ArticleAnnals of epidemiology2025
Validation of data in the Veteran health administration electronic medical record for identification of tobacco use.
Article in Annals of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
purposeTobacco use is not commonly represented as computable information in the electronic health record (EHR). We developed an algorithm in the Veterans Health Administration (VHA) to identify tobacco ever-use among Veterans.
methodsWe used the VHA corporate data warehouse to develop an algorithm comprised of multiple data types (health factors [semi-structured template data entry and decision support tools], billing, orders, medication, and encounter codes) to identify tobacco ever-use (current or former) versus never use. Algorithm accuracy was compared to two reference standards: (1) EHR abstraction cohort and (2) Veteran self-reported survey cohort. We calculated the sensitivity and positive predictive values (PPV) for the algorithm and stratified by its data types for the EHR abstraction cohort. We calculated the sensitivity, specificity, PPV, and negative predictive value (NPV) for the algorithm and stratified by its data types for the survey cohort.
resultsThe algorithm correctly identified 424 of 426 individuals with tobacco ever-use when compared to data abstracted from the EHR: sensitivity 1.00 (95 % CI 0.98-1.00); PPV 1.00 (95 % CI 0.98-1.00). Compared to survey data, the algorithm correctly identified 514 of 547 participants with tobacco ever-use: sensitivity 0.94 (95 % CI 0.92-0.96); PPV 0.88 (95 % CI 0.85-0.91). The specificity was 0.53 (95 % CI 0.45-0.62), and NPV of 0.70 (95 % CI 0.61-0.79). Of all data types, health factors had the highest sensitivity in both cohorts.
conclusionsThis novel tool had excellent sensitivity and PPV for tobacco ever-use in two cohorts. Future research should study this tool to support preventive healthcare services.
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.