Antibiotic Prescriptions Before, During and after the Corona Pandemic

in Schleswig-Holstein with Prescription Data from 2017 till 2023

Reinhard Schuster

, Timo Emcke

, Vera Ries

, Eva von Arnstedt

and Mareike Burmester

Chair of Department of Health Economics, Epidemiology and Medical Informatics, Medical Advisory Board of Statutory

Health Insurance in Northern Germany (MD Nord), 23554 L

ubeck, Germany

Chair of Department of Prescription Analysis, Association of Statutory Health Insurance Physicians, Germany

Medical Advisory Service Institution of the Statutory Health Insurance in North Rhine (MD Nordrhein), 40212 D

usseldorf,

Germany

Medical Advisory Board of Statutory Health Insurance in Northern Germany (MD Nord), 23554 L

ubeck, Germany

Keywords:

Corona Pandemic, Antibiotic Prescriptions, Big Routine Prescription Data, International ATC-Code.

Abstract:

The ongoing COVID-19 pandemic threatens the health of humans, causes great economic losses and may dis-

turb the stability of the societies and is a major challenge for physicians, politicians, scientists and many other

groups. The article focuses on patients with antibiotic prescriptions and considers their risks in comparison

to all patients. Time series are analyzed starting from the pre-Corona period till today. Mathematical analysis

can be used to understand aspects of the dynamics of epidemics and to improve strategies, i. e. regarding

effects of antibiotic stewardship programs or reaction to drug availability constraints.

1 INTRODUCTION

The Covid-19 pandemic is a major challenge for

physicians, politicians, scientists and many other

groups. Models help in the discussion of possible

scenarios, allow to monitor the consequences of in-

terventions and to generate more background knowl-

edge for the reﬁnement of policy impact research,

cf. (Chinazzi et al., 2020), (Rosenbaum, 2020), (Pan

et al., 2020), (Behrens et al., 2020), (Tang et al.,

2020). The outbreak of the COVID-19 pandemic in

March 2020 led to signiﬁcant changes in the burden

of disease and in the medication prescription patterns

in Germany. To turn up at work despite ﬂu symp-

toms, even though it would be appropriate to report

sick, used to be daily occurrence, especially among

service employees. By contrast during the pandemic,

this had been viewed much more critically due to

the general risk of infection. Persons showing ﬂu-

like symptoms were suspected as possible candidates

for SARS-CoV-2 virus infection. In most cases, they

were requested by the employer to stay at home to

prevent further spread of the illness. This effect as

well as the public pandemic-related infection protec-

tion measures starting in spring 2020 led to fewer res-

piratory infections and therefore fewer cases of inca-

pacity for work due to this diseases. The problem

of excessive use of reserve antibiotics has been dis-

cussed for a long time in the statutory health insur-

ance and changes during the pandemic are therefore

also relevant. Another problem are the delivery bot-

tlenecks, especially for antibiotics for children.

Nationwide antibiotic stewardship initiatives aim

to ban the inappropriately excessive use of antibiotics

and of reserve antibiotics for ﬂu-like symptoms, so

changes during the pandemic are relevant to moni-

tor. Another relevant problem are pandemic-related

delivery bottlenecks, regarding unit dosage forms es-

pecially made for children, respectively.

2 MATERIAL AND METHODS

We analyse prescription and diagnostic data of the

most northern federal state of Germany (Schleswig-

Holstein) from quarters 1/2017 till 2/2023. The analy-

sis relates to patients, quarters and physicians. Count-

ing a patient as often as pairs of quarters and physi-

cians appear results in 153 million drug prescription

data records.

The C-related programming language awk is used

for the computations. The visualization is performed

using Mathematica by Wolfram Research and Mi-

crosoft Excel.

412

Schuster, R., Emcke, T., Ries, V., von Arnstedt, E. and Burmester, M.

Antibiotic Prescriptions Before, During and after the Corona Pandemic in Schleswig-Holstein with Prescription Data from 2017 till 2023.

DOI: 10.5220/0012361700003657

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 2, pages 412-419

ISBN: 978-989-758-688-0; ISSN: 2184-4305

For the prescription analysis, the International

Anatomical Therapeutic Chemical classiﬁcation sys-

tem (ATC) with speciﬁcations provided by the Ger-

man National Institute for Drugs and Medical Devices

(BfArM) is used with ATC code J01 as identiﬁer for

antibiotic drugs, cf. (Fricke et al., 2009).

