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Assessing the impact of national antibiotic campaigns in Europe

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Abstract

Because of evidence of causal association between antibiotic use and bacterial resistance, the implementation of national policies has emerged as a interesting tool for controlling and reversing bacterial resistance. The aim of this study was to assess the impact of public policies on antibiotic use in Europe using a differences-in-differences approach. Comparable data on systemic antibiotics administered in 21 European countries are available for a 11-year period between 1997 and 2007. Data on national campaigns are drawn from the public health literature. We estimate an econometric model of antibiotic consumption with country fixed effects and control for the main socioeconomic and epidemiological factors. Lagged values and the instrumental variables approach are applied to address endogeneity aspects of the prevalence of infections and the adoption of national campaigns. We find evidence that public campaigns significantly reduce the use of antimicrobials in the community by 1.3–5.6 defined daily doses per 1,000 inhabitants yearly. This represents an impact of roughly 6.5–28.3 % on the mean level of antibiotic use in Europe between 1997 and 2007. The effect is robust across different measurement methods. Further research is needed to investigate the effectiveness of policy interventions targeting different social groups such as general practitioners or patients.

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Notes

  1. For more evidence on the impact of the French campaign to reduce inappropriate use of antibiotics, see also the recent study by Chahwakilian et al. [11], who analyse trends in antibiotic prescriptions between 1980 and 2009.

  2. Antibiotic consumption data generally derive from reimbursement data or distribution/sales data, depending on the method for measuring antibiotic use employed by each national database. Assuming patient’s non-compliance to be a negligible factor implies that the quantity of antibiotics sold matches the quantity actually consumed. The latter is associated to antimicrobial resistance and represents the target of antibiotic policies.

  3. Data are reliable and exhibit a good degree of comparability since the ESAC network screens for detection bias in sample and census data, bias by over-the counter sales and parallel trade, errors in assigning medicinal product packages to the Anatomical Therapeutic Chemical Classification (ATC), and errors in calculations of defined daily doses [20].

  4. Public campaigns and policies are used interchangeably throughout the remaining of the paper. The reader should be aware that public campaigns represent a subset of possible antibiotic policies. The Netherlands, for instance, have strong antibiotic policies in place although the country did not conduct any public campaign during the study period.

  5. In the literature, several approaches are discussed to estimate the causal impact of a “treatment variable” on an outcome variable, such as the DD estimator and the propensity score matching estimator. In this study, we use a differences-in-differences approach because of the relatively small panel data set with observations at the country level rather than at individual level. Using a propensity score matching approach requires, for instance, a large data set regarding the number of variables and the sample size. For a discussion on this issue, we refer the reader to studies by Frolich [21] and Heinrich et al. [22].

  6. Initially, we also estimate Eq. (1) using ordinary least-squares (OLS) and random effects (RE) approaches.

  7. As discussed by Bertrand et al. [14], conventional differences-in-differences standard errors may be biased because of serial correlation. A solution proposed by Arellano [28] is to compute cluster-robust standard errors. Kezdi [29] shows that cluster-robust estimates perform well in typical-sized panels, although they can be biased slightly downward if the number of countries is very small. In a Monte Carlo experiment, Kezdi [29] considers N = 10 to be a very small number of countries. In our case, N is equal to 20. Therefore, although the sample is relatively small, we believe that cluster-robust standard errors represent a viable solution to autocorrelation.

  8. Although comparing antibiotic use among countries using DDD has a large consensus among researchers, one limitation is that this measure is not appropriate for all age groups. Indeed, using other measures may give different results, as illustrated by Goossens et al. [31].

  9. Information on mortality for infectious diseases and price of pharmaceuticals are not available for all countries or years. This reduces the total number of observations in our final regressions.

  10. It is important to underline that this variable is obtained from OECD data and is likely difficult to compare between countries. Mortality for infectious diseases is generally based on diagnostic discharge codes. Consequently, differences among countries may depend on different methods of determining this variable.

  11. Preliminary OLS regressions show an R 2 adjusted of 0.59. The goodness-of-fit increases slightly with the inclusion of temporal dummy variables. The F test is 24.58 (12.51 with time dummies). This suggests that overall regressors has a significant impact on the dependent variable. Moreover, the mean variance inflation factor is lower than 3. Finally, the Shapiro-Wilk test as well as the Jarque-Bera test for normality of errors cannot be rejected using the conventional 95 % level of significance.

  12. The results are confirmed if we include in the model the dummy variable POLICY 2 instead of POLICY 1. This takes a value equal to 1 in the years of campaign adoption as well as in the years post-campaign. The rationale of this indicator is that policies may take some time to show their effects or may have carryover effects. Although POLICY 1 seems to reflect more closely information collected in the review by Huttner et al. [12], POLICY 2 may provide a robustness check of our results based on POLICY 1. Since countries in the treatment group are assumed to implement policies for longer periods under POLICY 2 than under POLICY 1, the effect of policies could be biassed. We find that the estimated coefficients of POLICY 2 are slightly less significant than the coefficients of POLICY 1, which confirms the results and may suggest that policies have carryover effects beyond the year of policy implementation.

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Authors and Affiliations

  1. Department of Economics, University of Lugano, Lugano, Switzerland

    M. Filippini, L. G. González Ortiz & G. Masiero

  2. ETH, Zurich, Switzerland

    M. Filippini

  3. Department of Economics and Technology Management, University of Bergamo, Bergamo, Italy

    G. Masiero

Authors
  1. M. Filippini
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  2. L. G. González Ortiz
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  3. G. Masiero
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Correspondence to G. Masiero.

Appendix

Appendix

See Tables 3 and 4.

Table 3 Mean values for the whole period for countries that did not implement any public campaign between 1997 and 2007
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Table 4 Mean values for the whole period for countries that implemented public campaigns between 1997 and 2007
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Filippini, M., Ortiz, L.G.G. & Masiero, G. Assessing the impact of national antibiotic campaigns in Europe. Eur J Health Econ 14, 587–599 (2013). https://doi.org/10.1007/s10198-012-0404-9

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