Analyzing World Health Statistics 2005 using TreeMap and Spotfire.

 

Abhinav Gupta

 

Link to Presentation

 

1.    Introduction- The Dataset

 

The World Health Organization (WHO) collects and summarizes a wide range of quantitative data from a variety of health domains through country offices, regional offices and headquarters departments. These data are used internally by WHO for estimation, advocacy, policy development and evaluation. They are also widely disseminated in formal publications and through more informal mechanisms, both in electronic and printed format.

 

We use one of the health statistics data for the year 2005 to find some interesting facts about the health status, services and system of the 192 nations of the world. The member nations of the UN have been divided in six WHO regions (See Figure 2). These six regions are:

 

·        African Region (AFR)

·        South East Asian Region (SEAR)

·        Region of the Americas (AMR)

·        European Region (EUR)

·        Eastern Mediterranean Region (EMR)

·        Western Pacific Region (WPR)

 

The data collected for member nations includes information on the demographics, morbidity, mortality, health system, risk factor and health services. We analyze the following data(23) for each member nation:

 

  • Demographics: Population, Growth Rate, Per-Capita Income, Literacy
  • Mortality: Maternal Mortality, Under-5 Mortality rate, Life-Expectancy
  • Morbidity: Number of HIV, TB, Polio cases and Children Under-5 stunted for age.
  • Services: Immunization of Measles, DPT3, HepB3, Births attended by skilled personnel.
  • Risk Factors: Alcohol and Tobacco Consumption, Sanitation.
  • System: Number of Physicians and Nurses, Percapita expenditure on health and as \% of GDP.

 

1.1       Missing Data / Values

 

Many member nations fail to collect several data due to lack of resources or the public policy specific to the nation. In these cases, we have chosen to filter those member nations from our analysis whose data is either not available or not applicable for comparison.

 

We used -1 value for all missing data and filtered the data having negative value while analyzing the data with the help of treemap and spotfire.

 

2.    Visualization Tools

 

2.1       Treemap

 

Treemap is a space-constrained visualization of hierarchical structures. It is very effective in showing attributes of leaf nodes using size and color coding. Treemap enables users to compare nodes and sub-trees even at varying depth in the tree, and help them spot patterns and exceptions

 

2.2       Spotfire

 

Spotfire Decision Site offers an interactive, visual approach to data analysis that empowers individuals to quickly and easily see trends, patterns, outliers and unanticipated relationships in data. For our case the legend for the spotfire across the figures is shown in Figure 1.

 

Figure 1: The Legend for Spotfire Figures.

3. The Demographics

 

We analyzed the demographics data using the Treemap software. This helped us analyze the regional distribution of Population. As it is evident from the figure 2, almost half of the world's population resides in SEAR and WPR region which includes India and China respectively. However, the higher growth rate seems to be in the AFR and EMR region. In most of the WHO regions there seems to be coherence in terms of growth rates suggesting that regional values/environment control the rate of growth of population however there seems to be a little incoherence in EUR nations. This is due to the fact that enivornment seems to be a lot different in Eastern and Western Europe.

 

 

Figure 2: The Population Distribution and the growth rate. The size of the rectangle is proportional to the population of the

country and the color depends on growth rate. The red shows a negative growth rate and green shows a positive growth-rate.

 

The per-capita income fails to show any regional coherence which could have been expected from the results above. Only a very few nations in every WHO region seem to have higher per-capita incomes. While most of the Western Europe has higher income eastern europe seems to have low per-capita incomes. From the AMR and WPR regions, USA, Canada, Japan, Korea, Singapore and Australia seem to stand out the rest. As expected, the AFR and SEAR regions are the poorest.

 

 

Figure 3: The Population Distribution and the per-capita income. The size of the rectangle is proportional to the population of the

country and the color depends on per-capita income. The red shows a lower per-capita income and green shows a higher per-capita income

 

4. Money Matters

 

4.1 Money is the key to better living conditions

We tried to understand the correlation between the per-capita income and living conditions. We plotted the sanitation access to per-capita income using spot-fire (See Figures 4,5). It was interesting to see that sanitation conditions improve with log of per-capita income. 

