Facts Only: Real life impacts of racism

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By: Daniella Saint-Phard
Date: April 6, 2021

Racism persists all around us, whether in education, housing and infrastructure, or healthcare systems. Racial groups face problems on every front. Some of these problems include more police presence, less funding, social interventions, opportunity, and credibility. Data is so important to combatting the everyday issues BIPOC folx face. It is literally a driver of change and we must be responsible with it.


“The PRMR [pregnancy-related mortality rate] for black women with at least a college degree was 5.2 times that of their white counterparts.”

“Cardiomyopathy, thrombotic pulmonary embolism, and hypertensive disorders of pregnancy contributed more to pregnancy-related deaths among black women than among white women.”


Have you ever wondered why pregnant black women fatalities are higher than all other races? Or wondered why women of color in general have higher pregnancy-related mortality rates than white women? These terrifying statistical findings are provided by the CDC. These statistics serve as a tool to illuminate the lived experiences of the BIPOC (Black, Indigenous, People of color) community. How can data then impact BIPOC experiences? It reflects realities and provides insight into areas (variables) of possible change. Throughout data analysis, it is important to be mindful of implicit racism while navigating the method planning, data framing, and historical context.

Based on the CDC’s findings, the following recommendations were made to hospitals and healthcare providers: provide higher quality care, pay closer attention when diagnosing, and learn more about warning signs across different races. The implementation of these recommendations can prevent at least 60% of these deaths and lower the PRMR. 

A glaringly important aspect of data collection, analysis, and presentation is utilizing ethical, responsible, and unbiased language

These recommendations do not address the implicit racial bias faced by black women and other minority groups. Data science and analysis should be for the good of people. As demonstrated in the PRMP statistics, you can have all the numbers, but clear and accurate presentation is equally important. A glaringly important aspect of data collection, analysis, and presentation is utilizing ethical, responsible, and unbiased language. There are three important takeaways from this mini case study of real life data to consider when doing analytic work: methods, framing, and context



Takeaways

Methods

The provided PRMR statistics reflect one aspect of the population, but many other aspects (variables) of life that impact outcomes are not included. It is important to be mindful of reflecting the analyzed population in all stages: planning, methodology, implementation, analysis, and reporting. Tailor data collection methods to your population including, but not limited to, survey design, population sampling, administration/implementation, and monitoring/evaluation.

Framing

The wording of the above reported data can read as accusatory, placing the blame on black women and other minority groups, when in reality the most effective intervention should be implemented by healthcare providers. The wording also includes high-level understanding of medical conditions that the average reader may not immediately understand, which is why it’s important to know your audience. Language is a powerful tool for advocating and presenting data. Check your biases. Peer-review. Communicate openly with the data’s reflected population. Report clear and concise information. Make raw data and findings accessible to affected communities. These are simple habits to develop while dealing with data collection, analysis, and reporting. 

notebook with pen on messy desk

Context

The historical and background context is so important to understanding figures and statistics and applying them in a beneficial and ethical manner. The goal should be to provide a full picture of the situation and the “why” behind data results, rather than reporting data for open interpretation. These reports impact expenditure, planning, and policy decisions that significantly impact life trajectory for many people. When context is not taken into consideration, racial biases and discrimination persists for the BIPOC community.  

In conclusion, data is a powerful undercurrent of lived experiences and gateway to change. Be an ethical and responsible data analysis change driver for not only BIPOC communities, but everyone. That’s it. That’s the message of this blog post.

For more in-depth information on data analysis, visit the META Lab bootcamp course. For more information on the insane reality for BIPOC pregnant women, visit the CDC.gov website.


References

[1] https://www.cdc.gov/media/releases/2019/p0905-racial-ethnic-disparities-pregnancy-deaths.html

[2] https://ocrdata.ed.gov/assets/downloads/FAQ.pdf

[3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194634/

[4] https://www.loyola.edu/academics/data-science/blog/2018/why-ethics-are-important-in-data-science

[5] https://www.cssny.org/news/entry/New-Neighbors

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