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Learn more: PMC Disclaimer | PMC Copyright Notice Indian J Community Med . 2026 Jan 12;51(Suppl 1):S129–S136. doi: 10.4103/ijcm.ijcm_344_24 Search in PMC Search in PubMed View in NLM Catalog Add to search Comparative Study of Delay Discounting Rates in Alcohol and Opioid Dependence in Northern India Md Abdus Sabir Md Abdus Sabir 1 Department of Psychiatry, National Drug Dependence Treatment Centre (NDDTC), All India Institute of Medical Sciences (AIIMS), Ansari Nagar, New Delhi, India Find articles by Md Abdus Sabir 1 , Ravindra Rao Ravindra Rao 1 Department of Psychiatry, National Drug Dependence Treatment Centre (NDDTC), All India Institute of Medical Sciences (AIIMS), Ansari Nagar, New Delhi, India Find articles by Ravindra Rao 1 , Ashwani Kumar Mishra Ashwani Kumar Mishra 1 Department of Psychiatry, National Drug Dependence Treatment Centre (NDDTC), All India Institute of Medical Sciences (AIIMS), Ansari Nagar, New Delhi, India Find articles by Ashwani Kumar Mishra 1, ✉ , Rachna Bhargava Rachna Bhargava 1 Department of Psychiatry, National Drug Dependence Treatment Centre (NDDTC), All India Institute of Medical Sciences (AIIMS), Ansari Nagar, New Delhi, India Find articles by Rachna Bhargava 1 , Atul Ambekar Atul Ambekar 1 Department of Psychiatry, National Drug Dependence Treatment Centre (NDDTC), All India Institute of Medical Sciences (AIIMS), Ansari Nagar, New Delhi, India Find articles by Atul Ambekar 1 Author information Article notes Copyright and License information 1 Department of Psychiatry, National Drug Dependence Treatment Centre (NDDTC), All India Institute of Medical Sciences (AIIMS), Ansari Nagar, New Delhi, India ✉ Address for correspondence: Mr. Ashwani Kumar Mishra, National Drug Dependence Treatment Centre (NDDTC), All India Institute of Medical Sciences (AIIMS), Ansari Nagar, New Delhi, India. E-mail: [email protected] Received 2024 May 20; Accepted 2025 Sep 17; Issue date 2026 Feb. Copyright: © 2026 Indian Journal of Community Medicine This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. PMC Copyright notice PMCID: PMC13068419 PMID: 41969728 Abstract Background: The study of delay discounting (DD) in addictive disorders helps in understanding why an individual chooses addictive substances immediately over possible long-term gains. Individuals with addictive disorders are seen to consistently have steeper DD compared to healthy controls. The aim of this study was to compare the delay discounting rate (DDR) between individuals seeking treatment for alcohol dependence syndrome (ADS) and opioid dependence syndrome (ODS). Materials and Methods: A total of 125 individuals (ADS: 61, ODS: 64) seeking treatment in an addiction treatment facility in North India were assessed. DDR was measured by a 27-item Monetary Choice Questionnaire (MCQ). Other assessment tools included were Addiction Severity Index-Lite (ASI), Clinical Institute Withdrawal Assessment of Alcohol Scale-Revised (CIWA-Ar), Clinical Opiate Withdrawal Scale (COWS), Mini International Neuropsychiatric Interview (MINI)-Screen, Barratt Impulsiveness Scale-11 (BIS-11), Verbal Adult Intelligence Scale (VAIS), and Likert Scale for craving. A nonparametric Mann–Whitney U test was applied as a test of significance for the quantitative characteristics, χ 2 for the categorical variables, and Karl Pearson’s correlation coefficient for the relationship between the scores of DDR and clinical variables. The estimate of the DDR was compared with the scores obtained on the assessment tools. Results: The sociodemographic (except marital status) and most of the clinical characteristics (barring duration, BIS-nonplanning impulsivity (NPI), ASI scores for domains of alcohol, drug, and legal) were comparable across the ADS and ODS groups. The MCQ-based estimated score for DDR was significantly higher for the ODS group across all the DD categories (overall, small, medium, and large). A consistent significant positive correlation was noted in both groups with DD, craving, BIS, BIS-MI, BIS-AI, BIS-NPI, and ASI-Medical (except with DD-small), and negative with VAIS. Conclusion: Patients with ODS discount future rewards more steeply than patients with ADS. Nonplanning impulsivity possibly affects the way in which individuals with ODS make decisions. Keywords: Alcohol dependence syndrome, delay discounting rate, general linear model, opioid dependence syndrome, impulsivity I NTRODUCTION An important application of behavioral economics is in understanding decision-making among individuals with substance use disorders (SUDs). Individuals with SUDs often prefer immediate rewards of getting high over the delayed rewards due to abstinence. This discounting of the delay in rewards, called delayed discounting (DD), has been the focus of attention in addiction research. DD refers to the subjective devaluation of rewards based on their delay in time.