Context about Race/Ethnicity
Examining the relationship between racial/ethnic disparities in HIV-vulnerable populations is crucial in ending the HIV pandemic. Racial and ethnic barriers have existed for decades, from the start of the HIV/AIDS epidemic to today’s barriers to HIV prevention. Braunstein et al. (2022) determined that race/ethnic disparities were the most pronounced among men who have sex with other men (MSM), specifically Black and Latino MSM; additionally, despite the introduction of highly active antiretroviral therapy (HAART), which is considered to be an extreme improvement in HIV healthcare, improvements for Black and Latino people lag behind.
When I speak of barriers to healthcare, we must address all of the institutional barriers, such as policing in the United States. Policing has historically impacted Black Americans, with roots in Chattel Slavery, Segregation, and the Civil Rights movement; the over-policing of predominantly Black neighborhoods has remained a systemic barrier for Black people in the United States in accessing a myriad of resources (Echols, 2022). The impact of policing in urban areas has directly impacted MSM and has exacerbated HIV vulnerability among Black and Latino MSM (Parker et al., 2018). A feature of policing includes perceived discrimination based on anti-blackness and homophobia. Policing practices, like misdemeanor arrests without probable cause, are directly linked to racial health disparities across multiple conditions, including diabetes, trauma and anxiety, and stress (Parker et al., 2018).
In addition to policing as an institutional barrier for Black and Brown people, within the context of recent years, due to the Corona Virus pandemic, there is a growing body of evidence that suggests the racial disparities, specifically those impacting Black people, were exacerbated due to the pandemic and include disparities in Black and Latino persons with HIV (PWH) (Gwadz et al., 2021; Khan et al., 2021).
Black MSM are disproportionately affected by HIV and are reported to have the worst outcomes regarding healthcare due to institutional barriers, as previously mentioned; additionally, Black men with HIV have been reported to experience disparities in most aspects of their lives, including social stigma and healthcare access (Mgbako et al., 2020). Regarding the discussion of disparities in HIV-related healthcare for Black MSM, it is incredibly important to mention the inaccessibility that Transfeminine people of color face. Public health research has determined a high prevalence of HIV in transfeminine people of color in New York City; some reasons include the need for many transfeminine people to participate in survival sex work and being subject to violence from male partners (Hwahng & Nuttbrock, 2014). Many factors surrounding vulnerabilities to HIV are intersectional, meaning that there are several reasons for why an individual may be marginalized when it comes to accessing healthcare; some of these reasons could include racism, homophobia, transmisogyny, and classism.
Context about Housing Conditions
Neighborhood racial segregation in NYC is independently associated with a lack of resources, such as food availability and access to healthcare, specifically for Black and Latine women of color (Bower et al., 2014; Damle et al., 2022). Housing and lack of housing and HIV have a direct link, as housing supplies an individual with resources that could disrupt the causal relationship between poverty and inequality; additionally, housing functions as a crucial resource in reducing vulnerability to HIV, including the reduction of poor outcomes regarding healthcare (Aidala & Sumartojo 2007).
There are many factors regarding the relationship between housing and HIV vulnerability; a significant factor is housing conditions, including location, affordability, and housing quality. Leaver et al. (2007) determined that there was a significant positive association between providing housing for PWH and their health outcomes regarding healthcare. In addition to housing, resource insecurity (food insecurity) drives HIV-related stigma because poverty is an incredibly stigmatized phenomenon often associated with poor housing, health, and opportunities (Logie et al., 2022).
Why I chose this topic?
HIV stigma and barriers directly impact me as a Queer person born and raised in New York City, specifically in a low-income community in Central Queens. HIV healthcare has improved tremendously in the past 30 years, including the development of PrEP and other preventative techniques; despite the improvements in healthcare for HIV, there are still many barriers for Black and Brown people in urban areas like New York City.
Many of these barriers directly relate to housing insecurity, over-policing, and segregation in New York City. I wanted to visualize data to support research that has determined the existence of housing insecurity and poor housing conditions in its relationship with HIV vulnerabilities.
Materials and Methodology
I accessed the data set from the NYC Open Data HIV/AIDS Diagnoses by Neighborhood, Sex, and Race/Ethnicity data set. This data set included total sums of HIV/AIDS diagnoses and diagnoses by a population of 100,000. Additionally, I utilized the Housing Maintenance Code Complaints dataset to grab insights on housing conditions around the city. Fortunately, this was a geotagged dataset with comprehensive explanations of data. NYC Open Data is an incredibly versatile and valuable tool with an extensive library of clean data sets ready for analysis.
I decided to make four different chart types all using Tableau Public, including a map, because having a versatile representation of data is essential in representing sensitive data such as HIV diagnostics. I combined the treemap, bubble chart, and scatterplot into a singular dashboard for ease of access but kept the geospatial visualization separate.
