Communicable Diseases Surveillance Centre, Abton House, Wedal Road, Cardiff CF4 3QX, UK
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Abstract |
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Keywords: antibiotic resistance , surveillance of antibiotic resistance , resistance epidemiology
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The resistance problem and epidemiology |
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Unfortunately, resistance epidemiology has been grossly neglected. Although some antibiotic or infection journals kindly host papers, the subject has no dedicated journal and there are few relevant papers and no systematic approach. Consequently, the many interventions that have been suggested for the resistance problem are almost entirely based on conjecture and unproven assumptions. Basic questions that might indicate a rational strategy, such as which factors affect the incidence of resistant infection, remain unanswered. Rational targets for the extent of reduction in antimicrobial usage have not been formulated, and we have no idea of the likely time-scale and extent of intervention effects. The functional knowledge base supporting intervention is only a little better than that for infectious disease in the mid-18th century.
Worse still, the major potential data source for such studiesroutine diagnostic susceptibilitieshas never been subjected to detailed critical validation studies for epidemiological use, and is regarded by many as subject to a host of potential biasing factors. Our all-Wales studies demonstrate that one of the most widely believed contentions of bias, inclusion of duplicate isolates,16 produces comparatively trivial effects, whereas another factor that has been largely ignoredlaboratory testing policyproduces much more significant effects.17 The belief structure that moulds the current view on resistance surveillance is based on assumptions, rather than proven facts. Current attitudes have proven singularly ineffective in providing answers to the many critical questions that are required to produce a rational intervention strategy.810
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The path to resistance epidemiologydata |
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It is unlikely that studies confined to the most common pathogensEscherichia coli, MSSA and MRSAwill provide an adequate picture of resistance epidemiology that can be applied across the full range of pathogens and antibiotics (see below). For many important pathogens, collection of resistance data on adequate numbers of isolates for rigorous statistical analysis, over a reasonable time period, requires data collection on a UK national basis. It would be sensible, rather than repeatedly restructuring national data collection for individual investigations, to institute an ongoing national resistance surveillance scheme for all organisms.
Collection of bulk routine data from computerized laboratory reporting systems is no longer a technical problem. Microbiology DataStore, developed in NPHS Wales, provides robust automated transfer of bacteriology and virology reports (negative and positive) from a wide variety of laboratory systems into an inexpensive industry-standard database system. The design provides clear advantages to participating laboratories. There is local ownership of the system, long-term continuity of records through system changes and facilities for rapid, flexible analysis of local data.18 For the most part, difficulties of varied proprietary reporting systems, local variation of result coding, patient confidentiality and automated transfer of results for regional analysis have been resolved. Institution of regional and national collection of data in the UK is now largely a matter of will, organization and comparatively minor finance.
One problem with national collection of routine diagnostic results is doubt on the validity of the data. Enthusiasm in this area seems to centre on advancing hypothetical criticism outside refereed journals, rather than on publication of a structured, detailed and critical examination of real data that aims for a consensus approach to discovery and elimination of confounding factors. A systematic co-operative effort to achieve a consensus pre-processing strategy for elimination of confounding and biasing factors is an essential first step.
Collection of validated bulk data is merely an intermediate (though essential) step in attacking the resistance problem. In our view, the primary purpose of national resistance surveillance is to provide data for a systematic analysis of resistance epidemiology. Provision of figures outlining geographical variation in resistance, current local levels and trends are an important immediate by-product. The longer-term goals for real public health impact are the resolution of the factors and mechanisms involved in the spread of resistance, and the formulation of rational interventions, with realistic targets and expectations. Clarity and constancy of purpose are essential to achieve these long-term goals in the face of a host of immediate, but minor and ephemeral requirements.
Close co-operation with other agencies, to obtain data on the factors that may influence resistance, is another essential aspect of this approach. Antibiotic consumption, including age-sex demographics, is the most obvious input, but access to data on other potential intervention factors, such as the psychology of the patient and practitioner in prescribing, social deprivation or infection control activity, would be crucial. Access to the growing number of relevant computerized data sources, such as the Welsh Medusa system for hospital pharmaceutical consumption19 and National Census data, requires commitment to co-operation, high levels of computer expertise, and a clear knowledge of the evolving range of information sources.
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Resistance epidemiology, what is known and what might be found |
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Co-resistancegenetically linked multi-resistancemay also have effects via co-selection processes. Ampicillintrimethoprim co-resistance is common in community urinary E. coli,22 so trimethoprim usage would be expected to select for ampicillin resistance and vice versa. Clear cross-associations between resistance and usage have been demonstrated for these antibiotics.22,23 Surveillance of multiple linked resistances is minimal, leaving a critical gap in our knowledge. The impact of resistance to a single agent on treatment is minor; multi-resistant strains present the major therapeutic problems. Surveillance schemes must monitor known associations between resistance to appropriate agents and seek evolving associations, in view of the therapeutic implications and likely impact on epidemiology.
