II. Money

     The first numbers that I looked at concerned the economics of Pittsburgh neighborhoods.  A good starting point is median income (map 8), which represents the median total household income.  The Census Bureau states that a household “includes all of the people who occupy a housing unit [. . . and] occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living quarters” (1).  For this statistic, the correlation coefficient was -0.483 (Table 5).  Because I was comparing 51 zip codes and used P < 0.05 as my N value (a confidence level of 95%), a coefficient greater than .279 was significant.  In this case, the correlation has a 99.9% confidence level, as it is greater than 0.451.  As one would expect, poorer neighborhoods, which are less prepared to defend themselves against this disease due to lack of money for healthcare, tend to have greater AIDS rates. 

     Other statistics that address the issue of severe poverty include the percent of the population below the poverty line and the percent relying on Social Security and Public Assistance.  The poverty lines used by the Census Bureau are called the Poverty Thresholds and are adjusted every year.  In 2000 the poverty thresholds looked like this: http://www.census.gov/hhes/www/poverty/threshld/thresh00.html (2).  In 1999, the median household income for Pennsylvania was $49,184 and $49,815 for Allegheny County.  As a point of reference, Manchester, the area in Pittsburgh with the highest AIDS rate, had an income in 1999 of only $23,841.  The correlation coefficient for percent of population below the poverty line was 0.416, at 99% confidence (Table 5), providing a significant link between those in dire financial need and the AIDS rate.  Citizens who receive Public Assistance or Social Security benefits can be elderly or retired, but a great deal of this money goes to the unemployed or impoverished through programs such as Temporary Assistance for Needy Families.  For Public Assistance the correlation coefficient was 0.438 (Table 5), showing a definite relationship with 99.9% confidence between areas with families on welfare and high AIDS rates.  Interestingly enough, the Social Security coefficient was negatively correlated at -0.346 (Table 5).  This negative correlation probably results from the high percentage of elderly or retired people in Pittsburgh that rely on Social Security.  These financial demographic statistics confirmed my hypothesis that areas with high poverty levels would also have high AIDS rates.

Proceed to III. Race

Back to I. Introduction