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Clinical-economic analysis of COVID-19 treatment
drugs at the stationary level
Ani A. Chakhoyan ( ani.chakhoyan@ysu.am )
Yerevan State University https://orcid.org/0000-0003-2404-8124
Albert E. Sahakyan
Yerevan State University
Research Article
Keywords: Clinical-economic analysis, ABC analysis, VEN analysis, ATC/DDD analysis, DU90% analysis,
COVID-19 treatment, rational use of drugs, stationary treatment
Posted Date: July 8th, 2022
DOI: https://doi.org/10.21203/rs.3.rs-1832859/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
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Abstract
Background A number of health system problems have arisen due to the coronavirus. Apart from these
problems, the pharmaceutical sector was not left out either, as prescriptions for new virus treatments
were unplanned and purchases were irregular. Aim To carry out an economic analysis of the drugs used
in the COVID-19 department of the hospital. Methods For the study we used the clinical-economic
methods: ABC/VEN analysis, ATC/DDD methodology and DU90% analysis method. Materials for the
study were data on the use of drugs by one of the multidisciplinary hospitals in Armenia in the
department of COVID-19. Results The ABC/VEN analysis of the drugs revealed that group A did not have
non-essential drugs, and group B had non-essential drugs - 18.75%. The combination of the ABC/VEN
and DU90% analysis revealed that the DU90% group lacked non-essential drugs, indicating that the
hospital had purchased and used the most vital and essential drugs for the COVID-19 department.
Conclusion Based on the results, a recommendation was made and sent to the pharmacist of the
hospital, who will take into account these results when planning the purchase of drugs for the next year,
aiming to reduce the use of non-essential drugs in the hospital.
Impact Statements
The clinical-economic methods used in this study are used for the rst time in Armenia and allow to
evaluate the effective use of drugs.
In the absence of a formulary list of drugs and a formulary system in Armenia, non-essential,
ineffective drugs are often prescribed and used, so it is necessary to monitor for accurate planning of
drug demand.
The results of this study also reect how drug purchases were made at the stationary level when
COVID-19-related drug purchases were irregular or unplanned.
Continued research will address a number of issues that contribute to the effective use of drugs.
1. Introduction
During the coronavirus epidemic, the problems of effective management of the activities of medical
institutions were also aggravated for the organization of pharmaceutical activities. There was a need to
change the procurement planning procedure to provide new (temporary) sections [1]. The urgent
establishment of outpatient computed tomography centers and mobile teams [2], the location of
infectious disease hospitals, and the cancellation of scheduled hospitalizations required considerable
effort on the part of staff to promptly select drugs for patients with varying degrees of COVID-19. Doctors,
pharmacists, and other professionals encountered a number of problems when choosing drugs, and there
were irregular and unplanned purchases of drugs, which in turn led to inecient cost planning.
2. Aim
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The aim of the research is to carry out an economic analysis of the drugs used in the COVID-19
department of the hospital.
Ethics approval was not required.
3. Methods
3.1 Data Source
The data for the study were the data on the use of drugs by one of the multi-prole hospitals in the
Republic of Armenia in the COVID-19 department. Data for 1 year (between 01-January-2021 and 31-
December-2021) were taken.
The le provided by the hospital presented the brand names of the drugs used in the COVID-19
department in units of measurement of dosage forms, quantities and monetary value of the unit.
3.2 Analyses
For the study we used ABC/VEN analysis, ATC/DDD methodology and DU90% analysis.
We used ABC analysis, which is a method of estimating the cost of drug supply, which allows you to
determine the most expensive cost directions of drugs by dividing 3 classes, depending on their
consumption over a period of time. The classication is carried out as follows:
Class A - drugs on which the main part of the drug budget is spent (80%);
Class B - drugs of the middle group, on which the expenses make up 15% of all consumptions;
Class C - the remaining drugs in the range with low frequency of use (≤ 5% of the total amount of
consumption).
VEN analysis is required to make form decisions in conjunction with ABC analysis. It allows you to
evaluate the effectiveness of the use of drugs and the dominance of a particular group of drugs in their
use. Therefore, all drugs prescribed to patients are divided into 3 categories.
