Validating the use of veterans affairs tobacco health factors for assessing change in smoking status: accuracy, availability, and approach

Abstract

BackgroundAccurate smoking status is key for research purposes, but can be costly and difficult to measure. Within the Veteran’s Health Administration (VA), smoking status is recorded as part of routine care as “health factors” (HF)—fields that researchers can query through the electronic health record (EHR). Many researchers are interested in using these fields to track changes in smoking status over time, however the validity of this measure for assessing change is unknown. The primary goal of this project was to examine whether HFs can be used to accurately measure change in tobacco status over time, with secondary goals of assessing the optimum timeframe for assessment and variation in accuracy by site.MethodsSecondary analysis of the Veterans VICTORY study, a pragmatic smoking cessation randomized controlled trial conducted from 2009 to 2011. Eligible subjects were identified via the EHR using a past 90-day HF indicating current tobacco use (for example: “CURRENT SMOKER”, “CURRENTLY USES TOBACCO”). Participants were surveyed at 1 year to determine prolonged smoking abstinence. We identified HFs for tobacco status within +/− 120 days of the follow-up survey mailing date and recorded the temporally closest HF. Among subjects with both measures, we compared the two for agreement using kappa statistics and concordance.Results1713 subjects (33%) had both follow-up survey and HF data, 1594 (31%) had only a survey response, 790 (15%) had only HF and 1026 (20%) had neither. For subjects with both measures, there was 90% concordance and moderate agreement (Kappa 0.48, 95%CI 0.41–0.55, Sensitivity 54.4, 95%CI 41.1–67.7, Specificity 94.3, 95%CI 87.5–100.0).ConclusionsWe found high concordance but only moderate agreement by kappa statistics between HFs and survey data. The difference is likely accounted for by the natural history of quit attempts, in which patients cycle in and out of quit attempts. HFs appear to provide an accurate measure of population level quit behavior utilizing data collected in the course of clinical care.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0501-2) contains supplementary material, which is available to authorized users.

Tobacco Cessation in Affordable Care Act Medicaid Expansion States Versus Non-expansion States

“…Our primary outcome was tobacco cessation ("quit") during the post-period, coded as a binary yes/ no variable. Using methods similar to prior EHR-based studies, 11,[19][20][21][22] a person was identified as "quit" if the last recorded tobacco-use status during the pre-period indicated that the patient was a current user, and if there was at least one subsequent measurement documented in the postperiod that indicated the patient's status was a "nonuser" (eg, former user, not a current user). We extracted the following tobacco cessation medications from EHR medication orders: bupropion, varenicline, and all nicotine replacement products; as a proxy for utilization of care, we extracted data on the number of post-period office visits per patient (≥6 vs. <6 visits) based on previous studies.…”

Real World Outcomes versus Clinical Trial Results of Durvalumab Maintenance in Veterans with Stage III Non-Small Cell Lung Cancer

“…Charlson Comorbidity Index (CCI) [4,5] was calculated from inpatient and outpatient ICD-10 diagnosis codes in the year before durvalumab start (Cohort 1) or the proxy durvalumab start date described below (Cohort 2). Smoking status was obtained through Health Factors data [6,7]. A concurrent chemotherapy regimen was obtained through intravenous infusion records and supplemented with the VACRS where available.…”

Affordable Care Act Medicaid expansion and access to primary-care based smoking cessation assistance among cancer survivors: an observational cohort study

“…Smoking cessation (‘quit’) at ≥1 visit during the post-period was coded as a binary yes/no variable. Using methods similar to prior EHR-based studies [ 31 – 35 ], a person was identified as ‘quit’ if there was at least one measurement documented in the post-period that indicated the patient’s status changed from one indicating current smoking to one indicating former smoking (regardless of whether the patient had a subsequent status that indicated a return to smoking). For smoking cessation medication provision, we extracted orders for bupropion, varenicline, and all nicotine replacement products from EHR medication orders.…”