3 RESULTS

Comparing the total number of antibiotic prescrip-

tions per quarter in the period between the ﬁrst quarter

of 2017 and the second quarter of 2023, there is both

a seasonal trend and a decrease per year in the pre-

Corona period from 2017 to 2020, cf . (Bornemann

and Tillmann, 2022). The decisive drop in the number

of prescriptions is observed following the pandemic

breakout in the second quarter of 2020 and continues

until the second quarter of 2021. The number of pre-

scriptions than rises again to reach the highest level

during the observation period in the ﬁrst quarter of

2023, cf. (Kolbe, 2021), (Tarazi et al., 2021), (Patel

et al., 2021), (Olsen et al., 2020) and (Smits et al.,

2019). The reductions in the pre-Corona period were

overcompensated by the development after 2021, sug-

gesting that increased health risks are met in the pe-

riod after the Corona pandemic. The time series is

shown in Figure 1.

50.000

100.000

150.000

200.000

250.000

300.000

350.000

400.000

171 172 173 174 181 182 183 184 191 192 193 194 201 202 203 204 211 212 213 214 221 222 223 224 231 232

number of prescriptions

quarter

number of prescriptions per quarter

Figure 1: Time series of antibiotic prescriptions.

In order to consider the age distribution for all and

for antibiotic drugs in 2019, that means that the area

under the curve in Figure 2 is normed. The age dis-

tribution in 2020 is nearly the same, the differences

in the prescription numbers are almost completely

reduced by the normalization. Because aspects of

polypharmacy are included, it differs from the age

distribution of the related patients.

In contrast to all prescriptions, there is initially a

local maximum at the age of two to four years for

antibiotic prescriptions. This is followed by a rel-

ative minimum at the age of 13 for both observa-

0,0%

0,5%

1,0%

1,5%

2,0%

2,5%

3,0%

fraction

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98

age

age distribution of drug prescriptions for all drugs and for antidiabetics

all drugs J01

Figure 2: Age distribution with respect to all drugs and for

antibiotic drugs.

tions. Again, there is another relative prescription

maximum for both observations at the age of 19 when

people start to assume working or studying. Consid-

ering antibiotic prescriptions, the absolute maximum

is reached between the ages of 53 and 56, while the

overall number of prescriptions continues to rise. At

the age of 54 years, there is an intersection of the nor-

malized curves. There is a narrow local minimum at

the end of the war in 1945, which corresponds to the

age of 74 in 2019. On one hand, the curves are char-

acterized by the slowly emerging baby boomer gen-

eration, on the other by the increased probability of

illness and death of older people. Both the choice of

antibiotics and the prescriptions as a whole peak at an

age of 80 (relatively for antibiotics, absolutely for all

prescriptions). Our age-related considerations of an-

tibiotic prescriptions in the pre-pandemic period and

during the pandemic are concordant with the ﬁndings

in (European Centre for Disease Prevention and Con-

trol, 2020), (Holstiege et al., 2019), (Augustin et al.,

2015), (Koller et al., 2013) and (Gillies et al., 2022).

The age spectrum of antibiotic prescriptions in the

pre-Corona year 2019 and the year of the outbreak in

2020 barely differs, with a certain shift towards older

ages, Figure 3. This accounts for the generally higher

risk, but the insigniﬁcance of this effect is noteworthy.

0,0%

0,2%

0,4%

0,6%

0,8%

1,0%

1,2%

1,4%

1,6%

1,8%

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98

fraction

age

Age distributions of antibiotic drugs 2019 and 2020

J 2019

J 2020

Figure 3: Age distributions for antibiotic drugs 2019 and

2020.

Antibiotic Prescriptions Before, During and after the Corona Pandemic in Schleswig-Holstein with Prescription Data from 2017 till 2023

413

We compare the prescription of antibiotics be-

tween 2019 and 2020 at different ATC levels and start

with the ATC 4 level, cf. Table 1.

Table 1: ATC 4 prescriptions 2019 and 2020.

ATC 4 nr. 2019 nr. 2020 diff.rel. drug group

J01C 301,672 218,899 -27.4 % beta-lactam antibacterials, peni-

cillins

J01D 205,392 151,232 -26.4 % other beta-lactam antibacterials

J01F 163,670 100,860 -38.4 % macrolides, lincosamides and

streptogramins

J01X 97,910 92,920 -5.1 % other antibacterials

J01E 72,058 58,767 -18.4 % sulfonamides and trimethoprim

J01M 78,787 57,409 -27.1 % quinolone antibacterials

J01A 48,368 42,672 -11.8 % tetracyclines

J01G 1,054 856 -18.8 % aminoglycoside antibacterials

Other changes appear considering the relative

fractions of prescriptions in the same context, cf. Ta-

ble 2.

Table 2: ATC 4 prescriptions 2019 and 2020.

