Antiviral prophylaxis during pandemic influenza may increase drug resistance

Emergency Medical Services Personnel's Pandemic Influenza Training Received and Willingness to Work during a Future Pandemic

Objective: Identify determinants of emergency medical service (EMS) personnel's willingness to work during an influenza pandemic. Background: Little is known about the willingness of EMS personnel to work during a future influenza pandemic or the extent to which they are receiving pandemic training. Methods: EMS personnel were surveyed in July 2018 - Feb 2019 using a cross-sectional approach; the survey was available both electronically and on paper. Participants were provided a pandemic scenario and asked about their willingness to respond if requested or required; additional questions assessed their attitudes and beliefs and training received. Chi-square tests assessed differences in attitude/belief questions by willingness to work. Logistic regressions were used to identify significant predictors of response willingness when requested or required, controlling for gender and race. Results: 433 individuals completed the survey (response rate = 82.9%). A quarter (26.8%, n = 116) received no pandemic training; 14.3% (n = 62) participated in a pandemic exercise. Significantly more EMS personnel were willing to work when required versus when only requested (88.2% vs 76.9%, X2 = 164.1, p < .001). Predictors of willingness to work when requested included believing it is their responsibility to work, believing their coworkers were likely to work, receiving prophylaxis for themselves and their family members, and feeling safe working during a pandemic. Discussion: Many emergency medical services personnel report lacking training or disaster exercises related to influenza pandemics, and a fair percentage are unwilling to work during a future event. This may limit healthcare surge capacity and could contribute to increased morbidity and mortality. Findings from this study indicate that prehospital staff's attitudes and beliefs about pandemics influence their willingness to work. Pre-event training and planning should address these concerns.

Dynamics of Drug Resistance: Optimal Control of an Infectious Disease

Antimicrobial resistance is a significant public health threat. In the U.S. alone, 2 million people are infected and 23,000 die each year from antibiotic resistant bacterial infections. In many cases, infections are resistant to all but a few remaining drugs. We examine the case where a single drug remains and solve for the optimal treatment policy for an SIS infectious disease model incorporating the effects of drug resistance. The problem is formulated as an optimal control problem with two continuous state variables, the disease prevalence and drug's "quality" (the fraction of infections that are drug-susceptible). The decision maker's objective is to minimize the discounted cost of the disease to society over an infinite horizon. We provide a new generalizable solution approach that allows us to thoroughly characterize the optimal treatment policy analytically. We prove that the optimal treatment policy is a bang-bang policy with a single switching time. The action/inaction regions can be described by a single boundary that is strictly increasing when viewed as a function of drug quality, indicating that when the disease transmission rate is constant, the policy of withholding treatment to preserve the drug for a potentially more serious future outbreak is not optimal. We show that the optimal value function and/or its derivatives are neither C ¹ nor Lipschitz continuous suggesting that numerical approaches to this family of dynamic infectious disease models may not be computationally stable. Furthermore, we demonstrate that relaxing the standard assumption of constant disease transmission rate can fundamentally change the shape of the action region, add a singular arc to the optimal control, and make preserving the drug for a serious outbreak optimal. In addition, we apply our framework to the case of antibiotic resistant gonorrhea.

Procalcitonin kinetics after burn injury and burn surgery in septic and non-septic patients - a retrospective observational study

BACKGROUND

Early sepsis diagnosis is crucial for the correct management of burn patients, and it clearly influences outcomes. The systemic inflammatory response triggered by burns mimics sepsis presentation and complicates early sepsis diagnosis. Biomarkers were advocated to aid the diagnosis of early sepsis. Serum procalcitonin (PCT) exhibits fair accuracy and good correlation with sepsis severity, being used in diverse clinical settings. However, few studies have evaluated perioperative changes in PCT levels in burn patients. The present study evaluated PCT kinetics during the first days after burn injury and subsequent surgical interventions to assess PCT utility in distinguishing septic from non-septic inflammatory responses.

METHODS

This study was a retrospective observational study of all burn patients admitted to the Coimbra Burns Unit (Portugal) between January 2011 and December 2014 who presented with a total burn surface area ≥ 15% and who underwent subsequent surgery. PCT kinetics were investigated a) during the first five days after burn injury and b) preoperatively during the five days after surgery in three subsets of patients, including those with no preoperative and no postoperative sepsis (NN), no preoperative but postoperative sepsis (NS), and preoperative and postoperative sepsis (SS). A total of 145 patients met the selection criteria and were included in the analysis.