[ 1 ] Wang and his team[ 2 ] conducted the first-ever large international study across 53 countries aimed at assessing the time preference. It revealed that all countries exhibit hyperbolic discounting patterns, a phenomenon characterized by having a greater preference for the immediate reward rather than the future reward. Such steep DD is well-known to be allied with complications such as addiction, obesity, and risky sexual behavior. Such behaviors are not only considered as an indication of impulsiveness and a deficiency of self-control but also a predicament to poor cognitive functioning. In a comprehensive study through the Human Connectome Project ( n = 1206), Yeh et al .[ 3 ] investigated the association between DD and 11 tests within seven cognitive domains,[ 4 , 5 ] as well as the Big Five fundamental personality traits.[ 6 ] It was revealed that after accounting for the income level and education, discounting was correlated with only four of the 11 cognitive abilities evaluated, although the correlation coefficients were all small (<0.20). Subsequently, the authors concluded that steep delay discounting serves as an individual difference characteristic in addition to being a pointer of poor cognitive functioning or psychometric impulsiveness. Moreover, a secondary analysis of a National Institute on Drug Abuse Clinical Trials Network on individuals with stimulant use disorder provided the evidence that decision-making, verbal learning/memory, executive function, and set shifting are vital cognitive domains to screen clinically and treat in aging adults with SUDs.[ 7 ] This is attributed to the fact that their propensity for continuing to abuse stimulants as they grow older cannot be overruled. DD is a cognitive process allowing individuals to compare values between the immediate and the delayed consumption of a determined commodity.[ 8 ] This means that when a choice is made, there is an automatic attribution of values for both the choice of the immediate value and the choice of the delayed value.[ 1 ] DD is usually measured using various experimental paradigms or through the use of a questionnaire. Studies demonstrate that DD precedes and predicts substance use and substance use behavior. Similarly, DD is also associated with the severity of addictive disorders. Additionally, DD is also seen to predict treatment response in addiction. Thus, studying DD will be important to understand different aspects of addiction. Studies show that individuals with SUD have steeper DD compared to healthy controls. This is true for many addictive substances studied, including alcohol, nicotine, cocaine, opioids, and gambling. Heavy drinkers and alcohol-dependent individuals discount delayed rewards more than healthy controls.[ 9 ] The steeper behavior of DD is also eminent to be associated with the use of much harder psychoactive substances such as opioids.[ 10 ] Such evidence suggests that the phenomenon of DD may be a central feature in addiction, but its specific role in the pathological characteristics of addiction remains unexplored.[ 11 , 12 ] It is important to understand whether DD is a common feature underlying all addictive disorders or whether the delayed discounting rate (DDR) differs from one substance to another. Such comparisons between addictive substances would provide insights into whether the decision-making paradigm changes with changes in the type of substances used by individuals. Most studies on DD have been conducted in Western countries. Few studies from Asian settings from China (opioid use disorder on methadone maintenance,[ 13 ] physical exercise reduces temporal discounting among methamphetamine-dependent individuals,[ 14 ] and DD differences between methamphetamines and methcathinone use disorders[ 15 ]) and Japan (nicotine dependence[ 16 ] and alcohol use[ 17 ]) exist. However, to the best of our knowledge, there is no study from India. A recent systematic review[ 18 ] on treatment outcomes and discounted methodology reported that although there is inconsistent evidence regarding prospective association with substance use treatment outcomes, the DD at treatment entry was more perceptible with a variety of poorer treatment outcomes, such as abstinence, relapse, and use frequency. This signature finding is more vivid when a greater level of granularity is considered in the methodology for understanding the