ATC 4 frac. 2019 frac. 2020 diff.rel. drug group

J01C 31.1 % 30.3 % -2.8 % beta-lactam antibacterials,

penicillins

J01D 21.2 % 20.9 % -1.4 % other beta-lactam antibacte-

rials

J01F 16.9 % 13.9 % -17.5 % macrolides, lincosamides

and streptogramins

J01X 10.1 % 12.8 % 27.1 % other antibacterials

J01E 7.4 % 8.1 % 9.2 % sulfonamides and trimetho-

prim

J01M 8.1 % 7.9 % -2.4 % quinolone antibacterials

J01A 5.0 % 5.9 % 18.1 % tetracyclines

J01G 0.1 % 0.1 % 8.7 % aminoglycoside antibacteri-

als

The largest absolute decrease occurs at

”macrolides, lincosamides and streptogramins”

(J01F), followed by ”beta-lactam antibacterials,

penicillins” (J01C) and ”quinolone antibacterials”

(J01M). The largest increase of the relative fractions

occurs in the unspeciﬁc drug group ”other antibac-

terials” (J01X), followed by ”tetracyclines” (J01A)

and the smallest group with absolute prescriptions

numbers ”aminoglycoside antibacterials” (J01G).

The entropy e deﬁned by e = −

∑

ln(p

) as a

measure of distribution differences increases slightly

from 0.772 to 0.785.

Prescription frequencies before the pandemic

(2019) and at the beginning of the Corona pandemic

(2020) are closely linked to the group of medical spe-

cialist prescribing. The largest decline in the num-

ber of prescriptions occurred among paediatricians (-

41.0%), followed by ENT doctors (-33.6%) and gen-

eral practitioners (-26.6%). There was a compara-

tively slight decline among gynaecologists (-3.2%),

followed by dermatologists (-3.8%), surgeons (-3.5%)

and urologists (-5.8%). Some ATC 4 drug groups

are primarily used in antibiotics by certain specialist

groups.

For the top 10 positions in terms of ATC 5 pre-

scription frequency in 2019, the changes from 2019

to 2020 are shown in Table 3.

Table 3: ATC 5 prescriptions 2019 and 2020.

ATC 5 nr. 2019 nr. 2020 diff.rel. drug group

J01CA 154,815 105,379 -31.9 % beta-lactam antibacterials, peni-

cillins

J01DC 152,422 101,789 -33.2 % other beta-lactam antibacterials

J01FA 139,762 79,163 -43.4 % macrolides, lincosamides and

streptogramins

J01CR 87,871 75,945 -13,6 % other antibacterials

J01MA 78,787 57,409 -27.1 % sulfonamides and trimethoprim

J01XX 77,724 73,215 -5.8 % quinolone antibacterials

J01EE 58,823 44,085 -25.1 % tetracyclines

J01CE 57,101 36,017 -36.9 % aminoglycoside antibacterials

J01AA 48,368 42,672 -11.8 % tetracyclines

J01DD 45,120 45456 0,7 % aminoglycoside antibacterials

The Drug group J01C (beta-lactam antibacteri-

als, penicillins) splits in subgroups: J01CA (beta-

lactam antibacterials, penicillins, -31.9%) and J01CE

(beta-lactam antibacterials, penicillins, -36.9%) whith

a marked decrease and J01CR (other antibacterials,

-13.6%) slowly decreasing. The drug group J01D

(other beta-lactam antibacterials, -26.4%) with de-

crease has the subgroup J01DD (aminoglycoside an-

tibacterials, +0.7%) with increased in drug prescrip-

tion numbers.

Next, we look at the ATC 7 drug level, cf. Table

Table 4: ATC 7 prescriptions 2019 and 2020.

ATC 7 nr. 2019 nr. 2020 diff.rel. drug group

J01CA04 147,205 91,798 -37,6 % amoxicillin

J01DC02 112,231 77,543 -30,9 % cefuroxime

J01FA10 79,345 46,094 -41,9 % azithromycin

J01XX01 73,950 69,450 -6,1 % fosfomycin

J01EE01 58,823 44,085 -25,1 % sulfamethoxazole and trimetho-

prim

J01CR02 58,006 49,080 -15,4 % amoxicillin and beta-lactase in-

hibitors

J01MA02 54,980 40,859 -25,7 % ciproﬂoxacin

J01CE02 52,548 33,498 -36,3 % phenoxymethylpenicillin

J01DD13 43,531 44,146 1,4 % cefpodoxime

J01AA02 41,143 36,112 -12,2 % doxycycline

Using the ATC 7, we have again a more differen-

tiated picture on the level of active substances.

