Correlations between Various Demographics and AIDS Rate in Allegheny County

I. Introduction

     Because of the nature of HIV transmission, the disease known as AIDS affects certain populations at a greater rate than others.  After finding the areas in my hometown of Pittsburgh, Pennsylvania, that had the highest cumulative rates of AIDS infection, I was curious to see why these specific communities were in greater peril than others.  To do this, I correlated different demographic statistics with the AIDS rates to see which features could be found in conjunction with AIDS cases.  The correlations I discovered simply show the relationship between two variables, and do not imply causation; however, they are still useful indicators of what populations are hit the hardest by the AIDS epidemic.  

     The three main categories of demographic data that I used related to money, race, and a miscellaneous category including average year building built, percent male male households, and median age.  Initially, I felt that money and race would be the strongest relationships because the zip codes with the highest rates in Pittsburgh, including East Liberty (see photos), Manchester, and the North Side (see photos), tended to be predominantly disadvantaged and African American, populations that represent high proportions of the national AIDS population.  While the main method of transmission in Pittsburgh was men having sex with men (see Project 1 Table 3.1), this statistic cannot be as easily applied to demographic data and only pertained to a small population, and so I thought that I wouldn’t find much data relating to this group of Pittsburghers.  This fact in conjunction with the high African American rates in low-income areas suggests that ‘down low’ homosexuality in the black community may also be a problem.  I also expected education statistics to correlate, but neither the percent of population with a high school education or less, nor the opposite, the percent of the population with a college degree or more, formed a significant correlation.  When I did the calculations, money and race certainly did correlate with the AIDS rate, but my strongest correlation was to be found between male male households and the AIDS rate.  By looking at the strength of these demographics-based correlations, I was able to gain a greater understanding of the nature of AIDS in Pittsburgh.

Proceed to II. Money