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 Table of Contents  
ORIGINAL ARTICLE
Year : 2020  |  Volume : 36  |  Issue : 3  |  Page : 221-224

Sociodemographic and clinical factors associated with treatment lag in substance use disorders


Department of Psychiatry, Government Medical College and Hospital, Chandigarh, India

Date of Submission25-Nov-2019
Date of Decision10-Dec-2019
Date of Acceptance07-Jan-2020
Date of Web Publication28-Sep-2020

Correspondence Address:
Dr. Ajeet Sidana
Department of Psychiatry, Government Medical College and Hospital, Sector-32, Chandigarh - 160 030
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijsp.ijsp_121_19

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  Abstract 


Introduction: Substance use disorder (SUD) has plagued the society. However, owing to late treatment seeking, this menace is ever growing. The reasons for treatment lag and variables responsible for the same would be of paramount importance in nipping this evil early. Aim: The aim of this study was to explore the sociodemographic and clinical variables of treatment lag in SUD. Materials and Methods: Patients of SUDs who sought treatment for the first time from an outpatient clinic were recruited in the study. Results: A total of 172 patients with SUDs were included in the study. The mean duration of treatment lag in SUD came out to be 102 months. Age at first use, duration of regular use, and drug-related problems were significantly associated with treatment lag. Conclusion: The study concludes that treatment lag is significantly associated with age of a patient at first use, duration of regular use, and drug-related problems experienced by the patient.

Keywords: Clinical factors, sociodemographic, substance use disorder, treatment lag


How to cite this article:
Sidana A, Bansal SP. Sociodemographic and clinical factors associated with treatment lag in substance use disorders. Indian J Soc Psychiatry 2020;36:221-4

How to cite this URL:
Sidana A, Bansal SP. Sociodemographic and clinical factors associated with treatment lag in substance use disorders. Indian J Soc Psychiatry [serial online] 2020 [cited 2022 Jun 30];36:221-4. Available from: https://www.indjsp.org/text.asp?2020/36/3/221/296255




  Introduction Top


Substance use disorders (SUDs) account for enormous global health burden, and in developing countries like India, it is one of the leading causes of disability and the number is expected to swell.[1] The magnitude of substance use report establishes that a substantial number of people use psychoactive substances in India, and substance use exists in all the population groups, but adult men bear the maximum brunt of SUDs.[2] Making prompt contact with a health-care facility after the first onset of a SUD is an essential and first step in obtaining effective treatment; unfortunately, little is known about the variables which influence this crucial first step.[3] The adverse effects of treatment lag are well documented in literature. Unawareness about the needs of treatment is one of the common reasons for not seeking treatment, resulting in lag in the first treatment, and further, not willing to stop the substance is another reason, adding up to the treatment lag.[4] A common clinical feature associated with patients of SUDs is an individual's tendency to underestimate the severity of substance use problem and to overestimate their ability to control it. This could be because of substance-induced changes in the brain circuits due to long-term substance use. Among others reasons for not seeking treatment are; users don't have health care coverage or could not afford, might have a negative consequences on job or may cause neighbours or community to have a negative opinion about them, didn't know where to go for treatment or no program for the type of desired treatment, lack of transportation, treatment services are too far away, or hours are inconvenient. Untreated SUDs tend to have a poorer outcome in the short as well as long term. As per available literature, only 1 in 10 substance users seeks treatment.[5] Research Highlights considerable time lag between the onset of substance use and first time help-seeking.[6] The treatment lag has been defined as “the time between onset of disorder and obtaining treatment.”[7] A reasonable number of disorder-related characteristics and sociodemographic factors have been implicated as contributors to treatment delay, for instance, young age at onset of the disorder.[8] Little is known about patterns and correlates of initial treatment contacts, as help-seeking research has generally focused on recent service use among prevalent cases over relatively short time periods rather than on delays in initial treatment contact among incident cases over longer time periods. The index study aimed to explore the sociodemographic and clinical factors associated with treatment lag in persons with SUD.


  Materials and Methods Top


The study was conducted at the Department of Psychiatry, Government Medical College and Hospital, Chandigarh, from July 01, 2019, to August 31, 2019. Patients who visited the Psychiatry Outpatient Department for the first with diagnosis of substance dependence as per the International Classification of Diseases-10[9] were included in the study. Patients were assessed on sociodemographic profile including age, gender, education, occupation, marital status, family income, locality, source of referral, age at first use of substance, duration of regular use, and treatment lag duration in months. Detailed history, clinical interview, and assessment were done to rule out patients with active psychotic illness and persons with intellectual disability. Furthermore, patients who did not accompany with reliable informant were excluded from the study. Written informed consent for use of information for research was taken from all the patients as per departmental protocol. The study was approved by the Institutional Ethics Committee.

