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Open Journal of Psychiatry, 2017, 7, 51-60
http://www.scirp.org/journal/ojpsych
ISSN Online: 2161-7333
ISSN Print: 2161-7325
DOI: 10.4236/ojpsych.2017.71005 January 11, 2017
Addiction Treatment Aftercare Outcome Study
Akikur Mohammad, Kristopher J. Irizarry, Rebecca Ninah Shub, Alexandria Sarkar
Department of Psychiatry, LAC-USC, Los Angeles, CA, USA
Abstract
Aftercare is crucial once an individual has completed drug or
alcohol treatment and
is in recovery. There is a continuity of care that should be followed once initial
treatment is completed. This usually involves a lower level of treatment such as ou
t-
patient care and a sober living environment. In order to assess the efficacy and ben
e-
fit of our addiction treatment program, we investigate a set of patients in which a
d-
diction treatment outcome and rehabilitation is determined for patients who
have
completed treatment and followed up. We determine abstinence rates and id
entify
predictors of treatment outcome.
Keywords
Addiction, Treatment, Dual Diagnosis, Dependence, Abstinence, Naltrexone,
Vivitrol, Antabuse, Acamprosate
1. Introduction
Addiction is a serious problem affecting between 20 million and 40 million individuals
in the United States. The economic impact on the country is estimated to be $200 bil-
lion dollars per year in terms of lost productivity, health-related treatment costs, and
criminal justice expenses [1]. The Minnesota Model of addiction treatment utilizes the
Twelve-Step Facilitation program from Alcoholics Anonymous. This approach began at
the state hospital in Willmar, Minnesota in 1950 and spread to the Hazelden Founda-
tion and eventually throughout the country [2]. Hazelden’s model has been assessed to
determine the outcome for patients. An analysis of 1083 male and female patients iden-
tified a 1-year abstinence rate of 53% [3].
A recent study investigating the outcome for 284 patients receiving treatment for ad-
diction in outpatient treatment, residential treatment and sober living environments
identified 1-year abstinence rates of 16.8%, 11.7%, and 23.8% respectively. When con-
sidering across all three treatments, 1-month abstinence rate was 74.6%; 3-month ab-
stinence was 63.7%; 6-month abstinence was 55.7%; and 1-year abstinence rate was
42.1% [4]. In a separate study, the efficacy associated with self-help groups and psy-
How to cite this paper:
Mohammad, A.,
Irizarry, K
.J., Shub, R.N. and Sarkar, A.
(201
7) Addiction Treatment Aftercare Out-
come Study
.
Open Journal of Psychiatry
,
7,
51
-60.
http://dx.doi.org/10.4236/ojpsych.2017.71005
Received:
November 12, 2016
Accepted:
January 8, 2017
Published:
January 11, 2017
Copyright © 201
7 by authors and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons
Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
A. Mohammad et al.
52
chotherapy was explored in a group of 200 outpatients (28.5% attended self-help groups
and 14.5% participated in group therapy). The results indicated that 1-month absti-
nence was 19.3%; 6-month abstinence was 22.8%; and 1-year or longer abstinence was
43.9% [5].
Similar rates of abstinence have been reported for substance specific treatment pro-
grams. For example, in a study of 202 outpatients treated for tobacco-dependence, 43%
abstained for at least 7 days after completing the treatment program [6]. Some studies
have sought to investigate demographic features that may be associated with treatment
success. In one study, African American and Latino smokers of menthol cigarettes ex-
hibited lower 1-month abstinence (30% and 23%, respectively) when compared to
white smokers (43%) of menthol cigarettes [7].
Efforts aimed at developing successful treatment programs for addiction have led to
the exploration of invasive brain based methods of treatment. In a study of five patients
undergoing deep brain stimulation of the nucleus accumbens for the treatment of al-
cohol dependence, 40% exhibited long-term abstinence [8]. A treatment method de-
signed to accomplish surgical ablation of the nucleus accumbens was performed in
1167 patients in China. Within a set of 272 patients undergoing this invasive surgical
treatment, 5-year abstinence was documented as 58%. In another group of 150 cases,
the non-relapse rate was reported as 50% [9].
