Evidence map›Paper›PMID 32778170›Full record

ArticleInfectious diseases of poverty2020

Temporal trends in areas at risk for concomitant tuberculosis in a hyperendemic municipality in the Amazon region of Brazil.

Alexandre Tadashi Inomata Bruce, Thais Zamboni Berra, Felipe Lima Dos Santos, Yan Mathias Alves, Ludmilla Leidianne Limirio Souza, Antônio Carlos Vieira Ramos, Luiz Henrique Arroyo, Juliane de Almeida Crispim, Ione Carvalho Pinto, Pedro Fredemir Palha and 6 more

Abstract read
In one paragraph

Article in Infectious diseases of poverty, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Alexandre Tadashi Inomata BruceDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil. alexandreinomata@gmail.com.ORCID http://orcid.org/0000-0002-7008-8023
Thais Zamboni BerraDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Felipe Lima Dos SantosDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Yan Mathias AlvesDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Ludmilla Leidianne Limirio SouzaDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Antônio Carlos Vieira RamosDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Luiz Henrique ArroyoDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Juliane de Almeida CrispimDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Ione Carvalho PintoDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Pedro Fredemir PalhaDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Aline Aparecida MonroeDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Mellina YamamuraDepartment of Nursing, Federal University of São Carlos, São Carlos, São Paulo, Brazil.
Regina Célia FioratiDepartment of Neurosciences and Behavioral Sciences, Ribeirão Preto Medical School of the University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
Ana Carolina Scarpel MoncaioDepartment of Nursing, Federal University of Catalão, Catalão, Goiás, Brazil.
Dulce Maria de Oliveira GomesMathematics Department, University of Évora, Évora, Portugal.
Ricardo Alexandre ArcêncioDepartment of Maternal-Infant and Public Health Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 130203/2019-0
6 · The paper itself

Abstract

backgroundAlthough preventable and curable, tuberculosis (TB) still occurs in poor or developing countries, mainly in metropolitan regions of larger cities. The disease is a serious public health problem, and is directly linked to social issues. We analyzed temporal trend variations in areas at risk for concomitant TB, and characterized the clinical and epidemiological profiles of cases in a hyperendemic municipality in the Amazon region of Brazil.

methodsThis ecological study was performed in the municipality of Manaus, in northern Brazil. The population comprised cases with concomitant pulmonary and extrapulmonary TB, registered on the Notifiable Diseases Information System (SINAN), between January 1, 2009 and December 31, 2018. For risk cluster detection, spatial and spatiotemporal scanning statistical techniques were used. The Spatial Variation in Temporal Trends (SVTT) approach was used to detect and infer clusters for significantly different time trends.

resultsBetween 2009 and 2018, 873 concomitant TB cases were registered in Manaus. By using purely spatial scanning statistics, we identified two risk clusters. The relative risk (RR) of the clusters was 2.21 (95% confidence interval [CI]: 1.57-2.88; P = 0.0031) and 2.03 (95% CI: 1.58-2.58; P = 0.0029). Using space-time scanning, we identified a risk cluster with an RR of 3.57 (95% CI: 2.84-4.41; P = 0.014), between 2017 and 2018. For SVTT analyses, three clusters with spatial variations were detected in the significant temporal trends: SVTT 1 (P = 0.042), SVTT 2 (P = 0.046) and SVTT 3 (P = 0.036).

conclusionsIn Brazil, several TB-determining factors such as race/color, gender, low educational level and low income overlap in needy urban areas and communities, demonstrating that it is unlikely to reach the goals, agreed and launched with the END TB Strategy within the deadlines of international agreements, if there is no reduction in existing inequities determinants and risk of illness in the country.

Indexed as

AdolescentAdultAgedAged, 80 and overBrazilChildChild, PreschoolFemaleGeographyHumansInfantMaleMiddle AgedRisk FactorsSpatio-Temporal AnalysisTuberculosisEpidemiologyPublic healthSpatial analysisSpatio-temporal analysisTuberculosis

Identifiers

PMID32778170
PMCPMC7418188

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.