Morbidity with temporary incapacity for work in the Southern Federal District of Russia. Selected indicators

Objective: To analyze the patterns of morbidity with temporary incapacity for work in the Southern Federal District of the Russian Federation.Materials and Methods: The object of the study is a unified interagency analytics platform. The subject of the research is the indicators of morbidity with temporary incapacity for work of the working population of the constituent entities of the Russian Federation of the Southern Federal District over 2005–2019: “The number of cases of temporary incapacity for work per 100 employees”, “The number of days of temporary incapacity for work per 100 employees”. The descriptive statistics methods were applied.Results: The studied statistical indicators of temporary incapacity for work reveal a steady positive trend in the morbidity rate of the working population in all the constituent entities of the Southern Federal District. From 2014 to 2015, there was a significant reduction in the indicators of temporary incapacity for work in all the constituent entities of the district. The indicators in all the studied constituent entities simultaneously declined, this decline being more prominent than in the previous 10 years. The data of the federal statistical monitoring does not represent an overall set of cases and days of temporary incapacity for work.Summary: It is deemed essential to amend Order of The Russian Federal Service for Statistics No. 723. All individuals and legal entities involved in medical activities for the examination of temporary incapacity for work in the relevant region must provide reports in accordance with Form 16-VN to the competent authorities of the constituent entities of the Russian Federation.

Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring

Background A central statistical assessment of the quality of data collected in clinical trials can improve the quality and efficiency of sponsor oversight of clinical investigations. Material and Methods The database of a large randomized clinical trial with known fraud was reanalyzed with a view to identifying, using only statistical monitoring techniques, the center where fraud had been confirmed. The analysis was conducted with an unsupervised statistical monitoring software using mixed-effects statistical models. The statistical analyst was unaware of the location, nature, and extent of the fraud. Results Five centers were detected as atypical, including the center with known fraud (which was ranked 2). An incremental analysis showed that the center with known fraud could have been detected after only 25% of its data had been reported. Conclusion An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.

The short-term mortality fluctuation data series, monitoring mortality shocks across time and space

The COVID-19 pandemic has revealed substantial coverage and quality gaps in existing international and national statistical monitoring systems. It is striking that obtaining timely, accurate, and comparable across countries data in order to adequately respond to unexpected epidemiological threats is very challenging. The most robust and reliable approach to quantify the mortality burden due to short-term risk factors is based on estimating weekly excess deaths. This approach is more reliable than monitoring deaths with COVID-19 diagnosis or calculating incidence or fatality rates affected by numerous problems such as testing coverage and comparability of diagnostic approaches. In response to the emerging data challenges, a new data resource on weekly mortality has been established. The Short-term Mortality Fluctuations (STMF, available at www.mortality.org) data series is the first international database providing open-access harmonized, uniform, and fully documented data on weekly all-cause mortality. The STMF online vizualisation tool provides an opportunity to perform a quick assessment of the excess weekly mortality in one or several countries by means of an interactive graphical interface.

Application of Statistical Monitoring Using Autocorrelated Data and With the Influence of Multicollinearity in a Steel Process

The purpose of this article is to demonstrate a practical application of control charts in an industrial process that has data auto-correlated with each other. Although the control charts created by Walter A. Shewhart are very effective in monitoring processes, there are very important statistical assumptions for Shewhart's control charts to be applied correctly. Choosing the correct Control Chart is essential for managers to be able to make coherent decisions within companies. With this study, it was possible to demonstrate that the original data collected in the process, which at first appeared to have many special causes of variation, was actually a stable process (no anomalies present). However, this finding required the use of autoregressive models, multivariate statistics, autocorrelation and normality tests, multicollinearity analysis and the use of the EWMA control chart. It was concluded that it is of fundamental importance to choose the appropriate control chart for monitoring industrial processes, to ensure that changes in processes do not incorporate non-existent variations and do not cause an increase in the number of defective products.

Dynamic methods for ongoing assessment of site-level risk in risk-based monitoring of clinical trials: A scoping review

Background/Aims It is increasingly recognised that reliance on frequent site visits for monitoring clinical trials is inefficient. Regulators and trialists have recently encouraged more risk-based monitoring. Risk assessment should take place before a trial begins to define the overarching monitoring strategy. It can also be done on an ongoing basis, to target sites for monitoring activity. Various methods have been proposed for such prioritisation, often using terms like ‘central statistical monitoring’, ‘triggered monitoring’ or, as in the International Conference on Harmonization Good Clinical Practice guidance, ‘targeted on-site monitoring’. We conducted a scoping review to identify such methods, to establish if any were supported by adequate evidence to allow wider implementation, and to guide future developments in this field of research. Methods We used seven publication databases, two sets of methodological conference abstracts and an Internet search engine to identify methods for using centrally held trial data to assess site conduct during a trial. We included only reports in English, and excluded reports published before 1996 or not directly relevant to our research question. We used reference and citation searches to find additional relevant reports. We extracted data using a predefined template. We contacted authors to request additional information about included reports. Results We included 30 reports in our final dataset, of which 21 were peer-reviewed publications. In all, 20 reports described central statistical monitoring methods (of which 7 focussed on detection of fraud or misconduct) and 9 described triggered monitoring methods; 21 reports included some assessment of their methods’ effectiveness, typically exploring the methods’ characteristics using real trial data without known integrity issues. Of the 21 with some effectiveness assessment, most contained limited information about whether or not concerns identified through central monitoring constituted meaningful problems. Several reports demonstrated good classification ability based on more than one classification statistic, but never without caveats of unclear reporting or other classification statistics being low or unavailable. Some reports commented on cost savings from reduced on-site monitoring, but none gave detailed costings for the development and maintenance of central monitoring methods themselves. Conclusion Our review identified various proposed methods, some of which could be combined within the same trial. The apparent emphasis on fraud detection may not be proportionate in all trial settings. Despite some promising evidence and some self-justifying benefits for data cleaning activity, many proposed methods have limitations that may currently prevent their routine use for targeting trial monitoring activity. The implementation costs, or uncertainty about these, may also be a barrier. We make recommendations for how the evidence-base supporting these methods could be improved.