Visualizations
I used a bubble chart to organize the frequency of HIV diagnosis per capita by neighborhood in New York City. The color code indicates differences in the neighborhood, and the size indicates HIV diagnoses per capita. Many things stood out to me, specifically “Chelsea – Clinton,” which is a historically gay neighborhood in New York City, but still the majority is white (65%) (United States Census Bureau. 2021 American Community Survey 5-Year Estimates). Since my project’s scope was analyzing health disparities for Black and Brown people in New York City, I became curious about neighborhood demographics and their relationship to HIV diagnostic data. More neighborhood bubbles that were much larger than others, such as High Bridge-Morrisania, Crotona-Treemont, East Harlem, and Bed Stuy / Flatbush, stood out to me.
To better understand the spread of this data, I organized the data into a scatter plot using total HIV/AIDS diagnoses. I also wanted to create a scatter plot and organize both AIDS and HIV diagnostic variables because, due to my research, I believed there would be a positive linear pattern.
Visualizing the spread of total HIV/AIDS diagnoses by neighborhood was important for me to determine which specific neighborhoods I wanted to compare to housing maintenance code violations. The two neighborhoods I wanted to look into were Bed Stuy / Crown Heights and the South Bronx (Treemont).
In the static version of this geospatial visualization, we can see that the highest total counts of Maintenance code violations are in the South Bronx, specifically in neighborhoods such as Treemont, High Bridge, and Jerome Park. Additionally, we can see that in Brooklyn, the highest total count of maintenance code violations is in Flatbush, Crown Heights, and Bed Stuy. All these neighborhoods are majority Black: Flatbush is a majority Black neighborhood (63.9%) with a median household income of $66,000, where almost 11% in this zipcode live below the poverty line (United States Census Bureau. 2021 American Community Survey 5-Year Estimates).
In addition to a bubble chart, a scatterplot, and a geospatial visualization, I also created a treemap to visualize the disparities in HIV diagnosis across race/ethnicity.
In creating the treemap, I wanted to visualize the racial disparities in HIV diagnosis in New York City. As we can see from this visualization, Black New Yorkers are the most vulnerable to contracting HIV compared to White New Yorkers, as discussed before, due to institutional barriers that Black people face in accessing healthcare, specific healthcare for HIV prevention.
UX Research and Design Process
I conducted two moderated user tests on my dashboard and geospatial visualization to ensure that accessibility recommendations were met and to potentially make changes as per user testimonies. I determined that the representative users for this test should be colleagues from Pratt who are both familiar with Tableau as a platform but are not experts in using it.
I asked them questions about readability if they could find specific data points, and if they could summarize findings just from the information presented in the data visualizations. They recommended changing the typeface to be more readable, having a thematic color palette, and collating the three visualizations into a dashboard. I followed all three of these recommendations, changing the typeface and colors and collating my three data visualizations regarding HIV/AIDS data into one singular interactive dashboard on Tableau Public.
I used the Red-Gold color palette for my visualizations regarding HIV/AIDS diagnostic data because of the association with the AIDS awareness movement. Red was used as a representative color for the AIDS awareness movement to symbolize the association between HIV/AIDS and blood (as it is a blood-transmissible disease) and a more symbolic meaning, to represent passion and anger in the blatant negligence that was paid to Gay people in the initial stages of the HIV/AIDS epidemic (UNAIDS, 2006; CDC, 2019).
I used Helvetica as a label for my graphs because it is a screen-readable san-serif font that is optimized for the computer/phone screen (Ali et al., 2013).
Reflection
I wanted to expand on my research topic from Lab 2 because HIV/AIDS healthcare is important to me, and being more informed / generate further research is important to me and my future research goals. I also have experience in sex psychology research, and HIV/AIDS as a research topic generally interests me. As for the technicalities of creating these charts, it was much easier now that I had a working repository of knowledge on using Tableau. In Lab 2, I had more difficulties since there was an initial learning curve.
The UX Research I did provided me with the most insights on the project because, following the user tests, I did research on typeface accessibility and association with certain colors out of curiosity, and I was able to apply that information to this project. I gained many insights from this final project and learned much more about design research. I look forward to expanding on this research in the future to make more advanced visualizations.
References
Data Source:
https://data.cityofnewyork.us/Health/HIV-AIDS-Diagnoses-by-Neighborhood-Sex-and-Race-Et/ykvb-493p
https://data.cityofnewyork.us/Housing-Development/Housing-Maintenance-Code-Complaints/uwyv-629c
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Logie, Carmen H., Nina Sokolovic, Mina Kazemi, Shaz Islam, Peggy Frank, Rebecca Gormley, Angela Kaida, Alexandra de Pokomandy, Mona Loutfy, and the CHIWOS Research Team. “Does Resource Insecurity Drive HIV-Related Stigma? Associations between Food and Housing Insecurity with HIV-Related Stigma in Cohort of Women Living with HIV in Canada.” Journal of the International AIDS Society 25, no. S1 (July 1, 2022): e25913. https://doi.org/10.1002/jia2.25913.
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