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. 2023 Jun 8;18:100502. doi: 10.1016/j.abrep.2023.100502

Higher perceived stress during admission is associated with shorter retention in short-term residential substance use disorder treatment

Orrin D Ware a,⁎, Paul Sacco b, John G Cagle b, Jodi J Frey b, Fernando A Wagner b, Alexandra S Wimberly b, Bernard Gyebi-Foster c, Mayra Diaz c, Kisun Peters c, Sarah E Zemore d
PMCID: PMC10758394  PMID: 38170055

Highlights

  • •

    We examined perceived stress during admission in residential treatment retention.

  • •

    Approximately half of the sample (51.3%) completed treatment.

  • •

    Of the sample that left treatment prematurely, they stayed an average of 9 days.

  • •

    Higher perceived stress during admission predicted shorter treatment retention.

Keywords: Perceived stress, Residential, Retention, Short-term, Substance use disorder, Treatment completion

Abstract

Introduction

Over one million people in the U.S. received residential treatment for a substance use disorder (SUD) in 2020. Longer treatment retention is associated with better outcomes (e.g., reduced substance use). Entering treatment with higher stress may be associated with shorter retention. This paper examines the impact of perceived stress at admission on SUD treatment retention in short-term residential treatment.

Methods

A sample of 271 treatment episodes with admissions between October 2019 and February 2020 were collected from de-identified records of an urban mid-Atlantic adult 28-day short-term residential SUD treatment facility. Treatment completion involved finishing 28 days. Sociodemographic, substance use, perceived stress, and treatment discharge variables were analyzed. Bivariate analyses examined differences between treatment completion and early discharge, and Cox regression investigated the effect of perceived stress on treatment retention with covariates.

Results

The sample was primarily male (73.8%) and non-Hispanic Black (71.6%). A majority used heroin as their primary substance (54.6%) and reported polysubstance use (72.3%). About half (51.3%) completed treatment, and completed an average of 18.7 (SD = 10.7) days. Those who prematurely discharged from treatment stayed an average of 8.9 (SD = 7.0) days. The Cox regression model found that higher perceived stress (adjusted hazard ratio (AHR) = 1.028; 95% CI = [1.005, 1.053], p =.019) and a race/ethnicity other than non-Hispanic Black (AHR = 1.546, 95% CI = [1.037, 2.305], p =.033) predicted premature discharge.

Conclusions

Perceived stress at admission is associated with shorter treatment retention. Early stress management interventions may help increase treatment retention.

1. Introduction

In 2020, over 1 million people ages 12 years and older in the U.S. received residential/inpatient treatment for a substance use disorder (SUD) (Substance Abuse and Mental Health Services Administration [SAMHSA], 2021). SUD treatment retention is associated with beneficial outcomes such as a reduction in the frequency of substance use (Daigre et al., 2021, Hser et al., 2004, Hubbard et al., 2003, SAMHSA, 2016; Schuman-Olivier et al., 2014a; Zhang et al., 2003). Reducing substance use among individuals with a SUD also reduces their risks of experiencing substance use-related morbidity and mortality, such as overdose (Hawk et al., 2015). However, the approximately 30% of individuals who leave SUD treatment prematurely (Lappan et al., 2020) may not experience these positive outcomes. Important individual level factors such as sociodemographic characteristics and stress may impact treatment retention.

1.1. Sociodemographic factors associated with treatment retention

Several factors are associated with treatment retention. Studies have found that younger persons (Baker et al., 2020, Mutter and Ali, 2019, Mutter et al., 2015; Schuman-Olivier et al., 2014b), those with unstable housing and employment (Mutter et al., 2015), and individuals in underserved groups (e.g., marginalized racial/ethnic groups) have lower retention rates in treatment (Saloner and Lê Cook, 2013, Stahler et al., 2016). Knowledge of which sociodemographic characteristics are associated with treatment retention can allow treatment providers to design specific interventions to increase treatment retention among these groups. Alongside sociodemographic factors, clinical factors such as stress may impact treatment retention.

1.2. Stress, substance Use, and treatment

Stress has been described by Lazarus and Folkman (1984) as “a particular relationship between the person and the environment that is appraised by the person as taxing or exceeding his or her resources and endangering his or her well-being” (p. 19). Further, perceived stress is defined as “feelings or thoughts that an individual has about how much stress they are under at a given point in time or over a given time period” (Phillips, 2013, p. 1453–1454). Substantial evidence has demonstrated associations between stress and substance use (MacLean et al., 2019, McHugh et al., 2020; Preston et al., 2018a; Preston et al., 2018b; Sinha, 2008). Some individuals use substances as a stress response to cope with stressors (Bornovalova et al., 2012, Buckner et al., 2007, Heggeness et al., 2019, Marshall-Berenz et al., 2011, Wolitzky-Taylor et al., 2018), sometimes referred to as self-medication (Khantzian, 1985).

