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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. Problem. Current Antibiotic Use in Korea

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

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

  2. Problem • 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)

  3. Policy Intervention • 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.

  4. Policy Intervention The PSPD’s petition for the MOHW to disclose the list of highest and lowest antibiotic prescribers the High Court of Justice’s decision 00 01 02 03 04 05 06 07 08 Separation of dispensing and prescribing Evaluation 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.

  5. Objective of the Study 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

  6. Outcome Variable • 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 total number of antibiotic prescriptions for patients diagnosed with AURI = total number of patients diagnosed with AURI and received a prescription

  7. Methods (1) Interrupted time series analysis APRict: the antibiotic prescribing rate for AURI of healthcare facility i in district c at t Timet: a continuous variable indicating the order of the quarter since Q1 04 Policyt: dummy for report cards Pi : the characteristics of health care facility i Dc : population characteristics in district c Hc: healthcare market characteristics in district c Ac : district or state fixed effect Bt : quarter fixed effect εict: error term Figure 1. Modeling of interrupted time series analysis

  8. Methods (2) To test the hypothesis that the impact of report cards will be larger on the healthcare facilities with high APR →Quantileregression 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

  9. Data • 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

  10. Results 1 After Q3 05 Before Q3 06 Figure 2. APR for AURI distribution before and after public disclosure

  11. Results 2 Figure 3. APR trends for AURI by type of health care facility

  12. 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.

  13. Results 4 percentage change (%) Figure 5. Quantile regression analysis for primary care clinics Note. 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.

  14. Summary • 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

  15. Policy Implications • 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.

  16. Thank you 

  17. Source: 2004~2009 Yearbook of National Health Insurance

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