Visualizing Data for Different Teams - Pres

CEO

Analyzing WHO HIV data to significantly dicrease the number of HIV cases and deaths.

Context: HIV infection has been a serious concern for decades. Ministries of health of several countries, including MSPP which is Haiti's ministry of healt (MoH), have been struggling since then to find a way to control the expansion of the disease following the World Health Organization (WHO) guidelines. Since Haiti is the poorest and thus most vulnerable country in the Americas region, I was interested in comparing its data with other countries in the region and see what action should be taken in order to significantly dicrease the disease burden and also save children lives.
This chart shows the missing ART coverage needed to match the 100% HIV+ people in the Americas region.

The treatment for HIV is called antiretroviral therapy (ART). ART involves taking a combination of HIV medicines (called an HIV treatment regimen) every day. ART is recommended for everyone who has HIV. ART can’t cure HIV, but HIV medicines help people with HIV live longer, healthier lives. ART also reduces the risk of HIV transmission. (Ref. https://hivinfo.nih.gov/understanding-hiv/fact-sheets/hiv-treatment-basics)
The negative numbers represent the ART coverage % remainder for each country to match the 100% HIV+ people.
Based on WHO methods, this chart shows an estimated number of children in need for ART. Haiti has by far the highest number of children in need for ART treatmnent comparing to other countries in the region with 8700 children. The main way to help them not be infected is to focus on enrolling all the pregnant women on the ART treatment.
Besides Colombia where the number of pregnant women on ART is low comparing to those in need of the treatment, Haiti has a high percentage of 83% of pregnant women who have received ART, it still has the highest number of pregnant women in need for antiretroviral. This also results in the fact that the country has the highest number of children living with HIV in the americas. To reduce the number of HIV+ children, the treatment should start with HIV+ mothers to prevent transmission from mother to child of the disease. Blue bars represent the number who received ART and orange bars represent the number in need of ART. 

Conclusion

After analysis we can realize that saving HIV+ people's lives largely depends on the ART treatment. The treatment both help save lives but also help limitate the transmission of the disease from mother to child. Haiti is the most vulnerable country in the region with the largest number of children and pregnant women in need of antiretrovirals. The next move would be to seach the patients lost of follow ups and have them enrolled in the treatment. This will help reduce the propagation of the disease and thus reduce the number of death cause by the disease.

Data science head team

Analyzing ART coverage missing to match 100% HIV+ people in the region - Cleaning and ploting data. 
The next slide shows the code and the output chart after cleaning the dataset. Congo was set to the Americas region, there were NaN values. The negative numbers represent the % of ART treatment needed to match the 100% people living with HIV in the region.
# select only the countries in Americas region
data = coverage.loc[coverage['WHO Region']=='Americas']
# select the columns from the df for analysis
art_cov = data[['Estimated ART coverage among people living with HIV (%)_median','Country']]

# set countries as indexes
country_index = art_cov.set_index('Country')

# calculate the ART coverage each country should meet to cover the 100% od people living with HIV
missing_cov = country_index - 100.0

#Drop any row with missing values (NaN)
no_empty = missing_cov.dropna(how='any')

# ploting the data
no_empty.plot(kind='barh')
plt.xlabel('missing ART coverage')
plt.ylabel('Missing % ART coverage (%)')
plt.title('Missing ART coverage to meet 100% HIV+ people')
plt.legend(['Missing ART coverage to match 100% people HIV+'], bbox_to_anchor=(1.05, 1), loc='upper left', fontsize='medium')

plt.savefig('artCoverage.jpeg')

Pediatric ART coverage in the region

The slide that follows showcase the pediatric ART coverage in the Americas region. Children are unfortunately vulnarable by this disease. I used the data provided to see the number of children left without the ART treatment. If this number of children can be enrolled in the treatment, this will help save their lives. After analysis, we can see that Haiti has by far the highest number of children in need for ART treatment based on WHO methods.
# importing the dataset pediatric = pd.read_csv('art_pediatric_coverage_by_country_clean.csv') # assign Congo to Africa region pediatric.loc[36,'WHO Region'] = 'Africa' # select only the countries in Americas region haiti_ped = pediatric.loc[pediatric['WHO Region'] == 'Americas'] # selct the columns for analysis data = haiti_ped[['Country','Estimated number of children needing ART based on WHO methods_median']] # set countries as indexes ind = data.set_index('Country') # remove empty rows (NaN) rmv_missing = ind.dropna(how='any') #Haiti has the highest number of HIV positive children in need of ART in the Americas based on WHO methods. # ploting data rmv_missing.plot(kind = 'barh') plt.xlabel('Number of children') plt.ylabel('Country') plt.title('Estimated number of children needing ART based on WHO methods') plt.legend(['Estimated number of children needing ART'], bbox_to_anchor=(1.05, 1), loc='upper left', fontsize='medium')

Prevention from mother to child transmission of HIV (PMTCT)

This next slide show the number of pregnant women who have received the ART treatment versus the number in need of the treatment. In order to plot the data, the recieved art row was converted into float. Besides Colombia where the number of pregnant women on ART is low comparing to those in need of the treatment, despite that Haiti has a percentage of 83% of pregnant women who have received ART, it still has a very high number of pregnant women in need for antiretroviral.
# import dataset pmtct = pd.read_csv('prevention_of_mother_to_child_transmission_by_country_clean.csv') # select only the countries in Americas region haiti_pmtct = pmtct.loc[pmtct['WHO Region']=='Americas'] # select columns need for analysis pmtct_data = haiti_pmtct[['Country','Received Antiretrovirals','Needing antiretrovirals_median']] # set coutries as indexes pmtct_ind = pmtct_data.set_index('Country') # drop Congo from the region only_amer = pmtct_ind.drop('Congo') # remove fields with no values (NaN) rmv_null = only_amer.dropna(how='any') # convert data in for ploting rmv_null = rmv_null.astype({"Received Antiretrovirals": float}) # ploting data rmv_null.plot(kind='bar') plt.title('PMTCT - ART coverage') plt.legend(['Received Antiretrovirals','Needing antiretrovirals_median'], bbox_to_anchor=(1.05, 1), loc='upper left', fontsize='medium') plt.ylabel('number received vs number needing ART')

Conclusion

From the datasets we could get some relevant information which can help dicrease the disease transmission. Some slight cleaning had to be done, including removing Congo from the Americas region and  removing the NaN values. Some of the datasets didn't have enough information to do further ananlysis, including deaths rate for all the chosen years or the HIV prevalence. Despite that, the most important indicator is the ART coverage for each group that determines the number of deaths and prevalence per year. Thus, Haiti needs to get serious about this since it remains a major concern.