Evidence map›Paper›PMID 37642047›Full record

ArticleAsian Pacific journal of cancer prevention : APJCP2023

A Geospatial Model to Identify Areas Associated with Late- Stage Breast Cancer: A Spatial Epidemiology Approach.

Antonio Reyna-Sevilla, Miguel Ernesto Gonzalez-Castañeda, Igor Martin Ramos-Herrera, Celida Duque-Molina, Gabriela Borrayo-Sanchez, Ricardo Aviles-Hernandez, Carlos Quezada-Sanchez, Abelardo Flores-Morales

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Asian Pacific journal of cancer prevention : APJCP, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
3.0field-weighted citation impact, top 8% 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

2 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 2 institutions in 1 country.

Antonio Reyna-SevillaHealth Planning and Innovation Unit, Directorate of Medical Benefits, Mexican Social Security Institute, Cuauhtémoc, CDMX, Mexico.
Miguel Ernesto Gonzalez-CastañedaDepartment of Public Health, University Center for Health Sciences, University of Guadalajara, Guadalajara, Jalisco, Mexico.
Igor Martin Ramos-HerreraDepartment of Public Health, University Center for Health Sciences, University of Guadalajara, Guadalajara, Jalisco, Mexico.
Celida Duque-MolinaDirectorate of Medical Benefits, Mexican Social Security Institute, Cuauhtémoc, CDMX, Mexico.
Gabriela Borrayo-SanchezHealth Innovation Coordination, Health Planning and Innovation Unit, Medical Benefits Directorate, Mexican Social Security Institute, Cuauhtémoc, CDMX, Mexico.
Ricardo Aviles-HernandezHealth Planning and Innovation Unit, Directorate of Medical Benefits, Mexican Institute of Social Security, Cuauhtémoc, CDMX, Mexico.
Carlos Quezada-SanchezDirectorate of Medical Benefits, Mexican Social Security Institute, Cuauhtémoc, CDMX, Mexico.
Abelardo Flores-MoralesHealth Innovation Coordination, Health Planning and Innovation Unit, Medical Benefits Directorate, Mexican Social Security Institute, Cuauhtémoc, CDMX, Mexico.
Mexican Social Security Institute · MXUniversidad de Guadalajara · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe aim of this study was to show how a geospatial model can be used to identify areas with a higher probability for late-stage breast cancer (BC) diagnoses.

methodsOur study considered an ecological design. Clinical records at a tertiary care hospital were reviewed in order to obtain the place of residence and stage of the disease, which was classified as early (0-IIA) and late (IIB-IV) and whose diagnoses were made during the 2013-2017 period. Then, they were geolocated to identify the distribution and spatial trend. Subsequently, the pattern of location, i.e. scattered, random and concentrated, was statistically assessed and a geospatial model was elaborated to determine the probability of late diagnoses in the state of Jalisco, Mexico.

resultThere were 1 954 (N) geolocated BC diagnoses: 58.3% were late. During the five-year period, a southwest-northeast trend was identified, nearly 9.5% of the surface of Jalisco, where 6 out of 10 (n= 751) late- stage diagnoses were concentrated. A concentrated and statistically significant pattern was identified in the southern, central and northern Pacific area of Jalisco, where the geospatial model delimited the places with the highest probability of late clinical stages (p <0.05).

conclusionThe geographical differences associated with the late diagnoses of BC suggest it is necessary to adapt and focus the strategies for early detection as an alternative to create a major impact on the population. Reproducible analysis tools were used in other contexts where geolocation data are available to complement public policies and strategies aimed to control BC.

Indexed as

Breast NeoplasmsFemaleHumansMexicoProbabilityPublic PolicyBreast NeoplasmsGeographic MappingLate DiagnosesSpatial-Temporal Analysis

Identifiers

PMID37642047
PMCPMC10685234
OpenAlexW4386258006

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.