Introduction

I wanted to examine the relationship between race, neighborhood, and HIV/AIDS diagnoses in New York City. I used Tableau Public to create data visualizations representing the relationships between HIV/AIDS diagnoses by neighborhood as an indicator of social conditions that may lead to the factors contributing to disproportionate HIV diagnoses in low-income neighborhoods (Ransome et al., 2016). Area-based conditions, such as social and environmental conditions, are recognized as determinants and risks for health inequities, such as HIV diagnosis (Wiewel et al., 2016).

Methods and Tools

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. NYC Open Data is an incredibly versatile and valuable tool with an extensive library of clean data sets ready for analysis.

In addition to using NYC Open Data as a source for my project, I also used Open Refine to clean the data. Since this project required a historical analysis (over time), the data set I accessed was not quite fit for that type of analysis. I was able to sort and view the data set chronologically by filtering and sorting columns with Open Refine.

I first analyzed the data sets by comparing HIV and AIDS Diagnoses per a population of 100,000 by Race. Per current ethnographic research, Black populations in New York City have higher rates of HIV/AIDS diagnosis compared to other racial groups due to area-based social inequalities and systemic racial inequalities among housing instability, food insecurity, and social stigma surrounding having HIV (Parker et al., 2016). Per the packed bubble data visualization, produced via tableau, it is apparent that there is a disproportionate diagnosis in Black populations between both males and females.

Figure 1

Next, I analyzed HIV/AIDS diagnosis, race, and neighborhood. We have already determined a disproportionate diagnosis of HIV in Black populations in New York City; the next step is to analyze HIV/AIDS diagnoses between neighborhoods. There is a history of disproportionate access to healthcare between neighborhoods, as seen through the COVID-19 pandemic (Zhong et al., 2022). By analyzing HIV/AIDS diagnosis by neighborhood, we can further determine the existing inequalities in diagnosis and care.

From the scatter plot, we can see an obvious outlier: Black populations in Chelsea and Clinton (also known as Hell’s Kitchen), which are both historically gay neighborhoods (Bitterman et al., 2016).

Figure 2

There is an existing disproportion in HIV and AIDS diagnosis. It is clear from both visualizations that Black and Latino populations in majority Black and Latino neighborhoods in New York City are diagnosed with HIV and AIDS at the highest rate. The next step in my methodology was to analyze diagnosis by sex and race. Neaigus et al. 2014 determined that Black gay men are at the highest risk of HIV exposure due to social conditions such as racial inequality in healthcare, economic barriers to getting tested and acquiring pre-exposure prophylaxis (PrEP), and barriers such as stigma. I created a side-by-side circle plot which represents HIV diagnosis between Male and Females in New York City by race/ethnicity.

The visualization represents an outlier, being Black men per a population of 100,000. Other disproportionate data includes Black women and Hispanic/Latino men.

Figure 3

Lastly, I devised a visualization that represents HIV/AIDS diagnosis over time for all groups to determine if there has been an increase over the years. A line graph shows increases and decreases in HIV/AIDS diagnoses per population of 100,000 people in New York City among all racial populations and sex.

Figure 4

It was important to properly label the graphs to represent the data being visualized accurately. My classmate and I discussed proper graph titles and how it is crucial to communicate what is being represented when designing a data visualization effectively. Initially, my titles were long, but through a brief conversation with my classmate, I was able to narrow down the titles to be shorter.

Conclusion

There are many barriers for Black gay men in New York City to receive PrEP, as it is a costly medication unless covered by insurance and to be tested for HIV/AIDS historically (Malebranche et al., 2004). My analysis of disproportionate HIV/AIDS diagnosis reveals a symptom of systemic inequality for low-income Black and Brown people in New York City, specifically Gay Black and Brown men.

I wanted to specialize in researching HIV/AIDS diagnosis in New York City because I am a queer person in New York City. I was interested in the open data surrounding issues that my community faces. As we can see in Figure 1 and Figure 2, Black people in New York City, specifically Black men in Chelsea and Hell’s Kitchen, are disproportionately diagnosed with HIV and AIDS. Per Figure 4, we can see that there is not a steady decrease over time in diagnosis, but increases, decreases, and plateaus.

Healthcare in New York City is inadequate for many different communities. There are many barriers to access for Black and Brown people, as we can see from the data. These graphs highlight the inequalities and barriers to testing, treatment, and access to PrEP in New York City.

References

Data Source: https://data.cityofnewyork.us/Health/HIV-AIDS-Diagnoses-by-Neighborhood-Sex-and-Race-Et/ykvb-493p

Bitterman, A., & Hess, D. B. (2016). Gay ghettoes growing gray: Transformation of Gay Urban Districts across North America reflects generational change. The Journal of American Culture, 39(1), 55–63. https://doi.org/10.1111/jacc.12523

Farhat, D., Greene, E., Paige, M. Q., Koblin, B. A., & Frye, V. (2016). Knowledge, stereotyped beliefs and attitudes around HIV chemoprophylaxis in two high HIV prevalence neighborhoods in New York City. AIDS and Behavior, 21(5), 1247–1255. https://doi.org/10.1007/s10461-016-1426-6

Malebranche, D. J., Peterson, J. L., Fullilove, R. E., & Stackhouse, R. W. (2004). Race and sexual identity: perceptions about medical culture and healthcare among Black men who have sex with men. Journal of the National Medical Association, 96(1), 97–107.

Neaigus, A., Reilly, K. H., Jenness, S. M., Wendel, T., Marshall, D. M., & Hagan, H. (2014). Multilevel risk factors for greater HIV infection of black men who have sex with men in New York City. Sexually Transmitted Diseases, 41(7), 433–439. https://doi.org/10.1097/olq.0000000000000144

Parker, C. M., Garcia, J., Philbin, M. M., Wilson, P. A., Parker, R. G., & Hirsch, J. S. (2016). Social Risk, stigma and space: Key concepts for understanding HIV vulnerability among black men who have sex with men in New York City. Culture, Health & Sexuality, 19(3), 323–337. https://doi.org/10.1080/13691058.2016.1216604

Wiewel, E. W., Bocour, A., Kersanske, L. S., Bodach, S. D., Xia, Q., & Braunstein, S. L. (2016). The association between neighborhood poverty and HIV diagnoses among males and females in New York City, 2010–2011. Public Health Reports, 131(2), 290–302. https://doi.org/10.1177/003335491613100213

Zhong, X., Zhou, Z., Li, G., Kwizera, M. H., Muennig, P., & Chen, Q. (2022). Neighborhood disparities in COVID-19 outcomes in New York City over the first two waves of the outbreak. Annals of Epidemiology, 70, 45–52. https://doi.org/10.1016/j.annepidem.2022.04.008