Kayipmaz San Usul 2020

Methods:

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This descriptive study adopted an ecological (correlation) design. We obtained the data on suicide only from the Ankara region. We collected data through a retrospective review of medical records.

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Rather than attempting to look at causal relationships between the relevant factors and individual suicides, or trying to develop a theoretical model through experiments, the study investigated a possible correlation between meteorological factors and suicide rates on groups. This was done by examining the historical medical records of suicides and suicide attempts in one particular population—that of Ankara

Introduction:

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As other social variables related to season such as unemployment, holidays, access to clinicians, etc. are known to affect suicides, the effect of these variables should be evaluated independently of the seasonal cycle to measure the direct effect of climatic variables on suicides (Vyssoki et al. 2014).
In this context, we aimed to reveal the relationship between the meteorological variables in Ankara, Turkey and suicide rates (completed suicides and suicide attempts) independently of the seasonal cycle.

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The introduction begins with an overview of recent research into the effects of meteorological variables on the rate of suicides and suicide attempts in a number of different geographical regions. Many of these previous studies have found that several additional factors that are tied to seasonality affect suicide rates. Because of this seasonal influence, this study examines the meteorological impact on suicides and suicide attempts in one region, while excluding the potential influence of seasonality.

Results:

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After smoothing the effect of the day of the week and seasonality, we found that an increase in the minimum temperature on the day of the call by 1 unit (1 °C) led to an increase in the number of suicides by 0.01 unit (0.01 ± 0.005, p = 0.046).

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In looking to see if meteorological factors impacted suicide rates, the researchers employed a statistical model that excluded the potential influence of other variables such as seasonality and days of the week. By doing so, they found that an increase in the minimum temperature of 1°C led to a 1% increase in suicide rates on that day (with a margin of error of plus or minus one fifth of a percent), indicating a positive correlation between higher temperatures and suicide rates.

Discussion:

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There is no widely accepted explanation for the effect of temperature on suicide; however, it is believed that the increase in the number of suicides in summer is due to changes in the serotonin system which is responsible for depression, impulsivity, and aggression (Luykx et al. 2013).

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It seems to be generally recognized in the scientific community that there is a positive correlation between higher temperatures and the number of suicides, but there’s no generally accepted explanation for why the correlation exists. Some studies point to a possible link between higher temperatures and disruptions in the serotonin system, which could lead to increases in depression, impulsivity, and aggression—moods and emotional states that likely play a role in suicides.

Future Directions:

• What future research should follow up on this work?

Because it wasn’t always available, the study did not look at data on sunshine duration. Other studies indicated that this can also play a role in suicide rates, and even that its effects may vary among populations with different mood disorders. One study even indicated that it can decrease suicide rates among some of these groups, so future studies should look into these questions.

Difficult Material:

• What did you not understand about this paper that someone else may be able to help you with? Or, if you understood everything, what did you find most challenging to understand?

As a number of other people mentioned, I found the section on Statistical Analyses to be the most challenging and there was a lot I didn’t understand. It refers to the Spearman rank correlation coefficient, generalized additive Poisson regression models, and penalized smoothing splines. I was able to find enough of a foothold in the section to have a sense of what the authors were doing with their statistical model, but I didn’t understand these terms at all.