Abstract
Background:
Suicide is a growing health concern and causes significant health burden. Patients with substance use disorders represent an especially vulnerable population in terms of self-harm. Data on risk factors for self-harm in substance-using population in the Indian context are limited. We aimed to determine the patterns and sociodemographic/clinical predictors of self-harm in patients with substance use disorders.
Materials and Methods:
We assessed 300 male patients on Deliberate Self-Harm Inventory to assess the patterns of self-harm. We performed mediation analysis to determine whether clinical variables acted via sociodemographic variables in their effect on self-harm.
Results:
The occurrence rate of self-harm was 32.7%. It was significantly associated with a younger age, being unmarried/separated, unemployed, history of injecting drug use, high-risk sexual behavior, and cannabis use disorders.
Conclusion:
Self-harm is an important consideration among patients with substance use disorders, and needs to be addressed by the clinicians involved in the care of such patients.
Key words: Epidemiology, substance use disorders, suicide risk
INTRODUCTION
Suicide is a growing health concern at both a national and global scale. The World Health Organization estimates that there are over 8 lakh suicide deaths every year, amounting to one death for every 40 s.[1] The number of suicide attempts is estimated to be almost twenty times higher. The number of suicide deaths in India in 2015 was more than 1.3 lakh,[2] and suicides contributed to more than 40 million disability-adjusted life years in the year 2016.[3] An important risk factor which has been shown to significantly contribute to an increased risk of suicide is substance use.[4] Almost 20% of patients visiting the emergency department with a history of self-harm have been found to suffer from at least one substance use disorder,[5] making substance use one of the most common risk factors associated with a suicide attempt.[6] Conversely, patients with substance use disorders have been shown to be at an increased risk of suicide, and are more likely to harm themselves than nonusers.[7] It has been theorized and demonstrated that intoxication impairs judgment, reduces inhibition and restraint,[8] as well as increases agitation, aggression, and distress,[9] thus enabling self-harm behaviors. Most of the evidence that exist are for alcohol use, and the influence of other substances on self-harm behavior is limited.[7]
Within the subgroup of patients with substance use disorders, the self-harm attempt itself may vary in severity and intent, ranging from a wish to die and nonspecific self-harm ideation to active suicidal ideation with a specific plan of action. Literature demonstrates that these different intensities of self-harm may be differentially associated with certain sociodemographic and clinical variables; for instance, a wish to be dead and nonspecific suicidal ideation being more associated with a higher income.[10] It is also known that patients with substance use sometimes attempt self-harm not with an intent to die, but as an expression of frustration and means of manipulation.[11] Recent studies have found a high prevalence of nonsuicidal self-harm behavior of up to 50% in patients with substance use, as compared to a 20% prevalence of suicidal behavior.[12] This is significant, as the prevention and management approaches for different intensities of attempts vary substantially, with high-intensity suicidal attempts necessitating intense observation and care, while such interventions may carry a risk of reinforcing demonstrative/nonsuicidal behavior. It is thus evident that the patterns and risks of self-harm behavior in this highly vulnerable group warrant investigation, as a better understanding will in turn lead to better, more effective management strategies.
Till date, there is limited literature investigating such patterns and risk factors in India.[13,14,15,16,17,18,19,20] Because culture is known to significantly influence and alter self-harm behavior,[21,22,23] we need to understand the determinants of self-harm specifically in the Indian context. This holds an even greater salience in case of patients with substance use disorders. We thus conducted this study with the aims of (1) assessing the rates and characteristics of self-harm behavior among patients with substance use disorders and (2) investigating the sociodemographic and clinical factors which are associated with an increased risk of self-harm behavior in these patients.
MATERIALS AND METHODS
Setting and participants
The present cross-sectional observational study was conducted at a substance use disorder treatment facility. The facility has outpatient and inpatient services, and provides medical as well as psychosocial interventions as treatment approaches. In the present study, male patients above 18 years of age were screened for inclusion into the study. Patients were included if they had a history of any substance use disorder as per the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 criteria and were willing to give informed consent. A liberal inclusion strategy was adopted so that the sample was representative of the population of patients visiting the outpatient clinic.
