how economic factors influence rates of hiv infection and survival
DESCRIPTION
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.). - PowerPoint PPT PresentationTRANSCRIPT
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How Economic Factors Influence Rates of HIV Infection and Survival
Mark Schenkel, Isi Oribabor, Magan Sethi, Shang-Jui Wang, Dylan Kelemen
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http://www.cnn.com/SPECIALS/2001/aids
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Background Information
•Infectious disease cases: tuberculosis (bronchitis, pneumonia, measles, etc.)
•Decreased as a result of demographic factors
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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
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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
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Least Developed Countries (LDCs)
49 Countries
610.5 million people
10.5% of world population (1997)
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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.
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Demographic Factors
Life ExpectancyGDP/GNPPer capita incomeTotal populationInfant mortality rateLiteracyAnnual population growth rate
Urbanized PopulationFertility rateImmunizationsAccess to safe waterSanitationPeople per televisionPeople per physician
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Methods
Collect data on demographic variables in both developing and developed countriesTransfer data to ExcelTransfer data to JMP INAnalyze Make Conclusions
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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
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Life Expectancy
Rsquare = 0.320881
Prob > f < .0001
Log (Percent AIDS Population) = 5.5516345 – 6.5608861 Log (Life Expectancy(Total Population))
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Significant Demographic Factors
Female LiteracyLife ExpectancyTotal Percent Access to Safe Water
Annual Population Growth RateFertility RatePer Capita Income
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Female Literacy
Rsquare Prob > f
0.465782 < .0001
y = 0.0269015x + 6.8029618
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Percent Access to Safe Water
Rsquare Prob > f
0.488917 < .0001
y= 4.1108261x + 3.1446294
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Annual Population Growth Rate
Rsquare Prob > f
0.201189 < .0001
y= -0.4451292x + 7.1854992
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Fertility Rate
Rsquare Prob > f
0.617951 <.0001
y= -0.5481316x + 8.3921602
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Per Capita Income (in $1,000)
Rsquare Prob > f
0.544437 < .0001
y= 0.1107245x + 5.6441095
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Research Findings
Bivariate Fit of total life expectancy By people per physician
Rsquare = 0.643446
Prob > f <0.0001
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Research Findings
Bivariate Fit of Total Life Expectancy by People per Television
Rsquare = 0.741966
Prob > f < .0001
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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
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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?
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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
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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
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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
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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
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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”.