1. Comparing HIV and AIDS Prevalence within Countries By: Dan Evans Ida Nordestgaard Lacey McLean By: Dan Evans Ida Nordestgaard Lacey McLean 30 April 2009Economic Statistics 120

2. Research Question: What is the social impact of HIV/AIDS? What can we do to lower the AIDS prevalence rate in countries? Over 1/5th of deaths in Africa are due to AIDS

3. Estimated number of people living with HIV, by region (1990-2006)

4. Countries We Included In Our Study: Wealthiest Countries USA China Japan India Germany UK Russia France Brazil Italy Wealthiest Countries USA China Japan India Germany UK Russia France Brazil Italy Liberia Madagascar Malawui Mali Mauritania Mauritius Mozambique Nambia Niger Nigeria Rwanda Senegal Sierra Leone Somalia South Africa Swaziland Tanzania Togo Uganda Zambia Zimbabwe Sub-Saharan African countries are home to 70% of total world HIV positive population Sub-Saharan African Countries Angola Benin Botswana Burkina Faso Burundi Cameroon Central African Republic Chad Comoros Congo Cote D’Ivoire Democratic Republic of the Congo Djibouti Equilateral Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho

5. Measuring Social Cost of HIV/AIDS: “It is no exaggeration to say that today AIDS is the greatest threat to Africa’s development.” -K.Y. Amoako, Executive Secretary UN Economic Commission for Africa Factors we considered: GDP per Capita Life Expectancy Literacy Rate GDP Real Growth Rate Unemployment Rate Factors we considered: GDP per Capita Life Expectancy Literacy Rate GDP Real Growth Rate Unemployment Rate

6. GDP Per Capita GDPP Mean6835.926 Standard Error1528.543 Median1600 Mode700 Standard Deviation11232.45 Sample Variance1.26E+08 Kurtosis3.988933 Skewness2.210648 Range46800 Minimum200 Maximum47000 Sum369140 Count54

7. Life Expectancy Mean56.43426 Standard Error1.669299 Median54.255 Mode59 Standard Deviation12.26679 Sample Variance150.4742 Kurtosis-0.31711 Skewness0.485418 Range50.24 Minimum31.88 Maximum82.12 Sum3047.45 Count54

8. Unemployment Rate Mean0.31575 Standard Error0.035746 Median0.29 Mode0.5 Standard Deviation0.237112 Sample Variance0.056222 Kurtosis-0.37837 Skewness0.700858 Range0.822 Minimum0.028 Maximum0.85 Sum13.893 Count44

9. GDP per Capita Coefficientst StatP-value Intercept8318.2134.1358780.000129 X Variable 1-19585.3-0.78580.435553 Life Expectancy Coefficientst StatP-value Intercept60.8063432.517773.13E-36 X Variable 1-89.2937-3.853320.000322 Unemployment Coefficientst StatP-value Intercept0.2421386.030621.72E-07 X Variable 10.5569341.1192960.268158

10. Funding Toward AIDS Global Spending on AIDS has increased from $3 million in 1996 to $6.1 in 2004. It was projected in 2008 the world would need to spend $22 billion to effectively respond to the epidemic. Sources of funding include: –National Governments –Multilateral Funding Organizations –Private Sector Funding –Domestic Resources Projected distribution of resources: –38% HIV care and treatment –35% for prevention –22% for orphan support –5% for policy, advocacy and administration costs Global Spending on AIDS has increased from $3 million in 1996 to $6.1 in 2004. It was projected in 2008 the world would need to spend $22 billion to effectively respond to the epidemic. Sources of funding include: –National Governments –Multilateral Funding Organizations –Private Sector Funding –Domestic Resources Projected distribution of resources: –38% HIV care and treatment –35% for prevention –22% for orphan support –5% for policy, advocacy and administration costs

