One profound claim and observations by the media is, that the rate of suicides for younger people in the UK have risen from the 1980s to the 2000s. You might find it generally on the news , in publications or it is just an accepted truth by the population. But how can you make this measurable?

Making an assumption tangible

In order to make this claim testable we look for data and find an overview of the suicide rates, specifically England and Wales, at the [Office for National Statistics (UK)]

Generally, one type of essential questions to ask — in everyday life, business or academia — is when changes have occurred. You are under the assumption that something has fundamentally changed over time. In order to prove that you have to quantify it. So you get some data on the subject-matter and build a model to display points at which changes in values have occurred as well as their magnitude. We are going to look at exactly how to do that.

Millennial Suicides Specifically

With our data at hand, we take the average for the age-group of Millennials. That entails ages in the range of 25 to 35, for the sake of simplicity. Given our dataset, we can visualize the average suicide count for Millennials since the 1980s like this:

suicide_millenials_df = suicide_data[suicide_data.Age.isin(range(23,35))].groupby(["Year"]).mean().reset_index()
# round values to closest integer
suicide_millenials_df["Suicides"] = suicide_millenials_df.Suicides.round()
suicide_millenials_df.head()

What the head of your dataframe should look like.

Fig. 1 — The average suicides (per 100.000 people) in the age group 25 to 35 for England and Wales from 1981 to 2017.

From this data we can clearly see a steady increase over time until the very early 2000s followed by a decline until the 2010s. The inquisitive side of us wants to know when exactly that happened so that we can go out and look for the root causes and the underlying issues associated with those impacting trends. Let’s build a model to find out exactly in which years to look for that substantial change. To look for a specific change-point we will use the Pyro PPL as a probabilistic framework.

#millenials #pyro #tutorial #timeseries #data-science #data analysis