BURDEN OF ANTIBIOTIC RESISTANCE
Antimicrobial resistance is widely regarded as one of the ma-
jor public health concerns of the 21st century [1,2], but there
are no good estimates of the net global health burden due to
resistance of bacteria to antibiotics. Although numerous
studies have provided estimates of the burden of resistance
of specific combinations of clinical disease, bacterial agent,
antibiotic and health care setting (primarily hospitals in de-
veloped countries), metrics vary, coverage is patchy and
methodologies are inconsistent. Such data have been used
to obtain partial estimates of resistance–related mortality and
other outcomes for Europe , the USA  and the world
, but because of huge information gaps and the need to
extrapolate from small–scale studies these estimates, though
helpful, should be regarded as tentative at best.
Multiple metrics are used to quantify the “burden” of infec-
tious diseases, including mortality, morbidity, disability ad-
justed life years, length of stay in hospital, or cost of care.
Here we focus on mortality, although similar considerations
apply to other metrics. An essential first step is to provide a
clear definition of the burden of antibiotic resistance. We
consider the most appropriate definition to be: the number of deaths attributable to the failure of antibiotic therapy due to anti- biotic resistance. Importantly, this is not equivalent to the total number of deaths among patients with antibiotic resistant
infections and may be much less than this for two main rea-
sons: not all patients who may have resistant infections are
treated with clinically indicated antibiotics and, for those that
are, the measurable difference in outcome for patients with
resistant vs susceptible infections may be relatively small.
Mark Woolhouse1,2, Catriona Waugh1, Meghan Rose Perry1,3, Harish Nair2
More formally, this definition of burden can be expressed as a population attributable fraction (PAF, also referred to as the aetiological fraction), ie, the number of deaths that would not occur if antibiotic resistance were eliminated. As set out in the Box, to calculate PAF for mortality due to an- tibiotic resistance requires data not only on the number of patients with resistant infections and the number that die but also enumeration of the population of interest, which includes patients who survived and/or had susceptible in- fections. Enumeration of the population of interest in turn requires information on the incidence of the relevant clin- ical condition, its aetiology, and coverage of the antibiotic therapy of choice. Because such information is rarely avail- able, PAF is rarely used to estimate the global burden of resistance; one recent example considered neonatal sepsis , but had to extrapolate key parameters from estimates obtained from a single hospital.
INCIDENCE AND AETIOLOGY
The main clinical conditions where antibiotic therapy can reduce mortality (Table 1) fall into three groups: commu-
1Centre for Immunity, Infection and Evolution, University of Edinburgh, Edinburgh, UK 2Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK 3Regional Infectious Diseases Unit, Western General Hospital, Edinburgh, UK
Global disease burden due to antibiotic resistance – state of the evidence
The absence of comprehensive and reliable
estimates of the global health burden due to
antibiotic resistance makes it difficult to assess
trends and harder to justify the allocation of
adequate resources to deal with the problem.
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Quantification of the burden of resistance re-
quires data on the incidence of clinical condi-
tions appropriately treated with antibiotics,
the frequency of treatment failures due to re-
sistance and their impact on clinical outcome.
Treatment failures in turn depend on the level
of resistance in the aetiological agent to the
antibiotic used. These data are not easily ob-
tained. One obstacle is that global health sta-
tistics as currently collected do not provide
the necessary information.
Possible ways forward include making some
categories of resistance notifiable, modifying
the International Classification of Diseases, use
of sentinel sites, and structured polling of cli-
nicable diseases, endogenous infections and prophylaxis to prevent endogenous infections in high risk patients. The number of patients in these categories defines the popula- tion potentially at risk of mortality attributable to antibi- otic resistance. Relatively good incidence estimates are available for only some of these categories, notably tuber- culosis and health care associated infections .
Of the clinical conditions listed in the Table only tubercu- losis has a specific aetiology. The remainder are associated with multiple kinds of bacteria and several, such as sexu- ally transmitted infections, diarrhoea and respiratory infec- tions, may also be caused by viral and/or fungal agents.
A restricted set of both gram negative and gram positive bacterial agents, plus Mycobacterium tuberculosis, are com- monly highlighted in the context of antibiotic resistance (eg, ). Some of these are of particular concern in hospi- tal settings, such as Acinetobacter spp, Enterobacteriaceae spp, Enterococcus spp, Pseudomonas aeruginosa, Staphylococ- cus aureus and Streptococcus spp. Others are associated with
communicable diseases typically acquired outside hospi- tals, such as Campylobacter spp, Neisseria gonorrhoeae, Sal- monella typhi, non–typhoidal Salmonella spp, Shigella spp, and Streptococcus pneumoniae. Several of these contribute to multiple clinical conditions of interest.
