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Time Trend of the Suicide Incidence in India: a Statistical Modelling

Brijesh Sathian*, Asis De, Edwin van Teijlingen, Padam Simkhada, Indrajit Banerjee, Bedanta Roy, HS Supram, Suresh Devkota, Rajesh E

Department of Community Medicine, Manipal College of Medical Sciences, Pokhara, Nepal

Abstract: Background: It is estimated that over 100,000 people die by suicide in India every year. India alone contributes to more than 10% of suicides in the world. The suicide rate in India has been increasing steadily and has reached 11.2 (per 100,000 of population) in 2011 registering 78% increase over the value of 1980 (6.3). Objective: Objective of the study was to forecasts the suicide incidence of India up to 2020. Material and Methods: Theoretical statistics was used for the statistical modelling of the retrospective data of suicide incidence data of 1989-2011 years collected from National Crime Records Bureau (NCRB). Results: Using curve fitting method, Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power and Exponential growth models were validated. Cubic Model was the best fitted model with R²>0.90, p<0.01. Suicide incidence of India has an increasing trend. In 2020, it is estimated that the suicide incidence of India will be 109814 with CI [ 86593, 133034] for male, 76224 with CI [55151, 97297] for female and 186038 with CI [145605, 226471] for total [both male and female]. Conclusion: Suicide incidence of India has an increasing trend. India requires the involvement of all governments and other organizations to contribute to the cause of suicide awareness and prevention through activities, events, conferences and campaigns to solve this public health problem.

Keywords: suicide, incidence, forecasting, statistical modelling, India

Citation: Brijesh Sathian, Asis De, Edwin van Teijlingen, Padam Simkhada, Indrajit Banerjee, Bedanta Roy, HS Supram, Suresh Devkota, Rajesh E, Time Trend of the Suicide Incidence in India: a Statistical Modelling. American Journal of Public Health Research, 3 (5A). 80-87. January 2015. doi:10.12691/ajphr-3-5A-17

1. Introduction

WHO predicts that almost 900000 people worldwide depart this life from suicide each year, counting about 200000 in China, 170000 in India, and 140000 in high-income countries [1]. The Government of India trust on its National Crime Records Bureau (NCRB) for national predicted values, and these reports show fewer suicide deaths (as regards 135000 suicide deaths in 2010) [2] than is predicted by WHO. The NCRB data is a foundation for police reports and suicide is still a crime in India, which might have an effect on the reliability of reporting.

Nowadays farmer’s suicide is a threat in India [3]. Apart from these there is no clear picture of socio demographic factors wise information viz. the age-specific and sex-specific death totals, rates, and risks, and in addition the mode of suicide. Predicted suicide incidence is required for the Government of India to execute its 12th Year Plan for 2012–17 which includes strategies to embark upon mental health and chronic disease [4]. Sathian et al. have done several studies to project communicable diseases and non communicable diseases using statistical modelling procedures, which furnish trustworthy estimates [5-11]. The aim of our study was to forecasts the suicide incidence of India up to 2020 from the NCRB available data.

2. Material and Methods

2.1. Setting

A retrospective study was carried out on the Suicide data collected from the National Crime Records Bureau (NCRB) India, between 1989 and 2011.

2.2. Dependent Variables

Suicide Incidence of Male, Female and Total

2.3. Independent Variable

Year.

2.4. Statistical Analysis

The collected and coded data were analysed using Statistical Package for the Social Sciences (SPSS) for Windows Version 16.0 (SPSS Inc; Chicago, IL, USA). A p-value of < 0.05 (two-tailed) was used to establish statistical significance. The annual Suicide incidence plotted in y-axis in opposition to the consequent year in the x-axis. Curve fitting, also branded as regression analysis, was applied to obtain the "best fit" line or curve for a series of data points. Linear, Quadratic, Inverse, Logarithmic, and Cubic were selected to fit to the derived curve. F-test was applied for obtaining the best suitable curve for the testing of hypothesis. P-value was selected as statistically significant when p< 0.05 (two tailed). R² - value > 0.80 was selected as statistically significant for prediction [10]. The verdict concerning the choice of a appropriate forecasting approach is governed by the comparative performance of the models for monitoring. It should also sufficiently sufficiently cover the observable fact under study. The Cubic model obtained here could strongly fit curves for predicted and reported suicide incidence (Figure 1, Figure 3, Figure 5). While structuring the model, the extremities (maximums and minimums) take part in a immense role. If the points are spread more, the curve tries to fiddle with the highest number of observed points. The cubic model in the equation below is a third degree polynomial, where m0 is the constant term and m₁, m₂, m3 are coefficient terms 11,12. Where Y is the number of number of suicide incidence annually and X is the consequent year; 1=1989, 2=1990, 3=1991 and so on.

