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.