Background This study analyzes the trajectories of antibiotic consumption using different indicators of patients’ socioeconomic status, category and age-group of physicians. Methods This study uses a pooled, cross-sectional, time series analysis. The data focus on 22 European countries from 2000 to 2014 and were obtained from the European Center for Disease and Control, Organization for Economic Co-operation and Development, Eurostat and Global Economic Monitor. Results There are large variations in community and hospital use of antibiotics in European countries, and the consumption of antibiotics has remained stable over the years. This applies to the community (b = 0.07, p = 0.267, 95% -0.06, 0.19, b-squared <0.01, p = 0.813, 95% = -0.01, 0.02) as well as the hospital sector (b = -0.02; p = 0.450; CI 95% = -0.06, 0.03; b-squared <0.01; p = 0.396; CI95% = > -0.01, <0.01). Some socioeconomic variables, such as level of education, income, Gini index and unemployment, are not related to the rate of antibiotic use. The age-group of physicians and general practitioners is associated with the use of antibiotics in the hospital. An increase in the proportion of young doctors (<45 years old) leads to a significant increase in antibiotics consumption, and as the percentage of generalist practitioners increases, there use of antibiotics in hospitals decreases by 0.04 DDD/ 1000 inhabitants. Conclusions Understanding that age-groups and categories (general/specialist practitioners) of physicians may predict antibiotic consumption is potentially useful in defining more effective health care policies to reduce the inappropriate antibiotic use while promoting rational use.

Gianino, M. M., Lenzi, J., Bonaudo, M., Fantini, M. P., Ricciardi, W., Damiani, G., Predictors and trajectories of antibiotic consumption in 22 EU countries: Findings from a time series analysis (2000–2014), <<PLOS ONE>>, 2018; 13 (6): e0199436-N/A. [doi:10.1371/journal.pone.0199436] [http://hdl.handle.net/10807/124972]

Predictors and trajectories of antibiotic consumption in 22 EU countries: Findings from a time series analysis (2000–2014)

Gianino, Maria Michela;Ricciardi, Walter;Damiani, Gianfranco
2018

Abstract

Background This study analyzes the trajectories of antibiotic consumption using different indicators of patients’ socioeconomic status, category and age-group of physicians. Methods This study uses a pooled, cross-sectional, time series analysis. The data focus on 22 European countries from 2000 to 2014 and were obtained from the European Center for Disease and Control, Organization for Economic Co-operation and Development, Eurostat and Global Economic Monitor. Results There are large variations in community and hospital use of antibiotics in European countries, and the consumption of antibiotics has remained stable over the years. This applies to the community (b = 0.07, p = 0.267, 95% -0.06, 0.19, b-squared <0.01, p = 0.813, 95% = -0.01, 0.02) as well as the hospital sector (b = -0.02; p = 0.450; CI 95% = -0.06, 0.03; b-squared <0.01; p = 0.396; CI95% = > -0.01, <0.01). Some socioeconomic variables, such as level of education, income, Gini index and unemployment, are not related to the rate of antibiotic use. The age-group of physicians and general practitioners is associated with the use of antibiotics in the hospital. An increase in the proportion of young doctors (<45 years old) leads to a significant increase in antibiotics consumption, and as the percentage of generalist practitioners increases, there use of antibiotics in hospitals decreases by 0.04 DDD/ 1000 inhabitants. Conclusions Understanding that age-groups and categories (general/specialist practitioners) of physicians may predict antibiotic consumption is potentially useful in defining more effective health care policies to reduce the inappropriate antibiotic use while promoting rational use.
2018
Inglese
Gianino, M. M., Lenzi, J., Bonaudo, M., Fantini, M. P., Ricciardi, W., Damiani, G., Predictors and trajectories of antibiotic consumption in 22 EU countries: Findings from a time series analysis (2000–2014), <<PLOS ONE>>, 2018; 13 (6): e0199436-N/A. [doi:10.1371/journal.pone.0199436] [http://hdl.handle.net/10807/124972]
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