Probabilistic modelling of effects of antibiotics and calendar time on transmission of healthcare-associated infection.

Healthcare-associated infection and antimicrobial resistance are major concerns. However, the extent to which antibiotic exposure affects transmission and detection of infections such as MRSA is unclear. Additionally, temporal trends are typically reported in terms of changes in incidence, rather than analysing underling transmission processes. We present a data-augmented Markov chain Monte Carlo approach for inferring changing transmission parameters over time, screening test sensitivity, and the effect of antibiotics on detection and transmission. We expand a basic model to allow use of typing information when inferring sources of infections. Using simulated data, we show that the algorithms are accurate, well-calibrated and able to identify antibiotic effects in sufficiently large datasets. We apply the models to study MRSA transmission in an intensive care unit in Oxford, UK with 7924 admissions over 10 years. We find that falls in MRSA incidence over time were associated with decreases in both the number of patients admitted to the ICU colonised with MRSA and in transmission rates. In our inference model, the data were not informative about the effect of antibiotics on risk of transmission or acquisition of MRSA, a consequence of the limited number of possible transmission events in the data. Our approach has potential to be applied to a range of healthcare-associated infections and settings and could be applied to study the impact of other potential risk factors for transmission. Evidence generated could be used to direct infection control interventions.

Cost-effectiveness and budget impact analyses for the prioritisation of the four available rotavirus vaccines in the national immunisation programme in Thailand.

BACKGROUND: Rotavirus is a major cause of diarrhoea in children less than five years old in Thailand. Vaccination has been shown to be an effective intervention to prevent rotavirus infections but has yet to be enlisted in the national immunisation programme. This study aimed to assess the cost-utility of introducing rotavirus vaccines, taking all WHO-prequalified vaccines into consideration. METHODS: A cost-utility analysis was performed using a transmission dynamic model to estimate, from a societal perspective, the costs and outcomes of four WHO-prequalified rotavirus vaccines: Rotarix®, RotaTeq®, ROTAVAC® and ROTASIIL®. The model was used to simulate the impact of introducing the vaccines among children aged < 1 year and compare this with no rotavirus vaccination. The vaccination programme was considered to be cost-effective if the incremental cost-effectiveness ratio was less than a threshold of USD 5,110 per QALY gained. RESULTS: Overall, without the vaccine, the model predicted the average annual incidence of rotavirus to be 312,118 cases. With rotavirus vaccination at a coverage of more than 95%, the average number of rotavirus cases averted was estimated to be 144,299 per year. All rotavirus vaccines were cost-saving. ROTASIIL® was the most cost-saving option, followed by ROTAVAC®, Rotarix® and RotaTeq®, providing average cost-savings of USD 32, 31, 23 and 22 million per year, respectively, with 999 QALYs gained. All vaccines remained cost-saving with lower QALYs gained, even when ignoring indirect beneficial effects. The net saving to the healthcare system when implementing any one of these vaccines would be between USD 13 and 33 million per year. CONCLUSION: Rotavirus vaccines should be included in the national vaccination programme in Thailand. Implementing any one of these four WHO-prequalified vaccines would reduce government healthcare spending while yielding health benefits to the population.

Enumerating the economic cost of antimicrobial resistance per antibiotic consumed to inform the evaluation of interventions affecting their use.

Background: Antimicrobial resistance (AMR) poses a colossal threat to global health and incurs high economic costs to society. Economic evaluations of antimicrobials and interventions such as diagnostics and vaccines that affect their consumption rarely include the costs of AMR, resulting in sub-optimal policy recommendations. We estimate the economic cost of AMR per antibiotic consumed, stratified by drug class and national income level. Methods: The model is comprised of three components: correlation coefficients between human antibiotic consumption and subsequent resistance; the economic costs of AMR for five key pathogens; and consumption data for antibiotic classes driving resistance in these organisms. These were used to calculate the economic cost of AMR per antibiotic consumed for different drug classes, using data from Thailand and the United States (US) to represent low/middle and high-income countries. Results: The correlation coefficients between consumption of antibiotics that drive resistance in S. aureus, E. coli, K. pneumoniae, A. baumanii, and P. aeruginosa and resistance rates were 0.37, 0.27, 0.35, 0.45, and 0.52, respectively. The total economic cost of AMR due to resistance in these five pathogens was $0.5 billion and $2.9 billion in Thailand and the US, respectively. The cost of AMR associated with the consumption of one standard unit (SU) of antibiotics ranged from $0.1 for macrolides to $0.7 for quinolones, cephalosporins and broad-spectrum penicillins in the Thai context. In the US context, the cost of AMR per SU of antibiotic consumed ranged from $0.1 for carbapenems to $0.6 for quinolones, cephalosporins and broad spectrum penicillins. Conclusion: The economic costs of AMR per antibiotic consumed were considerable, often exceeding their purchase cost. Differences between Thailand and the US were apparent, corresponding with variation in the overall burden of AMR and relative prevalence of different pathogens. Notwithstanding their limitations, use of these estimates in economic evaluations can make better-informed policy recommendations regarding interventions that affect antimicrobial consumption and those aimed specifically at reducing the burden of AMR.