Treatment, including admission to treatment, is often stressful, particularly when catalyzed by painful or traumatic circumstances such as an overdose (Alinsky et al., 2020, Kilaru et al., 2020, Pollini et al., 2006) or a criminal justice system referral (Pasareanu et al., 2017). Yet even when treatment is not begun under such circumstances, entering a new environment like a residential treatment facility for a SUD can increase clients’ stress. Admission to these facilities can involve a variety of new challenges, such as becoming oriented to the facility’s rules, treatment schedule, living quarters, staff, and other individuals receiving treatment. Entering residential treatment can also spark anticipatory fears of experiencing craving (Bergeria et al., 2021, Zemore et al., 2021) and withdrawal (Dunn et al., 2019, Summers et al., 2018) after losing access to substances, as other potential stressors (Ware et al., 2022). People who use substances to cope with stress may perceive residential treatment as more stressful than their typical lives with substance use, as their substance of choice is not easily accessible. In sum, not only are SUDs and stress often mutually reinforcing, but entering treatment for SUDs presents many new, unfamiliar stressors beyond the physical and psychological stress of ceasing to use a desired substance.

1.3. Stress and treatment retention

Identifying and mitigating these stressors can be crucial to SUD treatment outcomes, as the amount of stress an individual perceives and their ability to tolerate stressors may affect their likelihood of SUD treatment retention (Ali et al., 2017, Tull et al., 2013). Notably, stress experienced across an individual’s lifetime is associated with prematurely discharging from SUD treatment (Darke et al., 2012, Wallen, 1992). Prior studies on perceived stress among persons in the residential SUD treatment setting have found evidence of effects of perceived stress and ways of reducing it among this population. For instance, findings suggest that perceived stress predicts substance craving (Ames & Roitzsch, 2000) and increased stress is associated with lower retention in treatment for women (Rivera et al., 2021), whereas improved life satisfaction (Tang & Chan, 2017) and supportive text messaging may reduce perceived stress (O'Reilly et al., 2019).

However, the effect of perceived stress on treatment retention in short-term residential programs remains understudied. These settings typically serve high-need individuals with limited financial and psychosocial resources. It is therefore important to examine the impact of sociodemographic, substance use, and mental health stressors concurrently on treatment retention. The current study analyzed treatment data from a short-term residential inpatient treatment facility in an urban area serving primarily African American adults. Given the rising rates of drug overdose deaths among African Americans (Kariisa et al., 2020), providers must understand their experiences to inform culturally appropriate treatment. This study examined the relationship between perceived stress and treatment retention for adults receiving short-term residential treatment for a SUD in an urban area. Based on the strong connection between stress and substance use (MacLean et al., 2019, Ruisoto and Contador, 2019), individuals using substances to cope with stress (Bornovalova et al., 2012, Buckner et al., 2007, Heggeness et al., 2019, Marshall-Berenz et al., 2011, Wolitzky-Taylor et al., 2018), and residential treatment being a potential barrier to easily accessing substances, we hypothesized that higher levels of perceived stress would predict shorter treatment retention.

2. Material and Methods

2.1. Data source

De-identified data were extracted from the electronic health record (EHR) of individuals admitted to an urban mid-Atlantic adult (≥18 years old) 28-day short-term residential SUD treatment facility between October 2019 and February 2020. The original dataset contained 357 cases. An a priori Little’s Missing Completely at Random (MCAR) test was conducted using variables included in this study’s Cox regression model. The MCAR test provided a value of p =.69, suggesting no bias would result from listwise deletion. After excluding cases missing values for variables in the Cox regression model, the study included a sample of N = 271 treatment episodes. All study procedures were deemed not human subjects research by the University of Maryland, Baltimore’s Institutional Review Board. A data use agreement was signed by the researchers and the SUD treatment facility before obtaining the de-identified data. As part of the agreement, individuals with a sociodemographic characteristic identified in less than 10 cases in the data were included in a group listed as “Other” (e.g., “Other Race and Ethnicity” for races/ethnicities shared by less than 10 individuals in the sample) to further ensure anonymity.