Global consumption of antibiotics has recently been esti- mated at more than 70 billion doses per annum . By volume, antibiotic usage in 2010 was dominated by peni- cillins, cephalosporins, macrolides, fluoroquinolones, tri- methoprim and tetracyclines.
These data refer to sales by pharmacies; they do not link antibiotic consumption to the treatment of patients with specific clinical conditions. The WHO last published ge- neric guidelines for the therapeutic use of antibiotics in 2001  but these and more current national and interna- tional guidelines tend not to be prescriptive, emphasizing the need to account for local circumstances, not least local patterns of antibiotic resistance. Usage profiles can thus vary considerably between locations. For some countries antibiotic usage data are available at hospital level; again however, these data are not routinely linked to information on the conditions that were being treated .
Current antibiotic usage profiles are, of course, influenced by current patterns of antibiotic resistance. Resistance pat- terns mean that, for example, aminopenicillins alone may not be used to treat serious gram negative bacterial infec- tions, alternative drugs would be used additionally where available. In this scenario, aminopenicillin resistance does not contribute to the population attributable fraction as defined above, although it is arguably an element of the overall burden of antibiotic resistance.
The most comprehensive data on global levels of antibiot- ic resistance come from a recent WHO survey . Even so, for most combinations of bacterial species and antibiotic the countries providing the minimum data required (test- ing of 30 isolates) accounted for less than half the world’s population. A major contribution of this exercise was to
Table 1. Common clinical conditions for which antibiotic therapy reduces the risk of mortality
Category of Condition Condition Communicable diseases
Tuberculosis Sexually transmitted bacterial infections Respiratory bacterial infections (especially of the lower respiratory tract) Diarrhoea caused by bacteria* Healthcare associated bacterial infections
Urinary tract infections Skin and soft tissue infections Infective endocarditis Sepsis
Prevention of infection
Burns, wounds Caesarean sections Joint replacements Cancer therapy Organ transplants
*Antibiotics are not necessarily indicated for diarrhoeal cases.
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highlight significant variations in the kinds of isolates test- ed and in resistance testing protocols.
Moreover, bacteria–antibiotic combinations were not ex- plicitly linked to clinical condition, so it is unclear when the resistances tested were clinically relevant and when they were not. This, together with the lack of data relating antibiotic usage to clinical condition, makes it difficult to estimate the relevant component of the PAF calculation, the fraction of patients with bacterial infections that are resis- tant to the antibiotic used to treat them (Box 1).
TREATMENT FAILURE AND CLINICAL OUTCOME
Two key quantities for estimating the burden of antibiotic resistance are the frequency and clinical impact of failures of antibiotic therapy. Treatment failure is a complex phenom- enon that may well be attributable to factors other than an- tibiotic resistance, including misdiagnosis. Treatment failure can also occur in patients with antibiotic–susceptible infec- tions. Central to the calculation of burden is the distinction between the death of a patient who has an antibiotic resis- tant infection and the death of a patient that is attributable to having an antibiotic resistant infection (see Box).
Data on treatment failures are not routinely recorded. One source of data on mortality is the ICD–10 (International Classification of Disease, version 10) codes used by the WHO . ICD–10 covers many, though not all, of the clinical conditions listed in Table 1. However, ICD–10 submissions do not usually include treatment failures as- sociated with antibiotic resistant infections (reference to which is confined to the rarely used “Codes for Special Pur- poses”). Nor does the Institute for Health Metrics and Eval- uation’s Global Burden of Disease cause list have categories linked to antibiotic resistance .
Information currently collected at global or multi–national scales is not sufficient to generate estimates of the disease burden attributable to antibiotic resistance. As a result, cur- rent knowledge of the burden of antibiotic resistance is still based largely on the collation of one–off, small–scale, indi- vidual studies that vary greatly in setting, scope, sampling frame and methodology, and often requires bold extrapo- lations to be made from very limited data sets. For estima- tion of the global burden of antibiotic resistance and, even more, for monitoring changes in burden over time more systematic approaches would be helpful. There are several possibilities.
ICD–10 is due to be replaced by ICD–11 in 2017 . This provides an opportunity to create routinely used categories that record treatment failures, or at least linking treatments with outcomes, the most direct ways to estimate the bur- den of antibiotic resistance. Specific concerns, such as XDR– TB or carbapenem–resistant Enterobacteriaceae, might be pri- oritised for inclusion.
ICD facilitates passive reporting. An alternative is active re- porting by recruiting sentinel sites. For example, 660 hos- pitals from 67 countries responded to an internet survey on antimicrobial stewardship in 2012 . Monitoring treat- ment failures due to antibiotic resistance in these hospitals using standardised protocols would generate valuable data.
Box 1. Population attributable fraction (PAF) of mortality due to antibiotic resistance.
