Quantitative and ratings-based job exposure measures were each associated with CTS. Obesity increased the association between frequency of exertion and CTS.

Workplace and individual risk factors both contribute to the risk for CTS. Time spent in forceful exertion can be a greater risk for CTS than obesity if the job exposure is high. Preventive workplace efforts should target forceful exertions.

Studies of determinants of occupational exposure frequently involve left-censored lognormally distributed data, often with repeated measures. Left censoring occurs when observations are below the analytical limit of detection (LOD); repeated measures data results from taking multiple measurements on the same worker. A common method of dealing with this type of data has been to substitute a value (such as LOD/2) for the censored data followed by statistical analysis using the 'usual' methods. Recently, maximum likelihood estimation (MLE) methods have been employed to reduce bias associated with the substitution method. We compared substitution and MLE methods using simulated lognormally distributed exposure data subjected to varying amounts of censoring using two procedures available in SAS: LIFEREG and NLMIXED. In these simulations, the MLE method resulted in less bias and performed well even for censoring up to 80%, whereas the substitution method resulted in considerable bias. We illustrate the NLMIXED procedure using a dataset of chlorpyrifos air measurements collected from termiticide applicators on consecutive days over a 5-day workweek. We provide sample SAS code for several situations including one and two groups, with and without repeated measures, random slopes, and nested random effects.

These findings reaffirm that lung cancer and CBD, and suggest that COPD and nervous system and urinary tract cancers, are related to beryllium exposure. Cigarette smoking and exposure to other lung carcinogens are unlikely to explain these elevations.

This article compares several methods that were used for determining hand activity level and force in a large prospective ergonomics study. The first goal of this analysis was to determine the degree of correlation between hand activity/ force ratings using different assessment methods. The second goal was to determine if the hand activity/force methods were functionally equivalent for the purpose of calculating the ACGIH(R) hand activity level (HAL) threshold limit value (TLV(R)). A final goal was to investigate reasons for potential differences between methods. More than 700 task analyses were conducted on 484 workers at three study locations. Hand activity was assessed by two methods, including a trained observer on site using a 10-point visual analog scale for hand activity level and by offsite video analysis of the same task to calculate the frequency of exertions and the work/recovery ratio. Hand force was assessed by two on-site methods: ratings of perceived exertion (RPE) using a modified Borg CR-10 scale by a trained observer and RPE by the worker performing the task. The two methods for assessing hand activity level were correlated (Spearman rank = 0.49) and produced main TLV result categories (below Action Limit, Action Limit, TLV) with percent of exact agreement ranging from 71 to 91% and weighted Kappa ranging from 0.61 to 0.75. The two RPE methods for assessing hand force were correlated (Spearman rank ranging from 0.47 to 0.69) and produced TLVs with percent of exact agreement ranging from 64 to 83% and weighted Kappa ranging from 0.52 to 0.62. Differences between methods may be explained by a number of task and subject variables that were significantly associated with higher levels of hand activity and force. In summary, this study found substantial agreement between two methods for assessing hand activity level and moderate agreement between two methods for assessing hand force.

Background: Statin remains a mainstay in the prevention and treatment of cardiovascular diseases. Statin utilization has evolved over time in many countries, but data on this topic from China are quite limited. This study aimed to investigate the changing trends of statins prescription, as well as detail the statin utilization through a successive longitudinal study. Methods: The prescription database was established based on electronic health records retrieved from the hospital information system of Jinshan Hospital, Fudan University from January 2012 to December 2018 in Shanghai, China. The prescription rates and proportions of different statin types and doses among all patients were examined. Subanalyses were performed when stratifying the patients by age, gender, dose intensity, and preventative intervention. Results: During the study period, a total of 51,083 patients, who were prescribed for statins, were included in this study (mean [SD] age, 59.78 [±13.16] years; 53.60% male, n = 27, 378). The overall statins prescription rate in which patients increased from 2012 (1.24, 95% CI: 1.21-1.27%) to 2018 (3.16, 95% CI: 3.11-3.20%), P < 0.001. Over 90% of patients were given a moderate dose of statins. Patients with a history of coronary and cerebrovascular events (over 32%) were more likely to be prescribed with statins for preventative intervention. Furthermore, our study has witnessed a significant rise in statin therapy in primary and secondary prevention. Conclusions: In conclusion, statins were frequently prescribed and steadily increased over time in our study period. There were also changes in statin drug choices and dosages. A coordinated effort among the patient, clinical pharmacist, stakeholders and health system is still needed to improve statin utilization in clinical practice in the future.

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