1] Washington Univ St Louis, Sch Med, Dept Pathol & Immunol, St Louis, MO USA
[2] TriCore Reference Labs, 1001 Woodward Pl NE, Albuquerque, NM 87102 USA
[3] TriCore Reference Labs, Albuquerque, NM USA
关键词
Big Data; Illicit drug use; Laboratory surveillance; OPIOID OVERDOSE DEATHS; UNITED-STATES; CARE;
D O I
10.1016/j.drugalcdep.2022.109499
中图分类号
R194 [卫生标准、卫生检查、医药管理];
学科分类号
摘要
Background: Drug overdose is the leading cause of death among people 25-44 years of age in the United States. Existing drug surveillance methods are important for prevention and directing treatment, but are limited by delayed reporting and lack of geographic granularity.Methods: Laboratory urine drug screen and complete metabolic panel data from patients presenting to the emergency department was used to observe long-term and short-term temporal and geospatial changes at the zip code-level in St. Louis. Multivariate linear regression was performed to investigate associations between zip code level socioeconomic factors and drug screening positivity rates.Results: An increase in the fentanyl positive drug screens was seen during the initial COVID-19 shutdown period in the spring of 2020. A decrease in cocaine positivity was seen in the fall and winter of 2020, with a return to baseline coinciding with the second major COVID-19 shutdown in the summer of 2021. These changes appeared to be independent of changes in emergency department utilization as measured by complete metabolic panels ordered. Significant short-term changes in fentanyl and cocaine positivity rates between specific time periods were able to be localized to individual zip codes. Zip code-level multivariate analysis demonstrated independent associations between socioeconomic/demographic factors and fentanyl/cocaine positivity rates as determined by laboratory drug screening data.Conclusions: Analyzing clinical laboratory drug screening data can enable a more temporally and geographically granular view of population-level drug use surveillance. Additionally, laboratory data can be utilized to find population-level socioeconomic associations with illicit drug use, presenting a potential avenue for the use of this data to guide public health and healthcare policy decisions.
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页数:7
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