Open Access

A statistical study of suicidal behavior of Indians

Egyptian Journal of Forensic Sciences20177:12

https://doi.org/10.1186/s41935-017-0007-9

Received: 7 April 2017

Accepted: 12 June 2017

Published: 25 September 2017

Abstract

Background

According to the reports published by the National Crime Records Bureau (NCRB) India, every year, the number of suicides are continuously increasing. The number of suicidal deaths in 2014 is 15.8% more than 2004.

Objectives

To briefly overview the recent trends in the number of suicides and study risk factors for suicides, means of suicides in India. We explore the association of suicidal deaths and some attributes such as gender (sex), age group and profession of suicide victims.

Method

Multiple correspondence analysis (MCA) and subset MCA are used to study the association of attributes.

Results

In India, family problems and health problems are major reasons for suicides. The association between the risk factors for suicides, means of suicides, gender, profession, age group of suicide victims is studied and explored using biplots.

Conclusions

The leading risk factors for suicides are family problems, illness, drug addiction, failure in examination, etc. Hanging and poisoning are the common means adopted by males and females while females most frequently commit suicide by fire/self-immolation. As per the association of attributes studied, government of India has to launch ‘Anti-suicide campaigns’ at all levels regularly and the campaign should consider gender, age group, profession while structuring the campaign.

Keywords

SuicidesRisk factors for suicidesOdds ratioMCABiplots

Background

Suicide is the 10th leading cause of death worldwide. More than one million people commit suicide every year, representing an annual global suicide mortality rate of 16 per 100,000 (Nock et al. (2012)). World Health Organization (WHO) reports that suicide attempts are up to 20 times more frequent than completed suicides. According to recent statistics, among more than a million suicidal deaths worldwide, 20% are Indians while India is 17% of the world population Singh and Singh (2003). As per NCRB report, the total number of suicides reported in 2014 are 131,666 out of which 89,129 are males, 42,521 are females and 16 are transgender. According to WHO reports, India ranks 43rd in descending order of rates of suicide with a rate of 10.6 per 100,000 in 2009 (Radhakrishnan and Andrade (2012)).

Suicide is a major public and mental health problem which demands urgent action. Suicide is the act of intentionally terminating one’s own life (Nock et al. (2008b)). It is often carried out as a result of despair, the cause of which is frequently attributed to a mental disorder such as depression, borderline personality disorder, alcoholism or drug abuse, stress factors such as financial difficulties or troubles with interpersonal relationships. A suicide attempt possesses self-initiated, potentially injurious behavior, the presence of intent to die and non-fatal outcome (Levi et al. (2008)). The costs of suicide are not only loss of life, but the mental, physical and emotional stress imposed on family members and friends. Other costs are for the public resources, as people who attempt suicide often require help from health care and psychiatric institutes. Suicide is a final act of behavior that is probably the result of interactions of several different factors. Predictors of suicidal behavior and risk factors include a history of previous suicide attempts, particular demographic variables, clinical symptoms and issues related to medical and social support (Hawton and Heeringen (2009)).

An estimated 804,000 suicides occurred worldwide in 2012, representing an annual global age-standardized suicide rate of 11.4 per 100,000 population (15.0 for males and 8.0 for females). With regard to age, the suicide rate is highest in persons aged 70 or over for both men and women in almost all regions of the world. In some countries, the suicide rate is highest in the young age. According to WHO report, suicide is the second leading cause of death in 15–29 year-olds. The countries of the Eastern Europe and East Asia have the highest suicide rate in the world. While the region with the lowest suicide rate is Latin America. Asian countries account for approximately 60% of the world’s suicides (Chen et al. (2012)). Compared with Western countries, Asian countries have a higher average suicide rate, lower male-to-female suicide gender ratio, and higher elderly-to-general-population suicide ratios. Vijayakumar et al. (2005a,b,c) studied suicides in developing countries. Maher et al. (2011) studied suicide mortality in Cairo city during 1998 to 2002.

According to WHO, an estimate of number of suicides for the year 2020 is approximately 1.53 million and ten to twenty times more people are estimated to attempt suicide worldwide. These figures do not include the suicide attempts, which are up to 20 times more frequent than completed suicide (Kumar et al. (2013)). The estimates for the year 2020 represent on an average one death every 20 sec and one attempt every one to two seconds (Gvion and Apter (2012)). In most of the countries, suicides are under-reported. Even in some countries, suicides are treated as illegal act and it is very likely that it is unreported. In countries with good vital registration data, suicide may often be misclassified as an accident or other cause of death. Registering a suicide is a complicated procedure involving several different authorities, often including law enforcement. In countries without reliable registration of deaths, suicides simply dies uncounted.

We observe that, gender difference plays a significant role among all age groups in India as well as across the world. According to Nock et al. (2008a), suicide is more prevalent among men, whereas nonfatal suicidal behaviours are more prevalent among women and persons who are young, unmarried, or have a psychiatric disorder. Tousignant et al. (1998) reported that the gap between male and female suicide rates in India is relatively small. But since 2009, this gap has shown continuous increase. The overall male-female ratio of suicide victims in India for the year 2014 was 68:32 while it was 59:41 in 1998. Steen and Mayer (2004) studied the effect of modernization on male-female suicide ratio in India during 1967–1997.