3,0

3,2

3,4

3,6

3,8

4,0

4,2

4,4

4,6

J 2017 J 2018 J 2019 J 2020 J 2021 J 2022 HJ 2023

polypharmacy for all patients and for patients with antibiotica

all drug plus J01

Figure 4: Time series for polypharmacy for all patients and

for patients with antibiotic drugs.

We consider the mean number of prescribed ac-

tive substances per patient at the ATC 7 level as a

measure of polypharmacy with a quarter as time refer-

ence. This is considered for all patients as well as for

HEALTHINF 2024 - 17th International Conference on Health Informatics

414

those who get an antibiotic prescription. Assuming

that the additional antibiotic prescription increases the

level of polypharmacy by one, instead does not nec-

essarily hold true, because patient groups may differ

in their morbidity.

In the pre-Corona period, the difference was only

slightly less than 1 at 0.9. At the beginning of the

pandemic in 2020, the polypharmacy value rose sig-

niﬁcantly faster by 0.3 among antibiotic patients than

by 0.1 among all patients. In the following year 2022,

the polypharmacy value for all patients remains un-

changed compared to 2021, while the value for an-

tibiotic patients falls to 4.0, which is a lower value

than in the pre-Corona period. This trend intensiﬁes

in the ﬁrst half of 2023: There is an increase in all pa-

tients compared to 2021 and a reduction in antibiotic

patients, thus the difference is only 0.4.

Antimicrobial resistance (AMR) is a threat to

global health and development and it contributes to

millions of deaths worldwide each year, so the WHO

aims to improve the surveillance of antimicrobial re-

sistance through a global action plan on AMR in

order to reduce inappropriate antibiotic consump-

tion. The WHO Categories Access, Watch, Reserve

(AWaRe) provide concise, evidence-based guidance

on the choice of antibiotic, dose and route of ad-

ministration, cf. (World Health Organization, 2022b),

(World Health Organization, 2022a). As access an-

tibiotics show a narrow spectrum of activity, less side-

effects, a lower potential for the selection of antimi-

crobial resistance and lower cost, they are recom-

mended for the empiric treatment of most common

infections and should be widely available. Watch an-

tibiotics have a higher potential for the selection of

antimicrobial resistance, therefore, their use should

be restricted to sicker patients in hospital facility set-

tings carefully monitored to avoid overuse. Reserve

antibiotics are last-resort antibiotics that should only

be used to treat severe infections caused by multidrug-

resistant pathogens. The proportion of reserve antibi-

otics in our data is 0.17% for the entire period un-

der consideration, with no signiﬁcant deviations. The

proportion of infections in the WHO ”watch” cate-

gory has fallen moderately since the beginning of our

analyses until quarter 2 of 2021, cf. Figure 5. This

will be related to extensive consultations with doc-

tors on this topic in the Germen region of Schleswig-

Holstein. Surprisingly, this trend remains almost the

same at the beginning of the pandemic. Only in the

third quarter of 2021, there is a signiﬁcant change,

namely towards an even greater drop in the WHO

watch category fraction with a further moderate drop

until the second quarter of 2022. After a moderate

increase in the fourth quarter of 2022 and the ﬁrst

quarter of 2023, the proportion falls moderately again

in the second quarter of 2023. In the middle part of

the pandemic and also when it expires, the proportion

of prescriptions in the WHO watch category showns

a positive trend already signiﬁcantly reduced before

the pandemic. The extent to which this is caused by

prescription behavior or by changing disease states

should be investigated by further research.

10%

20%

30%

40%

50%

60%

70%

181 182 183 184 191 192 193 194 201 202 203 204 211 212 213 214 221 222 223 224 231 232

fraction

quarter

fraction of antibiotic prescriptions in the WHO watch category

Figure 5: Prescription switch between drug groups within

an antibiotic therapy 2021 with clusters.

Next, we consider therapeutic replacements of

prescribed drug groups at different ATC-levels in

2018 before the pandemic and in 2021 during the

pandemic. For each drug group, we determine the

n = 2 other drug groups that it is most frequently re-

placed with, using at least 100 prescription changes as

a threshold to reduce graphical complexity. We con-

sider a graph visualisation with community clusters

performed with Mathematica by Wolfram Research.

The clusters are determined by minimizing the tran-

sitions between the clusters compared to the transi-

tions within the clusters; about graph theory methods,

see (Brooks, 1991), (Buser, 1978), (Chakrabarti and

Faloutsos, 2006), (Chung, 1997) and (Alon, 1998).

