2007
DOI: 10.1016/j.drugalcdep.2006.12.016
|View full text |Cite
|
Sign up to set email alerts
|

Fatal overdoses and deaths by other causes in a cohort of Norwegian drug abusers—A competing risk approach

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

2
26
0
4

Year Published

2010
2010
2018
2018

Publication Types

Select...
8

Relationship

2
6

Authors

Journals

citations
Cited by 38 publications
(32 citation statements)
references
References 18 publications
2
26
0
4
Order By: Relevance
“…Only three of the deceased were females. This harmonises with other studies, showing that males have a significantly higher risk of death Gossop et al, 2002;Ødegård et al, 2007;Clausen et al, 2007).…”
Section: Discussionsupporting
confidence: 91%
“…Only three of the deceased were females. This harmonises with other studies, showing that males have a significantly higher risk of death Gossop et al, 2002;Ødegård et al, 2007;Clausen et al, 2007).…”
Section: Discussionsupporting
confidence: 91%
“…The competing risk approach is necessary because OST registrants are at increased risk of more than one cause of death, several of which share risk factors (Odegard et al, 2007). With this method, individuals who die from a cause other than the one under study remain in the risk set, and the subdistribution hazards estimate the 'real world' probabilities of death due to specific causes.…”
Section: Discussionmentioning
confidence: 99%
“…The variables used in the multiple regression models were based upon known risk factors for overdoses, injection frequency, substance use and criminal activity available in the data. These variables were age, gender, length of injecting career, homelessness and shelter use (Bird and Robertson, 2007, Hickman et al, 2007, Loyd-Smith et al, 2008, Nordentoft et al, 2003Ødegård et al, 2007). Length of injecting career is only known as a risk factor for overdoses (Ødegård et al, 2007), and it was therefore not used in the other multiple regression models.…”
Section: Data Analysesmentioning
confidence: 99%