title: A comparison of average wages with age-specific wages for assessing indirect productivity losses: analytic simplicity versus analytic precision.
abstract: OBJECTIVE
  Numerous approaches are used to estimate indirect productivity losses using various wage estimates applied to poor health in working aged adults. Considering the different wage estimation approaches observed in the published literature, we sought to assess variation in productivity loss estimates when using average wages compared with age-specific wages.
  
  
  METHODS
  Published estimates for average and age-specific wages for combined male/female wages were obtained from the UK Office of National Statistics. A polynomial interpolation was used to convert 5-year age-banded wage data into annual age-specific wages estimates. To compare indirect cost estimates, average wages and age-specific wages were used to project productivity losses at various stages of life based on the human capital approach. Discount rates of 0, 3, and 6 % were applied to projected age-specific and average wage losses.
  
  
  RESULTS
  Using average wages was found to overestimate lifetime wages in conditions afflicting those aged 1-27 and 57-67, while underestimating lifetime wages in those aged 27-57. The difference was most significant for children where average wage overestimated wages by 15 % and for 40-year-olds where it underestimated wages by 14 %.
  
  
  CONCLUSIONS
  Large differences in projecting productivity losses exist when using the average wage applied over a lifetime. Specifically, use of average wages overestimates productivity losses between 8 and 15 % for childhood illnesses. Furthermore, during prime working years, use of average wages will underestimate productivity losses by 14 %. We suggest that to achieve more precise estimates of productivity losses, age-specific wages should become the standard analytic approach.
authors.affiliation: Unit of PharmacoEpidemiology and PharmacoEconomics, Department of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands. m.connolly@rug.nl.|Global Market Access Solutions (GMAS), St-Prex, Switzerland. m.connolly@rug.nl.
authors.forename: Mark P
authors.initials: MP
authors.lastname: Connolly
authors.affiliation: Global Market Access Solutions (GMAS), St-Prex, Switzerland.
authors.forename: Cole
authors.initials: C
authors.lastname: Tashjian
authors.affiliation: Global Market Access Solutions (GMAS), St-Prex, Switzerland.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Kotsopoulos
authors.affiliation: John Radcliffe Hospital, Oxford, OX3 9DU, UK.
authors.forename: Aomesh
authors.initials: A
authors.lastname: Bhatt
authors.affiliation: Unit of PharmacoEpidemiology and PharmacoEconomics, Department of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV, Groningen, The Netherlands.|Institute of Science in Healthy Aging and Healthcare (SHARE), University Medical Center Groningen (UMCG), Groningen, The Netherlands.|Department of Epidemiology, University Medical Center Groningen (UMCG), Groningen, The Netherlands.
authors.forename: Maarten J
authors.initials: MJ
authors.lastname: Postma
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D002648:Child; D002675:Child, Preschool; D017281:Cost of Illness; D004526:Efficiency; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D018803:Models, Economic; D012456:Salaries and Fringe Benefits / Q000706:statistics & numerical data*; D006113:United Kingdom; D055815:Young Adult
keywords: Age-specific wages; Average wages; Indirect costs; Productivity