In order to assess the efficacy and benefit our addiction treatment program, we in-
vestigate a set of patients in which addiction treatment outcome and rehabilitation is
determined for patients who have completed treatment and followed up. We determine
abstinence rates and identify predictors of treatment outcome.
2. Methods
2.1. Detoxification
Patients are assessed for treatment based on a dual diagnosis consisting of substance
abuse coupled with mental health co-morbidity. Upon admission into the program pa-
tients undergo detoxification. Nurses acquire relevant intake history including what
substance is used, how much of the substance is used, duration of use for each sub-
stance and frequency. That information is used to customize a patient specific detoxifi-
cation protocol, conditions and length. Detox for each patient is individualized by
MD’s who are board certified in both addiction medicine and psychiatry. The resulting
individualized detox tapers utilize four FDA approved addiction drugs: naltrexone, an-
tabuse, acamprosate, and vivotrol. Taper duration ranges from 1 - 2 weeks and may be
extended if necessary.
2.2. Treatment
Duration of treatment is optimized for each patient based a variety of factors including
types of substances abused, and frequency/duration of abuse history. Typical treatment
durations last 30 - 45 days and may extend as long as 90 days. Treatment consists of
various modalities of science and evidence based therapies. Patients attend group ther-
apy 3 - 4 times daily and individual sessions 4 - 5 times per week.
A. Mohammad et al.
53
Pharmacological therapies used during treatment include Naltrexone which is FDA
approved for treating alcoholism and administered orally. Vivitrol is a longer acting
opioid antagonist given via injection once a month. Antabuse is an acetaldehyde dehy-
drogenase inhibitor used to reduce craving and acamprosate stabilizes glutamate and
GABA neurotransmitters during alcohol withdrawal. Additional medications may be
included in treatment and thereafter, provided they have been proven to be effective in
addiction clinical studies.
2.3. Aftercare
A component of the treatment program includes facilitating an effective aftercare plan
for each patient. This is accomplished using teams of doctors and treatment program
support staff in structured interactions with patients. The goal of an aftercare plan is to
seamlessly transition a patient into an independent substance free lifestyle. Aftercare is
typically successful when a patient fully participates in the plan-facilitation process.
Completing a drug/alcohol treatment program is a feat but the battle is still being
fought which is why aftercare is important in maintaining sobriety.
2.4. Aftercare Follow Up
Once patients exit the program, a follow up plan is enacted lasting up to a year. During
this time patients are contacted by phone and asked questions about substance use,
treatment efficacy and adherence to the aftercare plan. Responses to phone calls are
recorded in patient records.
2.5. Data Analysis
Seventy-two patients were included in this study. Demographic data for patients was
collected and analyzed to determine average age for all patients and for males and fe-
males. This data was used to characterize treatment outcome by age and gender over
12-month post-treatment period. Treatment outcome was analyzed to identify any re-
lationship between treatment length and treatment efficacy.
In order to identify predictors of addiction relapse, three different levels of treatment
success based on stringency of aftercare follow up data. Three different definitions for
treatment success were defined and the treatment outcome data was analyzed under
each of the different stringencies. The results of this analysis were used to identify pre-
dictors of addiction relapse following treatment.
3. Results
3.1. Patient 12-Month Treatment Efficacy by Age and Gender
A total of 72 patients underwent clinical treatment for addiction for which 32 were
male and 40 were female. The average age of all patients was 30.36 years (s.d. = 11.3
years) with very similar ages between the genders. The average age of males was 30.0
years (s.d = 10.4 yrs) and the average age of females was 30.7 years (s.d. = 12.2 yrs).
Overall, the average age of patients who successfully completed the treatment was 29.2
years (s.d. = 11.6 yrs) while the average age of those who failed to complete treatment
A. Mohammad et al.
54
successfully was 29.0 years (s.d. = 10.6 yrs).
The average age of male patients who successfully completed treatment was 26.7
years (s.d. = 8.6 yrs) while the average age of female patients who successfully com-
pleted the treatment was 31.8 years (s.d. = 12.9 yrs). Interestingly, the average age of
men who did not successfully complete treatment was 35.6 years (s.d. = 10.7 yrs) while
the average age of women who failed to complete treatment successfully was 28.8 years
(s.d. = 10.5 yrs).
