Raffaele Vardavas

The Emergence of HIV Transmitted Resistance in Botswana: “When Will the WHO Detection Threshold Be Exceeded?”

Background The Botswana antiretroviral program began in 2002 and currently treats 42,000 patients, with a goal of treating 85,000 by 2009. The World Health Organization (WHO) has begun to implement a surveillance system for detecting transmitted resistance that exceeds a threshold of 5%. However, the WHO has not determined when this threshold will be reached. Here we model the Botswana governments treatment plan and predict, to 2009, the likely stochastic evolution of transmitted resistance. Methods We developed a model of the stochastic evolution of drug-resistant strains and formulated a birth-death Master equation. We analyzed this equation to obtain an analytical solution of the probabilistic evolutionary trajectory for transmitted resistance, and used treatment and demographic data from Botswana. We determined the temporal dynamics of transmitted resistance as a function of: (i) the transmissibility (i.e., fitness) of the drug-resistant strains that may evolve and (ii) the rate of acquired resistance. Results Transmitted resistance in Botswana will be unlikely to exceed the WHOs threshold by 2009 even if the rate of acquired resistance is high and the strains that evolve are half as fit as the wild-type strains. However, we also found that transmission of drug-resistant strains in Botswana could increase to ∼15% by 2009 if the drug-resistant strains that evolve are as fit as the wild-type strains. Conclusions Transmitted resistance will only be detected by the WHO (by 2009) if the strains that evolve are extremely fit and acquired resistance is high. Initially after a treatment program is begun a threshold lower than 5% should be used; and we advise that predictions should be made before setting a threshold. Our results indicate that it may be several years before the WHOs surveillance system is likely to detect transmitted resistance in other resource-poor countries that have significantly less ambitious treatment programs than Botswana.

Mean-field analysis of an inductive reasoning game : Application to influenza vaccination

We define and analyze an inductive reasoning game of voluntary yearly vaccination in order to establish whether or not a population of individuals acting in their own self-interest would be able to prevent influenza epidemics. We find that epidemics are rarely prevented. We also find that severe epidemics may occur without the introduction of pandemic strains. We further address the situation where market incentives are introduced to help ameliorating epidemics. Surprisingly, we find that vaccinating families exacerbates epidemics. However, a public health program requesting prepayment of vaccinations may significantly ameliorate influenza epidemics.

Test-and-Treat in Los Angeles: A Mathematical Model of the Effects of Test-and-Treat for the Population of Men Who Have Sex With Men in Los Angeles County

BACKGROUND There is evidence to suggest that antiretroviral therapy (ART) and testing for human immunodeficiency virus (HIV) reduce the probability of transmission of HIV. This has led health officials across the United States to take steps toward a test-and-treat policy. However, the extent of the benefits generated by test-and-treat is debatable, and there are concerns, such as increased multidrug resistance (MDR), that remain unaddressed. METHODS We developed a deterministic epidemiologic model to simulate the HIV/AIDS epidemic for men who have sex with men (MSM) in Los Angeles County (LAC). We calibrated the model to match the HIV surveillance data from LAC across a 10-year period, starting in 2000. We then modified our model to simulate the test-and-treat policy and compared epidemiologic outcomes under the test-and-treat scenario to the status quo scenario over the years 2012-2023. Outcome measures included new infections, deaths, new AIDS cases, and MDR. RESULTS Relative to the status quo, the test-and-treat model resulted in a 34% reduction in new infections, 19% reduction in deaths, and 39% reduction in new AIDS cases by 2023. However, these results are counterbalanced by a near doubling of the prevalence of MDR (9.06% compared to 4.79%) in 2023. We also found that the effects of increasing testing and treatment were not complementary. CONCLUSIONS Although test-and-treat generates substantial benefits, it will not eliminate the epidemic for MSM in LAC. Moreover, these benefits are counterbalanced by large increases in MDR.

Theory versus Data: How to Calculate R0?