RESULTS

PCT levels in the first five days after burn injury were significantly higher in patients who developed at least one sepsis episode (n = 85) compared with patients who did not develop sepsis (n = 60). PCT values > 1.00 ng/mL were clearly associated with sepsis. Study participants (n = 145) underwent a total of 283 surgical interventions. Their distribution by preoperative/postoperative sepsis status was 142 (50.2%) in NN; 62 (21.9%) in NS; and 79 (27.9%) in SS. PCT values exhibited a parallel course in the three groups that peaked on the second postoperative day and returned to preoperative levels on the third day or later. The lowest PCT values were found in NN, and the highest values were observed in SS; the NS values were intermediate.

CONCLUSIONS

PCT kinetics coupled with a clinical examination may be helpful for sepsis diagnosis during the first days after burn injury and burn surgery.

Assessing the oseltamivir-induced resistance risk and implications for influenza infection control strategies

Background

Oseltamivir-resistant mutants with higher drug resistance rates and low trans-mission fitness costs have not accounted for influenza (sub)type viruses. Predicting the impacts of neuraminidase inhibitor therapy on infection rates and transmission of drug-resistant viral strains requires further investigation.

Objectives

The purpose of this study was to assess the potential risk of oseltamivir-induced resistance for influenza A (H1N1) and A (H3N2) viruses.

Materials and methods

An immune-response-based virus dynamic model was used to best fit the oseltamivir-resistant A (H1N1) and A (H3N2) infection data. A probabilistic risk assessment model was developed by incorporating branching process-derived probability distribution of resistance to estimate oseltamivir-induced resistance risk.

Results

Mutation rate and sensitive strain number were key determinants in assessing resistance risk. By increasing immune response, antiviral efficacy, and fitness cost, the spread of resistant strains for A (H1N1) and A (H3N2) were greatly decreased. Probability of resistance depends most strongly on the sensitive strain number described by a Poisson model. Risk of oseltamivir-induced resistance increased with increasing the mutation rate for A (H1N1) only. The ≥50% of resistance risk induced by A (H1N1) and A (H3N2) sensitive infected cells were 0.4 (95% CI: 0.28–0.43) and 0.95 (95% CI 0.93–0.99) at a mutation rate of 10⁻⁶, respectively. Antiviral drugs must be administrated within 1–1.5 days for A (H1N1) and 2–2.5 days for A (H3N2) virus infections to limit viral production.

Conclusion

Probabilistic risk assessment of antiviral drug-induced resistance is crucial in the decision-making process for preventing influenza virus infections.

Design of non-pharmaceutical intervention strategies for pandemic influenza outbreaks

BACKGROUND

As seen during past pandemic influenza outbreaks, pharmaceutical interventions (PHIs) with vaccines and antivirals are the most effective methods of mitigation. However, availability of PHIs is unlikely to be adequate during the early stages of a pandemic. Hence, for early mitigation and possible containment, non-pharmaceutical interventions (NPIs) offer a viable alternative. Also, NPIs may be the only available interventions for most underdeveloped countries. In this paper we present a comprehensive methodology for design of effective NPI strategies.

METHODS

We develop a statistical ANOVA-based design approach that uses a detailed agent-based simulation as an underlying model. The design approach obtains the marginal effect of the characteristic parameters of NPIs, social behavior, and their interactions on various pandemic outcome measures including total number of contacts, infections, and deaths. We use the marginal effects to establish regression equations for the outcome measures, which are optimized to obtain NPI strategies. Efficacy of the NPI strategies designed using our methodology is demonstrated using simulated pandemic influenza outbreaks with different levels of virus transmissibility.

RESULTS

Our methodology was able to design effective NPI strategies, which were able to contain outbreaks by reducing infection attack rates (IAR) to below 10% in low and medium virus transmissibility scenarios with 33% and 50% IAR, respectively. The level of reduction in the high transmissibility scenario (with 65% IAR) was also significant. As noted in the published literature, we also found school closure to be the single most effective intervention among all NPIs.

CONCLUSIONS

If harnessed effectively, NPIs offer a significant potential for mitigation of pandemic influenza outbreaks. The methodology presented here fills a gap in the literature, which, though replete with models on NPI strategy evaluation, lacks a treatise on optimal strategy design.

