Relapse in opioid dependence: Role of psychosocial factors.

BACKGROUND: Although our understanding about neurobiology of opioid dependence and availability of pharmacological treatment has gone a long way in the last few decades, psychosocial interventions play a pivotal role in the prevention of relapse owing to reasons such as less treatment-seeking behavior and poor penetrance of opioid substitution treatment. There are many studies assessing psychosocial factors in alcohol and nicotine dependence, yet the availability of such studies for opioid dependence is sparse. This study aimed at evaluating the association of relapse in opioid dependence with various psychosocial factors.
MATERIALS AND METHODS: This was a cross-sectional study with two groups of opioid dependence patients: In abstinence (N = 28) and relapse (N = 33). Psychosocial variables such as high-risk situations, coping behavior, stressful life events, self-efficacy, and social support were assessed in the two groups and analyzed for the association with opioid relapse.
RESULTS: This study reports that more high risk situations (odds ratio [OR] =1.58; 95% confidence interval [CI] =1.22-2.03; P = 0.017), especially negative mood state and undesirable stressful life events (OR = 2.08; 95% CI = 1.28-3.37; P = 0.05) were significantly associated with higher odds of relapse in patients of opioid dependence. Further, higher self-efficacy (OR = 0.92; 95% CI = 0.87-0.96; P = 0.017) was significantly associated with lower odds of relapse.
CONCLUSION: Psychosocial factors such as high risk situations, undesirable stressful life events, and self-efficacy were significantly associated with relapse in opioid dependence. Hence, practice of a holistic, multimodal, and individualized treatment plan addressing these factors might help in reducing the relapse rates in them. Copyright:

INTRODUCTION

According to the Recent National Survey on Extent and Pattern of Substance Use in India, 2019, there are approximately 77 lakh problem opioid users (i.e. those using in harmful or dependent pattern) in the country. The prevalence of current use of any opioid was 2.06% with heroin being the most commonly used opioid in India (1.14%), followed by pharmaceutical opioids (0.96%) and opium (0.52%).[1] The usage of illegal, more potent synthetic opiates, such as heroin and the nonmedical use of prescription opioid pain medications have risen to epidemic levels, with rates continuing to increase according to the Centers for Disease Control and Prevention report, 2011.[2] Although any type of addiction causes harm, opioid dependence is associated with particular risks, including transmission of various blood-borne viruses including human immunodeficiency virus and hepatitis B and C and increase in criminal activity.[3]

Although there are wide range of medications and psychosocial interventions available for the treatment of opioid dependence, the high prevalence of relapse within the 1st year, more so in the first 3 months of abstinence hinders the recovery of patients.[4] In spite of our improved understanding of the neurobiological basis of opioid addiction and the availability of medications for treatment, psychosocial interventions continue to be the cornerstone in the relapse prevention for many reasons. Stigma, lack of awareness, younger age of onset of dependence, poor penetrance of opioid substitution therapy (OST), inadequate number of trained health professional for the implementation of OST, and nonconducive legal and policy environment in India[5] emphasize the importance of psychosocial factors and interventions in relapse prevention.

There is an increasing emphasis on the interaction between biological changes such as dysfunctional brain circuits with psychosocial factors in establishing the vulnerability for relapse. Relapse has been viewed as a complex, dynamic, unfolding process in which the resumption of substance use is the last event in a long sequence of maladaptive responses to internal or external stressors according to the socialcognitive behavioral models proposed by Marlatt and Witkiewitz.[67] According to this model, when an abstinent individual faces a high risk situation (Relapse precipitant) defined as a circumstance that threatens the individual's effort to refrain from a particular undesirable behavior, the risk of lapse or relapse is determined by the interaction of a number of intrapersonal and interpersonal determinants. Coping, a critical intrapersonal determinant, is defined as the thoughts and behaviors used to manage the internal and external demands of situations that are appraised as stressful. Another important intrapersonal determinant is self-efficacy, defined as the individual's expectations concerning his/her ability to cope with the high-risk situation. Stressful life events overlap between self-efficacy and other areas of intrapersonal determinants, such as emotional states, by presenting more adaptational strain on the client. One of the key interpersonal determinant is social support including family, peers, and other networks.[6] There are very few studies[8910] which have examined the association between various psychosocial factors and relapse in patients of opioid dependence. Hence, this study has been aimed at examining the extent of association between demographic, clinical variables, various psychosocial factors with relapse in patients of opioid dependence when compared to abstinent patients.

