Our best estimate is that on average, substance abuse treatment costs $1,583 and is associated with a societal benefit of $11,487, representing a 7:1 ratio of benefits to costs (9:1 when arrest data are “inflated” to proxy for actual crimes committed). This ratio is based on weighted average treatment costs, which reflect expected costs of treatment; 9-month follow-up of clients in all modalities with follow-up survey data, so that as many sources of benefit as possible could be included in the analysis; and benefit measures that demonstrate significant change, so that the estimates are robust to rare events. Sixty-five percent of the total benefit was attributable to reductions in crime costs, including incarceration. Twenty-nine percent was because of increased employment earnings, with the remaining 6 percent because of reduced medical and behavioral health care costs.
A review of 11 studies (McCollister and French 2003
) found that the benefit–cost ratios associated with substance abuse treatment ranged from 1.33 to 23.33 and that benefits were overwhelmingly because of reductions in criminal activity, with smaller contributions of earnings, and averted health care. Our conclusion is similar, especially when inflating the arrest data. Our benefit–cost ratio is also similar to the CalDATA estimate, despite differences in study design and methodology. However, our estimates of substance abuse treatment costs tend to be lower than those in previous studies. An earlier literature review by Roebuck, French, and McLellan (2003)
suggested that the average cost per treatment episode was $7,358 for MM, $1,944 for standard outpatient, and $9,426 for residential. Our estimates were $2,737, $838, and $2,791, respectively, based on weighted per diem estimates. The lower episode costs in CalTOP were because of shorter lengths of treatment for MM and residential, as the weekly cost of treatment was actually higher ($99 and $235, respectively, in CalTOP, compared with $91 and $194 in Roebuck et al.). For outpatient, lower episode costs were also attributable to lower weekly costs, around $48 versus $121 in Roebuck et al. These discrepancies might reflect geographic differences in the intensity and duration of treatment.
Our findings should be interpreted with caution, given a number of study limitations. The results may not generalize to non-CalTOP providers, especially those in other states. Attrition may have biased the estimated cost–benefit ratio among the “intake+follow-up” cohort if the clients who were women, incarcerated, or could not be located were more costly on average than the clients who were successfully tracked. Compared with the statewide data, the CalTOP sample slightly underrepresented methadone clients, although statewide methadone clients only account for 10 percent of the total treatment population. We may have slightly overestimated benefit–cost ratios if they were based on the average across CalTOP programs of all modalities. Reductions in nontreatment costs may be overstated because of regression to the mean, i.e., persons entering substance abuse treatment often have hit the bottom and “have nowhere to go but up.” A related issue is whether clients who were court-mandated to enter treatment were deterred in the short run from committing further criminal activities. Unfortunately, randomization to treatment is neither logistically nor ethically possible in a large-scale, “real-world” study of this type, plus randomized-controlled studies lack the external validity of observational studies. The pre–post study design has strong advantages over observational studies comparing substance abusers who do and do not enter treatment, because of the selection bias inherent in the latter. The high ratio of benefits to costs makes it less plausible that the cost of substance abuse treatment would have outweighed its benefits if regression to the mean and deterrence effects could have been taken into account. Although it was not possible to study these effects using CalTOP data, we analyzed studies including a “no-treatment” control group from a published meta-analysis of drug abuse treatment outcomes (Prendergast et al. 2002
). These analyses suggested that the controls had pre–post differences in outcomes that were about half as large as those in the treatment group. Applying this ratio to CalTOP, the $1,583 in treatment costs would be compared with a benefit of $5,744 ($11,487/2).
The relatively short 9-month follow-up period may understate the monetary benefits associated with treatment if its effects persist over the longer run; alternatively, the additional benefits accrued beyond the 9-month window might be offset by additional costs if the patients relapse and require further treatment. Most of the other study limitations are likely to lead to conservative biases, e.g., the inability to cost out certain crimes (especially those related to drug manufacture and sales, which showed the largest reductions following treatment) and to measure probation and parole costs and costs imposed on family members and friends. Systematic underreporting of hospitalizations, ER use, days incarcerated, and employment income would tend to understate the benefits of treatment as long as the under-reporting was similar for a given client before and after treatment. The lack of comprehensive outpatient medical care data could have induced either a conservative or liberal bias, depending on whether engagement in substance abuse treatment increased referrals to medical providers or primarily improved physical health so that less medical care was needed. Treatment costs may have been slightly underestimated because providers estimated the depreciated costs of their furniture to be zero.
The CalTOP study provided a number of important lessons for conducting future analyses of the cost–benefit of substance abuse treatment. Given concerns about respondent burden, use of a shorter version of the DATCAP is desirable and we do not believe much critical information would be lost. A brief version of the DATCAP has been pilot tested (French, Roebuck, and McLellan 2004
) and is available for download and use by researchers at http://www.datcap.com
. Similarly, the ASI-6 will be better suited for economic evaluation studies than the older version used for CalTOP. The most important sources of monetary benefits (crime, hospitalizations, and earnings) occurred in domains that can be measured using administrative data. As omission of many other sources of monetary benefit induces only a conservative bias, a reasonable cost–benefit analysis might be conducted without the time and expense of primary data collection from clients. Use of administrative data only has the added advantage of allowing the entire client population to be included in the analysis. Long administrative data lags suggest that cost–benefit analyses may need to be based on older data, but lags pose less of a threat to the validity of the findings if treatment systems or client populations do not change rapidly over time. If primary data collection is used as the primary or a supplementary source of information, an instrument designed specifically for cost–benefit analyses should be administered. For example, the most recent version of the Addiction Severity Index (the forthcoming ASI-6) has been redesigned to permit economic evaluation.
Nontrivial differences by treatment modality were observed. Although the benefits associated with outpatient treatment were lower than for residential treatment, the costs were also lower, so the net return on investment was actually higher for outpatient than for residential treatment. No statistically significant monetary benefits were identified among the MM clients, likely because of the small sample size and low power. Alternatively, benefits may be smaller for the MM clients, because of the long-term nature of methadone treatment. The strongest effects of treatment are likely to occur soon after the client becomes drug-free. The overwhelming majority of MM clients had prior treatment admissions, suggesting that many may have been on methadone for a long time and hence already realized any reductions in crime in past years. The baseline level of crime costs was much lower for MM clients than for either outpatient or residential clients, suggesting little room for additional improvement. In other words, our “pre” admission measurement period may not actually precede the receipt of treatment for these clients, but rather, reflect a phase in ongoing treatment. Again, however, the lack of precision in the estimates when looking separately at MM clients precludes us from drawing firm conclusions about the relative magnitudes of the effects for methadone versus outpatient or residential clients. In general, caution must be exercised in making comparisons across modalities, because substance abusers tend to move in and out of treatment and across treatment modalities during their life course. Furthermore, the modality comparisons were based on initial treatment modality, so attribution of benefits to a single modality may be misleading.
Taken as a whole, our findings suggest that even without considering the health and quality-of-life benefits to the clients themselves, spending taxpayer dollars on substance abuse treatment may be a wise investment. Further research is needed to establish a link between the monetary benefits of treatment and the duration and intensity of treatment. Challenges in identifying this relationship include collecting reliable data on the services received by clients and addressing selection bias (i.e., more acute clients probably receive more intensive services, at least to begin with, but more motivated clients are likely to have higher retention rates). Despite these challenges, such an analysis would seem to be the logical next step in building on the CalTOP findings.