Evidence map›Paper›PMID 40025192›Full record

ArticleScientific reports2025

Scaling of cardiovascular risk factors in 230 Latin American cities.

Aureliano S S Paiva, Usama Bilal, Roberto F S Andrade, Claudiano C Cruz Neto, J Firmino de Sousa Filho, Gervásio F Santos, Maurício L Barreto, Daniel A Rodriguez, Pricila Mullachery, Brisa Sanchez and 6 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

16 authors.

Aureliano S S PaivaCenter of Data and Knowledge Integration for Health (CIDACS), Fundação Oswaldo Cruz, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil.
Usama BilalUrban Health Collaborative, Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, PA, USA.
Roberto F S AndradeCenter of Data and Knowledge Integration for Health (CIDACS), Fundação Oswaldo Cruz, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil.
Claudiano C Cruz NetoCenter of Data and Knowledge Integration for Health (CIDACS), Fundação Oswaldo Cruz, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil.
J Firmino de Sousa FilhoCenter of Data and Knowledge Integration for Health (CIDACS), Fundação Oswaldo Cruz, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil. jose.ffilho@fiocruz.br.
Gervásio F SantosCenter of Data and Knowledge Integration for Health (CIDACS), Fundação Oswaldo Cruz, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil.
Maurício L BarretoCenter of Data and Knowledge Integration for Health (CIDACS), Fundação Oswaldo Cruz, Instituto Gonçalo Moniz, Salvador, Bahia, Brazil.
Daniel A RodriguezDepartment of City and Regional Planning and Institute of Transportation Studies, University of California Berkeley, Berkeley, CA, USA.
Pricila MullacheryUrban Health Collaborative, Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, PA, USA.
Brisa SanchezUrban Health Collaborative, Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, PA, USA.
Ana V Diez-RouxUrban Health Collaborative, Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health, Philadelphia, PA, USA.
Felipe MontesDepartment of Industrial Engineering, Social and Health Complexity Center, Universidad de los Andes, Bogotá, Colombia.
Andrés TrottaInstitute of Collective Health, Universidad Nacional de Lanús, Buenos Aires, Argentina.
Tania AlfaroEscuela de Salud Pública, Facultad de Medicina, Universidad de Chile, Santiago, Chile.
J Jaime MirandaCRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru.
Tonatiuh Barrientos-GutierrezCenter for Population Health Research, National Institute of Public Health, Cuernavaca, Mexico.

Funding

Wellcome TrustWellcome Trust 205177/Z/16/Z
6 · The paper itself

Abstract

Urbanization results in increased numbers of people living in cities and poses challenges and opportunities to public health policies. Studies of urban scaling have unveiled how cities' socio-economic and infrastructural attributes vary systematically with city size. Previous studies have explored the scaling properties of health outcomes across metropolitan areas in different countries, but chronic diseases have been infrequently examined. This paper examines scaling behaviors of 4 cardiovascular risk factors: hypertension, diabetes, tobacco smoking, and obesity across 230 cities in six countries of Latin America. In analyses pooled across countries, diabetes and hypertension showed weakly superlinear scaling (higher prevalence in larger cities). In comparison, obesity showed linear scaling, and tobacco showed weakly sublinear scaling (lower prevalence in larger cities), although most coefficients did not differ significantly from the null. In country-specific analyses, hypertension and diabetes tended to show a superlinear pattern across most countries, obesity tended to show a sublinear pattern in most countries, and tobacco tended to be superlinear (in contrast to the analysis pooled across countries where it was sublinear). Results suggest the need to examine further the drivers of this varying scaling of risk factors.

Indexed as

Cardiovascular DiseasesHeart Disease Risk FactorsCitiesDiabetes MellitusFemaleHumansHypertensionLatin AmericaMaleMiddle AgedObesityPrevalenceRisk FactorsTobacco SmokingUrbanizationCardiovascular diseaseLatin AmericaProportionUrban healthUrban scaling

Identifiers

PMID40025192
PMCPMC11873263

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Registered trials

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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.