V (vital). are life-saving drugs (for example, thrombolytics in cardiology facilities) that are constantly
required to maintain quality of life (insulin, glucocorticosteroids) or that develop discontinuation
syndrome when discontinued.
E (essential). These drugs are more effective against less serious but important diseases, but are not
absolutely necessary for basic medical care.
N (non + essential- secondary, non-primary or auxiliary) are drugs used for secondary (mild) diseases
or self-medication, have questionable ecacy, or have a relatively high value for a small therapeutic
benet.
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When conducting VEN analysis, it is advisable to use two approaches: expert and formal. We did it in an
expert way [3–10].
The WHO recommends the use of the ATC/DDD system in pharmacoepidemiological studies. The rst
step in the ATC/DDD methodology is the classication of drugs according to the ATC (anatomical-
therapeutic-chemical) classication. Each drug that has an ATC code is dened by its DDD (Dened Daily
Dose) by WHO Collaborating Center for Drug Statistics Methodology [3, 4, 11, 12].
The ATC/DDD classication is followed by the DU90% (drug utilization) analysis method, which is one of
the quantitative analysis methods for drug use. In the DU90% analysis, the calculation of the cost of 1
DDD in both segments - DU90% DU10%, allows comparison of costs for frequently and infrequently
used drugs. DU90% analysis is often combined with ABC / VEN analysis, which allows you to analyze
and standardize the amount of information per year, make it understandable, evaluate the effectiveness
of drugs and justify the nancial costs incurred on them [3, 4, 13].
It should be noted that in the absence of DDD, we did not consider the drug in ATC/DDD and DU90%
analysis.
Summarizing the above, a research concept has been developed for the implementation of the research
goal. We:
1. compiled a list of drugs purchased by the hospital for the patients of COVID-19 department in the
last 1 year;
2. analysed drugs according to ATC classication;
3. sorted drugs according to the reduction of nancial resources spent on them;
4. calculated the percentage of costs for each drug;
5. classicated drugs in classes A, B, C in the created list;
. classicated all drugs on that list according to the VEN system in an expert way;
7. combinated ABC and VEN analyses;
. writted DDDs for each drug;
9. calculated the number of DDDs for each drug – NDDDs;
10. calculated the percentage of each drug in the total NDDD;
11. listed the drugs from the largest NDDD to the smallest;
12. created two groups of drugs: DU90% and DU10%;
13. calculated the cost of one DDD for each drug and in both segments: DU90% and DU10%, which will
allow to compare costs on frequently and infrequently used drugs;
14. combined DU90% analysis with ABC/VEN analysis.
4. Results
First of all, for the research, we have taken from the hospital the list of drugs with trade names, quantities
and unit values, which were bought and used in the COVID-19 department of the hospital during 1 year.
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The list of drugs was given by Microsoft Excel software, we also did further work in that program. We
reviewed and edited the list, lled in the International Nonproprietary Names (INN) of all the drugs, and as
a result received an information le that we used in the following steps.
Then we performed an analysis of the drugs used according to the ATC classication. ATC codes of
drugs were searched in the ATC/DDD Index[12].
As a result of our research, it became clear that the COVID-19 department of the hospital used 59 INN
drugs, the majority of which are drugs that affect the alimentary tract and metabolism (20.3%),
antiinfevtives for systemic use (16.9%), and drugs affecting the cardiovascular system (13.5%) (Table 1).
Among the drugs affecting the alimentary tract and metabolism(А)in the COVID-19 department, the
following subgroup drugs were mainly used: A02B- drugs for peptic ulcer and gastro-oesophageal reux
disease (gord), A11-Vitamins, A12- Mineral supplements.Of the J-antiinfevtives for systemic use, the
most commonly used are J01-antibacterials for systemic use.
In the next stage, we have sorted the drugs according to the reduction of the nancial means spent on
them, and the calculation of the percentage of costs for each drug. Then, in the created list, we classied
the drugs into classes A, B, C (Table 2).
Of the 59 INN drugs used, 13 INN drugs are in class A - 22%, class B - 27%, and the remaining about 51%
are in class C.
After ABC analysis, we performed the VEN classication of drugs in an expert way.We have given the
drugs V, E, N categories, using the recommendations in the literature, consulting with doctors and
pharmacists, as well as using clinical guidelines for the treatment of COVID-19 [14].It turned out that out
of 59 INN drugs, 11 are category V drugs: 18.6%, E66.1 % ,
N
15.3%. As a result, the table was
supplemented with VEN categories (Table 2).
