The relationship between anthropometric measures and cardiometabolic health in shift work: findings from the Atlantic PATH Cohort Study

PurposeTo evaluate the relationship between anthropometric measures and cardiometabolic health in shift workers compared to non-shift workers.MethodsA population health study was conducted with 4155 shift workers and 8258 non-shift workers from the Atlantic Partnership for Tomorrow’s Health (PATH) cohort. Linear and logistic regression models were used to assess the differences in anthropometric measures (body adiposity) and self-reported cardiometabolic disease outcomes (obesity, diabetes, and cardiovascular disease) between shift workers and non-shift workers.ResultsThere was a significant increased risk of cardiovascular disease, obesity, and diabetes among shift workers compared to matched controls despite higher levels of physical activity and lower levels of sedentary behaviour. Shift workers were 17% more likely to be obese (95% CI 7–27) and 27% more likely to have diabetes (95% CI 8–51). The strength of this association was demonstrated by also controlling for body mass index and fat mass index.ConclusionsShift work is associated with obesity, cardiovascular disease, and diabetes despite higher levels of physical activity and lower levels of sedentary behaviour. The association between shift work and cardiometabolic health was independent of body mass index for cardiovascular disease and diabetes, and independent of fat mass index for diabetes

Telemedicine across the globe-position paper from the COVID-19 pandemic health system resilience PROGRAM (REPROGRAM) international consortium (Part 1)

Coronavirus disease 2019 (COVID-19) has accelerated the adoption of telemedicine globally. The current consortium critically examines the telemedicine frameworks, identifies gaps in its implementation and investigates the changes in telemedicine framework/s during COVID-19 across the globe. Streamlining of global public health preparedness framework that is interoperable and allow for collaboration and sharing of resources, in which telemedicine is an integral part of the public health response during outbreaks such as COVID-19, should be pursued. With adequate reinforcement, telemedicine has the potential to act as the “safety-net” of our public health response to an outbreak. Our focus on telemedicine must shift to the developing and under-developing nations, which carry a disproportionate burden of vulnerable communities who are at risk due to COVID-19

Automated Coding of Job Descriptions From a General Population Study: Overview of Existing Tools, Their Application and Comparison

OBJECTIVES: Automatic job coding tools were developed to reduce the laborious task of manually assigning job codes based on free-text job descriptions in census and survey data sources, including large occupational health studies. The objective of this study is to provide a case study of comparative performance of job coding and JEM (Job-Exposure Matrix)-assigned exposures agreement using existing coding tools. METHODS: We compared three automatic job coding tools [AUTONOC, CASCOT (Computer-Assisted Structured Coding Tool), and LabourR], which were selected based on availability, coding of English free-text into coding systems closely related to the 1988 version of the International Standard Classification of Occupations (ISCO-88), and capability to perform batch coding. We used manually coded job histories from the AsiaLymph case-control study that were translated into English prior to auto-coding to assess their performance. We applied two general population JEMs to assess agreement at exposure level. Percent agreement and PABAK (Prevalence-Adjusted Bias-Adjusted Kappa) were used to compare the agreement of results from manual coders and automatic coding tools. RESULTS: The coding per cent agreement among the three tools ranged from 17.7 to 26.0% for exact matches at the most detailed 4-digit ISCO-88 level. The agreement was better at a more general level of job coding (e.g. 43.8-58.1% in 1-digit ISCO-88), and in exposure assignments (median values of PABAK coefficient ranging 0.69-0.78 across 12 JEM-assigned exposures). Based on our testing data, CASCOT was found to outperform others in terms of better agreement in both job coding (26% 4-digit agreement) and exposure assignment (median kappa 0.61). CONCLUSIONS: In this study, we observed that agreement on job coding was generally low for the three tools but noted a higher degree of agreement in assigned exposures. The results indicate the need for study-specific evaluations prior to their automatic use in general population studies, as well as improvements in the evaluated automatic coding tools

Canada’s role in strengthening global health security during the COVID-19 pandemic

The world is confronted by the current pandemic of Corona Virus Disease (COVID-19), which is a wake-up call for all nations irrespective of their development status or geographical location. Since the start of the century we have seen five big infectious outbreaks which proved that epidemics are no more regarded as historic and geographically confined threats. The Canadian government underlined that these infectious disease outbreaks are threats to global health security and disrupt societal wellbeing and development. In this context, the Public Health Agency of Canada is proactive and has shown its preparedness for outbreaks of emerging and epidemic-prone diseases, and in dealing with these pathogens. Even before the declaration of pandemic, Canada has proved its global health leadership by ensuring collective action and multisectoral coordination which still remains a serious challenge especially for low and middle- income countries with existing poor health systems. In this article we discuss how Canada is addressing the global challenges posed by the COVID-19 pandemic through its leadership and practice of global health diplomacy

An examination of the association between lifetime history of prostate and pancreatic cancer diagnosis and occupation in a population sample of Canadians.

BACKGROUND:Occupation was assessed as possible risk factors for prostate (PCa) and pancreatic cancer in a large Canadian worker cohort. METHODS:The Canadian Census Health and Environment Cohort (CanCHEC) was derived from linking the 1991 Canadian Census Cohort to the Canadian Cancer Database (1969-2010), Canadian Mortality Database (1991-2011), and Tax Summary Files (1981-2011). From the total sample of 1,931,110 persons, we identified and derived two samples of 28,610 men and 3,220 men and women with a past history of PCa and pancreatic cancer diagnoses, respectively. Cox proportional hazards models were used to estimate hazards ratios and 95% confidence intervals for occupation. RESULTS:In Canadian men aged 24-64 years, the highest elevated risks of PCa were observed for library clerks (HR = 2.36, 95% CI:1.12-4.97), medical radiation technologists (HR = 1.66, 95% CI:1.04-2.65), telecommunications and line cable workers (HR = 1.62, 95% CI: 1.22-3.16) and commissioned police officers (HR = 1.54, 95% CI: 1.10-2.16. The highest elevated risk for pancreatic cancer were observed for commissioned police officers (HR = 4.34, 95% CI: 1.85-10.21), photographic and film processors (HR = 3.97, 95% CI:1.69-9.34), railway and motor transport labourers (HR = 3.94, 95% CI: 1.67-9.29), and computer engineers (HR = 3.82, 95%CI: 1.52-9.61). CONCLUSION:These findings emphasize the need for further study of job-related exposures and the potential influence of non-occupational factors such as screening practices

Twitter analytics to inform provisional guidance for COVID-19 challenges in the meatpacking industry

The COVID-19 pandemic raised considerable challenges to obtain reliable guidance to help occupational health practitioners, workers, and stakeholders building up efficient prevention strategies at the workplace, between the constant increase of publications in the domain, the time required to run high-quality research and systematic reviews, and the urgent need to identify areas for prevention at the workplace. Social Media and Twitter, in particular, have already been used in research and constitute a useful source of information to identify community needs and topics of interest for prevention in the meatpacking industry. In this commentary, we introduce the methods and tools we used to screen relevant posts on Twitter. Twitter analytics is a way to capture real-time concerns of the community and help ensure compliance with the notion of social accountability. As such research has limitations in terms of exhaustiveness and level of evidence, it should be considered as provisional guidance to direct both actions at the workplace and further conventional research projects