Cross-over effects are also found between species. Therapy may be targeted on the infecting organism, but the effects spill into the general commensal flora of the patients, their contacts and the environment. For example, most community UK ampicillin/amoxicillin prescribing is for respiratory infection, yet this usage is clearly associated with ampicillin/amoxicillin resistance of distinct organisms affecting another siteurinary coliform isolates.22,24
Another interesting aspect is the contrast between the contribution of antibiotic usage to risk of resistant infection for individual treated patients, with its contribution to increased risk for the general population. There is an increased risk of resistant infection for individual patients post-antibiotic treatment.23,2527 The risk is associated with use of the specific antibiotic and other antibiotics, i.e. trimethoprim resistance in urinary tract infection is associated with prior trimethoprim treatment, and prior treatment with other antibiotics.23 It is large initially, but decreases to an insignificant level after 6 months.23 This individual risk will probably dominate short-term epidemiological effects for usage of many antibiotics.
However, the population affected by treatment is not the individual patients, but their pathogen and commensal flora, and there is no doubt that the affected organisms will be transmitted between hosts. The clonal spread of MRSA in hospitals and into the community provides a clear example of the existence of a real ecological effect, and there are many similar instances.2830 Resistance genes often show close sequence similarity between and within species,3133 demonstrating ecological spread by genetic transmission, and inter-species spread of resistance genes has been implicated in hospital outbreaks.3436 Equally, antibiotics spread from the patient to the environment,37 and may well select for resistance in the ubiquitous microbial ecology. A population effect due to clonal or genetic spread of resistance between hosts may be undetectable in short-term studies,23 but there are good reasons to believe that it exists, and that it may have more significant long-term effects than individual risk.
Resistance to an antibiotic generally follows an S-shaped curve with time from introduction (Figure 1).38 This is not compatible with a model based solely on individual risk. In particular, the early slow rise phase of this curve does not correspond to a plausible pattern of antibiotic usage, which usually rises much more rapidly to a plateau. The change in UK incidence of penicillin-resistant Streptococcus pneumoniae in 1976 was not due to any concurrent change in penicillin prescribing, but probably to the introduction of a resistant clone into the ecology via international travel.39
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The key arguments are that long-term trends in resistance and known fundamental mechanisms for generation of resistance indicate a slow cumulative effect. The individual risk effect is not cumulative; it decays rapidly,23 and so cannot be maintained or amplified in a host population where most individuals receive less than one antibiotic prescription per annum, as in the UK.44 Further, there is a real ecologyclonal and genetic-transmission spread of resistance between hosts is a well-established fact and a known key factor in the epidemiology of, for example, MRSA.
There are clear associations between resistant infection and patient age and gender. Resistance tends to decrease from age 1 to 15 years, plateau, and then increase beyond age 65 for many pathogens and antibiotics.22 Age emerged as a significant risk factor, independent of individual patient prescribing, for trimethoprim resistance in community urinary E. coli infection.23 This indicates that other factors may be involved in age differences. Increased inter-host transmission in child45,46 and geriatric-care facilities,47 and possible differences in colonization-susceptibility are clear candidates. A small but significant excess of resistance in males is commonly found,22 with the exception of trimethoprim resistance in community urinary E. coli, where excess resistance occurs in females.22,23 The causes of excess resistance in males are obscure. Overall UK antibiotic prescribing is higher in females, which supports a non-usage-associated effect.44 Unpicking this puzzle may prove difficult.
Associations between social deprivation and resistance may also be important. Examples include: penicillin-resistant pneumococcal carriage,48 MRSA infection,49 and resistance in community urinary tract infection.22 These associations seem to be independent of the high antibiotic usage found in deprived communities.22 It may be that behavioural and environmental patterns associated with community deprivation provide pathways favouring transmission of resistant organisms in these communities. It would be interesting to dissect the details of such effects, and determine whether analogous factors influence resistance in hospitals.
Finally, there have been growing efforts to produce mathematical models of the resistance problem,5055 a welcome input of external expertise that will be essential to progress. The models hypothecate a set of assumptions on the laws governing resistance, and predict the behaviour of resistance from these assumptions and their interactions. The next step in this approach is to test the predictions against many real datasets, to determine which of the assumptions are universally applicable, which are true, but only in special cases, and which are incorrect. The major problem is the lack of large, validated observational datasets to compare with the predictions,50 a deficiency that might be cured by the measures outlined below. This cyclic process of hypothesis, observation, and revision of hypotheses has been essential in producing sets of well-tested universal laws in other sciences. However, it has rarely been exploited in biology, and its adoption here might encourage a more general attempt to deduce generalized laws from the vast bulk of purely observational biological data. It would be particularly informative to explore a model of individual risk effects at prescribing rates above 23 prescriptions per patient per annum. These high rates are encountered in some countries, care homes or hospital wards, and could lead to self-amplifying effects.
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Specific recommendations |
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Conclusions |
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Acknowledgements |
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References |
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