2. 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

3. 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

4. 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)
Let me briefly introduce health policy change in Korea which are our research interest.
In July, 2000, there was fundamental change in drug utilization process. Korean government prohibited doctors from dispensing drugs and pharmacists from prescribing drugs. Before then, they were allowed to do. All physicians in clinics prescribed and dispensed drugs in their own clinics. But after this policy, they only prescribe and cannot dispense drugs. And this policy change removed physician’s economic gain of dispensing drugs, so it was expected to reduce unnecessary prescription which have been done from economic reason.
The second policy that started in the middle of 2001 is antibiotics utilization monitoring. National Health Insurance Organization of Korea began to quantify the total amount of antibiotics for every clinic and hospital and provide each organization with feedback of the result. They did not consider case mix, just quantified total amout. And they compare them with other clinics or hospitals of similar size and area. Each clinic and hospital was graded according to the results and got a report of its grade. This policy was to reduce antibiotics utilization which is one of the most serious drug overuse problems in Korea.
Both of the policies intended to decrease unnecessary drug prescription and to finally improve the quality of drug prescriptions, while the first policy was for all kinds of drugs and the second for antibiotics.

5. 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
The object of this research is to analyze the impact of these two policies on drug prescription.
So the first object is to….. and the second object is to…..

6. Bacterial Illness Group
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)

7. Characteristics of Cases

8. 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
In analyzing impacts of first policy on px, we used mixed effect model to control for the potential bias because drug prescription behavior of same clinics in different times and disease groups might be correlated.
In this model, the dependent variables are prescription variables. Some of the dependent variables were transformed square or square root for meeting the normality assumption. These are the fixed effect variables. They are disease, policy 1, and the interaction of disease and policy 1.
For disease, viral diseases were coded as 1, and bacterial 0. For the first policy, year 2001 was coded as 1, 2000 as 0. We excluded data of 2002 in this analysis.
The interaction term is to see if change of drug px after the first policy was different between viral disease group and bacterial disease group. The random effect was clinic.
We did this analysis four times separately for the four prescription variables.

9. Impact of the Policy on Prescribing
Antibiotic Prescribing

10. Impact of the Policy on Prescribing
Number of Different Antibiotics

11. Impact of the Policy on Prescribing
Gastrointestinal Drug Prescribing
Number of Different Non-antibiotic Drugs

12. 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 / pt / >= 251 pt
Age : <= 39 / / >= 50
Gender : Male / Female

13. Provider Characteristics Related to Decrease of Inappropriate Antibiotic Prescribing

14. 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

15. 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.