Objectives: Exposure-outcome studies, for instance on work-related low-back pain (LBP), often classify workers into groups for which exposures are estimated from measurements on a sample of workers within or outside the specific study. The present study investigated the influence on bias and power in exposure-outcome associations of the sizes of the total study population and the sample used to estimate exposures.

Methods: At baseline, lifting, trunk flexion, and trunk rotation were observed for 371 of 1131 workers allocated to 19 a-priori defined occupational groups. LBP (dichotomous) was reported by all workers during three years of follow-up. All three exposures were associated with LBP in this parent study (p<0.01).

All 21 combinations of n=10,20,30 workers per group with an outcome, and k=1,2,3,5,10,15,20 workers actually being observed were investigated using bootstrapping, repeating each combination 10,000 times. Odds ratios (OR) with p-values were determined for each of these virtual studies. Average OR and statistical power (p<0.05 and p<0.01) was determined from the bootstrap distributions at each (n,k) combination.

Results: For lifting and flexed trunk, studies including n≥20 workers, with k≥5 observed, led to an almost unbiased OR and a power >0.80 (p-level 0.05). A similar performance required n≥30 workers for rotated trunk. Small numbers of observed workers (k) resulted in biased OR, while power was, in general, more sensitive to the total number of workers (n).

Conclusions: In epidemiologic studies using a group-based exposure assessment strategy, statistical performance may be sufficient if outcome is obtained from a reasonably large number of workers, even if exposure is estimated from only few workers per group.

Cost-efficient assessments of job exposures in large populations may be obtained from models in which “true” exposures assessed by expensive measurement methods are estimated from easily accessible and cheap predictors. Typically, the models are built on the basis of a validation study comprising “true” exposure data as well as an extensive collection of candidate predictors from questionnaires or company data, which cannot all be included in the models due to restrictions in the degrees of freedom available for modeling. In these situations, predictors need to be selected using procedures that can identify the best possible subset of predictors among the candidates. The present study compares two strategies for selecting a set of predictor variables. One strategy relies on stepwise hypothesis testing of associations between predictors and exposure, while the other uses cluster analysis to reduce the number of predictors without relying on empirical information about the measured exposure. Both strategies were applied to the same dataset on biomechanical exposure and candidate predictors among computer users, and they were compared in terms of identified predictors of exposure as well as the resulting model fit using bootstrapped resamples of the original data. The identified predictors were, to a large part, different between the two strategies, and the initial model fit was better for the stepwise testing strategy than for the clustering approach. Internal validation of the models using bootstrap resampling with fixed predictors revealed an equally reduced model fit in resampled datasets for both strategies. However, when predictor selection was incorporated in the validation procedure for the stepwise testing strategy, the model fit was reduced to the extent that both strategies showed similar model fit. Thus, the two strategies would both be expected to perform poorly with respect to predicting biomechanical exposure in other samples of computer users.

Objectives

To investigate the statistical efficiency of strategies for sampling upper arm elevation data, which differed with respect to sample sizes and sample allocations within and across measurement days. The study was also designed to compare standard theoretical predictions of sampling efficiency, which rely on several assumptions about the data structure, with “true” efficiency as determined by bootstrap simulations.

Methods

Sixty-five sampling strategies were investigated using a data set containing minute-by-minute values of average right upper arm elevation, percentage of time with the arm elevated less than 15°, and percentage of time with an arm elevated more than 90° in a population of 23 house painters, 23 car mechanics and 26 machinists, all followed for four full working days. Total sample times per subject between 30 and 240 minutes were subdivided into continuous time blocks between 1 and 240 minutes long, allocated to one or four days per subject. Within day(s), blocks were distributed using either a random or fixed-interval principle. Sampling efficiency was expressed in terms of the variance of estimated mean exposure values of 20 subjects, and assessed using standard theoretical models assuming independence between variables and homoscedasticity. Theoretical performance was compared to empirical efficiencies obtained by a nonparametric bootstrapping procedure.