The dataset contains variables captured during treatment admission and treatment discharge. Self-report admission data were entered into the EHR by admission staff in the treatment facility, including counselors, nurses, peer recovery specialists, and social workers. Categorical variables captured during admission were selected from dropdowns in the EHR after admission staff asked individuals entering treatment to identify their response to a corresponding item. Discharge data were added to the EHR after an individual was discharged from treatment. Further details on the measures are described below.

2.2. Sample

Table 1 contains discharge information, sociodemographic characteristics, and substance use characteristics. The sample was primarily non-Hispanic Black (71.6%) and male (73.8%), with an average age of 44.7 years (SD = 10.8). Heroin was the primary substance for most of the sample (54.6%). Most of the sample engaged in polysubstance use (72.3%). Most of the sample indicated experiencing recent anxiety (68.6%) and recent depression (67.9%).

Table 1.

Sociodemographic and Substance Use Characteristics by Treatment Completion.

Characteristic Total

n (%)		 Treatment Complete

n (%)		 Premature Discharge

n (%)		 t or χ2 (p)
Sample Size 271 (100%) 139 (100.0%) 132 (100.0%)
Age, Mean (SD) 44.7 (10.8) 45.6 (10.5) 43.8 (11.2) −1.4 (0.177)
Days in treatment, Mean (SD)a 18.7 (10.7) 28 (0.0) 8.9 (7.0) −31.9 (<0.001)
Perceived Stress Scale, Mean (SD)b 22.1 (7.9) 20.8 (7.6) 23.5 (8.1) 2.8 (0.006)
Age of primary sub first use, Mean (SD)c 21.9 (9.2) 22.2 (9.4) 21.5 (9.0) −0.6 (0.528)
Gender 0.9 (0.345)
Man 200 (73.8%) 106 (76.3%) 94 (71.2%)
Woman 71 (26.2%) 33 (23.7%) 38 (28.8%)
Race and Ethnicity 5.7 (0.057)
Non-Hispanic Black 194 (71.6%) 108 (77.7%) 86 (65.2%)
Non-Hispanic White 67 (24.7%) 28 (20.1%) 39 (29.5%)
Other Race and Ethnicity 10 (3.7%) 3 (2.2%) 7 (5.3%)
Education Level 2.0 (0.567)
Less than HS or GEDd 85 (31.4%) 49 (35.3%) 36 (27.3%)
Completed HS or GED 131 (48.3%) 63 (45.3%) 68 (51.5%)
Some college or college degreee 55 (20.3%) 27 (19.4%) 28 (21.2%)
Marital Status 0.4 (0.947)
Divorced 26 (9.6%) 13 (9.4%) 13 (9.8%)
Married 25 (9.2%) 12 (8.6%) 13 (9.8%)
Never married 182 (67.2%) 93 (66.9%) 89 (67.4%)
Other marital status 38 (14.0%) 21 (15.1%) 17 (12.9%)
Employment Status 2.6 (0.279)
Employed FT or PTf 36 (13.3%) 20 (14.4%) 16 (12.1%)
Unemployed 224 (82.7%) 111 (79.9%) 113 (85.6%)
Other employment status 11 (4.1%) 8 (5.8%) 3 (2.3%)
Housing Status 0.3 (0.850)
Private residence 174 (64.2%) 91 (65.5%) 83 (62.9%)
Unstable housing 86 (31.7%) 42 (30.2%) 44 (33.3%)
Other housing status 11 (4.1%) 6 (4.3%) 5 (3.8%)
Recent Anxiety 186 (68.6%) 90 (64.7%) 96 (72.7%) 2.0 (0.157)
Recent Depression 184 (67.9%) 90 (64.7%) 94 (71.2%) 1.3 (0.255)
Primary Substance 2.5 (0.476)
Alcohol 66 (24.4%) 39 (28.1%) 27 (20.5%)
Cocaine 36 (13.3%) 16 (11.5%) 20 (15.2%)
Heroin 148 (54.6%) 74 (53.2%) 74 (56.1%)
Other primary substance 21 (7.7%) 10 (7.2%) 11 (8.3%)
Polysubstance Use 196 (72.3%) 93 (66.9%) 103 (78.0%) 4.2 (0.041)
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Note. Some percentages may not equal 100 due to rounding.

a

Treatment episodes were complete at 28 days.

b

Range 0–40.

c

Age of first use of the primary substance.

d

HS = High school; GED = General equivalency degree.

e

College degree included Associates of Arts and higher.

f

PT = Part time; FT = Full time.

