Program Capacity to Deliver Prevention Services to Children of Adult Clients Receiving Substance Use Disorder Treatment

Understanding the role of financial capacity in the delivery of opioid use disorder treatment

Opioid treatment programs must have adequate financial capacity to sustain operations and deliver a high standard of care for individuals suffering from opioid use disorder. However, there is limited consistency in the health services literature about the concept and relationship of organizational financial capacity and key outcome measures (wait time and retention). In this study, we explored five common measures of financial capacity that can be applied to opioid treatment programs: (a) reserve ratio, (b) equity ratio, (c) markup, (d) revenue growth, and (e) earned revenue. We used these measures to compare financial capacity among 135 opioid treatment programs across four data collection points: 2011 (66 programs), 2013 (77 programs), 2015 (75 programs), and 2017 (69 programs). We examined the relationship between financial capacity and wait time and retention. Findings from the literature review show inconsistencies in the definition and application of concepts associated with financial capacity across business and social service delivery fields. The analysis shows significant differences in components of financial capacity across years. We observed an increase in average earned revenue and markup in 2017 compared to prior years. The interaction between minorities and markup was significantly associated with higher likelihood of waiting (IRR = 1.077, p < .05). Earned revenue (IRR = 0.225, p < .05) was related to shorter wait time in treatment. The interaction between minorities and equity ratio is also significantly associated with retention (IRR = 0.796, p < .05). Our study offers a baseline view of the role of financial capacity in opioid treatment and suggests a framework to determine its effect on client-centered outcomes.

Individualized approach to primary prevention of substance use disorder: age-related risks

Background

The misuse of legal and illegal substances has led to an increase in substance use disorder (SUD) in the United States. Although primary prevention strategies have been successfully used to target chronic physical diseases, these strategies have been less effective with SUD, given misconceptions of SUD, shortages in behavioral health professionals, and the population-based focus on specific substances. A developmental approach to the identification and primary prevention of SUD that does not fully rely upon behavioral health workers is needed. The purpose of this paper was to examine age related risk factors for developing SUD and present a novel individualized approach to SUD prevention.

Methods

A literature search was conducted to identify risk factors for SUD among children, young adults, adults, and older adults. We searched CINAHL, PsycINFO, and PubMed between the years 1989–2019, and extracted data, analyzing similarities and differences in risk factors across life stages. Broader categories emerged that were used to group the risk factors.

Results

More than 370 articles were found. Across all age groups, risk factors included adverse childhood experiences, trauma, chronic health diseases, environmental factors, family history, social determinants, and grief and loss. Despite the similarities, the contextual factors and life challenges associated with these risks varied according to the various life stages. We proposed an approach to primary prevention of SUD based on risk factors for developing the disease according to different age groups. This approach emphasizes screening, education, and empowerment (SEE), wherein individuals are screened for risk factors according to their age group, and screening results are used to customize interventions in the form of education and empowerment. Given that trained persons, including non-healthcare providers, close to the at-risk individual could conduct the screening and then educate and mentor the individual according to the risk level, the number of people who develop SUD could decrease.

Conclusions

The risk factors for developing SUD vary across the various life stages, which suggests that individualized approaches that do not overtax behavioral healthcare workers are needed. Using SEE may foster early identification and individualized prevention of SUD.