11. Top U.S. Humanitarian Aid Recipients: 2000-2003 Country FY 2000FY 2001FY 2002FY 2003Total (millions) 4-Year Total Uganda$9.30$13.40$20.00$27.90 $70.60 Zambia$9.10$12.90$18.50$25.50 $66.00 Kenya$9.20$10.40$17.20$26.50 $63.30 Nigeria$6.70$12.80$14.50$23.90 $57.90 Ethiopia$7.60$18.82$11.30$19 $56.72 South Africa$6.50$10.90$15.00$22.90 $55.30

12. Nation Profiles HIV/AIDS prevalence Rate GDP Per Capita Unemployment Rate Life Expectancy Uganda 5.40% $ 1,100.003.20%52.72 Zambia 15.20% $ 1,500.0050.00%38.63 Ethiopia 2.10% $ 800.0050.00%55.41 Kenya 6.70% $ 1,600.0040.00%57.86 Nigeria 3.10% $ 2,300.004.90%46.94 South Africa 18.10% $ 10,000.0021.70%49.98

13. Nation Profiles Decreased Rate HIV/AIDS prevalence Rate Uganda4.86%5.40% Zambia13.68%15.20% Ethiopia1.89%2.10% Kenya6.03%6.70% Nigeria2.79% 3.10% South Africa16.29% 18.10%

14. Nation Profiles HIV/AIDS prevalence Rate GDP Per Capita Unemployment Rate Life Expectancy Uganda4.86% $ 1,133.002.88%54.8288 Zambia13.68% $ 1,545.0045.00%40.1752 Ethiopia1.89% $ 824.0045.00%57.6264 Kenya6.03% $ 1,648.0036.00%60.1744 Nigeria2.79% $ 2,369.004.41%48.8176 South Africa16.29% $ 10,300.0019.53%51.9792

15. The Social Costs of AIDS “ AIDS is negatively affecting every aspect of the social, economic and political development of Africa. It strikes people down in their prime, when they are raising their children, farming their lands, earning their wages, teaching future generations and leading their countries. By the same token, every development challenge that Africa is facing, whether poverty, poor health, gender inequality, or war and instability, is contributing and driving the spread of AIDS. ” - Mr. Kevin Perkins, Executive Director, Canada Africa Partnership on AIDS

16. For Additional Information: CIA World Fact Book AIDS.ORG UNICEF.ORG JournAIDS.ORG World Health Organization CIA World Fact Book AIDS.ORG UNICEF.ORG JournAIDS.ORG World Health Organization

17. GDPP Per Capita Our results indicate a negative relationship between HIV/AIDS Prevalence rate and GDPP

18. GDPP Per Capita Regression Statistics Multiple R0.351007 R Square0.123206 Adjusted R Square0.10233 Standard Error3351.607 Observations44 ANOVA dfSSMSFSignificance F Regression166296348 5.9017850.019483 Residual424.72E+0811233270 Total435.38E+08

19. Life Expectancy Our results indicate a negative relationship between HIV/AIDS Prevalence rate and Life Expectancy

20. Life Expectancy Regression Statistics Multiple R0.434662 R Square0.188931 Adjusted R Square0.16962 Standard Error7.563977 Observations44 ANOVA dfSSMSFSignificance F Regression1559.7514 9.7835140.003195 Residual422402.97757.21374 Total432962.729

21. Literacy Rate Mean0.589134 Standard Error0.031552 Median0.61 Mode0.679 Standard Deviation0.209295 Sample Variance0.043805 Kurtosis-0.53233 Skewness-0.38326 Range0.8461 Minimum0.0609 Maximum0.907 Sum25.9219 Count44

22. Literacy Rate Our results indicate a positive relationship between HIV/AIDS prevalence rate and literacy rate

23. Literacy Rate Regression Statistics Multiple R0.560812 R Square0.31451 Adjusted R Square0.298189 Standard Error0.175336 Observatio ns44 ANOVA dfSSMSFSignificance F Regression10.59241 19.270047.51E-05 Residual421.2911870.030743 Total431.883597

24. GDP Real Growth Rate GDP Real Growth Mean0.047 Standard Error0.005588 Median0.0475 Mode0.032 Standard Deviation0.037066 Sample Variance0.001374 Kurtosis10.67931 Skewness-2.05298 Range0.258 Minimum-0.126 Maximum0.132 Sum2.068 Count44

