title: Predicting the impact of chronic health conditions on workplace productivity and accidents: results from two US Department of Energy national laboratories.
abstract: OBJECTIVE
  Examine associations of chronic health conditions on workplace productivity and accidents among US Department of Energy employees.
  
  
  METHODS
  The Health and Work Performance Questionnaire-Select was administered to a random sample of two Department of Energy national laboratory employees (46% response rate; N = 1854).
  
  
  RESULTS
  The majority (87.4%) reported having one or more chronic health conditions, with 43.4% reporting four or more conditions. A population-attributable risk proportions analysis suggests improvements of 4.5% in absenteeism, 5.1% in presenteeism, 8.9% in productivity, and 77% of accidents by reducing the number of conditions by one level. Depression was the only health condition associated with all four outcomes.
  
  
  CONCLUSIONS
  Results suggest that chronic conditions in this workforce are prevalent and costly. Efforts to prevent or reduce condition comorbidity among employees with multiple conditions can significantly reduce costs and workplace accident rates.
authors: From the University of Maryland School of Social Work (Dr Frey and Ms Ko), Baltimore; Florida State University (Dr Osteen), Tallahassee; Institute for Social Research (Ms Berglund), University of Michigan, Ann Arbor; and Integrated Benefits Institute (Dr Jinnett), San Francisco, Calif.
authors: Jodi Jacobson
authors: JJ
authors: Frey
authors: Philip J
authors: PJ
authors: Osteen
authors: Patricia A
authors: PA
authors: Berglund
authors: Kimberly
authors: K
authors: Jinnett
authors: Jungyai
authors: J
authors: Ko
mesh_terms: D000041:Absenteeism; D000062:Accidents, Occupational / Q000706:statistics & numerical data; D000328:Adult; D000368:Aged; D002908:Chronic Disease / Q000453:epidemiology; D003430:Cross-Sectional Studies; D004526:Efficiency; D005260:Female; D006801:Humans; D059232:Laboratory Personnel; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D000069597:Presenteeism / Q000706:statistics & numerical data; D015995:Prevalence; D012307:Risk Factors; D014481:United States / Q000453:epidemiology; D037041:United States Government Agencies