statistics

A causal inference framework for analyzing large administrative healthcare databases with a focus on multiple sclerosis

Provincial health authorities routinely collect patient information on a massive scale, but health researchers face the challenge of exploring cause-and-effect relationships using these non-randomized population-based data sources. Machine learning methods are increasingly used to analyze these large datasets, although they do not inherently take causal structures (i.e., how the variables affect each other) into consideration and may lead to less-than-optimal or even erroneous conclusions.

Primary Investigator: 
Award Type: 
Year: 
2018

Operations research applied to assess different strategies to reduce the public health and economic burdens of HIV/AIDS in British Columbia

Although traditional HIV prevention strategies — behaviour modification, condoms, needle exchange – have been very successful, their effect has reached a plateau since they are not always available, practical, or fully adhered to. In the past five years, research has shown that using antiretroviral therapy (ART) to treat those infected with HIV not only decreases mortality and morbidity but also decreases HIV transmission. Unfortunately, many individuals are still unaware that they are HIV-positive or that they should be on ART, since they have not been linked to our health-care system.

Primary Investigator: 
Award Type: 
Year: 
2012
Health Category: 
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