Procedure
Patients visiting the outpatient services were screened for inclusion into the study through purposive sampling. Patients who met the inclusion criteria were recruited into the study after obtaining written informed consent. They were then assessed using a structured pro forma. Demographic details (age, gender, marital status, education, occupation, monthly income, type of family, number of family members, and area of residence), clinical details (primary substance of use, duration of substance use, history of injecting drug use, history of chronic medical/psychiatric illness, and family history of substance use), and legal complications (history of being caught by police, incarceration, active legal case, and involvement in drug peddling) were recorded. The DSM-5 criteria[24] were applied to ascertain the severity of dependence. Specific details about the nature of self-harm acts were assessed using the Deliberate Self-Harm Inventory.[25] This is a 17-item inventory assessing the history of ever committing self-harm under 16 different methods (including cutting oneself and burning oneself) as well as a provision to record any other method, if present. If a patient is found to have committed deliberate self-harm under any of the methods listed, that is further investigated in terms of the age at which it was committed for the first time and the last time, as well as the number of times it has been committed and whether it has led to the patient being hospitalized. It assesses self-harm independent of suicidal ideation, and has been shown to have high internal consistency (α = 0.83), test–retest reliability (φ = 0.68, P < 0.001), and validity.[25] If the patient scored positive for any item on the scale, a separate variable “history of any self-harm attempt in lifetime” was coded as “yes.”
Data were collected in a single sitting. Data were gathered from patients and family members whenever available. If any patient was found to have an active wish to die or suicidal ideation at the time of the interview, he/she was referred to his/her treating psychiatrist for management. Data collection lasted from January to December 2017. The study was approved by the Institutional Ethics Committee.
Statistical analysis
Statistical analysis was performed using IBM SPSS v24 (Released 2016, IBM Corp, Armonk, NY, USA). Descriptive analysis was performed to describe relevant variables such as means, standard deviations, frequencies, or percentages. The relation of continuous and categorical variables with nominal variables was explored using t-test and Chi-square test, respectively. Correlation between scale variables was explored using Pearson's correlation. Multivariable binary logistic regression was performed to further explore the predictors of a history of self-harm behavior being present in a patient. First, the association of all sociodemographic, clinical, and legal variables with the presence/absence of self-harm behavior was explored.
All variables which were found to be significantly associated with the presence/absence of self-harm behavior were then explored for multicollinearity using both Pearson's r (with multicollinearity being defined as r >0.75) and variance inflation factor (VIF) (with multicollinearity being defined as VIF >10).[26] In case multicollinearity was detected, the variable with a greater strength of association with the outcome variable was retained. Step-wise multivariable binary logistic regression using the maximum log-likelihood ratio method was performed, with the variables selected above as the independent variables and the presence/absence history of self-harm behavior as the outcome variable. Hosmer–Lemeshow statistic was used to test for goodness of fit of the regression model. Mediation analysis was performed using the SPSS plugin PROCESS v3.0 by Hayes.[27] PROCESS macro is an SPSS plugin for path analysis widely used in social and health sciences for investigating single and multiple mediator models. Mediation analysis was used to test whether the effect of any of the sociodemographic/clinical variable significantly associated with the outcome variable was being mediated by a second sociodemographic/clinical variable. Mediation analysis was performed only if the mediation was realistically possible and sound. The level of significance was set at P < 0.05.
RESULTS
A total of 364 patients were offered inclusion into the study, of whom 300 consented to participate. All the 300 patients completed all questionnaires, and thus comprised the study sample. Table 1 summarizes the sociodemographic and clinical details of the study sample. Majority of the patients were married (n = 169; 56.3%), were employed (n = 234; 78%), were living in a nuclear family (n = 168; 56%), and were in an urban area (n = 218; 72.7%). Majority of the patients were dependent on nicotine (n = 284; 94.7%) and opioids (n = 208; 68.7%).
Table 1.
