Evidence map›Paper›PMID 39141060›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2024

Disparities in Cancer Stage of Diagnosis by Rurality in California, 2015 to 2019.

Debora L Oh, Katarina Wang, Debbie Goldberg, Karen Schumacher, Juan Yang, Katherine Lin, Scarlett Lin Gomez, Salma Shariff-Marco

Abstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
  2. Assessment of Rebound in Cancer Diagnoses in California in 2022, by Stage, Sociodemographic Factors, and Rurality.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026
    Article
  3. Article
  4. 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.

Debora L OhDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.ORCID 0000-0001-8229-5372
Katarina WangDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.ORCID 0000-0002-7804-4850
Debbie GoldbergDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.ORCID 0009-0008-0428-2769
Karen SchumacherSchool of Nursing, University of California San Francisco, San Francisco, California.ORCID 0000-0002-3349-4727
Juan YangDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.ORCID 0000-0002-2291-7907
Katherine LinDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.ORCID 0000-0002-8948-7630
Scarlett Lin GomezDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.ORCID 0000-0002-5143-4867
Salma Shariff-MarcoDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.ORCID 0000-0002-2541-3333

Funding

California Department of Public Health (CDPH) Safety Code Section 103885Centers for Disease Control and Prevention (CDC) 5NU58DP006344NCCDPHP CDC HHS NU58DP006344NCI NIH HHS HHSN261201800009CNCI NIH HHS HHSN261201800009INCI NIH HHS HHSN261201800015CNCI NIH HHS HHSN261201800015INCI NIH HHS HHSN261201800032CNCI NIH HHS HHSN261201800032I
6 · The paper itself

Abstract

backgroundCancer rates in rural areas vary by insurance status, socioeconomic status, region, race, and ethnicity.

methodsCalifornia Cancer Registry data (2015-2019) were used to investigate the stage of diagnosis by levels of rurality for the five most common cancers. The percentage of residents in rural blocks within census tract aggregation zones was categorized into deciles up to 50%. Multivariable logistic regression was used to estimate associations with rurality, with separate models by cancer site, sex, race, and ethnicity (non-Hispanic White and Hispanic). Covariates included individual-level and zone-level factors.

resultsThe percentage of late-stage cancer diagnosis was 28% for female breast, 27% for male prostate, 77% for male lung, 71% for female lung, 60% for male colorectal, 59% for female colorectal, 7.8% for male melanoma, and 5.9% for female melanoma. Increasing rurality was significantly associated with increased odds of late-stage cancer diagnosis for female breast cancer (Ptrend < 0.001), male lung cancer (Ptrend < 0.001), female lung cancer (Ptrend < 0.001), and male melanoma (Ptrend = 0.01), after adjusting for individual-level and zone-level factors. The strength of associations varied by sex and ethnicity. For males with lung cancer, odds of late-stage diagnosis in areas with >50% rural population was 1.24 (95% confidence interval, 1.06-1.45) for non-Hispanic White patients and 2.14 (95% confidence interval, 0.86-5.31) for Hispanic patients, compared with areas with 0% rural residents.

conclusionsIncreasing rurality was associated with increased odds for late-stage diagnosis for breast cancer, lung cancer, and melanoma, with the strength of associations varying across sex and ethnicity. IMPACT: Our findings will inform cancer outreach to these rural subpopulations.

Indexed as

NeoplasmsNeoplasm StagingRural PopulationAdultAgedCaliforniaFemaleHealthcare DisparitiesHumansMaleMiddle AgedRegistries

Identifiers

PMID39141060
PMCPMC11530323

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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.