Presentation from the 3rd Joint Meeting of the Antimicrobial Resistance and Healthcare-Associated Infections (ARHAI) Networks, organised by the European Centre of Disease Prevention and Control - Stockholm, 11-13 February 2015

1. Appropriate measures for outpatient
antibiotic use in Europe
Ann Versporten
On behalf of Robin Bruyndonckx, Samuel Coenen, Herman Goossens
3rd Joint meeting of the Antimicrobial Resistance and Healthcare-Associated Infection (ARHAI) Networks
Stockholm, 11-13 February 2015

2. Outpatient antibiotic use in Belgium
Expressed as:
N defined daily doses,
N packages,
N treatments,
N insured individuals,
reimbursed per 1000 inhabitants
per day.
Coenen S, Gielen B, Blommaert A, Beutels P, Hens N, Goossens H.
Appropriate international measures for outpatient antibiotic prescribing
and consumption: recommendations from a national data comparison of
different measures. J Antimicrob Chemother 2014;69:529-534.

3. Explained discrepancies between the different measurement
units:
• Increased N DDD/pack for:
 Amoxicillin (up to 50%, from 7.1 in ’02-’03 to 10.7 in ‘08-
’09)
 Co-amoxiclav (up to 70%, from 8.7 to 14.2)
 These substances present 54% of all outpatient AB use in
DDD in 2008-2009
 Increase of N units/pack & amount active substance/unit
 From 16 to 20 units/pack
 From 500mg to 1000mg/unit
Outpatient antibiotic use in Belgium

4. Background – Research questions
What is the trend of
outpatient antibiotic use in
Europe?
What is the relationship
between outpatient
antibiotic use and
antimicrobial resistance in
Europe?

5. Methods – Data used
Outpatient antibiotic use:
• IMS data; all ATC J01 (antibacterials for systemic use);
31 EU countries; quarterly data; years 2000-2007
 N defined daily doses/1000 inhabitants/day (DID)
 N packages/1000 inhabitans/day (PID)
Antimicrobial resistance:
• EARSS data; 27 EU countries; years 2000-2009
 Proportion penicillin-non-susceptible S. pneumoniae (PNSP)
 Proportion erythromycin-non-susceptible S. pneumoniae
(ENSP)

6. Methods – Data analysis
1. Outpatient AB use (IMS data)
 Quarterly measurements per country  mixed effects model
 Seasonal fluctuation  nonlinear mixed model
2. Association of outpatient AB use in DID, PID and both; and
resistance
 Generalized linear mixed model
with 0, 1 or 2 years time lag between AB use and resistance
 Predictions of resistance for decreasing AB use

7. Results – Outpatient AB use in DID

8. Results – Outpatient AB use in PID

9. Results – Change in DDD per package
Average dose per package (DDD/pack) in year 2000
differs between AB subgroups and between countries
Average change in DDD/pack over time (increase 2000-2007)
differs substantially between countries and AB subgroups
No increase for quinolones (J01M)

10. Results – Change in DDD per package (DP)
 Overall increase in DDD/package over time
• Quarterly increase ranged between 0.01 and 0.08 DDD/package
• Yearly increase ranged from 0.04 to 0.31 DDD/package

11. Results – Estimated change in DDD per package
β0 (intercept), predicted average (standard errors) DDD per package in the first quarter of 2000; β1 (slope),
predicted average (standard errors) increase (if positive)/decrease (if negative) in DDD per package per quarter.
*P<0.05.
**P<0.0001.

12. Results – Association AB use and resistance
PNSP and β-lactam use (penicillin, cephalosporin)
 Best model fit for β-lactam use in PID & lag time=0 year
 Sign. increase of odds of PNSP with increasing β-lactam use in
PID (OR=1.96;1.57-2.44) which did not changed sign. over time
Predictions of PNSP if decrease of β-lactam use
• If decrease of β-lactam use in DID, then prediction of stable PNSP
• If decrease of β-lactam use in PID or in PID & DID, then prediction of decrease of PNSP

13. Results – Association AB use and resistance
ENSP and TMLS use (tetracycline, macrolide, lincosamide, streptogramin)
 Best model fit for TMLS in PID and DID & lag time=1 year
 Sign. increase of odds of ENSP with increasing TMLS use in PID
(OR=3.68;1.27-10.72) and decreasing TMLS use in DID
(OR=0.78;0.65-0.93), with no sign. changes over time
Predictions of ENSP if decrease of TMLS use
• If decrease of TMLS use in DID or in PID & DID, then prediction of increase of ENSP
• If decrease of TMLS use in PID, then prediction of decrease of ENSP

14. Conclusions – Measuring outpatient AB use
Present both DID and PID because:
Variable increase in dose/pack over time
(Country and AB subgroup)
Contrasting AB use trends between DID and PID
Inconsistent associations and predictions of resistance
whether AB use expressed in DID or PID
Model fit depend on time lag between use & resistence

15. Conclusions – Measuring outpatient AB use
Better understanding and interpretation of
outpatient antibiotic use and its relation to
resistance
Consider time lags and also data expressed in PID
when exploring AB-use & resistance relations