Sociodemographic and clinical details
| Variable | Mean±SD or frequency (n=300), n (%) |
|---|---|
| Age (years) | 33.07±11.24 |
| Years of education | 8.90±4.49 |
| Monthly per capita income (Rs.) | 6478±7780 |
| Marital status | |
| Unmarried/separated | 131 (43.7) |
| Married | 169 (56.3) |
| Occupational status | |
| Unemployed | 66 (22) |
| Employed | 234 (78) |
| Area of residence | |
| Urban | 218 (72.7) |
| Rural | 82 (27.3) |
| Living arrangement | |
| Alone/with friends | 12 (4) |
| Nuclear family | 168 (56) |
| Joint/extended family | 120 (40) |
| Duration of substance use (years) | 11.09±9.07 |
| Number of significant abstinent attempts | 1.48±3.04 |
| Substance use profile | |
| Nicotine use disorder | 284 (94.7) |
| Alcohol use disorder | 94 (31.3) |
| Opioid use disorder | 208 (68.7) |
| Cannabis use disorder | 54 (18) |
| Other (including inhalant use disorder) | 12 (4) |
| History of injecting drug use (present) | 73 (24.3) |
| History of high-risk sexual behavior (present) | 73 (24.3) |
| Legal complications | |
| Ever caught by police | 79 (26.3) |
| Criminal case pending | 15 (5) |
| History of incarceration | 35 (11.7) |
| History of peddling drugs | 4 (1.3) |
SD – Standard deviation
Table 2 summarizes the findings on the Deliberate Self-Harm Inventory, listing those methods of self-harm which had at least one respondent. Self-harm was present in 98 patients (i.e., 32.7%). The most common method of self-harm was cutting oneself (n = 48; 16%), followed by hitting one's head against a wall or other hard surface (n = 29; 9.7%) and burning oneself with a cigarette (n = 20; 6.7%). Ninety-eight (32.7%) patients had a history of committing deliberate self-harm in at least one form during their lifetime.
Table 2.
Deliberate Self-Harm Inventory
| Method | Lifetime prevalence, n (%) |
|---|---|
| Cutting self with sharp instruments | 48 (16) |
| Hitting one’s head against a hard surface | 29 (9.7) |
| Burning self with cigarette | 20 (6.7) |
| Biting oneself | 15 (5) |
| Boxing oneself | 15 (5) |
| Inscribing words into one’s skin | 3 (1) |
| Burning self with acid | 1 (0.3) |
| Rubbing glass on the skin | 1 (0.3) |
| Breaking one’s bone(s) | 1 (0.3) |
| Not letting a wound heal | 1 (0.3) |
| Others | |
| Hanging | 5 (1.7) |
| Hitting hand in glass/mirror | 2 (0.7) |
| Self-immolation | 1 (0.3) |
| Consuming disinfectant | 1 (0.3) |
| Voluntarily causing road traffic accident | 1 (0.3) |
| Any attempt | 98 (32.7) |
Tables 3 and 4 summarize the association of sociodemographic and clinical variables, respectively, with the presence/absence of self-harm. Those who had a history of self-harm were significantly younger, were more likely to be unmarried, were unemployed, and were living in an urban area. They were also likely to have a significantly shorter duration of substance use at presentation, were more likely to be involved in injecting drug use, had high-risk sexual behavior, were more likely to have been caught by the police in the past and incarcerated, and were more likely to be dependent on cannabis.
Table 3.
Association of sociodemographic variables with self-harm attempt
| Variable | History of deliberate self-harm |
Test statistic (df), P | |
|---|---|---|---|
| Present | Absent | ||
| Age (years) | 28.4±8.0 | 35.4±11.9 | t (267)=6.014, <0.001* |
| Years of education | 8.3±4.0 | 9.2±4.6 | t (298)=1.512, 0.132 |
| Per capita monthly income | 6106±5464 | 6658±8691 | t (298)=0.575, 0.565 |
| Marital status | |||
| Unmarried/separated | 59 | 72 | χ2 (1)=16.18, <0.001* |
| Married | 39 | 130 | |
| Occupational status | |||
| Unemployed | 31 | 35 | χ2 (1)=7.87, 0.004* |
| Employed | 67 | 167 | |
| Are of residence | |||
| Urban | 82 | 136 | χ2 (1)=8.87, 0.002* |
| Rural | 16 | 66 | |
| Living arrangement | |||
| Alone | 6 | 6 | χ2 (2)=1.82, 0.402 |
| Nuclear family | 55 | 113 | |
| Joint/extended family | 37 | 83 | |
*P<0.05
Table 4.
