Evidence map›Paper›PMID 41501284›Full record

ArticleScientific reports2026

Evolving spatiotemporal patterns and urban scaling of deaths from external causes.

Cesar I N Sampaio Filho, Humberto A Carmona, Antonio S Lima Neto, Monica V Prates, Haroldo V Ribeiro, Marcia C Castro, José S Andrade

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Cesar I N Sampaio FilhoDepartamento de Física, Universidade Federal do Ceará, Fortaleza, Ceará, 60451-970, Brazil.
Humberto A CarmonaDepartamento de Física, Universidade Federal do Ceará, Fortaleza, Ceará, 60451-970, Brazil.
Antonio S Lima NetoCentro de Ciências da Saúde, Universidade de Fortaleza, Fortaleza, 60811, Ceará, Brazil.
Monica V PratesDepartamento de Física, Universidade Estadual de Maringá, Maringá, PR, 87020-900, Brazil.
Haroldo V Ribeiro
Marcia C CastroDepartment of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
José S AndradeDepartamento de Física, Universidade Federal do Ceará, Fortaleza, Ceará, 60451-970, Brazil. soares@fisica.ufc.br.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 303533/2021-8Conselho Nacional de Desenvolvimento Científico e Tecnológico 303765/2017-8Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 88887.311932/2018-00Coordenação de Aperfeiçoamento de Pessoal de Nível Superior N/AFundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico 06849573/2023
6 · The paper itself

Abstract

Urban scaling theory posits that urban indicators follow power-law relations with population, yet the evolution of these patterns-and the role of regional differences in settings marked by social inequalities and unplanned urbanization-remains poorly understood. Here, we analyze nearly three decades of mortality data from Brazilian cities to investigate the scaling of external causes of death: homicides, suicides, and accidents. Using a hierarchical Bayesian framework and spatial correlation analysis, we find that these mortality indicators exhibit distinct, regionally heterogeneous scaling trajectories. Homicide mortality has significantly attenuated its typical superlinear scaling with increased spatial clustering, suggesting a redistribution of violence to smaller cities and intensified intercity interactions, possibly linked to the consolidation of organized crime. Suicide mortality, usually sublinear, has trended upward, implying a weakening of urban agglomerations' protective effect. Accident mortality remains superlinear, with transport fatalities scaling nearly proportionally, and non-transport accidents becoming superlinear. The scaling changes for suicides and accidents coincide with less correlated and stable spatial patterns, suggesting that the underlying processes predominantly operate within city boundaries. Finally, while scaling exponents have evolved more homogeneously across Brazilian states, scale-adjusted mortality remains highly heterogeneous, indicating that fundamental processes govern scaling laws, whereas state-specific factors drive scale-adjusted metrics.

Indexed as

AccidentsHomicideBayes TheoremBrazilCause of DeathCitiesHumansSpatio-Temporal AnalysisSuicideUrbanizationUrban Population

Identifiers

PMID41501284
PMCPMC12808102

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.