Tracking oseltamivir-resistance in New Zealand influenza viruses during a medicine reclassification in 2007, a resistant-virus importation in 2008 and the 2009 pandemic

INTRODUCTION

Oseltamivir (Tamiflu®) is an important pharmaceutical intervention against the influenza virus. The importance of surveillance for resistance to oseltamivir has been highlighted by two global events: the emergence of an oseltamivir-resistant seasonal influenza A(H1N1) virus in 2008, and emergence of the influenza A(H1N1)pdm09 virus in 2009. Oseltamivir is a prescription medicine in New Zealand, but more timely access has been provided since 2007 by allowing pharmacies to directly dispense oseltamivir to patients with influenza-like illness.

OBJECTIVE

To determine the frequency of oseltamivir-resistance in the context of a medicine reclassification in 2007, the importation of an oseltamivir-resistant seasonal influenza virus in 2008, and the emergence of a pandemic in 2009.

METHODS

A total of 1795 influenza viruses were tested for oseltamivir-resistance using a fluorometric neuraminidase inhibition assay. Viruses were collected as part of a sentinel influenza surveillance programme between the years 2006 and 2010.

RESULTS

All influenza B, influenza A(H3N2) and influenza A(H1N1)pdm09 viruses tested between 2006 and 2010 were shown to be sensitive to oseltamivir. Seasonal influenza A(H1N1) viruses from 2008 and 2009 were resistant to oseltamivir. Sequencing of the neuraminidase gene showed that the resistant viruses contained an H275Y mutation, and S247N was also identified in the neuraminidase gene of one seasonal influenza A(H1N1) virus that exhibited enhanced resistance.

DISCUSSION

No evidence was found to suggest that increased access to oseltamivir has promoted resistance. A probable importation event was documented for the global 2008 oseltamivir-resistant seasonal A(H1N1) virus nine months after it was first reported in Europe in January 2008.

Pandemic Influenza A H1N1 (2009) Virus: Lessons from the Past and Implications for the Future

The recent pandemic by novel influenza A (H1N1) 2009 (pH1N1) virus is an emerging viral infection, being of significant international concern and requires intensive research. The virus spread in pandemic proportions, and continues to be in the post-pandemic phase. Since, the pH1N1 is still circulating in the community, monitoring is required during the post-pandemic period. The pH1N1 defied influenza seasonality and rapidly became dominant over the seasonal influenza viruses. This new strain was antigenically different from the seasonal H1N1 influenza strains due to the genetic re-assortment. Surprisingly, this new reassortant virus emerged at the end of influenza season, caused a sudden toll of mild illness and is now co-circulating with the seasonal strains. The recent outbreak of pH1N1 consolidates the fact that a new reassortant virus may have originated in animal reservoirs and got transferred to human who were in close contact with these animals. There is a continued need for multisite surveillance to detect potentially dangerous influenza strains, which may emerge and establish themselves in human population. This review is an attempt to address the lessons learnt from the recent influenza pandemic and the future implications for prevention and control of influenza.

Developing guidelines for school closure interventions to be used during a future influenza pandemic

Background

The A/H1N1 2009 influenza pandemic revealed that operational issues of school closure interventions, such as when school closure should be initiated (activation trigger), how long schools should be closed (duration) and what type of school closure should be adopted, varied greatly between and within countries. Computer simulation can be used to examine school closure intervention strategies in order to inform public health authorities as they refine school closure guidelines in light of experience with the A/H1N1 2009 pandemic.

Methods

An individual-based simulation model was used to investigate the effectiveness of school closure interventions for influenza pandemics with R₀ of 1.5, 2.0 and 2.5. The effectiveness of individual school closure and simultaneous school closure were analyzed for 2, 4 and 8 weeks closure duration, with a daily diagnosed case based intervention activation trigger scheme. The effectiveness of combining antiviral drug treatment and household prophyaxis with school closure was also investigated.

Results

Illness attack rate was reduced from 33% to 19% (14% reduction in overall attack rate) by 8 weeks school closure activating at 30 daily diagnosed cases in the community for an influenza pandemic with R₀ = 1.5; when combined with antivirals a 19% (from 33% to 14%) reduction in attack rate was obtained. For R₀ >= 2.0, school closure would be less effective. An 8 weeks school closure strategy gives 9% (from 50% to 41%) and 4% (from 59% to 55%) reduction in attack rate for R₀ = 2.0 and 2.5 respectively; however, school closure plus antivirals would give a significant reduction (~15%) in over all attack rate. The results also suggest that an individual school closure strategy would be more effective than simultaneous school closure.

Conclusions

Our results indicate that the particular school closure strategy to be adopted depends both on the disease severity, which will determine the duration of school closure deemed acceptable, and its transmissibility. For epidemics with a low transmissibility (R₀ < 2.0) and/or mild severity, individual school closures should begin once a daily community case count is exceeded. For a severe, highly transmissible epidemic (R₀ >= 2.0), long duration school closure should begin as soon as possible and be combined with other interventions.