Cost-effectiveness of Ward Closure to Control Outbreaks of Norovirus Infection in United Kingdom National Health Service Hospitals.

BACKGROUND: Norovirus is the most common cause of outbreaks of acute gastroenteritis in National Health Service hospitals in the United Kingdom. Wards (units) are often closed to new admissions to stop the spread of the virus, but there is limited evidence describing the cost-effectiveness of ward closure. METHODS: An economic analysis based on the results from a large, prospective, active-surveillance study of gastroenteritis outbreaks in hospitals and from an epidemic simulation study compared alternative ward closure options evaluated at different time points since first infection, assuming different efficacies of ward closure. RESULTS: A total of 232 gastroenteritis outbreaks occurring in 14 hospitals over a 1-year period were analyzed. The risk of a new outbreak in a hospital is significantly associated with the number of admission, general medical, and long-stay wards that are concurrently affected but is less affected by the level of community transmission. Ward closure leads to higher costs but reduces the number of new outbreaks by 6%-56% and the number of clinical cases by 1%-55%, depending on the efficacy of the intervention. The incremental cost per outbreak averted varies from £10 000 ($14 000) to £306 000 ($428 000), and the cost per case averted varies from £500 ($700) to £61 000 ($85 000). The cost-effectiveness of ward closure decreases as the efficacy of the intervention increases, and the cost-effectiveness increases with the timing of the intervention. The efficacy of ward closure is critical from a cost-effectiveness perspective. CONCLUSIONS: Ward closure may be cost-effective, particularly if targeted to high-throughput units.

Comparative efficacy of interventions to promote hand hygiene in hospital: systematic review and network meta-analysis.

OBJECTIVE: To evaluate the relative efficacy of the World Health Organization 2005 campaign (WHO-5) and other interventions to promote hand hygiene among healthcare workers in hospital settings and to summarize associated information on use of resources. DESIGN: Systematic review and network meta-analysis. DATA SOURCES: Medline, Embase, CINAHL, NHS Economic Evaluation Database, NHS Centre for Reviews and Dissemination, Cochrane Library, and the EPOC register (December 2009 to February 2014); studies selected by the same search terms in previous systematic reviews (1980-2009). REVIEW METHODS: Included studies were randomised controlled trials, non-randomised trials, controlled before-after trials, and interrupted time series studies implementing an intervention to improve compliance with hand hygiene among healthcare workers in hospital settings and measuring compliance or appropriate proxies that met predefined quality inclusion criteria. When studies had not used appropriate analytical methods, primary data were re-analysed. Random effects and network meta-analyses were performed on studies reporting directly observed compliance with hand hygiene when they were considered sufficiently homogeneous with regard to interventions and participants. Information on resources required for interventions was extracted and graded into three levels. RESULTS: Of 3639 studies retrieved, 41 met the inclusion criteria (six randomised controlled trials, 32 interrupted time series, one non-randomised trial, and two controlled before-after studies). Meta-analysis of two randomised controlled trials showed the addition of goal setting to WHO-5 was associated with improved compliance (pooled odds ratio 1.35, 95% confidence interval 1.04 to 1.76; I(2)=81%). Of 22 pairwise comparisons from interrupted time series, 18 showed stepwise increases in compliance with hand hygiene, and all but four showed a trend for increasing compliance after the intervention. Network meta-analysis indicated considerable uncertainty in the relative effectiveness of interventions, but nonetheless provided evidence that WHO-5 is effective and that compliance can be further improved by adding interventions including goal setting, reward incentives, and accountability. Nineteen studies reported clinical outcomes; data from these were consistent with clinically important reductions in rates of infection resulting from improved hand hygiene for some but not all important hospital pathogens. Reported costs of interventions ranged from $225 to $4669 (£146-£3035; €204-€4229) per 1000 bed days. CONCLUSION: Promotion of hand hygiene with WHO-5 is effective at increasing compliance in healthcare workers. Addition of goal setting, reward incentives, and accountability strategies can lead to further improvements. Reporting of resources required for such interventions remains inadequate.

Seasonal influenza vaccination for children in Thailand: a cost-effectiveness analysis.