Suicide is a leading cause of death among teenagers and young people under 35 years of age across the world. Even in India, 66.28% (87, 252 out of n = 1, 31, 650 male and female suicides) of the suicide victims are between the age group 18–45 years according to NCRB report for 2014. Specifically, in India, the suicide victims’ boy-girl ratio (below 18 years of age) is 51:49. Mayer and Ziaian (2002) studied gender and age variations in suicides in India. Lasrado et al. (2016) studied suicidal behavior in South India whereas Issa et al. (2016) studied suicidal deaths in Saudi Arabia. Hobson and Leech (2014) studied the youths’ suicidal behavior and noted that there is a significant relationship between media coverage and youth suicide.

The consumption of insecticides (poisoning) (Argo et al. (2010)), hanging and firearms are the most common means of suicide globally, but many other methods are used with the choice of method, often varying according to population group such as age-group, gender, profession, social status, educational status, etc. In India hanging, poisoning, firearm/self-immolation, and drowning are the prominent means of suicides. During 2014, almost 51.12% (67,303 out of n) of the total male suicides are committed by hanging, poisoning and drowning while near about 24.56% (32, 333  out of n) of the total female suicide are committed by hanging, poisoning and fire/self-immolation. Overall, 67.83% (89, 295  out of n) of the total suicides are committed by hanging and poisoning.

There is no single reason why someone may try to take its own life, but certain factors can increase the risk, such as illness, family problems, financial loss, harmful use of alcohol, act cumulatively to increase a person’s vulnerability to suicidal behavior, etc. According to NCRB report 2014, ‘Family Problems (other than marriage related problems)’ and ‘Illness’ have together accounted 39.76% (52, 341  out of n) of the total suicides.

Knowledge about suicidal behavior has increased greatly in recent decades. Research at different levels, has shown the importance of the interplay between biological, psychological, social, environmental and cultural factors in determining suicidal behaviors. At the same time, literature has helped to identify many risks and protective factors for suicide, both in the general population and in vulnerable groups.

In this paper, we study the recent trends in the number of suicides in India and briefly review various risk factors for suicide. The main objective of this study is to explore the association between various attributes such as gender, age of suicide victims, the social, economic, educational and professional status of suicide victims, means of suicides, risk factors for suicides, etc. We study the association between various attributes through the Correspondence Analysis (CA). The detail of the methodology to study the suicidal behavior of Indians is discussed in the next section.

Methods

The present study includes the association of various attributes such as gender, age category of victims, social status, economic status, educational status, professional status, risk factors for suicides, means of suicides, etc. for the number of suicides in India during 2013/2014. At primary level, an odds ratio is calculated to measure an association between gender and risk factors for suicides, the means of suicides. Further, we use CA to study the association between various attributes related to the suicides. We summarize the conclusions and findings which may be helpful for policy makers, researchers, NGO’s, etc. to develop preventive measures of suicides.

The chi-square test for independence is commonly used to determine whether there is a significant association between the categorical variables (CVs) expressed in a contingency table. Usually if the CVs are found to be dependent through this test, the interest would be, how the levels of CVs are related with each other. To portray the interrelations between the CVs, CA is used. CA is a multivariate statistical technique to visualize graphically the association between CVs in a contingency table. Particularly Simple CA (SCA) and Multiple CA (MCA) are useful for exploring the relations amongst two and more than two CVs respectively. The association between the categories of the variables are visualized on a map, called a biplot, allowing interpretations of their similarities and differences.

The biplot shows the best two dimensional approximations of the distances between row and column profiles of a matrix data. The distance between any row points or column points gives a measure of their similarity (or dissimilarity). Biplot consists of lines and dots. Lines are used to reflect the variables of the dataset, and dots are used to show the observations. In a biplot, the length of a line approximates the variance of the variable. The longer the line, the higher is the variance. The angle between the lines, or, to be more precise, the cosine of the angle between the lines, approximates the correlation between the variables they represent. The closer the angle to 900 or 2700, smaller the correlation, while an angle of 00 or 1800 degrees reflects a correlation of 1 or −1, respectively.

In common practice, CA includes all the categories of the CVs under consideration in the analysis, since this gives the most comprehensive and global view of their interrelationships. Sometimes it is likely that after a global view of the data, it would be of interest to focus attention on a reduced set of response categories of the data. Another reason for restricting the categories to be analysed for a subset is when there are many variables and thus many biplot points in the graphical display. In such situation, it is better to study a subset of variables of interest and their categories. This approach is known as subset CA.

The same concept is applicable for MCA, which is known as subset MCA. Since the variables of interest and their categories are few in Subset MCA, the corresponding biplot is easily interpretable. The idea is to maintain the original relative frequencies of the categories and not to re-express them relative to totals within the subset. Since, the interpretations of biplot obtained from the classical MCA are more delicate, Khangar and Kamalja (2017) used a method for performing MCA based on separate singular value decompositions (SVDs) where the overlaid biplots are used to visualize the association of pairs of CVs across all the categories of others. The main contribution of this paper is application of subset MCA to study the suicidal behavior. The subset MCA based on separate SVDs is applied to study the association of key risk factors for suicides and means of suicides with other attributes related to number of suicides in India.

Statistical analyses

The data about the number of accidental and suicidal deaths in India is recorded and analyzed by NCRB, India every year since 1967. The data is maintained according to various attributes such as gender, age group, social, economic, educational status, profession, etc. Further, the data are also classified according to the risk factors for suicides and the means of suicides. The suicide death rate for a year represents the number of suicides per 100,000 of the population during the reference year.