Influenza--insights from mathematical modelling

BACKGROUND

When the first cases of a new infectious disease appear, questions arise about the further course of the epidemic and about the appropriate interventions to be taken to protect individuals and the public as a whole. Mathematical models can help answer these questions. In this article, the authors describe basic concepts in the mathematical modelling of infectious diseases, illustrate their use with a simple example, and present the results of influenza models.

METHOD

Description of the mathematical modelling of infectious diseases and selective review of the literature.

RESULTS

The two fundamental concepts of mathematical modelling of infectious diseases-the basic reproduction number and the generation time-allow a better understanding of the course of an epidemic. Modelling studies based on past influenza epidemics suggest that the rise of the epidemic curve can be slowed at the beginning of the epidemic by isolating ill persons and giving prophylactic medications to their contacts. Later on in the course of the epidemic, restricting the number of contacts (e.g., by closing schools) may mitigate the epidemic but will only have a limited effect on the total number of persons who contract the disease.

CONCLUSION

Mathematical modelling is a valuable tool for understanding the dynamics of an epidemic and for planning and evaluating interventions.

Intervention strategies for an influenza pandemic taking into account secondary bacterial infections

Influenza infections often predispose individuals to consecutive bacterial infections. Both during seasonal and pandemic influenza outbreaks, morbidity and mortality due to secondary bacterial infections can be substantial. With the help of a mathematical model, we investigate the potential impact of such bacterial infections during an influenza pandemic, and we analyze how antiviral and antibacterial treatment or prophylaxis affect morbidity and mortality. We consider different scenarios for the spread of bacteria, the emergence of antiviral resistance, and different levels of severity for influenza infections (1918-like and 2009-like). We find that while antibacterial intervention strategies are unlikely to play an important role in reducing the overall number of cases, such interventions can lead to a significant reduction in mortality and in the number of bacterial infections. Antibacterial interventions become even more important if one considers the--very likely--scenario that during a pandemic outbreak, influenza strains resistant to antivirals emerge. Overall, our study suggests that pandemic preparedness plans should consider intervention strategies based on antibacterial treatment or prophylaxis through drugs or vaccines as part of the overall control strategy. A major caveat for our results is the lack of data that would allow precise estimation of many of the model parameters. As our results show, this leads to very large uncertainty in model outcomes. As we discuss, precise assessment of the impact of antibacterial strategies during an influenza pandemic will require the collection of further data to better estimate key parameters, especially those related to the bacterial infections and the impact of antibacterial intervention strategies.

Age-prioritized use of antivirals during an influenza pandemic

Background

The WHO suggested that governments stockpile, as part of preparations for the next influenza pandemic, sufficient influenza antiviral drugs to treat approximately 25% of their populations. Our aim is two-fold: first, since in many countries the antiviral stockpile is well below this level, we search for suboptimal strategies based on treatment provided only to an age-dependent fraction of cases. Second, since in some countries the stockpile exceeds the suggested minimum level, we search for optimal strategies for post-exposure prophylactic treatment of close contacts of cases.

Methods

We used a stochastic, spatially structured individual-based model, considering explicit transmission in households, schools and workplaces, to simulate the spatiotemporal spread of an influenza pandemic in Italy and to evaluate the efficacy of interventions based on age-prioritized use of antivirals.

Results

Our results show that the antiviral stockpile required for treatment of cases ranges from 10% to 35% of the population for R₀ in 1.4 – 3. No suboptimal strategies, based on treatment provided to an age-dependent fraction of cases, were found able to remarkably reduce both clinical attack rate and antiviral drugs needs, though they can contribute to largely reduce the excess mortality. Treatment of all cases coupled with prophylaxis provided to younger individuals is the only intervention resulting in a significant reduction of the clinical attack rate and requiring a relatively small stockpile of antivirals.

Conclusion

Our results strongly suggest that governments stockpile sufficient influenza antiviral drugs to treat approximately 25% of their populations, under the assumption that R₀ is not much larger than 2. In countries where the number of antiviral stockpiled exceeds the suggested minimum level, providing prophylaxis to younger individuals is an option that could be taken into account in preparedness plans. In countries where the number of antivirals stockpiled is well below 25% of the population, priority should be decided based on age-specific case fatality rates. However, late detection of cases (administration of antivirals 48 hours after the clinical onset of symptoms) dramatically affects the efficacy of both treatment and prophylaxis.