In[20]:=

atc = {{"J01A", "J01C"}, {"J01A", "J01F"}, {"J01C", "J01D"},

{"J01C", "J01F"}, {"J01D", "J01C"}, {"J01D", "J01F"}, {"J01E", "J01X"},

{"J01E", "J01M"}, {"J01F", "J01C"}, {"J01F", "J01D"}, {"J01M", "J01X"},

{"J01M", "J01D"}, {"J01X", "J01M"}, {"J01X", "J01D"}};

atcGraph =

Graph

Graph[

Tabelle

Table[atc[[i]][[1]] -> atc[[i]][[2]], {i, 1,

Länge

Length[atc]}],

Knotenbeschriftungen

VertexLabels → "Name"]

Out[20]=

J01A

J01C

J01F

J01D

J01E

J01X

J01M

In[21]:=

stelle Gemeinschaft graphisch dar

CommunityGraphPlot[atcGraph]

Out[21]=

J01A

J01C

J01F

J01D

J01E

J01X

J01M

Figure 6: Prescription switch between drug groups within

an antibiotic therapy 2018 with clusters.

The community cluster changed between 2018

(cf. Figure 6) and 2021 (cf. Figure 7) in J01M

(quinolone antibacterials) forming an own minimal

cluster in 2021 and J01D (other beta-lactam antibac-

terials) moving to the other primary cluster in 2018.

At ATC 5 level, there are major differences in

the community clusters, 2018 (cf. Figure 8) showing

three clusters and 2021 (cf. Figure 9) two cluster and

one more vertex. The drug group J01CA (penicillins

with extended spectrum) has one incoming edge in

2018 and ﬁve incoming edges in 2021, this drug group

Antibiotic Prescriptions Before, During and after the Corona Pandemic in Schleswig-Holstein with Prescription Data from 2017 till 2023

415

In[9]:=

stelle Gemeinschaft graphisch dar

CommunityGraphPlot[atcGraph]

Out[9]=

J01A

J01C

J01F

J01D

J01X

J01E

J01M

atc_4_2021.nb

Figure 7: Prescription switch between drug groups within

an antibiotic therapy 2021 with clusters.

Table 5: ATC 4 drug groups in transition graphs.

ATC 4 drug group

J01A tetracyclines

J01C beta-lactam antibacterials, penicillins

J01D other beta-lactam antibacterials

J01E sulfonamides and trimethoprim

J01F macrolides, lincosamides and streptogramins

J01M quinolone antibacterials

J01X other antibacterials

In[12]:=

stelle Gemeinschaft graphisch dar

CommunityGraphPlot[atcGraph]

Out[12]=

J01CA

J01FA

J01MA

J01CR

J01DC

J01AA

J01DD

J01EE

J01XX

J01FF

J01XE

atc_5_2018.nb

Figure 8: Prescription switch between drug groups within

an antibiotic therapy 2018 with clusters.

was changed to another more frequently than another

was changed to this group. Due to the extended spec-

trum of action of J01CA, the replacement may be mo-

tivated by theapeutic safety reasons, cf. (Holstiege

et al., 2022), (Langford et al., 2021), (Kern et al.,

2006) and (Filippini et al., 2006). Another possibil-

ity could be a change due to delivery difﬁculties.

In[ ]:=

Eigensystem

Eigensystem[ad2]

