Evidence map›Paper›PMID 37200576›Full record

ArticleHeliyon2023

Spatio-temporal multidisciplinary analysis of socio-environmental conditions to explore the COVID-19 early evolution in urban sites in South America.

Gilma C Mantilla Caicedo, Matilde Rusticucci, Solange Suli, Verónica Dankiewicz, Salvador Ayala, Alexandra Caiman Peñarete, Martín Díaz, Silvia Fontán, Francisco Chesini, Diana Jiménez-Buitrago and 2 more

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

12 authors.

Gilma C Mantilla CaicedoGlobal Consortium on Climate and Health Education, Columbia University, New York, United States.
Matilde RusticucciUniversidad de Buenos Aires, Departamento de Ciencias de la Atmósfera y los Océanos, CONICET, Argentina.
Solange SuliUniversidad de Buenos Aires, Departamento de Ciencias de la Atmósfera y los Océanos, CONICET, Argentina.
Verónica DankiewiczUniversidad de Buenos Aires, Departamento de Ciencias de la Atmósfera y los Océanos, CONICET, Argentina.
Salvador AyalaUniversidad de Chile, Programa de Doctorado en Salud Pública, Instituto de Salud Pública de Chile, Chile.
Alexandra Caiman PeñareteSubred Integrada de Servicios Hospitalarios Centro Oriente ESE, Red Hospitalaria Bogotá Distrito Capital, Colombia.
Martín DíazUniversidad Nacional de La Matanza, Departamento de Ciencias de la Salud, Argentina.
Silvia FontánUniversidad Nacional de La Matanza, Departamento de Ciencias de la Salud, Argentina.
Francisco ChesiniMinisterio de Salud de Argentina, Argentina.
Diana Jiménez-BuitragoMinisterio de Salud y Protección Social, Mesa de Variabilidad y Cambio Climático de la CONASA, Colombia.
Luis R Barreto PedrazaInstituto de Hidrología, Meteorología y Estudios Ambientales - IDEAM, Subdirección de Meteorología, Mesa de Variabilidad y Cambio Climático de la CONASA, Miembro del grupo QuASAR UPN, Colombia.
Facundo BarreraCentro Austral de Investigaciones Científicas (CADIC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ushuaia, Argentina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman's non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.

Indexed as

Climate variabilityGINIPandemicParametric and non-parametric analysisSARS-CoV-2

Identifiers

PMID37200576
PMCPMC10162854

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.