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Decreased Inappropriate
Antibiotic Use Following a
Korean National Policy to
Prohibit Medication
Dispensing by Physicians
Sylvia Park, Stephen B. Soumerai, Alyce S. Adams, Jonathan A.
Finkelstein, Sunmee Jang*, Dennis Ross-Degnan
Harvard Medical School, USA. *Health Insurance Review Agency, Korea
Research on Dispensing Doctors
 Dispensing Doctors were found to
 prescribe greater numbers of drugs
 prescribe more antibiotics and injections
 have higher prescribing costs
 Little is known about quality of
prescribing
 Cross-sectional research
Dispensing Policy and
Antibiotic Use in Korea
 New policy (July 2000)
 prohibiting doctors from dispensing drugs
and pharmacists from prescribing drugs
 Antibiotic Use
 most commonly used drugs – 20% of
ambulatory drug expenditures (2000)
 overused and inappropriately used
 high resistance rate - 86% of Streptococcus
pneumoniae resistant to penicillin (2001)
Objects
 To evaluate the impact of the new policy
in Korea on the quantity and quality of
physician prescribing
 selectivity in the decrease of antibiotic
prescribing in viral and bacterial illness
 To investigate provider characteristics
related to the decrease of inappropriate
antibiotic prescribing in viral illness
New Policy (Jul. 2000)
Jan. 2000
Jan. 2001
Korean National Health Insurance monthly claims data
Viral Illness Group
: Common Cold / URI /
Bronchiolitis
Bacterial Illness Group
: Penumonia/ Otitis media/ Tonsilitis/
Strep. Sore throat/ Sinusitis/ UTI/ SST
10% clinics, 20% cases
No commorbidity
50,999 Cases (1,372 clinics)
Prescription Analysis
-Antibiotics (antibiotic prescribing/ # of different antibiotics)
-Non-antibiotic Drugs (GI drug prescribing/ # of non-antibiotic drugs)
Characteristics of Cases
Characteristics (n= 50 999)
Gender of patient
Age of patient
Location of clinic
Practice size
Type of practice
Gender of physician
Age of physician
%
Male
46.4
Female
53.6
≤2
18.7
3 – 18
34.3
19 – 64
42.6
≥ 65
4.4
Urban
89.4
Rural
10.6
≤ 150 patients
151 – 250 patients
22.7
34.0
≥ 251 patients
43.3
Solo
89.2
Group
10.8
Male
90.9
Female
9.1
≤ 39
30.5
40 – 49
47.4
≥ 50
22.1
Impact of the Policy on Prescribing
 Generalized Estimating Equations
Y= ß0 + ß1×Policy + ß2×Illness + ß3×Policy×Illness
(+ ß4 Patient or provider char. + ß5 … ) + 
- Y: Prescription Variables (patient level)
- X: Policy: after policy=1 / before policy=0
: Illness: viral=1 / bacterial=0
: Policy×Illness: Interaction
(different policy effect between illnesses)
: Patient or provider characteristics
: gender, age, location, size, type
- Cluster effect : clinic
Impact of the Policy on Prescribing
 Antibiotic Prescribing
100%
91.6
89.7
90
80.8
80
72.8
70
60
50
Before
After
Viral illness
Explanatory variables
Policy effect
in bacterial illness
Additional policy effect
in viral illness
Bacterial illness
Adjusted relative risk
0.98
(95% CI)
(0.97, 0.99)
P value
0.0171
0.90
(0.87, 0.93)
< 0.0001
Impact of the Policy on Prescribing
 Number of Different Antibiotics
2
1.8
1.7
1.6
1.6
1.4
1.5
1.4
1.2
1
Before
After
Viral illness
Explanatory variables
Bacterial illness
Adjusted rate of change
(95% CI)
P value
Policy effect
in bacterial illness
-6.38%
(- 8.25%, - 4.47%)
< 0.0001
Additional policy effect
in viral illness
-1.28%
(- 3.95%, 1.46%)
0.3571
Impact of the Policy on Prescribing
 Gastrointestinal Drug Prescribing
Explanatory variables
Adjusted relative risk
(95% CI)
P value
Policy effect
in bacterial illness
0.96
(0.93, 0.98)
< 0.0001
Additional policy effect
in viral illness
0.97
(0.93, 1.01)
0.1243
 Number of Different Non-antibiotic Drugs
Explanatory variables
Policy effect
in bacterial illness
Additional policy effect
in viral illness
Adjusted rate of change
(95% CI)
-7.55%
(- 10.37%, - 4.63%)
0.65%
(- 3.19%, 4.64%)
P value
< 0.0001
0.7450
Provider Characteristics Related to Decrease
of Inappropriate Antibiotic Prescribing
 Multiple Regression – Clinic level
Y= ß0 + ß1Location + ß2Type + ß3Size (+ ß4Age + ß5Gender) + 
 Y: Antibiotic prescribing rate (baseline & change)
: Average # of different antibiotics per case (baseline & change)
(age, gender, diagnosis mix adjusted)
 X: Location : Urban / Rural
Type : Group / Solo
Size : <= 150 pt / 151 - 250 pt / >= 251 pt
Age : <= 39 / 40 - 49 / >= 50
Gender : Male / Female
Provider Characteristics Related to Decrease
of Inappropriate Antibiotic Prescribing
Provider variable
Estimate
95% CI
P value
Baseline antibiotic prescribing rate (n= 435)
Type (Group)
-14.3% (- 23.4%, - 5.2%)
0.0021
Changes in number of antibiotics (n= 307)
Age (≤ 39)
-0.13
(- 0.24, - 0.02)
0.0235
Age (40 – 49)
-0.16
(- 0.26, - 0.06)
0.0022
Location (Urban)
-0.14
(- 0.27, - 0.01)
0.0298
Test on Changes in Diagnostic Coding
 Bacterial or possibly bacterial diagnoses did not increase
as either primary or secondary diagnosis.
(primary: 52.2% -> 51.4%; secondary: 33.7% -> 33.6%)
Pre-intervention Trends of Prescribing
 Antibiotic prescribing rate for URI had hardly changed
before study period.
: 1994 – 2000 : 85.6% -> 88.7% (adults) / 90.6% -> 89.0% (children)
 In viral illness, it dropped after the policy (80.8 % -> 72.8%).
 Number of drugs per case with URI in 1994 was same as
that for viral illness cases before the policy in the study (5.1)
 It dropped after the policy.
 GI drug prescribing had increased in 1997-2000
 It dropped after the policy.
Conclusions
 Prohibiting doctors from dispensing drugs
reduced prescribing overall, both antibiotics and
other drugs, and selectively reduced
inappropriate antibiotic prescribing for patients
with viral diagnosis.
 Still high rate of antibiotic prescribing for viral
illness after policy indicates the need for further
targeted interventions
 Further study using longitudinal data is needed
to evaluate whether these reductions in
prescribing and improvements in quality are
maintained.