About AMR and Stata
The IBM Stata software will help us to analyze the antimicrobial resistance data. We can use cross tabulation, pearson's chi-squared test, bar, pie, line, box, histogram, and regression analysis to determine frequency distributions.
Outine
- Stata installtion
- Import CSV data
- Data cleaning
- Frequency check
- Number of isolates count by year
- Cross tabulation
- Pearson's Chi-squared test
- Plotting results
- Yearly antibiotics by organism
1. Stata installation
Visit the following website and follow the installation steps: https://julhas.com/jsedutech/materials/Level-1/Stata-Session-1.pdf
2. Import CSV/ Excel data
Visit the following website and follow the steps: https://julhas.com/jsedutech/materials/Level-1/Stata-Session-3.pdf
Output:
3. Data management
Visit the following website and follow the steps: https://julhas.com/jsedutech/materials/Level-1/Stata-Session-6.pdf
4. Frequency check
Frequency check - Gender, Year, Sample, Organism, Age group: A frequency distribution is a list, table or graph that displays the frequency of various outcomes in a sample.
Commands:
tab sex
tab year
tab sample
tab organism
Output:
. count
23,411
. tab sex
Sex | Freq. Percent Cum.
------------+-----------------------------------
f | 13,568 57.96 57.96
m | 9,843 42.04 100.00
------------+-----------------------------------
Total | 23,411 100.00
tab year
Year | Freq. Percent Cum.
------------+-----------------------------------
2017 | 6,878 29.38 29.38
2018 | 8,072 34.48 63.86
2019 | 8,461 36.14 100.00
------------+-----------------------------------
Total | 23,411 100.00
. tab sample
Sample | Freq. Percent Cum.
------------+-----------------------------------
Blood | 1,214 5.19 5.19
Pus | 5,753 24.57 29.76
Sputum | 1,079 4.61 34.37
Stool | 2 0.01 34.38
Urine | 12,814 54.73 89.11
Wound Swab | 2,549 10.89 100.00
------------+-----------------------------------
Total | 23,411 100.00
. tab organism
Organism | Freq. Percent Cum.
----------------------+-----------------------------------
Escherichia coli | 11,887 50.78 50.78
Klebsiella sp. | 7,709 32.93 83.70
Staphylococcus aureus | 3,815 16.30 100.00
----------------------+-----------------------------------
Total | 23,411 100.00
5. Number of isolates count by year
Commands:
count if year == 2019
tab amk if year == 2017
Output:
. count if year == 2019
8,461
. tab amk if year == 2017
AMK | Freq. Percent Cum.
------------+-----------------------------------
I | 2 0.03 0.03
R | 999 14.91 14.94
S | 5,697 85.06 100.00
------------+-----------------------------------
Total | 6,698 100.00
6. Cross tabulation
Cross tabulation: It is used to quantitatively analyze the relationship between multiple variables.
Commands:
tab sex year
tab organism year, col nofreq // Display only percentage
tab sample year, col // Number and percentage
Output:
. tab sex year
| Year
Sex | 2017 2018 2019 | Total
-----------+---------------------------------+----------
f | 4,038 4,602 4,928 | 13,568
m | 2,840 3,470 3,533 | 9,843
-----------+---------------------------------+----------
Total | 6,878 8,072 8,461 | 23,411
. tab organism year, col nofreq // Display only percentage
| Year
Organism | 2017 2018 2019 | Total
----------------------+---------------------------------+----------
Escherichia coli | 54.04 49.55 49.28 | 50.78
Klebsiella sp. | 28.58 34.64 34.83 | 32.93
Staphylococcus aureus | 17.37 15.81 15.88 | 16.30
----------------------+---------------------------------+----------
Total | 100.00 100.00 100.00 | 100.00
. tab sample year, col // Number and percentage
+-------------------+
| Key |
|-------------------|
| frequency |
| column percentage |
+-------------------+
| Year
Sample | 2017 2018 2019 | Total
-----------+---------------------------------+----------
Blood | 361 399 454 | 1,214
| 5.25 4.94 5.37 | 5.19
-----------+---------------------------------+----------
Pus | 1,578 1,979 2,196 | 5,753
| 22.94 24.52 25.95 | 24.57
-----------+---------------------------------+----------
Sputum | 336 376 367 | 1,079
| 4.89 4.66 4.34 | 4.61
-----------+---------------------------------+----------
Stool | 0 0 2 | 2
| 0.00 0.00 0.02 | 0.01
-----------+---------------------------------+----------
Urine | 3,993 4,256 4,565 | 12,814
| 58.05 52.73 53.95 | 54.73
-----------+---------------------------------+----------
Wound Swab | 610 1,062 877 | 2,549
| 8.87 13.16 10.37 | 10.89
-----------+---------------------------------+----------
Total | 6,878 8,072 8,461 | 23,411
| 100.00 100.00 100.00 | 100.00
7. Pearson's Chi-squared test
Pearson's Chi-squared test: Pearson's chi-squared test (χ2) is a statistical test applied to sets of categorical data to evaluate how likely it is that any observed difference between the sets arose by chance.