Suicidal death rate in India

Figure 1 shows the variation in the suicide death rate in India from 1967 to 2014. The overall increasing trend in the suicide death rate indicates the need to handle this critical issue very carefully.
Fig. 1

Suicidal Death Rate in India

The number of suicidal deaths in 2014 is 15.8% more than 2004. There is a wide variation in the suicide rates within different states of the country. In the year 2014, a total of 131,166 suicidal deaths is recorded, out of which 67.70% (89, 129 out of n) are males and 32.30% (42, 521 out of n) are females. The percent share of males in the number of suicidal deaths is continuously increasing as compared to females since last two decades. Fig. 2 shows the gender wise percent of suicides of the total suicides since 1967 to 2014.
Fig. 2

Percent of Suicides for males and females

Most of the suicides in India are from a younger age. Fig. 3a and b shows percent of suicidal deaths across each age group and gender. Percent of suicidal deaths for age group below 14 years of age and between 15 and 18 years of age is almost equal for male and female for 2013 and 2014. Also, the percent share of male suicidal deaths for age groups 30–45 and 45–60 is too high as compared to females. Near about 34.08% (44, 870 out of n) of suicides are committed by people between the age group 18–30 in 2014. The fact that, the percent share of suicides committed by younger age group people imposes the huge social, emotional and economic burden on the society.
Fig. 3

a. Age-group wise percent share of Suicidal deaths for year 2013. b. Age-group wise percent share of Suicidal deaths for year 2014

Figure 4 represents the percent of suicide victims by their social status and gender. The highest percent share in suicidal deaths is of married male. Fig. 5 presents the percent share of suicides by gender and their educational status. Around 69.74% (91, 820 out of n) of suicidal deaths are by people belonging to less than 100,000 of the annual income group. It indicates the impact of poverty on suicidal deaths in India.
Fig. 4

Social Status of Suicide Victims

Fig. 5

Economic Status of Suicide Victims

The percent share of suicidal deaths by gender and educational status of suicide victims is represented in Fig. 6. Overall, less educated people are more likely to commit suicide. Fig. 7 shows the suicide percent by professional status of suicide victims and gender. Out of the total female suicides, 47.38% (20, 148 out of 42, 521) suicides are committed by housewives followed by 8.95% (3, 807 out of 42, 521) suicides by students.
Fig. 6

Educational Status of Suicide Victims

Fig. 7

Professional Status of Suicide Victims

The parameters such as age, gender, the social, economic, educational and professional status of suicide victims play a significant role towards the suicidal behavior. However, the risk factors for suicides and means of suicides need to be studied for the prevention of suicidal deaths and to develop the new strategies for the vulnerability of the people tending to suicide. The brief study of risk factors for suicides and means of suicides with respect to age category and gender is presented in the next subsection.

Study of risk factors of suicides

There are various risk factors which ultimately result in the suicide. Suicidal behaviors may be observed when there is a situation or event that the person finds overwhelming, such as aging, the death of loved one, drug or alcohol use, emotional trauma, serious physical illness, unemployment or money problems, etc. So it is imperative to design interventions that can address distress among various demographic groups, and not aggravate the problem by focusing on health and family problems alone.

From Fig. 8, it can be observed that family problems (other than marriage related issues) and illness/health problems are the key risk factors for suicides. During 2014, out of 10 major risk factors for suicides, family problems and illness together caused 69.59% (52,341 out of 75,212) suicides. The percent share of other reported risk factors such as cancellation and non-settlement of marriage, love affairs, drug abuse/addiction, failure in examination, bankruptcy, unemployment, poverty have a relatively lesser share than these two major risk factors. It can be seen that if family problems are handled with care and precaution, the majority of the suicides can be prevented. If the patients are counseled/theropied with respect to possible suicide attempt, to some extent suicidal deaths due to illnesses can also be reduced.
Fig. 8

Causes of Suicides

To further explore the risk factors for suicides, we consider the key risk factors only. We study the risk factors for the suicides by gender through odds ratio. Table 1 gives odds ratios for respective risk factors. From Table 1, it can be seen that, males are 39 times more likely to commit suicide than females due to drug abuse/addiction while males are 10 times more likely to commit suicide than females due to bankruptcy or sudden change in economic status. Unemployment is also a subsidiary cause of males’ suicide. Males are 8 times more likely to commit suicide due to unemployment than females. The leading cause of the suicide among females is dowry dispute, since females are 56 times more likely to commit suicide than males due to dowry dispute. Thus, bankruptcy, unemployment, drug abuse/addiction are the common risk factors for suicides among males and dowry dispute is a major risk factor for suicide among females.
Table 1

Odds ratio for causes of suicides for males and females

Sr. No.

Causes

Males

Females

Total

Odds favouring males

Odds favouring females

1

Drug Abuse/Addiction

3555

91

3646

39.0659

0.0256

2

Bankruptcy or sudden change in Economic status

2098

210

2308

9.9905

0.1001

3

Unemployment

1965

242

2207

8.1198

0.1232

4

Poverty

1419

280

1699

5.0679

0.1973

5

Illness

16078

7663

23741

2.0981

0.4766

6

Family problems

18623

9977

28600

1.8666

0.5357

7

Love Affairs

2441

1727

4168

1.4134

0.7075

8

Failure in examination

1358

1045

2403

1.2995

0.7695

9

Cancellation/Non-settlement of Marriage

2173

2006

4179

1.0832

0.9231

10

Dowry Dispute

39

2222

2261

0.0176

56.9744

Now to study the pattern of association of these two attributes with respect to number of suicidal deaths in a specific age group, we use MCA based on separate SVDs.

