A framework for studying risk factors for lower extremity musculoskeletal discomfort in nurses.

Lower extremity musculoskeletal discomfort (MSD) is prevalent, but understudied, in nurses. A comprehensive, theoretical, aetiological model of lower extremity work-related MSD in hospital in-patient staff nurses was developed through a review of the literature to provide a framework for aetiological and intervention research. The framework informed the design of a survey of 502 hospital staff nurses. Symptom prevalence ranged from 32% in hip/thigh to 59% in ankle/foot regions. Logistic regression modelling using survey data showed that different work and personal factors were associated with discomfort in different regions of the lower extremity. Individual factors (e.g. older age, higher BMI or having any foot condition), physical factors (e.g. higher frequency of patient handling), psychosocial factors (e.g. lower job satisfaction) were associated with discomfort in one or more parts of the lower extremity. Future research should target these factors for intervention, to attempt to reduce occurrence of lower extremity discomfort in nurses. Practitioner Summary: Practitioners may find useful the illustrated, theoretical aetiological model of factors that could influence the prevalence of lower extremity discomfort in nurses. The model could guide conversations with nurses and observational analyses of nursing work. The model and survey results may provide ideas for intervention exploration. Abbreviations: MSD: musculoskeletal discomfort; BMI: body mass index; MSK: musculoskeletal; ICU: intensive care unit; NLERF: nurses' lower extremity MSD risk factor; NASA-TLX: NASA-task load index.

Bayesian nonparametric models for ranked set sampling.

Ranked set sampling (RSS) is a data collection technique that combines measurement with judgment ranking for statistical inference. This paper lays out a formal and natural Bayesian framework for RSS that is analogous to its frequentist justification, and that does not require the assumption of perfect ranking or use of any imperfect ranking models. Prior beliefs about the judgment order statistic distributions and their interdependence are embodied by a nonparametric prior distribution. Posterior inference is carried out by means of Markov chain Monte Carlo techniques, and yields estimators of the judgment order statistic distributions (and of functionals of those distributions).

Improved procedures for estimation of disease prevalence using ranked set sampling.

Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). When the variable of interest is binary, ranking of the sample observations can be implemented using the estimated probabilities of success obtained from a logistic regression model developed for the binary variable. The main objective of this study is to use substantial data sets to investigate the application of RSS to estimation of a proportion for a population that is different from the one that provides the logistic regression. Our results indicate that precision in estimation of a population proportion is improved through the use of logistic regression to carry out the RSS ranking and, hence, the sample size required to achieve a desired precision is reduced. Further, the choice and the distribution of covariates in the logistic regression model are not overly crucial for the performance of a balanced RSS procedure.

Unbalanced ranked set sampling for estimating a population proportion.

The application of ranked set sampling (RSS) techniques to data from a dichotomous population is currently an active research topic, and it has been shown that balanced RSS leads to improvement in precision over simple random sampling (SRS) for estimation of a population proportion. Balanced RSS, however, is not in general optimal in terms of variance reduction for this setting. The objective of this article is to investigate the application of unbalanced RSS in estimation of a population proportion under perfect ranking, where the probabilities of success for the order statistics are functions of the underlying population proportion. In particular, the Neyman allocation, which assigns sample units for each order statistic proportionally to its standard deviation, is shown to be optimal in the sense that it leads to minimum variance within the class of RSS estimators that are simple averages of the means of the order statistics. We also use a substantial data set, the National Health and Nutrition Examination Survey III (NHANES III) data, to demonstrate the feasibility and benefits of Neyman allocation in RSS for binary variables.

Ranked set sampling for efficient estimation of a population proportion.

Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgement or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. In this paper we propose the use of logistic regression to aid in the ranking of a binary variable of interest. We illustrate the application of RSS to estimation of a population proportion with an example based on the National Health and Nutrition Examination Survey III data set. Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a population proportion.

Judgement post-stratification with imprecise rankings.

Judgement post-stratification, which is based on ideas similar to those in ranked set sampling, relies on the ability of a ranker to forecast the ranks of potential observations on a set of units. In practice, the authors sometimes find it difficult to assign these ranks. This note shows how one can borrow techniques from the literature on finite population sampling to allow a probabilistic ranking of the units in a set, thus facilitating use of these sampling plans and improving estimation. The same techniques provide one approach to estimation using a judgement post-stratified sample with multiple rankers. The technique is illustrated on allometric data relating brain weight to body weight in different species of mammals, and on a study of student performance in graduate school.