Citation

Kerry R, Yoo E, Ingram B. Drug Alcohol Depend. 2019; 204: e107598.

Affiliation

Facultad de Ingeniería, Universidad de Talca, Chile.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.drugalcdep.2019.107598

PMID

31606724

Abstract

BACKGROUND: The USA has seen dramatic increases in drug poisoning deaths (DPD) recently. State-level rates have responded to federal and state initiatives, yet the counties with the highest rates are stable. Spatial analysis enables investigators to identify the highest risk counties and most important risk factors, although results are often confounded by spatial autocorrelation and multicollinearity.

METHODS: Profile regression (PR) is an integrated method for cluster and regression analysis, which adjusts for spatial-autocorrelation and multi-collinearity.

RESULTS: With PR, three clusters were identified in the Western USA with most of NM, NV and UT and several counties in AZ, CO, ID and WY being high-risk. Cluster analysis in a previous study only identified high-risk counties in northern CA, NM and NV. Elevation, suicide and LDS population were positively, and population density was negatively linked with DPD for PR and standard regression (SR) showing differences between the mountain west and coastal areas. Complex relationships between DPD and several variables were identified by PR which was not possible with SR.

CONCLUSIONS: Statistically principled methods like PR are needed for appropriate identification of the highest risk counties and important risk factors given the complex relationships with DPD. Funding for prevention, education and medical services should be targeted at rural, mountain communities in the west which have high %LDS and suicide rates. Counties with high %poverty and %Hispanic were also at high-risk. Individual-level studies are needed to confirm important risk factors in high-risk counties.

Copyright © 2019 Elsevier B.V. All rights reserved.

Language: en

Keywords

Altitude; American West; Drug poisoning deaths; Multi-collinearity; Spatial autocorrelation; Spatial profile regression