Evidence map›Paper›PMID 34586304›Full record

ArticleRevista do Instituto de Medicina Tropical de Sao Paulo2021

Geoclimatic, demographic and socioeconomic characteristics related to dengue outbreaks in Southeastern Brazil: an annual spatial and spatiotemporal risk model over a 12-year period.

Sebastian Vernal, Andressa K Nahas, Francisco Chiaravalloti Neto, Carlos A Prete Junior, André L Cortez, Ester Cerdeira Sabino, Expedito José de Albuquerque Luna

Abstract read
In one paragraph

Article in Revista do Instituto de Medicina Tropical de Sao Paulo, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
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.

Sebastian VernalUniversidade de São Paulo, Faculdade de Medicina, Departamento de Moléstias Infecciosas e Parasitárias, São Paulo, São Paulo, Brazil.ORCID http://orcid.org/0000-0001-8589-0089
Andressa K NahasUniversidade de São Paulo, Faculdade de Saúde Pública, Departamento de Epidemiologia, São Paulo, São Paulo, Brazil.
Francisco Chiaravalloti NetoUniversidade de São Paulo, Faculdade de Saúde Pública, Departamento de Epidemiologia, São Paulo, São Paulo, Brazil.
Carlos A Prete JuniorUniversidade de São Paulo, Escola Politécnica, Departamento de Engenharia de Sistemas Eletrônicos, São Paulo, São Paulo, Brazil.
André L CortezUniversidade de São Paulo, Faculdade de Medicina, Departamento de Moléstias Infecciosas e Parasitárias, São Paulo, São Paulo, Brazil.
Ester Cerdeira SabinoUniversidade de São Paulo, Instituto de Medicina Tropical de São Paulo, São Paulo, São Paulo, Brazil.ORCID http://orcid.org/0000-0003-2623-5126
Expedito José de Albuquerque LunaUniversidade de São Paulo, Instituto de Medicina Tropical de São Paulo, São Paulo, São Paulo, Brazil.ORCID http://orcid.org/0000-0002-1145-9672

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dengue fever is re-emerging worldwide, however the reasons of this new emergence are not fully understood. Our goal was to report the incidence of dengue in one of the most populous States of Brazil, and to assess the high-risk areas using a spatial and spatio-temporal annual models including geoclimatic, demographic and socioeconomic characteristics. An ecological study with both, a spatial and a temporal component was carried out in Sao Paulo State, Southeastern Brazil, between January 1st, 2007 and December 31st, 2019. Crude and Bayesian empirical rates of dengue cases following by Standardized Incidence Ratios (SIR) were calculated considering the municipalities as the analytical units and using the Integrated Nested Laplace Approximation in a Bayesian context. A total of 2,027,142 cases of dengue were reported during the studied period. The spatial model allocated the municipalities in four groups according to the SIR values: (I) SIR<0.8; (II) SIR 0.8<1.2; (III) SIR 1.2<2.0 and SIR>2.0 identified the municipalities with higher risk for dengue outbreaks. "Hot spots" are shown in the thematic maps. Significant correlations between SIR and two climate variables, two demographic variables and one socioeconomical variable were found. No significant correlations were found in the spatio-temporal model. The incidence of dengue exhibited an inconstant and unpredictable variation every year. The highest rates of dengue are concentrated in geographical clusters with lower surface pressure, rainfall and altitude, but also in municipalities with higher degree of urbanization and better socioeconomic conditions. Nevertheless, annual consolidated variations in climatic features do not influence in the epidemic yearly pattern of dengue in southeastern Brazil.

Indexed as

DengueBayes TheoremBrazilDisease OutbreaksHumansIncidenceSocioeconomic Factors

Identifiers

PMID34586304
PMCPMC8494490

What OpenQuestion holds

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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.