2.3. Measures

2.3.1. Sociodemographic characteristics

Sociodemographic variables captured during admission included age, gender, race and ethnicity, educational level, marital status, employment status, and housing status.

Age. Age-in-years in the EHR was calculated when the individual provided their birthdate.

Gender. Gender was a binary variable in the EHR, and included male and female.

Race and ethnicity. Race and ethnicity were two separate variables that were recoded into one variable. Ethnicity was a binary variable: Hispanic and Non-Hispanic. The race and ethnicity variables were combined into three categories: Non-Hispanic Black, Non-Hispanic White, and Other Race and Ethnicity. Individuals that did not fall into the categories of Non-Hispanic Black or Non-Hispanic White were added to the category Other Race and Ethnicity due to small sample sizes of less than 10. In the final analyses, Non-Hispanic White (n = 67, 24.7%) and Other Race and Ethnicity 10 (3.7%) were combined into the sample category Other Race and Ethnicity to account for the small sample size.

Education level. Education level included three categories: less than a high school (HS) diploma or general equivalency degree (GED), completed HS or GED, and some college or college degree. Individuals who reported having an educational level of some college/college casework (n = 41, 15.1%) and some college degree, including an Associate of Arts, Bachelor of Arts, or Bachelor of Science (n = 14, 5.2%), were recoded as some college or college degree due to small sample size.

Marital status. Marital status included four categories: divorced, married, never married, and other marital status. Individuals who self-identified as separated (n = 20, 7.4%), widowed (n = 14, 5.2%), or another marital status (n = 4, 1.5%) were recoded as other marital status due to small sample size.

Employment status. Employment status included three categories: employed full time (FT) or part time (PT), unemployed, and other employment status. Individuals who reported being employed FT (n = 23, 8.5%) and employed PT (n = 13, 4.8%), were recoded as employed FT or PT due to small sample size. Individuals that did not fall into the categories unemployed or employed FT or PT were added to the category other employment status due to small sample sizes of less than 10.

Housing status. Housing status included three categories: private residence, unstable housing, and other housing status. Individuals who self-identified their housing status as homeless – no shelter (n = 65, 24.0%) and homeless – shelter (n = 21, 7.7%) were recoded as unstable housing. Individuals that did not fall into the categories private residence or unstable housing were added to the category other housing status due to sample sizes of less than 10.

2.3.2. Substance use characteristics

Substance use variables captured during admission included primary substance and polysubstance use.

Primary substance. Primary substance indicates the main substance used in the past 30 days and included four categories: alcohol, cocaine (including crack), heroin, and other primary substance. Individuals with a primary substance other than alcohol, cocaine, or heroin were added to the category other primary substance due to small sample sizes of less than 10.

Polysubstance use. Polysubstance use (indicating whether an individual used multiple substances) included two categories: yes and no. Treatment episodes with a secondary substance in the past 30 days listed in the EHR were coded as polysubstance use.

2.3.3. Clinical characteristics

Clinical variables captured during admission included recent anxiety, recent depression, and perceived stress.

Recent anxiety. Recent anxiety included two response categories: yes and no. This binary variable was captured in the EHR via admission staff’s selected response to the following item: “Past 30 Day: experienced serious anxiety, tension, inability to relax, unreasonable worry?” This question in the EMR was adapted from the Addiction Severity Index (McLellan et al., 1992).

Recent depression. Recent depression included two response categories: yes and no. This binary variable was captured in the EHR via admission staff’s selected response to the following item, “Past 30 Day: experienced serious depression, sadness, hopelessness, lack of interest?” This question was adapted from the Addiction Severity Index (McLellan et al., 1992).

Perceived stress. Perceived stress is a continuous variable calculated via the Perceived Stress Scale (PSS) (Cohen et al., 1983), with possible scores ranging from 0 to 40. The PSS is a 10-item measure that asks about thoughts and feelings related to stress over the past month (e.g., “In the last month, how often have you felt that you were unable to control the important things in your life?”, “In the last month, how often have you felt nervous and ‘stressed’?”). Responses use a five-point Likert-type scale ranging from 0 (never) to 4 (very often). Four of the items are reverse scored and therefore were recoded to align with the rest of the items. The total measure is summed, and lower scores on the measure indicate lower perceived stress. A previous study found this measure has good internal consistency α ≥ 0.70 (Lee, 2012). The Cronbach’s alpha for the PSS in this sample is α = 0.87, indicating strong inter-item consistency.