phenomenon of DD. Against this background, this study aimed to study and compare DDRs in treatment-seeking patients with alcohol and opioid dependence and assessed the association of DDRs with socio-demographic and clinical characteristics. Such studies from the treatment-seeking individuals may generate empirical evidence from India, which may possibly provide deeper insight into our understanding of the determinants of SUD. M ETHODOLOGY Setting and study design This study was carried out between March and June 2019 at the outpatient department of the National Drug Dependence Treatment Centre (NDDTC), All India Institute of Medical Sciences (AIIMS), New Delhi. It followed a cross-sectional, comparative design with two groups of participants: one with alcohol dependence syndrome (ADS) and the other with opioid dependence syndrome (ODS). Patients who came to the clinic for the first time seeking help for alcohol or opioid use problems were included, based on specific eligibility criteria. Inclusion and exclusion criteria The inclusion criteria for patients to be enrolled in the ADS group were male gender, age more than 20 years, fulfilling the criteria of ADS (diagnosed as per International Classification of Diseases and Related Health Problems, ICD-10); alcohol use for at least 15 days in the past month; presenting to the treatment facility for the first time for treatment and willing to provide informed consent. The inclusion criteria for patients to be enrolled in the ODS group were male gender, age more than 20 years, fulfilling the criteria of ODS (as per ICD-10), having used heroin for at least 15 days in the past month, presenting to the treatment facility for the first time for treatment, and willing to provide informed consent. The starting age limit was 20 years for the acceptable applicability of VAIS. The exclusion criteria for both groups were dependence on other psychoactive substances (except nicotine), intoxication at the time of assessment, and unwillingness/inability to participate in the study due to psychiatric or physical conditions. In addition, if any individual scored more than 20 on the Clinical Institute Withdrawal Assessment of Alcohol Scale-Revised (CIWA-Ar) and on treatment for alcohol during the last one month, they were excluded from the ADS group. Similarly, those with severe withdrawals (score > 36 on Clinical Opiate Withdrawal Scale (COWS)) and on treatment for opioid use for the last one month before the assessment were excluded from the ODS group. Only males were included in the study because of the lower footfall of female patients in the treatment setting. Ethical considerations The study was conducted after getting clearance from the ethical committee of the institution. All participants recruited in the study were informed about the nature and purpose of the study and were assured that their personal information would be kept strictly confidential. The participants were given the right to withdraw from the study at any stage. The participants were included in the study after they provided written informed consent. Sample size The sample requirement for this study was estimated based on previous work on DD.[ 19 ] The estimated value of the mean DDR ( k ) in these two groups was 0.019 and 0.083, with standard deviation of 0.07 and 0.19, respectively. Hence, with an equal allocation ratio of 1:1, a minimum of 51 cases were estimated to be required in each group to achieve the confidence level of 90% and power of 70%. The descriptive statistic of k clearly suggests its skewed nature (variance greater than the mean). We arrived at the estimation based on the most relevant study aligned to the chosen study design, and provided the necessary inputs for the estimation of the sample size. Instruments Clinical instruments The following instruments were used to assess various clinical factors: Mini International Neuropsychiatric Interview (MINI)-Screen: MINI-Screen was used for screening for the presence of psychiatric illnesses in the participants. MINI-Screen[ 20 ] is a screening instrument that assesses for the presence of psychiatric illnesses based on DSM-5 diagnosis. The inter-rater reliability is generally very good, with a kappa ranging from 0.75 to 0.95, and a reasonably sound estimate of sensitivity (0.70–0.95) and specificity (0.85–0.95). Likert Scale for Craving: Craving for alcohol in the ADS group, and for opioids in the ODS group, was assessed through a Likert scale of 0–100, with 0 as no craving and 100 as maximum craving. This also holds sound psychometric properties, such as Cronbach’s alpha > 0.80, moderate to high test–retest reliability. Clinical Institute Withdrawal Assessment of Alcohol Scale-Revised (CIWA-Ar): CIWA-Ar is a 10-item scale that assesses the severity of alcohol withdrawal.