Showing papers by "M. Elizabeth Halloran published in 2012"

Controlling dengue with vaccines in Thailand.

Abstract: Background Dengue is a mosquito-borne infectious disease that constitutes a growing global threat with the habitat expansion of its vectors Aedes aegyti and A. albopictus and increasing urbanization. With no effective treatment and limited success of vector control, dengue vaccines constitute the best control measure for the foreseeable future. With four interacting dengue serotypes, the development of an effective vaccine has been a challenge. Several dengue vaccine candidates are currently being tested in clinical trials. Before the widespread introduction of a new dengue vaccine, one needs to consider how best to use limited supplies of vaccine given the complex dengue transmission dynamics and the immunological interaction among the four dengue serotypes.

Estimating within-school contact networks to understand influenza transmission

Abstract: Many epidemic models approximate social contact behavior by assuming random mixing within mixing groups (e.g., homes, schools, and workplaces). The effect of more realistic social network structure on estimates of epidemic parameters is an open area of exploration. We develop a detailed statistical model to estimate the social contact network within a high school using friendship network data and a survey of contact behavior. Our contact network model includes classroom structure, longer durations of contacts to friends than non-friends and more frequent contacts with friends, based on reports in the contact survey. We performed simulation studies to explore which network structures are relevant to influenza transmission. These studies yield two key findings. First, we found that the friendship network structure important to the transmission process can be adequately represented by a dyad-independent exponential random graph model (ERGM). This means that individual-level sampled data is sufficient to characterize the entire friendship network. Second, we found that contact behavior was adequately represented by a static rather than dynamic contact network. We then compare a targeted antiviral prophylaxis intervention strategy and a grade closure intervention strategy under random mixing and network-based mixing. We find that random mixing overestimates the effect of targeted antiviral prophylaxis on the probability of an epidemic when the probability of transmission in 10 minutes of contact is less than 0.004 and underestimates it when this transmission probability is greater than 0.004. We found the same pattern for the final size of an epidemic, with a threshold transmission probability of 0.005. We also find random mixing overestimates the effect of a grade closure intervention on the probability of an epidemic and final size for all transmission probabilities. Our findings have implications for policy recommendations based on models assuming random mixing, and can inform further development of network-based models.

Causal Inference for Vaccine Effects on Infectiousness

Abstract: If a vaccine does not protect individuals completely against infection, it could still reduce infectiousness of infected vaccinated individuals to others. Typically, vaccine efficacy for infectiousness is estimated based on contrasts between the transmission risk to susceptible individuals from infected vaccinated individuals compared with that from infected unvaccinated individuals. Such estimates are problematic, however, because they are subject to selection bias and do not have a causal interpretation. Here, we develop causal estimands for vaccine efficacy for infectiousness for four different scenarios of populations of transmission units of size two. These causal estimands incorporate both principal stratification, based on the joint potential infection outcomes under vaccine and control, and interference between individuals within transmission units. In the most general scenario, both individuals can be exposed to infection outside the transmission unit and both can be assigned either vaccine or control. The three other scenarios are special cases of the general scenario where only one individual is exposed outside the transmission unit or can be assigned vaccine. The causal estimands for vaccine efficacy for infectiousness are well defined only within certain principal strata and, in general, are identifiable only with strong unverifiable assumptions. Nonetheless, the observed data do provide some information, and we derive large sample bounds on the causal vaccine efficacy for infectiousness estimands. An example of the type of data observed in a study to estimate vaccine efficacy for infectiousness is analyzed in the causal inference framework we developed.