3.2. Statistical Analysis of Patient Demographic Factors Associated with
Treatment Efficacy
We chose to investigate if patient gender or age was a factor associated with treatment
outcome. In our study, a total of 72 patients underwent clinical treatment for addiction
for which 32 were male and 40 were female. The average age of males was 30.0 years
(s.d = 10.4 yrs) and the average age of females was 30.7 years (s.d. = 12.2 yrs). There
was no statistically significant difference between the average age of males and the age
of females in our study population (p-value = 0.3966, alpha = 0.05).
The average age of all patients who successfully completed the treatment was 29.2
years (male and female, n = 45, s.d. = 11.6 yrs) while the average age of all those who
failed to complete treatment successfully was 29.0 years (male and female, n = 27, s.d. =
10.6 yrs). There was no statistically significant difference between the average ages of
patients who successfully completed treatment versus those for which treatment was
not successful (p = 0.52972, alpha = 0.05).
As no significant difference was identified between the average age of patients by
gender, or by treatment outcome, we next wondered if there was a significant difference
between the average age of males patients who successfully completed treatment (n =
23, avg = 26.7 yrs, s.d. = 8.6 yrs) versus the average age of female patients who success-
fully completed the treatment (n = 22, 31.8 yrs, s.d. = 12.9 yrs). Again, although the
p-value was just barely larger than 0.05, there was no statistically significant difference
between the average age of successfully treated men versus women (p = 0.06551, alpha
= 0.05). In a similar manner, we tested to see if there was a significant difference be-
tween the average age of male patients (n = 32 − 23 = 9, avg = 35.6 yrs, s.d. = 10.7 yrs)
versus female patients (n = 40 − 22 = 18, avg = 28.8 yrs, s.d. = 10.5 yrs) for whom
treatment was not successful. Likewise, there was no statistically significant difference
detected (0.93256, alpha = 0.05).
Overall we found no significant differences between the patient demographic factors
of gender and age with 12-month treatment outcome.
3.3. Patient 12-Month Treatment Efficacy by Treatment Length
Of the 72 patients who participated in the treatment program, 53 had a treatment
length of 30 days while just 19 underwent treatment for more than 30 days. Patients
participating in the longer treatment program included those in a 33-day program (n =
1), 35-day program (n = 3), 40-day program (n = 2), 45-day program (n = 9) and a
60-day program (n = 4). Patients undergoing the 30 day treatment program exhibited a
54.7% treatment success rate. In contrast, patients that participated in a treatment pro-
A. Mohammad et al.
55
gram lasting more than 30 days experienced a success rate of 84.2%.
3.4. Statistical Analysis of Clinical Treatment Length with 12-Month
Treatment Efficacy
We next wanted to investigate whether the length of the treatment program (30 days vs.
more than 30 days) was significantly associated with treatment efficacy. Our initial
analysis revealed that of the 53 patients undergoing the 30-day treatment program, 29
patients (54.7%) exhibited a successful 12-month treatment outcome. In contrast,
among the 19 patients that participated in a treatment program lasting more than 30
days, 16 patients (84.2%) experienced a successful 12-month treatment outcome. To
assess whether the treatment efficacy was significantly different between treatment
length, we performed a two-sided Z-Test for population proportions using an alpha =
0.05, which resulted in a p-value of 0.0226. This result indicates that within our clinical
addiction treatment program, treatment lengths greater than 30 days were significantly
associated with better treatment outcome.
3.5. Patient 12-Month Treatment Efficacy by Addiction Type
Patients were treated for a number of chemical dependencies including alcohol, am-
phetamine, benzodiazepines, and opioids. Among the 72 total patients, 29 were treated
for alcohol dependency (13 males, 16 females), 2 were treated for amphetamine depen-
dency (2 males, 0 females), 13 were treated for benzodiazepine addiction (5 males, 8
females) and 28 were treated for opioid dependency (12 males, 16 females). The overall
12-month treatment success rate across all patients was 62.5 percent. Overall, treatment
success varied from a high of 100% for amphetamine addiction, to 69.2% for benzodia-
zepine dependency, with 64.3% percent for opioid dependency, and the lowest treat-
ment success rate of 55.2% percent for alcohol addiction.