[ 21 ] In this study, the scale was used to exclude those who scored more than 20 (corresponding to severe alcohol withdrawals) as well as to measure the severity of alcohol withdrawals. It has an excellent intraclass correlation coefficient (ICC) > 0.90, and an acceptable high Cronbach’s alpha of 0.70–0.95. Clinical Opiate Withdrawal Scale (COWS): COWS is an 11-item scale used to assess the severity of opioid withdrawal.[ 22 ] In this study, the scale was used to exclude patients scoring greater than 36 (corresponding to severe opioid withdrawals) as well as to measure the severity of opioid withdrawals. COWS’ internal consistency is generally good, with Cronbach’s ranging from 0.78 to 0.89 across validation studies, strong inter-rater reliability, and high ICC > 0.90. Barratt Impulsiveness Scale (BIS-11): BIS-11 is a 30-item scale to assess impulsivity.[ 23 ] BIS-11 provides an overall score of impulsivity as well as scores in three sub-domains: attentional impulsivity (AI), motor impulsivity (MI), and nonplanning impulsivity (NPI). The total scores as well as sub-domain scores obtained in the scale were used for estimating the overall impulsiveness among the participants. The Cronbach’s α for BIS-11 ranges typically as 0.79–0.83 (good) for the total scale, and for subscales (AI, MI, and NPI): (0.59–0.74), reflecting some instability in subfactor structure; test–retest reliability is moderate to high over short intervals (coefficients 0.72–0.89). Verbal Adult Intelligence Scale (VAIS): VAIS is an Indian adaptation of the Wechsler Adult Intelligence Scale (WAIS). It measures verbal intelligence in four domains, viz., Information, Digit span, Arithmetic, and Comprehension. It can be applied to subjects whose age is 20 years or more, and gives the verbal quotient along with the individual subtests quotient. An Indian norm for use in the Indian population was used in this study.[ 24 ] Addiction Severity Index-Lite Version (ASI-Lite): ASI-Lite is a shorter version of the Addiction Severity Index (ASI) and was used in this study for deriving quantitative scores of addiction severity across different domains (medical, employment/support, drug and alcohol use, legal, family/social, and psychiatric).[ 25 ] The composite scores demonstrate moderate to high reliability (Cronbach’s α typically 0.70–0.85 across domains), test–retest reliability is usually strong for most of the domains, and the ICC is 0.80. Instrument for delay discounting—monetary choice questionnaire (MCQ) The MCQ was used to elicit individual inter-temporal discount rates k , providing a set of choices between lower, more immediate amounts of money and higher, delayed amounts of money.[ 26 ] An estimate of the respondent’s discounting rate can be calculated as the geometric mean of the k at indifference between the two questions that reflect when the respondent changes between choosing the delayed reward versus the immediate reward. The questionnaire includes a fixed set of 27 items based on size, with nine items per category belonging to the small reward value, ranging from (25$–36$), medium ($50–$60), and large ($75–$85). The “ k ” ranges from 0.00016 to 0.25. We used ten times the value of reward money in $ for each item across both sets of rewards (immediate and delayed) for use in an Indian setting. This strategy of using equivalent proportionate amounts for each set of choices was used as it simplifies the task of asking a question with the respondents, and it retains the same range of estimated value of “ k .” This was tested by empirically generating a hyperbolic discounting curve and confirming equivalence. The equivalence was ascertained by obtaining the same range of k values (0.00016–0.25). To be more specific, on average, the quantum of small immediate reward (SIR) value in the original MCQ is 38.8 (range: 11–80), while the longer delayed reward (LDR) value is 55 (range: 25–85). With respect to progression, the average successive increase in the SIR value is 1.07, indicating a 7% rise over the previous value (range: 1–1.27). Furthermore, the average ratio of LDR to SIR is 1.57 (range: 1.08–2.75). Taking these findings into account, we proceeded with estimating the value of k with a hypothetical value of reward in INR (dollar into INR, but same quantum), followed by ten times the dollar (rate in the year 2019), and then 60 times (the true quantum of reward in INR). Given the absence of Indian studies and the limitations in directly using the dollar as a unit, we considered