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Statistical signs of social influence on suicides.

Melo HP, Moreira AA, Batista É, Makse HA, Andrade JS - Sci Rep (2014)

Bottom Line: Under the same framework, he considered that crime is bound up with the fundamental conditions of all social life.The social effect on the occurrence of homicides has been previously substantiated, and confirmed here, in terms of a superlinear scaling relation: by doubling the population of a Brazilian city results in an average increment of 135% in the number of homicides, rather than the expected isometric increase of 100%, as found, for example, for the mortality due to car crashes.Differently from homicides (superlinear) and fatal events in car crashes (isometric), we find sublinear scaling behavior between the number of suicides and city population, with allometric power-law exponents, β = 0.84 ± 0.02 and 0.87 ± 0.01, for all cities in Brazil and US counties, respectively.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Física, Universidade Federal do Ceará, 60451-970 Fortaleza, Ceará, Brazil.

ABSTRACT
By treating the suicide as a social fact, Durkheim envisaged that suicide rates should be determined by the connections between people and society. Under the same framework, he considered that crime is bound up with the fundamental conditions of all social life. The social effect on the occurrence of homicides has been previously substantiated, and confirmed here, in terms of a superlinear scaling relation: by doubling the population of a Brazilian city results in an average increment of 135% in the number of homicides, rather than the expected isometric increase of 100%, as found, for example, for the mortality due to car crashes. Here we present statistical signs of the social influence on the suicide occurrence in cities. Differently from homicides (superlinear) and fatal events in car crashes (isometric), we find sublinear scaling behavior between the number of suicides and city population, with allometric power-law exponents, β = 0.84 ± 0.02 and 0.87 ± 0.01, for all cities in Brazil and US counties, respectively. Also for suicides in US, but using the Metropolitan Statistical Areas (MSAs), we obtain β = 0.88 ± 0.01.

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Scaling relationship between suicides and population for US counties and MSAs.The small circles show the total number of suicides over five years (2003 to 2007) vs the average population for counties (a) and MSAs (b). The solid gray line indicate the best fit of a power law, using OLS regression, between the average total number of suicides and population. The dashed black lines delimit the 95% confidence band given by the Nadaraya-Watson kernel regression (solid black line)1718. The allometric exponents are obtained through an ordinary least-squares (OLS) fit19 over the Nadaraya-Watson kernel regression applied to the suicides data. The values of the Pearson correlation coefficients ρ associated with these scaling relations are shown in each plot. We find β = 0.87 ± 0.01 for counties and β = 0.88 ± 0.01 for MSAs with a 95% confidence interval estimated by bootstrap. The insets in each graph show the systematic decreases of suicide rates with population in both cases.
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f3: Scaling relationship between suicides and population for US counties and MSAs.The small circles show the total number of suicides over five years (2003 to 2007) vs the average population for counties (a) and MSAs (b). The solid gray line indicate the best fit of a power law, using OLS regression, between the average total number of suicides and population. The dashed black lines delimit the 95% confidence band given by the Nadaraya-Watson kernel regression (solid black line)1718. The allometric exponents are obtained through an ordinary least-squares (OLS) fit19 over the Nadaraya-Watson kernel regression applied to the suicides data. The values of the Pearson correlation coefficients ρ associated with these scaling relations are shown in each plot. We find β = 0.87 ± 0.01 for counties and β = 0.88 ± 0.01 for MSAs with a 95% confidence interval estimated by bootstrap. The insets in each graph show the systematic decreases of suicide rates with population in both cases.

Mentions: We also analyzed data available for suicides in all US counties and MSAs. The data is an accumulation of the total number of suicides during a period of five years, from 2003 to 2007. In Figs. 3a and 3b we show the dependence of the total number of suicides during these five years on the average population for each county and MSA, respectively. As depicted, the number of suicides also scales with a sub-linear power law with exponent β = 0.87 ± 0.01 for counties, and β = 0.88 ± 0.01 for MSAs, which are in agreement with our previous results for Brazil.


Statistical signs of social influence on suicides.

