Sat Gupta

Nurses’ Presenteeism and Its Effects on Self-Reported Quality of Care and Costs

ObjectiveAlthough research has been conducted on how nurse staffing levels affect outcomes, there has been little investigation into how the health-related productivity of nurses is related to quality of care. Two major causes of worker presenteeism (reduced on-the-job productivity as a result of health problems) are musculoskeletal pain and mental health issues, particularly depression. This study sought to investigate the extent to which musculoskeletal pain or depression (or both) in RNs affects their work productivity and self-reported quality of care and considered the associated costs. MethodsUsing a cross-sectional survey design, a random sample of 2,500 hospital-employed RNs licensed in North Carolina were surveyed using a survey instrument sent by postal mail. Specific measures included questions on individual and workplace characteristics, self-reported quality of care, and patient safety; a numeric pain rating scale, a depression tool (the Patient Health Questionnaire), and a presenteeism tool (the Work Productivity and Activity Impairment Questionnaire: General Health) were also incorporated. A total of 1,171 completed surveys were returned and used for analysis. ResultsAmong respondents, the prevalence of musculoskeletal pain was 71%; that of depression was 18%. The majority of respondents (62%) reported a presenteeism score of at least 1 on a 0-to-10 scale, indicating that health problems had affected work productivity at least “a little.” Pain and depression were significantly associated with presenteeism. Presenteeism was significantly associated with a higher number of patient falls, a higher number of medication errors, and lower quality-of-care scores. Baseline cost estimates indicate that the increased falls and medication errors caused by presenteeism are expected to cost

Estimation of sensitivity level of personal interview survey questions

1,346 per North Carolina RN and just under

On improvement in estimating the population mean in simple random sampling

2 billion for the United States annually. Upper-boundary estimates exceed

On estimation of finite population variance

9,000 per North Carolina RN and

A New Estimator of Population Mean in Stratified Sampling

13 billion for the nation annually. ConclusionMore attention must be paid to the health of the nursing workforce to positively influence the quality of patient care and patient safety and to control costs.

Parameter estimation in low order fractionally differenced ARMA processes

In this paper, an optional randomized response model is proposed. The estimator of the mean of the stigmatized variable based on the optional randomized response sampling is shown to be more efficient than the usual estimator of the mean based on randomized response technique method. In addition to estimating population mean and variance, it has been shown that the optional randomized response technique is useful in quantifying the sensitivity levels of the questions in the personal interview surveys. An estimator for the sensitivity level of a question is proposed and an empirical study is carried out to show the validity of the proposed estimation technique. We also propose a test for the sensitivity level of the stigmatized variable.

Improved Ratio Estimators in Stratified Sampling

Abstract Kadilar and Cingi [Ratio estimators in simple random sampling, Appl. Math. Comput. 151 (3) (2004), pp. 893–902] introduced some ratio-type estimators of finite population mean under simple random sampling. Recently, Kadilar and Cingi [New ratio estimators using correlation coefficient, Interstat 4 (2006), pp. 1–11] have suggested another form of ratio-type estimators by modifying the estimator developed by Singh and Tailor [Use of known correlation coefficient in estimating the finite population mean, Stat. Transit. 6 (2003), pp. 655–560]. Kadilar and Cingi [Improvement in estimating the population mean in simple random sampling, Appl. Math. Lett. 19 (1) (2006), pp. 75–79] have suggested yet another class of ratio-type estimators by taking a weighted average of the two known classes of estimators referenced above. In this article, we propose an alternative form of ratio-type estimators which are better than the competing ratio, regression, and other ratio-type estimators considered here. The results are also supported by the analysis of three real data sets that were considered by Kadilar and Cingi.

On Improvement in Variance Estimation Using Auxiliary Information

Abstract Following Searls (1964), we propose an estimator for estimating the finite population variance. This estimator is the combination of Singh et al. (1973), and Prasad and Singh (1992) estimators and has an improvement over Singh et al. (1973), Prasad and Singh (1992), and several other estimators under certain conditions. Validity of proposed estimator is examined by using seven numerical examples.

Differences in health, productivity and quality of care in younger and older nurses

Kadilar and Cingi (2005) have suggested a new ratio estimator in stratified sampling. The efficiency of this estimator is compared with the traditional combined ratio estimator on the basis of mean square error (MSE). We propose another estimator by utilizing a simple transformation introduced by Bedi (1996). The proposed estimator is found to be more efficient than the traditional combined ratio estimator as well as the Kadilar and Cingi (2005) ratio estimator.

On Estimating Finite Population Mean in Simple and Stratified Random Sampling

A class of regression type estimators of the parameterd in a fractionally differencedARMA (p, q) process is introduced. This class is an extension of the estimator considered by Geweke and Porter-Hudak. In a simulation study, we compared three estimators from this class together with two approximate maximum likelihood estimators which are based on two separate approximations to the likelihood. One approximation ignores the determinant term in the likelihood and the other includes a compensating factor for the determinant. When the determinant term is included, the estimate tends to be much less biased and is in general superior to the other estimate. The approximate maximum likelihood estimator out performed, by a large margin, the regression type estimators for pureARIMA (0,d,0) processes. However, forARIMA (1,d,1) processes, a regression type estimator turned out to be the best for realizations of length 400 in 3 out of the 5 cases we tried.