Association of clinical variables with the presence of self-harm attempt
| Variable | History of deliberate self-harm |
Test statistic (df), P | |
|---|---|---|---|
| Present | Absent | ||
| Duration of substance use | 9.2±6.5 | 12.0±9.9 | t (272)=2.587, 0.003* |
| Number of significant abstinence attempts | 1.8±3.7 | 1.3±2.7 | t (298)=−1.322, 0.187 |
| Injecting drug use | |||
| Present | 33 | 40 | χ2 (1)=6.896, 0.007* |
| Absent | 65 | 162 | |
| High-risk sexual behavior | |||
| Present | 35 | 38 | χ2 (1)=10.24, 0.001* |
| Absent | 63 | 164 | |
| Ever caught by police | |||
| Yes | 39 | 40 | χ 2 (1)=13.59, <0.001* |
| No | 59 | 162 | |
| Criminal case pending | |||
| Yes | 7 | 8 | χ2 (1)=1.41, 0.182 |
| No | 91 | 194 | |
| History of incarceration | |||
| Yes | 19 | 16 | χ2 (1)=8.42, 0.004* |
| No | 79 | 186 | |
| History of peddling drugs | |||
| Yes | 2 | 2 | χ 2 (1)=0.55, 0.395 |
| No | 96 | 200 | |
| Nicotine dependence | |||
| Present | 96 | 188 | χ2 (1)=3.13, 0.061 |
| Absent | 2 | 14 | |
| Alcohol dependence | |||
| Present | 28 | 66 | χ 2 (1)=0.52, 0.280 |
| Absent | 70 | 136 | |
| Opioid dependence | |||
| Present | 69 | 139 | χ 2 (1)=0.08, 0.444 |
| Absent | 29 | 63 | |
| Cannabis dependence | |||
| Present | 25 | 29 | χ 2 (1)=5.56, 0.015* |
| Absent | 73 | 173 | |
| Other substance dependence | |||
| Present | 2 | 10 | χ 2 (1)=1.46, 0.189 |
| Absent | 96 | 192 | |
*P<0.05
The variables which were found to be significantly associated with the presence of self-harm in univariate analysis were assessed for multicollinearity. No two variables were correlated with each other with a correlation coefficient of more than ±0.75. Furthermore, the maximum VIF for any variable when all were entered simultaneously in a model was <5. Thus, multicollinearity was not detected.
Table 5 summarizes the results of univariable and multivariable binary logistic regression analyses with the presence of self-harm as dependent variable and age, marital status, occupational status, area of residence, duration of substance use, injecting drug use, high-risk sexual behavior, history of being caught by police, and presence of cannabis dependence as independent variables. The model was statistically significant (χ2(4) = 44.951, P < 0.001). Hosmer–Lemeshow test for goodness of fit was not statistically significant (χ2(8) =5.453, P = 0.708), indicating a good fit. The model explained 13.9% of the variance. In this model, age, duration of substance use, and being ever caught by police were the independent predictors of having self-harm behavior.
Table 5.
Step-wise binary logistic regression for predictors of self-harm
| Variable | Multivariate analysis |
||
|---|---|---|---|
| Exp(B) | 95% CI | P | |
| Age | 0.909 | 0.868-0.951 | <0.001 |
| Duration of substance use | 1.057 | 1.003-1.113 | 0.038 |
| Ever caught by police | 2.159 | 1.224-3.808 | 0.008 |
CI – Confidence intervals
We performed mediation analysis [Figure 1] to ascertain whether socio-occupational functioning (marital status and occupational status in the present scenario) mediated the effect of clinical variables on the presence/absence of self-harm. Mediation was explored only if the proposed independent variable and mediating variable were individually significantly associated with the outcome variable (history of self-harm). The significant direct effect of “cannabis dependence” (independent variable) on “self-harm” (dependent variable) (B = 0.714, P = 0.020) was found to become insignificant when “marital status” (mediating variable) was introduced (B = 0.441, P = 0.169). The effect of the mediating variable on the dependent variable, however, was found to be statistically significant (B = 0.922, P < 0.001). Thus, a marital status of “not married” was found to mediate the effect of a longer duration of substance use and cannabis use on the presence of a history of self-harm.
Figure 1.
Analysis of mediation of the effect of “cannabis use” on “presence of self-harm” by “marital status.” The figure depicts the results of mediation analysis. “Cannabis dependence” is the independent variable (X), “presence of self-harm is the dependent variable (Y), and “marital status” is the mediating variable (M)
DISCUSSION
The present study was conducted to determine the patterns and predictors of a history of self-harm in patients with substance use disorders attending the outpatient clinic in a tertiary drug dependence treatment center. It found a history of deliberate self-harm in almost a third of the patients with substance use disorders and found several significant sociodemographic and clinical associations including a younger age, being unmarried/separated, unemployed, living in an urban area, a shorter duration of substance use, a greater severity of substance use disorder, history of injecting drug use and high-risk sexual behavior, of being caught by police and of being incarcerated, as well as cannabis dependence. Age, duration of substance use, and being ever caught by police were the independent predictors of self-harm in this sample.
