Burden of depression and its predictors among prisoners in a central jail of Odisha, India.

Background: Mental illness disproportionately affects the physical, psychological, and social well-being of prisoners worldwide at a far higher rate than the general population. Depression is one of the common mental illnesses. However, in low- and middle-income countries, relatively little research has been undertaken to assess the prevalence and the factors that contribute to depression among prisoners. Aim: This paper aims to assess the burden and predictors of depression among male inmates in a central jail in Odisha. Method: This cross-sectional study was conducted among male prisoners in one of the central jails of Odisha. A total of 146 samples were selected using a random sampling method and 105 completed the interview. The socio-demographic characteristics and PHQ-9 scale were used for data collection. Descriptive and inferential statistics were applied for data analysis. Result: In the overall study sample, 25.7% of inmates were diagnosed with moderately severe depression, and 27.6% were diagnosed with severe depression. Binary logistic regression showed that higher education, being accused of a crime, and having low social support are all significant predictors of depression in prisoners.
Conclusion: The study emphasizes the significance of understanding the role of social support in prison and assisting policymakers in developing policies that make it more inmate-oriented, resulting in increased prisoner mental well-being and health. Copyright:

INTRODUCTION

Mental disorders are public health concern in all countries around the world. They affect an estimated 450 million people worldwide.[1] These disorders are found to be disproportionally more prevalent among the prisoners.[1] Imprisonment has a substantial effect on the psychological well-being of the person who is locked up. According to the American Psychological Association (APA), prisoners suffer more from serious mental illness (10-20%) as compared to the general population (5%) (APA, 2014).[2] The inmates commonly suffer from mood disorders, anxiety disorders, schizophrenia, or post-traumatic stress disorders. These mental issues may be either pre-existing or may develop over time after incarceration (WHO, 2019).[3] The mental disorders can develop as a result of a variety of negative factors in the jail environment, including a lack of social interaction, forced isolation, overcrowding, hostility, a lack of personal space, insecurities about prospects, a lack of access to adequate health services, and so on.[4] A study conducted in one of Nepal’s largest prison[5] found that over 35% of the detainees were battling with depression similar to the findings of studies carried out in the USA,[6] Nigeria,[7] and Iran.[8] Despite this, these disorders go unnoticed by the prisoners or prison staff, letting the person suffer in silence.

India has the fifth largest prisoner population in the world, with 1,350 functioning jails.[9] According to estimates, over 400,000 people were imprisoned in Indian jails in 2019.[9] With poor facilities in the prisons, the prisoners may find it difficult to access mental health services. While India has a large pool of qualified staff in general health care, mental health professionals are scarce. This gap in demand and supply of mental health services in prison aggravates the risk of recidivism.

Odisha, one of the Indian states with a large prisoner population, has 91 jails including 1 open jail. The total prison population of Odisha is 16,010 of which 3,550 are convicts and 12,460 are undertrial prisoners.[10] Some jails in Odisha have a smaller number of prisoners than their actual capacity whereas some prisons are overcrowded. There is a lack of scientific data on the mental health conditions and causes of mental illnesses among the prison inmates in Odisha. Many of the studies done to assess mental morbidities and their causes among the inmates were done in western jails, and it is questionable whether these results can be applied to other contexts, such as Indian detention centers.[11] Suicide is the major complication of depression and often leads to the deaths of inmates in prison, with approximately 33% of inmates reporting lifetime attempts of self-harm and approximately 20% contemplating suicide. Besides this, depression is found as the main cause of disability among prisoners accounting for 18% of all cases.[12] Despite the severity of the problem, there is a dearth of studies available on prisoners assessing the prevalence of depression in Odisha. Thus, the findings of this study will be beneficial for both the prison inmates and policymakers to design better policies and interventions and to bring out new reformation in prison for the mental well-being of the prison inmates.

Aims of the study:

To assess the prevalence of depression among prisoners.

To identify the predictors associated with depression among prisoners.

MATERIAL AND METHODS

Study design, study setting, and population

This is an institutional-based, cross-sectional study carried out during 2020 in one of the central jails of Odisha, which has a capacity of 500 male inmates. Male prisoners aged between 18 and 75 years, who were imprisoned for more than 3 months and were willing to participate in the study were considered for inclusion in the study. The prisoners who were mentally retarded or had a grave medical issue were excluded from taking part in this study.

Sample size and sampling technique

The sample size was calculated using the single proportion formula[13]: Ni = (z2 × p × q/d2), where Ni = initial required sample size, p = prevalence of depression which was 16.1%,[14] ‘q’=1-p, ‘d2’= margin error of 5%, and ‘z’ was confidence interval taken at 95% (1.96).

