Evidence map›Paper›PMID 21144791›Full record

ArticleHealth & place2011

Geographic disparities in late-stage breast cancer diagnosis in California.

Tzy-Mey Kuo, Lee R Mobley, Luc Anselin

Abstract read
In one paragraph

Article in Health & place, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Phenotype Discovery and Geographic Disparities of Late-Stage Breast Cancer Diagnosis across U.S. Counties: A Machine Learning Approach.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022
    Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Modeling Geospatial Patterns of Late-Stage Diagnosis of Breast Cancer in the US.International journal of environmental research and public health · 2017
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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.

Tzy-Mey KuoRTI International, 3040 Cornwallis Road, Research Triangle Park, NC 27709-2194, USA. mkuo@rti.org
Lee R Mobley
Luc Anselin

Funding

Geospatial Factors & Impacts: Measurement and UseR01CA126858 · NCI · RESEARCH TRIANGLE INSTITUTE · PI ANSELIN, LUC, MOBLEY, LEE R · 2007 to 2015
$3.4M
NCI NIH HHS 1R01CA126858-01A1NCI NIH HHS R01 CA126858
6 · The paper itself

Abstract

Using cancer registry data for the population of California women aged 67+ with breast cancers, we estimated random intercept logistic models to examine how two socio-ecological predictors (residential isolation and poverty) were associated with probability of late-stage diagnosis for breast cancer. Using the multilevel modeling results, we calculated fully adjusted predicted probabilities associated with women in each Medical Service Study Area (MSSA) in California and classified the areas into two distinct groups: MSSAs with predicted rates below the 25th percentile (presumably the better outcome areas) and MSSAs with predicted rates above the 75th percentile (presumably the worse outcome areas) for two minority groups. Some areas had better outcomes for one group but worse outcomes for the other, suggesting that interventions to improve outcomes need different strategies for different groups in the same areas. Using information from geographic risk factors and multilevel modeling, this study informs interventions designed to reduce disparities in breast cancer outcomes.

Indexed as

AgedAged, 80 and overBreast NeoplasmsCaliforniaDelayed DiagnosisFemaleGeography, MedicalHealthcare DisparitiesHumansMinority GroupsRacial GroupsRisk FactorsSEER Program

Identifiers

PMID21144791
PMCPMC3091974

What OpenQuestion holds

Textmetadata
LicenceTDM
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.