Evolving notes, images and sounds by Luis Apiolaza

Suicide statistics and the Christchurch earthquake

Suicide is a tragic and complex problem. This week New Zealand’s Chief Coroner released its annual statistics on suicide, which come with several tables and figures. One of those figures refers to monthly suicides in the Christchurch region (where I live) and comes with an interesting comment:

Suicides in the Christchurch region (Timaru to Kaikoura) have risen from 67 (2010/11) to 81 (2011/12). The average number of suicides per year for this region over the past four years is 74. The figure of 67 deaths last year reflected the drop in suicides post-earthquake. The phenomenon of a drop in the suicide rate after a large scale crisis event, such as a natural disaster, has been observed elsewhere. [my emphasis]

‘Provisional Suicide deaths in relation to the Christchurch earthquakes’ this is the original header for the graph in the report. The first earthquake was in September 2010 and is marked with red dots.

The figure highlights the earthquake and its major aftershocks using different colors. It is true that we have faced large problems following the 4th September 2010 earthquake and thousands of aftershocks, but can we really make the coroner’s interpretation (already picked up by the media)? In fact, one could have a look at the data before the earthquake, where there are big drops and rises (What would be the explanation for that? Nothing to do with any earthquake). In fact, the average number of suicides hasn’t really changed after the quake.

I typed the data in to this file, calculated the mean number of suicides per month (~6.3) and generated a few random realizations of a Poisson process using that mean; here I’m plotting the real data in panel 1 and 4 other randomly generated series in panels 2 to 5.

su <- read.csv('suicide-canterbury-2012.csv')

su$Day <- ifelse(Month %in% c(1, 3, 5, 7, 8, 10, 12), 31,
                 ifelse(Month %in% c(4, 6, 9, 11), 30, 28))
su$Date <- as.Date(paste(Day, Month, Year, sep = '-'), format = '%d-%m-%Y')

# No huge autocorrelation

# Actual data
xyplot(Number ~ Date, data = su, type = 'b')

# Mean number of monthly suicides: 6.283
# Simulating 4 5-year series using Poisson for
# panels 2 to 5. Panel 1 contains the observed data
simulated <-  data.frame(s = factor(rep(1:5, each = 60)),
                         d = rep(su$Date, 5),
                         n = c(su$Number, rpois(240, lambda = mean(su$Number))))

xyplot(n ~ d | s, simulated, type = 'b', cex = 0.3, layout = c(1, 5),
       xlab = 'Date', ylab = 'Suicides per month')

Observed suicide data for Christchurch (panel 1) and four 60-month simulations (panels 2-5).

Do they really look different? We could try to fit any set of peaks and valleys to our favorite narrative; however, it doesn't necessarily make any sense. We'll need a larger data set to see any long-time effects of the earthquake.

P.S. 2012-09-18. Thomas Lumley comments on this post in StatsChat.


  1. naught101

    You’re assuming that all the other large spikes and drops in the time series are natural variability, but there’s nothing in the corroner’s comment thatIm implies that all large drops must relate to earthquakes, just *large scale crisis events. Presumably large spikes relate to other occurrences, like Big Brother returning to TV…

  2. Luis

    I’ve never watched Big Brother and, after your comment, I will avoid it in case I feel like jumping off a bridge. 🙂 I’m saying that it’s possible to simulate random variation (natural variability) that looks like the observed data.

    It is interesting to me that the coroner chose to put forward an interpretation for the earthquakes (so it fits the ‘international experience’) but chose not to comment about the effect of Big Brother (or whatever happened with the previous spike).

    My point is that there is no reason to run with a narrative just because it is justified by our prejudices. Sort of an XKCD moment:

    Also, all financial analysis. And, more directly, D&D

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