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首页> 外文期刊>Quality of life research: An international journal of quality of life aspects of treatment, care and rehabilitation >Predicting presenteeism using measures of health status
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Predicting presenteeism using measures of health status

机译：Predicting presenteeism using measures of health status

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Abstract Objectives To identify whether it is feasible to develop a mapping algorithm to predict presenteeism using multiattribute measures of health status.Methods Data were collected using a bespoke online survey in a purposive sample (n?=?472) of working individuals with a self-reported diagnosis of Rheumatoid arthritis (RA). Survey respondents were recruited using an online panel company (ResearchNow). This study used data captured using two multiattribute measures of health status (EQ5D-5 level; SF6D) and a measure of presenteeism (WPAI, Work Productivity Activity Index). Statistical correlation between the WPAI and the two measures of health status (EQ5D-5 level; SF6D) was assessed using Spearman’s rank correlation. Five regression models were estimated to quantify the relationship between WPAI and predict presenteeism using health status. The models were specified based in index and domain scores and included covariates (age; gender). Estimated and observed presenteeism were compared using tenfold cross-validation and evaluated using Root mean square error (RMSE).Results A strong and negative correlation was found between WPAI and: EQ5D-5 level and WPAI (r?=???0.64); SF6D (r?=??0.60). Two models, using ordinary least squares regression were identified as the best performing models specifying health status using: SF6D domains with age interacted with gender (RMSE?=?1.7858); EQ5D-5 Level domains and age interacted with gender (RMSE?=?1.7859).Conclusions This study provides indicative evidence that two existing measures of health status (SF6D and EQ5D-5L) have a quantifiable relationship with a measure of presenteeism (WPAI) for an exemplar application of working individuals with RA. A future study should assess the external validity of the proposed mapping algorithms.

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