Welcome to the anatomy of morbidity project

As part of the three day challenge for #hackseq18 on October 12-14, 2018 at the University of British Columbia, we analyzed the most recent open dataset called “Death, causes of death, and life expectancy, 2016” from Canadian Vital Statistics. We used descriptive, diagnostic, and predictive data analyses for this project to enable prescriptive recommendations.
We encourage participants to form their own interests/questions from the data and join the project slack channel.

Project abstract

With the rise of aging population and chronic diseases, Canada continues to suffer from the high costs of disease management [1-3]. Additionally, studies on prevalence and patterns of causes of morbidity or co-morbidity and associated determinants in Canada remain scarce and outdated [4]. Canadian Vital Statistics has recently released a comprehensive and well-annotated dataset on causes of death and mortality of all Canadians from 2012 until 2016. We hypothesize that understanding the major causes of mortality (i.e. various diseases) among males and females in different age categories across all provinces can inform health care and governmental policies. In this project, we will formulate hypotheses about what we most die of as a nation and test them by exploring these datasets. The overarching goal is to enable evidence-informed policymaking in Canada with these visuals..

Participants

Passionate about the scientific inquiry and inspired to put the scientific method to good use using an interesting dataset. Basic familiarity with data analysis in Python or RStudio is an asset, for instance, this could include data wrangling (tidyverse, purr, tidyr, dplyr), data visualization and modelling (ggplot2, maptools, ggmap, nlme, reshape), as well as data reporting (Rshiny, Rmarkdown) in RStudio. Concerned about reproducibility in science and so familiar with Git and Github project management and organization.

Level of proficiency

Skills can vary from 1 to 3 stars because enthusiasm to learn is key!

• 1 Star: “I can work with this language if I have access to Stack Overflow.”
• 2 Stars: “I know this language and use it in my work/projects regularly.”
• 3 Stars: “I’m am one with the language. I’ve developed software using it.”

Use this poll:

Optional competency

Result reporting and story telling abilities, knowledge of RStudio and R-packages, teaching that to fellow team-mates, and ready to have fun!

Products of hackseq18

• This shinyapp contains what we worked on as a team
• Recorded video presentation of our project: minute 39:00
• Medium article on hacking disease and death

Resources

• Daniel Shiffman’s Git and GitHub for Poets
• Useful R packages
• On hypothesizing, Strong Inference by John Platt
• Multi-Level Modeling by Singer and Willet
• Population-level time series regression analysis

References

[1]. Canada’s aging population will strain the health-care system
[2]. Caring for aging parents costs Canadians $33 billion a year — and it’s just going to get worse
[3]. Canada ‘woefully unprepared’ to deal with senior population surge, Senate committee hears
[4]. Roberts, K. C. et al. “Prevalence and Patterns of Chronic Disease Multimorbidity and Associated Determinants in Canada.” Health Promotion and Chronic Disease Prevention in Canada : Research, Policy and Practice 35.6 (2015): 87–94.

Project Team Members:

Eva Yap, Katarina Priecelova, Shannon Lo, Rachel Miller, Mariam Arab, chuhan zhang, Emily Gong, Sophia Chan, Adil Imtiaz, Uyen Nguyen, Lisa Cao, Marion Shadbolt, Raissa Phillibert, Noushin Nabavi

Project lead: Noushin Nabavi: Github
Project co-lead: Marion Shadbolt: Github
Project co-lead: Raissa Philibert: Github