25. GDP Real Growth Rate Our results indicate a negative relationship between HIV/AIDS Prevalence rate and GDP Real Growth Rate

26. GDP Real Growth Rate Regression Statistics Multiple R0.228344 R Square0.052141 Adjusted R Square0.029573 Standard Error0.036513 Observations44 ANOVA dfSSMSFSignificance F Regression10.00308 2.310380.136006 Residual420.0559960.001333 Total430.059076

27. Unemployment Rate Our results indicate a positive relationship between HIV/AIDS Prevalence rate and the unemployment rate

28. Unemployment Rate Regression Statistics Multiple R0.03775 R Square0.001425 Adjusted R Square-0.02235 Standard Error0.239747 Observations44 ANOVA dfSSMSFSignificance F Regression10.003445 0.0599380.807786 Residual422.4140990.057479 Total432.417544

29. Results We can conclude statistically that the AIDS prevalence rate affects Life Expectancy. We were surprised to find that these factors were affected by the AIDS rate, and will not dismiss the possibility that GDP per Capita, GDP Growth, Literacy Rate and Unemployment may be affected. If relief funding from organizations increases, lowering the AIDS prevalence rate, it can be inferred from our data that Life Expectancy will rise. Possible Limitations: Data collected within the Sub-Saharan African region is questionable because of instability within the country due to the AIDS epidemic We collected the most recent data available, however the AIDS epidemic continues to growth and expand Selection of countries We can conclude statistically that the AIDS prevalence rate affects Life Expectancy. We were surprised to find that these factors were affected by the AIDS rate, and will not dismiss the possibility that GDP per Capita, GDP Growth, Literacy Rate and Unemployment may be affected. If relief funding from organizations increases, lowering the AIDS prevalence rate, it can be inferred from our data that Life Expectancy will rise. Possible Limitations: Data collected within the Sub-Saharan African region is questionable because of instability within the country due to the AIDS epidemic We collected the most recent data available, however the AIDS epidemic continues to growth and expand Selection of countries

30. Nation Profiles HIV/AIDS prevalence Rate GDP Per Capita Unemployme nt Rate Life Expectancy Uganda 5.40% $ 1,100.003.20%52.72 Zambia 15.20% $ 1,500.0050.00%38.63 Ethiopia 2.10% $ 800.0050.00%55.41 Kenya 6.70% $ 1,600.0040.00%57.86 Nigeria 3.10% $ 2,300.004.90%46.94 South Africa 18.10% $ 10,000.0021.70%49.98

31. Nation Profiles Decreased Rate HIV/AIDS pervalance Rate Uganda4.86%5.40% Zambia13.68%15.20% Ethiopia1.89%2.10% Kenya6.03%6.70% Nigeria2.79% 3.10% South Africa16.29% 18.10%

32. Nation Profiles HIV/AIDS prevalence Rate GDP Per Capita Unemployment Rate Life Expectancy Uganda4.86% $ 1,133.002.88%54.8288 Zambia13.68% $ 1,545.0045.00%40.1752 Ethiopia1.89% $ 824.0045.00%57.6264 Kenya6.03% $ 1,648.0036.00%60.1744 Nigeria2.79% $ 2,369.004.41%48.8176 South Africa16.29% $ 10,300.0019.53%51.9792

33. The Social Costs of AIDS “ AIDS is negatively affecting every aspect of the social, economic and political development of Africa. It strikes people down in their prime, when they are raising their children, farming their lands, earning their wages, teaching future generations and leading their countries. By the same token, every development challenge that Africa is facing, whether poverty, poor health, gender inequality, or war and instability, is contributing and driving the spread of AIDS. ” - Mr. Kevin Perkins, Executive Director, Canada Africa Partnership on AIDS

34. For Additional Information: CIA World Fact Book AIDS.ORG UNICEF.ORG JournAIDS.ORG World Health Organization CIA World Fact Book AIDS.ORG UNICEF.ORG JournAIDS.ORG World Health Organization