How Economic Factors Influence Rates of HIV Infection and Survival

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

2. http://www.cnn.com/SPECIALS/2001/aids

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

4. 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

5. 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

6. Least Developed Countries (LDCs) • 49 Countries • 610.5 million people • 10.5% of world population (1997)

7. Hypotheses • H0: There is no relationship between demographic factors and the rates of infection and survival of HIV. • Ha: There is a relationship between demographic factors and the rates of infection and the survival of HIV.

8. 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 Demographic Factors

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

10. Direct Correlation to AIDS Percentages Rsquare = 0.0989 Prob > f 0.0003 Rsquare = 0.0454 Prob > f 0.0152 Rsquare = 0.048 Prob > f 0.0126 Rsquare = 0.031299 Prob > f 0.0814

11. Life Expectancy Rsquare = 0.320881 Prob > f < .0001 Log (Percent AIDS Population) = 5.5516345 – 6.5608861 Log (Life Expectancy(Total Population))

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

13. Female Literacy y = 0.0269015x + 6.8029618 Rsquare Prob > f 0.465782 < .0001

14. Percent Access to Safe Water y= 4.1108261x + 3.1446294 Rsquare Prob > f 0.488917 < .0001

15. Annual Population Growth Rate y= -0.4451292x + 7.1854992 Rsquare Prob > f 0.201189 < .0001

16. Fertility Rate y= -0.5481316x + 8.3921602 Rsquare Prob > f 0.617951 <.0001

17. Per Capita Income (in $1,000) y= 0.1107245x + 5.6441095 Rsquare Prob > f 0.544437 < .0001

18. Research Findings Bivariate Fit of total life expectancy By people per physician Rsquare = 0.643446 Prob > f <0.0001

19. Research Findings Bivariate Fit of Total Life Expectancy by People per Television Rsquare = 0.741966 Prob > f < .0001

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

21. 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?

22. 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

23. 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

24. References • www.thebody.com/unaids/update/overview.html • www.unaids.org/epidemic_update/report/Table_E.htm • www.unaids.org/epidemic_update/report/Epi_report • www.unicef.org/sowc00/stat6.htm • www.who.int/emc-hiv/fact-sheets/index.html • www.cdc.gov/hiv/dhap.htm • www.cia.gov/cia/publications/factbok/index.html • www.un.org/Depts/unsd/social/litteracy.html • www.state.gov/r/pa/bgn/index.cfm • www.aegis.com/news/ct/1999/CT990402.html

25. More References • http://countweb.med.harvard.edu/web_resources/med/aidshiv.html • www.lib.umich.edu/libhome/Documents.center/forstats.html • Lewontin, R.C. Biology as Ideology: The Doctrine of DNA • www.pitt.edu/~super1/lecture/lec2561/007.htm • www.unicef.org/statis • www.unctad.org/en/subsites/ldcs/ldc11.htm • www.mara.org.za/data.htm

26. 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”.