Evidence map›Paper›PMID 32836331›Full record

ArticleGeographical analysis2021

A Spatio-Temporal Analysis of the Environmental Correlates of COVID-19 Incidence in Spain.

Antonio Paez, Fernando A Lopez, Tatiane Menezes, Renata Cavalcanti, Maira Galdino da Rocha Pitta

Open access · bronzeAbstract read
In one paragraph

Article in Geographical analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 2 pooled it
4.0field-weighted citation impact, top 4% of its field
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

58 citing papers in PubMed, 2 syntheses or guidelines pooled it, 135 citations in OpenAlex.

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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

5 authors at 3 institutions in 3 countries.

Antonio PaezSchool of Geography and Earth Sciences McMaster University Hamilton ON Canada.ORCID 0000-0001-6912-9919
Fernando A LopezDepartamento de Metodos Cuantitativos, Ciencias Juridicas, y Lenguas Modernas Universidad Politecnica de Cartagena Cartagena Spain.
Tatiane MenezesDepartamento de Economia Universidade Federal de Pernambuco Recife Brazil.
Renata CavalcantiNúcleo de Pesquisa em Inovação Terapêutica NUPIT/UFPE Universidade Federal de Pernambuco Recife Brazil.
Maira Galdino da Rocha PittaNúcleo de Pesquisa em Inovação Terapêutica NUPIT/UFPE Universidade Federal de Pernambuco Recife Brazil.
Universidade Federal de Pernambuco · BRMcMaster University · CAUniversidad Politécnica de Cartagena · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The novel SARS-CoV2 has disrupted health systems and the economy, and public health interventions to slow its spread have been costly. How and when to ease restrictions to movement hinges in part on whether SARS-CoV2 will display seasonality due to variations in temperature, humidity, and hours of sunshine. Here, we address this question by means of a spatio-temporal analysis in Spain of the incidence of COVID-19, the disease caused by the virus. Use of spatial Seemingly Unrelated Regressions (SUR) allows us to model the incidence of reported cases of the disease per 100,000 population as an interregional contagion process, in addition to a function of temperature, humidity, and sunshine. In the analysis we also control for GDP per capita, percentage of older adults in the population, population density, and presence of mass transit systems. The results support the hypothesis that incidence of the disease is lower at higher temperatures and higher levels of humidity. Sunshine, in contrast, displays a positive association with incidence of the disease. Our control variables also yield interesting insights. Higher incidence is associated with higher GDP per capita and presence of mass transit systems in the province; in contrast, population density and percentage of older adults display negative associations with incidence of COVID-19.

Identifiers

PMID32836331
PMCPMC7300768
OpenAlexW3033384130

What OpenQuestion holds

Textmetadata
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.