this an optimal scenario and explored the use of hypothetical values. Notably, the series of calculations aligns with the average value range reported in the original MCQ (DD) (0.00156–0.25). Therefore, regardless of the initial hypothetical reward value chosen, the estimated k value will fall within this established range of the original MCQ questionnaire. Study procedure Patient recruitment is illustrated in Figure 1 . The patients seeking treatment for the first time from the outpatient department of the addiction treatment facility of the authors’ institute for their alcohol or opioid use disorder were recruited and screened for inclusion in the study. Those who fulfilled the selection criteria were recruited, and various study instruments, as mentioned above, were applied. A total of 125 individuals were finally recruited in this study (ADS = 61, ODS = 64). The assessment for each participant took about 1 h. The data collection was completed in 4 months from March 2019 to June 2019. Figure 1. Open in a new tab Flowchart for recruitment of patients Statistical analysis Descriptive statistics for the quantitative variables (age, income per month, duration of substance use; scores on Likert Scale for Craving, BIS, BIS-AI, BIS-MI, BIS-NPI, VAIS, ASI-Lite domains—Medical, Employment, Alcohol Use, Drug Use, Legal, Family, DD-Overall/Small/Medium/Large were summarized through mean (SD); while that of the categorical variables (Marital status, Occupation, Education, Employment, Residence) by frequency (%). Test of normality for the quantitative variables was performed by the Shapiro–Wilk statistic. The bivariate association across the ADS and ODS for the categorical variables was tested through the Chi-square test statistic, degrees of freedom, and P value. The association of the quantitative variables (except VAIS) with two clinical variables was assessed through the Z -score obtained by applying a nonparametric Mann–Whitney U test, as the test for normality was found to be significant ( P < 0.05). The response on the 27-item MCQ for everyone ( n = 125) was coded into the automated sheet in accordance with the suggested mechanism by Kaplan and colleagues[ 27 ] for the calculation of the discount rate ( K ), and a nonparametric Mann–Whitney U test was applied for a test of significance across ADS and ODS groups. Karl Pearson’s correlation coefficient was also estimated for assessing the correlation of quantitative variables with a measure of DD (overall k , small k , medium k , and large k ). The Mazur’s[ 28 ] hyperbolic function [ V = A /(1 + KD ) -1 ] was fitted to the dataset, where the terms V , A , K , and D refer, respectively, to the point of indifference, amount of delayed reward, rate with which the delayed reward is discounted, and period of delay in days. The pooled median values of the point of indifference were plotted against the delay period [ Figure 2 ]. For pooling, we utilized the raw score extracted from the scoring sheet of the MCQ (available in the public domain), separately for the ADS and ODS groups. The 27 items of MCQ are clubbed into set of nine items corresponding to three levels of rewards, namely, small (items: 3, 5, 7, 11, 13, 18, 20, 22, and 26); medium (1, 6, 8, 10, 14, 16, 21, 24, and 27); and large (2, 4, 9, 12, 15, 17, 19, 23, and 25). The individual-level DDR estimates were matched across three respective categories and aggregated by the median value. Subsequently, these median values (point of indifferences) were plotted against the median value of delay in months [ Figure 2 ]. The statistical analysis was performed using licensed SPSS 25.0 version software.[ 29 ] Figure 2. Open in a new tab Line diagram for hyperbolic curve fit demonstrating the median of discounted values as a function of reward delays measured in months R ESULTS The mean (SD) age of participants was 32 (9.2) years. More than half were married or cohabiting ( n = 69, 55.5%), and the majority had completed middle or high school education ( n = 83, 66.4%). Approximately 72.8% ( n = 91) were employed full-time. Participants in the ODS group were significantly younger than those in the ADS group, and their average monthly income was also significantly higher. In contrast, a greater proportion of participants in the ADS group were married or cohabiting (73.8%, n = 45) compared with the ODS group. Other socio-demographic characteristics did not differ significantly between groups [ Table 1 ]. Table 1. Distribution of sociodemographic characteristics across ADS ( n =61) and ODS ( n =64) groups Variables ADS ODS ( χ 2 , df, P )/Z, P Mean (SD)/ n (%) Age 34.7 (8.8) 29.5 (9.0) 3.7, <0.001 Income 