Spatial analysis of drug poisoning deaths in the American west: A comparison study using profile regression to adjust for collinearity and spatial correlation.
Altitude
American West
Drug poisoning deaths
Multi-collinearity
Spatial autocorrelation
Spatial profile regression
Journal
Drug and alcohol dependence
ISSN: 1879-0046
Titre abrégé: Drug Alcohol Depend
Pays: Ireland
ID NLM: 7513587
Informations de publication
Date de publication:
01 11 2019
01 11 2019
Historique:
received:
11
12
2018
revised:
18
07
2019
accepted:
19
07
2019
pubmed:
14
10
2019
medline:
14
7
2020
entrez:
14
10
2019
Statut:
ppublish
Résumé
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. Profile regression (PR) is an integrated method for cluster and regression analysis, which adjusts for spatial-autocorrelation and multi-collinearity. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 31606724
pii: S0376-8716(19)30375-8
doi: 10.1016/j.drugalcdep.2019.107598
pii:
doi:
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
107598Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.