For the purpose of the study, the treatment lag was considered for patients who had regular use of substance for at least 1 year and did not seek any treatment during this period. Patients having past history of treatment for SUDs, the duration of treatment lag was considered after the last treatment sought.

The primary drug of use would mean the drug which is causing most health, social, or other problems to the patient and has led to the consultation/contact with service. Drug-related problems were clubbed into health, finance, legal, family, marital, and social problems.

Statistical analysis

The Statistical Package for the Social Sciences (SPSS Inc., Chicago, IL, USA, version 16.0) was used for statistical analysis.[10] Descriptive statistics were calculated. The proportions were compared using either Chi-square or Fisher's exact test, as applicable. Multivariate regression analysis was done to identify the effect of variables responsible for lag period.


  Results Top


A total of 172 cases of SUDs were included in the study. The sociodemographic and clinical variables are depicted in [Table 1]. The mean age was 34 years. Majority of the patients were in the age group of 21–40 years. Males predominantly constituted the sample (97.6%). More than 50% were educated more than matriculation. Thirty two percent of the sample was constituted of students.
Table 1: Sociodemographic and clinical profile of patients (n=172)

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The most common substance of use was opioid 48.8% (84), followed by alcohol 36.6% (63), cannabis 4.0% (7), and tobacco 9.8% (17). The mean treatment lag duration in months was 102 (1–600 range). The maximum mean lag was seen in tobacco users, 167 months, followed by alcohol users, 156 months. Cannabis users had a treatment lag duration of 58 months. However, most conspicuous is the mean treatment lag of only 52 months in opioid users, almost half of the average of cumulative lag of all substances [Figure 1].
Figure 1: Duration of treatment lag in months for various substances

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Multiple regression analysis has been depicted in [Table 2] with duration of treatment lag in months as a dependent variable against 15 other independent sociodemographic and clinical variables. Significant values have been obtained in age, age at first use, duration of regular use, and drug-related problems.
Table 2: Association of sociodemographic, clinical variables with treatment lag using multivariate regression analysis

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  Discussion Top


The index study found that treatment lag duration was significantly affected by age of patient at first use, duration of regular use, and drug-related problems experienced by the patient. The findings of the study are unique. The authors could not find any similar study to the best of their knowledge.

In our study, early age of use and long duration of regular use of drugs were the clinical variables which can be implicated for long treatment lag. Furthermore, the drug-related health problems can be the reason for the treatment lag, since few had them early on, leading to not feeling the need to seek treatment. The findings of the study clearly indicate that younger age of substance use is associated with more drug-related complications. It has been reported in research that accidental and intentional fatality that is associated with drug and alcohol use is more common in 15–24-year-old population and which is a leading preventable cause of death. Moreover, alcohol and other drug use in the young population carries a high risk for academic underachievement, delinquency, teenage pregnancy, and depression.[11]

The mean age for onset of opioid use was 23.70 years, alcohol 23.54 years, cannabis 15.40 years and tobacco was 20.11 years. First, opioid users might be using semi-synthetic and synthetic opioids, which causes more financial burden onto the patients and their families, and hence, they came little early. Second, the large number of subpopulation of the opioid users might be IDU, more likely to contract infections and referred by the physician for treatment. Third, a small number of patients of opioid users might be using crude form of opioid and nonavailability of the same might have forced them to seek treatment. Fourth, this subpopulation might be little more aware about the need and consequence of the treatment or it could be combination of all these factors propelling them to come forward for the treatment. However, the treatment lag was more in alcohol use disorder, probable reason could be inability to realize the need for treatment, and moreover, the gap between initiation of alcohol use and getting dependent on alcohol is more than rest of the substance; hence, they reported late in the OPD. Inability to feel the need of treatment, procrastination in treatment-seeking decision, and poor treatment facilities have been reported as major contributions in treatment lag in SUD.[5]

Tobacco and cannabis users have altogether different stories. Treatment lag for tobacco users is mammoth 167 months which is probably due to easy availability, callous attitude of authorities in curbing the sale and purchase of tobacco despite the legal provisions to curb the same and late, fewer but lethal complications after long-term tobacco use like carcinoma lung, oral and dental malignancy, late referral to specialist for deaddiction, hence escalating the treatment lag duration.[12],[13] Treatment lag for cannabis is 58 months. Cannabis, on the other side, is even more freely available, no need to purchase, just pass by nearby herb land, fetch the leaves off the Papaver plant, and experience the effect of the drug. Moreover, sociocultural sanction may also influence the treatment-seeking behavior. Many people, who use them, seldom consider tobacco or cannabis to be harmful or addictive and continue the substance. Attitude of the clients toward the substance is also an important factor that may influence the treatment lag.

