Finding the Connections
Part of finding a solution to the problem of high AIDS rates is searching for connections between those rates and characteristics of the places they exist. When looking at demographic information and then correlating it with AIDS rates, some areas stand out and show a greater connection with AIDS rates. In no way are these the causes of the rates, but by changing the characteristics in various ways, we might be able to decrease the AIDS rates. In the Bronx, minority status, income status, and educational background are three major areas that show a compelling connection to AIDS rates. I originally chose these three areas because they seemed to be the most obvious ones to choose that would show strong correlations. Perhaps by looking at these areas more closely, governmental programs or laws can be implemented to help stimulate some change in AIDS rates.
Income status proved to be the most useful, as I had originally thought. I had a feeling that the poorer a community was, the higher the AIDS rates would be. Poorer families tend to live in poor neighborhoods, which have bad schools that poorly educate the children. This can lead to bad choices about sex and drugs in the future. In addition, poorer areas have less awareness of the problem, as shown in the previous paper, so they are more likely to have higher AIDS rates. As seen in table 1, there is an immensely strong correlation between the various levels of income and AIDS rates. (Correlations are highlighted in yellow)
For families living below the poverty line there is a positive correlation very close to one. This simply means that the more people who live below the poverty line, the more likely they are to have AIDS. However, this does not mean that being poor causes AIDS; it simply shows a connection that points out that more poor people have AIDS. I decided to try three levels of income that might clarify the point made by the correlation between AIDS rates and people living below the poverty line. I chose the lowest, the highest, and a middle area of income to correlate with AIDS rates. Again, the correlations display that more people with AIDS show up in families making less than 10 thousand dollars a year rather than families who make 50 to 60 thousand dollars a year. The tables clearly show that there are a higher percentage of people in the lower class then any class above it. This correlates positively with the high AIDS rates in the Bronx, thus proving that there is a connection between income status and AIDS rates, although it is not the cause.
The next area I thought would correlate strongly was minority status. Again, this seemed like an obvious one to me considering I have already seen many statistics that show higher percentages of minorities with AIDS than percentages of whites with it. Also, I was already aware that there are high AIDS rates in the Bronx and that the Bronx has more minorities than whites so it was pretty clear to see that connection before finding the exact data. Before making any correlations, there are noticeably more minorities in the Bronx than there are white people, so this only reinforced my notion that there would be a strong positive correlation. The Hispanic population shows a strong positive correlation as seen in table 2. (Correlations highlighted in yellow)
I had expected this because growing up in the Bronx, my experience has taught me that there are many more Hispanic people than any other race. The black population showed a surprisingly weak correlation to AIDS rates. This threw me off a little, but the reason becomes clear when looking at a graph of the data. In the graph below (insert graph below) there are clearly many areas that fall outside of the correlation line.
There are a few outliers that explain the weak correlation. In the zip codes of 10466 and 10469 (highlighted in green in table 2 and shown by the higher points in the graph) there is a high percentage of black people but relatively low AIDS rates. Were it not for areas like these, the correlation would be much stronger. The white population showed a correlation that I had expected. There is a relatively strong negative correlation between the white population and AIDS rates, which means that if you are white in the Bronx, you are less likely to have AIDS, but once again, it is not the cause. This correlation would be weak but in the zip codes of 10461, 10464, 10465, and 10471 (highlighted in blue in table2) there are very high percentages of white people but low AIDS rates. That contributes to the stronger correlation. Again, my initial thoughts about the relationship between race and AIDS rates were proven right because the data clearly shows that if one is a minority, he/she is more likely to have AIDS.
The final category I considered was education. Here I was unsure about what correlations I would get, but my first thought was that there would be a relatively strong positive correlation because the public school system in the Bronx is not very great. By not having a strong education system, teenagers and young adults are more likely to lead a life of poverty and crime, which includes bad choices such as having unprotected sex or sharing needles to do drugs. I decided to look at percentages of high school graduates. My thought is that the higher the percentage is, the lower the AIDS rates would be because these people would be more educated and mature about making decisions concerning sex and drugs, which would lower the risk of getting AIDS. However, I was disappointed to find that there are very small percentages of high school graduates in the Bronx. For the males, table 3 shows that no zip code had a higher percentage than 15.649 for male high school graduates and no zip code had a higher percentage than 19.39 for female high school graduates. (Correlations highlighted in yellow)
Before making the correlations, I knew they would be negative. Sure enough the correlations were negative, but the male correlation was not particularly strong. I guess the major factor playing into that correlation is sexuality. Even if a male does graduate from high school, if he is gay and has unprotected sex then he is more likely to get AIDS, so that would make the correlation weaker. Although these were not strong correlations, I think they can be used to affect some change.
Before ending my research, I thought it might be useful to find some other area where a positive correlation can be found so that something could be done to affect it. Immigration was the first idea to come to mind. My original thought was that the more immigrants there are in an area the higher the AIDS rates would be. Since most of the Hispanics living in the Bronx are immigrants and I had already seen a correlation between Hispanics and AIDS rates, I figured immigrants in general which show a similar correlation. Shown in table 4, there is a fairly strong correlation for those who are not U.S. citizens. Basically, if one is an immigrant in the Bronx, he/she is more likely to have AIDS. (Correlations highlighted in yellow)
Once again, it is important to note that these areas (income status, minority status, education, and immigration) are not the cause for the high AIDS rates. For example, immigrants tend to be poor so they fit into two categories that show strong correlations. The point is that I am not showing causes for AIDS, but rather connections that could possibly help towards finding a way to reduce AIDS rates in the Bronx.