Purpose: Pneumococcal disease is a leading cause of mortality and morbidity, particularly in immunocompromised persons, but the currently recommended pneumococcal polysaccharide vaccine (PPSV) has limited effectiveness in this group. Some evidence suggests that the pneumococcal conjugate vaccine (PCV), newly approved for adults and more costly than PPSV, is effective in the immunocompromised, but its costeffectiveness is unknown.
Method: We used a Markov model to estimate the cost effectiveness of 4 vaccination strategies in immunocompromised persons: no vaccine, a single PPSV, two PPSV doses 5 years apart (the CDC recommendation), and a single PCV. We considered, over a 15year time horizon, immunocompromised persons aged 1864 years (average life expectancy 11.7 years). Pneumococcal disease rates were obtained from US databases, as were childhood vaccination indirect effect projections. PCV effectiveness was estimated by a Delphi expert panel; PPSV protection was modeled relative to PCV effectiveness. In the model, both vaccines prevented invasive pneumococcal disease (IPD), but only PCV prevented nonbacteremic pneumococcal pneumonia (NPP), consistent with published data. Illness costs were obtained from the Nationwide Inpatient Sample and utilities taken from the literature. We used 2006 US costs, took a societal perspective, discounted costs and effectiveness 3%/yr, and used a $100,000/QALY costeffectiveness criterion.
Result: Compared to no vaccination, PCV cost $70,900/QALY gained if PPSV relative effectiveness compared to PCV was <53%; if PPSV relative effectiveness is >72%, singledose PPSV was favored. Extended dominance eliminated twodose PPSV in all analyses. In HIV patients, who have longer life expectancy (22.5 years), PCV was favored unless PPSV effectiveness is >93% of PCV's. A major driver of results was PCV effectiveness against NPP, which is unclear, particularly in the immunocompromised; PCV is not favored in the base case if its NPP effectiveness relative to its IPD effectiveness was ≤49% (expert estimate 70%, dashed line in figure). The Figure depicts a 2way sensitivity analysis, varying PPSV relative effectiveness (xaxis) and PCV effectiveness against NPP (yaxis). Probabilistic sensitivity analyses supported these results.

Conclusion: PCV in immunocompromised patients appears to be economically reasonable; however, the decision is sensitive to assumptions regarding overall PPSV effectiveness and PCV effectiveness against NPP. PCV is more strongly favored in HIV patients, due to their longer life expectancy. A twodose PPSV strategy, as recommended by the CDC, is dominated.
Purpose: We created a mathematical model of syphilis transmission dynamics to inform optimal syphilis screening strategies in urban areas in Ontario, Canada.
Method: Given that the syphilis resurgence among men who have sex with men (MSM) continues despite attempts at heightened screening and testing, we developed an agentbased dynamic model representing a core population of 2,000 MSM, forming a network of sexual contacts along which syphilis transmission can occur. Model parameters describing the epidemiology of the current epidemic and syphilis disease natural history were drawn from Ontario surveillance data supplemented by literaturederived estimates. Model outputs for the preintervention period were compared to surveillance data to identify credible simulations. A total of 380 to 405 wellcalibrated simulations were used for the analysis of each intervention. Evaluated strategies included: (i) increased frequency of syphilis screening; (ii) increasing coverage of annual syphilis screening; or (iii) a combination of (i) and (ii). Intervention impact was measured as the cumulative incidence of detected and total infectious syphilis cases per year over a 5year time period.
Result: Model outputs indicated that increasing frequency of syphilis screening to every three months was most effective in reducing reported and total infectious syphilis infections. By contrast, increasing test numbers by increasing the fraction of individuals tested, without increasing test frequency, resulted in no appreciable change in syphilis incidence, as the reduction in the number of infectious individuals, due to treatment, was counterbalanced by increased infectious syphilis in individuals who had previously had latent (noninfectious) infection.
Conclusion: Our model reproduced the (counterintuitive) persistence of elevated syphilis incidence that has been noted empirically in the face of screening “blitzes” targeting MSM at high risk of infectious syphilis. By contrast, strategies that focus on higher frequency of testing in smaller fractions of the population were more effective in reducing syphilis incidence in a simulated MSM population. These findings highlight how treatmentinduced loss of protective immunity creates nuances in screeningbased control strategies.
Purpose: Nearly 2 million Americans are unaware that they are infected with chronic hepatitis C (HCV). HCV screening and treatment may be more efficient in identifiable subgroups with higher HCV prevalence, especially when coupled with programs to reduce mortality risks from comorbidities. No single study contains data needed to estimate subgroupspecific prevalence of HCV, risk factor status, and mortality risks. We developed a combined modeling approach to infer necessary riskgroupspecific mortality rates for chronically HCVinfected U.S. adults.
