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Group A streptococcal cases and treatments during the COVID-19 pandemic: a rapid report

Description
This rapid report describes weekly changes in the recording of diagnoses and symptoms related to group A streptococcal infection and the prescribing of antibiotics used to treat group A streptococcal infection. The supporting monthly report is linked below. A full description of the methods and results can be found in the published paper linked below.
Authors
Christine Cunningham, Louis Fisher, Christopher Wood, The OpenSAFELY Collaborative, Victoria Speed, Andrew D Brown, Helen J Curtis, Rose Higgins, Richard Croker, Ben FC Butler-Cole, David Evans, Peter Inglesby, Iain Dillingham, Sebastian CJ Bacon, Elizabeth Beech, Kieran Hand, Simon Davy, Tom Ward, George Hickman, Lucy Bridges, Thomas O’Dwyer, Steven Maude, Rebecca M Smith, Amir Mehrkar, Liam C Hart, Chris Bates, Jonathan Cockburn, John Parry, Frank Hester, Sam Harper, Ben Goldacre, Brian MacKenna
Contact
Get in touch and tell us how you use this report or new features you'd like to see: team@opensafely.org
First published
06 Feb 2023
Last released
19 Apr 2023
Links

Group A streptococcal cases and treatments during the COVID-19 pandemic: a rapid report

01-09-22 through 05-04-23 by week

Background

During the COVID-19 pandemic there has been a substantial change to the pattern of circulating viruses and bacteria that cause illnesses. In order to support ongoing response and recovery of NHS services from the COVID-19 pandemic, it is useful to have detailed information on patterns of disease being reported by the NHS and on treatments such as antibiotics.

In the winter of 22/23 UKHSA (December 8th) reported an unseasonal increase of scarlet fever and group A streptococcus infections. Sadly, between 19th September 2022 and 26th March 2023 there have been 355 deaths in England across all age groups, including 40 children under 18. UKHSA indicates that the increase is likely to reflect increased susceptibility to these infections in children due to low numbers of cases during the COVID-19 pandemic, along with current circulation of respiratory viruses. As of March 26th 2023, scarlet fever notifications have returned to expected levels, but invasive group A strep notifications remain higher than normal.

This rapid report describes changes in the recording of diagnoses and symptoms related to group A streptococcal infection and the prescribing of antibiotics used to treat group A streptococcal infection. We will routinely update the data in this report and invite anyone who finds it useful to get in touch and tell us how you use this report or new features you'd like to see.

Methods

This study used data from OpenSAFELY-TPP, which covers 40% of the population of England. For a description of the representativeness of this sample, please see our manuscript here. Individuals were included if they were alive and registered at a TPP practice each week, across the study period. Patients were excluded if their listed age was not between 0 and 120 years.

Counts represent patients with at least one prescription or clinical event in that week. Patients with more than one of the same prescription or clinical event in a week were only counted once. Rates divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.

Counts <=5 have been redacted and all numbers rounded to the nearest 10 to avoid potential re-identification of individuals. The rates displayed were computed with these rounded counts.

Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol.

Clinical events data is based on a clinical code being added to a patient's record. This is often added by a clinician during a consultation to indicate the presence of a sign/symptom (e.g. sore throat) or that a clinical diagnosis has been made (e.g. Scarlet Fever). These codes do not necessarily indicate positive test results.

Weeks run Thursday to Wednesday to enable the extraction of the most up-to-date data.

Links to the codelist for each analysis can be found beneath the relevant section.

Antibiotic Prescribing

The below charts show the count and rate of patients prescribed the following antibiotics each week: phenoxymethylpenicillin, amoxicillin, clarithromycin, erythromycin, azithromycin, flucloxacillin, cefalexin and co-amoxiclav. This is based on the antibiotic recommendation given in NHS England Group A streptococcus in children: Interim clinical guidance summary 22nd December 2022.