Exploration of the association between risk factors for suicide and gender across Age-category of suicide victims

We perform MCA based on separate SVDs for the CVs gender (A) and risk factors for suicides (B) across age-category (C) of suicide victims. The categories of the CVs are summarized as follows.

Name of the CV

Categories of CV

Gender (A)

Male (A 1), Female (A 2)

Risk factors for Suicides (B)

Family problems (B 1), Illness (B 2),

Cancellation/Non-settlement of Marriage (B 3), Love Affairs (B 4),

Drug Abuse/Addiction (B 5), Failure in examination (B 6),

Bankruptcy or sudden change in Economic status (B 7), Dowry Dispute (B 8),

Unemployment (B 9), Poverty (B 10), Property Dispute (B 11),

Death of a dear person (B 12), Professional/Career problem (B 13),

Social Disrepute (B 14), Suspected/Illicit relation (B 15),

Divorce (B 16), Barrenness/Impotency (B 17),

Physical Abuse (Rape, Incest) (B 18), Ideological Causes/Hero Worshipping (B 19),

Illegitimate Pregnancy (B 20)

Age Category (C)

Up to 14 years (C 1), 15–18 years (C 2), 18–30 years (C 3),

30–45 years (C 4), 45–60 years (C 5), 60 years and above (C 6)

For the year 2014, the distribution of the number of suicidal deaths with reference to these 3-attributes is presented in 3-way contingency table in Table 2. The details of nonzero Singular Values (SV), Inertia (I) and % of inertia obtained by performing MCA based on separate SVDs are provided in Table 3 while Fig. 9 shows the corresponding biplot.
Table 2

Distribution of suicides in 2014 by causes of suicides and gender across age group

Sr. No.

Causes of suicides

A 1 (Male)

A 2 (Female)

Total suicides

Percent suicides

C 1

C 2

C 3

C 4

C 5

C 6

C 1

C 2

C 3

C 4

C 5

C 6

1

B 1

129

625

5541

7041

3990

1297

116

716

4221

3238

1228

458

28600

35.77

2

B 2

96

428

3756

5027

4147

2624

85

496

2299

2083

1496

1204

23741

29.69

3

B 3

9

109

1001

753

253

48

10

136

1139

591

113

17

4179

5.23

4

B 4

12

346

1581

429

63

10

30

511

985

193

8

0

4168

5.21

5

B 5

5

51

967

1419

893

220

6

5

24

35

15

6

3646

4.56

6

B 6

91

582

655

28

2

0

72

539

407

25

2

0

2403

3.01

7

B 7

1

14

432

918

579

154

2

3

41

101

51

12

2308

2.89

8

B 8

1

0

19

12

5

2

0

32

1707

427

44

12

2261

2.83

9

B 9

0

48

838

724

276

79

2

9

125

84

21

1

2207

2.76

10

B 10

1

9

358

589

370

92

4

8

91

117

48

12

1699

2.12

11

B 11

0

15

201

343

263

52

0

5

63

87

26

12

1067

1.33

12

B 12

1

13

169

218

174

83

3

26

126

75

47

46

981

1.23

13

B 13

0

33

244

307

171

37

0

8

61

33

9

0

903

1.13

14

B 14

2

7

85

136

98

33

7

15

55

34

12

6

490

0.61

15

B 15

0

4

100

119

26

4

4

23

83

79

15

1

458

0.57

16

B 16

0

0

55

59

28

8

0

10

88

77

5

3

333

0.42

17

B 17

0

5

54

50

15

3

1

11

100

77

13

3

332

0.42

18

B 18

0

0

3

0

3

0

4

20

29

12

2

1

74

0.09

19

B 19

1

2

21

10

7

2

2

0

8

3

0

0

56

0.07

20

B 20

0

0

0

0

0

0

1

10

34

11

0

0

56

0.07

Grand Total

349

2291

16080

18182

11363

4748

349

2583

11686

7382

3155

1794

79962

100

Table 3

Results of MCA based on separate SVDs for Table 2 along B and A across the categories of C

Categories of C

SV

Inertia

Percent inertia

C 1

0.1215

0.0148

3.24

0.0417

0.0017

0.38

C 2

0.3550

0.1261

27.62

0.1113

0.0124

2.72

C 3

0.3708

0.1375

30.13

0.1431

0.0205

4.49

C 4

0.1925

0.0370

8.12

0.1173

0.0138

3.02

C 5

0.1914

0.0366

8.03

0.1207

0.0146

3.19

C 6

0.1963

0.0385

8.44

0.0533

0.0028

0.62

Total

0.4563

100

Fig. 9

Biplot showing association between B (Risk factors of Suicide) and A (Gender) across C (Age Category)

The biplot points far from the origin and close to each other are considered for interpretations, since more the vector length, better is the discrimination ability. The interest would be in pairs of biplot points of CVs B and A across the same category of C. The biplot obtained by performing MCA based on separate SVDs on 3-way contingency data for three attributes A, B and C in Table 2 includes all the risk factors for suicides. Fig. 9 consists of too many biplot points associated with the risk factors for suicides. This causes the biplot difficult to interpret. Hence we perform subset MCA based on separate SVDs to the subset of response categories which include 10 key risk factors for suicides contributing almost 94% of the total suicides. In fact, we drop out the risk factors for suicides with a relatively small percent share in total suicides. We perform a subset MCA based on separate SVDs to the cross-classified data in Table 2 with first 10 risk factors for suicides. The details of nonzero singular values, inertia and % of inertia obtained by performing subset MCA based on separate SVDs is provided in Table 4 while Fig. 10 shows the corresponding biplot.
Table 4