Performance of rapid influenza H1N1 diagnostic tests: a meta-analysis

Abstract: Please cite this paper as: Chu et al. (2011) Performance of rapid influenza H1N1 diagnostic tests: a meta-analysis. Influenza and Other Respiratory Viruses DOI: 10.1111/j.1750-2659.2011.00284.x. Background Following the outbreaks of 2009 pandemic H1N1 infection, rapid influenza diagnostic tests have been used to detect H1N1 infection. However, no meta-analysis has been undertaken to assess the diagnostic accuracy when this manuscript was drafted. Methods The literature was systematically searched to identify studies that reported the performance of rapid tests. Random effects meta-analyses were conducted to summarize the overall performance. Results Seventeen studies were selected with 1879 cases and 3477 non-cases. The overall sensitivity and specificity estimates of the rapid tests were 0·51 (95%CI: 0·41, 0·60) and 0·98 (95%CI: 0·94, 0·99). Studies reported heterogeneous sensitivity estimates, ranging from 0·11 to 0·88. If the prevalence was 30%, the overall positive and negative predictive values were 0·94 (95%CI: 0·85, 0·98) and 0·82 (95%CI: 0·79, 0·85). The overall specificities from different manufacturers were comparable, while there were some differences for the overall sensitivity estimates. BinaxNOW had a lower overall sensitivity of 0·39 (95%CI: 0·24, 0·57) compared with all the others (P-value <0·001), whereas QuickVue had a higher overall sensitivity of 0·57 (95%CI: 0·50, 0·63) compared with all the others (P-value = 0·005). Conclusions Rapid tests have high specificity but low sensitivity and thus limited usefulness.

The Minicommunity Design to Assess Indirect Effects of Vaccination

Abstract: We propose the minicommunity design to estimate indirect effects of vaccination. Establishing indirect effects of vaccination in unvaccinated subpopulations could have important implications for global vaccine policies. In the minicommunity design, the household or other small transmission unit serves as the cluster in which to estimate indirect effects of vaccination, similar to studies in larger communities to estimate indirect, total, and overall effects. Examples from the literature include studies in small transmission units to estimate indirect effects of pertussis, pneumococcal, influenza, and cholera vaccines. We characterize the minicommunity design by several methodologic considerations, including the assignment mechanism, ascertainment, the role of transmission outside the transmission unit, and the relation of the size of the transmission unit to number of people vaccinated. The minicommunity study for indirect effects is contrasted with studies to estimate vaccine effects on infectiousness and protective effects under conditions of household exposure within small transmission units. The minicommunity design can be easily implemented in individually randomized studies by enrolling and following-up members of households of the randomized individuals. The methodology for the minicommunity design for estimating indirect effects of vaccination deserves much future research.

A Hybrid EM and Monte Carlo EM Algorithm and Its Application to Analysis of Transmission of Infectious Diseases

Abstract: In epidemics of infectious diseases such as influenza, an individual may have one of four possible final states: prior immune, escaped from infection, infected with symptoms, and infected asymptomatically. The exact state is often not observed. In addition, the unobserved transmission times of asymptomatic infections further complicate analysis. Under the assumption of missing at random, data-augmentation techniques can be used to integrate out such uncertainties. We adapt an importance-sampling-based Monte Carlo Expectation-Maximization (MCEM) algorithm to the setting of an infectious disease transmitted in close contact groups. Assuming the independence between close contact groups, we propose a hybrid EM-MCEM algorithm that applies the MCEM or the traditional EM algorithms to each close contact group depending on the dimension of missing data in that group, and discuss the variance estimation for this practice. In addition, we propose a bootstrap approach to assess the total Monte Carlo error and factor that error into the variance estimation. The proposed methods are evaluated using simulation studies. We use the hybrid EM-MCEM algorithm to analyze two influenza epidemics in the late 1970s to assess the effects of age and preseason antibody levels on the transmissibility and pathogenicity of the viruses.

Comparing bounds for vaccine effects on infectiousness.