The mean age of the patients was in the thirties. Majority of the patients were married, were employed, and were living in an urban area in nuclear families. This demographic profile is similar to those reported by other studies from India.[28,29] Majority of the patients had opioid use disorder, followed by alcohol and cannabis use disorder. Most of the patients were also dependent on nicotine.
The prevalence figures for a lifetime wish to be dead (39%) and suicidal ideation (22%) are comparable to those found by other authors in the substance-using population from other cultures, as are the rates of actual self-harm (32.7%).[10,30] These rates are significantly higher than those observed in the general population in India.[31] This difference from the general population is expected, as patients with substance use are known to be at a higher risk of suicide.[10] World literature reveals that patients with substance use disorders are more likely to suffer from personality disorders and other axis I psychiatric disorders and are more likely to have greater life stresses and poorer psychosocial support than the general population, all of which have been shown to significantly increase suicidal ideation.[30]
A majority of the patients harming themselves did so using means with potentially serious or even fatal outcomes. The most common means of self-harm was cutting oneself. This carries the risk of irreversible nerve damage, leading to lifetime handicap, and death in case of major vessel injury. Hitting one's head against the wall can potentially lead to a concussion/bleed, which may result in lifetime neurological deficit. A frontal lobe lesion, for example, may lead to further disinhibition and personality changes, leading to further loss of control of impulsive behavior. Thus, even if committed as nonsuicidal acts, such means of self-harm are potentially lethal, and should be considered in this light.
In our study, upon analyzing the effect of sociodemographic variables, it was found that patients with a history of self-harm were younger and had a shorter duration of substance use. This finding has been replicated in previous studies,[30] which may be explained by various factors. First of all, it has been shown that suicide in India is more common in the young population, which itself may contribute to this finding.[32] Another reason could be that patients with self-harm represent a subclass of substance users who have a more severe disease, and thus present earlier to the outpatient for management. Third, it may be possible that because those patients with suicidal ideation or attempts are more likely to die prematurely from suicide, a reduced life expectancy of this population may lead to the lower mean age observed. The association with a shorter duration of substance use may be confounded with lower age of those with self-harm. Indeed, this association is reversed when controlling for age, as seen in the results of the multivariable binary logistic regression.
We found that a significantly greater proportion of patients with a history of self-harm were unmarried/separated, as compared to those without such a history. Furthermore, living alone or in a nuclear family afforded a greater risk for harming oneself. Those who were unemployed were at a significantly greater risk of committing self-harm as well. All these findings are in line with previous world literature.[32] It is known that poor psychosocial support and unemployment are associated with a greater risk of suicide.[33,34] The finding that living in an urban area increased a person's chances of having committed self-harm is different from those of other authors, who have found that living in rural regions carries a greater risk of suicide.[35] This may be because previous studies have investigated a community sample, whereas ours is a hospital-based one.
Among clinical variables, the presence of injecting drug use, high-risk sexual behavior, and cannabis dependence were found to be significantly associated with the presence of self-harm. Studies from India have previously demonstrated that patients with injecting drug use are at a significantly greater risk of suicide, with more than half of this population experiencing suicidal ideation and more than a third making an actual attempt.[20] This may be explicable by the fact that patients with injecting drug use represent a population with a more severe dependence, and either the determinants or the consequences of a more severe addiction (e.g., personality factors and poor socioeconomic status) may also contribute to this higher rates of suicidal ideation and attempts. The association of high-risk sexual behavior and greater legal complications may also be explained by personality factors, as impulsivity has been shown to increase the risk of legal complications, high-risk sexual behavior, as well as self-harm behavior.[18]
On attempting to devise a model for predicting self-harm using binary logistic regression, it is seen that a younger age, a longer duration of substance use, and the history of being caught by the police are significant predictors. This would suggest that sociodemographic factors, in addition to other factors (e.g., personality traits), are the primary determinants of self-harm behavior, rather than the substance use profile. This is also suggested by other studies in this area.[17]
The presence of cannabis dependence was significantly associated with a history of self-harm. Previous literature has shown that the increase in suicidality conferred by cannabis is usually due to another psychiatric disorder.[36] Because this is not the case here, other reasons need to be explored. Previous data have suggested that cannabis use is associated with a poorer psychosocial status, which may be the causative factor for a higher suicide rate.[37] On exploring the relationship of cannabis dependence with sociodemographic predictors, it was observed that having a status of “not married” significantly mediated (partially) the effect of cannabis use on the history of self-harm. Because studies have demonstrated that cannabis use is indeed associated with an unmarried status,[38] and that being unmarried/separated increases the risk of self-harm, it seems intuitive that cannabis use does predispose to patients remaining unmarried or being separated, which in turn leads to a greater risk of self-harm.