MATERIALS AND METHODS

This was a cross-sectional and nonblind study done over a period of 1 year. A total sample size of 61 participants were recruited in the study using the purposive sampling method. There is no single generally accepted definition of abstinence and relapse. Different studies have used varied definitions of relapse ranging from any deviation from abstinence, increased frequency of reuse to the presence of consequences such as physical, social, or hospitalization.[1112] Hence, opioid abstinence and relapse was operationally defined for this study purpose. The time frame adapted was based on the high risk of relapse in the first 3 months after abstinence.[4] Patients between 18 and 65 years of age who met the DSM IV-TR criteria for opioid dependence and were currently abstinent for a minimum period of 3 months were considered to be “in Abstinence”. “Relapse” is defined as those opioid dependence patients who were abstinent for at least 1 month (according to early full remission criteria of DSM IV-TR) after which they relapsed and currently fulfilling the DSM IV-TR criteria for dependence for at least 1 month. The study was conducted after approval from the Institutional Ethics Committee.

Patients of age group of 18–65 years who attended psychiatry outpatient services in a tertiary care hospital, satisfying the DSM IV-TR criteria of opioid dependence were recruited into the study. Among them, those who fulfilled the above-mentioned operational definition for abstinence or relapse for opioid dependence were included in the study. Patients who had comorbid Axis-I psychiatric disorder (assessed using SCID-1[13]), other substance dependence except nicotine, organic brain syndrome or mental retardation, major physical illness, who were in a state of intoxication or where he/she was unable to give consent or participate in the study were excluded. Written informed consent was obtained from all patients who were willing to participate in the study.

A semi-structured pro forma was used to collect the demographic and clinical variables from all patients who were included in the study. Hindi version of Relapse Precipitant Inventory (RPI),[14] Coping Behavior Inventory,[15] Hindi translation of Self–Efficacy Scale,[16] Presumptive Stressful Life Events Scale (PSLES),[17] and Social Support Questionnaire[18] were applied to assess the high risk situations for relapse, coping strategies, self-efficacy, life events in the past year, and their social support, respectively. Appropriate statistical analyses were performed, and the results were compared between the opioid abstinent and relapse groups.

RESULTS

A total of 61 participants were included in this study, among which 33 of them (54.1%) were in the relapse group and 28 (45.9%) in the abstinence group. Forty-seven patients (77.1%) were found to use opioids through oral or inhalational routes and 14 patients (22.9%) used injectable opioids predominantly. The occurrence of relapse was considered as the primary outcome variable. The occurrence of high-risk situations, coping behavior, self-efficacy, stressful life events, and social support were considered primary explanatory variables. Other explanatory variables were sociodemographic factors such as age, marital status and education, and clinical factors such as the age at onset of dependence, duration of dependence, time to develop dependence, history of previous relapses, and family history of dependence. The two study groups were compared in terms of various sociodemographic and clinical variables. The study participants of both the groups belonged to the male gender and there was no significant difference between the groups on the basis of socioeconomic status, domicile, marital status, education, or route of opioid administration.

The mean age at the presentation was 29.03 ± 8.51 years in the relapse group and 31.89 ± 7.58 years in the abstinent group, and the difference was not statistically significant. The mean time to develop dependence was 1.93 years lesser in the relapse group (95% confidence interval [CI] =1.13–2.74, P = 0.001) as compared to the abstinent group. The mean duration of use was 2.88 years lesser in relapse patients when compared to abstinence patients (95% CI = 1.1–4.67, P = 0.002) [Table 1]. Relapse was observed in 63.4% of patients who had a family history of dependence, whereas only 35% of patients without family history of dependence had a relapse (P = 0.05). The proportion of patients with current relapse was 75.0% and 43.9%, respectively, in those with greater than or equal to two relapses versus <2 relapses in the past (P = 0.03).

