Evidence map›Paper›PMID 41588111›Full record

ArticleScientific reports2026

Spatial neighborhood patterns of pulmonary tuberculosis in a large urban area: the case of Santiago, Chile.

S Ayala, N Escobar, L Vizeu Barrozo, F Chiaravalloti-Neto, M Canals

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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
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0citing papers in PubMed
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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

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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

5 authors.

S AyalaCentro de Epidemiología y Políticas de Salud, Facultad de Medicina Clínica Alemana, Universidad Del Desarrollo, Santiago, Chile. salvadorayala@udd.cl.
N EscobarPrograma de Control y Eliminación de la Tuberculosis, Ministerio de Salud, Gobierno de Chile, Santiago, Chile.
L Vizeu BarrozoFaculty of Philosophy, Languages and Human Sciences, Department of Geography, University of São Paulo, São Paulo, Brazil.
F Chiaravalloti-NetoDepartment of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brasil.
M CanalsPrograma de Salud Ambiental, Escuela de Salud Pública, Facultad de Medicina, Universidad de Chile, Santiago, Chile.

Funding

ANID FONDECYT Regular 1251995ANID's National Doctoral Fellowship 21191111
6 · The paper itself

Abstract

In recent years, global concern about tuberculosis (TB) has grown due to the slow progress in achieving control goals, with a record number of cases reported worldwide in 2022. This study analyzed the spatial distribution of all pulmonary TB cases reported between 2016 and 2020 in the Gran Santiago (n = 3348), Chile's largest urban area. Cases were geocoded using an automated cascade method (Bing Maps, Google Maps, and manual verification) and aggregated at the neighborhood level. To detect spatial clusters, we estimated gender-adjusted rates, assessed spatial autocorrelation using Moran's I, and identified spatial clusters with Flexible Scan Statistics. Bivariate analyses were used to explore associations with demographic and residential variables. We observed higher incidence rates in males compared to females (13.23 vs. 7.03 per 100,000), with no statistically significant changes over time. A total of 29.4% (n = 984) of all cases were concentrated in 11 significant spatial clusters. These clusters were characterized by a higher proportion of immigrant, Indigenous, and older populations, along with indicators of precarious living conditions, including overcrowding and tenement housing. Identifying spatial patterns of pulmonary TB can support the design of targeted, community-based case-finding strategies in urban settings, particularly in populations at higher social and structural risk.

Indexed as

Neighborhood CharacteristicsTuberculosis, PulmonaryChileFemaleHumansIncidenceMaleResidence CharacteristicsSpatial AnalysisUrban PopulationNeighborhood characteristicsSpatial analysisTuberculosis

Identifiers

PMID41588111
PMCPMC12905264

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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.