The robust negative relationship between mean temperature and national suicide rates
Matt N. Williams, Massey University, New Zealand | [email protected]
Abstract
This study demonstrates that there is a negative relationship between the
mean temperature and the suicide rate of geographical areas. This finding
holds both across countries (N = 171) and across counties of the US (N =
3147). The relationship is robust to controls for several plausible confounds.
Introduction
A number of studies have investigated how the incidence of suicide is affected
by variation in temperature over time within particular geographic areas,
generally finding that warmer time periods are associated with higher suicide
incidence1. However, the relationship between geographical variation in
temperature and suicide has not been so extensively studied, leaving an
interesting observation rather underappreciated: That warmer geographical
areas (e.g., warmer countries) tend to have lower suicide rates.
This negative relationship was identified in studies using data from the 1970s
and 1980s2–4. However, it has not been extensively examined using more
contemporary data, nor previously subjected to controls for potential
confounds.
Results: Analysis across countries
Results: Analysis across counties of the USA
Across the 171 countries examined, there was a correlation of r = -.20
between mean temperature and suicide rate, 95% credible interval [-.35, .05]. There were ~0.16 fewer suicides per 100,000 p.a. for each °C higher
temperature. The effect remained present and in fact increased in size when
controlling for the set of selected potential confounds (Table 1).
Across the 3147 US counties examined, there was a correlation of r = -.16
between mean temperature and suicide rate, 95% credible interval [-.20,
-.12]. There were ~0.20 fewer suicides per 100,000 p.a. for each °C higher
temperature. The effect remained present and again increased in size when
controlling for the set of selected potential confounds (see Table 2).
Table 1 Regression model for age-standardized suicide rate (per 100,000) by country
Table 2 Regression model for age-standardized suicide rate (per 100,000) by county
Intercept
Mean temperature (°C), 1961-1990
GDP per capita in 2012 (000s USD, purchasing power
parity)
Sunlight exposure: Absolute latitude of country
centroid, °
Alcohol consumed per capita (L), 2010
Population density in 2012 (000s/km2)
Percent female labour force participation (age 15+)
in 2012
Percent Catholic or Muslim in 2010
95%
CI lo
Posterior
mean
95%
CI hi
IV data
source
7.44
-0.63
-0.09
18.77
-0.32
-0.03
30.29
-0.02
0.03
World Bank
World Bank
-0.30
0.03
-0.95
-0.06
-0.06
-0.14
0.33
0.95
0.01
-0.03
0.01
0.64
2.97
0.08
WHO
UN/CIA
World Bank
0.00
Pew
95%
CI hi
IV data
source
Intercept
-0.97
3.16
7.45
-
Mean temperature (°C), 1999–2011
Median household income (000s USD), 2005-2014
-0.91
-0.08
-0.82
-0.06
-0.73
-0.04
CDC
ACS
1.80
2.05
2.28
CDC
0.15
0.28
0.40
12
Population density (000s/km2), mean 1999–2014
-0.20
-0.14
-0.08
CDC/ACS
Percent female labour force participation (age
16+), mean 2005–2014
Percent Catholic, mean 2000/2010
-0.22
-0.19
-0.15
ACS
-0.07
-0.05
-0.04
US Religion census
0.00
0.01
0.02
Census
Sunlight exposure: Mean radiation (MJ/m2), 1979–
2011
Percent heavy drinkers of alcohol*, mean 2005–2012
11
95% Posterior
CI lo
mean
Percent White, mean 2005–2014
Methods
Analysis across countries: Suicide and temperature data sources
• Suicide rates were obtained from the WHO for 2012 for 171 countries
• Mean temperatures for the period 1961-1990 were obtained from the
World Bank, gridded to country level
Analysis across US counties: Suicide and temperature data sources
Age-standardized suicide rates for 1999–2014 and meteorological data by
county for 1999–2011 were both obtained from the CDC. Data were obtained
for 3147 counties, of which 2851 had complete suicide and temperature data.
Control variables
Control variables were selected on the basis of availability of data and
existence of evidence for the variable likely having an effect on suicide rates.
Data analyses
Data were analysed in R. Visually weighted regressions were completed per
Hsiang5,6 with loess span selected using fANCOVA7. Missing data were
multiply imputed using mice8. Bayesian regression models were estimated
with uninformative priors using MCMCpack9. Choropleth mapping was
completed using choroplethr10.
Figure 3. Visually weighted loess regression of county suicide rate on mean
temperature. Shading indicates uncertainty around line of best fit.
Figure 2 Visually weighted loess regression of country suicide rate on mean
temperature. Shading indicates uncertainty around line of best fit.
Discussion
The apparent positive relationship between temporal variation in temperature and suicide incidence within geographical locations has led some researchers to
suggest that global warming will increase suicide rates13. However, this study shows that there is a reasonably robust negative correlation between geographic
variation in temperature and suicide incidence. This raises the possibility that sustained exposure to warmer temperatures have different effects than do shortterm fluctuations in temperature, complicating inferences about the effects of climate change on suicide1.
It would therefore be valuable for researchers to attempt to determine the reason for the apparent negative relationship between geographical variation in
temperature and suicide rates. This presentation seeks to draw attention to this research topic, rather than offer an explanation. The true explanation could be:
• Confounding: The presence of some uncontrolled variable correlated with temperature (but not affected by temperature) that affects suicide incidence
• Spuriousness: The presence of some measurement or statistical problem that renders the apparent relationship entirely spurious
• Causality: An actual (direct or indirect) protective effect of sustained higher temperatures on suicide incidence.
References
Figure 1. Choropleth of suicide rates in US counties, 1999-2014
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