Results

We found the assumptions of independence and homoscedasticity in the theoretical model to be violated, most notably expressed through an autocorrelation between measurement units within working days. The empirical variance of the mean exposure estimates decreased, i.e. sampling efficiency increased, for sampling strategies where measurements were distributed widely across time. Thus, the most efficient allocation strategy was to organize a sample into one-minute blocks collected at fixed time intervals across four days. Theoretical estimates of efficiency generally agreed with empirical variances if the sample was allocated into small blocks, while for larger block sizes the empirical, “true” variance was considerably larger than predicted by theory. Theory overestimated efficiency in particular for strategies with short total sample times per subject.

Conclusions This study demonstrates that when exposure data are autocorrelated within days – which we argue is the major reason why theory overestimates sampling performance – sampling efficiency can be improved by distributing the sample widely across the day or across days, preferably using a fixed-interval strategy. While this guidance is particularly valid when small proportions of working days are assessed, we generally recommend collecting more data than suggested by theory if a certain precision of the resulting exposure estimate is needed. More data per se give a better precision, and sampling larger proportion(s) of the working day(s) also alleviate the negative effects of possible autocorrelation in data

Objectives. Observational work sampling is often used in occupational studies to assess categorical biomechanical exposures and occurrence of specific work tasks. The statistical performance of data obtained by work sampling is, however, not well understood, impeding informed measurement strategy design. The purpose of this study was to develop a procedure for assessing the statistical properties of work sampling strategies evaluating categorical exposure variables, and to illustrate the usefulness of this procedure to examine bias and precision of exposure estimates from samples of different sizes.

Methods. From a parent data set of observations on 10 construction workers performing a single operation, the probabilities were determined for each worker of performing four component tasks and working in four mutually exclusive trunk posture categories (neutral, mild flexion, severe flexion, twisted). Using these probabilities, 5000 simulated data sets were created via probability-based re-sampling for each of six sampling strategies, ranging from 300 to 4500 observations. For each strategy, mean exposure and exposure variability metrics were calculated at both the operation- and task-levels and, for each of these, bias and precision were assessed across the 5000 simulations.

Results. Estimates of exposure variability were substantially more uncertain at all sample sizes than estimates of mean exposures and task proportions. Estimates at small sample sizes were also biased. With only 600 samples, proportions of the different tasks and of working with a neutral trunk posture (the most common) were within 10% of the true target value in at least 80% of all the simulated data sets; rarer exposures required at least 1500 samples. For most task-level mean exposure variables and for all operation- and task-level estimates of exposure variability, performance was low, even with 4500 samples. In general, the precision of mean exposure estimates did not depend on the exposure variability between workers.

Conclusions. The suggested probability-based simulation approach proved to be versatile and generally suitable for assessing bias and precision of data collection strategies using work sampling to estimate categorical data. The approach can be used in both real and hypothetical scenarios, in ergonomics as well as in other areas of occupational epidemiology and intervention research. The reported statistical properties associated with sample size are likely widely relevant to studies using work sampling to assess categorical variables.

When designing a strategy for collecting occupational exposure data, both economic and statistical performance criteria should be considered. However, very few studies have addressed the trade-off between the cost of obtaining data and the precision/accuracy of the exposure estimate as a research issue. To highlight the need of providing cost-efficient designs for assessing exposure variables in occupational research, the present review explains and critically evaluates the concepts and analytical tools used in available cost efficiency studies. Nine studies were identified through a systematic search using two algorithms in the databases PubMed and ScienceDirect. Two main approaches could be identified in these studies: comparisons of the cost efficiency associated with different measurement designs, and optimizations of resource allocation on the basis of functions describing cost and statistical efficiency. In either case, the reviewed studies use simplified analytical tools and insufficient economic analyses. More research is needed to understand whether these drawbacks jeopardize the guidance on cost-efficient exposure assessment provided by the studies, as well as to support theoretical results by empirical data from occupational life.