In addition, only abysmally small number of cannabis users experience schizophrenia-like psychotic phenomenon making them seek treatment early on as has been reflected in the index study finding.[14]

Furthermore, research into treatment delay and the factors associated with it, in a wider national framework, is clearly warranted. The World Health Organization (WHO) has taken up the issue of treatment gap and lag very seriously. The WHO has laid down ten recommendations which include mainly integration of mental health care with primary health care, ready availability of psychotropic drugs, shifting of care away from institutions and toward community facilities, public education about mental health, involvement of families, communities and consumers in advocacy, policy-making and forming self-help groups, establishment of national mental health programs, improving the training of mental health professionals in community work, establishing and enhancing links with other governmental and nongovernment agencies, monitoring the mental health systems using quality indicators, and providing more support for research.[7] Like the coin has two sides, this study too has a few limitations such as the comorbid diagnosis was not assessed, and only the primary diagnosis was focused upon. Moreover, the severity of disorders and patients' knowledge and attitudes toward SUDs was not assessed. In addition, there are chances of recall bias regarding age at first use and duration of regular use too. The treatment lag is a major reason for the unmet need seen in SUDs. Most of the treatment lags can be attributed to reasons that can be potentially modified.


  Conclusion Top


This can be concluded from the study that there is a huge treatment lag in patients with SUDs. The early age of initiation, duration of regular use, and drug-related problems have a significant association with treatment lag.

Acknowledgment

We would like to specially thank medical social workers and psychiatric record keepers.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Murray CJ, Lopez AD. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries and Risk Factors in 1990 and Projected to 2020. Cambridge, Mass: Harvard University Press; 1990.  Back to cited text no. 1
    
2.
Ambekar A, Agrawal A, Rao R, Mishra AK, Khandelwal SK, Chadda RK. On Behalf of the Group of Investigators for the National Survey on Extent and Pattern of Substance Use in India. Magnitude of Substance Use in India. New Delhi: Ministry of Social Justice and Empowerment, Government of India; 2019.  Back to cited text no. 2
    
3.
Gallo JJ, Marino S, Ford D, Anthony JC. Filters on the pathway to mental health care, II. Sociodemographic factors. Psychol Med 1995;25:1149-60.  Back to cited text no. 3
    
4.
Arun P, Chavan BS, Kaur H. A Study of Reasons for not Seeking Treatment for Substance Abuse in Community. Indian J Psychiatry 2004;46:256-60.  Back to cited text no. 4
[PUBMED]  [Full text]  
5.
Center for Behavioral Health Statistics and Quality. Results from the 2015 National Survey on Drug Use and Health: Detailed Tables. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2016.  Back to cited text no. 5
    
6.
Ponizovsky AM, Geraisy N, Shoshan E, Kremer I, Smetannikov E. Treatment lag on the way to the mental health clinic among Arab- and Jewish-Israeli patients. Isr J Psychiatry Relat Sci 2007;44:234-43.  Back to cited text no. 6
    
7.
Kohn R, Saxena S, Levav I, Saraceno B. The treatment gap in mental health care. Bull World Health Organ 2004;82:858-66.  Back to cited text no. 7
    
8.
Kessler RC, Olfson M, Berglund PA. Patterns and predictors of treatment contact after first onset of psychiatric disorders. Am J Psychiatry 1998;155:62-9.  Back to cited text no. 8
    
9.
World Health Organization. The ICD 10 Classification of Mental and Behavioral Disorders: Clinical Description and Diagnostic Guidelines. World Health Organization; 1992.  Back to cited text no. 9
    
10.
IBM Corp. IBM SPSS Statistics for Windows. Ver. 16.0. Armonk, NY: IBM Corp; 2011.  Back to cited text no. 10
    
11.
Belcher HM, Shinitzky HE. Substance abuse in children: Prediction, protection, and prevention. Arch Pediatr Adolesc Med 1998;152:952-60.  Back to cited text no. 11
    
12.
Garg J, Sidana A, Chavan BS, Singh J. Impact of pictorial warnings on tobacco users. JMCC 2019;9:11-7.  Back to cited text no. 12
    
13.
Chavan BS, Sidana A, Kaushal T. Impact of smoke-free law on tobacco consumption in Chandigarh: A community-based study. Indian J Med Sci 2010;64:125-31.  Back to cited text no. 13
  [Full text]  
14.
Sidana A, Mamta, Chavan BS. Cannabis induced psychosis: A phenomenological perspective. J Ment Health Hum Behav 2007;12:77-80.  Back to cited text no. 14
    


    Figures

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    Tables

  [Table 1], [Table 2]



 

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