seemoon choi * , michael r. reich harvard school of public health, boston, ma, usa

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Report Cards: Assessing the Impacts of the Public Disclosure of Antibiotic Prescribing Rate for Acute Upper Respiratory Tract Infection Seemoon Choi* , Michael R. Reich Harvard School of Public Health, Boston, MA, USA

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Report Cards : Assessing the Impacts of the Public Disclosure of Antibiotic Prescribing Rate for Acute Upper Respiratory Tract Infection. Seemoon Choi * , Michael R. Reich Harvard School of Public Health, Boston, MA, USA. Problem. Current Antibiotic Use in Korea - PowerPoint PPT Presentation

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Page 1: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Report Cards: Assessing the Impacts of the Public Disclosure of Antibiotic Prescribing Rate for Acute Upper Respiratory Tract Infection

Seemoon Choi* , Michael R. Reich

Harvard School of Public Health, Boston, MA, USA

Page 2: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

1. Current Antibiotic Use in Korea National average: 23.0 DDD/1,000/day,

for children under 7: 45.6DDD/1,000/day in 2003 (KFDA, 2003)

90.6% of Korean children received antibiotics to treat acute upper respiratory tract infection (AURI) (Park and Moon, 1998)

2. Antibiotic Resistance Erythromycin resistance rate: more than

70% and continuously increasing (Lee, 2009)

Problem

Page 3: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

After the People’s Solidarity for Participatory Democracy submitted administrative litigation for the public disclosure of high antibiotic-prescribing health care facilities in 2005, the High Court of Justice ruled that the government was obligated to release the information.

Since February 9, 2006, the Health Insurance Review & Assessment Service (HIRA) has released the AURI antibiotic prescribing rate (APR) of health care facilities that have more than 100 patients on a quarterly basis on HIRA’s website.

Policy Intervention

Page 4: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Policy Intervention

00 01 02 03 04 05 06 07 08

the High Court of Justice’s decision

Separation of dispensing and prescribingEvaluation of ambulatory prescribing and providing feedback

Report Cards for APR

Since February 9, 2006, the Health Insurance Review & Assessment Service (HIRA) has released the AURI antibiotic prescribing rate (APR) of health care facilities that have more than 100 patients on a quarterly basis on HIRA’s website.

The PSPD’s petition for the MOHW to disclose the list of highest and lowest antibiotic prescribers

Page 5: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

The study focuses on three issues:

To assess the effect of public disclosure on changing prescribing behavior

To explore the differences in the change of prescribing behavior among different provider groups

To examine the gradient of the impact of public disclosure on antibiotic prescribing

Objective of the Study

Page 6: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

• Antibiotic prescribing rate (APR) for AURI is defined by HIRA

• Acute Upper Respiratory tract Infection (AURI) covers 7 diagnoses according to Korean Classification of Disease▫ J00 (Acute nasopharyngitis, common cold), J01 (Acute sinusitis) ▫ J02 (Acute pharyngitis), J03 (Acute tonsilitis), J04 (Acute laryngitis and

tracheitis)▫ J05 (Acute obstructive laryngitis and epiglottitis), J06 (Acute upper respiratory

infections of multiple and unspecified sites)• APR data available for all health care facilities with > 100

patients diagnosed with AURI over a three-month period

Outcome Variable

total number of antibiotic prescriptions for patients diagnosed with AURI

total number of patients diagnosed with AURIand received a prescription

=

Page 7: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Interrupted time series analysis

APRict: the antibiotic prescribing rate for AURI of healthcare facility i in district c at t

Methods (1)

Figure 1. Modeling of interrupted time series analysis

Timet : a continuous variable indicating the order of the quarter since Q1 04Policyt : dummy for report cardsPi : the characteristics of health care facility iDc : population characteristics in district cHc : healthcare market characteristics in district cAc : district or state fixed effectBt : quarter fixed effectεict : error term

Page 8: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

To test the hypothesis that the impact of report cards will be larger on the healthcare facilities with high APR

→ Quantile regression analysis allows researchers to estimate the heterogeneous impact on the distributional outcomes by estimating quantile treatment effects (QTE) across the outcome variable distribution nine quantiles: .05, .10, .25, .40, .50, .60, .75, .90, .95 the standard error are estimated using bootstrapping

method using 500 replicates

Methods (2)

Page 9: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Publicly released APR datasets between Q1 2004 and Q4 2008 based on National Health Insurance claim data

Dataset of health care facilities in 2005 2005 Census: gender, age structure, occupation,

education (Korea National Statistical Office) 254,432 observations, 15,669 health care facilities

at 249 districts and 16 metropolitan areas or states between Q1 2004 and Q4 2008

Data

Page 10: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Results 1

Before Q3

06Q3 05

After

Figure 2. APR for AURI distribution before and after public disclosure

Page 11: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Results 2

Figure 3. APR trends for AURI by type of health care facility

Page 12: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Results 3

Figure 4. Variation in the impact of public disclosure on APR by specialty among primary care clinics

Note. The coefficient estimates were statistically significant at 5% significant level except general surgery, NS+OS+Obgyn (neurosurgery, orthopedic surgery, and obstetrics and gynecology), and Others (other specialties). The regression model includes the number of beds, dummy for solo practice, dummy for private ownership, female education at district level, population clinic ratio, number of medical appliance, district fixed effect, and quarter fixed effect. The standard error are clustered at individual healthcare facility level.

Page 13: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Results 4

Figure 5. Quantile regression analysis for primary care clinicsNote. Except q5, all of the coefficient estimates of the impact of Report Cards are statistically significant at 5% significant level based on 500 bootstrap replicates. The regression model includes the number of beds, dummy for solo practice, dummy for private ownership, number of medical appliance, female education at district level, population clinic ratio, district fixed effect, and quarter fixed effect. The standard error are clustered at individual healthcare facility level.

percentage change (%)

Page 14: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

The Internet-based report cards of APR for AURI is an effective policy intervention to reduce antibiotic prescription for AURI across all types of healthcare facilities in Korea (8.9%, 14.7 percentage change).

Different response for different healthcare facility group By healthcare facilities: primary care clinics >

tertiary hospitals > secondary hospital By specialties among primary care clinics: pediatrics

> ENT > internal medicine Heterogeneous impact of report cards by the level of

antibiotic prescribing rate q25 > q40 > q50 > q60 > q10 > q75 > q90 > q95

Summary

Page 15: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

The Internet-based report cards is a new intervention to effectively modify antibiotic prescribing behavior for AURI.

Report cards can be applied in the countries that need the effective intervention to reduce overuse of antibiotics.

More-tailored interventions such as additional educational session for high APR healthcare facilities should be considered to minimize the unintended consequences of report cards.

Policy Implications

Page 16: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Thank you

Page 17: Seemoon Choi * , Michael R. Reich  Harvard School of Public Health, Boston, MA, USA

Source: 2004~2009 Yearbook of National Health Insurance