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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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Fatality per capita versus population for homicides, traffic accidents, and suicides.The color map represents the conditional probability density obtained by kernel density estimation. The bottom and top lines correspond to the 10% and 90% bounds of the distribution for each population size, that is 80% of the sampled points are between these lines. The middle line is the 50% level or “median” expected for each population size. The diagonal shape observed in the left side of density maps are cases of low number of fatal events, one or two fatalities. After this region we observe that the three level lines wiggle around an average power-law behavior. In the case of homicides the three level lines indicate an increase in the expected density of fatality with the population size. Similarly, for traffic accidents the lines are close to horizontal, that is, the probability distribution for the rate of fatality is near independent of the population. For suicides, the median shows a slight decrease with population size, while the 90% level, that is associated with cases of extreme rates of suicide, shows a pronounced decrease. The sublinear growth observed for suicides, as depicted in Fig. 1c, is likely due to the suppression of these extremely high rates in large urban areas.
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f4: Fatality per capita versus population for homicides, traffic accidents, and suicides.The color map represents the conditional probability density obtained by kernel density estimation. The bottom and top lines correspond to the 10% and 90% bounds of the distribution for each population size, that is 80% of the sampled points are between these lines. The middle line is the 50% level or “median” expected for each population size. The diagonal shape observed in the left side of density maps are cases of low number of fatal events, one or two fatalities. After this region we observe that the three level lines wiggle around an average power-law behavior. In the case of homicides the three level lines indicate an increase in the expected density of fatality with the population size. Similarly, for traffic accidents the lines are close to horizontal, that is, the probability distribution for the rate of fatality is near independent of the population. For suicides, the median shows a slight decrease with population size, while the 90% level, that is associated with cases of extreme rates of suicide, shows a pronounced decrease. The sublinear growth observed for suicides, as depicted in Fig. 1c, is likely due to the suppression of these extremely high rates in large urban areas.

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.


Statistical signs of social influence on suicides.

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

Fatality per capita versus population for homicides, traffic accidents, and suicides.The color map represents the conditional probability density obtained by kernel density estimation. The bottom and top lines correspond to the 10% and 90% bounds of the distribution for each population size, that is 80% of the sampled points are between these lines. The middle line is the 50% level or “median” expected for each population size. The diagonal shape observed in the left side of density maps are cases of low number of fatal events, one or two fatalities. After this region we observe that the three level lines wiggle around an average power-law behavior. In the case of homicides the three level lines indicate an increase in the expected density of fatality with the population size. Similarly, for traffic accidents the lines are close to horizontal, that is, the probability distribution for the rate of fatality is near independent of the population. For suicides, the median shows a slight decrease with population size, while the 90% level, that is associated with cases of extreme rates of suicide, shows a pronounced decrease. The sublinear growth observed for suicides, as depicted in Fig. 1c, is likely due to the suppression of these extremely high rates in large urban areas.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Fatality per capita versus population for homicides, traffic accidents, and suicides.The color map represents the conditional probability density obtained by kernel density estimation. The bottom and top lines correspond to the 10% and 90% bounds of the distribution for each population size, that is 80% of the sampled points are between these lines. The middle line is the 50% level or “median” expected for each population size. The diagonal shape observed in the left side of density maps are cases of low number of fatal events, one or two fatalities. After this region we observe that the three level lines wiggle around an average power-law behavior. In the case of homicides the three level lines indicate an increase in the expected density of fatality with the population size. Similarly, for traffic accidents the lines are close to horizontal, that is, the probability distribution for the rate of fatality is near independent of the population. For suicides, the median shows a slight decrease with population size, while the 90% level, that is associated with cases of extreme rates of suicide, shows a pronounced decrease. The sublinear growth observed for suicides, as depicted in Fig. 1c, is likely due to the suppression of these extremely high rates in large urban areas.
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.

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