Ni = (1.96) 2 × 0.161 (1-0.161)/(0.05) 2

Ni = 0.5189/0.0025 = 207

As the central jail of Odisha inhabited a population of 500 inmates (N = 500), by using the finite correction formula, we obtained a final sample size of:

Nf = 146

The final samples were selected based on a random sampling method. A list of 500 prisoners was obtained from the prison administration to create a sampling frame for the study. A total of 146 participants gave consent, out of which only 105 participants completed the interview and were included in the study.

Data collection technique

The data were gathered using socio-demographic characteristics such as age gender, educational status, marital status, family income, the status of imprisonment and imprisonment characteristics, living conditions inside the jail, habits, and social support. Odia and English versions of the PHQ-9[15] scale were used to measure depression among the inmates. Before the start of data collection, the PHQ-9 scale was translated into regional language (Odia) and then back-translated to the English language to evaluate consistency. Then, a pilot study was conducted among local youths and 12 ex-convicts to check the feasibility, reliability, and validity of the study tool. Based on the responses of the participants of the pilot study, a few minor amendments to the questions were made. The male prisoners of the central jail were encouraged to participate in the study. To maintain the quality, integrity, and confidentiality of the data, they were collected by the first author itself. It took approximately 20-25 min to collect data from each participant and there were no incentives offered for completing the study.

Ethical consideration

Ethical consideration was taken from the institutional ethical committee of the Asian Institute of Public Health and permission was taken from the Jail Superintendent and DIG prisons, Bhubaneswar. Consent was taken from each of the study participants both written as well as verbal before data collection. All subjects were informed about the purpose of the study and their right to withdraw from the study at any point in time. Confidentiality and privacy were maintained throughout the process.

Statistical analysis

Data were coded, cleaned, and entered into Microsoft Excel and imported to SPSS for further analysis. Tables were used to present the data. Descriptive statistics were computed and presented in the form of tables and figures. Jail environment was also re-categorized as bad (including bad and very bad), moderate (same as moderate), and good (including good and very good). The depression variable was re-categorized into depressed and not depressed for use in the regression analysis. Depressed consists of moderate, moderately severe, and severe depression as a score above 10 is considered diagnostic for depression; while the not depressed category consisted of minimal and mild depression levels as a score below 9 represents either no depression or sub-threshold depression.[15] Binary logistic regression analysis was used to identify factors associated with depression. An odds ratio with 95% confidence interval and a P value of 0.05 was used to determine the factors associated with depression.

RESULTS

Socio-demographic characteristics

A total of 105 male prisoners who met the inclusion criteria and agreed to participate in the study were interviewed, accounting for 21% of the total population of sentenced male prisoners. About 71.4% of participants stated that they had been charged with a crime, while the remainder had been convicted of a crime. Overall, the sample (N = 105) consisted of male inmates with a mean age of 32.12 (±11.90 SD) years. A majority (64.7%) of them were young adults; 47.6% of the participants were married, while 50.5% were unmarried. The mean education of the prisoners in the studied years was found to be 6.94 (±4.10 SD) years, with 16.2% (n = 17) being illiterate and only 1.9% (n = 2) having completed education up to the post-graduate level. Before their conviction, nearly two-thirds (63.8%) of them lived in rural areas. Close to 60% of them came from households that had a monthly income of less than Rs. 10,000. The detailed socio-demographic characteristics of the participants are presented in Table 1.

Table 1

Socio-demographic characteristics of prisoners (n=105)

Variable	Number (%)
Age (years)
Mean±SD	32.12 (±11.90)
Marital status
Single	53 (50.5)
Married	50 (47.6)
Divorced	2 (1.9)
Place of residence
Urban	36 (34.3)
Rural	67 (63.8)
Semiurban	2 (1.9)
Education
Mean±SD	6.94±4.10
Family income
<Rs. 5,000	32 (30.5)
Rs. 5,000-Rs. 9,999	28 (26.7)
Rs. 10,000-Rs. 14,999	15 (14.3)
≥Rs. 15,000	30 (28.6)
Status of imprisonment
Convicted	30 (28.6)
Accused	75 (71.4)

Living environment in the jail

Perceived jail environment—The ‘perceived jail environment’ variable was assessed using the question “How do you rate the environment of jail?” The score ranged between 1 and 5 with 1 meaning very bad and 5 meaning very good jail environment. Overall, the jail environment was also re-categorized as bad (score 1-2), moderate (score 3), and good (score 4-5) for use in the regression analysis.

Quality of food—Quality of food was reported through the question “How do you rate the quality of food inside the jail?” which used a Likert scale of 1-5. A score of 1 denoted very bad and a score of 5 denoted very good quality of food.

Cleanliness of toilets and bathroom—Similarly, the ‘cleanliness of toilets’ variable was studied using a Likert rating scale ranging from 1 to 5 using the question “How do you rate the cleanliness of toilets and bathrooms inside jail?” A score of 1 denoted very poor cleanliness while a score of 5 indicated a very good standard of cleanliness.