Given these findings, the clinical implications of this study include the identification of a more vulnerable group among patients with substance use disorders (i.e., young unmarried unemployed men) who may require greater vigilance for self-harm and possibly more intense intervention. Clinicians need to be aware of the history of self-harm among substance users and understand the reasons of the same. Moreover, they would need to deal with the acute risk of further self-harm, and deal with the consequences of a previous attempt, if that is the case. Health policy too may be guided by these results, as demographics are a viable target for intervention, and considering their role in increasing self-harm, such an intervention may go a long way in reducing the occurrence of self-harm.
The strengths of this study include a fairly large sample size, the consideration of all substance use disorders together, and the investigation of mediation effects among the clinical and sociodemographic variables. The understanding of mediation effects of factors effecting self-harm better equips the mental health professional to intervene and nullify the effects of any such factor, even if the factor itself may not be removed. There are some limitations of the study which require mention. One limitation of the study is that personality was not assessed. Because personality factors have been shown to increase the risk of self-harm, and substance use as well, it is possible that the effects which have been observed in this study may, in fact, be moderated by personality factors. Second, as this was a retrospective cross-sectional study, it was subject to recall bias. Third, being a cross-sectional study, it is difficult to differentiate causality from association, i.e., it is difficult to say whether the associations observed in this study are causal in nature. Fourth, there could be chances of response bias in divulging on this topic, despite adequate privacy being ensured to the patients. Finally, a detailed structured assessment for other axis-I mental illnesses was not performed, as it is extremely difficult to establish in retrospect whether a diagnosable disorder was present at the time of commitment of self-harm. An assessment along these lines, though highly informative, would be subject to extensive recall bias, limiting its reliability.
CONCLUSION
The prevalence of suicidal ideation and self-harm attempts in the substance-using population is much higher than that in the general population. It is comparable to the prevalence rates reported in world literature, as well as Indian literature. Certain sociodemographic variables such as age, marital status, and occupational status, as well as clinical variables including injecting drug use, high-risk sexual behavior, and cannabis dependence, may predict the occurrence of self-harm in patients with substance use disorders. Further studies are needed which explore the role of other factors, including personality factors, in increasing the risk of self-harm in this population. Furthermore, longitudinal studies are required to establish causality. A better understanding of such risk factors of self-harm will better equip us to manage this health hazard in the vulnerable group of substance-dependent patients.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
REFERENCES
- 1.World Health Organization | Suicide Data. World Health Organization. 2018 [Google Scholar]
- 2.National Health Profile. Central Bureau of Health Intelligence. National Health Profile. 2008 [Google Scholar]
- 3.Institute for Health Metrics and Evaluation. India: Health of the Nation's States. ICMR Public Health Foundation of India | Institute for Health Metrics and Evaluation. 2017 [Google Scholar]
- 4.Vijayakumar L, Kumar MS, Vijayakumar V. Substance use and suicide. Curr Opin Psychiatry. 2011;24:197–202. doi: 10.1097/YCO.0b013e3283459242. [DOI] [PubMed] [Google Scholar]
