Forecasting suicide rates in India: An empirical exposition

Abstract

Introduction Suicide is a major social and health issue in India. Yearly statistics show a concerning increasing pattern of suicidal deaths in India which is higher in comparison to the global trend. There is limited evidence regarding historical analysis of suicide or any forecasting for suicide in India towards predicting the possible risks of death due to suicide. Methods This paper examines the trend of suicide rate and characteristics of suicide victims in India, based on the longitudinal time series data over the last 50 years—collected from the National Crime Record Bureau Reports (1969 to 2018) of the Government of India. In our analysis, we have used the time series model to forecast the suicide rates in India for the next decade. ARIMA (4,1,0) model is found to be the best fit model for forecasting the data. Findings There has been an observable and rising trend of suicide rates in India over the last five decades. The forecast indicates a continuance of rising suicide cases for an upcoming couple of years in India with a limited decline in the following years. The prediction model indicates a future relatively consistent pattern of suicide in India which does not seem to be a very encouraging trend. As we have not included the period staring the year 2020 onwards affected by Covid-19 and which has several disruptions in personal and family spaces, the projected suicide trend during the period of next two to three years (2020–22) may rise far high and then it may show a declining path. Along with this, there is a shift in means of suicide in the last couple of decades. Constituting the second-highest number of cases, Illness associated suicide was visibly a serious concern. Conclusion The present analysis finds that there is no visible substantial relief for suicide deaths during the coming years in India. On the other hand, more extensive exploration of sample cases may provide important information for suicide prevention. Availability of detailed and more inclusive data will be highly useful for analysis and suicide preventive policies. Investment in public health care and other welfare activities like education and employment generation will yield visible positive results in suicide control.

Forecasting Road Accidental Deaths in India: An Explicit Comparison between ARIMA and Exponential Smoothing Method

“…ARIMA model is generally referred to as an ARIMA (p, d, q) where p signifies the order of autoregression, d denotes the degree of difference, and q is the order of moving average. In other words, the p and q are the number of significant lags of the autocorrelation function (ACF) and the partial autocorrelation function (PACF) plots, respectively, and d is the different order needed to remove the ordinary non-stationarity in the mean of the error terms (Swain et al, 2021).…”

The patterns, trends and major risk factors of suicide among Indian adolescents—A scoping review protocol

“…Suicide is one of the major mental health concerns that is found in every society across the world [ 1 ]. Suicide is the intentional or deliberate act of killing own self [ 2 ]. Globally, suicide is the 15 th leading cause of death for all ages [ 3 , 4 ].…”

Analysing the Impacts of Financial Expenditure of Prefectures on Methods of Suicide Completion in Japan

“…Similar to Japan, several studies also reported that increasing governmental financial support possibly played important roles in the reduction or prevention of increasing suicide mortalities in Asian and European countries and the USA. Indeed, the increasing governmental expenditures to public health, welfare, education and employment was related to the reduction of suicide mortality in India [27]. The enhancement of governmental support to the social welfare facilities for the elderly people was effective for reduction of suicide mortality in the regions with higher proportion of elderly residents at risk of suicide, in South Korea [28].…”