As part of a mental health strategic plan initiative, between 2005 and 2008 the VA provided targeted funding for specialty substance use disorder (SUD) treatment programs. This SUD "fenced funding" was intended to improve access to SUD treatment. In total, about $150 million were allocated for SUD treatment as part of the larger initiative. This project complements the other projects in this CREATE because it applies some of the same performance metrics featured in the other projects to important policy questions and it investigates the mediating effects of these variables on outcomes for Veterans.
The project was extended for six months at no additional cost to validate mental health performance measure with patient satisfaction and to estimate the effect of capacity expansion on demand for mental health care.
Aim 1: Determine the proportion of fenced funds that can be accounted for by expanded staffing for specialty substance abuse treatment services.
Aim 2: Estimate the effect of fenced funding-related staffing on access and process quality.
We used 18 access and process quality metrics developed and used by OMHO.
Aim 3: Measure the effects of fenced funding-related staffing on patient-level outcomes.
Aim 4. Assess the association between mental health performance measures and patient satisfaction.
Aim 5. Estimate the effect of capacity expansion on demand for mental health care.
This was a retrospective, observational analysis of administrative data. The principal population studied was a cohort of approximately 500,000 Veterans who were diagnosed or treated for a substance use disorder in 2003 or 2004. We followed these patients through 2011 to assess patient outcomes under Aim 3. Analyses under Aims 1 and 2 examined the population of VA facilities from 2004 through 2011. Statistical models used station-level fenced funding allocations as instrumental variables to isolate quasi-random variations in SUD staffing leading to changes in access and quality metrics and patient-level outcomes. Aim 4 relied principally on facility-level MHIS and SAIL performance metrics provided to us by OMHS/PERC. Aim 5 relied on wait time, workload, and FTE data.
Aim 1: To increase funding for treatment, the VA has implemented several initiatives over the past decade to direct funds toward SUD treatment, supplementing the unrestricted funds VA medical centers receive. In this aim, we study the 'flypaper effect' or the extent to which these directed funds have actually increased SUD treatment spending. The sample for this aim included all VA facilities and used observational data spanning years 2002 to 2010. Data were analyzed with a fixed effects, ordinary least squares specification with monetized workload as the dependent variable and funding dedicated to SUD specialty clinics the key dependent variable, controlling for unrestricted funding. With these data, we observed different effects of dedicated SUD specialty clinic funding over the period 2002 to 2008 versus 2009 to 2010. In the earlier period, there is no evidence of a significant portion of the dedicated funding sticking to its target. In the later period, a substantial proportion-38% in 2009 and 61% in 2010-of funding dedicated to SUD specialty clinics did translate into increased medical center spending for SUD treatment. In comparison, only five cents of every dollar of unrestricted funding is spent on SUD treatment. Relative to unrestricted funding, dedicated funding for SUD treatment was much more effective in increasing workload, but only in years 2009 and 2010.
Aim 2: This aim addressed whether access to and intensity of VA SUD treatment kept pace with rising demand during the 2000s or declined. Overall, we observed an increase in access to and intensity of VA SUD care associated with increased funding. The number of VA patients with a SUD diagnosis grew from about 310,000 in 2005 to 439,000 in 2010, an increase of 42%. Over this period, the proportion of SUD diagnosed patients receiving intensive treatment, intensive residential treatment, or intensive outpatient treatment grew 41%, 23%, and 7%, respectively. In 2008-2010 both the percent of patients receiving intensive outpatient and residential care increased, but the former more so. The average number of weeks of intensive outpatient care increased in 2008 and 2010, but the average number of weeks of intensive inpatient care did not. Overall, the VA was able to increase funding for and expand the population to which it offered SUD treatment without diminishing internal access and intensity.
Aim 3: Unfeasible due to methodological barriers. The issue arose from the fact that outcomes are only observed (i.e., available in administrative data) if patients are seen. A patient is more likely to be seen if he is receiving SUD treatment. Therefore, treated patients are more likely to have observed outcomes (like overdoses and adverse events). So, one cannot use treatment as the independent variable because results would suggest that treatment causes bad outcomes. One needs to find something that randomly influences treatment. We tried using dedicated SUD funding as an "instrument" in an instrumental variable analysis. It's an institutional factor that has randomizing effect on treatment probability. But, it turned out to have too little influence on treatment and there is no good alternative. Hence, this aim was not feasible.
Aim 4: Satisfaction with access to care was higher than with experiences with care encounters; Broad measures of mental health care program reach and intensity were positively associated with both kinds of satisfaction while no measures of psychosocial service access were positively associated with both access and encounter satisfaction and nearly all measures of treatment continuity were strongly and positively associated with both kinds of satisfaction.
Aim 5: High elasticity of wait times with respect to MH FTEs (the proportion by which wait times increased with MH FTE increases) is an indicator of adequate VA capacity. We found that elasticity grew between FY10-FY14. But, we observed large variation in elasticity across stations, ranging between 0.2 - 11.3. Increasing elasticity at all stations to 2 (as a proxy for adequate capacity) would require an additional 1,150 FTEs (10% of total FTE in 2014).
This component of our CREATE proposal used fenced funding from the Mental Health Strategic Plan to investigate the effects of evidence-based treatment on health outcomes for individual Veterans. In addition, this study evaluated the effectiveness of the fenced funding approach for improving access to and quality of specialty SUD treatment services. Our findings will help the Office of Mental Health Services refine its efforts to measure and promote access and quality in the future and inform current discussions regarding whether fenced funding should be used in the coming years. Our operational partners in this research were the Office of Mental Health Operations, the Program Evaluation and Resource Center, and the Office of Mental Health Services. The findings of this study will also inform VA clinicians and managers at the station and VISN levels about the effects of evidence-based treatment on health outcomes for individual Veterans.
- Hanchate AD, Frakt AB, Kressin NR, Trivedi A, Linsky A, Abdulkerim H, Stolzmann KL, Mohr DC, Pizer SD. External Determinants of Veterans' Utilization of VA Health Care. Health services research. 2018 Dec 1; 53(6):4224-4247.
- Frakt AB, Trafton J, Pizer SD. Maintenance of access as demand for substance use disorder treatment grows. Journal of substance abuse treatment. 2015 Aug 1; 55:58-63.