Background. Information on exposure variability, expressed as exposure variance components, is of vital use in occupational epidemiology, including informed risk control and efficient study design. While accurate and precise estimates of the variance components are desirable in such cases, very little research has been devoted to understanding the performance of data sampling strategies designed specifically to determine the size and structure of exposure variability. The aim of this study was to investigate the accuracy and precision of estimators of betweensubjects, between-days and within-day variance components obtained by sampling strategies differing with respect to number of subjects, total sampling time per subject, number of days per subject and the size of individual sampling periods.

Methods. Minute-by-minute values of average elevation, percentage time above 90degrees and percentage time below 15degrees were calculated in a data set consisting of measurements of right upper arm elevation during four full shifts from each of 23 car mechanics. Based on this parent data, bootstrapping was used to simulate sampling with 80 different combinations of the number of subjects (10, 20), total sampling time per subject (60, 120, 240, 480 minutes), number of days per subject (2, 4), and size of sampling periods (blocks) within days (1, 15, 60, 240 minutes). Accuracy (absence of bias) and precision (prediction intervals) of the variance component estimators were assessed for each simulated sampling strategy.

Results. Sampling in small blocks within days resulted in essentially unbiased variance components. For a specific total sampling time per subject, and in particular if this time was small, increasing the block size resulted in an increasing bias, primarily of the between-days and the within-days variance components. Prediction intervals were in general wide, and even more so at larger block sizes. Distributing sampling time across more days gave in general more precise variance component estimates, but also reduced accuracy in some cases.

Conclusions. Variance components estimated from small samples of exposure data within working days may be both inaccurate and imprecise, in particular if sampling is laid out in large consecutive time blocks. In order to estimate variance components with a satisfying accuracy and precision, for instance for arriving at trustworthy power calculations in a planned intervention study, larger samples of data will be required than for estimating an exposure mean value with a corresponding certainty

Background. Reliable exposure data is a vital concern in medical epidemiology and intervention studies. The present study addresses the needs of the medical researcher to spend monetary resources devoted to exposure assessment with an optimal cost-efficiency, i.e. obtain the best possible statistical performance at a specified budget. A few previous studies have suggested mathematical optimization procedures based on very simple cost models; this study extends the methodology to cover even non-linear cost scenarios.

Methods. Statistical performance, i.e. efficiency, was assessed in terms of the precision of an exposure mean value, as determined in a hierarchical, nested measurement model with three stages. Total costs were assessed using a corresponding three-stage cost model, allowing costs at each stage to vary non-linearly with the number of measurements, according to a power function. Using these models, procedures for identifying the optimally cost-efficient allocation of measurements under a constrained budget were developed, and applied on 225 scenarios combining different sizes of unit costs, cost function exponents, and exposure variance components.

Results. Explicit mathematical rules for identifying optimal allocation could be developed when cost functions were linear, while non-linear cost functions implied that parts of or the entire optimization procedure had to be carried out using numerical methods.

For many of the 225 scenarios, the optimal strategy consisted in measuring on one occasion from each of as many subjects as allowed by the budget. Significant deviations from this principle occurred if costs for recruiting subjects were large compared to costs for setting up measurement occasions, and, at the same time, the between-subjects to within-subject variance ratio was small. In these cases, non-linearities had a profound influence on the optimal allocation and on the eventual size of the exposure data set.

Conclusions. The analysis procedures developed in the present study can be used for informed design of exposure assessment strategies, provided that data are available on exposure variability and the costs of collecting and processing data.  The present shortage of empirical evidence on costs and appropriate cost functions however impedes general conclusions on optimal exposure measurement strategies in different epidemiologic scenarios.

Background. Upper arm postures are believed to be an important risk determinant for musculoskeletal disorder development in the neck and shoulders. The 10th and 90th percentiles of the angular elevation distribution have been reported in many studies as measures of neutral and extreme postural exposures, and variation has been quantified by the 10th-90th percentile range. Further, the 50th percentile is commonly reported as a measure of "average" exposure. These four variables have been estimated using samples of observed or directly measured postures, typically using sampling durations between 5 and 120 min.

Methods. The present study examined the statistical properties of estimated full-shift values of the 10th, 50th and 90th percentile and the 10th-90th percentile range of right upper arm elevation obtained from samples of seven different durations, ranging from 5 to 240 min. The sampling strategies were realized by simulation, using a parent data set of 73 full-shift, continuous inclinometer recordings among hairdressers. For each shift, sampling duration and exposure variable, the mean, standard deviation and sample dispersion limits (2.5% and 97.5%) of all possible sample estimates obtained at one minute intervals were calculated and compared to the true full-shift exposure value.

Results. Estimates of the 10th percentile proved to be upward biased with limited sampling, and those of the 90th percentile and the percentile range, downward biased. The 50th percentile was also slightly upwards biased. For all variables, bias was more severe with shorter sampling durations, and it correlated significantly with the true full-shift value for the 10th and 90th percentiles and the percentile range. As expected, shorter samples led to decreased precision of the estimate; sample standard deviations correlated strongly with true full-shift exposure values.