 

 

Figure 4:  The Sanitation Access improves with per-capita income but logarithmically.

 

 

                

 

Figure 5:  The Sanitation Access improves with per-capita income (In a Treemap). Rectangle size

being per-capita income and bright green implies more easy access to sanitation.

 

 

4.2 Money leads to better health statistics.

Improved living conditions also lead to better health status among nations. This can be seen by plotting percentage of children under 5 stunted for age against log of per-capita income (See Figures 6, 7). Again a linear correlation can be easily seen.

        

 

Figure 6:  Children Under-5 are less stunted if the per capita income of the nation is high.

           

 

 

 

Figure 7:  Children Under-5 are less stunted if the per capita income of the nation is high  (In a Treemap). Rectangle size

being per-capita income and bright green implies lesser children stunted for age

 

                  

 

4.3 But Money brings vices.

People earning more tend to drink more alcohol. The nations with higher per-capita income have higher alchol consumption rates then nations with lower per-capita income. It is interesting to see the black dots in Figure 8 and EMR region in the Treemap (Figure 9). Here, the alchol consumption seems to be generally less than other nations.

 

 

Figure 8:  Earn More Drink More. Nations with higher per-capita income also have higher alcohol consumptions.

 

 

 

Figure 9:  Earn More Drink More. Nations with higher per-capita income also have higher alcohol consumptions (In a Treemap). Rectangle

size being per-capita income and bright green implies more alcohol consumption

 

 

 

5. Money buys LIFE everywhere except Africa

If money can be so important, can money buy life? We were particularly interested in looking at correlations between life of males (similar observations in life of females) and the per-capita income. It was surprising to see that while money really does buy life everywhere in the world, it fails to do so in the land of Africa (See Figures 10, 11).

Figure 10:  Money Buys LIFE

 

 

Figure 11: But not in Africa  

 

 

6. Are Health Services and System in United States more-expensive than we can afford?

We compared per-capita health expenditures with per-capita incomes for 192 member nations. Most of the nations tend to follow the linear pattern where Nations with higher per-capita income tend to have higher expenditures on health services and systems. However, United States stands out among these member nations. The ratio of per-capita health expenditure to per-capita income is very high for United States (See Figure 12).

 

 

Figure 12:  United States stands out of the rest of the nations in terms of Health Expenditure to Income ratio.

 

7. Region of Americas faces a severe Nurse shortage as compared to European Region

We compared the number of physicians to number of nurses per 10,000 people for 192 member nations.  The AFR, EMR and SEAR regions are short of nurses and physicians. However, when we only plotted EUR and AMR region nations we found that AMR nations face a sever Nurse shortage as compared to EUR region. European region seems to have a good Nurse/Physician ratio (See Figure 13).

 

 

Figure 13:  AMR region is facing a shortage of nurses.

 

 

8. TB: Effecting Life Expectancy across Africa and South East Asia

We plotted number of TB Cases per 10000 people and the life expectancy for 192 member nations. We found that for AFR and SEAR Life Expectancy is inversely proportional to number of TB cases reported in these regions (See Figure 14). This affect is particularly strong in the regions of AFR and SEAR. While the EUR and AMR regions seem to be less affected by it.

 

 

 

Figure 14:  TB a major problem in land of Africa and South East Asia.

 

 

 

 

Figure 15:  TB has not such severe effect in AMR and EUR.

 

9. Tools

Both the Treemap and Spotfire are easy to use softwares. Spotfire even suggests some graphs which have some interesting observations. However, both seem to require very specific inputs. The treemap software fails to take in the inputs with extra empty lines. There is no special way to handle missing data in the either so I had to use -1 filter in both the softwares.

 

Spotfire has lot of tools for data analysis like scatter plots, profile plots, histograms, pie charts. Treemap seems a very nice concept to understand hierarchal relationships in data.

 

10. Conclusion

While the study suggests that money seems to improve life conditions it seems that money is not properly being used in major african nations. The study also suggests that United States spends a huge amount in health services which its purse can afford. It also suggests that TB seems to be a dangerous factor in the land of AFR and SEAR.