Any antibiotic

Counts: represent patients with at least one prescription event in that week. Patients with more than one of the same prescription in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The below charts show the count of patients prescribed any of the above listed antibiotics each week, followed by a table with the underlying counts.

Date Patient Count Amoxicillin Azithromycin Cefalexin Clarithromycin Co Amoxiclav Erythromycin Flucloxacillin Phenoxymethylpenicillin
01-09-22 25382020 35780 5220 6570 12230 8870 3220 31660 14760
08-09-22 25389400 38630 5270 6430 12410 9040 3180 30720 15560
15-09-22 25395470 38270 4830 5460 11250 7430 3000 24770 14510
22-09-22 25401690 54680 5660 6690 14420 9160 3730 29000 18740
29-09-22 25409950 61970 5470 6480 15620 9460 3890 28390 19670
06-10-22 25422880 64500 5400 6340 15630 9170 4000 28080 19820
13-10-22 25430840 66900 5320 6450 16420 9180 4100 27680 20910
20-10-22 25442150 65530 5450 6730 16010 9060 4020 27200 19780
27-10-22 25452780 64330 5380 6610 15830 9240 4020 27500 20270
03-11-22 25463090 65180 5460 6480 16320 9350 4140 27700 21980
10-11-22 25472540 69920 5490 6580 16680 9390 4440 28010 23220
17-11-22 25480730 79790 5680 6740 17910 9500 4700 26860 25430
24-11-22 25487660 92150 5650 6720 20090 9590 5400 26800 28530
01-12-22 25494320 115250 6820 6870 26290 10380 8830 27140 54740
08-12-22 25499490 143400 8420 8710 35340 12000 13670 26590 61200
15-12-22 25502920 162250 8290 9680 35560 14080 13120 28380 48930
22-12-22 25506370 101280 3810 5600 20710 9010 6500 17540 23580
29-12-22 25504260 115100 5470 6780 24260 10990 6020 23320 26490
05-01-23 25510030 99930 6520 7700 23650 11820 5710 28310 28920
12-01-23 25513710 73660 5910 7130 18720 10530 4910 27650 27010
19-01-23 25519520 68100 5530 6710 17410 9860 4560 27470 27130
26-01-23 25525290 73050 5660 6860 17850 10090 4520 28400 28940
02-02-23 25529810 75650 5800 6940 18430 10180 4640 28450 29840
09-02-23 25534750 73030 5660 6580 17900 10080 4380 27780 27850
16-02-23 25538850 70460 5450 6710 17830 10100 4280 27840 26340
23-02-23 25542400 67140 5650 6740 17320 10060 4270 27660 26560
02-03-23 25546500 65600 5900 6820 17110 10070 4340 28020 27750
09-03-23 25550090 66090 5750 6530 17090 9940 4370 28240 27200
16-03-23 25553580 67430 5590 6700 17140 9950 4500 28680 27470
23-03-23 25553650 65880 5730 6730 17400 9980 4540 27890 27420
30-03-23 25551790 62790 5750 6820 16750 9980 4600 28390 25290

The below charts show the weekly count and rate of patients with any of the listed antibiotics across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Phenoxymethylpenicillin (Codelist)

The below charts show patients prescribed phenoxymethylpenicillin between 01-09-22 and 05-04-23. The codelist used to identify phenoxymethylpenicillin is here.

Counts: represent patients with at least one prescription event in that week. Patients with more than one of the same prescription in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the top 5 used codes within the phenoxymethylpenicillin codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
0501011P0AAAJAJ Phenoxymethylpenicillin 250mg tablets 548,570 65.63
0501011P0AAAFAF Phenoxymethylpenicillin 250mg/5ml oral solution 132,580 15.86
0501011P0AAADAD Phenoxymethylpenicillin 125mg/5ml oral solution 123,830 14.82
0501011P0AAASAS Phenoxymethylpenicillin 250mg/5ml oral solution sugar free 17,140 2.05
0501011P0AAARAR Phenoxymethylpenicillin 125mg/5ml oral solution sugar free 13,720 1.64

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded phenoxymethylpenicillin events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Amoxicillin (Codelist)

The below charts show patients prescribed amoxicillin between 01-09-22 and 05-04-23. The codelist used to identify amoxicillin is here.