2.3.4. Discharge variables

Discharge variables captured after an individual discharged from treatment included discharge reason and days in treatment.

Discharge reason. Discharge reason included two categories: treatment completed or premature discharge. Individuals in the EHR listed as discharged against medical advice (n = 116, 42.8%), administrative discharge (n = 13, 4.8%), and other discharge reason (n = 3, 1.1%) were recoded as premature discharge.

Days in treatment. Days in treatment is a continuous variable with a range of 0–28. This variable reflected how many days an individual was in treatment from the day of admission to discharge.

2.4. Data analytic plan

All analyses were conducted using IBM SPSS Version 27 (IBM Corp, 2020). Univariate statistics were used to describe the sample. Bivariate statistics were used to examine discharge reason differences.

We used a multivariable Cox regression analysis to examine the effect of perceived stress and covariates on the number of days retained in treatment. The number of days in treatment was used for time, and premature discharge was the event. Perceived stress (continuous) was the primary predictor. Covariates included gender (reference group: Women), race and ethnicity (reference group: Non-Hispanic Black), education level (reference group: Less than HS or GED; selected as the reference since this is a potential proxy variable for lower socioeconomic status), housing status (reference group: Unstable housing; selected as the reference since this is a potential proxy variable for lower socioeconomic status), recent anxiety (reference group: No), recent depression (reference group: No), primary substance (reference group: Heroin; selected as the reference since heroin was the primary substance for a slight majority of the sample), and polysubstance use (reference group: No). While our primary focus was perceived stress, these covariates were included to control for the potential effects of sociodemographic, substance use, and mental health stressors on treatment retention. All data were examined for collinearity and Schoenfeld residuals were used to assess the proportional hazards assumption. The alpha level was set to the conventional 0.05.

3. Results

3.1. Treatment retention

A slight majority of the sample completed treatment (51.3%). The average length of stay for the full sample was 18.7 days (SD = 10.7); for treatment episodes ending in premature discharge, the average length of stay was 8.9 days (SD = 7.0). The sample had a mean score of 22.1 (SD = 7.9) for the PSS (Table 1). Those who completed treatment reported significantly lower levels of stress at admission (M = 20.8; SD = 7.6) than those who were discharged from treatment before 28 days (M = 23.5; SD = 8.1).

Table 2 provides results from the Cox regression model. Higher perceived stress (adjusted hazard ratio [AHR] = 1.028; 95% CI = [1.005, 1.053]; p = 0.019) and being of another race/ethnicity compared to being Non-Hispanic Black (AHR = 1.546; 95% CI = [1.037, 2.305]; p =.033) predicted premature discharge. For each one-point increase in PSS score, individuals had a 3% higher risk of prematurely discharging from treatment.

Table 2.

Cox Regression Analyses Predicting Premature Treatment Discharge.

Variable AHR (95% CI) p
Perceived Stress 1.028 (1.005, 1.053) 0.019
Age 0.994 (0.977, 1.011) 0.495
Gender (Ref: Woman)
Man 0.971 (0.649, 1.453) 0.887
Race and Ethnicity (Ref: Non-Hispanic Black)
Other Race and Ethnicity 1.546 (1.037, 2.305) 0.033
Education Level (Ref: Less than HS or GED)a
Completed HS or GED 1.303 (0.865, 1.963) 0.205
Some college or college degree 1.213 (0.722, 2.038) 0.465
Housing Status (Ref: Unstable housing)
Private residence 1.109 (0.750, 1.639 0.605
Other housing status 0.866 (0.322, 2.333) 0.776
Recent Anxiety (Ref: No)
Yes 1.083 (0.671, 1.749) 0.743
Recent Depression (Ref: No)
Yes 1.091 (0.692, 1.720) 0.708
Primary Substance (Ref: Heroin)
Alcohol 0.824 (0.526, 1.291) 0.398
Cocaine 1.266 (0.753, 2.114) 0.377
Other primary substance 0.963 (0.499, 1.860) 0.911
Polysubstance Use (Ref: No)
Yes 1.291 (0.842, 1.978) 0.241
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a

AHR = Adjusted Hazard Ratio; HS = High school; GED = General equivalency degree.

4. Discussion

We found that higher perceived stress during admission predicted shorter treatment retention. These findings may be attributable to different factors, including the stressful nature of recovery and clients’ inability to use substances to cope with this stress (Maddox-Rooper et al., 2022). This study also found that individuals being of another race/ethnicity compared to being Non-Hispanic Black predicted premature discharge in this sample. These findings may stem from the lower level of treatment engagement that is identified of other racial/ethnic groups in a primarily African American treatment setting (Melnick et al., 2011) and the greater socioeconomic similarities across racial/ethnic groups in this sample compared to the socioeconomic disparities seen in other national studies examining treatment retention (Stahler et al., 2016, Stahler and Mennis, 2018).