Results of Subset MCA based on separate SVDS for Table 2 along B and A across the categories of C

Categories of C

SV

Inertia

Percent inertia

C 1

0.1171

0.0137

3.17

0.0309

0.0010

0.22

C 2

0.3512

0.1233

28.53

0.0992

0.0098

2.27

C 3

0.3637

0.1323

30.60

0.1409

0.0198

4.59

C 4

0.1840

0.0338

7.83

0.1113

0.0124

2.87

C 5

0.1826

0.0333

7.71

0.1146

0.0131

3.04

C 6

0.1925

0.0370

8.57

0.0508

0.0026

0.60

Total

0.4323

100

Fig. 10

Biplot for subset MCA showing the association between B (Risk factors of Suicide) and A (Gender) across C (Age Category)

From the pair of biplot points (A 1 C 3, B 9 C 3), (A 1 C 3, B 4 C 3), (A 1 C 3, B 6 C 3) it is seen that Males (A 1) between age group 18–30 (C 3) commit suicide due to unemployment (B 9), Love Affairs (B 4) and Failure in examination (B 6) respectively. The other conclusions about the association of risk factors for suicides with gender and age group from biplot in Fig. 10 are summarized in the following table sorted on the attributes’ discrimination ability and strength of association of pair of attributes. The biplot points are displayed in respective symbol colors in biplot.

Age group

Gender

Pair of Biplot Points

Interpretation of pair of Biplot Points (Association between risk factors for suicides and gender across age category)

18–30 year (C 3)

Male (A 1)

(A 1 C 3, B 9 C 3)

(A 1 C 3, B 4 C 3)

(A 1 C 3, B 6 C 3)

Males (A 1) between age group 18–30 (C 3) commit suicide due to: Unemployment (B 9), Love Affairs (B 4) and Failure in examination (B 6).

Female (A 2)

(A 2 C 3, B 8 C 3)

(A 2 C 3, B 3 C 3)

Females (A 2) between age group 18–30 (C 3) commit suicide due to: Dowry Dispute (B 8) and Cancellation/Non-settlement of Marriage (B 3).

45–50 year (C 5)

Male (A 1)

(A 1 C 5, B 7 C 5)

(A 1 C 5, B 10 C 5)

(A 1 C 3, B 6 C 3)

Males (A 1) between age group 45–60 (C 5) commit suicide due to: Bankruptcy/sudden change in Economic status (B 7), Poverty (B 10) and Failure in examination (B 6).

30–45 year (C 4)

Male (A 1)

(A 1 C 4, B 1 C 4)

Males (A 1) between age group 30–45 (C 4) commit suicide due to Family problems (B 1).

Female (A 2)

(A 2 C 4, B 3 C 4)

(A 2 C 4, B 1 C 4)

Females (A 2) between age group 30–45 (C 4) commit suicide due to: Cancellation/Non-settlement of Marriage (B 3) and Family problems (B 1).

15–18 year (C 2)

Male (A 1)

(A 1 C 2, B 6 C 2)

Males (A 1) between age group 15–18 (C 2) commit suicide due to Failure in examination (B 6).

Female (A 2)

(A 2 C 2, B 4 C 2)

Females (A 2) between age group 15–18 (C 2) commit suicide due to Love Affairs (B 4).

Up to 14 years (C 1)

Male (A 1) and Female (A 2)

(A 1 C 1, B 6 C 1)

(A 2 C 1, B 6 C 1)

Males (A 1) and Females (A 2) up to 14 years (C 1) age group commit suicide due to Failure in examination (B 6).

Study of means of suicides

Along with the risk factors for suicides, means of suicides is also an important attribute for the prevention of suicides. Fig. 11 shows % of suicides in 2014 by means adopted and gender. It is observed that hanging (42% out of n), consuming poison (26% out of n) are the common means among males and females for suicides.
Fig. 11

Means of Suicides

Table 5 shows the odds ratio for the means of suicides. It can be concluded that males are 5.46 times more likely than females to commit suicide by coming under running vehicles/trains while males are 4.08 times more likely than females to commit suicide by touching an electric wire. Males are 2.52 times and 2.08 times more likely than females to commit suicide by hanging and poisoning respectively, whereas females are 1.5 times more likely to commit suicide by fire/self-immolation.
Table 5

Odds ratio for means adapted for suicides for males

Sr. No.

Means Adapted

Males

Females

Total

Odds favouring males

Odds favouring females

1

Coming under running vechiles/trains

2862

524

3386

5.4618

0.1831

2

By touching Electric Wire

604

148

752

4.0811

0.2450

3

Jumping

1013

395

1408

2.5646

0.3899

4

Hanging

39410

15631

55041

2.5213

0.3966

5

Other means

12650

5825

18475

2.1717

0.4605

6

Fire-Arms

346

161

507

2.1491

0.4653

7

Poisoning

23128

11126

34254

2.0787

0.4811

8

Drowning

4765

2661

7426

1.7907

0.5584

9

By Self Inflicting Injury

363

203

566

1.7882

0.5592

10

By consuming sleeping pills

443

271

714

1.6347

0.6117

11

Fire/Self Immolation

3545

5576

9121

0.6358

1.5729

To study the pattern of association of gender and the means of suicides across age categories with respect to number of suicidal deaths, we use MCA based on separate SVDs.

