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School and Suicide

There’s a well-known seasonal effect to suicide in that people are more likely to end their lives during the spring and summer months and less likely to do so during the winter. However, when looking at youth suicides (ages 18 and under) the pattern reverses during the summer vacation months, the opposite of the adult pattern. Which could be interpreted as evidence that school is really, really bad for one’s mental health.

The trend of increasing suicides is evident from the trend-line in the following charts:

Suicide trends in 18 and under

Suicide trends in adults

But what we are really interested in is the seasonal pattern.

When plotting the monthly means of the number of suicides from 1999 to 2017 the different patterns are readily apparent: Youths are less likely to kill themselves during the summer months compared to adults whose suicide rates peak just when youths are the least likely to kill themselves.

Mean number of suicides in children, by month

Mean number of suicides in adults, by month

We can also examine the association between summer vacation and suicides by fitting a poisson regression with the month of year included as a categorical variable. The model includes an offset to account for the uneven number of days in each month.

Coefficients

Coefficients

For completeness sake I’m including the suicide deaths means of college age adults

Suicide in college age adults

I also fitted a regression model to the 2014-2018 suicide data to see how the marginal effects of the interaction age * month affect suicide rates. It’s pretty much what you expect, June and July have the lowest suicide rates when people are younger, but once people leave school their rates start rising.

I added a spline to the model cause the effect of age isn’t linear as should be obvious from the plot.

1fit2 <- glm(rate ~ splines::bs(age) * month_fac, data = df)

Suicide in college age adults

 rate
PredictorsEstimatesCIp
(Intercept)0.460.32 – 0.61<0.001
age [1st degree]1.981.56 – 2.40<0.001
age [2nd degree]0.640.37 – 0.91<0.001
age [3rd degree]1.120.90 – 1.35<0.001
month_fac [August]-0.13-0.33 – 0.080.219
month_fac [December]-0.12-0.32 – 0.090.258
month_fac [February]-0.06-0.26 – 0.150.579
month_fac [January]-0.06-0.26 – 0.150.568
month_fac [July]-0.22-0.43 – -0.020.034
month_fac [June]-0.23-0.43 – -0.020.028
month_fac [March]-0.07-0.27 – 0.140.505
month_fac [May]-0.06-0.26 – 0.150.574
month_fac [November]0.02-0.19 – 0.220.872
month_fac [October]-0.00-0.21 – 0.200.965
month_fac [September]-0.13-0.33 – 0.080.215
age [1st degree] *
month_fac [August]
0.790.20 – 1.390.009
age [2nd degree] *
month_fac [August]
-0.10-0.49 – 0.290.610
age [3rd degree] *
month_fac [August]
0.26-0.06 – 0.580.107
age [1st degree] *
month_fac [December]
0.27-0.32 – 0.870.367
age [2nd degree] *
month_fac [December]
-0.27-0.65 – 0.120.176
age [3rd degree] *
month_fac [December]
0.03-0.29 – 0.350.866
age [1st degree] *
month_fac [February]
-0.08-0.68 – 0.510.780
age [2nd degree] *
month_fac [February]
-0.19-0.57 – 0.200.345
age [3rd degree] *
month_fac [February]
-0.21-0.53 – 0.110.203
age [1st degree] *
month_fac [January]
0.19-0.40 – 0.790.528
age [2nd degree] *
month_fac [January]
-0.18-0.57 – 0.200.353
age [3rd degree] *
month_fac [January]
0.00-0.32 – 0.320.977
age [1st degree] *
month_fac [July]
0.910.31 – 1.500.003
age [2nd degree] *
month_fac [July]
-0.14-0.53 – 0.250.479
age [3rd degree] *
month_fac [July]
0.440.12 – 0.760.007
age [1st degree] *
month_fac [June]
0.750.16 – 1.350.013
age [2nd degree] *
month_fac [June]
0.01-0.38 – 0.390.974
age [3rd degree] *
month_fac [June]
0.29-0.03 – 0.610.071
age [1st degree] *
month_fac [March]
0.28-0.31 – 0.880.348
age [2nd degree] *
month_fac [March]
-0.06-0.45 – 0.320.747
age [3rd degree] *
month_fac [March]
0.07-0.25 – 0.390.670
age [1st degree] *
month_fac [May]
0.35-0.24 – 0.950.243
age [2nd degree] *
month_fac [May]
0.03-0.35 – 0.420.862
age [3rd degree] *
month_fac [May]
0.09-0.23 – 0.410.590
age [1st degree] *
month_fac [November]
-0.05-0.65 – 0.540.865
age [2nd degree] *
month_fac [November]
-0.28-0.67 – 0.110.155
age [3rd degree] *
month_fac [November]
-0.18-0.50 – 0.140.272
age [1st degree] *
month_fac [October]
0.35-0.24 – 0.950.242
age [2nd degree] *
month_fac [October]
-0.35-0.73 – 0.040.079
age [3rd degree] *
month_fac [October]
0.16-0.16 – 0.480.321
age [1st degree] *
month_fac [September]
0.55-0.04 – 1.150.068
age [2nd degree] *
month_fac [September]
-0.09-0.47 – 0.300.664
age [3rd degree] *
month_fac [September]
0.17-0.15 – 0.490.288
Observations876
R2 Nagelkerke0.785
agemonthsuicides 2014-2018population 2014-2018ratemonth_factor
12128206238070.1357654January
12237206238070.1794043February
12326206238070.1260679March
12428206238070.1357654April
12539206238070.1891018May
12625206238070.1212191June
12722206238070.1066728July
12826206238070.1260679August
12925206238070.1212191September
121050206238070.2424383October
121136206238070.1745556November
121228206238070.1357654December
13155206755570.2660146January
13264206755570.3095443February

August 8th, 2019


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