Dataset Information

ICD-9 tobacco use codes are effective identifiers of smoking status.

ABSTRACT:

Objective

To evaluate the validity of, characterize the usage of, and propose potential research applications for International Classification of Diseases, Ninth Revision (ICD-9) tobacco codes in clinical populations.

Materials and methods

Using data on cancer cases and cancer-free controls from Vanderbilt's biorepository, BioVU, we evaluated the utility of ICD-9 tobacco use codes to identify ever-smokers in general and high smoking prevalence (lung cancer) clinic populations. We assessed potential biases in documentation, and performed temporal analysis relating transitions between smoking codes to smoking cessation attempts. We also examined the suitability of these codes for use in genetic association analyses.

Results

ICD-9 tobacco use codes can identify smokers in a general clinic population (specificity of 1, sensitivity of 0.32), and there is little evidence of documentation bias. Frequency of code transitions between 'current' and 'former' tobacco use was significantly correlated with initial success at smoking cessation (p<0.0001). Finally, code-based smoking status assignment is a comparable covariate to text-based smoking status for genetic association studies.

Discussion

Our results support the use of ICD-9 tobacco use codes for identifying smokers in a clinical population. Furthermore, with some limitations, these codes are suitable for adjustment of smoking status in genetic studies utilizing electronic health records.

Conclusions

Researchers should not be deterred by the unavailability of full-text records to determine smoking status if they have ICD-9 code histories.

SUBMITTER: Wiley LK

PROVIDER: S-EPMC3721171 | biostudies-literature | 2013 Jul-Aug

REPOSITORIES: biostudies-literature

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Publications

ICD-9 tobacco use codes are effective identifiers of smoking status.

Wiley Laura K LK Shah Anushi A Xu Hua H Bush William S WS

Journal of the American Medical Informatics Association : JAMIA 20130209 4

<h4>Objective</h4>To evaluate the validity of, characterize the usage of, and propose potential research applications for International Classification of Diseases, Ninth Revision (ICD-9) tobacco codes in clinical populations.<h4>Materials and methods</h4>Using data on cancer cases and cancer-free controls from Vanderbilt's biorepository, BioVU, we evaluated the utility of ICD-9 tobacco use codes to identify ever-smokers in general and high smoking prevalence (lung cancer) clinic populations. We ...[more]

PMID: 23396545

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Project description:BACKGROUND:The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). OBJECTIVE:The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. METHODS:We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. RESULTS:We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). CONCLUSIONS:This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.

Project description:IntroductionHealth professionals' own beliefs and practices, especially their smoking status, has been described to strongly influence their willingness to provide brief tobacco interventions (5 A's) to their patients. This study examines the association between the smoking status of faculty members in US dental programmes and (1) practice pattern; (2) perceived confidence; and (3) perceived educational preparedness of new graduates in providing the 5 A's to their patients.MethodsThis study presents data from the National Tobacco Survey of Personnel in Dental and Allied Academic Programs (TSPDAP) conducted in 2018. Faculty members in US dental/allied dental schools were invited to participate in this survey. Data were stratified based on the smoking status of the respondents as "never" and "ever" smokers (smoked <100 and ≥100 cigarettes during their lifetime, respectively). Multiple logistic regression models were used to calculate the adjusted odds ratios (aORs) and 95% confidence intervals (CIs).ResultsData of 1896 participants were analysed, of whom 1032 (54.4%) were categorised as "ever" smokers. In the final regression model, low perceived barrier score was significantly associated with high practice pattern (aOR, 0.94; 95% CI, 0.92-0.97), high perceived confidence (aOR, 0.92; 95% CI, 0.90-0.95), and high perceived educational preparedness (aOR, 0.97; 95% CI, 0.94-0.98) in delivering the 5 A's to patients. Similarly, high perceived effectiveness was significantly associated with high practice pattern (aOR, 1.08; 95% CI, 1.05-1.11), high perceived confidence (aOR, 1.10; 95% CI, 1.06-1.13), and high perceived educational preparedness (aOR, 1.06; 95% CI, 1.03-1.09) in delivering the 5 A's to their patients. The smoking status of the dental personnel did not show any significant association with practice pattern, perceived confidence, or perceived educational preparedness in delivering the 5 A's to their patients.ConclusionsThe smoking status of oral health care personnel was not significantly associated with their participation in tobacco cessation interventions.

