Rates of increase of antibiotic resistance and ambient temperature in Europe: a cross-national analysis of 28 countries between 2000 and 2016

Background The rapid increase of bacterial antibiotic resistance could soon render our most effective method to address infections obsolete. Factors influencing pathogen resistance prevalence in human populations remain poorly described, though temperature is known to contribute to mechanisms of spread. Aim To quantify the role of temperature, spatially and temporally, as a mechanistic modulator of transmission of antibiotic resistant microbes. Methods An ecologic analysis was performed on country-level antibiotic resistance prevalence in three common bacterial pathogens across 28 European countries, collectively representing over 4 million tested isolates. Associations of minimum temperature and other predictors with change in antibiotic resistance rates over 17 years (2000–2016) were evaluated with multivariable models. The effects of predictors on the antibiotic resistance rate change across geographies were quantified. Results During 2000–2016, for Escherichia coli and Klebsiella pneumoniae, European countries with 10°C warmer ambient minimum temperatures compared to others, experienced more rapid resistance increases across all antibiotic classes. Increases ranged between 0.33%/year (95% CI: 0.2 to 0.5) and 1.2%/year (95% CI: 0.4 to 1.9), even after accounting for recognised resistance drivers including antibiotic consumption and population density. For Staphylococcus aureus a decreasing relationship of −0.4%/year (95% CI:  −0.7 to 0.0) was found for meticillin resistance, reflecting widespread declines in meticillin-resistant S. aureus across Europe over the study period. Conclusion We found evidence of a long-term effect of ambient minimum temperature on antibiotic resistance rate increases in Europe. Ambient temperature might considerably influence antibiotic resistance growth rates, and explain geographic differences observed in cross-sectional studies. Rising temperatures globally may hasten resistance spread, complicating mitigation efforts.


Additional Limitations
In this paper, we performed an ecologic analysis of association between country level outcomes with country level predictors, and as such we cannot infer causality. When choosing measures of antibiotic consumption, we selected only those corresponding to the class of antibiotic susceptibility (or resistance), i.e. we related fluoroquinolone consumption to fluoroquinolone resistance. We did seek to evaluate non-linear forms of consumption (in the forms of splines), however we did not seek to evaluate the impacts of other classes of antibiotics (to model coselection) due to potential complexity and uncertainty of effect. This includes use of antimicrobials for food animals.
The antibiotic resistance and antibiotic consumption data have a number of limitations which are acknowledged by their source. Namely, the degree of data comprehensiveness across countries is initially sparse, but generally increases over time. The methods/measures to evaluate antibiotic consumption and antibiotic resistance may vary by country and may change over time. Most relevant to this paper would be changes in breakpoints over time. However, consistency of findings supports likely limited impacts of these changes, and the data are sourced from the most comprehensive database available. Lastly, the bacterial isolate sources (for determining antibiotic prevalence) come from usually sterile sites and are thus may be more reflective of severe and/or hospital origin infections.

Sensitivity Analyses
In order to explore potential non-linear relationships between antimicrobial consumption and antibiotic resistance, we also developed multivariable models with the relationship between antibiotic resistance and antibiotic consumption modeled as a natural cubic spline with k=3 knots, in order to account for potentially non-linear relationships. This sensitivity analysis did not demonstrate material differences in our findings (Table S2).
In addition, in order to identify long-term climatic influences on antibiotic resistance, we repeated the original analysis using a fixed value for temperature, representing the average minimum temperature over the 17-year study period. We recovered nearly identical coefficients and pvalues compared to the original analysis (Table S5).