17721.3 (22273.40) 30609.3 (40703.2) 2.4, 0.02 Marital status Married and staying together 45 (73.8) 24 (37.5) 16.6, <0.001 Others 16 (26.2) 40 (62.5) Education Illiterate 4 (6.6) 2 (3.1) 3.0, 0.22 Primary-high school 36 (59) 47 (73.4) Above high school 21 (34.4) 15 (23.4) Occupation Highly skilled 6 (9.8) 13 (20.3) 3.9, 0.27 Skilled 10 (16.4) 12 (18.8) Semi-skilled 37 (60.7) 29 (45.3) Unskilled 8 (13.1) 10 (15.6) Employment Currently employed (full time) 48 (78.7) 43 (67.2) 2.1, 0.34 Currently employed (part time) 5 (8.2) 9 (14.1) Currently unemployed 8 (13.1) 12 (18.8) Open in a new tab Regarding clinical characteristics [ Table 2 ], the mean duration of substance use was significantly higher in the ADS group (12.1 years) compared with the ODS group (7.4 years, P < 0.001). On the BIS, scores for NPI were significantly higher in the ODS group. On the ASI-Lite, alcohol-related scores were higher in the ADS group, whereas drug use and legal domain scores were higher in the ODS group. Table 2. Distribution of clinical characteristics across ADS ( n =61) and ODS ( n =64) groups Parameters Test of normality (total) Shapiro Wilk’s statistic, P ADS ODS Test of significance Z**, P / t *, P Mean (SD) Clinical characteristics Duration of use 0.862, <0.001 12.13 (7.70) 7.4 (7.6) 4.740, <0.001 Craving 0.910, <0.001 45.9 (28.3) 53.7 (30.4) 1.436, 0.151 FTND @ 0.902, <0.001 3.44 (3.01) 5.7 (2.47) 3.937, <0.001 FTND-ST @@ 0.412, <0.001 1.48 (2.79) 0.28 (1.58) 3.183, <0.001 BIS 0.919, <0.001 57.9 (19.5) 62.0 (20.0) 1.364,0.173 BIS- AI #1 0.874, <0.001 12.6 (4.1) 13.1 (4.3) 0.564,0.573 BIS- MI #2 0.928, <0.001 20.4 (7.0) 21.5 (7.1) 0.854,0.395 BIS- NPI #3 0.932, <0.001 24.0 (8.7) 27.36 (9.0) 2.168,0.030 VAIS #4 score 0.990, 0.487* 117.6 (10.4) 119.4 (14.6) 0.807,0.421 ASI ^ lite-medical 0.460, <0.001 0.1 (0.3) 0.1 (0.25) 0.503,0.615 ASI ^ lite-employment 0.811, <0.001 0.4 (0.3) 0.36 (0.28) 103.15,0.189 ASI ^ lite-alcohol use 0.687, <0.001 1.7 (0.3) 0.05 (0.01) 10.134, <0.001 ASI ^ lite-drug use 0.735, <0.001 0.06 (0.03) 0.38 (0.02) 9.885, <0.001 ASI ^ lite-legal 0.471, <0.001 0.07 (0.19) 0.37 (0.55) 5.305, <0.001 ASI ^ lite-family 0.462, <0.001 0.25 (0.31) 0.29 (0.33) 1.903, 0.057 Task performance of delay discounting DD overall 0.769, <0.001 0.07 (0.09) 0.15 (0.1) 4.675, <0.001 DD small 0.781, <0.001 0.08 (0.1) 0.17 (0.09) 4.941, <0.001 DD medium 0.748, <0.001 0.06 (0.09) 0.15 (0.11) 4.765, <0.001 DD large 0.739, <0.001 0.06 (0.09) 0.13 (0.11) 4.450, <0.001 Open in a new tab @ FTND: Fagerström Test for Nicotine Dependence, @@ FTND: Fagerström Test for Nicotine Dependence, #1 AI: Attentional Impulsiveness, #2 MI: Motor Impulsiveness, #3 NPI: Non-Planning Impulsiveness, except #4 VAIS: Verbal Adult Intelligence Test (*normally distributed in the whole clinical sample), ^ Addiction Severity Index, ** Z score and P value based on Mann-Whitney U Test for all variables The estimated value of DDR was found to be significantly higher for the ODS [overall—0.15 (0.1); small—0.17 (0.09); medium—0.15 (0.11); large—0.13 (0.11)] group than the ADS group [overall—0.07 (0.09); small—0.08 (0.1); medium—0.06 (0.09); large—0.06 (0.09)]. The pairwise correlations between DD variants and clinical characteristics are presented in Table 3 . In both groups, DD showed consistent significant positive correlations with craving, BIS (total and subscales: motor, attentional, and non-planning), and ASI-Medical (except for DD-small), and negative correlations with VAIS. In the ADS group, duration of alcohol use was negatively correlated with DD, whereas CIWA-Ar, craving, and ASI-Employment scores were positively associated. In the ODS group, COWS and ASI-Legal scores were significantly positively correlated with all DD measures (except DD-small). The fitted curve [ Figure 2 ] illustrates that discounting for larger rewards was more rapid in the ODS group compared with the ADS group. Table 3. Correlation between delay discounting and clinical characteristics across ADS ( n =61) and ODS ( n =64) groups Clinical variables Task performance on delay discounting (DD) ADS ODS Overall Small Medium Large Overall Small Medium Large Duration of use –0.263* –0.269* –0.282* –0.263* –0.142 –0.130 –0.151 –0.170 Craving 0.522** 0.554** 0.470** 0.514** 0.404** 0.399** 0.411** 0.390** FTND 0.122 –0.062 –0.156 –0.100 0.245 0.213 0.252* 0.233 FTND-ST 0.100 0.089 0.118 0.035 0.174 0.141 0.179 0.195 CIWA #1 -Ar 0.339** 0.383** 0.284* 0.341** NA NA NA NA COWS #2 NA NA NA NA 0.384** 0.381** 0.388** 0.392** BIS #3 0.835** 0.856** 0.802** 0.768** 0.642** 0.546** 0.674** 0.652** BIS-AI #4 0.791** 0.783** 0.769** 0.764** 0.571** 0.459** 0.602** 0.594** BIS-MI #5 