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Comparison of Employer Productivity Metrics to Lost Productivity Estimated by Commonly Used Questionnaires.

ABSTRACT: The aim of the study was to assess construct and discriminant validity of four health-related work productivity loss questionnaires in relation to employer productivity metrics, and to describe variation in economic estimates of productivity loss provided by the questionnaires in healthy workers.Fifty-eight billing office workers completed surveys including health information and four productivity loss questionnaires. Employer productivity metrics and work hours were also obtained.Productivity loss questionnaires were weakly to moderately correlated with employer productivity metrics. Workers with more health complaints reported greater health-related productivity loss than healthier workers, but showed no loss on employer productivity metrics. Economic estimates of productivity loss showed wide variation among questionnaires, yet no loss of actual productivity.Additional studies are needed comparing questionnaires with objective measures in larger samples and other industries, to improve measurement methods for health-related productivity loss.

SUBMITTER: Gardner BT

PROVIDER: S-EPMC5009902 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Comparison of Employer Productivity Metrics to Lost Productivity Estimated by Commonly Used Questionnaires.

Gardner Bethany T BT Dale Ann Marie AM Buckner-Petty Skye S Van Dillen Linda L Amick Benjamin C BC Evanoff Bradley B

Journal of occupational and environmental medicine 20160201 2

<h4>Objective</h4>The aim of the study was to assess construct and discriminant validity of four health-related work productivity loss questionnaires in relation to employer productivity metrics, and to describe variation in economic estimates of productivity loss provided by the questionnaires in healthy workers.<h4>Methods</h4>Fifty-eight billing office workers completed surveys including health information and four productivity loss questionnaires. Employer productivity metrics and work hours ...[more]

PMID: 26849261

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Project description:National working groups identify the need for return on investment research conducted from the purchaser perspective; however, the field has not developed standardized methods for measuring the basic components of return on investment, including costing out the value of work productivity loss due to illness. Recent literature is divided on whether the most commonly used method underestimates or overestimates this loss. The goal of this manuscript is to characterize between and within variation in the cost of work productivity loss from illness estimated by the most commonly used method and its two refinements.One senior health benefit specialist from each of 325 companies employing 100+ workers completed a cross-sectional survey describing their company size, industry and policies/practices regarding work loss which allowed the research team to derive the variables needed to estimate work productivity loss from illness using three methods. Compensation estimates were derived by multiplying lost work hours from presenteeism and absenteeism by wage/fringe. Disruption correction adjusted this estimate to account for co-worker disruption, while friction correction accounted for labor substitution. The analysis compared bootstrapped means and medians between and within these three methods.The average company realized an annual $617 (SD?=?$75) per capita loss from depression by compensation methods and a $649 (SD?=?$78) loss by disruption correction, compared to a $316 (SD?=?$58) loss by friction correction (p?<?.0001). Agreement across estimates was 0.92 (95% CI 0.90, 0.93).Although the methods identify similar companies with high costs from lost productivity, friction correction reduces the size of compensation estimates of productivity loss by one half. In analyzing the potential consequences of method selection for the dissemination of interventions to employers, intervention developers are encouraged to include friction methods in their estimate of the economic value of interventions designed to improve absenteeism and presenteeism. Business leaders in industries where labor substitution is common are encouraged to seek friction corrected estimates of return on investment. Health policy analysts are encouraged to target the dissemination of productivity enhancing interventions to employers with high losses rather than all employers.NCT01013220.

Project description:BackgroundEvidence-based conservation is urgently needed to identify, apply and promote effective interventions for mitigation of threats and recovery of the natural environment. Estimation of intervention effectiveness is subject to robust study design and statistical analysis, and much progress is documented in these fields. In contrast, little is understood about the accuracy and biases (underestimation and overestimation) of different effectiveness metrics and how they are affected by sample size.MethodsIn this study, a dataset (n = 500 cases) consisting of random, positive, integer numbers was simulated to produce frequency input data for the 2 × 2 contingency table. For each case, three metrics of the relative risk, odds ratio and the magnitude of change were calculated, their disparity was estimated and the characteristics of treatment (with intervention) and control (without intervention) samples significantly affecting this disparity were studied by means of linear regression. Further, four case studies from different conservation interventions are provided to support the results.ResultsThe study has shown that the relative risk and the magnitude of change produce identical estimates of intervention effectiveness only when treatment and control samples are equal, or when the number of target outcomes (e.g., number of livestock killed by predators) in treatment sample reaches zero. In other situations, the magnitude of change gives overestimates or underestimates, depending on relationships between treatment and control sample sizes. The table summarizing the conditions of equalities and biases between these two metrics is provided. These conditions are valid for both reduction-aimed interventions reducing negative target outcomes (e.g., livestock protection to reduce livestock losses to predators) and for addition-aimed interventions increasing positive target outcomes (e.g., establishment of protected areas to increase species presence). No significant effects on the odds ratio were found.ConclusionResearchers should set equal treatment and control sample sizes so that to produce identical estimates of intervention effectiveness by the relative risk and the magnitude of change. Otherwise, these estimates are biased if produced by the magnitude of change and the relative risk should be used instead. As setting equal treatment and control samples can be impractical, I encourage researchers and practitioners to use the relative risk in estimation of intervention effectiveness. This will not take additional efforts as both metrics are calculated from the same contingency table. Treatment and control sample sizes, along with their sub-samples affected and not by an intervention, should be explicitly reported by researchers to allow independent evaluation of intervention effectiveness. This approach can help obtain more accurate information on intervention effectiveness for making better decisions in conservation actions.

Dataset Information

Comparison of Employer Productivity Metrics to Lost Productivity Estimated by Commonly Used Questionnaires.

Publications

Comparison of Employer Productivity Metrics to Lost Productivity Estimated by Commonly Used Questionnaires.

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