To predict the potential severity of outbreaks of infectious diseases such as SARS, HIV, TB and smallpox, a summary parameter, the basic reproduction number R0, is generally calculated from a population-level model. R0 specifies the average number of secondary infections caused by one infected individual during his/her entire infectious period at the start of an outbreak. R0 is used to assess the severity of the outbreak, as well as the strength of the medical and/or behavioral interventions necessary for control. Conventionally, it is assumed that if R0>1 the outbreak generates an epidemic, and if R0<1 the outbreak becomes extinct. Here, we use computational and analytical methods to calculate the average number of secondary infections and to show that it does not necessarily represent an epidemic threshold parameter (as it has been generally assumed). Previously we have constructed a new type of individual-level model (ILM) and linked it with a population-level model. Our ILM generates the same temporal incidence and prevalence patterns as the population-level model; we use our ILM to directly calculate the average number of secondary infections (i.e., R0). Surprisingly, we find that this value of R0 calculated from the ILM is very different from the epidemic threshold calculated from the population-level model. This occurs because many different individual-level processes can generate the same incidence and prevalence patterns. We show that obtaining R0 from empirical contact tracing data collected by epidemiologists and using this R0 as a threshold parameter for a population-level model could produce extremely misleading estimates of the infectiousness of the pathogen, the severity of an outbreak, and the strength of the medical and/or behavioral interventions necessary for control.

Is there any evidence that syphilis epidemics cycle

We re-examine the evidence behind the controversial hypothesis that syphilis epidemics cycle. We used the same methods (spectral analysis) used by the proponents of this hypothesis to reanalyse a longitudinal dataset provided by the US Centers for Disease Control and Prevention (CDC). We also analysed a longitudinal CDC mortality dataset. To investigate the theoretical results generated by the transmission model that was used to support the hypothesis, we simulated the model and predicted the expected dynamics of syphilis epidemics. By contrast with previous findings, we found that neither of the CDCs datasets provides compelling evidence that syphilis epidemics cycle, and the transmission model (if more reasonable parameter values are used) does not predict cycling behaviour. We explain the possible reasons for the previous proposal that syphilis epidemics cycle. Our findings imply that it is quite possible that the CDC could be successful in eliminating syphilis within the next few decades.

A universal long-term flu vaccine may not prevent severe epidemics

BackgroundRecently, the promise of a new universal long-term flu vaccine has become more tangible than ever before. Such a vaccine would protect against very many seasonal and pandemic flu strains for many years, making annual vaccination unnecessary. However, due to complacency behavior, it remains unclear whether the introduction of such vaccines would maintain high and stable levels of vaccination coverage year after year.FindingsTo predict the impact of universal long-term flu vaccines on influenza epidemics we developed a mathematical model that linked human cognition and memory with the transmission dynamics of influenza. Our modeling shows that universal vaccines that provide short-term protection are likely to result in small frequent epidemics, whereas universal vaccines that provide long-term protection are likely to result in severe infrequent epidemics.ConclusionsInfluenza vaccines that provide short-term protection maintain risk awareness regarding influenza in the population and result in stable vaccination coverage. Vaccines that provide long-term protection could lead to substantial drops in vaccination coverage and should therefore include an annual epidemic risk awareness programs in order to minimize the risk of severe epidemics.

Modeling Influenza Vaccination Behavior Via Inductive Reasoning Games

Past experiences with seasonal influenza and immunization may affect individual decisions about whether to obtain vaccinations. Individuals continually adapt to recent influenza-related experiences, using inductive thought to reevaluate their options to obtain vaccinations. We explore this concept by constructing an individual-level model of adaptive decision-making. We couple this model with a population-level model of influenza that includes vaccination dynamics. The coupled models allow us to explore how individual-level decisions may change influenza epidemiology and, conversely, how influenza epidemiology might change individual-level decisions. By including the effects of adaptive decision-making within an epidemic model, we show that the behavioral dynamics of vaccination uptake could lead to severe influenza epidemics even without the presence of a pandemic strain. We further show that these severe epidemics might be prevented if vaccination programs provided commitment-based incentives or if mass media released epidemiological information that individuals can use to evaluate the prudence of vaccination. Finally we discuss and present some preliminary results of the model when social networks offer preferential paths for transmission.

Modeling Employer Self-Insurance Decisions after the Affordable Care Act

OBJECTIVE To present a microsimulation model that addresses the methodological challenge of estimating the firm decision to self-insure. METHODOLOGY The model considers the risk that the firm bears when self-insuring and the opportunity to mitigate that risk by purchasing stop-loss insurance. The model makes use of a structural, utility maximization framework to account for numerous aspects of the firm decision, and a multinomial probit to reproduce the elasticity of the firms demand for health insurance. FINDINGS AND CONCLUSIONS Our simulations provide three important conclusions. First, they project significant increases in self-insurance rates among small firms--presumably induced by the desire to avoid ACAs rate-banding and risk adjustment regulations-only if generous stop-loss policies become widely available. Second, they show that this increase would be due to this hypothetical adoption of widespread, generous reinsurance by the market and not by passage of the ACA. Third, even with a substantial increase of self-insurance rates among small firms, they project negligible adverse selection in the exchanges, as indicated by our finding that the increase in exchange premium is less than 0.5% when assuming very generous stop-loss policies after implementation of the ACA.