Table 1

Comparison of clinical variables between the two study groups

Parameters	Mean±SD		Mean difference (95% CI)	P
	Opioid abstinence (n=28) (years)	Opioid relapse (n=33) (years)
Age of onset of use	23.43±4.84	23.45±6.69	−0.02 (−3.07-3.01)	0.98
Duration of use	8.46±3.55	5.58±3.39	2.88 (1.1-4.67)	0.002*
Age of onset of dependence	27.04±6.63	25.03±6.86	2 (−1.47-5.48)	0.25
Duration of dependence	4.82±2.05	4.06±3.23	0.76 (−0.65-2.17)	0.28
Time to `develop dependence	3.57±2.13	1.64±0.82	1.93 (1.13-2.74)	<0.001*

*P<0.05 is considered statistically significant. CI - Confidence interval; SD - Standard deviation

The association between the primary explanatory variables and outcome was assessed by calculating the appropriate parameters of interest like odds ratio (OR) and their 95% CIs. Univariate logistic regression analysis was used to assess the statistical significance of the association. In view of small sample size and multiple comparisons, Bonferroni corrected P values were calculated. Higher RPI total score (OR = 1.58; 95% CI = 1.22–2.03; P = 0.017) and negative mood state individual domain score (OR = 1.64; 95% CI = 1.22–2.22; P = 0.017) were associated with higher odds of relapse in patients of opioid dependence. Higher self-efficacy total score (OR = 0.92; 95% CI = 0.87–0.96; P = 0.017) and general self-efficacy individual domain score (OR = 0.90; 95% CI = 0.86–0.96; P = 0.017) were associated with lower odds of relapse in patients of opioid dependence. Higher undesirable events domain score of PSLES was associated with higher odds of relapse (OR = 2.08; 95% CI = 1.28–3.37; P = 0.05). There was no significant association found between coping behavior, social support, and relapse in patients of opioid dependence in the current study [Table 2].

Table 2

Association between psychosocial parameters and relapse in the study population (univariate binary logistic regression)

Psychosocial variables	Unadjusted OR	95% CI for OR		P	Adjusted/corrected Bonferroni P
		Lower	Upper
Relapse precipitant inventory
Negative mood state	1.64	1.22	2.22	0.001*	0.017*
Euphoric mood state	1.68	1.11	2.56	0.015*	0.255
Lessened cognitive vigilance	2.40	1.25	4.62	0.008*	0.136
Total	1.58	1.22	2.03	0.001*	0.017*
Coping behavior inventory
Positive thinking	2.41	1.16	4.99	0.018*	0.306
Negative thinking	1.44	0.74	2.79	0.27	1.00
Avoidance	2.36	1.12	4.99	0.024*	0.408
Seeking social support	1.84	0.81	4.17	0.14	1.00
Total	7.48	0.75	74.63	0.08*	1.00
Self-efficacy scale
General	0.90	0.86	0.96	0.001*	0.017*
Social	0.87	0.78	0.98	0.023*	0.391
Total	0.92	0.87	0.96	0.001*	0.017*
Presumptive stressful life events scale
Desirable events	1.00	0.65	1.55	0.98	1.00
Ambiguous	1.08	0.71	1.64	0.70	1.00
Undesirable events	2.08	1.28	3.37	0.003*	0.05*
Total	1.33	1.05	1.69	0.01*	0.17
Social Support	0.98	0.93	1.02	0.40	1.00

*P<0.05 is considered statistically significant. CI - Confidence interval; OR - Odds ratio

DISCUSSION

This is among the few studies which focuses on the extent of association between multiple psychosocial factors, sociodemographic, few clinical variables, and relapse in patients of opioid dependence. This study reports that more high risk situations (relapse precipitants), especially negative mood state and more undesirable stressful life events were associated with higher odds of relapse in patients of opioid dependence. The association of negative mood states such as anger, loneliness, sadness, and boredom with relapse has been highlighted by many researchers like Baker in their affective processing model of negative reinforcement. This model explains that the escape and avoidance of negative affect are the major motive for addictive drug use.[19]