Data Use Disclaimer
The views and opinions of the authors expressed herein do not necessarily state or reflect those of ECDC. The accuracy of the authors' statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data. Table S1. List of cities or sources from which weather station data were obtained. Weather stations were available for 26 of the 28 countries in the main analysis, and data were compiled by the European Climate Data & Assessment (ECD&A) project of the Royal Netherlands Meteorological Institute (KNMI). Table S2. Adjusted multivariable analyses by pathogen and antibiotic class, for European capital cities using MERRA-2 reanalysis weather data. Coefficients with standard errors (95% confidence intervals) were adjusted for country, minimum temperature, year, population density, antibiotic consumption, and the interaction between year and minimum temperature. For interpretability, year was zeroed at baseline (2000). A log transform was applied to antibiotic consumption to improve linear fit. All available pathogen-antibiotic combinations of 3 pathogens (E. coli, K. pneumoniae, and S. aureus) and 4 antibiotic classes (aminoglycosides, 3rdgeneration cephalosporins, fluoroquinolones, and penicillins) were analyzed. For each capital city, daily minimum temperature from MERRA-2 were obtained from the 0.5° x 0.625° grid cell covering the centroid of the city, and the annual mean for each country was computed for each calendar year. Penicillin resistance in E.coli was measured as resistance to aminopenicillins, and for S. aureus as methicillin resistance. Table S3. Adjusted multivariable analyses by pathogen and antibiotic class, for European cities using weather station data. Coefficients with standard errors (95% confidence intervals) were adjusted for country, minimum temperature, year, population density, antibiotic consumption, and the interaction between year and minimum temperature. For interpretability, year was zeroed at baseline (2000). A log transform was applied to antibiotic consumption to improve linear fit. All available pathogen-antibiotic combinations of 3 pathogens (E. coli, K. pneumoniae, and S. aureus) and 4 antibiotic classes (aminoglycosides, 3rd-generation cephalosporins, fluoroquinolones, and penicillins) were analyzed. Penicillin resistance in E.coli was measured as resistance to aminopenicillins, and for S. aureus as methicillin resistance. Weather station data for capital or populous cities were obtained from the European Climate Data & Assessment (ECD&A) project of the Royal Netherlands Meteorological Institute (KNMI). 26 of the 28 countries had available weather station data, and the list of cities used for each country can be found in Table S1. Table S4. Adjusted multivariable analyses by pathogen and antibiotic class, with a cubic spline for antibiotic consumption. Coefficients with standard errors (95% confidence intervals) are adjusted for country, minimum temperature, year, population density, antibiotic consumption, and the interaction between year and minimum temperature. For interpretability, year is zeroed at baseline (2000). To improve linear fit, antibiotic consumption was modeled with a natural cubic spline (k=3 knots). All available pathogen-antibiotic combinations of 3 pathogens (E. coli, K. pneumoniae, and S. aureus) and 4 antibiotic classes (aminoglycosides, 3rd-generation cephalosporins, fluoroquinolones, and penicillins) were analyzed. Penicillin resistance in E.coli was measured as resistance to aminopenicillins, and for S. aureus as methicillin resistance. Table S5. Adjusted multivariable analyses by pathogen and antibiotic class, using a fixed 17-year average for minimum temperature. Coefficients with standard errors (95% confidence intervals) are adjusted for country, the 17-year average minimum temperature, year, population density, antibiotic consumption, and the interaction between year and the 17-year average minimum temperature. For interpretability, year is zeroed at baseline (2000). A log transform was applied to antibiotic consumption to improve linear fit. All available pathogenantibiotic combinations of 3 pathogens (E. coli, K. pneumoniae, and S. aureus) and 4 antibiotic classes (aminoglycosides, 3rd-generation cephalosporins, fluoroquinolones, and penicillins) were analyzed. Penicillin resistance in E.coli was measured as resistance to aminopenicillins, and for S. aureus as methicillin resistance.        (C) Density distributions of association measures (slopes) between antibiotic resistance and minimum temperature, stratified by time and with median densities (by year) marked by vertical dashed lines. Each country is represented by a collection of vertical points. (D) Change of antibiotic resistance over time as a function of minimum temperature for methicillin, with 95% confidence intervals. Estimates for (D) were obtained from multivariable models adjusting for country, minimum temperature (°C), year, population density (persons/km²), antibiotic consumption, and the interaction between year and minimum temperature. Beta coefficients and p-values are given for the interaction between minimum temperature and year, with 95% confidence intervals calculated using the standard error of Equation 3, √ ( 2 ) + ² ×      The hit rate captures the proportion of years for which the annual changes in antibiotic resistance and predictors are in the same direction, and was computed by country, pathogen, and antibiotic class for minimum temperature lags of between 0 and 9 years. Here, the mean hit rate across countries is shown. Figure S14. E. coli hit rates measuring the congruence of directional changes in antibiotic resistance and (A) minimum temperature and (B) antibiotic consumption, for multiple lags by country and antibiotic class. The hit rate captures the proportion of years for which the annual changes in antibiotic resistance and predictors are in the same direction, and was computed for temperature lags of between 0 and 9 years. Minimum temperature and antibiotic consumption precede antibiotic resistance at negative lags. The hit rate captures the proportion of years for which the annual changes in antibiotic resistance and predictors are in the same direction, and was computed for temperature lags of between 0 and 7 years (due to shorter availability of K. pneumoniae resistance data). Minimum temperature and antibiotic consumption precede antibiotic resistance at negative lags. The hit rate captures the proportion of years for which the annual changes in antibiotic resistance and predictors are in the same direction, and was computed for temperature lags of between 0 and 9 years. Minimum temperature and antibiotic consumption precede antibiotic resistance at negative lags.