BACKGROUND: Seasonal influenza is a major cause of mortality worldwide. Routine immunization of children has the potential to reduce this mortality through both direct and indirect protection, but has not been adopted by any low- or middle-income countries. We developed a framework to evaluate the cost-effectiveness of influenza vaccination policies in developing countries and used it to consider annual vaccination of school- and preschool-aged children with either trivalent inactivated influenza vaccine (TIV) or trivalent live-attenuated influenza vaccine (LAIV) in Thailand. We also compared these approaches with a policy of expanding TIV coverage in the elderly. METHODS AND FINDINGS: We developed an age-structured model to evaluate the cost-effectiveness of eight vaccination policies parameterized using country-level data from Thailand. For policies using LAIV, we considered five different age groups of children to vaccinate. We adopted a Bayesian evidence-synthesis framework, expressing uncertainty in parameters through probability distributions derived by fitting the model to prospectively collected laboratory-confirmed influenza data from 2005-2009, by meta-analysis of clinical trial data, and by using prior probability distributions derived from literature review and elicitation of expert opinion. We performed sensitivity analyses using alternative assumptions about prior immunity, contact patterns between age groups, the proportion of infections that are symptomatic, cost per unit vaccine, and vaccine effectiveness. Vaccination of children with LAIV was found to be highly cost-effective, with incremental cost-effectiveness ratios between about 2,000 and 5,000 international dollars per disability-adjusted life year averted, and was consistently preferred to TIV-based policies. These findings were robust to extensive sensitivity analyses. The optimal age group to vaccinate with LAIV, however, was sensitive both to the willingness to pay for health benefits and to assumptions about contact patterns between age groups. CONCLUSIONS: Vaccinating school-aged children with LAIV is likely to be cost-effective in Thailand in the short term, though the long-term consequences of such a policy cannot be reliably predicted given current knowledge of influenza epidemiology and immunology. Our work provides a coherent framework that can be used for similar analyses in other low- and middle-income countries.

Understanding and managing zoonotic risk in the new livestock industries.

BACKGROUND: In many parts of the world, livestock production is undergoing a process of rapid intensification. The health implications of this development are uncertain. Intensification creates cheaper products, allowing more people to access animal-based foods. However, some practices associated with intensification may contribute to zoonotic disease emergence and spread: for example, the sustained use of antibiotics, concentration of animals in confined units, and long distances and frequent movement of livestock. OBJECTIVES: Here we present the diverse range of ecological, biological, and socioeconomic factors likely to enhance or reduce zoonotic risk, and identify ways in which a comprehensive risk analysis may be conducted by using an interdisciplinary approach. We also offer a conceptual framework to guide systematic research on this problem. DISCUSSION: We recommend that interdisciplinary work on zoonotic risk should take into account the complexity of risk environments, rather than limiting studies to simple linear causal relations between risk drivers and disease emergence and/or spread. In addition, interdisciplinary integration is needed at different levels of analysis, from the study of risk environments to the identification of policy options for risk management. CONCLUSION: Given rapid changes in livestock production systems and their potential health implications at the local and global level, the problem we analyze here is of great importance for environmental health and development. Although we offer a systematic interdisciplinary approach to understand and address these implications, we recognize that further research is needed to clarify methodological and practical questions arising from the integration of the natural and social sciences.

Estimating the effectiveness of isolation and decolonization measures in reducing transmission of methicillin-resistant Staphylococcus aureus in hospital general wards.

Infection control for hospital pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) often takes the form of a package of interventions, including the use of patient isolation and decolonization treatment. Such interventions, though widely used, have generated controversy because of their significant resource implications and the lack of robust evidence with regard to their effectiveness at reducing transmission. The aim of this study was to estimate the effectiveness of isolation and decolonization measures in reducing MRSA transmission in hospital general wards. Prospectively collected MRSA surveillance data from 10 general wards at Guy's and St. Thomas' hospitals, London, United Kingdom, in 2006-2007 were used, comprising 14,035 patient episodes. Data were analyzed with a Markov chain Monte Carlo algorithm to model transmission dynamics. The combined effect of isolation and decolonization was estimated to reduce transmission by 64% (95% confidence interval: 37, 79). Undetected MRSA-positive patients were estimated to be the source of 75% (95% confidence interval: 67, 86) of total transmission events. Isolation measures combined with decolonization treatment were strongly associated with a reduction in MRSA transmission in hospital general wards. These findings provide support for active methods of MRSA control, but further research is needed to determine the relative importance of isolation and decolonization in preventing transmission.

Screening, isolation, and decolonisation strategies in the control of meticillin resistant Staphylococcus aureus in intensive care units: cost effectiveness evaluation.

