Counting Farm Injuries and Fatalities: An Assessment of Irish Occupational Health and Safety Surveillance Data Systems

OBJECTIVE

Generation of reliable data underpins the effectiveness of Occupational Health and Safety (OHS) surveillance systems. Despite the importance of understanding OHS data systems, there are few papers that provide overviews of their structure and/or content. This paper introduces a basic framework for assessing OHS data systems that will be of use to researchers internationally. We applied this approach to assess the Irish OHS data system by undertaking a data mapping exercise.

METHOD

We developed a checklist based on recommendations of monitoring and measurement of OHS proposed by the National Academies of Sciences, Engineering, and Medicine (USA). An assessment of published reports that present systematic OHS surveillance data was undertaken to identify the institutions or organisations responsible for collecting and curating the data, their remit, and, associated with this, their respective case definitions. We then provide an overview of the variables collected and these are then mapped against the checklist.

RESULTS

The assessment highlights that whilst the farm fatalities dataset provides complete coverage of all fatalities, regardless of age or employment status, the same is not true of the three non-fatal injuries datasets reviewed. There are important differences in the data collection methods and, associated with this, which populations are covered.

PRACTICAL APPLICATION

The assessment approach provides valuable insights into the strengths and weaknesses of a critical element of OHS surveillance systems, namely the production of datasets. This knowledge is important for researchers as understanding the data that informs their research is fundamental to good science. It is critical for policy-makers and other stakeholders to understand the strengths and weaknesses on which OHS policy, strategies, or education and training interventions are developed.

Taiwan renal data report system: Framework, function, and development

The world healthcare system is actively seeking possible solutions for the rapid growth of kidney disease threats. The Taiwan Renal Data System (TWRDS) was central in assisting kidney health and care policymaking to reduce end-stage kidney disease incidence and mortality. This article summarizes the TWRDS framework, recent applications, and developments to provide new insights for some international researchers to promote planetary kidney health. The TWRDS originated in 1987 for the accreditation and quality monitoring of dialysis units and was connected with enriched health claim databases after the implementation of universal national health insurance in Taiwan in 1995. As a healthcare information centre, TWRDS has published annual reports forming indispensable instructions for renal care improvement since 2014. The TWRDS possesses three main functions: (1) kidney disease surveillance; (2) offering rich materials for research purposes; (3) achieving precision prevention and care through complex algorithms. In the new era, TWRDS can help build a more resilient society against communicable disease threats by integrating remote sensor techniques for developing future remote healthcare structures, as well as identifying kidney health inequity populations and promoting healthcare resources distributed equity. The global healthcare system is facing escalating burdens of non-communicable disease care due to the rapidly growing elderly population. Therefore, a considerable-scale data system is an essential decision-supportive tool in promoting an evidence-based, resilient, sustainable, equity care environment. Undoubtedly, TWRDS experience is a practical example of leveraging healthcare providers' decisions, care outcomes, and renovation.

A comparative study on superb microvascular imaging and conventional ultrasonography in differentiating BI-RADS 4 breast lesions

This prospective study aimed to explore the diagnostic value of superb microvascular imaging (SMI) in differentiating Breast Imaging Reporting and Data System (BI-RADS) 4 breast lesions compared with conventional ultrasonography (US). A total of 111 patients with 116 breast lesions underwent grayscale ultrasound (US), colour Doppler flow imaging (CDFI) and SMI breast imaging between February 2016 and May 2018. CDFI and SMI were performed to evaluate vascular quantity, morphology, and distribution characteristics. The detection of malignancy was compared between grayscale US alone, US + CDFI and US + SMI in terms of the BI-RADS stratification system. SMI was observed to be significantly more accurate in distinguishing malignant breast lesions (86.67%) compared with CDFI (80.00%) (P<0.001). Among malignant lesions, SMI detected 80.00% of those that contained ≥4 vessels, while CDFI only detected 56.67%. Penetrating and branching vessels were identified by SMI in 53.33% of malignant breast lesions and by CDFI in 10.00%. There was no significant difference in vascular distribution by SMI (P=0.094) and by CDFI (P=0.087). US + SMI was associated with higher sensitivity, specificity, and accuracy rates (86.67, 83.72 and 84.48%, respectively) compared with US + CDFI (80.00, 72.09 and 74.14%, respectively). The area under the curve values from receiver operating characteristic analysis of US + SMI, US + CDFI and US alone were 0.852 [95% confidence interval (CI): 0.768–0.936)] 0.760 (95% CI: 0.660–0.860), 0.698 (95% CI: 0.589–0.807), respectively (P<0.001). SMI yielded more detailed vascular information associated with malignant breast masses when compared with conventional US. Therefore, as an adjunct to grayscale, SMI exhibited a markedly improved diagnostic capability in distinguishing malignant and benign breast lesions, particularly those of BI-RADS category 4.

Digital data acquisition and processing

A flow cytometer is made up of many different subsystems that work together to measure the optical properties of individual cells within a sample. The data acquisition system (also called the data system) is one of these subsystems, and it is responsible for converting the electrical signals from the optical detectors into list-mode data. This unit describes the inner workings of the data system, and provides insight into how the instrument functions as a whole. Some of the information provided in this unit is applicable to everyday use of these instruments, and, at minimum, should make it easier for the reader to assemble a specific data system. With the considerable advancement of electronics technology, it becomes possible to build an entirely functional data system using inexpensive hobbyist-level electronics. This unit covers both analog and digital data systems, but the primary focus is on the more prevalent digital data systems of modern flow cytometric instrumentation.