2.5. Models

1 Linear Model

2 Logarithmic Model

3 Inverse Model

4 Quadratic Model

5 Cubic Model

6 Compoind Model

7 Power Model

8 S-curve Model

9 Growth Model

10 Exponential Model

3. Results

Table 1, Table 3, Table 5, Figure 1, Figure 3, and Figure 5 depict that using curve fitting method, Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power and Exponential growth models were validated. Cubic Model was the best fitted model with R²>0.90, p<0.01 for Male, Female and Total Suicide incidence. Table 2, Table 4, Table 6, Figure 2, Figure 4, and Figure 6 reveal that the Male, Female and Total Suicide incidence is increasing by time.

Figure 1. Fitted curves and observed male suicide incidence in India

Table 1. Model summary and parameter estimates for fitted equations for male suicide incidence

Table 2. Male suicide incidence up to 2020

Table 3. Model summary and parameter estimates for fitted equations for female suicide incidence

Figure 2. Time trend of estimated and observed male suicide incidence in India

Table 4. Female suicide incidence up to 2020

Figure 3. Fitted curves and observed suicide incidence of females in India

Figure 4. Time trend of estimated and observed female suicide incidence in India

Table 5. Model summary and parameter estimates for fitted equations for total suicide incidence

Figure 5. Fitted curves ad observed suicide incidence of total cases in India

Table 6. Total suicide incidence up to 2020

Figure 6. Time trend of estimated and observed total suicide incidence in India

4. Discussions

In the Curve fitting method accumulated data depicted in a graph to assess the relationship between the dependent variable and time by joining the `points' with a line. After that the researcher should search out the best fitted model to the observed data. Once the best fitted model is obtained then it will be utilized to predict the trend of the dependent variable for a time variable [13,14]. Sathian et al. have successfully applied this methods to predict the non-communicable and communicable disease trends in Nepal [5-11]. Our study hereby commences the appropriateness of statistical modelling in estimating the annual incidence of suicide in the Indian context.

It is scientifically proven that the suicide prevalence have an association with age and sex [12,13,14]. Some studies reveals that it is more among adults aged 18-29 years than others [13-16]. It was supported by few other surveys like the National Comorbidity Survey [NCS], the National Comorbidity Survey Replication [NCS-R], and the Injury Control and Risk Survey [ICARIS] [12,13,14,15]. In contrary to the Indian scenario, previous studies without fail have established female preponderance in rates of having suicidal thoughts [12,13,14,15,16]. In these analyses, the incidence of suicide was more among the males compared to female in India.

It should be mandatory to provide psychiatric interventional programmes towards suicidal ideation of all psychiatric disorder patients because they carry an increased risk of suicide. Even pregnant women are at risk of psychiatric illness [17]. However, 90% of suicides can be traced to depression, linked either to bipolar disorder, major depressive disorder, schizophrenia or personality disorders, and particularly borderline personality disorder. Comorbidity of mental disorders increases suicide risk, especially anxiety or panic attacks.

Most common methods of committing suicide are bleeding: wrist cutting, hanging, suffocation, hypothermia, drowning, electrocution, firearms, vehicular impact – rail, traffic collisions, Jumping from a height, Poisoning – pesticide, drug overdose, and Immolation.

5. Conclusion

Suicide incidence of India has an increasing trend. India requires the involvement of all governments and other organizations to contribute to the cause of suicide awareness and prevention through activities, events, conferences and campaigns to solve this public health problem.

Authors’ Contributions

BS: Conceptualized the research, planned and conducted the data analysis, interpreted the results, wrote the first draft of the manuscript for publication and is the guarantor; AD, EvanT and PS: Conceptualized the research, co-drafted the first draft of the manuscript for publication; IB, BR, SHS, SD and RE: Helped conceptualizing the research, planned data analysis and revised earlier drafts of the manuscript. All the authors read and approved the final version of the manuscript to be submitted for publication in a scientific journal.

References

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