Exploration of the association between means of suicides and gender across Age-category of suicide victims

To study the suicidal behavior and to implement new strategies, we explore the association between the means of suicides and gender across the age category of suicide victims. We use the 3-way contingency data for 2013 given in Table 6. For 2014 such classification of number of suicidal deaths is not available. The details of categories of these variables and the labelling used in the analysis are given below.
Table 6

Distribution of suicides by means adopted and gender across age group in 2013

Sr. No.

Means Adopted for suicides

A 1

A 2

Total suicides

Percent of suicides

C 1

C 2

C 3

C 4

C 5

C 1

C 2

C 3

C 4

C 5

1

B 1

415

12172

13862

8489

3031

391

7847

4528

2111

790

53636

44.76

2

B 2

210

7032

8793

6428

2596

286

5458

3940

1941

941

37625

31.40

3

B 3

60

1081

1466

770

295

143

2882

1981

811

475

9964

8.32

4

B 4

244

1490

1737

1028

442

160

1073

763

434

275

7646

6.38

5

B 5

42

1122

1240

1017

441

30

323

244

175

104

4738

3.95

6

B 6

1

272

619

456

205

0

26

10

17

3

1609

1.34

7

B 7

36

263

341

262

82

25

143

96

41

30

1319

1.10

8

B 8

31

242

284

176

37

16

60

62

33

11

952

0.79

9

B 9

2

186

193

100

26

0

66

46

15

3

637

0.53

10

B 10

5

99

131

107

36

4

81

54

24

14

555

0.46

11

B 11

3

82

109

85

31

0

95

69

31

21

526

0.44

12

B 12

4

112

150

71

13

2

81

52

17

8

510

0.43

13

B 13

1

23

37

17

3

1

9

6

4

1

102

0.09

Grand Total

1054

24176

28962

19006

7238

1058

18144

11851

5654

2676

119819

100

Name of the CV

Categories of CV

Gender (A)

Male (A 1), Female (A 2)

Means of Suicides (B)

Hanging (B 1), Poisoning (B 2),

Fire/Self Immolation (B 3), Drowning (B 4),

Coming under running vehicles/trains (B 5), Excessive Alcoholism (B 6), Jumping (B 7), Self-electrocution (B 8),

Jumping of moving vehicles or trains (B 9), Self-Infliction of Injury (B 10),

Overdose of sleeping pills (B 11), Fire-Arms (B 12),

By machine (B 13),

Age Category (C)

Up to 14 years (C 1), 15–29 years (C 2),

30–44 years (C 3), 45–59 years (C 4), 60 years and above (C 5),

The distribution of number of suicides by means of suicides and gender across the age group in 2013 is given in Table 6.

We perform MCA for CVs Means of suicides (B) and gender (A) across age-category (C) of suicide victims. The numerical results are summarized in Table 7. The biplot for this analysis is shown in Fig. 12.
Table 7

Results of MCA for Table 6 along B and A across the categories of C

Categories of C

SV

Inertia

Percent inertia

C 1

0.0801

0.0064

6.68

0.0545

0.0030

3.09

C 2

0.1949

0.0380

39.54

0.0493

0.0024

2.53

C 3

0.1338

0.0179

18.64

0.0181

0.0003

0.34

C 4

0.1310

0.0171

17.85

0.0420

0.0018

1.84

C 5

0.0820

0.0067

7.01

0.0489

0.0024

2.49

Total

0.0961

100

Fig. 12

Biplot showing association between B (Means of Suicides) and A (Gender) across C (Age Category)

To study the key means of suicides, we perform the subset MCA for the first five means of suicides from Table 6, since these means of suicides contribute almost 94% of the total suicides. The details of nonzero singular values, inertia and % of inertia obtained by performing subset MCA based on separate SVDs are provided in Table 8 while Fig. 13 shows the corresponding biplot, which can be well interpreted.
Table 8

Results of subset MCA for Table 6 along B and A across C

Categories of C

SV

Inertia

Percent inertia

C 1

0.0714

0.0051

6.14

0.0542

0.0029

3.54

C 2

0.1920

0.0368

44.41

0.0366

0.0013

1.62

C 3

0.1201

0.0144

17.37

0.0111

0.0001

0.15

C 4

0.1162

0.0135

16.29

0.0390

0.0015

1.84

C 5

0.0731

0.0053

6.44

0.0426

0.0018

2.19

Total

0.0830

100

Fig. 13

Biplot showing association between (Means adapted for Suicides) and (Gender) across (Age Category)

From the pair of biplot points (A 2 C 2, B 3 C 2), it is seen that Females (A 2) between age group 15–29 (C 2) commit suicide by fire/self-immolation (B 3). The other conclusions about association of means of suicides with gender and age group from biplot in Fig. 13 are summarized in the following table.

Age group

Gender

Biplot points

Interpretation of pair of Biplot Points (Association between means of suicides and gender across age category)

15–29 year (C 2)

Female (A 2)

(A 2 C 2, B 3 C 2)

(A 2 C 2, B 1 C 2)

Females (A 2) between age group 15–29 (C 2) commit suicide by: Fire/self-immolation (B 3) and Hanging (B 1).

30–44 year (C 3)

Male (A 1)

(A 1 C 3, B 1 C 3)

(A 1 C 3, B 2 C 3)

(A 1 C 3, B 5 C 3)

Males (A 1) between age group 30–44 (C 3) commit suicide by: Hanging (B 1), Poisoning (B 2) and Coming under running vehicles/trains (B 5).

Female (A 2)

(A 2 C 3, B 3 C 3)

Females (A 2) between age group 30–44 (C 3) commit suicide by Fire/self-immolation (B 3).