The results of the ABC/VEN analysisof drugsare shown in percentages in the form of a table (Table 3).
For the next DDD and DU90% analysis, we searched DDDs for the drugs. In the ATC Indexes published by
the WHO, a separate column next to the chemical substance indicates how it is introduced into the body
and DDD [12]. Drugs for which DDDs were not prescribed were not included in the follow-up study (39 of
59 INN drugs had DDD).
We then calculated the number of DDDs for each drug (NDDD), and then the percentage of each drug in
the total NDDD. The resulting list was edited from the largest NDDD to the smallest and 2 groups were
created: DU90% and DU10% (Table 4).
We calculated the cost of 1 DDD for each drug, based on which we also calculated in the DU90% and
DU10% groups.
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We also performed a combination of DU90% and ABC / VEN analyses: we have observed what category
of drugs (VEN) is included in the group of the most commonly used drugs (DU90%) (Table 5).
5. Discussion
The results of the ATC analysis (Table1) of drugs were reviewed with physicians. By usage, antiinfective
drugs (mainly antibiotics) are in the second place, but we should not forget the fact that COVID-19 is
caused by a virus, it is not a bacterium and antibiotics have no effect on the virus. Some patients with
COVID-19 may have a concomitant bacterial infection, in which case doctor may prescribe antibiotics
[15].
As can be seen from the data in the Table3, class A lacks drugs of N category, which speaks of the
ecient use of costs, as 80% of the nancial resources were spent on vital and essential drugs. However,
18.75% of class B consists of drugs of N category: vaseline, dimetinden and glycerol, as well as class C
has drugs of V category: amoxicillin, colchicine, epinephrine, prednisolone and levothyroxine (16.67%).
DDD analysis revealed that the largest NDDDs corresponded to Colecalciferol, followed by Ascorbinic
acid, Dexamethasone, and others (Table4).
Only 9 INN drugs were included in DU90%. It turned out that the cost of 1 DDD in DU10% is about 4 times
higher than DU90%, which suggests that the use of cheap drugs has prevailed.
Therefore, a combination of ABC/VEN and DU90% analyses was performed to show which drugs are in
the group of most frequently used drugs (DU90%) according to the vital need. As can be seen from the
data in Table5, the DU90% group lacks N category drugs, indicating that the vital and essential drugs
were purchased and used for the COVID-19 ward by the hospital, and that nancial resources were spent
eciently.
6. Conclusion
Based on the results, a recommendation was made and sent to the pharmacist of the hospital, who will
take into account the results of this work when planning the purchase of drugs for the next year, aiming
to reduce the use of non-essential drugs in the hospital.
Declarations
ACKNOWLEDGMENTS
The authors thank the additional members for their additional review and discussion of the research, in
particular, hospital management, pharmacists, doctors who shared their experience. Unfortunately, their
names are not emphasized, as the information about the purchase of drugs was provided by the hospital
on a closed, contractual basis. The results were discussed with the hospital's pharmacists and doctors,
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and received a good response, as in the absence of a hospital formulary system, it is impossible to plan
drug demand without error and ensure ecient use of drugs and the nancial resources spent on them.
FUNDING
The authors declare that no funds, grants, or other support were received during the preparation of this
manuscript.
CONFLICTS OF INTEREST
The authors have no relevant nancial or non-nancial interests to disclose.
