title: Predicting Productivity Losses from Health-Related Quality of Life Using Patient Data.
abstract: OBJECTIVE
  This paper estimates productivity loss using the health of the patient in order to allow indirect estimation of these costs for inclusion in economic evaluation.
  
  
  METHODS
  Data from two surveys of inpatients [Health outcomes data repository (HODaR) sample (n = 42,442) and health improvement and patient outcomes (HIPO) sample (n = 6046)] were used. The number of days off paid employment or normal activities (excluding paid employment) was modelled using the health of the patients measured by the EQ-5D, international classification of diseases (ICD) chapters, and other health and sociodemographic data. Two-part models (TPMs) and zero-inflated negative binomial (ZINB) models were identified as the most appropriate specifications, given large spikes at the minimum and maximum days for the dependent variable. Analysis was undertaken separately for the two datasets to account for differences in recall period and identification of those who were employed.
  
  
  RESULTS
  Models were able to reflect the large spike at the minimum (zero days) but not the maximum, with TPMs doing slightly better than the ZINB model. The EQ-5D was negatively associated with days off employment and normal activities in both datasets, but ICD chapters only had statistically significant coefficients for some chapters in the HODaR.
  
  
  CONCLUSIONS
  TPMs can be used to predict productivity loss associated with the health of the patient to inform economic evaluation. Limitations include recall and response bias and identification of who is employed in the HODaR, while the HIPO suffers from a small sample size. Both samples exclude some patient groups.
authors.affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK. c.mukuria@sheffield.ac.uk.|Health Economics and Decision Science, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK. c.mukuria@sheffield.ac.uk.
authors.forename: Clara
authors.initials: C
authors.lastname: Mukuria
authors.affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.|Health Economics and Decision Science, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK.
authors.forename: Donna
authors.initials: D
authors.lastname: Rowen
authors.affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.|Health Economics and Decision Science, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK.
authors.forename: Mónica
authors.initials: M
authors.lastname: Hernández-Alava
authors.affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.|Health Economics and Decision Science, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK.
authors.forename: Simon
authors.initials: S
authors.lastname: Dixon
authors.affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.|Health Economics and Decision Science, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK.
authors.forename: Roberta
authors.initials: R
authors.lastname: Ara
mesh_terms: D000328:Adult; D003362:Cost-Benefit Analysis; D004651:Employment / Q000191:economics* / Q000706:statistics & numerical data*; D005260:Female; D005382:Financing, Personal / Q000191:economics* / Q000706:statistics & numerical data*; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011788:Quality of Life; D011795:Surveys and Questionnaires; D006113:United Kingdom
keywords: Akaike Information Criterion; Bayesian Information Criterion; Normal Activity; Productivity Loss; Recall Period