Project 1: Deaths by tuberculosis¶

by Jake Stokes, 1st July 2016.

This is the project notebook for Week 1 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 in each country.

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.593?lang=en (deaths). The uncertainty bounds of the number of deaths were ignored.

The data was collected into an Excel file which should be in the same folder as this notebook.

import warnings
warnings.simplefilter('ignore', FutureWarning)

from pandas import *
data = read_excel('WHO POP TB all.xls')
data

The range of the problem¶

The column of interest is the last one.

tbColumn = data['TB deaths']

The total number of deaths in 2013 is:

tbColumn.sum()

1072677.97

The largest and smallest number of deaths in a single country are:

tbColumn.max()

240000.0

tbColumn.min()

0.0

From less than 0 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.

tbColumn.mean()

5529.2678865979378

tbColumn.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.

data.sort('TB deaths')

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.

populationColumn = data['Population (1000s)']
data['TB deaths (per 100,000)'] = tbColumn * 100 / populationColumn
data.sort('TB deaths (per 100,000)')

Conclusions¶

Across the world there were almost 1.1 million deaths due to TB in 2013. The median shows that half of these coutries had fewer than 315 deaths. The much higher mean (over 5,529) indicates that some countries had a very high number. The least affected were San Marino, Niue, and Monaco, with 0, 0.01 and 0.03 deaths respectively, and the most affected were Nigeria and India with 160 thousand and 240 thousand deaths in a single year. However, taking the population size into account, the least affected were San Marino, Niue, and Monaco with less than 0.03 deaths per 100 thousand inhabitants, and the most affected were Nigeria and Dijbouti, with over 90 deaths per 100,000 inhabitants.

One should not forget that most values are estimates, and that the chosen countries are a small sample of all the world's countries. 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.

	Country	Population (1000s)	TB deaths
0	Afghanistan	30552	13000.00
1	Albania	3173	20.00
2	Algeria	39208	5100.00
3	Andorra	79	0.26
4	Angola	21472	6900.00
5	Antigua and Barbuda	90	1.20
6	Argentina	41446	570.00
7	Armenia	2977	170.00
8	Australia	23343	45.00
9	Austria	8495	29.00
10	Azerbaijan	9413	360.00
11	Bahamas	377	1.80
12	Bahrain	1332	9.60
13	Bangladesh	156595	80000.00
14	Barbados	285	2.00
15	Belarus	9357	850.00
16	Belgium	11104	18.00
17	Belize	332	20.00
18	Benin	10323	1300.00
19	Bhutan	754	88.00
20	Bolivia (Plurinational State of)	10671	430.00
21	Bosnia and Herzegovina	3829	190.00
22	Botswana	2021	440.00
23	Brazil	200362	4400.00
24	Brunei Darussalam	418	13.00
25	Bulgaria	7223	150.00
26	Burkina Faso	16935	1500.00
27	Burundi	10163	2300.00
28	CÃ´te d'Ivoire	20316	4000.00
29	Cabo Verde	499	150.00
...	...	...	...
164	Suriname	539	12.00
165	Swaziland	1250	1100.00
166	Sweden	9571	13.00
167	Switzerland	8078	17.00
168	Syrian Arab Republic	21898	450.00
169	Tajikistan	8208	570.00
170	Thailand	67010	8100.00
171	The former Yugoslav republic of Macedonia	2107	33.00
172	Timor-Leste	1133	990.00
173	Togo	6817	810.00
174	Tonga	105	2.50
175	Trinidad and Tobago	1341	29.00
176	Tunisia	10997	230.00
177	Turkey	74933	310.00
178	Turkmenistan	5240	1300.00
179	Tuvalu	10	2.80
180	Uganda	37579	4100.00
181	Ukraine	45239	6600.00
182	United Arab Emirates	9346	64.00
183	United Kingdom of Great Britain and Northern I...	63136	340.00
184	United Republic of Tanzania	49253	6000.00
185	United States of America	320051	490.00
186	Uruguay	3407	40.00
187	Uzbekistan	28934	2200.00
188	Vanuatu	253	16.00
189	Venezuela (Bolivarian Republic of)	30405	480.00
190	Viet Nam	91680	17000.00
191	Yemen	24407	990.00
192	Zambia	14539	3600.00
193	Zimbabwe	14150	5700.00