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Tables
Table1.Drug analysis results according to ATC classification
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ATC - 1st level, anatomical main group %
A-alimentary tract and metabolism 20,3%
B-blood and blood forming organs 10,2%
C-cardiovascular system 13,5%
D-dermatologicals 5,1%
H-systemic hormonal preparations, excl. sex hormones and insulins 6,8%
J-antiinfevtives for systemic use 16,9%
M-musculo-skeletal system 5,1%
N-nervous system 5,1%
R-respiratory system 10,2%
S-sensory organs 5,1%
V-various 1,7%
Table2.Results of ABC/VEN analysis of drugs used in COVID-19 department
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№ ATC
codes
INN VEN
analysis
Cost: absolute
value
Cost:
%
ABC
class
1 J01CR02 Amoxicillin + clavulanic acid V 561,234.70 34.52% A
2 J01MA14 Moxifloxacin V 133,297.10 8.20% A
3 A12CA01 Sodium chloride V 131,226.19 8.07% A
4 J01DD04 Ceftriaxone V112,500.00 6.92% A
5 B01AF01 Rivaroxaban E 96,723.46 5.95% A
6 A11CC05 Colecalciferol E 64,357.10 3.96% A
7 R01AA05 Oxymetazoline E 38,060.00 2.34% A
8 A12CB Zinc E 35,892.00 2.21% A
9 B02BA01 Phytomenadione E 29,969.52 1.84% A
10 A07FA51 Lactic acid producing organisms,
combinations
E
27650.72
1.70% A
11 A02BA03 Famotidine E 27406.80 1.69% A
12 J01FA10 Azithromycin E 26763.53 1.65% A
13 R03CC02 Salbutamol E 26520.00 1.63% A
14 H02AB02 Dexamethasone V 24,158.00 1.49% B
15 A06AX01 Glycerol N 22,387.00 1.38% B
16 B05BB01 Compound solution of sodium chloride E 20,502.00 1.26% B
17 D04AA13 Dimetindene N 20056.00 1.23% B
18 M01AE01 Ibuprofen E 18,648.40 1.15% B
19 A11GA01 Ascorbic acid E 18,360.00 1.13% B
20 B05CX01 Glucose E 17,394.00 1.07% B
21 N02BE01 Paracetamol E 15,893.59 0.98% B
22 D02AC Vazelin N 15,473.28 0.95% B
23 J01CA01 Ampicillin E 15,388.00 0.95% B
24 J01XA01 Vancomycin V 14,250.00 0.88% B
25 A03AX13 Simeticone E 10,620.57 0.65% B
26 A11GB Acidum Ascorbinicum + Zn E 9,854.32 0.61% B
27 A02BC02 Pantoprazole E 9,547.85 0.59% B
28 M01AB05 Diclofenac E 7,488.00 0.46% B
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29 J01DD08 Cefixime E 7,003.20 0.43% B
30 J01XD01 Metronidasole E 6,249.60 0.38% C
31 H01BA02 Desmopressin E 6,048.90 0.37% C
32 S03CA01 Ciprofloxacine+ Dexamethasone E 5,640.00 0.35% C
33 C07AB12 Nebivolol E 5,374.50 0.33% C
34 J01CA04 Amoxicillin V 4,831.20 0.30% C
35 S02DA30 Lidocaine + phenazone E 4,502.40 0.28% C
36 A11AA03 Vitamines, minerales N 3,267.90 0.20% C
37 R06AE09 Levocetirizine E 3,204.80 0.20% C
38 J01XE03 Furazidin E 2,896.00 0.18% C
39 N03AX14 Levetiracetam E 2,786.00 0.17% C
40 D03AX03 Dexpanthenol N 2,524.00 0.16% C
41 B02BX01 Etamsylate E 2,293.60 0.14% C
42 R03BA02 Budesonide E 2,125.00 0.13% C
43 C05AX03 Oleum Hippophea N 2,105.60 0.13% C
44 S01AA12 Tobramycin E 2,034.40 0.13% C
45 R01AA07 Xylometazoline E 2,027.39 0.12% C
46 R06AE07 Cetirizine E 1,917.70 0.12% C
47 A12CX Potassium aspartate & magnesium
aspartate
E1,521.54 0.09% C
48 S01AA17 Erythromicin E 1,138.40 0.07% C
49 M04AC01 Colchicine V 902.70 0.06% C
50 C01CA24 Epinephrine V 700.00 0.04% C