Commands:
tabulate ageGroup1 sample, chi2
tabulate organism sample, chi2
Output:
. tabulate ageGroup1 sample, chi2
| Sample
ageGroup1 | Blood Pus Sputum Stool Urine Wound S.. | Total
------------+------------------------------------------------------------------+----------
<=4 Years | 47 66 0 1 241 202 | 557
5-14 Years | 21 54 2 0 103 347 | 527
15-24 Years | 27 120 23 1 257 329 | 757
25-34 Years | 34 299 41 0 731 328 | 1,433
35-44 Years | 117 818 91 0 1,388 275 | 2,689
45-54 Years | 247 1,580 215 0 2,792 404 | 5,238
55-64 Years | 337 1,636 299 0 3,503 392 | 6,167
>= 65 Years | 384 1,180 408 0 3,799 272 | 6,043
------------+------------------------------------------------------------------+----------
Total | 1,214 5,753 1,079 2 12,814 2,549 | 23,411
Pearson chi2(35) = 3.9e+03 Pr = 0.000
. tabulate organism sample, chi2
| Sample
Organism | Blood Pus Sputum Stool Urine Wound S.. | Total
----------------------+------------------------------------------------------------------+----------
Escherichia coli | 640 1,041 105 2 9,800 299 | 11,887
Klebsiella sp. | 351 2,299 848 0 2,508 1,703 | 7,709
Staphylococcus aureus | 223 2,413 126 0 506 547 | 3,815
----------------------+------------------------------------------------------------------+----------
Total | 1,214 5,753 1,079 2 12,814 2,549 | 23,411
Pearson chi2(10) = 9.9e+03 Pr = 0.000
8. Plotting results
// Histogram histogram age // Box plot graph box age // Bar graph graph bar (count), over(ageGroup1) stack // Bar graph with more than one categorical variables graph bar (count), over(ageGroup1) over(sex) stack // Pie chart graph pie, over(sex) graph pie, over(sex) title(Gender distribution) graph pie, over(sex) title(Gender distribution) legend(on) scheme(s2color) Output:
9. Yearly antibiotics by organism
Commands:
tab amc year if organism == "Escherichia coli", col nofreq
tab amc year if organism == "Klebsiella sp.", col nofreq
tab amc year if organism == "Staphylococcus aureus", col
Output:
. tab amc year if organism == "Escherichia coli", col nofreq
| Year
AMC | 2017 2018 2019 | Total
-----------+---------------------------------+----------
R | 75.14 70.07 73.57 | 72.84
S | 24.86 29.93 26.43 | 27.16
-----------+---------------------------------+----------
Total | 100.00 100.00 100.00 | 100.00
. tab amc year if organism == "Klebsiella sp.", col nofreq
| Year
AMC | 2017 2018 2019 | Total
-----------+---------------------------------+----------
R | 68.89 71.05 70.59 | 70.33
S | 31.11 28.95 29.41 | 29.67
-----------+---------------------------------+----------
Total | 100.00 100.00 100.00 | 100.00
. tab amc year if organism == "Staphylococcus aureus", col
+-------------------+
| Key |
|-------------------|
| frequency |
| column percentage |
+-------------------+
| Year
AMC | 2017 2018 2019 | Total
-----------+---------------------------------+----------
R | 412 446 530 | 1,388
| 38.98 36.44 40.30 | 38.60
-----------+---------------------------------+----------
S | 645 778 785 | 2,208
| 61.02 63.56 59.70 | 61.40
-----------+---------------------------------+----------
Total | 1,057 1,224 1,315 | 3,596
| 100.00 100.00 100.00 | 100.00