	Country	Population (1000s)	TB deaths
147	San Marino	31	0.00
125	Niue	1	0.01
111	Monaco	38	0.03
3	Andorra	79	0.26
129	Palau	21	0.36
40	Cook Islands	21	0.41
118	Nauru	10	0.67
76	Iceland	330	0.93
68	Grenada	106	1.10
5	Antigua and Barbuda	90	1.20
113	Montenegro	621	1.20
152	Seychelles	93	1.40
105	Malta	429	1.50
143	Saint Kitts and Nevis	54	1.60
11	Bahamas	377	1.80
14	Barbados	285	2.00
144	Saint Lucia	182	2.20
99	Luxembourg	530	2.20
44	Cyprus	1141	2.30
174	Tonga	105	2.50
50	Dominica	72	2.70
137	Qatar	2169	2.70
179	Tuvalu	10	2.80
145	Saint Vincent and the Grenadines	109	3.10
126	Norway	5043	4.40
146	Samoa	190	6.10
121	New Zealand	4506	6.30
103	Maldives	345	7.60
12	Bahrain	1332	9.60
164	Suriname	539	12.00
...	...	...	...
160	South Sudan	11296	4500.00
119	Nepal	27797	4600.00
2	Algeria	39208	5100.00
193	Zimbabwe	14150	5700.00
184	United Republic of Tanzania	49253	6000.00
181	Ukraine	45239	6600.00
46	Democratic People's Republic of Korea	24895	6700.00
4	Angola	21472	6900.00
158	Somalia	10496	7700.00
31	Cameroon	22254	7800.00
170	Thailand	67010	8100.00
88	Kenya	44354	9100.00
163	Sudan	37964	9700.00
30	Cambodia	15135	10000.00
100	Madagascar	22925	12000.00
0	Afghanistan	30552	13000.00
141	Russian Federation	142834	17000.00
190	Viet Nam	91680	17000.00
115	Mozambique	25834	18000.00
159	South Africa	52776	25000.00
116	Myanmar	53259	26000.00
134	Philippines	98394	27000.00
58	Ethiopia	94101	30000.00
36	China	1393337	41000.00
47	Democratic Republic of the Congo	67514	46000.00
128	Pakistan	182143	49000.00
78	Indonesia	249866	64000.00
13	Bangladesh	156595	80000.00
124	Nigeria	173615	160000.00
77	India	1252140	240000.00

	Country	Population (1000s)	TB deaths	TB deaths (per 100,000)
147	San Marino	31	0.00	0.000000
111	Monaco	38	0.03	0.078947
126	Norway	5043	4.40	0.087250
120	Netherlands	16759	20.00	0.119339
137	Qatar	2169	2.70	0.124481
166	Sweden	9571	13.00	0.135827
121	New Zealand	4506	6.30	0.139814
185	United States of America	320051	490.00	0.153101
16	Belgium	11104	18.00	0.162104
32	Canada	35182	62.00	0.176226
8	Australia	23343	45.00	0.192777
113	Montenegro	621	1.20	0.193237
44	Cyprus	1141	2.30	0.201578
82	Israel	7733	16.00	0.206905
167	Switzerland	8078	17.00	0.210448
45	Czech Republic	10702	28.00	0.261633
76	Iceland	330	0.93	0.281818
60	Finland	5426	17.00	0.313306
43	Cuba	11266	37.00	0.328422
3	Andorra	79	0.26	0.329114
9	Austria	8495	29.00	0.341377
105	Malta	429	1.50	0.349650
65	Germany	82727	300.00	0.362639
81	Ireland	4627	18.00	0.389021
177	Turkey	74933	310.00	0.413703
99	Luxembourg	530	2.20	0.415094
48	Denmark	5619	24.00	0.427122
11	Bahamas	377	1.80	0.477454
86	Jordan	7274	35.00	0.481166
83	Italy	60990	310.00	0.508280
...	...	...	...	...
29	Cabo Verde	499	150.00	30.060120
58	Ethiopia	94101	30000.00	31.880639
4	Angola	21472	6900.00	32.134873
131	Papua New Guinea	7321	2400.00	32.782407
31	Cameroon	22254	7800.00	35.049879
106	Marshall Islands	53	21.00	39.622642
160	South Sudan	11296	4500.00	39.837110
193	Zimbabwe	14150	5700.00	40.282686
0	Afghanistan	30552	13000.00	42.550406
153	Sierra Leone	6092	2600.00	42.678923
39	Congo	4448	2000.00	44.964029
95	Lesotho	2074	960.00	46.287367
159	South Africa	52776	25000.00	47.370017
33	Central African Republic	4616	2200.00	47.660312
116	Myanmar	53259	26000.00	48.818040
96	Liberia	4294	2100.00	48.905449
13	Bangladesh	156595	80000.00	51.087199
100	Madagascar	22925	12000.00	52.344602
92	Lao People's Democratic Republic	6770	3600.00	53.175775
62	Gabon	1672	910.00	54.425837
117	Namibia	2303	1300.00	56.448111
30	Cambodia	15135	10000.00	66.072019
47	Democratic Republic of the Congo	67514	46000.00	68.134017
115	Mozambique	25834	18000.00	69.675621
71	Guinea-Bissau	1704	1200.00	70.422535
158	Somalia	10496	7700.00	73.361280
172	Timor-Leste	1133	990.00	87.378641
165	Swaziland	1250	1100.00	88.000000
124	Nigeria	173615	160000.00	92.157936
49	Djibouti	873	870.00	99.656357