- 5.Grover S, Sarkar S, Bhalla A, Chakrabarti S, Avasthi A. Demographic, clinical and psychological characteristics of patients with self-harm behaviours attending an emergency department of a tertiary care hospital. Asian J Psychiatr. 2016;20:3–10. doi: 10.1016/j.ajp.2016.01.006. [DOI] [PubMed] [Google Scholar]
- 6.Srivastava MK, Sahoo RN, Ghotekar LH, Dutta S, Danabalan M, Dutta TK, et al. Risk factors associated with attempted suicide: A case control study. Indian J Psychiatry. 2004;46:33–8. [PMC free article] [PubMed] [Google Scholar]
- 7.Conner KR, Bridge JA, Davidson DJ, Pilcher C, Brent DA. Meta-analysis of mood and substance use disorders in proximal risk for suicide deaths. Suicide Life Threat Behav. 2019;49:278–92. doi: 10.1111/sltb.12422. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Cherpitel CJ, Borges GL, Wilcox HC. Acute alcohol use and suicidal behavior: A review of the literature. Alcohol Clin Exp Res. 2004;28:18S–28S. doi: 10.1097/01.alc.0000127411.61634.14. [DOI] [PubMed] [Google Scholar]
- 9.Hufford MR. Alcohol and suicidal behavior. Clin Psychol Rev. 2001;21:797–811. doi: 10.1016/s0272-7358(00)00070-2. [DOI] [PubMed] [Google Scholar]
- 10.Al-Sharqi AM, Sherra KS, Al-Habeeb AA, Qureshi NA. Suicidal and self-injurious behavior among patients with alcohol and drug abuse. Subst Abuse Rehabil. 2012;3:91–9. doi: 10.2147/SAR.S22515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Nock MK, Joiner TE, Jr, Gordon KH, Lloyd-Richardson E, Prinstein MJ. Non-suicidal self-injury among adolescents: Diagnostic correlates and relation to suicide attempts. Psychiatry Res. 2006;144:65–72. doi: 10.1016/j.psychres.2006.05.010. [DOI] [PubMed] [Google Scholar]
- 12.Guvendeger Doksat N, Zahmacioglu O, Ciftci Demirci A, Kocaman GM, Erdogan A. Association of suicide attempts and non-suicidal self-injury behaviors with substance use and family characteristics among children and adolescents seeking treatment for substance use disorder. Subst Use Misuse. 2017;52:604–13. doi: 10.1080/10826084.2016.1245745. [DOI] [PubMed] [Google Scholar]
- 13.Bhattacharjee S, Bhattacharya A, Thakurta RG, Ray P, Singh OP, Sen S. Putative effect of alcohol on suicide attempters: An evaluative study in a tertiary medical college. Indian J Psychol Med. 2012;34:371–5. doi: 10.4103/0253-7176.108224. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Jonas JB, Nangia V, Rietschel M, Paul T, Behere P, Panda-Jonas S. Prevalence of depression, suicidal ideation, alcohol intake and nicotine consumption in rural central India. The central India eye and medical study. PLoS One. 2014;9:e113550. doi: 10.1371/journal.pone.0113550. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Borges G, Cherpitel CJ, Orozco R, Ye Y, Monteiro M, Hao W, et al. Adose-response estimate for acute alcohol use and risk of suicide attempt. Addict Biol. 2017;22:1554–61. doi: 10.1111/adb.12439. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lavania S, Ram D, Praharaj SK, Khan AH, Pattojoshi A. Deliberate self-harm in nondepressed substance-dependent patients. J Addict Med. 2012;6:247–52. doi: 10.1097/ADM.0b013e31826508c0. [DOI] [PubMed] [Google Scholar]
- 17.Kattimani S, Menon V, Sarkar S, Arun AB, Venkatalakshmi P. Role of demographic and personality factors in mediating vulnerability to suicide attempts under intoxication with alcohol: A record-based exploratory study. Indian J Psychol Med. 2016;38:540–6. doi: 10.4103/0253-7176.194919. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Sharma MK, Salim A. Suicidal behavior among alcohol dependents: Relationship with anger and personality dimensions. Ind Psychiatry J. 2014;23:61–4. doi: 10.4103/0972-6748.144971. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Menon V, Kattimani S, Sarkar S, Muthuramalingam A. Gender differences among suicide attempters attending a crisis intervention clinic in South India. Ind Psychiatry J. 2015;24:64–9. doi: 10.4103/0972-6748.160936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Armstrong G, Jorm AF, Samson L, Joubert L, Singh S, Kermode M. Suicidal ideation and attempts among men who inject drugs in Delhi, India: Psychological and social risk factors. Soc Psychiatry Psychiatr Epidemiol. 2014;49:1367–77. doi: 10.1007/s00127-014-0899-8. [DOI] [PubMed] [Google Scholar]