Stress can be a response that helps individuals avoid potentially uncomfortable situations. Individuals in this sample with higher perceived stress may have left treatment prematurely in response to their discomfort. Further, abstaining from substances during treatment and recovery is itself inherently stressful (Maddox-Rooper et al., 2022). Changing patterns related to substance use is also stressful as it may cause different interactions with an individual’s environment after treatment discharge.

Along with the inherently stressful nature of SUD recovery, being unable to use substances to cope with stress during treatment may have a greater impact on individuals with higher perceived stress. Except for rule infractions (e.g., filtering through illicit substances), entering residential treatment is a barrier to accessing substances. As some individuals use substances to cope (Bornovalova et al., 2012, Buckner et al., 2007, Khantzian, 1985, Marshall-Berenz et al., 2011; Preston et al., 2018a; Preston et al., 2018b), a lack of other stress management techniques may prompt premature discharge among individuals that enter treatment with higher perceived stress. The absence of substances as a coping mechanism may even further increase clients’ perception of distress. Given that the prematurely discharged group stayed an average of approximately nine days, stress management interventions provided by clinical staff within the first week may be particularly critical to enhancing the retention of these individuals in treatment.

Along with higher perceived stress, this study found that being of a race/ethnicity other than Non-Hispanic Black predicted premature discharge. These results contradict those of prior large national studies on treatment retention, which have found Non-Hispanic Black individuals representing a larger proportion of those with a lower socioeconomic status (based on education level, employment status, and housing status) and leaving treatment prematurely (e.g., Stahler and Mennis, 2018, Stahler et al., 2016, Ware et al., 2021). However, the present study’s sample is atypical compared to those of these national studies, as in our sample there were more socioeconomic similarities than differences across racial/ethnic groups.

We performed supplemental analyses and found no statistically significant differences in education level or employment status between those who were Non-Hispanic Black and those of another race/ethnicity. However, we observed significant differences in housing status, with a smaller proportion of Non-Hispanic Black individuals (n = 52, 26.8%) having unstable housing compared to those of another race/ethnicity (n = 34, 44.2%) p <.05. Future national studies examining treatment retention across racial/ethnic groups with similar socioeconomic characteristics may be a benefit to the literature. Also, a prior study of an SUD treatment setting primarily serving African Americans found that White participants tended to be less engaged in treatment than their African American counterparts (Melnick et al., 2011). A similar lack of engagement in treatment may also explain lower treatment retention among individuals who were Non-Hispanic Black in this study, which gathered data from a treatment setting primarily serving African American adults.

4.1. Limitations

We used a self-report measure of stress; repeated measures during treatment and physiological measures may provide a more complete picture of the role of stress in retention (Kyriakou et al., 2019, Ligabue et al., 2020, Maddox-Rooper et al., 2022). However, the PSS at admission has clinical utility and is correlated with cortisol levels (van Eck & Nicolson, 1994). The current study was limited to a single treatment setting, making it impossible to assess programmatic variables (Miles et al., 2022). COVID-19 might also have affected both stress and treatment retention for admissions in February 2020. Although this study focused on any premature discharge reason, a future study would benefit from examining differences in stress and patient demographics across all four discharge reasons. Finally, we were unable to control for the experience of craving and withdrawal on treatment retention.

4.2. Conclusions

This study highlights the importance of assessing stress among those entering residential treatment and tracking treatment retention. Studies have shown that longer treatment retention is associated with beneficial treatment outcomes such as harm reduction related to substance use (Daigre et al., 2021, Hser et al., 2004, Hubbard et al., 2003). Because higher stress during treatment admission is a predictor of treatment retention, appropriate stress management interventions may benefit those with higher perceived stress, especially during the first week of residential treatment (Black and Amaro, 2019, Bornovalova et al., 2012). Stress management interventions, combined with substance-dependent medications for SUD and behavioral therapy, may encourage longer treatment retention (Dalton et al., 2021). Stress management is an essential factor to incorporate in all treatments and may improve treatment outcomes.

Funding

This research was funded by the University of Maryland Social Work Ph.D. Program Dissertation Grant (Ware).

CRediT statement

All authors contributed to the creation of this research article.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

The data that has been used is confidential.

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