Counts: represent patients with at least one prescription event in that week. Patients with more than one of the same prescription in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the top 5 used codes within the amoxicillin codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
0501013B0AAABAB Amoxicillin 500mg capsules 1,669,110 70.61
0501013B0AAATAT Amoxicillin 250mg/5ml oral suspension sugar free 437,630 18.51
0501013B0AAAKAK Amoxicillin 250mg/5ml oral suspension 102,030 4.32
0501013B0AAASAS Amoxicillin 125mg/5ml oral suspension sugar free 82,840 3.50
0501013B0AAAAAA Amoxicillin 250mg capsules 35,310 1.49

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded amoxicillin events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Clarithromycin (Codelist)

The below charts show patients prescribed clarithromycin between 01-09-22 and 05-04-23. The codelist used to identify clarithromycin is here.

Counts: represent patients with at least one prescription event in that week. Patients with more than one of the same prescription in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the top 5 used codes within the clarithromycin codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
0501050B0AAADAD Clarithromycin 500mg tablets 413,360 71.57
0501050B0AAABAB Clarithromycin 125mg/5ml oral suspension 66,010 11.43
0501050B0AAAAAA Clarithromycin 250mg tablets 51,550 8.93
0501050B0AAAHAH Clarithromycin 250mg/5ml oral suspension 45,860 7.94
0501050B0AAAEAE Clarithromycin 500mg modified-release tablets 640 0.11

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded clarithromycin events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Erythromycin (Codelist)

The below charts show patients prescribed erythromycin between 01-09-22 and 05-04-23. The codelist used to identify erythromycin is here.

Counts: represent patients with at least one prescription event in that week. Patients with more than one of the same prescription in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the top 5 used codes within the erythromycin codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
0501050C0AAABAB Erythromycin 250mg gastro-resistant tablets 92,730 58.08
0501050H0AAABAB Erythromycin ethyl succinate 250mg/5ml oral suspension 20,220 12.66
0501050H0AAAMAM Erythromycin ethyl succinate 250mg/5ml oral suspension sugar free 18,930 11.86
0501050H0AAAAAA Erythromycin ethyl succinate 125mg/5ml oral suspension 12,640 7.92
0501050H0AAALAL Erythromycin ethyl succinate 125mg/5ml oral suspension sugar free 10,090 6.32

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded erythromycin events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Azithromycin (Codelist)

The below charts show patients prescribed azithromycin between 01-09-22 and 05-04-23. The codelist used to identify azithromycin is here.

Counts: represent patients with at least one prescription event in that week. Patients with more than one of the same prescription in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the top 5 used codes within the azithromycin codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
0501050A0AAAGAG Azithromycin 250mg tablets 94,030 52.82
0501050A0AAADAD Azithromycin 500mg tablets 32,790 18.42
0501050A0AAAAAA Azithromycin 250mg capsules 25,300 14.21
0501050A0AAABAB Azithromycin 200mg/5ml oral suspension 25,190 14.15
0501050A0BBABAB Zithromax 200mg/5ml oral suspension 340 0.19

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded azithromycin events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Flucloxacillin (Codelist)

The below charts show patients prescribed flucloxacillin between 01-09-22 and 05-04-23. The codelist used to identify flucloxacillin is here.

