ArticleJournal of registry management2019
Utility of Using Cancer Registry Data to Identify Patients for Tobacco Treatment Trials.
Article in Journal of registry management, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Leveraging large language models to extract smoking history from clinical notes for lung cancer surveillance.NPJ digital medicine · 2025Article
- Population-Based Data Linkage Describing Patterns of Cancer Clinical Trial Enrollment Among Children and Adolescents.JCO oncology practice · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundMany tobacco dependent cancer survivors continue to smoke after diagnosis and treatment. This study investigated the extent to which hospital-based cancer registries could be used to identify smokers in order to offer them assistance in quitting. The concordance of tobacco use coded in the registry was compared with tobacco use as coded in the accompanying Electronic Health Records (EHRs).
methodsWe gathered data from three hospital-based cancer registries in New York City during June 2014 to December 2016. For each patient identified as a current combustible tobacco user in the cancer registries, we abstracted tobacco use data from their EHR to independently code and corroborate smoking status. We calculated the proportion of current smokers, former smokers, and never smokers as indicated in the EHR for the hospitals, cancer site, cancer stage, and sex. We used a logistic regression model to estimate the log odds of the registry-based smoking status correctly predicting the EHR-based smoking status.
resultsAgreement in current smoking status between the registry-based smoking status and the EHR-based smoking status was 65%, 71%, and 90% at the three participating hospitals. Logistic regression results indicated that agreement in smoking status between the registry and the EHRs varied by hospital, cancer type, and stage, but not by age and sex.
conclusionsThe utility of using tobacco use data in cancer registries for population-based tobacco treatment interventions is dependent on multiple factors including accurate entry into EHR systems, updated data, and consistent smoking status definitions and registry coding protocols. Our study found that accuracy varied across the three hospitals and may not be able to inform interventions at these hospitals at this time. Several changes may be needed to improve the coding of tobacco use status in EHRs and registries.
Indexed as
Identifiers
32010425PMC6993933What 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.