Occupational injury research depends on the ability to accurately assess workplace exposures for large numbers of workers. This study used mixed modeling to identify observed and self-reported predictors of mean, 90th percentile, and cumulative low back muscle activity to help researchers efficiently assess physical exposures in epidemiological studies. Full-shift low back electromyography (EMG) was measured for 133 worker-days in heavy industry. Additionally, full-shift, 1-min interval work-sampling observations and post-shift interviews assessed exposure to work tasks, trunk postures, and manual materials handling. Data were also collected on demographic and job variables. Regression models using observed variables predicted 31-47% of the variability in the EMG activity measures, while self-reported variables predicted 21-36%. Observation-based models performed better than self-report-based models and may provide an alternative to direct measurement of back injury risk factors.

Objectives: The present study assessed full shift trunk and upper arm postural exposure amplitudes, frequencies, and durations among Swedish airport baggage handlers, and aimed to determine whether exposures differ between workers at the ramp (loading and unloading aircraft) and baggage sorting areas.

Methods: Trunk and upper arm postures were measured using inclinometers during three full work shifts on each of 27, male baggage handlers working at a large Swedish airport. Sixteen of the baggage handlers worked on the ramp and 11 in the sorting area. Variables summarizing postures and movements were calculated, and mean values and variance components between subjects and within subject (between days) were estimated using restricted maximum likelihood algorithms in a one-way random effect model.

Results: In total, data from 79 full shifts (651 hours) were collected with a mean recording time of 495 minutes per shift (range 319-632). On average, baggage handlers worked with the right and left arm elevated >60° for 6.4% and 6.3% of the total workday, respectively. The 90^th percentile trunk forward projection (FP) was 34.1° and the 50^th percentile trunk movement velocity was 8°s^-1. For most trunk (FP) and upper arm exposure variables, between-subject variability was considerable, suggesting that the flight baggage handlers were not a homogeneously exposed group. A notable between-days variability pointed to the contents of the job differing on different days. Peak exposures (>90°) were higher for ramp workers than for sorting area workers (trunk 0.6% ramp vs 0.3% sorting; right arm 1.3% ramp vs 0.7% sorting).

Conclusions: Trunk and upper arm postures and movements among flight baggage handlers measured by inclinometry were similar to those found in other jobs comprising manual material handling, known to be associated with increased risks for musculoskeletal disorders. The results showed that full-shift trunk (FP), and to some extent peak arm exposures, were higher for ramp workers compared to sorting workers.

OBJECTIVES: To describe upper arm postures and movements among female hairdressers, including the variability between hairdressers, between days within hairdresser, and between tasks, as a basis for understanding the characteristics of exposures in the job, considering possible sources of variation and recovery, and discussing appropriate exposure assessment strategies. METHODS: Data on upper arm postures were collected using inclinometers during four working days the same week from 28 female hairdressers working in 13 salons. Twenty of the hairdressers noted customer on and off times in a diary, to allow separate analyses of customer tasks (CT) and auxiliary non-customer tasks (AT), including breaks. For a number of posture and movement variables, mean values and variance components between subjects (BS) and within subjects between days (BD) were estimated using restricted maximum likelihood algorithms in one-way random effect models. RESULTS: For the 20 hairdressers with diaries, CT accounted for 279 min (58%) (SD(BS) = 39 min and SD(BD) = 85 min) of the working day and AT and breaks for 207 min (42%) (SD(BS) = 46 min and SD(BD) = 88 min). The hairdressers worked with the right arm elevated >60 degrees for 6.8% of the whole job (SD(BS) = 2.8% and SD(BD) = 2.0%). On average, the hairdressers worked with the right arm elevated >60 degrees for 9.0% of the time during CT, compared to 3.7% during AT, resulting in a contrast between tasks of 0.35. CONCLUSIONS: Hairdressers may be at risk for developing musculoskeletal disorders in the neck and shoulders due to a considerable occurrence of highly elevated arms, especially during CT. On the other hand, we do not find reasons to classify hairdressing as a job with too little variation. Posture variability between days within hairdressers was in the same order of magnitude as that between hairdressers, suggesting that 'typical' workdays do not exist. The exposure contrast between CT and AT for variables describing elevated arm postures indicates that for these variables a simple task-based approach for estimating job exposure could be successful.