Clinical Research Directory

Cytisine vs Nicotine Replacement Therapy

Tobacco is the most preventable cause of disease and death in Canada. Although the tobacco use rate has substantially gone down in the general population, significant differences exist between sub-populations in Canada, for example Ottawa's highly vulnerable homeless or at-risk for homelessness population has an almost 100% tobacco smoking rate relative to 9-18% in the rest of the Canadian general population. This stark inequity in tobacco use translates into devastating healthcare outcomes such as a disproportionate amount of cancer, stroke, heart disease and death. Canadians who are homeless or at-risk for homelessness die 25 years earlier than housed Canadians, mostly due to tobacco. In order to tackle this tobacco use related inequity - a novel approach is urgently needed. Despite commonly held dogma that People Who Use Drugs (PWUD) don't want to quit smoking, many studies have demonstrated that in fact they are very interested in quitting. Moreover, the investigators pragmatic peer-led community-based action approach used in their PROMPT project has demonstrated that tobacco dependence strategies can be implemented with great success in this population. The majority of PROMPT participants reduced or quit tobacco use, in addition to reducing or quitting all other drug use. Importantly, the investigators have demonstrated that it is possible to gain the trust and engagement of marginalized populations and that researchers can create a community space that is low-threshold, safe and non-judgmental. The investigators aim to compare two tobacco dependence management strategies in the homeless (or at-risk for homelessness) multi-drug use population in Ottawa and Toronto. They will use the same peer-led approach in PROMPT with community peer researchers with lived experience; with the hope that the cost-effective community based framework derived from this trial will serve as a template for interventions and treatments in community settings for chronic diseases such as obesity and diabetes.

Using Artificial Intelligence for the Detection of Respiratory Diseases Associated With Pollution

Generating a picture at the city level, through geospatial and temporal grouping a lung diseases associated or exacerbated by pollution (COPD, AB and neoplasm pulmonary or pleural) and correlating this data with pollution data. Development of an accurate and prognostic HRCT imaging diagnostic tool, computer assisted in the mentioned pathology, by generating an algorithm capable of to detect early the follow-up tomographic imaging lesions, as well as to evaluate objective their speed of evolution. Validation of the proposed algorithm by comparison with medical diagnosis.