ASA Trends between Treatment Programs
By: Elijah D. Sprague, Ph.D, NCC, LPCC-S (OH), LICDC, Director of Data Analytics & Research at The Counseling Center
Patients who leave treatment against staff advice (ASA) prior to their successful
completion of treatment, also known as attrition, present a significant barrier to the integrity of
recovery services offered in both medical and drug treatment agencies alike. Attrition places the
patient’s health at increased risk as their presenting concern remains untreated (Hwang, Gupta, &
Martin, 2003). Consequently, patients who leave treatment against staff advice, are more likely
to be readmitted and often fare worse than their successful counterparts (Choi, Kim, & Qian,
2011). These patients often continue to use at greater amounts and are more likely to suffer a
fatal drug-overdose. Additional evidence suggests patients who leave treatment too soon,
contribute to rising financial costs due in part to the amount of time clinicians devote to
paperwork related to admissions and discharges. As a result of the increasing regularity of
patient attrition, additional burden is then placed upon, not only the patient, but also the
clinicians who treat them.
Unfortunately, attrition is an increasingly frequent phenomena as evidenced by trends of
0.8% to 2.2% making up global hospital admissions (Paul, Gautam, Mahajan, Gautam, and
Ragavaiah, 2019). These numbers are likely to worsen as evidenced by an annual increase of 1-
2% being observed over the past year (Albayati et al., 2021). In comparison, substance use
treatment centers experience far greater amounts of patient attrition. Accordingly, attrition rates
within the first 90-days of outpatient drug treatment (without methadone) can exceed 50% or
more (Palmer, Murphey, Piselli, & Ball, 2009). As seen in Figure 1, the counseling center—an
established drug treatment provider within the Portsmouth, OH area—reaches average attrition
rates of 27.8% within total admissions over a 3-year period.
Previous research identifies multiple factors contributing to attrition, including age,
gender, and minority status, to name a few (Anderson & Berg, 2001; Sweet & Noones, 1989;
Agosti, Nunes, & Ocepek-Welikson, 1996). In her dissertation, Abeyesinhe (2013) discovered
in her sample, that the type of drug used to be the primary predictor of treatment attrition.
Accordingly, persons who abuse opiates are more likely than any other type of drug use to
experience higher levels of attrition. Moreover, Abeyesinhe (2013) found that over half of the
opiate using patients—admitted into the Loma Linda Behavioral Medical Center
(LLUMBC)—had dropped out of treatment during the study. Notwithstanding the localized
nature of her research, these data provide further confirmation regarding the ambiguous nature of
patient attrition, requiring further investigation as to contributing factors.
Upon closer inspection, differences in male vs. female populations have been observed in
the Counseling Center (TCC). This data was retrieved from Next Gen (Ver. 220.127.116.11) electronic
health records (EHR) and was analyzed via Microsoft Excel. These data are depicted in both fig.2 and fig. 3, respectively. Data reveal trends in ASA rates which correspond to different
times of the year. To this end, we see an uptick—within male populations—during the months
of May and August. Conversely, a gradual increase is observed—within female
populations—throughout the year with a dramatic increase in attrition in the month of
November. The percentage difference is presented in Fig. 3 with corresponding red and green
directional arrows to demonstrate the negative (red) vs positive (green) to help contrast the
differences between male and female ASA rates.
Although the potential reason for these trends is unknown, a provisional list of ongoing
assumptions includes the following. For the female population, 1.) Increase in seasonal
affective disorder among females, and 2.) social pressures for women to be with family during
the holidays. In regard to the first category, a growing body of evidence suggests that females
tend to experience seasonal affective symptoms (i.e. anhedonia and tiredness), at greater amounts
than do men (Lyall et al., 2018). In their study, shorter days were correlated with an uptick in
low mood and diminished pleasure in life. According to Rosenthal (n.d.) women are four times
more likely to experience seasonal affective disorder than men. This could further explain why
the proportion of ASA continue to be significantly different for men and women in the colder
months. In addition, social pressures for women may require a return to family during the
holidays. As seen in Fig 2, there is a 75% difference between men and women rates of attrition
specifically during the month of November. At no other point in the year do we see as much of a
dramatic difference between the two groups. Further investigation of this phenomenon may
reveal interaction and directs effects between potential variables including family ties,
medication, severity of mental health, to name a few.