Project description:Background On October 1, 2015, the Center for Medicare and Medicaid Services transitioned from the International Classification of Diseases, Ninth Revision ( ICD-9) to the ICD, Tenth Revision ( ICD-10) compendium of codes for diagnosis and billing in health care, but translation between the two is often inexact. Here we describe a validated crosswalk to translate ICD-9 codes into ICD-10 codes, with a focus on complications after carotid revascularization and endovascular aortic aneurysm repair. Methods and Results We devised an 8-step process to derive and validate ICD-10 codes from existing ICD-9 codes. We used publicly available sources, including the General Equivalence Mapping database, to translate ICD-9 codes used in prior work to ICD-10 codes. We defined ICD-10 codes as validated if they were concordant with the initial ICD-9 codes after manual comparison by two physicians. Our primary validation measure was the percent of valid ICD-10 codes out of the total ICD-10 codes obtained during translation. We began with 126 ICD-9 diagnosis codes used for complication identification after carotid revascularization procedures, and 97 ICD-9 codes for complications after endovascular aortic aneurysm procedures. Translation generated 143 ICD-10 codes for carotid revascularization, a 14% increase from the initial 126 codes. Manual comparison demonstrated 98% concordance, with 99% agreement between the reviewers. Similarly, we identified 108 ICD-10 codes for endovascular aortic aneurysm repair, an 11% increase from the initial 97 ICD-9 codes. We again noted excellent concordance and agreement (98% and 100%, respectively). Manual review identified 4 ICD-10 codes incorrectly translated from ICD-9 codes for carotid revascularization, and 3 codes incorrectly translated for endovascular aortic aneurysm repair. Conclusions Algorithms to crosswalk lists of ICD-9 codes to ICD-10 codes can leverage electronic resources to minimize the burden of code translation. However, manual review for code validation may be necessary, with collaboration across institutions for researchers to share their efforts.

Project description: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.

Project description:Background and aimsLongitudinal electronic health record (EHR) data offer a large-scale, untapped source of phenotypical information on harmful alcohol use. Using established, alcohol-associated variants in the gene that encodes the enzyme alcohol dehydrogenase 1B (ADH1B) as criterion standards, we compared the individual and combined validity of three longitudinal EHR-based phenotypes of harmful alcohol use: Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) trajectories; mean age-adjusted AUDIT-C; and diagnoses of alcohol use disorder (AUD).DesignWith longitudinal EHR data from the Million Veteran Program (MVP) linked to genetic data, we used two population-specific polymorphisms in ADH1B that are associated strongly with AUD in African Americans (AAs) and European Americans (EAs): rs2066702 (Arg369Cys, AAs) and rs1229984 (Arg48His, EAs) as criterion measures.SettingUnited States Department of Veterans Affairs Healthcare System.ParticipantsA total of 167 721 veterans (57 677 AAs and 110 044 EAs; 92% male, mean age = 63 years) took part in this study. Data were collected from 1 October 2007 to 1 May 2017.MeasurementsUsing all AUDIT-C scores and AUD diagnostic codes recorded in the EHR, we calculated age-adjusted mean AUDIT-C values, longitudinal statistical trajectories of AUDIT-C scores and ICD-9/10 diagnostic groupings for AUD.FindingsA total of 19 793 AAs (34.3%) had one or two minor alleles at rs2066702 [minor allele frequency (MAF) = 0.190] and 6933 EAs (6.3%) had one or two minor alleles at rs1229984 (MAF = 0.032). In both populations, trajectories and age-adjusted mean AUDIT-C were correlated (r = 0.90) but, when considered separately, highest score (8+ versus 0) of age-adjusted mean AUDIT-C demonstrated a stronger association with the ADH1B variants [adjusted odds ratio (aOR) 0.54 in AAs and 0.37 in AAs] than did the highest trajectory (aOR 0.71 in AAs and 0.53 in EAs); combining AUDIT-C metrics did not improve discrimination. When age-adjusted mean AUDIT-C score and AUD diagnoses were considered together, age-adjusted mean AUDIT-C (8+ versus 0) was associated with lower odds of having the ADH1B minor allele than were AUD diagnostic codes: aOR = 0.59 versus 0.86 in AAs and 0.48 versus 0.68 in EAs. These independent associations combine to yield an even lower aOR of 0.51 for AAs and 0.33 for EAs.ConclusionsThe age-adjusted mean AUDIT-C score is associated more strongly with genetic polymorphisms of known risk for alcohol use disorder than are longitudinal trajectories of AUDIT-C or AUD diagnostic codes. AUD diagnostic codes modestly enhance this association.

Dataset Information

ICD-9 tobacco use codes are effective identifiers of smoking status.

Objective

Materials and methods

Results

Discussion

Conclusions

Publications

ICD-9 tobacco use codes are effective identifiers of smoking status.

Similar Datasets

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