Does altitude moderate the impact of lithium on suicide? A spatial analysis of Austria
AbstractSuicide, the tenth leading cause of death worldwide, is a complex phenomenon. Models aiming to explain the interaction of ambient variables such as socioeconomic factors, lithium content of drinking water and altitude are poorly developed. While controlling for several risk factors, this research bridges two different, but complementary research lines by investigating statistically the relationship on suicide mortality between lithium levels in drinking water in response to altitude above sea level. Besides regression models with main effects, a multiplicative interaction model between lithium and altitude has been developed providing estimates at the district-level for Austria where spatial autocorrelation was accounted for through spatial filtering. The correlation results showed a negative association between lithium levels and altitude. The regression confirmed a negative association of lithium levels and suicide mortality. Altitude was found to be positively associated with suicide mortality. On the other hand, lithium effects on suicide mortality were found to be moderated by altitude. In lower altitude regions the effect turned out to be negatively related to suicide mortality, while lithium had a positive association in high-altitude regions. These results provide evidence for the fact that the relationship between lithium, altitude and suicide rates is more complex than hitherto assumed. Further research on the effects of ambient variables such as low levels of lithium on suicide is needed and particularly the lithium-altitude interaction is worth further investigation to understand possible underlying neurochemical processes.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2013 Marco Helbich, Victor Blüml, Michael Leitner, Nestor D. Kapusta
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.