Counts: represent patients with at least one prescription event in that week. Patients with more than one of the same prescription in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the top 5 used codes within the flucloxacillin codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
0501012G0AAABAB Flucloxacillin 500mg capsules 716,980 84.14
0501012G0AAAFAF Flucloxacillin 125mg/5ml oral solution 47,060 5.52
0501012G0AAAAAA Flucloxacillin 250mg capsules 34,200 4.01
0501012G0AAAGAG Flucloxacillin 250mg/5ml oral solution 32,490 3.81
0501012G0AAAQAQ Flucloxacillin 250mg/5ml oral solution sugar free 14,950 1.75

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded flucloxacillin events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Cefalexin (Codelist)

The below charts show patients prescribed cefalexin between 01-09-22 and 05-04-23. The codelist used to identify cefalexin is here.

Counts: represent patients with at least one prescription event in that week. Patients with more than one of the same prescription in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the top 5 used codes within the cefalexin codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
0501021L0AAABAB Cefalexin 500mg capsules 108,440 51.42
0501021L0AAAAAA Cefalexin 250mg capsules 42,250 20.03
0501021L0AAAGAG Cefalexin 250mg tablets 21,110 10.01
0501021L0AAADAD Cefalexin 250mg/5ml oral suspension 10,320 4.89
0501021L0AAACAC Cefalexin 125mg/5ml oral suspension 9,910 4.70

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded cefalexin events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Co-Amoxiclav (Codelist)

The below charts show patients prescribed co-amoxiclav between 01-09-22 and 05-04-23. The codelist used to identify co-amoxiclav is here.

Counts: represent patients with at least one prescription event in that week. Patients with more than one of the same prescription in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Prescribing data is based on prescriptions issued within the Electronic Health Record. Prescriptions may not always be dispensed or in some cases the dispensed item may differ from the prescribed item due to the use of a Serious Shortage Protocol
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the top 5 used codes within the co-amoxiclav codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
0501013K0AAAJAJ Co-amoxiclav 500mg/125mg tablets 249,690 81.16
0501013K0AAAGAG Co-amoxiclav 250mg/62mg/5ml oral suspension sugar free 17,590 5.72
0501013K0AAAAAA Co-amoxiclav 250mg/125mg tablets 17,180 5.58
0501013K0AAADAD Co-amoxiclav 125mg/31mg/5ml oral suspension sugar free 10,370 3.37
0501013K0AAAHAH Co-amoxiclav 125mg/31mg/5ml oral suspension 4,800 1.56

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded co-amoxiclav events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Recorded Clinical Events

The below charts show the count and rate of patients with a recording of the following clincial events each week: scarlet fever, sore throat/tonsillitis and invasive strep A.

Any clinical

Counts: represent patients with at least one clinical event in that week. Patients with more than one of the same clinical event in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Clinical events data is based on a clinical code being added to a patient's record. This is often added by a clinician during a consultation to indicate the presence of a sign/symptom (e.g. sore throat) or that a clinical diagnosis has been made (e.g. Scarlet Fever). These codes do not necessarily indicate positive test results.
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The below charts show the count of patients with any of the above listed clinical events each week, followed by a table with the underlying counts.

Date Patient Count Invasive Strep A Scarlet Fever Sore Throat Tonsillitis
01-09-22 25382020 [REDACTED] 80 8980
08-09-22 25389400 [REDACTED] 120 9840
15-09-22 25395470 10 150 9540
22-09-22 25401690 10 200 12220
29-09-22 25409950 [REDACTED] 280 12950
06-10-22 25422880 [REDACTED] 280 13050
13-10-22 25430840 10 400 13870
20-10-22 25442150 [REDACTED] 390 12870
27-10-22 25452780 10 240 13360
03-11-22 25463090 10 410 15090
10-11-22 25472540 10 510 15990
17-11-22 25480730 10 710 17580
24-11-22 25487660 20 1030 19560
01-12-22 25494320 30 3220 31800
08-12-22 25499490 30 4800 40810
15-12-22 25502920 40 3540 37300
22-12-22 25506370 20 1190 19040
29-12-22 25504260 30 760 20490
05-01-23 25510030 30 790 20880
12-01-23 25513710 10 860 19300
19-01-23 25519520 20 1000 19430
26-01-23 25525290 20 1010 20390
02-02-23 25529810 20 1000 21070
09-02-23 25534750 10 940 19400
16-02-23 25538850 20 590 18800
23-02-23 25542400 30 670 18770
02-03-23 25546500 20 820 19100
09-03-23 25550090 20 870 19270
16-03-23 25553580 20 840 18820
23-03-23 25553650 10 790 18770
30-03-23 25551790 10 650 16600