OBJECTIVE: To assess the cost effectiveness of screening, isolation, and decolonisation strategies in the control of meticillin resistant Staphylococcus aureus (MRSA) in intensive care units. DESIGN: Economic evaluation based on a dynamic transmission model. SETTING: England and Wales. Population Theoretical population of patients on an intensive care unit. MAIN OUTCOME MEASURES: Infections, deaths, costs, quality adjusted life years (QALYs), incremental cost effectiveness ratios for alternative strategies, and net monetary benefits. RESULTS: All decolonisation strategies improved health outcomes and reduced costs. Although universal decolonisation (regardless of MRSA status) was the most cost effective in the short term, strategies using screening to target MRSA carriers may be preferred owing to the reduced risk of selecting for resistance. Among such targeted strategies, universal admission and weekly screening with polymerase chain reaction coupled with decolonisation using nasal mupirocin was the most cost effective. This finding was robust to the size of intensive care units, prevalence of MRSA on admission, proportion of patients classified as high risk, and precise value of willingness to pay for health benefits. All strategies using isolation but not decolonisation improved health outcomes but costs were increased. When the prevalence of MRSA on admission to the intensive care unit was 5% and the willingness to pay per QALY gained was between £20,000 (€23,000; $32,000) and £30,000, the best such strategy was to isolate only those patients at high risk of carrying MRSA (either pre-emptively or after identification by admission and weekly screening for MRSA using chromogenic agar). Universal admission and weekly screening using polymerase chain reaction based detection of MRSA coupled with isolation was unlikely to be cost effective unless prevalence was high (10% of patients colonised with MRSA on admission). CONCLUSIONS: MRSA control strategies that use decolonisation are likely to be cost saving in an intensive care unit setting provided resistance is lacking, and combining universal screening using polymerase chain reaction with decolonisation is likely to represent good value for money if untargeted decolonisation is considered unacceptable. In intensive care units where decolonisation is not implemented, evidence is insufficient to support universal screening for MRSA outside high prevalence settings.

The role of mathematical modelling in guiding the science and economics of malaria elimination.

Unprecedented efforts are now underway to eliminate malaria from many regions. Despite the enormous financial resources committed, if malaria elimination is perceived as failing it is likely that this funding will not be sustained. It is imperative that methods are developed to use the limited data available to design site-specific, cost-effective elimination programmes. Mathematical modelling is a way of including mechanistic understanding to use available data to make predictions. Different strategies can be evaluated much more rapidly than is possible through trial and error in the field. Mathematical modelling has great potential as a tool to guide and inform current elimination efforts. Economic modelling weighs costs against characterised effects or predicted benefits in order to determine the most cost-efficient strategy but has traditionally used static models of disease not suitable for elimination. Dynamic mathematical modelling and economic modelling techniques need to be combined to contribute most effectively to ongoing policy discussions. We review the role of modelling in previous malaria control efforts as well as the unique nature of elimination and the consequent need for its explicit modelling, and emphasise the importance of good disease surveillance. The difficulties and complexities of economic evaluation of malaria control, particularly the end stages of elimination, are discussed.

An augmented data method for the analysis of nosocomial infection data.

The analysis of nosocomial infection data for communicable pathogens is complicated by two facts. First, typical pathogens more commonly cause asymptomatic colonization than overt disease, so transmission can be only imperfectly observed through a sequence of surveillance swabs, which themselves have imperfect sensitivity. Any given set of swab results can therefore be consistent with many different patterns of transmission. Second, data are often highly dependent: the colonization status of one patient affects the risk for others, and, in some wards, repeated admissions are common. Here, the authors present a method for analyzing typical nosocomial infection data consisting of results from arbitrarily timed screening swabs that overcomes these problems and enables simultaneous estimation of transmission and importation parameters, duration of colonization, swab sensitivity, and ward- and patient-level covariates. The method accounts for dependencies by using a mechanistic stochastic transmission model, and it allows for uncertainty in the data by imputing the imperfectly observed colonization status of patients over repeated admissions. The approach uses a Markov chain Monte Carlo algorithm, allowing inference within a Bayesian framework. The method is applied to illustrative data from an interrupted time-series study of vancomycin-resistant enterococci transmission in a hematology ward.

Confronting models with data.

Until now, most of the mathematical modelling work on nosocomial infections has used simple models that have permitted qualitative, but not reliable quantitative predictions about the likely effect of different interventions. Increasingly, researchers would like to use models to provide reliable quantitative answers to both scientific and policy questions. This requires confronting models with data. Here, we discuss the importance of this confrontation with data with reference to previous modelling work, and outline the standard methods for doing this. We then describe a powerful new set of tools that promises to allow us to provide better answers to such questions, making far greater use than current methods of the information content of highly detailed hospital infection datasets. These tools should allow us to address questions that would have been impossible to answer using previous analytical techniques.