Productivity loss due to presenteeism among patients with arthritis: estimates from 4 instruments.
ABSTRACT To estimate and compare lost work hours attributable to presenteeism, defined as reduced productivity while working, in individuals with osteoarthritis (OA) or rheumatoid arthritis (RA), according to 4 instruments.
In our prospective study, 250 workers with OA (n = 130) or RA (n = 120) were recruited from community and clinical sites. Lost hours due to presenteeism at baseline were estimated using the Health and Labor Questionnaire (HLQ), the Work Limitations Questionnaire (WLQ), the World Health Organization's Health and Work Performance Questionnaire (HPQ), and the Work Productivity and Activity Impairment Questionnaire (WPAI). Only those respondents working over the past 2 weeks were included. Repeated-measures ANOVA was used to compare the lost-time estimates, according to each instrument.
Of the 212 respondents included in the analyses, the frequency of missing and "0" values among the instruments was different (17% and 61% for HLQ, 8% and 5% for WLQ, 1% and 16% for HPQ, 0% and 27% for WPAI, respectively). The average numbers of lost hours (SD) per 2 weeks due to presenteeism using HLQ, WLQ, HPQ, and WPAI were 1.6 (3.9), 4.0 (3.9), 13.5 (12.5), and 14.2 (16.7). The corresponding costs for the 2-week period were CAN$30.03, $83.05, $284.07, and $285.10. The differences in the lost-hour estimates according to instruments were significant (p < 0.001).
Among individuals with arthritis, estimates of productivity losses while working vary widely according to the instruments chosen. Further research on instrument design and implications for a standardized approach to estimate lost time due to presenteeism is needed.
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ABSTRACT: Early referral and management of high-risk chronic kidney disease may prevent or delay the need for dialysis. Automatic eGFR reporting has increased demand for out-patient nephrology consultations and in some cases, prolonged queues. In Canada, a national task force suggested the development of waiting time targets, which has not been done for nephrology. We sought to describe waiting time for outpatient nephrology consultations in British Columbia (BC). Data collection occurred in 2 phases: 1) Baseline Description (Jan 18-28, 2010) and 2) Post Waiting Time Benchmark-Introduction (Jan 16-27, 2012). Waiting time was defined as the interval from receipt of referral letters to assessment. Using a modified Delphi process, Nephrologists and Family Physicians (FP) developed waiting time targets for commonly referred conditions through meetings and surveys. Rules were developed to weigh-in nephrologists', FPs', and patients' perspectives in order to generate waiting time benchmarks. Targets consider comorbidities, eGFR, BP and albuminuria. Referred conditions were assigned a priority score between 1-4. BC nephrologists were encouraged to centrally triage referrals to see the first available nephrologist. Waiting time benchmarks were simultaneously introduced to guide patient scheduling. A post-intervention waiting time evaluation was then repeated. In 2010 and 2012, 43/52 (83%) and 46/57 (81%) of BC nephrologists participated. Waiting time decreased from 98(IQR44,157) to 64(IQR21,120) days from 2010 to 2012 (p = <.001), despite no change in referral eGFR, demographics, nor number of office hrs/wk. Waiting time improved most for high priority patients. An integrated, Provincial initiative to measure wait times, develop waiting benchmarks, and engage physicians in active waiting time management associated with improved access to nephrologists in BC. Improvements in waiting time was most marked for the highest priority patients, which suggests that benchmarks had an influence on triaging behavior. Further research is needed to determine whether this effect is sustainable.BMC Nephrology 08/2013; 14(1):182. · 1.64 Impact Factor
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ABSTRACT: Productivity costs occur when the productivity of individuals is affected by illness, treatment, disability or premature death. The objective of this paper was to review past and current developments related to the inclusion, identification, measurement and valuation of productivity costs in economic evaluations. The main debates in the theory and practice of economic evaluations of health technologies described in this review have centred on the questions of whether and how to include productivity costs, especially productivity costs related to paid work. The past few decades have seen important progress in this area. There are important sources of productivity costs other than absenteeism (e.g. presenteeism and multiplier effects in co-workers), but their exact influence on costs remains unclear. Different measurement instruments have been developed over the years, but which instrument provides the most accurate estimates has not been established. Several valuation approaches have been proposed. While empirical research suggests that productivity costs are best included in the cost side of the cost-effectiveness ratio, the jury is still out regarding whether the human capital approach or the friction cost approach is the most appropriate valuation method to do so. Despite the progress and the substantial amount of scientific research, a consensus has not been reached on either the inclusion of productivity costs in economic evaluations or the methods used to produce productivity cost estimates. Such a lack of consensus has likely contributed to ignoring productivity costs in actual economic evaluations and is reflected in variations in national health economic guidelines. Further research is needed to lessen the controversy regarding the estimation of health-related productivity costs. More standardization would increase the comparability and credibility of economic evaluations taking a societal perspective.PharmacoEconomics 04/2013; · 2.86 Impact Factor
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ABSTRACT: BACKGROUND: Productivity costs are often ignored in economic evaluations. In order to facilitate productivity cost inclusion, it has been suggested to estimate productivity costs indirectly using quality of life data. OBJECTIVE: This study aimed to derive and validate an algorithm for predicting productivity losses on the basis of quality-of-life data using the EQ-5D-3L. METHODS: A large representative sample of the Dutch general public (n = 1,100) was asked in a web-based questionnaire to state their expected level of productivity in terms of absenteeism and presenteeism for multiple EQ-5D health states. Based on these data, two generalized estimating equations (GEE) models were constructed: (1) a model predicting levels of absenteeism and (2) a model predicting presenteeism. The models were validated by comparing model predictions with conventionally measured productivity within a group of low back pain patients. RESULTS: Predicted absenteeism levels based on EQ-5D health state closely resembled conventionally measured absenteeism levels. Productivity losses related to presenteeism seemed somewhat overestimated by our prediction model. Measured and predicted productivity were moderately but highly significantly correlated. CONCLUSIONS: Overall, it appears possible to make reasonable productivity predictions based on EQ-5D data. Further exploration and validation of prediction algorithms remains necessary, however, especially for presenteeism.The European Journal of Health Economics 06/2013; · 2.10 Impact Factor