The below charts show the weekly count and rate of patients with any of the listed clinical events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Scarlet Fever (Codelist)

The below charts show patients with recorded events of scarlet fever between 01-09-22 and 05-04-23. The codelist used to identify scarlet fever is here.

Counts: represent patients with at least one clinical event in that week. Patients with more than one of the same clinical event in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Clinical events data is based on a clinical code being added to a patient's record. This is often added by a clinician during a consultation to indicate the presence of a sign/symptom (e.g. sore throat) or that a clinical diagnosis has been made (e.g. Scarlet Fever). These codes do not necessarily indicate positive test results.
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the 5 most used codes within the scarlet fever codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
30242009 Scarlet fever 22,090 75.86
1087781000000109 Suspected scarlet fever 5,080 17.45
170523002 Notification of scarlet fever 1,640 5.63
186357007 Streptococcal sore throat with scarlatina 260 0.89
Other - 50 0.17

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded scarlet fever events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Sore Throat/Tonsillitis (Codelist)

The below charts show patients with recorded events of sore throat/tonsillitis between 01-09-22 and 05-04-23. The codelist used to identify sore throat/tonsillitis is here.

Counts: represent patients with at least one clinical event in that week. Patients with more than one of the same clinical event in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Clinical events data is based on a clinical code being added to a patient's record. This is often added by a clinician during a consultation to indicate the presence of a sign/symptom (e.g. sore throat) or that a clinical diagnosis has been made (e.g. Scarlet Fever). These codes do not necessarily indicate positive test results.
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the 5 most used codes within the sore throat/tonsillitis codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
90176007 Tonsillitis 267,070 46.45
162397003 Pain in throat 98,270 17.09
267102003 Sore throat symptom 84,180 14.64
17741008 Acute tonsillitis 55,600 9.67
405737000 Pharyngitis 34,180 5.94

The second chart illustrates top code usage over time. Codes that were in the top 5 either in the first or last week of the study period were included.

The below charts show the weekly count and rate of patients with recorded sore throat/tonsillitis events across the study period, with a breakdown by key demographic subgroups.

Count
Rate

Invasive Strep A (Codelist)

The below charts show patients with recorded events of invasive strep a between 01-09-22 and 05-04-23. The codelist used to identify invasive strep a is here.

Counts: represent patients with at least one clinical event in that week. Patients with more than one of the same clinical event in a week were only counted once. Counts <=5 were redacted and all numbers were rounded to the nearest 10.
Rates: divide the count by the included study population and multiply by 1,000 to achieve a rate per 1,000 registered patients.
Note: Clinical events data is based on a clinical code being added to a patient's record. This is often added by a clinician during a consultation to indicate the presence of a sign/symptom (e.g. sore throat) or that a clinical diagnosis has been made (e.g. Scarlet Fever). These codes do not necessarily indicate positive test results.
Note: Weeks run from Thursday to Wednesday to enable the extraction of the most up-to-date data

The following table shows the 5 most used codes within the invasive strep a codelist after summing code usage over the entire study period. Codes with low usage that would have been redacted have been grouped into the category 'Other'. The proportion was computed after rounding. If more than 5 codes in the codelist are used, the proportion will not add up to 100%.

Code Description Count of patients with code Proportion of total patients with code (%)
406614006 Invasive Group A beta-hemolytic streptococcal disease 180 35.29
Other - 130 25.49
449504009 Sepsis caused by Streptococcus pyogenes 120 23.53
1087891000000103 Suspected invasive Group A beta-haemolytic streptococcal disease 80 15.69