Evidence map›Paper›PMID 33244433›Full record

ArticleJournal of clinical and translational science2020

Mapping mammography in Arkansas: Locating areas with poor spatial access to breast cancer screening using optimization models and geographic information systems.

Sean G Young, Meghan Ayers, Sharp F Malak

Abstract read
In one paragraph

Article in Journal of clinical and translational science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. A Novel Approach for Conducting a Catchment Area Analysis of Breast Cancer by Age and Stage for a Community Cancer Center.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024
    Article
  3. Article
  4. Article
  5. Breast Cancer Disparities and the Impact of Geography.Surgical oncology clinics of North America · 2022
    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

3 authors.

Sean G YoungDepartment of Environmental and Occupational Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA.ORCID https://orcid.org/0000-0002-0054-0627
Meghan Ayers *Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Sharp F Malak *Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArkansans have some of the worst breast cancer mortality to incidence ratios in the United States (5th for Blacks, 4th for Whites, 7th overall). Screening mammography allows for early detection and significant reductions in mortality, yet not all women have access to these life-saving services. Utilization in Arkansas is well below the national average, and the number of FDA-approved screening facilities has decreased by 38% since 2001. Spatial accessibility plays an important role in whether women receive screenings.

methodsWe use constrained optimization models within a geographic information system (GIS) to probabilistically allocate women to nearby screening facilities, accounting for facility capacity and patient travel time. We examine accessibility results by rurality derived from rural-urban commuting area (RUCA) codes.

resultsUnder most models, screening capacity is insufficient to meet theoretical demand given travel constraints. Approximately 80% of Arkansan women live within 30 minutes of a screening facility, most of which are located in urban and suburban areas. The majority of unallocated demand was in Small towns and Rural areas.

conclusionsGeographic disparities in screening mammography accessibility exist across Arkansas, but women living in Rural areas have particularly poor spatial access. Mobile mammography clinics can remove patient travel time constraints to help meet rural demand. More broadly, optimization models and GIS can be applied to many studies of healthcare accessibility in rural populations.

Indexed as

accessibilityBreast cancerGISmammographyrural healthscreening

Identifiers

PMID33244433
PMCPMC7681135

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