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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. http://www.cnn.com/SPECIALS/2001/aids. Background Information. Infectious disease cases: tuberculosis (bronchitis, pneumonia, measles, etc.).

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how economic factors influence rates of hiv infection and survival

How Economic Factors Influence Rates of HIV Infection and Survival

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

background information
Background Information
  • Infectious disease cases: tuberculosis (bronchitis, pneumonia, measles, etc.)
  • Decreased as a result of demographic factors
aim of research
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
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
Least Developed Countries (LDCs)
  • 49 Countries
  • 610.5 million people
  • 10.5% of world population (1997)
hypotheses
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 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
methods
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
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

life expectancy
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 Literacy

Life Expectancy

Total Percent Access to Safe Water

Annual Population Growth Rate

Fertility Rate

Per Capita Income

Significant Demographic Factors
female literacy
Female Literacy

y = 0.0269015x + 6.8029618

Rsquare Prob > f

0.465782 < .0001

percent access to safe water
Percent Access to Safe Water

y= 4.1108261x + 3.1446294

Rsquare Prob > f

0.488917 < .0001

annual population growth rate
Annual Population Growth Rate

y= -0.4451292x + 7.1854992

Rsquare Prob > f

0.201189 < .0001

fertility rate
Fertility Rate

y= -0.5481316x + 8.3921602

Rsquare Prob > f

0.617951 <.0001

per capita income in 1 000
Per Capita Income (in $1,000)

y= 0.1107245x + 5.6441095

Rsquare Prob > f

0.544437 < .0001

research findings
Research Findings

Bivariate Fit of total life expectancy By people per physician

Rsquare = 0.643446

Prob > f <0.0001

research findings1
Research Findings

Bivariate Fit of Total Life Expectancy by People per Television

Rsquare = 0.741966

Prob > f < .0001

life expectancy fit model
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

conclusions
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
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
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
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
more references
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
acknowledgements
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”.

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