Out[]=

2., -1., - 1., - 1., 1., 1.11022 × 10

-16

, -5.55112 × 10

-17

, 0., 0., 0., 0., 0., 0.,

{0.285714, 0.285714, 0.285714, 0.285714, 0.285714, 0.285714,

0.285714, 0.285714, 0.142857, 0.285714, 0.285714, 0.285714, 0.285714},

{0.229416, 0.229416, -0.458831, 0.229416, 0.229416, 0.229416, 0.573539,

-0.114708, - 0.229416, 0.229416, -0.114708, 0.229416, - 0.114708},

{0.229416, 0.229416, -0.458831, 0.229416, 0.229416, 0.229416, 0.573539,

-0.114708, - 0.229416, 0.229416, -0.114708, 0.229416, - 0.114708},

{-0.229416, - 0.229416, 0.458831, -0.229416, - 0.229416, -0.229416,

-0.573539, 0.114708, 0.229416, - 0.229416, 0.114708, - 0.229416, 0.114708},

5.97306 × 10

-17

, -4.61881 × 10

-17

, 6.15841 × 10

-17

, -4.61881 × 10

-17

, 5.97306 × 10

-17

-4.61881 × 10

-17

, 0.5, 0.5, -4.65664 × 10

-17

, -4.61881 × 10

-17

, 0.5, 5.97306 × 10

-17

, 0.5,

1.84183 × 10

-16

, -3.70074 × 10

-17

, 3.70074 × 10

-17

, 3.70074 × 10

-17

1.84183 × 10

-16

, -3.70074 × 10

-17

, 0.666667, 3.70074 × 10

-17

, -0.333333,

-3.70074 × 10

-17

, 3.70074 × 10

-17

, 0.666667, -9.24072 × 10

-17

,

1.4457 × 10

-16

, 9.45405 × 10

-18

, -9.45405 × 10

-18

, -9.45405 × 10

-18

, 1.4457 × 10

-16

9.45405 × 10

-18

, -0.574192, 4.13281 × 10

-17

, 0.744501, -4.13281 × 10

-17

, 4.13281 × 10

-17

0.340618, 5.27262 × 10

-17

, {1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.},

{0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.},

{0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.},

{0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.},

{0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.},

{0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.}

In[14]:=

stelle Gemeinschaft graphisch dar

CommunityGraphPlot[atcGraph]

Out[14]=

J01AA

J01FA

J01CA

J01CR

J01CE

J01DC

J01DD

J01EE

J01EA

J01XX

J01MA

J01FF

J01XE

atc_5_2021.nb

Figure 9: Prescription switch between drug groups within

an antibiotic therapy 2021 with clusters.

Table 6: ATC 5 drug groups in transition graphs.

ATC 5 drug group

J01AA tetracyclines

J01CA penicillins with extended spectrum

J01CE beta-lactamase sensitive penicillins

J01CR combinations of penicillins, incl. beta-lactamase inhibitors

J01DC second-generation cephalosporins

J01DD third-generation cephalosporins

J01EA trimethoprim and derivatives

J01EE combinations of sulfonamides and trimethoprim, incl. derivatives

J01FA macrolides

J01FF lincosamides

J01MA ﬂuoroquinolones

J01XE nitrofuran derivatives

J01XX other antibacterials

In[ ]:=

Eigensystem

Eigensystem[ad2]

Out[]=

{{2, -1, -1, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},

{{2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2},

{-1, 1, 0, -1, 0, 0, 0, 0, 0, -1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1},

{0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, -1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0},

{-1, 0, 1, -1, 1, 1, 1, - 1, 0, 0, 0, -1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0},

{1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 2, 1, 1, 0, 0, 3, 1, 1, 1, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},

{1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},

{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}}

In[16]:=

stelle Gemeinschaft graphisch dar

CommunityGraphPlot[atcGraph]

Out[16]=

J01AA02

J01DC02

J01FA10

J01CA04

J01CE02

J01CR02

J01CR04

J01DB05

J01MA02

J01XX01

J01DC04

J01DD08

J01DD13

J01EA01

J01EE01

J01FA01

J01FA06

J01FA09

J01FF01

J01MA01

J01MA06

J01MA12

J01XE01

J01XX07

atc_7_2018.nb

Figure 10: Prescription switch between drug groups within

an antibiotic therapy 2018 with clusters.

In[ ]:=

Eigensystem

Eigensystem[ad2]

Out[]=

2., -1.41421, 1.41421, -1., -1., 9.7795 × 10

-17

, 0., 0., 0., 0., 0., 0., 0., 0.,

{-0.389867, -0.389867, -0.389867, -0.0974668, - 0.194934, - 0.194934, -0.2924,

-0.194934, -0.2193, -0.243667, -0.0974668, - 0.341134, - 0.2193, -0.231484},

{0., 0., 0., 0.3668, -0.518733, 0., 0.3668, 0., 0.290833, 0.107433, 0.3668,

-0.259367, 0.290833, -0.281617}, {0., 0., 0., 0.196808, 0.278329, 0.,

0.196808, 0., 0.434377, 0.335973, 0.196808, 0.139165, 0.434377, 0.54472},

0.267261, 0.267261, - 0.534522, 0.267261, -0.267261, -0.267261, 2.33274 × 10

-16

-0.267261, 3.63406 × 10

-16

, 0.267261, 0.267261, - 0.267261, 3.63406 × 10

-16

, -0.267261,

0.261341, -0.34631, 0.0849695, -0.34631, 0.34631, - 0.261341, 4.77034 × 10

-17

-0.261341, 0., -0.34631, -0.34631, -0.261341, 0., 0.34631,

0., 0., 0., 9.35299 × 10

-49

, -1.19937 × 10

-64

, 0., - 9.35299 × 10

-49

, 0., - 9.7795 × 10

-17

-9.56387 × 10

-33

, -1.22642 × 10

-48

, -9.56387 × 10

-33

, -9.7795 × 10

-17

, -1.,

{0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.},

{0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.},

{0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.},

{0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.},

{0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.},

{0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.},

{0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.},

{0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}

In[18]:=

stelle Gemeinschaft graphisch dar

CommunityGraphPlot[atcGraph]

Out[18]=

J01CA04

J01CR02

J01FF01

J01CA08

J01XX01

J01CE02

J01DC02

J01DC04

J01DD13

J01EE01

J01EA01

J01FA10

J01MA02

J01XE01

atc_7_2021.nb

Figure 11: Prescription switch between drug groups within

an antibiotic therapy 2021 with clusters.