Further the study reports that higher self-efficacy is associated with lower odds of relapse in patients with opioid dependence. There are many similar studies that have highlighted the critical role of higher self-efficacy in reducing the risk of relapse in addiction.[2021] The relationship between self-efficacy and relapse is usually considered bidirectional, meaning that individuals who are more successful report greater self-efficacy and individuals who have lapsed report lower self-efficacy.[6] The findings of the current study regarding various psychosocial factors are similar to few other similar studies in alcohol and nicotine dependence.[222324] This implies that largely similar mechanisms appear to be operating in the unfolding of relapse across different substances of abuse. Research indicates that relapse rates are high among patients who are not capable to use effectively coping skills in stressful events such as, peer pressure, family conflict, financial difficulties, or temptations.[25] However, the current study did not find any significant association between coping behaviours and relapse probably because of a small sample size. Although perceived social support has been considered an important factor in relapse, the current study could not find such an association.

Consequently, the study also found that greater proportion of patients with family history of substance dependence and more than or equal to 2 past relapses in the opioid relapse group as compared to the abstinent group. This might indicate a higher genetic predisposition and vulnerability in the patients of relapse group, and similar finding has been reported by many studies.[8] The study also reports that there is shorter time to dependence in opioid relapse patients, which is similar to the results of few other studies.[2224] Less duration of use in opioid relapse patients might be due to younger age at presentation and shorter time to develop dependence in them.

Enhancing self-efficacy, improving coping skills, and applying reinforcement contingencies are the some of the basic tasks that helps individuals recognize difficult situations, if possible avoid them and apply effective coping strategies to manage the situations resulting in reduced relapse rates.[26] These components can be bundled into different psychosocial interventions delivered in various formats such as motivational interviewing, cognitive-behavioral coping skills therapy, social skills training, family therapy, contingency management, 12-step facilitation therapy, group therapy, and others provided either in inpatient or outpatient settings.[2728] These psychosocial interventions become all the more important in young adults in view of peer pressure, poor coping skills, and more psychological difficulties.[29]

Psychosocial interventions also play an important role in improving the outcome of pharmacological management of opioid dependence, especially OST. In a study conducted in China, there were significantly reduced relapse rates in the enhanced methadone maintenance group (MMT and psychological counseling services, together with medical, spiritual, family, and employment treatment service) compared to minimum (only methadone) and standard methadone maintenance groups (MMT and psychological counseling services).[3031] These evidences give a strong argument to the fact that the more holistic, multimodal and individualised a treatment plan is, the more effective it will be in reducing the relapse rates.

The current study has few methodological limitations like being a cross-sectional, nonblind study in a hospital setting with a small sample size limits the generalizability of the findings. Many biological factors, including physical condition and treatment-related parameters, were not studied, as the study focussed more on psychosocial factors. The objective tests of opioid intake like urine or blood analysis were not used to confirm the reporting of abstinence or relapse. In addition, assessment of many clinical and psychosocial factors considered the subjective account of the patient and there is a possibility of recall bias. The dynamic nature of relapse can be better studied with a data set that includes multiple measures of risk factors over multiple days using ecological momentary assessments through electronic devices or interactive voice response methodology.[3233] The post hoc power of the study calculated taking into consideration the mean total scores of various psychosocial variables ranged from 50.2% to 99.9%. The post hoc power of the study for few psychosocial variables was less, possibly due to small sample size. The authors suggest a minimum sample size[34] of 150 for future prospective studies, considering the expected incidence of relapse as 37%.[35] Further studies addressing the above discussed limitations can help us understand the complex phenomenon of relapse better and improve our management accordingly.

CONCLUSION

This study reports that more high-risk situations, especially negative mood state and higher undesirable stressful life events were significantly associated with increased odds of relapse in patients with opioid dependence. Further, the study also found that higher self-efficacy was significantly associated with lesser odds of relapse in these patients. Developing a holistic, individualized treatment plan aimed at improving the various above-mentioned psychosocial variables can reduce the relapse rates and might also improve the effectiveness of pharmacological management in patients of opioid dependence.

Financial support and sponsorship

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Conflicts of interest

There are no conflicts of interest.