51 H02AB06 Prednisolone V 570.40 0.03% C
52 N05CM09 Valerianae N 508.77 0.03% C
53 C01EB02 Camphora N 409.60 0.02% C
54 C03DA01 Spironolactone E 389.80 0.02% C
55 B01AC06 Acetylsalicylic acid E 357.10 0.02% C
56 V07AB Aqua destillata N 320.40 0.01% C
57 C09AA01 Captopril E 318.80 0.01% C
58 C03CA01 Furosemide E 287.20 0.01% C
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59 H03AA01 Levothyroxine sodium V 108.90 0.01% C
Table3.Results of the ABC/VEN analysis of drugs in percentages
Number of INNs V E N
A13 30.77% 69.23% 0
B16 12.5% 68.75% 18.75%
C30 16.67% 63.33% 20%
Table4.Results of DDD and DU90% analyses of drugs used in COVID-19 department
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№ INN ATC
codes
DDD NDDD % in total
NDDD
Cost of
1DDD
DU90% /
DU10%
1 Colecalciferol A11CC05 0,02mg 2587.5 44.38% 24.87 DU90%
(6435.18)
2 Ascorbinic acid A11GA01 200mg 850 14.58% 21.60
3 Dexamethasone H02AB02 1,5mg 666.6 11.43% 36.24
4 Sodium chloride A12CA01 1000mg 558 9.57% 235.17
5 Oxymetazoline R01AA05 0,4mg 218.75 3.75% 173.98
6
Amoxicillin + clavulanic
acid
J01CR02 1500mg 142.5 2.44%
3938.5
7 Ceftriaxone J01DD04 2000mg 75 1.29% 1500.00
8 Ibuprofen M01AE01 1200mg 70.8 1.21% 263.39
9 Paracetamol N02BE01 3000mg 65.83 1.13% 241.43
10 Pantoprazole A02BC02 40mg 65 1.11% 146.89 DU10%
(26247.65)
11 Rivaroxaban B01AF01 20mg 63.75 1.09% 1517.23
12 Moxifloxacin J01MA14 400mg 51 0.87% 2613.67
13 Azithromycin J01FA10 300mg 50 0.86% 535.27
14 Nebivolol C07AB12 5mg 50 0.86% 107.49
15 Camphora C01EB02 150mg 40 0.69% 10.24
16 Prednisolone H02AB06 10mg 40 0.69% 14.26
17 Colchicine M04AC01 1mg 30 0.51% 30.09
18 Salbutamol R03CC02 12mg 25 0.43% 1060.8
19 Desmopressin H01BA02 0,025mg 20 0.34% 302.445
20 Levocetirizine R06AE09 5mg 20 0.34% 160.24
21 Famotidine A02BA03 40mg 15 0.26% 1827.12
22 Ampicillin J01CA01 6000mg 15 0.26% 1025.87
23 Simeticone A03AX13 500mg 14.4 0.25% 737.54
24 Phytomenadione B02BA01 20mg 14 0.24% 2140.68
25 Xylometazoline R01AA07 0,8mg 12.5 0.21% 162.19
26 Diclofenac M01AB05 100mg 10 0.17% 748.8
27 Cetirizine R06AE07 10mg 10 0.17% 191.77
28 Furazidin J01XE03 300mg 8.3 0.14% 348.91
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29 Metronidasole J01XD01 1500mg 6.67 0.11% 937.44
30 Captopril C09AA01 50mg 5 0.09% 63.76
31 Furosemide C03CA01 40mg 5 0.09% 57.44
32 Cefixime J01DD08 400mg 5 0.09% 1,400.64
33 Epinephrine C01CA24 0,5mg 3.6 0.06% 194.44
34 Spironolactone C03DA01 75mg 3.34 0.06% 116.70
35 Levetiracetam N03AX14 1500mg 3.34 0.06% 834.13
36 Budesonide R03BA02 0,8mg 3.125 0.05% 680.00
37 Vancomycin J01XA01 2000mg 2.5 0.04% 5700.00
38 Amoxicillin J01CA04 3000mg 1.92 0.03% 2516.25
39 Levothyroxine sodium H03AA01 0,15mg 1.67 0.03% 65.34
Table5.Results ofcombination ofABC/VEN and DU90% analyses
DU90% group of drugs ABC VEN
ATC codes INN
A11CC05 Colecalciferol A E
A11GA01 Ascorbinic acid B E
H02AB02 Dexamethasone B V
A12CA01 Sodium chloride A V
R01AA05 Oxymetazoline A E
J01CR02 Amoxicillin + clavulanic acid A V
J01DD04 Ceftriaxone A V
M01AE01 Ibuprofen B E
N02BE01 Paracetamol B E