Melo HP, Moreira AA, Batista É, Makse HA, Andrade JS - Sci Rep (2014)

Scaling relationship between suicides and population for US counties and MSAs.The small circles show the total number of suicides over five years (2003 to 2007) vs the average population for counties (a) and MSAs (b). The solid gray line indicate the best fit of a power law, using OLS regression, between the average total number of suicides and population. The dashed black lines delimit the 95% confidence band given by the Nadaraya-Watson kernel regression (solid black line)1718. The allometric exponents are obtained through an ordinary least-squares (OLS) fit19 over the Nadaraya-Watson kernel regression applied to the suicides data. The values of the Pearson correlation coefficients ρ associated with these scaling relations are shown in each plot. We find β = 0.87 ± 0.01 for counties and β = 0.88 ± 0.01 for MSAs with a 95% confidence interval estimated by bootstrap. The insets in each graph show the systematic decreases of suicide rates with population in both cases.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150102&req=5

f3: Scaling relationship between suicides and population for US counties and MSAs.The small circles show the total number of suicides over five years (2003 to 2007) vs the average population for counties (a) and MSAs (b). The solid gray line indicate the best fit of a power law, using OLS regression, between the average total number of suicides and population. The dashed black lines delimit the 95% confidence band given by the Nadaraya-Watson kernel regression (solid black line)1718. The allometric exponents are obtained through an ordinary least-squares (OLS) fit19 over the Nadaraya-Watson kernel regression applied to the suicides data. The values of the Pearson correlation coefficients ρ associated with these scaling relations are shown in each plot. We find β = 0.87 ± 0.01 for counties and β = 0.88 ± 0.01 for MSAs with a 95% confidence interval estimated by bootstrap. The insets in each graph show the systematic decreases of suicide rates with population in both cases.
Mentions: We also analyzed data available for suicides in all US counties and MSAs. The data is an accumulation of the total number of suicides during a period of five years, from 2003 to 2007. In Figs. 3a and 3b we show the dependence of the total number of suicides during these five years on the average population for each county and MSA, respectively. As depicted, the number of suicides also scales with a sub-linear power law with exponent β = 0.87 ± 0.01 for counties, and β = 0.88 ± 0.01 for MSAs, which are in agreement with our previous results for Brazil.

Bottom Line: Under the same framework, he considered that crime is bound up with the fundamental conditions of all social life.The social effect on the occurrence of homicides has been previously substantiated, and confirmed here, in terms of a superlinear scaling relation: by doubling the population of a Brazilian city results in an average increment of 135% in the number of homicides, rather than the expected isometric increase of 100%, as found, for example, for the mortality due to car crashes.Differently from homicides (superlinear) and fatal events in car crashes (isometric), we find sublinear scaling behavior between the number of suicides and city population, with allometric power-law exponents, β = 0.84 ± 0.02 and 0.87 ± 0.01, for all cities in Brazil and US counties, respectively.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Física, Universidade Federal do Ceará, 60451-970 Fortaleza, Ceará, Brazil.

ABSTRACT
By treating the suicide as a social fact, Durkheim envisaged that suicide rates should be determined by the connections between people and society. Under the same framework, he considered that crime is bound up with the fundamental conditions of all social life. The social effect on the occurrence of homicides has been previously substantiated, and confirmed here, in terms of a superlinear scaling relation: by doubling the population of a Brazilian city results in an average increment of 135% in the number of homicides, rather than the expected isometric increase of 100%, as found, for example, for the mortality due to car crashes. Here we present statistical signs of the social influence on the suicide occurrence in cities. Differently from homicides (superlinear) and fatal events in car crashes (isometric), we find sublinear scaling behavior between the number of suicides and city population, with allometric power-law exponents, β = 0.84 ± 0.02 and 0.87 ± 0.01, for all cities in Brazil and US counties, respectively. Also for suicides in US, but using the Metropolitan Statistical Areas (MSAs), we obtain β = 0.88 ± 0.01.

Show MeSH
Related in: MedlinePlus