title: Changes in Veteran Tobacco Use Identified in Electronic Medical Records.
abstract: BACKGROUND
  Electronic medical records represent a new source of longitudinal data on tobacco use.
  
  
  METHODS
  Electronic medical records of the U.S. Department of Veterans Affairs were extracted to find patients' tobacco use status in 2009 and at another assessment 12-24 months later. Records from the year prior to the first assessment were used to determine patient demographics and comorbidities. These data were analyzed in 2015.
  
  
  RESULTS
  An annual quit rate of 12.0% was observed in 754,504 current tobacco users. Adjusted tobacco use prevalence at follow-up was 3.2% greater with alcohol use disorders at baseline, 1.9% greater with drug use disorders, 3.3% greater with schizophrenia, and lower in patients with cancer, heart disease, and other medical conditions (all differences statistically significant with p<0.05). Annual relapse rates in 412,979 former tobacco users were 29.6% in those who had quit for <1 year, 9.7% in those who had quit for 1-7 years, and 1.9% of those who had quit for >7 years. Among those who had quit for <1 year, adjusted relapse rates were 4.3% greater with alcohol use disorders and 7.2% greater with drug use disorders (statistically significant with p<0.05).
  
  
  CONCLUSIONS
  High annual cessation rates may reflect the older age and greater comorbidities of the cohort or the intensive cessation efforts of the U.S. Department of Veterans Affairs. The lower cessation and higher relapse rates in psychiatric and substance use disorders suggest that these groups will need intensive and sustained cessation efforts.
authors.affiliation: VA Health Economics Resource Center, Menlo Park, California; VA Center for Innovation to Implementation, Menlo Park, California; Department of Health Research Policy, Stanford University School of Medicine, Stanford, California. Electronic address: paul.barnett@va.gov.
authors.forename: Paul G
authors.initials: PG
authors.lastname: Barnett
authors.affiliation: VA Health Economics Resource Center, Menlo Park, California; VA Center for Innovation to Implementation, Menlo Park, California.
authors.forename: Adam
authors.initials: A
authors.lastname: Chow
authors.affiliation: VA Health Economics Resource Center, Menlo Park, California; VA Center for Innovation to Implementation, Menlo Park, California.
authors.forename: Nicole E
authors.initials: NE
authors.lastname: Flores
authors.affiliation: New York Harbor VA Health Care System, New York, New York; Department of Population Health, New York University School of Medicine, New York, New York.
authors.forename: Scott E
authors.initials: SE
authors.lastname: Sherman
authors.affiliation: VA Center for Clinical Management Research, Ann Arbor, Michigan; College of Nursing, Ohio State University, Columbus, Ohio.
authors.forename: Sonia A
authors.initials: SA
authors.lastname: Duffy
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000437:Alcoholism / Q000453:epidemiology*; D015331:Cohort Studies; D015897:Comorbidity; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D005500:Follow-Up Studies; D006331:Heart Diseases / Q000453:epidemiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000453:epidemiology*; D015995:Prevalence; D012008:Recurrence; D012559:Schizophrenia / Q000453:epidemiology*; D016540:Smoking Cessation / Q000706:statistics & numerical data*; D014029:Tobacco Use Disorder / Q000453:epidemiology* / Q000517:prevention & control; D014481:United States / Q000453:epidemiology; D014493:United States Department of Veterans Affairs / Q000706:statistics & numerical data; D014728:Veterans / Q000523:psychology / Q000706:statistics & numerical data*