For the male population, sparce research exists involving male attrition rates. Although
purely speculative, a possible reason for the uptick in the months of May and August—which
reveal a 66.7% and 22.2% difference, respectively—may be attributable to the increased
availability of employment opportunities. Adding to this, social pressures may influence men to
be more independent and less dependent on staff members in residential settings.
Notwithstanding the surprising trends of male attrition, no current studies exist which elucidate
potential factors. This gap in research demands further exploration to shed light on causal
mechanisms inherent in male attrition rates.
Solutions and Interventions
As previously mentioned, attrition is a rapidly growing problem in substance use
agencies which requires our immediate attention. This problem, if left untreated, will preclude
treatment efficacy in both client and clinician at ameliorating problems associated with substance
use disorder. Despite the growing evidence of high patient turnover, limited research exists
which examine interventions to reduce the total number of attritions. The following is a brief
exploration and explanation of interventions. These interventions, although mostly untested in
substance use populations, have dramatic implications for implementation.
Social Interaction. As seen in Fig 2 and 3 the rate of attrition appears to be higher for
females during winter months, suggesting the presence of seasonal affective symptoms. As it
happens, the 75% increase in ASAs—in comparison to the male population during the month of November— may in part be related to the holiday blues. Mothers, with a desire to reunite with
families and are unable due to treatment restrictions, may experience greater amounts of distress.
Accordingly, extant literature reveals significant relationships between social isolation and
depressive symptoms (Vanhalst et al., 2012; Cacioppo et al., 2010; Matthews et al., 2019).
Therefore, interventions consisting of familial interaction in conjunction with social activities
with peers, may in fact offset these holiday blue which in some ways are likely connected to the
separation from family coupled with the decrease in outside social events have during the colder
Supplements. Moreover, as seen in fig 2 and 3, ASA trends are largely different in the
colder months which may attributable higher rates of seasonal affective disorder observed at
greater amounts within females. As such, a potential remedial course may involve increased
activity i.e., diet and exercise in conjunction with supplements. Accordingly, some research
identifies a link between depressive symptoms and vitamin D deficiency (Wilkins, Sheline, Roe,
Birge, & Morris, 2006; May, et al., 2010). According to their meta-analysis, Rebecca, Anglin,
Samaan, Walter and McDonald, (2013) examined 14 separate publications (N=31,424), finding a
significant increase in depression corresponding to low levels of vitamin D (Rebecca, Anglin,
Samaan, Walter & McDonald, 2013). As such, a critical element of colder months corresponds
to a decrease exposure to the sun, which provides naturally occurring Vitamin D. By
supplementing patients with Vitamin D, this may help to decrease depressive symptoms which
may in turn, reduce voluntary treatment termination.
Despite the growing number of patient attrition, there appears to be cause for hope. In
our analysis, attrition rapidly declines the longer the patient remains in treatment. In my
analysis, 14% of patients leave within the first 1-2 weeks of admission followed by a sharp
decline (Shown in Figure 4). This implies that the patient’s length of stay (LOS) plays a
significant role in determining treatment success. Accordingly, the longer a patient remains
within the agency, the less likely they are to prematurely leave. As a result, the patient then
receives continued access to a stable environment allowing their mind and body to heal from
the torment of active use. In short, helping our patients remain in treatment—at least 1-2
weeks longer—reduces the likelihood of leaving against staff advice by over 30% (shown in
Understanding contributing factors involved in attrition and relapse will improve quality
of care. Therefore, the Department of Data Analytics (DDA), is working to assess
antecedents of attrition and relapse through the use longitudinal data acquisition. Using
measurements of mental health (i.e. depression, anxiety, PTSD), addiction severity,
demographic concerns, our team will begin to create predictive models which will help in
identifying key variables related to voluntary early termination. The relevance of this
information coincides with the increased demand placed on treatment providers by insurance
companies. With the data collected, agencies will be able to demonstrate to the public and to
insurance providers the relevancy of recovery services. Furthermore, the data provided will
serve as a source feedback which will help providers improve quality of care.
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