The quality of food provided in the prison was rated as poor by 41% and very poor by 14.3% of the inmates. Similarly, about 70.4% of inmates asserted that jail toilets and bathrooms were not clean enough. The majority of the respondents reported easy access to television (81.0%) but inaccessibility to telephones (79.0%). Out of 105 respondents, only 19 (18.1%) mentioned that they practice yoga or other recreational activities. Three-quarters (76.2%) of the inmates did not report engagement in any type of work that includes physical effort. More than half of the participants (54.3%) rated the jail environment as bad or very bad [Table 2].

Table 2

The living environment in jail (n=105)

Variable	Number (%)
Quality of food
Very bad	15 (14.3)
Bad	43 (41.0)
Moderate	29 (27.6)
Good	17 (16.2)
Very good	1 (1.0)
Cleanliness of toilets and bathroom
Very poor	16 (15.2)
Poor	58 (55.2)
Moderate	28 (26.7)
Good	3 (2.9)
Very good	0 (0.0)
Accessibility to television
Yes	85 (81.0)
No	20 (19.0)
Accessibility to phone
Yes	22 (21.0)
No	83 (79.0)
Participation in Yoga/Recreational activities
Yes	19 (18.1)
No	86 (81.9)
Physical activity
Yes	25 (23.8)
No	80 (76.2)
Overall rating for jail environment
Very bad	29 (27.6)
Bad	28 (26.7)
Moderate	21 (20.0)
Good	16 (15.2)
Very good	11 (10.5)

Social support

Social support—The ‘social support’ variable was measured using two questions—”How do you rate the support and help you receive from your family?” and “How do you rate the support and help you receive from your friends inside jail?” The responses to both of these questions were recorded using Likert scale ranging from 1 to 5 with 1 as very low support and 5 as very high support. An overall social support construct was calculated by taking the average score of both the social support variables (social support from family members and that from friends inside the jail). The overall construct was categorized into low, moderate, and high social support with scores of 1-2, 2.5-3.5, and 4-5, respectively, for use in the regression analysis.

The majority of the detainees (60.0%) reported very high levels of social support from their family members, while only less than a third (30.5%) reported high levels of social support from their friends who were incarcerated in the same jail. Table 3 shows the level of social support the participants received in the jail.

Table 3

Social support from friends inside the jail and their family members (n=105)

Variable	Number (%)
Support from family members
Very low	14 (13.3)
Low	8 (7.6)
Moderate	16 (15.2)
High	4 (3.8)
Very high	63 (60.0)
Support from friends inside jail
Very low	15 (14.3)
Low	43 (41.0)
Moderate	8 (7.6)
High	7 (6.7)
Very high	32 (30.5)

Depression and its predictors

While all jail inhabitants were found to have some level of depression, more than half of the inmates (53.3%) were identified to have severe or moderately severe depression using the PHQ-9 scale. Another 34.2% of prisoners were categorized to have mild and moderate levels of depression. The characteristic of depression among the inmates is presented in Table 4.

Table 4

Depression characteristics (n=105)

Variable	Number (%)
Depression status
Minimal depression	13 (12.4)
Mild depression	18 (17.1)
Moderate depression	18 (17.1)
Moderately severe depression	27 (25.7)
Severe depression	29 (27.6)

The binary logistic regression showed that education, imprisonment status, and social support are the significant predictors of depression status among the prisoners. With the increase in education level, the likelihood of depression levels increased (OR = 1.167, 95% CI: 1.015 – 1.342, P = 0.030) among prisoners. The convicted prisoners had less chances (OR = 0.205, 95% CI: 0.062 – 0.678, P = 0.009) of suffering from depression than their accused counterparts. As compared to prisoners with high social support, those with low social support (OR = 4.221, 95% CI: 1.023 – 18.057, P = 0.048) and those with moderate social support (OR = 3.219, 95% CI: 1.046 – 9.902, P = 0.041) had significantly higher chances of suffering from depression. The same has been represented in Table 5.

Table 5

Binary logistic regression to examine predictors of depression among prisoners (n=105)

Variables	OR	95% CI	P
Age	1.042	0.987-1.100	0.137
Education	1.167	1.015-1.342	0.030
Family income	1.093	0.972-1.216	0.662
Imprisonment status
Convicted	0.205	0.062-0.678	0.009
Accused	1.000
Social support
Low	4.221	1.023-18.057	0.048
Moderate	3.219	1.046-9.902	0.041
High	1.000
Overall perceived jail environment
Bad	2.747	0.844-8.943	0.093
Moderate	1.175	0.296-4.665	0.818
Good	1.000

OR=Odds ratio, CI=Confidence Interval, Values (P<0.05) stands statistically significant

DISCUSSION

In our study, 53% of male inmates reported severe depression symptoms in central jail in Odisha. This was found to be consistent with a study conducted in the Central Jail of Guwahati which reported a similar 62.5 per 100 male prisoners with depressive symptoms.[16] Comparatively, a lesser prevalence of depression, i.e., 16.2, 16.1, 14, and 12% was reported among prisoners in Central Jails of Amritsar, Rajasthan and South India, respectively.[11141718] This might be due to the differences in the study period and study settings.

