Statistical Analysis of Suicidal Death in India During 2021

: A social and public health response in addition to mental health response is crucial to prevent suicide behavior in India. Yearly statistics show a concerning increasing pattern of suicidal deaths in India. The current report reviews the data from the series accidental death and suicide in India published by India’s National Crime Records Bureau (NCRB) reporting official suicide rates based on police reports.


Research Methodology:
 Data collection: The data used for project is secondary data.The current report reviews the data from the series accidental death and suicide in India published by India's National Crime Records Bureau (NCRB) reporting official suicide rates based on police reports.We collected the data from National Crime Record Bureau (NCRB), India, the nodal agency for collecting the data on suicide incidences across India.

Rate of suicides during 2011 to 2021 :
Finding from Analysis: According to analysis the year 2021 has the highest suicides rate of 12% in India since 2011.

Gender wise distribution of suicides:
Finding: Out of total 73% Male committed suicides in India during2021.

Causes wise distribution of suicides:
Finding from Analysis: Family problems are the highest reason for suicides in India.Out of total 33.2% people committed suicides in 2021.Hypothesis: H0 : In the population, the two categorical variables are independent.

Gender wise distribution of suicides
Vs. H1 : In the population, the two categorical variables are dependent.
Where Oi is the observed value and Ei is the expected value.
Interpretation: For a Chi-square test, a p-value that is less than or equal to the specified significance level indicates sufficient evidence to conclude that the observed value is not the same as the expected value.
Here, we can conclude that a relationship exists between the given categorical variables.
Chi-square Test: Hypothesis H0: Attributes A and B are independent Vs H1: Attributes A and B are dependent

Sr.
No.    The people whose economic condition is bad committed suicide than other people. Economic status, marital status and professional status and education status depends on gender wise distribution of suicides. By forecasting rate of suicide using ARIMA model, the rate of suicide for year 2022 is  12.1 with 95% confidence interval is (11.4,12.7).

Prevention:
The current high rates of suicide in India highlight an urgent need for a coordinated national suicide prevention plan that will raise awareness and help make suicide prevention a national priority.A comprehensive approach across country at community, regional and national levels including all the stakeholders such as departments of health and education, social welfare, police and the judiciary is required.One of the observations was a high demand for prevention efforts, but lack of resources and scarcity of available program in populous countries like India.These factors play a more salient role as risk and protective factors for suicide than they do in Europe or the USA.

Suicide Prevention:
 Reducing social isolation. Preventing social is integration. Treating mental disorders. Regulating the sale of pesticides and ropes. Promoting psychological motivational sessions and meditation and yoga.
Crime Record Bureau (NCRB) functions under the Ministry of Home Affairs, Government of India and it publishes time series data on accidental deaths and suicides on annual basis from the year 1969 onwards for this study, we have collected data on suicide for a period of 11 years (2011-2021) and for year 2021 from NCRB annual reports on Accidental Deaths and Suicides in India (ADSI), freely available at https://ncrb.gov.in/en/adsi-reports-of-previous-years Statistical Analysis: Statistical Analysis of the data was executed by using graphical methods like pie chart, column chart, bar diagram, Histogram in Microsoft Excel.

4 .
Distribution of suicides by means /modes Finding from Analysis: Hanging (93580), consuming Poison (41197), Drowning (8370) are the prominent mean/mode of committing suicides.Chi square test of independence: Analysis of chi square test was executed in the Microsoft Excel .The chi-square test of independence also known as the chi-square test of association which is used to determine the association between the categorical variables.It is considered as a non-parametric test.It is mostly used to test statistical independence.

Forecasting
737 0.048 -0.596 -0.081 -0.149 C5 0.331 -0.640 -0.067 0.642 -0.231 0.104 C6 0.493 -0.088 0.309 -0.016 0.55 1-0.591ScreePlot Interpretation: This scree plot shows that the eigenvalues start to form a straight line after the second principal component.Therefore, the remaining principal components account for a very small proportion of the variability (close to zero) and are probably unimportant.Family problems and illness Accounts 82.1% variation in the data.Family problems and illness are main causes for suicidal rate in India.For first two principal components the equations are Y1=0.475X1+0.304X2+0.507X3-0.267X4+0.331X5+0.493X6Y2= 0.178 X1+ 0.048X2-0.081X3-0.737X4-0.640X5-0.088X6 Dickey-Fuller = 0.27675, Lag order = 2, p-value = 0.99 Alternative hypothesis: stationary In adf.test(datatime) : p-value greater than printed p-value ARIMA(2,0,2) with non-zero mean : Inf ARIMA(0,0,0) with non-zero mean : 23.25845 ARIMA(1,0,0) with non-zero mean : 18.46923 ARIMA(0,0,1) with non-zero mean : Inf ARIMA(0,0,0) with zero mean : 85.57994 ARIMA(2,0,0) with non-zero mean : 14.4225 ARIMA(3,0,0) with non-zero mean : Inf ARIMA(2,0,1) with non-zero mean : Inf ARIMA(1,0,1) with non-zero mean : Inf ARIMA(3,0,1) with non-zero mean : Inf ARIMA(2,0,0) with zero mean : Inf Best model: ARIMA(2,0,0) with non-zero mean Series: datatime ARIMA (2,0,0) with non-zero mean Coefficientssigma^2 estimated as 0.1162: log likelihood= -3.21 Statistical analysis of principal Component Analysis was executed by using R software version 4.0.1 and Minitab.Principal component analysis, or PCA, is a dimensionality reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.In multivariate statistics, a scree plot is a line plot of the eigen values of factors or principal components in an analysis.The scree plot is used to determine the number of factors to retain in exploratory principal components to keep in a principal component analysis (PCA).A scree plot always displays the eigen values in a downward curve, ordering the eigen values from largest to smallest.According to the scree test, the "elbow" of the graph where the eigen values seem to level off is found and factors or components to the left of this point should be retained as significant.
The rate of suicide decreases for next some years by using Forecasting of ARIMA model.According to NCRB data the rates of suicide for year 2022 is 12.4 and by forecasting using ARIMA model the rate of suicide for year 2022 is 12.1 with confidence interval (11.4,12.7) which is approximately near about NCRB rate.The NCRB rate of suicide 12.4 is in between confidence interval of forecasting hence the ARIMA (2,0,0) model is good fit for our data.The rate of suicides is increases year by year. The rate of suicide 12% in 2021 is recorded as highest suicide rate in India. Male count for commuting suicides is more than female or transgender. A family problem is the main reason for commuting suicide. The age group (18below 30 years) and (30below 45 years) are prominent age group for commuting suicides in India. Hanging is the most prominent means/modes for suicide.