3.6. Statistical Analysis of Patient Addiction Type with 12-Month
Treatment Efficacy
Within our after-care study, the number of patients being treated for alcohol depen-
dency (n_TXsuccess = 16, n_total = 29) and opioid dependency (n_TXsuccess = 18,
n_total = 28) was nearly identical. The remaining patients (n_TXsuccess = 11, n_total =
15) represent 13 with benzodiazepine addiction and just 2 with amphetamine addic-
tion. We first decided to perform statistical analysis to compare treatment efficacy be-
tween alcohol addiction and opioid addiction. Using the two-sided Z-Test for popula-
tion proportions with an alpha = 0.05, produced a p-value of 0.48392. This result indi-
cates that, within our clinical addiction treatment program, there was no statistically
significant difference between 12-month treatment efficacies for patients treated for al-
cohol dependency versus patients treated for opioid dependency.
Next we tested to see if there was a significant difference between the 12-month
treatment efficacy between the remaining patients (amphetamine and benzodiazepine
dependencies) compared to those with alcohol dependency. The two-sided Z-Test for
population proportions with an alpha = 0.05 produced a p-value of 0.242 indicating
that there is no difference between treatment outcome between patients with alcohol
A. Mohammad et al.
56
dependency compared to those with amphetamine or benzodiazepine dependency.
Similarly, we tested to see if there was a significant difference in 12-month treatment
efficacy between the amphetamine/benzodiazepine dependent patients compared to the
opioid dependent patients. Once again, we employed the two-sided Z-Test for popula-
tion proportions with an alpha = 0.05 and obtained a p-value of 0.5485, indicating that
there is not a significant difference between treatment outcome for patients treated for
opioid dependency versus patients treated for amphetamine/benzodiazepine depen-
dency. No significant difference between patient addiction type and 12-month treat-
ment outcome was identified.
3.7. Analysis of Treatment Success Using Three Distinct Stringency
Models for Efficacy
Since our study design employed self-reporting from patients who suffered from addic-
tion, we hypothesized that it was likely that a patient might misrepresent abstinence
during the 12-month after-care follow up period. As we reviewed the phone call res-
ponses from the patients, it became clear that some patients were readily available for
our phone calls, while other patients consistently failed to answer the phone. Initially,
we struggled with how to best to identify legitimate reasons for missing a follow-up
phone call, versus intentional avoidance associated with substance abuse relapse. Al-
though there is no perfect strategy for accurately classifying missed phone calls, we de-
cided to perform three separate analyses of the data using a sliding scale of stringency
for assessing treatment success. Specifically, we assessed treatment efficacy at 1-month,
3-month, 6-month and 12-month time points for each of our levels of stringency based
explicitly on how we interpreted unanswered phone calls.
The most stringent level of treatment success was designed to assess the lowest possi-
ble success rate, under the most rigorous criteria for considering treatment successful.
In this model, only patients who answered the phone and explicitly stated that they re-
mained substance free (“Y”) for entire post-treatment interval were classified as suc-
cessfully treated at each of the four time points. Under this most stringent model, 63.8%
of patients exhibited success at the 1 month time point. While only 51.4% were suc-
cessful at 3 months, with 38.9% successful at 6 months and just 23.6% remaining sub-
stance free 12 months after completing the treatment program.
For the moderate stringency model, we relaxed the inclusion criteria classifying pa-
tients as successful with the following three conditions for classifying a patient as suc-
cessfully treated: 1) no “N” responses at all; 2) no more than 4 “NA” responses in any
12-month period; and 3) no “NA” responses allowed if they were immediately followed
by a “N” response in the very next phone call. Under these criteria, 81.9% of patients
were successful at 1-month, 72.2% were successful at 3-month, 62.5% were successful at
6-month and 48.6% reported success at the 12-month time point.
Finally, in the least stringent model for treatment success, our only exclusion criteria
for success was any patient that replied “N” to the question of being substance free
within the time period being assessed. Under this model, 95.8% of patients were suc-
cessful at the 1-month time point, 86.1% were successful at the 3-month time point,
83.3% were successful at the 6-month time point and 69.4% were successfully substance
A. Mohammad et al.
57
free 12-month following treatment.