Abstract: Halloran and Hudgens1 and VanderWeele and Tchetgen Tchetgen2 (henceforth HH and VT) consider inference about causal vaccine effects for infectiousness. The two papers make assumptions that result in different bounds. Assume a random sample of N households with two individuals. Let Zij = 1 if individual j in household i receives vaccine and 0 otherwise, i = 1,…, N, j = 1, 2. Let Zi = (Zi1, Zi2) denote the treatment assignment vector for household i, and zi, zij denote possible values of Zi, Zij. Let Yij(zi1, zi2) denote the potential infection outcome for individual j in household i if the two individuals in household i have vaccine status (zi1, zi2). Let Yijobs be the observed value of Yij under an actual assignment, i.e., Yijobs=Yij(Zi). Assume throughout only individual 1 can be assigned to vaccine or control, vaccine assignment is randomized, and there is no interference across transmission units. Let p1=E[Yi2obs∣Zi1=1,Yi1obs=1] and p0=E[Yi2obs∣Zi1=0,Yi1obs=1]. The net vaccine effect on infectiousness based on the observed data when the exposed individual has vaccine status 0 is RDInet(0)=p1−p0. This net vaccine effect is difficult to interpret without additional assumptions because of selection bias, i.e., households that become infected when randomized to vaccine might not be comparable to households that become infected under control. Let pv = E[Yi2(1, 0)∣Yi1(1, 0) = Yi1(0, 0) = 1] and pu = E[Yi2(0, 0)∣Yi1(1, 0) = Yi1(0, 0) = 1]. The causal risk difference in the stratum of households where individual 1 becomes infected whether receiving vaccine or control is CRDI(0) = pv − pu. The CRDI(0) is not subject to selection bias. Even though this causal effect is not identifiable, the observable data provide information such that bounds can be estimated. Monotonicity assumes the vaccine does not increase the risk that individual 1 becomes infected. HH develop bounds on CRDI(0) assuming monotonicity. Under monotonicity, there are three principal strata based on the joint potential outcomes under vaccine and control of person j = 1 (eTable 1), namely the immune stratum (never infected), the protected stratum (not infected if vaccinated, infected if not vaccinated), and the doomed stratum (always infected). Under monotonicity, p1 = pv. Given the observed data, the proportion ρ of households where individual 1 is randomized to control and becomes infected that is in the doomed principal stratum is identifiable, just not which ones. Thus the HH bounds on CRDI(0) are CRDIH H,low(0)=p1−min{1,p0/ρ}, (1) CRDIH H,up(0)=p1−max{0,(p0−(1−ρ))/ρ}. (2) In contrast, VT make the additional assumption that in the absence of vaccination, individuals who become infected regardless of whether they are vaccinated are more infectious than individuals who are protected by the vaccine. Under this assumption, VT show the net risk difference is an upper bound for the causal risk difference. The net risk difference is always less than or equal (2). The main difference between the VT and HH bounds is the reliance on this assumption, which is untestable. Consider the study of 3000 households with two individuals in Table 1. The net vaccine effect on infectiousness is RDInet(0)=0.2−0.4=−0.2. Because p0 = 0.4 and ρ = 0.5, min{1, p0/ρ} = 0.8 and max{0, (p0−(1−ρ))/ρ} = 0. Thus CRDIH H,low(0)=0.2−0.8=−0.6 and CRDIH H,up(0)=0.2−0=0.2. The HH upper bound is positive, allowing that vaccination might actually enhance infectiousness. The VT upper bound is RDInet(0)=−0.2, indicating that, ignoring statistical variability, vaccination decreases infectiousness. Thus, in contrast to the HH bounds, the VT bound leads to the conclusion the vaccine is beneficial in reducing secondary transmissions. The different bounds could lead to different qualitative conclusions about whether the vaccine decreases, or possibly increases, infectiousness. Thus, one needs to examine critically the underlying biological and selection assumptions when determining bounds. Rather than relying on untestable assumptions, the bounds (1) and (2) can be combined with a sensitivity analysis (as in Hudgens and Halloran3) to make clear the degree to which conclusions about the vaccine having an effect on infectiousness depend on merging the data with strong prior beliefs. Further details are in the Online Supporting Material. Table 1 Identifiability and bounds on the causal risk difference CRDI(0). 3000 households of size 2 with individual 1 randomized to vaccine or control 1:1. In the 1500 households with Zi = (0, 0), individual 1 became infected in 1000, and individual 2 became ...