- 21.Cutright P, Fernquist RM. Effects of societal integration, period, region, and culture of suicide on male age-specific suicide rates: 20 developed countries, 1955-1989. Soc Sci Res. 2000;29:148–72. doi: 10.1006/ssre.1999.0658. [DOI] [PubMed] [Google Scholar]
- 22.Lester D, Colucci E. Suicide and Culture: Understanding the Context. Boston, MA: Hogrefe Publishing; 2013. Culture and suicide; pp. 59–89. [Google Scholar]
- 23.Maharajh H, Abdool P. Suicidal Behavior in Adolescence: An International Perspective. Tel Aviv, Israel: Freund Publishing House; 2005. Culture and suicide; pp. 19–32. [Google Scholar]
- 24.American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington, VA: American Psychiatric Association; 2013. [Google Scholar]
- 25.Gratz KL. Measurement of deliberate self-harm: Preliminary data on the Deliberate Self-Harm Inventory. J Psychopathol Behav Assess. 2001;23:253–63. [Google Scholar]
- 26.Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate Data Analysis. 3rd ed. New York: Macmillan; 1995. [Google Scholar]
- 27.Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York: The Guilford Press; 2017. [Google Scholar]
- 28.Basu D, Aggarwal M, Das PP, Mattoo SK, Kulhara P, Varma VK. Changing pattern of substance abuse in patients attending a de-addiction centre in North India (1978-2008) Indian J Med Res. 2012;135:830–6. [PMC free article] [PubMed] [Google Scholar]
- 29.Balhara YP, Mishra A, Sethi H, Ray R. A retrospective chart review of treatment seeking middle aged individuals at a tertiary care substance use disorder treatment centre in North part of India over five successive years: Findings from drug abuse monitoring system. ScientificWorldJournal. 2013;2013:316372. doi: 10.1155/2013/316372. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Cottler LB, Campbell W, Krishna VA, Cunningham-Williams RM, Abdallah AB. Predictors of high rates of suicidal ideation among drug users. J Nerv Ment Dis. 2005;193:431–7. doi: 10.1097/01.nmd.0000168245.56563.90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Bertolote JM, Fleischmann A, De Leo D, Bolhari J, Botega N, De Silva D, et al. Suicide attempts, plans, and ideation in culturally diverse sites: The WHO SUPRE-MISS community survey. Psychol Med. 2005;35:1457–65. doi: 10.1017/S0033291705005404. [DOI] [PubMed] [Google Scholar]
- 32.Vijayakumar L, Rajkumar S. Are risk factors for suicide universal? A case-control study in India. Acta Psychiatr Scand. 1999;99:407–11. doi: 10.1111/j.1600-0447.1999.tb00985.x. [DOI] [PubMed] [Google Scholar]
- 33.Mayer P, Ziaian T. Indian suicide and marriage: A research note. J Comp Fam Stud. 2002;33:297–305. [Google Scholar]
- 34.Boor M. Relationships between unemployment rates and suicide rates in eight countries, 1962-1976. Psychol Rep. 1980;47:1095–101. doi: 10.2466/pr0.1980.47.3f.1095. [DOI] [PubMed] [Google Scholar]
- 35.Patel V, Ramasundarahettige C, Vijayakumar L, Thakur JS, Gajalakshmi V, Gururaj G, et al. Suicide mortality in India: A nationally representative survey. Lancet. 2012;379:2343–51. doi: 10.1016/S0140-6736(12)60606-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Lynskey MT, Glowinski AL, Todorov AA, Bucholz KK, Madden PA, Nelson EC, et al. Major depressive disorder, suicidal ideation, and suicide attempt in twins discordant for cannabis dependence and early-onset cannabis use. Arch Gen Psychiatry. 2004;61:1026–32. doi: 10.1001/archpsyc.61.10.1026. [DOI] [PubMed] [Google Scholar]
- 37.Beautrais AL, Joyce PR, Mulder RT. Cannabis abuse and serious suicide attempts. Addiction. 1999;94:1155–64. doi: 10.1046/j.1360-0443.1999.94811555.x. [DOI] [PubMed] [Google Scholar]
- 38.Gentes EL, Schry AR, Hicks TA, Clancy CP, Collie CF, Kirby AC, et al. Prevalence and correlates of cannabis use in an outpatient VA posttraumatic stress disorder clinic. Psychol Addict Behav. 2016;30:415–21. doi: 10.1037/adb0000154. [DOI] [PMC free article] [PubMed] [Google Scholar]