Global HIV Prevalence

Set working directory

list.files()

[1] "gapminder_aids.nb.html" "gapminder_aids.R"       "gapminder_aids.Rmd"     "hiv_prev.csv"           "lesson3_student.rmd"   
[6] "pseudo_facebook.tsv"    "rsconnect"             

Steps:

• Read data from csv into a dataframe
• Look at the data’s rows and values
• Put the original dataframe into a holder variable

Reasoning:

• Get a sense of what to expect
• Determine what kind of questions need to be asked
• Determine what kind of problems may arise
• Save the data in it’s original form

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Load data from csv file

Steps:

• Install tidyr package
• Get row names for the years
• Collapse original dataframe into “years” and “cases” columns

Reasoning:

• Resulting dataframe is more intuituve

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Clean the data

Summary of HIV prevalence in 1999

summary(hp_1999)

      country        year             prevalence    
 Algeria  :  1   Length:146         Min.   : 0.060  
 Angola   :  1   Class :character   1st Qu.: 0.100  
 Argentina:  1   Mode  :character   Median : 0.300  
 Armenia  :  1                      Mean   : 2.067  
 Australia:  1                      3rd Qu.: 1.475  
 Austria  :  1                      Max.   :25.700  
 (Other)  :140                                      

max_row$country

[1] Zimbabwe
275 Levels: \x81land Abkhazia Afghanistan Akrotiri and Dhekelia Albania Algeria American Samoa Andorra Angola Anguilla ... Zimbabwe

Notes:

Highest HIV rate is nearly over a quarter of the population. The average HIV rate globally was measured at around 2%. 34 countries, including Algeria, Bangladesh, and Egypt reported an HIV prevalence of 0.06. Zimbabwe had the highest prevalence of HIV (25.7).

I have a vague distrust of the data, however, when I consider how culture and social norms may play into reporting. In the US, for example, gay and bisexual people make up only 2% of the population but 55% of PLWH (people living with HIV). In more conservative countries where homosexuality is supressed and hidden, I’m afraid that some men who are gay and bisexual are less likely to come out and make their conditions known or that officials might be understating HIV prevalence in order to hide poor leadership or downplay the issue. Some countries may also overstate their HIV prevalence in order to attract more international funding. The possibility of such distortions must be taken into account.

Resources:

• https://www.hrc.org/resources/hrc-issue-brief-hiv-aids-and-the-lgbt-community
• http://files.unaids.org/en/media/unaids/contentassets/documents/unaidspublication/2004/GAR2004_en.pdf

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Summary of HIV prevalence in 1999

Notes:

Data points for the various countries are shown. Vast majority of countries have HIV prevalance below 2%, there are quite a few that are above 2% and some that are extremely high.

Make a boxplot of the data

Notes:

The IQR for HIV prevalence appears to fall between .1 and 1.5 with a median at .3

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Separate the values above and below 2% prevalence

Notes:

Highest prevalence seems to situated in Sub-Saharan Africa and countries with a sizable diaspora from Sub-Saharan Africa. Researchers have shown that a genetic variation called Duffy Antigen Receptor for Chemokines (DARC) which was evolved as a protection against a now extinct form of malaria has led to a greater susceptibility to HIV. DARC is present in an overwhelming percentage of people with African descent making them more susceptible (but also more resilient) to HIV. This increased susceptibility, in concert with behavioral factors, helps to explain the high prevalence of HIV in geographically dispersed countries like Botswana and Haiti.

Resources:

• http://www.nature.com/news/2008/080716/full/news.2008.948.html
• http://www.sciencedirect.com/science/article/pii/S0753332299800213

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Has prevalence diminished over time?

Notes:

In Zimbabwe, consistently the hardest hit country, it looks like HIV prevalence increased throughout the 1980s and 90s and began to level-off after 1997. Prevalence seems to have started decreasing in the 2000s as testing and treatment were made more accessible. The most significant cause of the decrease in the case of Zimbabwe, however, was the reduction of the number of secual partners by 30% for men (UNAIDS, 2011). This reductions in new cases of HIV spells good news for future reduction efforts because it shows that the issue, despite whatever negative predispositions may be at play, can be contained and current cases can be treated with increasing effectiveness.

Resources:

• http://www.unaids.org/en/resources/presscentre/featurestories/2011/march/20110315zimbabwe