ArticleCancer causes & control : CCC2019
Racial disparities of liver cancer mortality in Wisconsin.
Article in Cancer causes & control : CCC, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Take charge during treatment: a protocol for a pilot study to evaluate the impact of exercise on cardiovascular biomarkers among black and white breast cancer patients undergoing treatment.Cardio-oncology (London, England) · 2025Article
- Comparing the effectiveness of implementation strategies to improve liver and colon cancer screening for Veterans: protocol for a large cluster-randomized implementation study.Implementation science : IS · 2025Article
- Historical redlining and mortality in children, adolescents, and young adults with cancer in California, 2000-2019.Journal of the National Cancer Institute · 2025Article
- Burden of liver cancer mortality by county, race, and ethnicity in the USA, 2000-19: a systematic analysis of health disparities.The Lancet. Public health · 2024Article
- Ecological Study of Variability in the Relationship between Liver Cancer Mortality and Racial Residential Segregation.International journal of environmental research and public health · 2021Article
- Community and Research Perspectives on Cancer Disparities in Wisconsin.Preventing chronic disease · 2020Article
- Prediction Model for Immunotherapy Efficacy in Hepatocellular Carcinoma Based on Alternative Splicing Sequencing Data.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo calculate tract-level estimates of liver cancer mortality in Wisconsin and identify relationships with racial and socioeconomic variables.
methodsCounty-level standardized mortality ratios (SMRs) of liver cancer in Wisconsin were calculated using traditional indirect adjustment methods for cases from 2003 to 2012. Tract-level SMRs were calculated using adaptive spatial filtering (ASF). The tract-level SMRs were checked for correlations to a socioeconomic advantage index (SEA) and percent racial composition. Non-spatial and spatial regression analyses with tract-level SMR as the outcome were conducted.
resultsCounty-level SMR estimates were shown to mask much of the variance within counties across their tracts. Liver cancer mortality was strongly correlated with the percent of Black residents in a census tract and moderately associated with SEA. In the multivariate spatially-adjusted regression analysis, only Percent Black composition remained significantly associated with an increased liver cancer SMR.
conclusionsUsing ASF, we developed a high-resolution map of liver cancer mortality in Wisconsin. This map provided details on the distribution of liver cancer that were inaccessible in the county-level map. These tract-level estimates were associated with several racial and socioeconomic variables.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.