Transcript How Economic Factors Influence Rates of HIV Infection and

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 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
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
Rsquare = 0.0989
Rsquare = 0.0454
Prob > f
Prob > f
0.0003
0.0152
Rsquare = 0.048
Rsquare = 0.031299
Prob > f
Prob > f
0.0126
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 Literacy
Life Expectancy
Total Percent Access to
Safe Water
Annual Population
Growth Rate
Fertility Rate
Per Capita Income
Female Literacy
y = 0.0269015x + 6.8029618
Rsquare
Prob > f
0.465782
< .0001
Percent Access to Safe Water
y= 4.1108261x + 3.1446294
Rsquare
Prob > f
0.488917
< .0001
Annual Population Growth Rate
y= -0.4451292x + 7.1854992
Rsquare
Prob > f
0.201189
< .0001
Fertility Rate
y= -0.5481316x + 8.3921602
Rsquare
Prob > f
0.617951
<.0001
Per Capita Income (in $1,000)
y= 0.1107245x + 5.6441095
Rsquare
Prob > f
0.544437
< .0001
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
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
 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 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
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
http://countweb.med.harvard.edu/web_resources/med/a
idshiv.html
www.lib.umich.edu/libhome/Documents.center/forstats.h
tml
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
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”.