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COVID-19 disrupted cancer control worldwide, impacting preventative screening, diagnoses, and treatment services. This modelling study estimates the impact of disruptions on colorectal cancer cases and deaths in Canada and Australia, informed by data on screening, diagnosis, and treatment procedures. Modelling was used to estimate short- and long-term effects on colorectal cancer incidence and mortality, including ongoing impact of patient backlogs. A hypothetical mitigation strategy was simulated, with diagnostic and treatment capacities increased by 5% from 2022 to address backlogs. Colorectal cancer screening dropped by 40% in Canada and 6.3% in Australia in 2020. Significant decreases to diagnostic and treatment procedures were also observed in Australia and Canada, which were estimated to lead to additional patient wait times. These changes would lead to an estimated increase of 255 colorectal cancer cases and 1,820 colorectal cancer deaths in Canada and 234 cases and 1,186 deaths in Australia over 2020–2030; a 1.9% and 2.4% increase in mortality, respectively, vs a scenario with no screening disruption or diagnostic/treatment delays. Diagnostic and treatment capacity mitigation would avert 789 and 350 deaths in Canada and Australia, respectively. COVID-related disruptions had a significant impact on colorectal cancer screening, diagnostic, and treatment procedures in Canada and Australia. Modelling demonstrates that downstream effects on disease burden could be substantial. However, backlogs can be managed and deaths averted with even small increases to diagnostic and treatment capacity. Careful management of resources can improve patient outcomes after any temporary disruption, and these results can inform targeted approaches early detection of cancers. [ABSTRACT FROM AUTHOR]

Unlike some other high-income counties, Canada does not provide universal prescription drug coverage. The various extent of coverage may left some Canadians vulnerable to cost-related non-adherence (CRNA) to medications. Using data from the 2015 national cycle of the Canadian Community Health Survey, we examine the impact of having private and public drug coverage on mitigating the risk of CRNA with a logit model and a Heckman selection model. CRNA was only observed in respondents who had prescriptions to fill, and respondents did not randomly make decisions on whether to get a prescription. This results in a classic sample selection problem. We found a higher estimated probability of reporting CRNA for uninsured respondents from the Heckman selection model than from the logit model. Respondents with government coverage only had a slightly higher probability of reporting CRNA relative to respondents with private coverage. These findings suggest that, without accounting for sample selection, the risk of not having drug insurance coverage is likely to be underestimated. Moreover, despite covering a less healthy cohort of respondents, the government insurance plans reduce risk of CRNA to a comparable level with private insurance. [ABSTRACT FROM AUTHOR]

In this study we explored nurse practitioner-provided medication abortion in Canada and identified barriers and enablers to uptake and implementation. Between 2020–2021, we conducted 43 semi-structured interviews with 20 healthcare stakeholders and 23 nurse practitioners who both provided and did not provide medication abortion. Data were analyzed using interpretive description. We identified five overarching themes: 1) Access and use of ultrasound for gestational dating; 2) Advertising and anonymity of services; 3) Abortion as specialized or primary care; 4) Location and proximity to services; and 5) Education, mentorship, and peer support. Under certain conditions, ultrasound is not required for medication abortion, supporting nurse practitioner provision in the absence of access to this technology. Nurse practitioners felt a conflict between wanting to advertise their abortion services while also protecting their anonymity and that of their patients. Some nurse practitioners perceived medication abortion to be a low-resource, easy-to-provide service, while some not providing medication abortion continued to refer patients to specialized clinics. Some participants in rural areas felt unable to provide this service because they were too far from emergency services in the event of complications. Most nurse practitioners did not have any training in abortion care during their education and desired the support of a mentor experienced in abortion provision. Addressing factors that influence nurse practitioner provision of medication abortion will help to broaden access. Nurse practitioners are well-suited to provide medication abortion care but face multiple ongoing barriers to provision. We recommend the integration of medication abortion training into nurse practitioner education. Further, widespread communication from nursing organizations could inform nurse practitioners that medication abortion is within their scope of practice and facilitate public outreach campaigns to inform the public that this service exists and can be provided by nurse practitioners. [ABSTRACT FROM AUTHOR]

Background: With the increasing concerns about the health and economic burden attributed to sugar-sweetened beverages (SSBs) consumption, SSB taxation has been proposed and implemented in many countries. Many previous economic evaluations of SSB taxation have shown that this kind of policy is cost-effective. However, the magnitude of impact varies. This study aims to design a comprehensive model to estimate the impact and cost-effectiveness of the SSB tax in Canada. Methods: A proportional multi-state life table-based Markov model was chosen to estimate the impacts of SSB tax in Canada. The health-related quality of life (including disability-adjusted life years (DALYs) and quality-adjusted life years (QALYs)), the costs (including health care costs and intervention costs), and the tax revenue were the main health and economic outcomes. We compared the simulated SSB tax with the current practice from the public health care payer perspective, and the tax was applied to the 2015 adult Canadian population up to 100 years. The economic model was built following guidelines from the Canadian Agency for Drugs and Technologies in Health. Results: After implementing a CAD$0.015/oz SSB tax, 282,104 cases of overweight and obesity, 210,542 cases of diseases, and 2,189 deaths could be prevented. The simulated SSB tax has the potential to avert 2.3 million DALYs, gain 1.5 million QALYs, and save CAD$32,583 million in health care costs in a lifetime period. The incremental cost-effectiveness ratio for the SSB tax was CAD$ -24,933/QALY. The SSB tax with different tax levels (CAD$0.01/oz and CAD$0.02/oz) remained cost-effective. Conclusion: Implementing the SSB tax in Canada is a potential cost-effective policy option for reducing obesity and related chronic diseases. The model built in this study provides a more accurate estimate of health and economic impact of SSB tax and could be used to estimate other sugar tax options. [ABSTRACT FROM AUTHOR]

Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy. [ABSTRACT FROM AUTHOR]

Recent developments in the social epidemiological literature indicate that health inequalities between the employed and the unemployed are widening in many advanced capitalist countries. At present, we know relatively little about why these inequalities are worsening. Drawing on nationally-representative data from the largest health survey in Canada, we explored this question by analyzing changes in self-rated health inequalities between employed and unemployed Canadians from 2000 to 2014. Using a regression-based method that decomposes a given inequality into its component sources, we investigated the extent to which risk factors that account for unemployment-related health inequalities at a single point in time can also explain the extent and direction of change in these unemployment-related health inequalities over time. Our results indicate that relative and absolute health inequalities between employed and unemployed Canadians widened over the study period. Between 2000 and 2014, the prevalence of poor self-rated health among unemployed Canadians increased from 10.8% to 14.6%, while rates among employed Canadians were stable at around 6%. Our findings suggest that the demographic, socioeconomic, and proximal risk factors that are routinely used to explain unemployment-related health inequalities may not be as powerful for explaining how and why these inequalities change over time. In the case of unemployment-related health inequalities in Canada, these risk factors explain neither the increasing prevalence of poor self-rated health among the unemployed nor the growing gap between the unemployed and their employed counterparts. We provide several possible explanations for these puzzling findings. We conclude by suggesting that widening health inequalities may be driven by macrosocial trends (e.g. widening income inequality and declining social safety nets) which have changed the meaning and context of unemployment, as well as its associated risk factors, in ways that are not easy to capture using routinely available survey data. [ABSTRACT FROM AUTHOR]