Above 60 years (C 5)

Male (A 1)

(A 1 C 5, B 2 C 5)

(A 1 C 5, B 5 C 5)

Males (A 1) above 60 years (C 5) age group commit suicide by Poisoning (B 2) and

Coming under running vehicles/trains (B 5).

Female (A 2)

(A 2 C 5, B 4 C 5)

Females (A 2) above 60 years (C 5) age group commit suicide by Drowning (B 4).

45–59 year (C 4)

Male (A 1)

(A 1 C 4, B 5 C 4)

(A 1 C 4, B 2 C 4)

Males (A 1) between age group 45–59 (C 4) commit suicide by: Coming under running vehicles/trains (B 5) and Poisoning (B 2).

Up to 14 years (C 1)

Female (A 2)

(A 2 C 1, B 3 C 1)

(A 2 C 1, B 1 C 1)

(A 2 C 1, B 2 C 1)

Females (A 2) up to 14 years (C 1) commit suicide by: Fire/self-immolation (B 3), Hanging (B 1) and Poisoning (B 2).

Exploration of the association between profession of suicide victims and gender across Age-category of suicide victims

To study the association of profession of suicide victims with age category and gender of suicide victims, we perform MCA based on separate SVDs. We consider the cross-classified data in Table 9 for analysis. The details of nonzero singular values, inertia and % of inertia obtained by performing MCA based on separate SVDs is provided in Table 10 while Fig. 14 shows the corresponding biplot.
Table 9

Distribution of incidences of suicides according to the profession during 2013

Sr No

Profession of suicide victims

A 1 (Male)

A 2 (Female)

Total suicide

Percent of suicide

C 1

C 2

C 3

C 4

C 5

C 1

C 2

C 3

C 4

C 5

1

B 1

259

11135

16779

11693

4467

271

2628

2191

1214

597

51234

46.58

2

B 2

0

0

0

0

0

26

9697

7809

3546

1664

22742

20.68

3

B 3

2

4239

5857

3624

635

8

1089

813

357

82

16706

15.19

4

B 4

15

2776

3102

1846

532

17

698

467

234

81

9768

8.88

5

B 5

709

3660

231

23

11

614

3064

90

16

5

8423

7.66

6

B 6

0

0

44

175

697

0

24

19

41

117

1117

1.02

Grand Total

109990

100

Table 10

Results of MCA based on separate SVDS for Table 9 along B and A across the categories of C

Categories of C

SV

Inertia

Percent inertia

C 1

0.2656

0.0705

9.94

0.0425

0.0018

0.26

C 2

0.4658

0.2170

30.58

0.1909

0.0364

5.13

C 3

0.4125

0.1702

23.98

0.1440

0.0207

2.92

C 4

0.2991

0.0894

12.61

0.1096

0.0120

1.69

C 5

0.2621

0.0687

9.68

0.1508

0.0227

3.21

Total

0.7095

100

Fig. 14

Biplot showing association between B (Profession of Suicides victims) and A (Gender) across C (Age Category)

Name of the CV

Categories of CV

Gender (A)

Male (A 1), Female (A 2)

Profession of Suicide Victim (B)

Self-employment (B 1), House wife (B 2), Service (B 3),

Unemployed (B 4), Student (B 5), Retired Person (B 6)

Age Category (C)

Up to 14 years (C 1), 15–29 years (C 2), 30–44 years (C 3), 45–59 years (C 4), 60 years and above (C 5),

From the pair of biplot points (A 1 C 2, B 4 C 2) it is seen that Unemployed (B 4) Males (A 1) between age group 15–29 (C 2) commit suicide. The other conclusions about association profession of suicide victims and gender across age category from biplot in Fig. 14 are summarized in the following table.

Age group

Gender

Biplot points

Interpretation of pair of Biplot Points (Association between profession of suicide victims and gender across age)

15–29 year (C 2)

Male (A 1)

(A 1 C 2, B 4 C 2)

Unemployed (B 4) Males (A 1) between age group 15–29 (C 2) commit suicide.

Female (A 2)

(A 2 C 2, B 2 C 2)

House wife (B 2) between age group 15–29 (C 2) commit suicide.

Up to 14 years (C 1)

Male (A 1)

(A 1 C 1, B 5 C 1)

Male (A 1) Students (B 5) up to age 14 (C 1) commit suicide.

Female (A 2)

(A 2 C 1, B 5 C 1)

Female (A 2) Students (B 5) up to age 14 (C 1) commit suicide.

30–44 year (C 3)

Male (A 1)

(A 1 C 3, B 3 C 3)

Males (A 1) with profession service (B 3) between age group 30–44 (C 3) commit suicide.

45–59 year (C 4)

Female (A 2)

(A 2 C 4, B 2 C 4)

House wife (B 2) between age group 45–59 (C 4) commit suicide.

Above 60 years (C 5)

Female (A 2)

(A 2 C 5, B 2 C 5)

House wife (B 2) above age 60 years (C 5) commit suicide.

Results

The summary of results about risk factors, means of suicides and profession of suicide victims studied through the subset MCA are summarized in this section.

Results related to the risk factors for suicides

  • Males and Females up to 14 years’ age group commit suicide due to ‘Failure in examination’.

  • Males between age group 15–18 commit suicide due to ‘Failure in examination’ whereas Females within the same age group commit suicide due to ‘Love affairs’.

  • Males between age group 18–30 commit suicide due to ‘Unemployment’, ‘Love Affairs’ and ‘Failure in examination’, while Females between the same age group are observed to commit suicide due to ‘Dowry Dispute’ and ‘Cancellation/Non-settlement of Marriage’.