The socio-demographic profile revealed that the majority of the prisoners were young adults, married, and belonging to rural areas. Similar characteristics were reported in the study conducted by Ayirolimeethal et al., Goyal et al., and Kumar and Daria.[111419] A study conducted in Ethiopia found that a large proportion of prisoners had education less than the primary level.[20] This was found to be inconsistent with our study results as 68.6% of prisoners in our study were educated above the primary level. These variations might be due to the geographical, political, and socio-cultural environment of the two countries. We found that a large proportion of inmates had a monthly income below Rs. 10,000, similar to the studies conducted by Goyal et al. and Ayirolimeethal et al.[1119] This might be due to the loss of livelihood, earning source, and suffering in their business/job.

The physical and social environment in which a person lives substantially impacts their mental health. A study conducted by Bonner et al.[21] found that 51% variation in suicide ideation among prisoners could be attributed to low reasons for living, irrational beliefs, jail stress, and loneliness. Consistently, 54.3% of inmates in our study rated the jail environment as unsuitable for living. According to one study, those who reported a poor prison meal were more than twice as likely to develop depression as those who reported a good prison meal.[12] Hence, it is necessary to evaluate and address the living conditions of jail to ensure the adequate functioning of prisoners.

The study’s findings revealed a high prevalence of depression among those with higher education. This was discovered to be similar to the findings of Reta et al.[20] in an Ethiopian study. They discovered that participants with a college or university education were five times more likely to suffer from depression than those who were illiterate.[20] In contrast, Abdu et al.[22] found no link between education and depression in their study. Depression among highly educated inmates may be due to their expectations of better treatment in prison or their concerns about the future after their release. Unfulfilled expectations and uncertainty about the future may lead to depression among well-educated inmates.

Our study also found that accused prisoners are more likely to get depressed in comparison to those who were convicted criminals. A systematic review revealed that accused prisoners face depression and suicidal ideation at some point in time.[23] Alexander-Bloch et al.[24] reported that 46% of their 13 accused participants had moderate to severe depression. This could be due to the accusation’s negative impact on self-identity, reputation, relationships with family members or relatives, and difficulty adjusting to the jail environment. As a result, figuring out how to best support those who have been wrongfully accused, as well as their families, is critical.

Different studies have shown that depression in prisoners is strongly linked to the social support they receive. According to a study by Abdu et al.,[22] prisoners without social support are twice as likely to develop depression as those who do. These results were similar to our findings which show that the odds of depression are four times more in inmates with low social support than in those with social support. Prison in and of itself means a period of increased risk, which can lead to extreme psychosocial distress as the prisoner feels loneliness without social support. Providing social assistance in prisons may prevent the development of psychosocial distress to depressive disorder.

Limitations

Although the study revealed very important factors associated with mental illnesses among prison inmates, it suffers from the limitations of cross-sectional study designs, such as it does not indicate a strong cause and effect relationship. Besides this, although the PHQ-9 scale used for screening depression was translated into the local language and piloted for participants’ comprehension, it was not validated. Also, female inmates were not housed in the prison understudy, so the number of females in this study is zero. Because the research was conducted in just one prison with a limited sample size, the findings cannot be applied to the entire Indian prison population. Hence, similar studies on a larger scale can be conducted to get a full picture of the problem.

Recommendation

Based on our findings, we recommend that both the government and prison managers should try to strengthen social support in each prison and the support for prisoners from family members, peers, and families. More research should be conducted to assess the mental health of prisoners. The following measures can help reduce the prevalence of depression among prisoners such as increasing the number of prison cells, involvement in recreational and religious activities, and fast hearing of trials in court. Furthermore, training on how to cope with a new environment just before imprisonment and release should be provided to develop the coping mechanism of prisoners. Further research into the negative effects of depression on inmates may be beneficial. Interventional research should be carried out to recognize effective treatment regimens for depression in prison inmates.

CONCLUSION

This study highlighted that depression level is comparatively high among those inmates who were accused, are well-educated, and have a lack of social support. Routine screening for depression and access to treatment in prison may be critical. The study emphasizes the importance of comprehending the role of social support in prison and assisting policymakers in developing policies to make it more inmate-oriented, resulting in an increase in prisoner mental well-being and health.

Financial support and sponsorship

We acknowledge Asian Institute of Public Health for providing approval for conducting this research. We would also like to thank the Jail Superintendent and DIG prisons, Bhubaneswar for their support.

Conflicts of interest

There are no conflicts of interest.