0.869** 0.904** 0.827** 0.789** 0.658** 0.559** 0.689** 0.669** BIS-NPI #6 0.818** 0.841** 0.783** 0.733** 0.608** 0.531** 0.637** 0.608** VAIS #7 score –0.325* –0.268* –0.335** –0.375** –0.259* –0.283* –0.256* –0.234 ASI ^ lite-medical 0.277* 0.242 0.252* 0.287* 0.292* 0.204 0.307* 0.302* ASI ^ lite-employment 0.387** 0.400** 0.381** 0.298* 0.191 0.098 0.221 0.164 ASI ^ lite-alcohol use 0.052 0.089 0.043 0.001 0.143 0.129 0.146 0.163 ASI ^ lite–drug use –0.037 0.008 –0.049 –0.094 0.102 0.213 0.088 0.084 ASI ^ lite-legal 0.129 0.225 0.136 0.010 0.267* 0.184 0.275* 0.276* ASI ^ lite-family 0.201 0.189 0.223 0.190 0.007 –0.029 0.032 0.012 Open in a new tab #1 Clinical Institute Withdrawal Assessment of Alcohol Scale, revised, #2 The Clinical Opiate Withdrawal Scale, #3 Barratt Impulsiveness Scale, #4 Barratt Impulsiveness Scale-Attentional Impulsiveness, #5 Barratt Impulsiveness Scale-Motor Impulsiveness, #6 Barratt Impulsiveness Scale-Non-Planning Impulsiveness, #7 Verbal Adult Intelligence Test, ^ Addiction Severity Index, *Significant at 5%, **Significant at 1%, NA-Not applicable as CIWA-Ar cannot be applied for ODS group and COWS cannot be applied for ADS group D ISCUSSION This study adopted a cross-sectional, comparative study to assess and compare DDRs between individuals with ADS and those with ODS. The study participants comprised patients attending an outpatient clinic of an addiction treatment facility for treatment of their SUD in a metropolitan city of India. Almost all studies on DD and SUD are from Western countries and very few from South Asian countries.[ 13 , 14 , 15 , 16 , 17 ] However, there are very few studies that have compared DDRs between two or more substances.[ 12 , 30 ] A total of 125 patients were included in this study [ Figure 1 ]. There were significant differences for a few of the socio-demographic characteristics between the ADS and the ODS groups. The participants in the ADS group were significantly older [ Table 2 ] compared to the ODS group, suggesting that patients with ADS seek treatment for their addiction later than patients with ODS.[ 19 ] Similarly, a significantly greater proportion of individuals in the ADS group were married and staying together as compared to the ODS group [ Table 2 ]. This finding is also supported by other studies.[ 31 ] The average income of participants in the ODS group was significantly higher than that of participants in the ADS group [ Table 2 ]. This may be because people using illicit opioids often support their earnings by indulging in illegal activities to support their drug use habit, which is much costlier than alcohol use. This is also supported by the finding that the scores in the legal domain of ASI-Lite instrument were significantly higher among participants in the ODS group in our study compared to the ADS group. There were no significant differences between the ADS and ODS groups on most clinical variables, including scores on craving, intelligence, as well as medical, employment, and family domains of ASI [ Table 2 ]. Understandably, the ADS group scored significantly higher on the alcohol use domain, while the ODS group scored significantly higher on the drug use domain of the addiction severity instrument, in accordance with the criteria set for inclusion in the study. While the total scores on impulsivity scales were comparable between the two groups, the scores on sub-domain of “Non-Planning Impulsivity” were significantly higher in the opioid group, signifying that individuals with ODS have greater problems with forethought or “futuring” compared to individuals with ADS.[ 23 ] Other studies have shown that scores on impulsivity did not discriminate between the alcohol and heroin user groups.[ 31 ] In this study, the estimated value of DDR was found to be significantly higher for the ODS (overall, small, medium, and large) group than the ADS [ Table 2 ]. This signifies that the ODS group chose the smaller immediate reward and discounted the delay in rewards to a significantly higher degree than the ADS group, which is equally supported by a steeper decline in the fitted hyperbolic function. A study conducted by Kirby and Petry[ 19 ] also showed similar findings between the alcohol and heroin user groups. However, the same study showed that the DDRs were similar between the heroin and cocaine user groups. This seems to suggest that DDRs may differ between legal and illegal substances, while DDRs may remain the same when different illegal substances are compared. Some studies have also been carried out showing that using more than one substance is associated with higher DD as compared to using one substance only or control.