3.8. Patterns of Post-Treatment Patient Behavior with 12-Month
Treatment Efficacy
In the course of this study we analyzed 3240 clinical data points (72 patients × 15 time
points × 3 stringency models) in an attempt to assess the efficacy of our addiction
treatment program. Interestingly, the process of defining the inclusion and exclusion
criteria for treatment success across the different stringency models offered a unique
opportunity to characterize trends in patient post-treatment behavior with 12-month
treatment outcome. We reasoned that certain behavioral patterns in the after-care fol-
low up phase of our study might have prognostic value for inferring the likelihood of
treatment success or failure. Ultimately, we are interested in identifying any factors as-
sociated with treatment outcome that can prove informative in identifying patients at
risk for poor treatment response.
We chose to base our analysis of patient behavioral patterns using only the data con-
tained in the most stringent model for assessing treatment outcome; in which only pa-
tients who always answered the phone and explicitly stated that they remained sub-
stance free (“Y”) for the entire duration of the post-treatment interval (1-month,
3-month, 6-month & 12-month) were classified as successfully treated. We reasoned
that markers of poor treatment outcome are more easily identifiable when the classifi-
cation of poor patient response is as broad and sensitive as possible.
In order to identify putative patterns of patient behavior during the after-care por-
tion of the study, we counted the total number of “no telephone answers” (NA) and
“negative responses to substance free question” (N) for each of the 72 patients. The
number of “NA’s” ranged from 0 (18 patients), to 15 (1 patient). Similarly, the number
of “N” responses ranged from 0 (47 patients) to 4 (4 patients). The average number of
“NA’s” per patient was 3.55 with a standard deviation of 3.92, while the average num-
ber of “N” responses per patient was 0.681 with a standard deviation of 1.165. Based on
the relationship between “NA” and “N” across the patients, we decided to investigate
the feasibility of using the frequency of “NA’s” among patients as a prognostic marker
for “N” responses.
To quantify the value of specific frequencies of “NA’s” as predictors of treatment
outcome, we calculated the sensitivity and specificity for specific frequencies of NA
among patients. Within our data set, all patients with 0 NA’s also had 0 “N” responses.
We next considered patients for which at least 1 NA occurred during the 12 month
post-treatment period. The sensitivity for NA ≥ 1 was 1.00 while the specificity was
0.383. Similarly, the sensitivity associated with NA ≥ 2 was also 1.00 while the specifici-
ty increased to 0.574. When we calculated the sensitivity and specificity calculations for
NA ≥ 3, we obtained a sensitivity of 0.92 and a specificity of 0.894. Finally, we calcu-
lated a specificity of 0.8 and a sensitivity of 0.936 for NA ≥ 4. During the analysis we
noticed that increasing the frequency of NA’s resulted in fewer true positives and more
false negatives. Subsequently we selected NA ≥ 3 as the optimal balance between sensi-
tivity and specificity because when NA ≥ 3, true positives were 23 and true negatives
were 42 while there were only 3 false positives and 5 false negatives.
A. Mohammad et al.
58
3.9. Relative Risk of Addiction Relapse for Patients with Specific
Post-Treatment Behavior
Given the results of sensitivity and specificity calculations for different frequencies of
“NA” as behavioral markers for treatment response, we identified a value of NA ≥ 3 as
exhibiting an optimal balance of high sensitivity and specificity. Subsequently, we were
curious to discover if patients having at least 3 occurrences of NA during the 12-month
post treatment period were at an increased relative risk for substance abuse relapse
compared to patients having fewer than 3 NA’s in the same period.
A total of 28 patients exhibited at least three instances of NA in the 12-month post
treatment period. Of these, 23 patients exhibited evidence of unsuccessful treatment
response resulting in an incidence of 23/28 = 0.8214. In contrast, 44 patients exhibited
fewer than 3 NA instances during the 12 month post treatment period. Among these,
just 2 patients experienced poor treatment outcome, associated with an incidence of
0.04545. We calculated the relative risk of poor treatment outcome by dividing the in-
cidence of poor treatment outcome among patients with NA ≥ 3 (0.8214) by the inci-
dence of poor treatment outcome among patients for whom NA < 3 (0.04545).
The corresponding relative risk of substance abuse relapse following treatment is
18.1 for patients who fail to answer the phone at least three times during the follow up
period compared to patients who only failed to answer the phone either 0, 1 or 2 times
during the entire 12-month post treatment period. These results strongly suggest that
behavioral patterns of patients associated with non-answered phone calls exhibit a
much greater relative risk of substance abuse relapse.
