Methodology: Antibiotic Use

Methodology information for antibiotic resistance is available here.

Sources

Data on antibiotic use for all countries currently included comes from the IQVIA MIDAS database. This database estimates antibiotic consumption from the volume of antibiotics sold in retail and hospital pharmacies based on national sample surveys done by pharmaceutical sales distribution channels (i.e. from manufacturer to wholesaler to retailer). In each sector, data are collected regularly to estimate direct sales from antibiotic drug manufacturers and indirect sales from wholesalers. The sales estimates from this sample are projected with use of an algorithm developed by IQVIA to approximate total volumes for sales and consumption. The algorithm uses regional, sectorial-specific and distribution-channel-specific factors to project national estimates of antibiotic consumption. However, precise details of the algorithm are withheld for proprietary reasons.

Data on antibiotic sales in standard units (SUs) and kilograms were obtained from the IQVIA MIDAS database. SU is an IQVIA designation that represents a single dose unit such as a pill, capsule, or equal amount of liquid. Sales expressed in kilograms were converted into defined daily doses (DDDs) using the Anatomical Therapeutic Chemical Classification System (ATC/DDD, 2016) developed by the WHO Collaborating Centre for Drug Statistics Methodology. For molecules not included in the ATC/DDD index, DDD values were estimated from other sources or as the average of DDD unit values by class. DDD unit values were provided in the ATC/DDD index for 199 of the molecules in the IQVIA MIDAS database. When possible, DDD unit values not available through the ATC/DDD index were estimated from other sources. Data for SUs were available for all years, whereas kilogram data were available only for the period 2005-2015. The ratio of SUs to kilograms for 2005-2015 was used to estimate kilograms and DDDs for 2000 to 2004. Countries' annual antibiotic consumption rate in DDDs per 1,000 inhabitants was calculated using population estimates from the World Bank DataBank. In countries where hospital and retail data were both reported for some but not all years (2000-2015), consumption in the missing sector was estimated by interpolation, using the ratio of antibiotic consumption in the hospital and retail sectors for the years data had been reported. Data collection procedures imposed additional limitations for a few countries that could not be (completely) accounted for. For example, some countries had sales data reported for only hospital or retail sectors, and in some cases, certain types of antibiotic sales—such as those in supermarkets or through government channels—were not included.

To allow for a meaningful comparison across countries, standard units/DDDs per 1,000 population was calculated by dividing the reported number of standard units/DDDs by population estimates from the World Bank. Taiwan’s population size data was not available in the World Bank’s database, so the values from Penn World Table 7.1 were used. Antibiotic use data was available only at a grouped regional level for some countries. For the two regional groupings—Central America and French West Africa—that had such data, we pooled the population estimates for the constituent countries to generate standard units/DDDs per 1,000 population.

For the United States, additional data was available at a sub-national level. This data comes from the IMS Health Xponent database. The Xponent database contains data on dispensed drug prescriptions collected from retail pharmacies (chain, mass merchandisers, independent pharmacies and food stores) in the United States. The database covers more than 70% of all prescriptions filled in the United States, and records are then weighted to project 100% of total prescriptions dispensed. Precise details of the weighting algorithm are withheld for proprietary reasons. These data are available at the zipcode level and have been aggregated into state-level values. Data were then divided by state population estimates from the US Census to give the number of prescriptions per 1,000 people.

Antibiotics

All the antibiotic products listed in MIDAS and Xponent databases constituted 90 different antibiotic molecule types. These generic antibiotics have been further combined into 18 different classes for comparisons across countries. The distribution of antibiotics into classes is listed in the following table.


Antibiotic class

Generic antibiotics

Aminoglycosides

Amikacin, Gentamicin, Kanamycin, Tobramycin, Neomycin

Broad-spectrum Penicillins

Amoxicillin, Ampicillin, Amoxicillin-clavulanate, Ampicillin-sulbactam, Carbenicillin, Carfecillin, Carindacillin, Piperacillin-tazobactam, Ticarcillin, Sultamicillin, Piperacillin, Temocillin

Carbapenems

Doripenem, Imipenem-cilastatin, Ertapenem, Meropenem

Cephalosporins

Cefazolin, Cefaclor, Ceftibuten, Cefadroxil, Cefdinir, Cefditoren pivoxil, Cefepime, Ceftizoxime, Cefotetan, Cefotaxime, Cefoxitin,Cefpodoxime proxetil, Cefprozil, Ceftazidime, Cefuroxime axetil, Ceftriaxone, Cefuroxime, Cefalexin, Cefradine, Loracarbef, Cefixime, Ceftaroline fosamil, faropenem

Chloramphenicols

Chloramphenicol

Glycopeptides

Vancomycin, Telavancin

Glycylcyclines

Tigecycline

Lipopeptides

Daptomycin

Macrolides

Erythromycin, Azithromycin, Clarithromycin, Dirithromycin, Telithromycin, Clindamycin, Fidaxomicin, Lincomycin,Troleandomycin, Dalfopristin-quinupristin

Monobactams

Aztreonam

Narrow-spectrum Penicillins

Oxacillin, Cloxacillin, Dicloxacillin, Nafcillin, Penicillin V, Penicillin G

Others

Bacitracin, Metronidazole, Streptomycin

Oxazolidinones

Linezolid

Phosphonics

Fosfomycin

Polymyxins

Colistin, Polymyxin B

Quinolones

Moxifloxacin, Ciprofloxacin, Gemifloxacin, Ofloxacin, Levofloxacin, Lomefloxacin, Norfloxacin, Enoxacin, Gatifloxacin, Trovafloxacin, Sparfloxacin

Tetracyclines

Oxytetracycline, Tetracycline, Doxycycline, Minocycline, Demeclocycline

Trimethoprim and combinations

Sulfamethoxazole-trimethoprim, Sulfamethoxazole, Sulfisoxazole, Sulfadiazine, Trimethoprim