[ 30 ] The individual-level discounting of the value of delayed rewards has been found to be associated with important health and disorder-related outcomes: the more discounting, the more unhealthy or problematic choices.[ 32 ] DD in the overall sample as well as in the ADS and ODS groups was significantly correlated with many clinical characteristics [ Table 3 ]. Overall, DDRs in the ADS and ODS groups were positively associated with craving scores. Studies have shown that craving for alcohol is associated with subsequent choices of alcohol versus money.[ 9 ] Similar findings have been seen for other substances as well, including opioids.[ 33 ] Stoltman[ 34 ] studied the phenomenon of DD with pharmacological state in heroin users. The authors found that heroin discounting was greater during withdrawal than during satiation. Moreover, the overall DD in the ADS and ODS groups was positively correlated with impulsivity, providing the gist that DDR increases as impulsivity increases. A recent systematic review has shown that people with impulsive traits discount more steeply.[ 35 ] A study done by Bozkurt and colleagues[ 31 ] also found that aggression and impulsivity scores were higher among both the alcohol and the heroin dependent groups than the healthy controls. However, we need more such studies to explore the ramifications of impulsivity with DD, as one study[ 36 ] from the United States in the general population ( N = 5949) provided age-cohort effect [18–29 (21.7%), 30–44 (30.1%), 45–64 (30.9%), 65+ (17.4)], with impulsivity. Such studies specifically from India will shed more light on the role of impulsivity with sociodemographic characteristics and their effect on DD. The same study spotted a greater extent of impulsivity among males, and additionally reported its association with a broad range of axis I and II disorders, including drug dependence, cluster B, dependent and schizotypal personality disorders, bipolar disorders, and adult deficit/hyperactivity disorder. There were a few limitations in this study. This was a hospital-based study; only those patients who came for treatment-seeking were included in the study, and thus, it may not truly represent a community-based population. Only males were included in the study. Hence, the findings of the study cannot be generalized to the female population. Very few women visit the addiction treatment facility where the data was collected; hence, we did not include women in our study. More studies from the Indian subcontinent, involving both genders, can possibly broaden our knowledge of DD and the role of sociodemographic and clinical characteristics. Specifically, with impulsivity pronounced more among males and proven to be associated with psychiatric disorders and multiple adverse events, an urgent necessity to target impulsivity in prevention and treatment efforts is desirable. Furthermore, incorporating dimensions of cognitive functioning, personality types, treatment outcome, neural correlates, and genetic epidemiology is worth reconnoitering for stemming maximum benefits in treatment settings for drug dependence. C ONCLUSION This study demonstrates that patients with OPD discount future rewards more steeply than patients with ADS, and that their NPI possibly affects the way in which these individuals make decisions. More studies are required for understanding the role of NPI and its relationship with substance use, as there are still inconsistent findings in the literature. This may have implications for planning effective management strategies among individuals with addictive disorders. Abbreviations ADS: Alcohol Dependence Syndrome ODS: Opioid Dependence Syndrome DD: Delay Discounting DDR: Delay Discounting Rate MCQ: Monetary Choice Questionnaire ASI-Lite: Addiction Severity Index-Lite CIWA-Ar: Clinical Institute Withdrawal Assessment of Alcohol Scale Revised COWS: Clinical Opiate Withdrawal Scale MINI: Mini International Neuropsychiatric Interview BIS: Barratt Impulsivity Scale VAIS: Verbal Adult Intelligence Scale Conflict of interest Nil. Funding Statement Nil. R EFERENCES 1. Madden GJ, Bickel WK, editors. Impulsivity: The Behavioral and Neurological Science of Discounting. Washington, DC, US: American Psychological Association; 2010. p. 453. [ Google Scholar ] 2. Wang M, Rieger MO, Hens T. How time preferences differ: Evidence from 53 countries. J Econ Psychol. 2016;52:115–35. [ Google Scholar ] 3. Yeh YH, Myerson J, Green L. Delay discounting, cognitive ability, and personality: What matters? Psychon Bull Rev. 2021;28:686–94. doi: 10.3758/s13423-020-01777-w. 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