How Economic Factors Influence Rates of HIV Infection and Survival

Mark Schenkel, Isi Oribabor, Magan Sethi, Shang-Jui Wang, Dylan Kelemen

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

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

610.5 million people

10.5% of world population (1997)

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.

Demographic Factors

Life ExpectancyGDP/GNPPer capita incomeTotal populationInfant mortality rateLiteracyAnnual population growth rate

Urbanized PopulationFertility rateImmunizationsAccess to safe waterSanitationPeople per televisionPeople per physician

Methods

Collect data on demographic variables in both developing and developed countriesTransfer data to ExcelTransfer data to JMP INAnalyze Make Conclusions

Direct Correlation to AIDS Percentages

Rsquare = 0.048

Prob > f

0.0126

Rsquare = 0.0989

Prob > f

0.0003

Rsquare = 0.0454

Prob > f

0.0152

Rsquare = 0.031299

Prob > f

0.0814

Life Expectancy

Rsquare = 0.320881

Prob > f < .0001

Log (Percent AIDS Population) = 5.5516345 – 6.5608861 Log (Life Expectancy(Total Population))

Significant Demographic Factors

Female LiteracyLife ExpectancyTotal Percent Access to Safe Water

Annual Population Growth RateFertility RatePer Capita Income

Female Literacy

Rsquare Prob > f

0.465782 < .0001

y = 0.0269015x + 6.8029618

Percent Access to Safe Water

Rsquare Prob > f

0.488917 < .0001

y= 4.1108261x + 3.1446294

Annual Population Growth Rate

Rsquare Prob > f

0.201189 < .0001

y= -0.4451292x + 7.1854992

Fertility Rate

Rsquare Prob > f

0.617951 <.0001

y= -0.5481316x + 8.3921602

Per Capita Income (in $1,000)

Rsquare Prob > f

0.544437 < .0001

y= 0.1107245x + 5.6441095

Research Findings

Bivariate Fit of total life expectancy By people per physician

Rsquare = 0.643446

Prob > f <0.0001

Research Findings

Bivariate Fit of Total Life Expectancy by People per Television

Rsquare = 0.741966

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 dataGrossly Under Reported AIDS dataDirect correlation to AIDS percentages were minor with much variability– Fit Model with Life Expectancy– Percent AIDS Equation

References

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

More References

http://countweb.med.harvard.edu/web_resources/med/aidshiv.htmlwww.lib.umich.edu/libhome/Documents.center/forstats.htmlLewontin, R.C. Biology as Ideology: The Doctrine of DNAwww.pitt.edu/~super1/lecture/lec2561/007.htm www.unicef.org/statiswww.unctad.org/en/subsites/ldcs/ldc11.htm www.mara.org.za/data.htm

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”.