PAF calculations are a standard method of quantifying dis- ease burden associated with a specified risk exposure , in this case bacterial infections resistant to the antibiotic used to treat them. The first step is to enumerate the population of interest. For current purposes, this would be the incidence (number per unit time) of patients with one of the clinical conditions of concern (see Table 1) and for whom antibi- otic therapy is clinically indicated and is provided. The inci- dence of such patients is denoted I.
PAF calculation is routinely expressed in terms of the pro- portion of population exposed to the risk factor (here, pa- tients with antibiotic–resistant infections) and the risk ratio for mortality standardised to the unexposed group (patients with antibiotic–susceptible infections) . An equivalent, easily understood version is: PAF = (IF−DR)/(ID−DR), where I is the overall incidence (number of patients per unit time); F is the number of patients with resistant infections that die; D is the number of patients that die; R is the number of pa- tients with resistant infections. If all deaths are associated with resistance (F = D) then PAF = 1; if deaths are not dispro- portionately associated with resistance (corresponding to F = DR/I) then PAF = 0. Importantly, PAF = 0 does not equate F = 0.
Intuitively, it seems natural to equate F with treatment “fail- ures”. However, some care is required because, given PAF<1, it is implicit that some of these patients (estimated as DR/I) would have died anyway, even if they had not had a resistant infection (this number reflecting the ‘background’ level of mortality observed in patients who were appropriately treat- ed and had a susceptible infection). Similarly, of patients with susceptible infections who survive, some would have sur- vived anyway, even had they had a resistant infection; that is, not all positive outcomes can be attributed to successful antibiotic therapy.
As detailed in the main text, although there is sometimes in- formation available on F, D and/or R, there is often insuffi- cient information to determine I. To do so requires addition- al data on one or more of the following: i) the total number of patients of interest that survive; ii) the number with sus- ceptible infections; or iii) the number with susceptible infec- tions that survive.
Obtaining a single global estimate of mortality attributable to antibiotic resistance presents the additional challenges of combining and extrapolating estimates of PAF for given com- binations of clinical condition, antibiotic, aetiological agent and location.
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Making selected, high priority antibiotic resistant infections ‘notifiable’ at national level could further improve data cap- ture, extending existing mandatory reporting for specific conditions (for example in the UK for scarlet fever or inva- sive streptococcal group A disease). Another possibility is a more qualitative approach of recruiting a global panel of individual clinicians who are polled to determine trends in the impact of antibiotic resistance on their patients. Polling has been used successfully in other clinical contexts .
As well as estimating the global burden of antibiotic resis- tance another useful exercise would be to estimate the glob- al burden due to lack of access to suitable antibiotics. For some clinical conditions, this may be a substantially great- er burden at the present time . The two issues poten- tially overlap where there is a lack of knowledge of local resistance profiles (perhaps due to lack of testing facilities) and alternative drugs would have been effective.
Estimation of the global burden of antibiotic resistance is extremely challenging and arguably not an attainable objec- tive with currently available health data. We stress that this conclusion does not contradict the generally accepted view that antibiotic resistance is a major public health problem of global significance. There is a large number of studies documenting levels of resistance and its clinical impact, and well–founded concerns that both will rise, perhaps dramat- ically, in the foreseeable future. However, as reviewed here,
the valuable insights provided by such studies do not sum to a comprehensive, coherent picture of the global antibi- otic resistance burden and how it is changing. Improving this situation will require changes to the ways in which global health statistics are collected; existing approaches are not up to the task. The primary benefit will be more accu- rate assessment of the global disease burden due to antibi- otic resistance and its forward trajectory, helping make the case for investment in combating the problem, and allow- ing assessment of future trends.
Acknowledgements: We thank Ana Clayton–Smith for assistance with literature searches and Mar- go Chase–Topping for help with statistical analysis. This work was funded by the Wellcome Trust (grant number 093724) and by a European Union project grant (EvoTAR, grant number HEALTH– F3–2011–282004).
Funding: This work was funded by the Wellcome Trust (grant number 093724) and by a Euro- pean Union project grant (EvoTAR, grant number HEALTH-FS-2011-282004).
Authorship declaration: MW conceived the study and led manuscript writing. CW carried out literature searches and collated data. MRP advised on clinical practice. HN advised on disease bur- den estimation. All authors contributed to manuscript preparation.
Competing interests: All authors have completed the Unified Competing Interest form at www. icmje.org/coi_disclosure.pdf (available on request from the corresponding author). All authors de- clare no competing interests.
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Photo: Courtesy of Kit Yee Chan, personal collection
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Correspondence to: Prof Mark Woolhouse Centre for Immunity, Infection and Evolution University of Edinburgh Kings Buildings Charlotte Auerbach Rd Edinburgh EH9 3FL, UK [email protected]
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