How Economic Factors Influence Rates of HIV Infection and Survival Mark Schenkel, Isi Oribabor, Magan Sethi, Shang-Jui Wang, Dylan Kelemen

Background Information Infectious disease cases: tuberculosis (bronchitis, pneumonia, measles, etc.) Decreased as a result of demographic factors

Aim of Research Correlate demographic factors to the disproportionate cases of HIV/AIDS in developing nations around the world Identify the key demographic factors that regulate the spread and survival of HIV cases

Developing vs. Developed United Nations Conference on Trade and Development Criteria (UNCTAD): Low income (as measured in GDP) < $800 Weak Human Resources Low level of economic diversification

Least Developed Countries (LDCs)  49 Countries  million people  10.5% of world population (1997)

Hypotheses H 0 : There is no relationship between demographic factors and the rates of infection and survival of HIV. H a : There is a relationship between demographic factors and the rates of infection and the survival of HIV.

Demographic Factors Life Expectancy GDP/GNP Per capita income Total population Infant mortality rate Literacy Annual population growth rate Urbanized Population Fertility rate Immunizations Access to safe water Sanitation People per television People per physician

Methods Collect data on demographic variables in both developing and developed countries Transfer data to Excel Transfer data to JMP IN Analyze Make Conclusions

Direct Correlation to AIDS Percentages Rsquare = Prob > f Rsquare = Prob > f Rsquare = Prob > f Rsquare = Prob > f

Life Expectancy Rsquare = Prob > f <.0001 Log (Percent AIDS Population) = – Log (Life Expectancy(Total Population))

Significant Demographic Factors Female Literacy Life Expectancy Total Percent Access to Safe Water Annual Population Growth Rate Fertility Rate Per Capita Income

Female Literacy Rsquare Prob > f <.0001 y = x

Percent Access to Safe Water RsquareProb > f <.0001 y= x

Annual Population Growth Rate RsquareProb > f <.0001 y= x

Fertility Rate RsquareProb > f <.0001 y= x

Per Capita Income (in $1,000) RsquareProb > f <.0001 y= x

Research Findings Bivariate Fit of total life expectancy By people per physician Rsquare = Prob > f <0.0001

Research Findings Bivariate Fit of Total Life Expectancy by People per Television Rsquare = Prob > f <.0001

Life Expectancy Fit Model Percent AIDS Population<.0001 Total Percent Access to Safe water<.0001 Fertility Rate<.0001 Female Literacy<.0001 Annual Population Growth Rate.0007 Actual by Predicted Residual Plot

Conclusions  There are no strong, direct correlations between the demographic factors with available statistics and AIDS percentages.  Life expectancy is dependent on percent AIDS population, total percent access to safe water, fertility rate, female literacy, and annual population growth rate.  If percent AIDS population is dependent on life expectancy, would it be possible to create an equation in which life expectancy was dependent on the percent AIDS population?

Long-term Research Keep working on present data Why did the demographic factors not directly correlate to AIDS percentages? Percent AIDS Population Equation Include more variables (ex. Malaria populations) CCR5 Evidence indicates Malaria alone may explain much of the problem (Journal of Infectious Diseases) Try to find more accurate AIDS Populations and AIDS percentages

Difficulties Non-uniform and limited data Grossly Under Reported AIDS data Direct correlation to AIDS percentages were minor with much variability –Fit Model with Life Expectancy –Percent AIDS Equation

References

More References idshiv.html html Lewontin, R.C. Biology as Ideology: The Doctrine of DNA

Acknowledgements We would like to thank the Institute faculty for contributing their time to make our program memorable. Specifically, we would like to thank Dr. Fleischman, Dr. Norton, Dr. Gardner, Dr. Short, Donna, and Mr. Clarke for being helpful resources. Lastly, we would like to extend our thanks to Mr. Newman for his guidance and support. Shout-outs to “The Family”.