R Notebook

The Effect of Gender and Age on Suicides Across Diiferent Countries

I will use the Health Data of 101 countries with different demographic characteristics (variables as age groups, sex, population, and generation type (Boomers, Milleniums, Generation Z, etc.) to explore the effect of gender and age on the suicide rates in these countries. Data provides the suicide numbers across those characterisitcs. This data has information of two conceptual levels, individual and country-based. Individuals with their demographic characteristics are nested within the countries. My hypothesis is that there is a correlation between sex and suicide attempts. Men commit suicide at highter rate than women.

I will work with the following packages: library(nlme) library(dplyr) library(magrittr) library(tidyr) library(haven) library(lmerTest) library(ggplot2) library(texreg)

Now, importing data for analysis:

library (readr)
master<-read_csv("C:/Users/Marcy/Documents/soc 712/master.csv")
head (master)

First, I will ignore the countly level data and analyzis the data on individual level by performing complete pooling model.

cpooling <- lm(suicides_no ~ sex, data = master)
summary(cpooling)

Based on complete pooling model shown above, sex is a sole considered factor while running this linear model. The coefficient of sex is statistically very significant. Evidently, males commit suicides at more than double rate than females, 261 to 112.

Now, I will run a no-pooling model to conduct an effect of sex on suicide rates within countries to see if there any variance exists.

dcoef <- master %>% 
    group_by(`country`) %>% 
    do(mod = lm(suicides_no ~ sex, data =.))
coef <- dcoef %>% do(data.frame(intc = coef(.$mod)[1]))
ggplot(coef, aes(x = intc)) + geom_density()+xlab("country")

dcoef=master %>% 
    group_by(`country`) %>% 
    do(mod = lm(suicides_no ~ sex, data = .))
coef <- dcoef %>% do(data.frame(difference = coef(.$mod)[2]))

ggplot(coef, aes(x = difference)) + geom_histogram()+xlab("Difference in Female and Male Suicide Rates by Country")

As shown above, there is no variation among the vast majority of the countries in difference of the patterns of male and female suicide rates. However, in a few of them, the difference in variation by sex is very significant and can vary from 20% to 60%.

Now, I will use a random effect model to allow for group variation within our regression model. Also, I add the interaction of sex and age as the effect of combination of age and sex, perspectively, could have a significant added effect on suicide rates.

randomeffect=lme(suicides_no ~ sex*age, data = master, random = ~1|country, method = "ML")
summary(randomeffect)

Thus, adding effect of age groups provides relevant results. Sex, age, and interaction of sex and age have sifnificant effect of suicide rates of the population. Males of 35-54 years of age are the most volnurable group (as much as three times higher) for attempting suicides. Whereas, reaching 75+ years, their risk of commiting a suicide (-107) drops significantly below many age groups and even below women of 75+ age whose number is 20 in this group. Overall, the lowest rates of suicide is among the age group of 5-14 years old. Interestingly, young age of males between 5-14 committ suicides at the rate of -189 compared to this particupar age group of females whose number is -69. Thus, I can conclude that overall, children are less promt to commit a suicide than older generation, but female children are more than twice more promt to attempt suicides than male children.

Now, I will use a random slope affect model. Unlike a random intercept model, a random slope model allows each group line to have a different slope and that means that the random slope model allows the explanatory variable to have a different effect for each group.

Slope=lme(suicides_no ~ sex*age, data = master, random = ~sex|country, method = "ML")
summary(Slope)

Data analysis reveals that there is a significant gender and age correlation between suicide rates of population in all reported countries. Except the few, the country origin does not have much variation in gender difference of suicide attempts. Across the board, men are more than twice likely to commit a suicide. Adding age as an interaction term with person’s gender plays a very important role. Men of age 35-54 are in the higherst risk group among those who committ suicide. Being a male, your chances of committing a suicide are 150.8 against being a women with 56.7. If you also consider the age, the chances of males between 34-54 years who commit suicides are tripled.