Conclusions. The documented risk of pronounced bias and low precision of percentile estimates obtained from short posture samples presents a concern in ergonomics research and practice, and suggests that alternative, unbiased exposure variables should be considered if data collection resources are restricted.

Background: Static posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation.

Methods: For this purpose, we simulated a repeated cyclic exposure varying within each cycle between “low” and “high” exposure levels in a “near” or “far” range, and with  “low” or “high” velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a “small” or “large” standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity. Each simulation trace included two realizations of 100 concatenated cycles with either low (r=0.1), medium (r=0.5) or high (r=0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined.

Results: C-EVA classified exposures more correctly than uni 1 variate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p<0.001). All three methods performed poorly in discriminating exposure patterns differing with respect to the variability in cycle time duration.

Conclusion: While C-EVA had a higher accuracy than conventional EVA, both failed to detect differences in temporal similarity. The data-driven optimality of data reduction and the capability of handling multiple exposure time lines in a single analysis are the advantages of the C-EVA.

Objectives. Data processing contributes a non-trivial proportion to total research costs, but documentation of these costs is rare. This paper employed a priori cost tracking for three posture assessment methods (self-report, observation of video, and inclinometry), developed a model describing the fixed and variable cost components, and simulated additional study scenarios to demonstrate the utility of the model. 

Methods. Trunk and shoulder postures of aircraft baggage handlers were assessed for 80 working days using all three methods. A model was developed to estimate data processing phase costs, including fixed and variable components related to study planning and administration, custom software development, training of analysts, and processing time.   

Results. Observation of video was the most costly data processing method with total cost of € 31,433, and was 1.2-fold more costly than inclinometry (€ 26,255), and 2.5-fold more costly than self-reported data (€ 12,491). Simulated scenarios showed altering design strategy could substantially impact processing costs. This was shown for both fixed parameters, such as software development and training costs, and variable parameters, such as the number of work-shift files processed, as well as the sampling frequency for video observation.  When data collection and data processing costs were combined, the cost difference between video and inclinometer methods was reduced to 7%; simulated data showed this difference could be diminished and, even, reversed at larger study sample sizes. Self-report remained substantially less costly under all design strategies, but produced alternate exposure metrics. 

Conclusions. These findings build on the previously published data collection phase cost model by reporting costs for post-collection data processing of the same data set.  Together, these models permit empirically based study planning and identification of cost-efficient study designs.

Background. Documentation of posture measurement costs is rare and cost models that do exist are generally naïve. This paper provides a comprehensive cost model for biomechanical exposure assessment in occupational studies, documents the monetary costs of three exposure assessment methods for different stakeholders in data collection, and uses simulations to evaluate the relative importance of cost components.  Trunk and shoulder posture variables were assessed for 27 aircraft baggage handlers for 3 full shifts each using three methods typical to ergonomic studies: self-report via questionnaire, observation via video film, and full-shift inclinometer registration.  The cost model accounted for expenses related to meetings to plan the study, administration, recruitment, equipment, training of data collectors, travel, and onsite data collection.  Sensitivity analyses were conducted using simulated study parameters and cost components to investigate the impact on total study cost.

Results. Inclinometery was the most expensive method (with a total study cost of € 66,657), followed by observation (€ 55,369) and then self report (€ 36,865). The majority of costs (90%) were borne by researchers.  Study design parameters such as sample size, measurement scheduling and spacing, concurrent measurements, location and travel, and equipment acquisition were shown to have wide-ranging impacts on costs. 

Conclusions. This study provided a general cost modelling approach that can facilitate decision making and planning of data collection in future studies, as well as investigation into cost efficiency and cost efficient study design. Empirical cost data from a large field study demonstrated the usefulness of the proposed models.

Background: Self-Rated Health (SRH) correlates with risk of illness and death. But how are different questions of SRH to be interpreted? Does it matter whether one asks: "How would you assess your general state of health?"(General SRH) or "How would you assess your general state of health compared to persons of your own age?"(Comparative SRH)? Does the context in a questionnaire affect the answers? The aim of this paper is to examine the meaning of two questions on self-rated health, the statistical distribution of the answers, and whether the context of the question in a questionnaire affects the answers. Methods: Statistical and semantic methodologies were used to analyse the answers of two different SRH questions in a cross-sectional survey, the MONICA-project of northern Sweden. Results: The answers from 3504 persons were analysed. The statistical distributions of answers differed. The most common answer to the General SRH was "good", while the most common answer to the Comparative SRH was "similar". The semantic analysis showed that what is assessed in SRH is not health in a medical and lexical sense but fields of association connected to health, for example health behaviour, functional ability, youth, looks, way of life. The meaning and function of the two questions differ - mainly due to the comparing reference in Comparative SRH. The context in the questionnaire may have affected the statistics. Conclusions: Health is primarily assessed in terms of its sense-relations (associations) and Comparative SRH and General SRH contain different information on SRH. Comparative SRH is semantically more distinct. The context of the questions in a questionnaire may affect the way self-rated health questions are answered. Comparative SRH should not be eliminated from use in questionnaires. Its usefulness in clinical encounters should be investigated.