4. Discussion
This study investigated the efficacy and benefit of our addiction treatment program on
patients who completed treatment and participated in aftercare follow-up. Specifically
we conducted an aftercare follow study to determine abstinence rates and identify pre-
dictors of treatment outcome. Our results demonstrated that patients undergoing the
30 days treatment program exhibited a 54.7% treatment success rate while patients
treated for more than 30 days experienced a success rate of 84.2% (p-value = 0.0226).
Our results indicated no statistically significant difference in treatment outcome across
addiction types of amphetamine addiction, benzodiazepine dependency, opioid depen-
dency, and alcohol addiction.
As part of the analysis, we developed three stringency models of treatment outcome
success. The low-stringency model provided the greatest treatment success rate by ex-
cluding the fewest patients from the “success” category. Low stringency model pro-
duced treatment success rates of 95.8% (1 month), 86.1% (3 months), 83.3% (6 months),
and 69.4% (12 months) following treatment.
The moderate stringency model was constructed as an intermediate model of treat-
ment success. Treatment outcome results were 81.9%, 72.2%, 62.5%, and 48.6% for the
1 month, 3 months, 6 months and 12 months aftercare time points respectively.
The high stringency model produced the lowest possible success rate using the most
rigorous criteria for considering treatment success. Under the high stringency model
A. Mohammad et al.
59
63.8% of patients exhibited success at the 1 month time point; 51.4% were successful at
3 months; 38.9% were successful at 6 months and 23.6% remained substance free 12
months after completing the treatment program.
The rationale for choosing multiple levels of stringency in this study was to accurate-
ly and objectively assess the efficacy of our addiction treatment program. The criteria
for characterizing success can sometimes be subjective based on the inclusion criteria
chosen. It is common for studies to report the treatment outcome in the most favorable
manner. We believe that such reporting makes it difficult to compare studies across
treatment programs. Accordingly, we created multiple definitions of treatment success
and assessed our data under each stringency model. Regardless of the stringency level
selected, we identified a trend of higher treatment at earlier time points (1 month and 3
months) versus later time points (6 months and 12 months).
This study was data intensive as 3240 clinical data points (72 patients × 15 time
points × 3 stringency models) were analyzed in an attempt to assess the efficacy of our
addiction treatment program. In the course of this comprehensive analysis, we were
able to identify trends in patient post-treatment behavior with 12-month treatment
outcome. Specifically we counted the total number of “no telephone answers” (NA) and
“negative responses to substance free question” (N) for each of the 72 patients.
We chose to base our analysis of patient behavioral patterns using only the data con-
tained in the most stringent model for assessing treatment outcome based on the ratio-
nale that poor treatment outcome is most easily identified when the classification of
poor patient response is as broad and sensitive as possible. Subsequently only patients
who always answered the phone and explicitly stated that they remained substance free
(“Y”) for the entire duration of the post-treatment interval (1 month, 3 months, 6
months & 12 months) were classified as successfully treated.
In order to determine if there was any specific behavioral pattern of post-treatment
follow-up that might be indicative of poor treatment outcome, we selected NA ≥ 3 as an
aftercare response pattern that provided the optimal balance between sensitivity and
specificity because when NA ≥ 3, treatment outcome true positives and true negatives
were maximized while false positives and false negatives were minimized.
The corresponding relative risk of substance abuse relapse following treatment is
18.1 for patients who fail to answer the phone at least three times during the follow-up
period compared to patients who only failed to answer the phone either 0, 1 or 2 times
during the entire 12-month post treatment period. These results suggest that aftercare
follow-up can identify patients at risk for relapse or additional treatment.
Recovery is an ongoing process once a client leaves treatment. Clients who adhere to
their discharge plan and immerse themselves in recovery related activities and lifestyle
are likely to achieve sobriety for longer periods of time if not indefinitely. Clients who
remain in treatment longer have higher success rates. Most clients ideally just want to
return to normality once they’ve left inpatient treatment. At Inspire Malibu drug and
alcohol treatment center, we offer aftercare follow-up as a courtesy to our clients once
they leave treatment. While our clients are in treatment, we develop a rapport that al-
lows us to maintain an aftercare relationship where we document their progress and
sobriety.
A. Mohammad et al.
60
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