Preventing, Identifying, and Treating Prescription Drug Misuse Among Active-Duty Service Members

Prescription drug misuse (PDM) is of critical concern for the military because of its potential impact on military readiness, the health and well-being of military personnel, and associated health care costs. The purpose of this study is to summarize insights gleaned from a series of activities that the RAND Corporation undertook for the Deputy Assistant Secretary of Defense for Readiness to address this important health and military readiness issue. The authors completed a review of U.S. Department of Defense policies and a comprehensive literature review of clinical guidelines and the empirical literature on the prevention and treatment of PDM and conducted individual face-to-face interviews with 66 health and behavioral health care providers at nine medical treatment facilities across three regions within the contiguous United States to identify best practices in the prevention, identification, and treatment of PDM and the extent to which those practices are known and followed. The study also presents the framework of an analytic tool that, once informed by data available to the military but not available to the authors, can assist the military in predicting future trends in PDM based on current demographics of active-duty service members and rates of injury and prescribing of prescription drugs. The findings from this work led the authors to formulate a set of key insights that they believe might improve the rapid identification and treatment of service members dealing with PDM, thereby improving future force readiness.

Reply to Gonzalez-Serna et al

We thank Gonzalez-Serna and his colleagues for initiating a discussion on our paper that models the impact of the test-and-treat policy on the human immunodeficiency virus (HIV) epidemic in Los Angeles County. Gonzalez-Serna et al contest the relevance of our findings that test-and-treat could potentially increase multidrug resistance (MDR) in Los Angeles by 89% [1]. Using observational data from British Columbia, Canada, they show that MDR and total drug resistance prevalence in British Columbia decreased over a period during which antiretroviral treatment (ART) prevalence increased by 60%. There are several reasons why this finding might not be particularly relevant for evaluating the impact of test-and-treat in Los Angeles or other regions of the world. First, British Columbia is a distinct setting with vastly different demographics and healthcare characteristics than Los Angeles. There is evidence that MDR prevalence in Canada is generally much lower than in the United States [2]. As with any mathematical model, our results are a product of the assumptions we make about parameter values and initial conditions, which are setting-specific. Because MDR prevalence in Los Angeles is higher at baseline than in British Columbia, the force of infection of transmitted resistance is higher, causing faster growth. Model results may be different with dynamics based on British Columbia characteristics. Second, these findings from British Columbia are inconsistent with findings in other parts of the world [3–6]. A recent World Health Organization report highlights the growth of drug-resistant HIV in low- and middle-income countries over the past decade, and shows a positive association between ART coverage and prevalence of transmitted drug-resistant HIV [7]. Third, a recent Canadian surveillance report shows that the results Gonzales-Serna et al report from British Columbia might not even generalize to other provinces in Canada. This report surveys 6 Canadian provinces and shows that prevalence of resistance increased by approximately 70% from 1999 to 2008 and there was no reduction in MDR [8]. Furthermore, a considerable proportion of transmitted drug resistance in both the United States and Canada remains undetected [9]. Fourth, early-stage HIV (ESH) treatment—a hallmark of test-and-treat—stayed relatively stable in British Columbia from 1995 to 2008 [10]. It is likely that ESH will coincide with lower levels of adherence, a strong predictor of increases in acquired drug resistance. Gonzalez-Serna et al also note a decrease in clinical significance of MDR, as dozens of different drug classes are now available. They suggest that pandrug resistance is the appropriate resistance measure instead of triple-therapy resistance, which we use. Even if this were true, MDR will likely raise HIV treatment costs for cash-strapped patients or healthcare systems. In addition, with expansion of early treatment, MDR detection might be more difficult as patients are asymptomatic and might not be monitored closely. Without close monitoring of resistance, patients could unknowingly develop MDR and thus delay initiation of second-line treatment. Therefore, the clinical significance of MDR may be greater with a larger ESH treatment prevalence. We agree with Gonzalez-Serna et al (and state in the main text) that test-and-treat is likely to bring epidemiologic benefits even with MDR growth, and we do not recommend abandoning this policy. However, Gonzalez-Serna et al downplay potential implications of MDR growth. We believe a prudent approach would be to evaluate the cost-effectiveness of test-and-treat compared to other policies, and if adoption of test-and-treat is warranted, it should be accompanied by initiatives to control and closely monitor MDR such as expanded MDR surveillance and interventions to improve adherence.