A Tiered Model of Substance use Severity and Life Complexity: Potential for Application to Needs-Based Planning

Abstract

Background: In order to improve long-term outcomes for individuals with substance use problems, one approach is to adopt a system planning model that considers both addiction severity and life complexities. The tiered approach has been developed and tested to describe systems-level need based on levels of risk and problem severity. Methods: An existing tiered model was modified to accommodate Australian data, incorporating substance use severity and life complexity. The hypothesis was that tiers would reflect differences in well-being amongst help seekers such that an increase in tier would be associated with a reduction in well-being, suggesting the need for more intensive (and integrated) interventions. The model was tested using 2 data sets of screening data, collected from face-to-face alcohol and other drug (AOD) service (n = 430) and online help (n = 309) seekers, drawn from a larger sample of 2,766 screens. The screen included demographic information and substance use, mental health, and quality of life measures. Results: There was a significant relationship between well-being and tier ranking, suggesting that the model adequately captured elements of severity and complexity that impact on well-being. There were notable differences between the help-seeking populations with a higher proportion of online respondents allocated to lower tiers and more face-to-face respondents allocated to higher tiers. However, there was an overlap in these populations, with more than half of online respondents classified as higher tiers and one fifth of face-to-face respondents classified as lower tiers. This suggests that the model can be used both to assess unmet need in out-of-treatment groups and demand in the absence of dependence in a subpopulation of the face-to-face treatment population. Conclusions: The tiered model provides a method to understand levels of AOD treatment need and, as part of needs-based planning, may be used to optimize treatment responses and resourcing.