V. Anomalies

In order to discover if the current AIDS and HIV situation in Pittsburgh is any different than that shown by the AIDS rate correlations, I also calculated correlations that compared the same demographics to the HIV and People Living With AIDS rates.  While most of the correlations remained very similar, there were a few disparities.  For example, the HIV correlation for average year building built was not significant (Table 5).  This result is less surprising after consulting this map (3), as it is apparent that many zip codes with higher HIV rates are not located directly downtown, the oldest part of the city.  Also, while the correlation for median age was significant for AIDS and PLWA, it was not for HIV (Table 5).  This can be interpreted as showing that while AIDS affects younger populations more than older, HIV doesn’t.  This statistic does not seem to fit in with my knowledge of HIV, but could perhaps be due to the fact that HIV is spreading to new populations, such as heterosexual black women, whose ages could be different than those of the populations affected by AIDS.  A final point that should be mentioned is that because of its extremely high AIDS rate of 1805 per 100,000, zip code 15233, Manchester, appears to be an outlier in most correlations.  However, when I removed it from the data, the correlations did not change significantly, and so I allowed it to remain in my calculations.

Proceed to VI. Conclusions

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