Statistical signs of social influence on suicides.
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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.
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Affiliation: Departamento de Física, Universidade Federal do Ceará, 60451-970 Fortaleza, Ceará, Brazil.
ABSTRACT
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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. Related in: MedlinePlus |
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Mentions: In Fig. 4 we show the countour plot for the conditional probability of the rate of fatality, given a population size. In order to obtain the approximate density, we perform a kernel-density estimation over the sample of all Brazilian cities in 2009. The lines in Fig. 4 indicate the limits below which 10%, 50% and 90% of the data points are located. Besides confirming the superlinear, linear, and sublinear behaviors, these results also show how the probability distribution of rates of fatality varies with the population size. Also, the 10% and 90% lines are representative of expected extreme cases of low and high fatalities, respectively. |
View Article: PubMed Central - PubMed
Affiliation: Departamento de Física, Universidade Federal do Ceará, 60451-970 Fortaleza, Ceará, Brazil.