Developing and evaluating a framework for the rational design of antibiotic prescribing policies in resource-constrained hospital settings

When hospitalised patients have a serious bacterial infection, they are usually given antibiotics. In rich countries, one third of the time the antibiotics are ineffective. This is often because the bacteria have acquired a gene that makes them resistant to that antibiotic. While it is possible to test for such resistance, it takes three or four days to get a result. For patients with serious infections this delay can be the difference between life and death.

In lower income countries the same is likely to be true, but little data are available. We do, however, know that patients in hospitals in poorer countries get infections more often and when they do they are more likely to die. Infections caused by antibiotic-resistant bacteria are also a major problem. As well as delaying effective treatment, such resistance can claim lives in low-income settings because remaining effective antibiotics are not available. Even if they are, they may be too expensive for many patients. The research aims to address the question of how we can more often give patients effective antibiotics when they need them and less often when they don't in hospitals with limited resources. We also want to find out how different antibiotics affect the spread of the most dangerous antibiotic-resistant bacteria, and we want to see if by changing patterns of antibiotic prescribing we can reduce the number of infections with resistant bacteria.

One part of the proposed work will use patient data (age, time in hospital, date last hospitalised, etc) and look for patterns that help to predict how likely infections with different types of bacteria are. For example, we know that patients who have been in hospital a long time are more likely to have infections with resistant bacteria. We can use this information to help choose which antibiotic is most likely to be effective. Our hunch is that by using computer models we can make optimal use of the information and choose an effective antibiotic more often than currently happens.

The second consideration doctors have to take into account when prescribing antibiotics is how this will affect other patients. The reason is that the more an antibiotic is used the more it creates an environment favourable to antibiotic-resistant bacteria. In general, increasing antibiotic use is associated with increased resistance to that antibiotic. The specifics, however, are complicated: some antibiotics promote resistance much more than others, and sometimes use of one antibiotic can cause an increase in resistance against a completely different antibiotic. To help design good antibiotic policies we need to understand these complex mechanisms better. Another part of the work will therefore use a computer modelling approach and new statistical techniques to develop and apply better methods to understand how levels of resistance change in response to changing antibiotic use.

The next stage of the research will combine these computer models and make extensive use of expertise from infectious disease doctors to design the best antibiotic policy we can for two hospitals. We will evaluate new policies in two ways: first we will run computer simulations, using real data from the hospitals to predict how well the new policy performs. If it performs worse than current practice we will redesign the policy until it performs better. Then, in one of the hospitals, we will perform an intervention study where we introduce the new policy and evaluate whether it really does improve antibiotic prescribing and reduce resistance as predicted.

Finally, use of new rapid tests that help determine what type of bugs are causing an infection can mean a patient has more chance of getting effective antibiotic treatment when it is needed and less chance of unnecessary treatment. We will use the previously-developed computer models to estimate how much patients would benefit from such tests, and evaluate which would represent good value for money.

Empirical antibiotic therapy for hospitalized patients should be chosen based on consideration of the likely pathogens, local antibiotic susceptibility patterns, patient-specific factors relating to severity of illness and risk of infection with a resistant organism, cost-effectiveness, and the impact of the antibiotic on the selection for resistant bacteria within the patient and the hospital.

Designing antibiotic polices to satisfy these complex and competing requirements is difficult. Wide variation in resistance patterns and different local incidence rates with different organisms prevent simple generalization of findings from clinical trials. Currently setting-specific choices of empirical therapy are made using clinical judgement alone, without the support of quantitative analysis or a coherent framework for balancing the competing requirements. We hypothesize that patient outcomes could be improved in a cost-effective (and potentially cost-saving) manner and antibiotic resistance reduced by implementing an evidence-based approach to the design of antibiotic policies.

This proposal aims to develop and evaluate such a framework for the rational design of antibiotic policies and to quantify the value of improving prescribing through investment in enhanced diagnostic capability. This will involve developing, extending and synthesizing a suite of phenomenological and mechanistic modelling approaches for quantifying the impact of prescribing decisions on patient and population-level outcomes. We propose to evaluate this approach using both in silico experiments using real hospital data and in a cluster-randomized trial.

While this proposal has global significance, we will focus exclusively on the choice of empirical therapy in low and middle income countries in Asia, where the potential health benefits are greatest and where the prevalence, local impact and international significance of antibiotic resistance make it a global health priority.

Though the precise burden of disease is unclear, serious bacterial infections are one of the most important causes of morbidity and mortality in developing countries. Bacterial infections acquired in hospital are also a major cause of morbidity and mortality globally. While healthcare associated infections (HCAIs) affect 5-10% of acute-care patients in developed countries, infection rates are far higher in developing countries and the consequences more severe. The best estimate is that about 15 in every 100 acute care patients in developing countries acquire an infection. Serious infections are also common. At the proposed study site in Thailand, for example, at least one in every 200 in-patients staying for more than two days acquires a bacteraemia. Though reliable data are lacking and few high quality studies have been conducted, there are reasons for thinking that hospital infections could be amongst the leading causes of death in developing countries. Such infections also place a large burden on already stretched health services in resource-poor settings. Efforts to reduce neonatal mortality by referring high-risk births to healthcare settings may also be undermined if neonates have high probabilities of contracting HCAIs. For all these reasons, control of HCAIs is considered a major priority for developing countries by the World Health Organization.

Few studies from developing countries have reported data on antibiotic resistance, but it is clear that this represents a major problem, particularly in Southeast Asia. Previous research at our study sites in Thailand and Cambodia has shown a high prevalence of infection with methicillin-resistant Staphylococcus aureus. Gram-negative bacteria account for an even greater burden of disease in Southeast Asia and here resistance is an even bigger concern. At the study site in Thailand Gram-negative infections account for 78% of bacteraemias and 59% of these are multiply antibiotic resistant (resistant to three or more classes of antibiotics). The situation could get far worse. The emergence of NDM-1 plasmids conferring resistance to nearly all antibiotics and capable of transfer to most important Gram-negative bacteria is a profound global concern. The NDM-1 pandemic appears to have originated in healthcare institutions in New Delhi and spread has recently been documented to many other countries and continents. One of the best ways to slow the spread of such multiply resistant bacteria in (and between) hospitals is to reduce antibiotic selection pressure by reducing unnecessary use and changing the antibiotics used (other measures are also important, and at the site in Thailand we are evaluating a hospital-wide hand hygiene programme).

The research will address these problems in three ways. First, amongst patients with serious bacterial infections, it will aim to increase the proportion who receive adequate antibiotic therapy by developing simple algorithms to identify those at risk of harbouring resistant bacteria. Second, it will aim to develop algorithms to inform antibiotic policies that reduce unnecessary use of antibiotics and reduce resistance. Third, it will aim to identify cost-effective investments in improved diagnostic capability that developing countries can make to help target antibiotic use more effectively.

The research also has significance beyond Southeast Asia. In the short term this is because the region, which has a large population and high antibiotic use (in hospitals, the community, and in animal husbandry) is a major potential source for novel resistance determinants capable of rapid global dissemination. Ultimately, however, the aim is to have a much larger impact by taking the guesswork out of antibiotic prescribing and policy-making, and to develop tools and approaches that can be used to help design the robust, effective and evidence-based prescribing policies that are needed to mitigate the global problem of antibiotic resistance.