Table 7: ATC 7 drug groups in transition graphs.

ATC 7 drug group

J01CA04 amoxicillin

J01CA08 pivmecillinam

J01CE02 phenoxymethylpenicillin

J01CR02 amoxicillin and beta-lactase inhibitors

J01DC02 cefuroxime

J01DC04 cefaclor

J01DD13 cefpodoxime

J01EA01 trimethoprim

J01EE01 sulfamethoxazole and trimethoprim

J01FA10 azithromycin

J01FF01 clindamycin

J01MA02 ciproﬂoxacin

J01XE01 nitrofurantoin

J01XX01 fosfomycin

In contrast to the ATC 4 and ATC 5 drug groups,

the graph for the active ingredients according to ATC

7 is divided into two community clusters in 2018 and

three in 2021. The active ingredients J01FF01 (clin-

damycin), J01CR02 (amoxicillin and beta-lactase in-

hibitors) and J01CA04 (amoxicillin) appear in pair-

wise and in both directions as the main versions of a

therapy change.

HEALTHINF 2024 - 17th International Conference on Health Informatics

416

4 DISCUSSION

The number of prescriptions for antibiotics in our data

has risen back to the level of 2017 in 2022 after a

sharp drop, particularly in 2020 being the ﬁrst year

of the pandemic. The WHO declared the outbreak of

the novel coronavirus to be a public health emergency

of international concern on January 30th 2020. The

German parliament passed a ﬁrst legal act on medical

drugs on March 25th 2020 easing the formerly very

restrictive regulations upon drug disposal, enabling

the dispensing pharmacists to change the medically

prescribed disposal variant and to exchange substitu-

tional substances on their own judgement. Delivery

bottlenecks for industrial produced antipyretic cough

syrups for children were avoided by allowing phar-

macists to supply individual preparations instead and

by suspension of reimbursement limits for children

preparations of drugs.

Since Germany rather met to a small part issues

for antibiotic drugs during the pandemic, the prescrip-

tion decrease observed may rather reﬂect a changed

health situation. Staying at home during the lock-

down in 2020 reduced the number of GP consultations

in Germany (Kolbe, 2021) and telemedicine appoint-

ments took a full ﬂight, replacing face-to-face con-

tacts (Mangiapane et al., 2022), (Patel et al., 2021)

and (Tarazi et al., 2021). Prior to the pandemic, res-

piratory tract infections and the need for a sick leave

certiﬁcate were among the main reasons to consult a

GP. The lockdown as well as the mitigation strategies

such as mask wearing decreased the risk for coron-

avirus infection, but lowered the rate of inﬂuenza and

all other respiratory tract infections as well (Lepak

et al., 2021), (Olsen et al., 2020), (Nawrocki et al.,

2021). To prevent the spread of coronavirus during

the pandemic, legal restrictions have been relaxed to

allow doctors to issue sick notes upon request by tele-

phone. Employees presenting with respiratory tract

infections, with fever or feeling unwell were encour-

aged to stay in remote work depending on their own

judgement. In Belgium, respiratory tract infections

were found to be the main diagnosis for overprescrib-

ing of antibiotics by GPs (Colliers et al., 2019), (Smits

et al., 2019). As shown in Table 8, most of the drugs

prescribed in our study can contribute to antibiotic

drug resistance (Ventola, 2015). Antibiotic drug re-

sistance was described for the ﬁrst time for penicilline

in 1940.

Affecting more and more substances, antibiotic

resistance represents a serious health threat world-

wide now (European Centre for Disease Prevention

and Control, 2020). The phenomenon of poly drug

resistance emerging in 2009 worsens this critical pub-

Table 8: Cross linking our data with the list by Ventola 2015

for reports upon antibiotic resistance in the U. S..

ATC Codes Our data ATC/ introduc-

tion/ drug

Resistance re-

ports

J01C Penicilline and beta-

lactam AB

J01CA04 amoxicilline J01C penicilline 1940 Staphylo-

coccus

J01CA08 piymecillinam 1965 Pneumo-

coccus

J01CR02 amoxicilline and

beta-lactamase inh.

