S A Hendricks — Page 2

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Power determination for geographically clustered data using generalized estimating equations.

Study designs in public health research often require the estimation of intervention effects that have been applied to a cluster of subjects in a common geographic area, rather than randomly assigned to individual subjects, and where the outcome is dichotomous. Statistical methods that account for the intracluster correlation of measurements must be used or the standard errors of regression coefficients will be under-estimated. Generalized estimating equations (GEE) can be used to account for this correlation, although there are no straightforward methods to determine sample-size requirements for adequate power. A simulation study was performed to calculate power in a GEE model for a proposed study of the effect of an intervention, designed to reduce lower-back injuries among nursing personnel employed in nursing homes. Nursing homes will be randomly assigned to either an intervention or control group and all employees within a nursing home will be treated alike. Historical injury data indicates that the baseline-injury risk for each home can be reasonably modelled using a beta distribution. It is assumed that the risk for any individual nurse within a nursing home follows a Bernoulli probability distribution expressed as a logit function of fixed covariates, which have values of odds ratios determined from previous studies which represent characteristics of the study population, and a random-intercept term which is specific for each home. Results indicate that failure to account for intracluster correlation can lead to overestimates of power as well as inflation of type I error by as much as 20 per cent. Although the GEE method accounted for the intracluster correlation when present, estimates of the intracluster correlation were negatively biased when no intracluster correlation was present. In addition, and possibly related to the negatively biased estimates of intracluster correlation, we also found inflated type I error estimates from the GEE method.

Algorithms↗

PubMedDate not supplied

Ergonomic exposure assessment: an application of the PATH systematic observation method to retail workers. Postures, Activities, Tools and Handling.

This study examined biomechanical stressor variables (physical work exposures) in relation to job title, gender, and back-belt status in 134 retail store workers. The principal concerns were to quantitatively describe physical work exposures and to determine the degrees to which these quantitative variables correlated with job title and with the use of back belts. An additional objective was to assess the inter-rater reliability of the observation method. The systematic observation method employed was based on a modification of the PATH (Postures, Activities, Tools, and Handling) measurement method. Chi-square analysis indicated that the frequencies of bent or twisted postures followed the pattern of unloaders > stockers > department managers. For weight handled per lift, lower, or carry, the pattern was unloaders > department managers > stockers. The mean lifting frequencies per hour were 35.9 for department managers, 48.8 for stockers, and 137.4 for unloaders. Back-belt-wearing percentages were higher for unloaders (63%) compared with stockers (48%) and department managers (25%). Back-belt-wearing workers had higher levels of biomechanical stressor variables, including arm position, twisting, weight handled, and number of lifts per hour. Kappa statistics ranged from 0.5 to 0.63, a level of adequate or good reliability beyond chance. The method employed in this study is applicable in studies that require only fairly crude distinctions among biomechanical stressor variables. Nevertheless, this level of distinction may be sufficient when implementing intervention studies and control strategies for many material-handling-intensive jobs.

Back Injuries↗