Rachana Tanksali, Sia Jumani and Sumaya Jashim present to you Public Health Data Visualization. We used public health data to create vizzes that we hope will inspire doctors and legislators worldwide to bridge the gap in treatment and accessible, affordable healthcare for all groups, especially low-income and minority groups.

Rachana - This visualization showcases the continental distribution of new COVID-19 cases correlated with various preexisting health conditions. The dark blue star includes global data, whereas the 6 other stars include data from South America, Oceania, North America, Europe, Asia, and Africa. The information included in each of these stars are the continent name, cardiovascular death rate, diabetes prevalence, extreme poverty rate, and the new cases. As shown in the visualization, the data on cardiovascular death rate is more or less the same for all continents whereas diabetes prevalence has an upward trend. The data visualization also mentions living conditions, such as extreme poverty, and how there could be certain anomalies in the data, in this case, Africa, which has a much higher extreme poverty rate. This visualization can essentially serve as a breakthrough to help doctors and scientists first treat such preexisting conditions in order to lower infection rates in their continent. Data Sources: https://devpost.com/software/public-health-data-visualization https://ourworldindata.org/

Sia - In my first graph of my visualization I evaluate the ratio of different ethnicities to the total population in the US. I then used that data and compared it to the testing data to see if the ratio of tests are equal to the ratio of people in each of those demographics. In my second graph I show the percentage of the total tests that each racial group had in 2020, 2021, and overall. We see that the percentage of tests was unequal to the percentage of the population in most of the racial groups and years with the exception of Native Hawaiian Pacific Islander population overall and in 2021 as well as in the Hispanic and Latino population overall and in 2021. This data is important because it will cause researchers and policy makers to not only question why these gaps exist and why they changed for some groups in 2021, but also how to bridge these gaps. Data Sources: https://www.census.gov/quickfacts/fact/table/US/RHI525219 https://covidtracking.com/analysis-updates/federal-covid-data-single-stream/

Sumaya - For my Data Visualization, I chose to use data from the United Census Bureau from the 2019 American Community Survey. I only looked at the percentage of insured individuals for all demographics as was given by the data. In my viz, you can see that it is categorized by race, income, sex, nationality/citizenship status, employment status, work experience, and then the overall sample percentage of the population. The data was further categorized into a tree map based upon the percentage insured with blue being at the higher end and orange being at the lower end. From my visualization, I was able to figure out that the overall percentage of individuals insured is still less than ideal, with an estimated ~9 percent not being insured. Individuals who are not citizens or are unemployed are less likely to be uninsured, with more than 35% of them not being insured. In addition to that, about 20% of Native Americans and Hispanics and low-income families are also uninsured in comparison to only about 10% for other minority groups and high-income families. This data has been eye-opening because it shows that we need to focus on bridging the gap for health insurance for these groups. Data Source: American Community Survey & United States Census Bureau. (2019). Explore Census Data. United States Census Bureau. https://data.census.gov/cedsci/table?q=health&tid=ACSST1Y2019.S2701&hidePreview=true

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