J01CE02 phenoxymethylpenicillin

J01D Other beta-lactam

J01DC02 cefuroxime (3rd gen) J01DD02 1985

ceftazidin 3rd gen

1987 Enter-

obacter

J01DC04 cefaclor (2nd gen) J01DD04 1982

ceftriaxon 3rd gen

2009 Neisseria

gon.

J01DD13 cefpodoxim (2nd gen) J01DI02 2010 cef-

taroline 5th gen

2011 Staphylo-

coccus

J01F Macrolide, lin-

cosamide, strep-

togramine

J01FA10 azithromycine J01CF03 1960 me-

thicilline

1962 Staphylo-

coccus

J01FF01 clindamycine J01FA01 1953 ery-

thromycin

1968 Strepto-

coccus

J01E Sulfonamide and

trimethoprime

J01EA01 trimethriprime J01XA01 1972

Vancomycin

1988 Entero-

coccus

J01EE01 sulfamethoxazole

trimethroprime

2002 Staphylo-

coccus

J01EE01 sulfamethoxazole

trimethroprime

2004 Acineto-

bacter

J01EE01 sulfamethoxazole

trimethroprime

2005

Pseudono-

mas

J01X Other AB

J01CE01 nitrofurantoine J01XX08 2000

linezolide

2001 Staphylo-

coccus

J01XX01 fosfomycine

J01M Quinolone AB

J01MA02 ciproﬂoxacine J01MA12 1996

Levoﬂoxacin

1996 pneumo-

coccus

J01AA tetracycline

J01AA02 doxycycline J01AA 1950 tetra-

cycline

1959 Shigella

J01G Aminogycoside AB

J01GB03 J01GB03 1967

gentamicine

1979 Entereo-

coccus

lic health situation (Ventola, 2015). As there is only

limited ﬁnancial interest in the market (Astrup et al.,

2017), we lack the development of new antibiotic

drugs and keep prescribing substances that entered

the market several decades ago. Previous exposure

to antibiotics is a key driver for antibiotics resistance

(Chatterjee et al., 2018). Inappropriate prescribing of

antibiotic drugs fuels antibiotic resistance, so it is cru-

cial to limit the treatment with watch (and reserve)

antibiotics to intensive care patients as a last resort.

Thanks to antibiotic stewardship programs, Germany

got off to a ﬂying start ranking ﬁfth among 30 Euro-

pean countries with one of the lowest amounts of out-

patient prescription of antibiotics at the begin of the

pandemic, (European Centre for Disease Prevention

and Control, 2020).

Nevertheless, the prescription pattern for all an-

tibiotics dropped again considerably with the onset

of the pandemic. Other countries, show an increased

prescription for the macrolide antibiotic azithromycin

Antibiotic Prescriptions Before, During and after the Corona Pandemic in Schleswig-Holstein with Prescription Data from 2017 till 2023

417

(J01FA10) at the beginning of the pandemic (Col-

liers et al., 2021). It remains unclear if it was ap-

plied to treat suspected opportunistic co-infections or

”just in case”. In the US, the empiric treatment with

antimicrobial drugs was explicitly repurposed, result-

ing in an increase in the general prescription of tetra-

cycline (J01AA) and an increased prescription pat-

tern in long-term care settings regarding azithromycin

(J01FA10), (Kolbe, 2021). Large-scale empirical pre-

scriptions carry the risk of new resistance develop-

ments, as previously described for both of these an-

tibiotics, refer to Table 8. Our data show a de-

crease of prescription for all antibiotic drugs in Ger-

many. In Belgium, after the short initial increase of

azithromycin had passed, the prescription of antibi-

otics decreased considerably, too. Instead, there was

no decrease for one of the ﬁrst-choice antibiotic drugs

for urinary tract infections, nitrofurantoin (Colliers

et al., 2021). In our data, the prescription of nitro-

furantoin (J01CE01) showed no different prescription

pattern, decreasing in the same way as all other an-

tibiotic drugs did.

5 CONCLUSION

After a sharp drop in 2020, the ﬁrst year of the pan-

demic, the number of prescriptions for antibiotics in

2022 equals the prescription level in 2017. This over-

all decline in prescription rates for all and especially

critical antibiotics observed in our data and in other

regional studies as well suggests an effective imple-

mentation of the antibiotic stewardship program in

Germany (Scholle et al., 2022).

Indeed, our data indicates an increase in the mean

age of all patients receiving pharmacotherapy, includ-

ing those receiving antibiotic therapy. This suggests

potential health issues for middle-aged and older pa-

tients that go beyond demographic changes. The con-

sequences of the COVID-19 pandemic continue to

pose a challenge to the healthcare system and will re-

main a focus of research.

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419