  • Males and Females between age group 30–45 commit suicide due to ‘Family problems’ while Males between age group 45–60 commit suicide due to ‘Bankruptcy/sudden change in Economic status’ and ‘Poverty’.

Results related to means of suicides

  • Females between age group up to 14 years’ and 15–29 commit suicide by ‘Fire/self-immolation’, ‘Hanging’ and ‘Poisoning’.

  • Males between age group 30–44 commit suicide by ‘Hanging’, ‘Poisoning’ and coming under running vehicles/trains while Females commit suicide by ‘Fire/self-immolation’.

  • Males between age group 45–59 and above 60 years commit suicide by ‘Coming under running vehicles/trains’ and ‘Poisoning’ while Females above 60 years’ commit suicide by ‘Drowning’.

Results related to the profession of suicide victims

  • Male and Female with profession ‘Students’ up to age 14 and ‘Unemployed Males’ between age group 15–29 commit suicide.

  • Males with profession ‘Service’ between age group 30–44 and ‘House wife’ between age group 15–29, 45–59 and above 60 years’ age commit suicide.

Discussion

Suicides have a significant impact on economic growth and development. The NCRB, Ministry of Home Affairs, Government of India is compiling and collating the data on accidents and suicides in India since 1967. NCRB is effectively using a well-developed network of State Crime Records Bureau (SCRB) and CID of states and union territories to get correct and validated data. NCRB is continuously upgrading the suicidal/accidental death reports as per the trends and significance of the current events.

In India, the cultural, religious, geographical, socioeconomic, sociodemographic diversities have significant impact on number of suicides. Due to these reasons, suicide rate varied from 0.6 (Nagaland) to 40.4 (Puducherry) between different states and union territories of India in 2014 while the all India rate of suicide is 10.6. Most of the states of the country have reported significant percentage decrease in suicides in 2014 over 2013. The number of suicides are found to be affected by social, economic, educational, professional status of the suicide victims.

In this paper, we study the association of number of suicidal deaths in India in 2014, with some attributes. From the suicide data analysis, it is observed that the young people between age group 18–45 years commit suicide more frequently. Since, the clash of values within families is an important factor for young people in their lives and as young Indians become more progressive, their traditionalist households become less supportive of their choices pertaining to financial independence, marriage age, premarital sex, rehabilitation and taking care of the elderly. However, the proportion of suicides is relatively less in persons aged above 60 years, because taking care of the elderly has been an important part of Indian tradition. Their needs are widely recognized and addressed and they enjoy a measure of respect by virtue of their age. The proportion of male suicides is high as compared to the female suicides. In India, near about 96% of suicides are observed from low (less than 100,000) and middle-income group (100,000–500,000).

Though not all suicides in world can be prevented, some strategies can help to reduce the suicidal risks. Key elements in developing a national suicide prevention strategy are to make prevention a multisector priority that involves not only the health sector but also education, employment, social welfare, the judiciary and others. The strategy should be tailored to each country’s cultural and social context, establishing best practices and evidence-based interventions in a comprehensive approach. Resources should be allocated for achieving both short-to-medium and long-term objectives, there should be effective planning and the strategy should be regularly evaluated, with evaluation findings feeding into future planning.

Conclusions

Overall the suicidal death rate in India is observed to be continuously increasing. From 1967 to 1970 suicidal death rate increased from 7.8 to 9.1. A declining trend is observed, up to 1981. Since 1982, the suicidal death rate has continuously increased from 5.9 to 11.2 in 1999. Since 1999, the overall suicidal death rate has declining trend. The percent of male suicides is observed to be more as compared to percent of female suicides since 1967.

The leading risk factors for suicides are family problems, illness, drug addiction, failure in examination, etc. Overall, we observe that the proportion of female victims are comparatively higher under the heads dowry dispute and cancellation/non-settlement of marriage whereas the proportion of male victims are comparatively higher under the heads family problems, bankruptcy/sudden change in economic status, unemployment, poverty, etc. Family problems and illness are major issues for committing the suicides in India.

As per NCRB reports, the means of suicides varied from easily available means such as poisoning, drowning into a well to more painful means such as hanging, fire/self-immolation, coming under running vehicles/trains, etc. Overall it is observed the means adopted by males to commit suicide are hanging, poisoning and coming under running vehicles/trains whereas females commit suicide by fire/self-immolation, drowning and hanging.

Declarations

Acknowledgement

Authors would like to thank the referee for useful suggestions which improved the presentation of work. Second author would also like to thank UGC, New Delhi for providing support for this work through the Rajiv Gandhi National Fellowship (Award Letter No.: F1-17.1/2011-12/RGNF-SC-MAH-5331).

Funding

UGC, New Delhi (Through Rajiv Gandhi National Fellowship Award, Letter No.: F1-17.1/2011-12/RGNF-SC-MAH-5331).

Authors’ contributions

We briefly overview the recent trends in the number of suicides in India and study the risk factors and means of suicides. We explore the association of number of suicidal deaths with some attributes such as gender (sex), age category of suicide victims, profession, etc. We use odds ratio and subset multiple correspondence analysis. Based on conclusions, some strategies may be planned to prevent suicides. Both authors read and approved the final manuscript.

Ethics approval and consent to participate

Ethical guidelines were respected.

Competing interest

The authors declare that they have no conflict of interest.

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Authors’ Affiliations

(1)
Department of Statistics, School of Mathematical Sciences, North Maharashtra University

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