I don't have a bunch of answers. What I have are questions. In fact, when I talk about data fluency I prefer to "group think" solutions. We try and unpack the information available to us and trace a path to critical thinking.
Reading through the article below, once again probably launched more questions than it attempted to answer.
Low-Dose Aspirin for Primary Prevention of Cardiovascular Disease: Use Patterns and Impact Across Race and Ethnicity in the Southern Community Cohort Study--click for article
American Heart Association news summarized the findings, A racial gap in the heart attack benefits of aspirin?
"We think the reason aspirin use did not have a beneficial effect for African Americans could involve a different genetic response to aspirin therapy and poor control of other risk factors," said Dr. Rodrigo Fernandez-Jimenez, lead author of the study. He is a cardiologist and researcher at Centro Nacional de Investigaciones Cardiovasculares in Madrid.
But when I look at the classification of "black" race contrasted with birth country of origin you can see why lumping a diverse population under a poor proxy for biology or a substitute for a political construct makes no sense in medicine (or anywhere else for that matter).
Data visualization below is from my workshop on racial bias in data...think about how heterogenous genetic profiles would be in an aggregated classification of "race".
Reading through the graphics in the article I was unable to look at the variables classified by race--they were aggregated at too high of a level to invite inquiry or additional analyses.
For example, why isn't the data stratified by race AND risk? Is it me or am I unable to determine how many blacks are low, intermediate, or high risk stratified by education or health insurance status? Wouldn't access to healthcare be a potential driver of being "prescribed" low dose aspirin?
I recently examined how access to healthcare influences algorithms attempting to manage the health of populations within a large health system. The algorithm was adopted to help identify patients with complex needs that would benefit from an intervention to help mitigate co-morbid disease and higher risk profiles.
It is worth digging into the article to see how the referral to the intervention becomes more equitable once actual levels of disease are implemented instead of healthcare costs. When the algorithm is applied as developed, black patients have to be far more ill before being referred to the program as compared to white patients.
Back to the Low Dose aspirin study. To add more confusion or obfuscation--the measure regarding health insurance status is not included in the final graphic...
Keep an eye on the YouTube space if you would like to see the full presentation from Tableau Fringe Festival. They should have videos posted by the end of the month.