Project 1: Deaths by Tuberculosis
by Michel Wermelinger and Freya Stewart-Grant, 14 July 2015, edited 5 April 2016, updated 18 October and 20 December 2017, 26 July 2021 This is the project notebook for the first part of The Open University’s Learn to code for Data Analysis course.
In 2000, the United Nations set eight Millenium Development Goals (MDGs) to reduce poverty and diseases, improve gender equality and environmental sustainability, etc. Each goal is quantified and time-bound, to be achieved by the end of 2015. Goal 6 is to have halted and started reversing the spread of HIV, malaria and tuberculosis (TB). TB doesn’t make headlines like Ebola, SARS (severe acute respiratory syndrome) and other epidemics, but is far deadlier. For more information, see the World Health Organisation (WHO) page http://www.who.int/gho/tb/en/.
Given the population and number of deaths due to TB in some countries during one year, the following questions will be answered:
What is the total, maximum, minimum and average number of deaths in that year? Which countries have the most and the least deaths?
What is the death rate (deaths per 100,000 inhabitants) for each country?
Which countries have the lowest and highest death rate?
The death rate allows for a better comparison of countries with widely different population sizes.
The data
The data consists of total population and total number of deaths due to TB (excluding HIV) in 2013 accross the globe.
The data was taken in July 2015 from http://apps.who.int/gho/data/node.main.POP107?lang=en (population) and http://apps.who.int/gho/data/node.main.1317?lang=en (deaths). The uncertainty bounds of the number of deaths were ignored.
The range of the problem
The column of interest is the last one.
The total number of deaths in 2013 is:
1072677.97
The largest and smallest number of deaths in a single country are:
240000.0
0.0
From zero to almost a quarter of a million deaths is a huge range. The average number of deaths, over all countries in the data, can give a better idea of the seriousness of the problem in each country. The average can be computed as the mean or the median. Given the wide range of deaths, the median is probably a more sensible average measure.
Mean: 5529.27
Median: 315.0
The median is far lower than the mean. This indicates that some of the countries had a very high number of TB deaths in 2013, pushing the value of the mean up.
The most affected
To see the most affected countries, the table is sorted in ascending order by the last column, which puts those countries in the last rows.
The table raises the possibility that a large number of deaths may be partly due to a large population. To compare the countries on an equal footing, the death rate per 100,000 inhabitants is computed.
Conclusions
The world had a total of over 1 million deaths due to TB in 2013. The median shows that half of these countries had fewer than 315 deaths. The much higher mean (over 5,500) indicates that some countries had a very high number. The least affected were San Mario, Niue and Monaco, with 0.00, 0.01 and 0.03 deaths retrospectively, and the most affected were Nigeria and India with 1.6 million and 2.4 million deaths in a single year. However, taking the population size into account, the least affected were San Marino and Monaco with less than 0.00 and 0.08 deaths per 100 thousand inhabitants, and the most affected were Nigeria and Djbouti with over 92 deaths per 100,000 inhabitants.
One should not forget that most values are estimates. Nevertheless, they convey the message that TB is still a major cause of fatalities, and that there is a huge disparity between countries, with several ones being highly affected.
Code Used (Appendix)
from pandas import *
data = read_excel(‘WHO POP TB all.xls’) data
tbColumn = data[‘TB deaths’]
tbColumn.sum()
tbColumn.max()
tbColumn.min()
tbColumn.mean()
tbColumn.median()
data.sort_values(‘TB deaths’)
populationColumn = data[‘Population (1000s)’]
data[‘TB deaths (per 100,000)’] = tbColumn * 100 / populationColumn data
data.sort_values(‘TB deaths (per 100,000)’)