Method: We used logistic regression to estimate age, sex, and racespecific HCV infection and riskfactor prevalence using the 200108 National Health and Nutrition Examination Survey (NHANES). We defined highrisk status as prior injection drug use, transfusion before 1992, or >20 lifetime sex partners. We analyzed NHANES III (198894) linked mortality data using Cox proportional hazards model to obtain hazard ratios (HR) by sex, race, risk, and HCV infection status. We incorporated these estimates into a Markov model to infer the age, sex, race, risk, and HCV infection statusspecific mortality rates that best fit overall agespecific population mortality rates (2006 life tables).
Result: We estimated HCV antibody prevalence for subgroups above age 40. For example, in 5059 yearolds, prevalence is higher for blacks (7.3% males; 4.8% females) than for nonblacks (4.9% males; 3.2% females). Depending on subgroup, 1531% are highrisk, and HCV antibody prevalence is higher for highrisk individuals (1117%) compared to lowrisk individuals (23%). Adjusting for age in a multivariate model, allcause mortality rates are higher in men (HR: 1.3 [1.11.7]); blacks (HR: 1.7 [1.52.1]); highrisk individuals (HR: 1.4 [1.01.9]); and HCV infected individuals (HR: 3.5 [2.06.0]). We also estimated that for HCVinfected individuals, 20% of mortality is liverrelated. Combining these estimates in a Markov model, we inferred sixteen life tables by sex, race, risk, and HCV infection status. Within each subgroup, the life expectancy of highrisk individuals is up to 3 years shorter; similarly, the life expectancy of chronically HCVinfected individuals is up to 9 years shorter.
Conclusion: Quantifying mortality rates of highrisk HCVinfected individuals permits more accurate estimates of the potential benefits of HCV screening and treatment. With 5% of older Americans infected with HCV, costeffectiveness analyses of expanded HCV screening and treatment require methods to appropriately quantify differential mortality risks.
Purpose: Recent data showing a high incidence of HIV infection among men who have sex with men (MSM) and other groups at high risk for acquiring HIV suggest that HIV screening more frequently than annually may be warranted. We assessed the costeffectiveness of HIV screening for MSM, high risk heterosexuals (HRH) and injection drug users (IDUs) at 3 and 6 month intervals compared with annual screening.
Methods: We used a published mathematical model of HIV transmission to evaluate screening intervals for each population using cohorts of 10,000 MSM, HRH and IDU ages 1464. We incorporated HIV transmissions averted due to serostatus awareness for each screening interval, as well as HIV testing costs and treatment costs saved from averted transmissions. Using surveillance and demographic data, we estimated HIV incidence to be 1.27% for MSM, 0.39% for IDU and 0.08% for HRH and conducted threshold analyses on incidence. We assumed conventional testing and 80% receipt of results.
Results: For MSM, HIV screening was costsaving for both 6month compared to annual screening, and quarterly compared to 6month screening. Threshold values for HIV at which screening MSM was <$100,000 per QALY saved was 0.08% and 0.3% at the 6month and quarterly screening intervals, respectively. Costeffectiveness was below $100,000 per QALY saved for screening IDUs and greater than $100,000 per QALY saved for screening HRHs at 6month intervals. For IDU and HRH the incidence threshold at which 6month screening was <$100,000 per QALY saved was .12% for IDU and .10% for HRH.
Conclusion: HIV screening as frequently as quarterly for MSM and every 6months for IDU populations is very costeffective, while more frequent screening for HRH was greater than $100,000 per QALY saved. Reexamination of HIV screening intervals for MSM and IDU populations should be considered on the basis of the economic evidence. Table: Costeffectiveness of HIV Screening at Different Intervals for MSM, IDU and HRH
Purpose: To develop an algorithm that extends survival probabilities based on the Seattle Heart Failure Model (SHFM) to generate estimates of survival time and mode of death for its integration in a customizable model designed to evaluate the costeffectiveness of patientcentered interventions for heart failure (TEAMHF).
Method: The SHFM is a multivariate risk model that has been shown to provide accurate 1, 2, and 3year estimates for the survival of heart failure patients. These estimates are obtained by first calculating a SHFM score, which is based on various demographic, clinical and laboratory characteristics, and then using this score within an exponential hazard function. Since medical costs incurred from sudden cardiac death differ from other nonsudden modes of death, it is desirable to have the capability of accounting for different modes of death in the TEAMHF model. To accomplish this, we made the immediate modification of declaring a causespecific hazard function in a competing risks setting. Furthermore, in an effort to obtain more realistic longterm projections, we replaced the standard exponential hazard function with a Gompertzbased hazard function. Model parameters were then calibrated using the pooled data from several randomized trials and prospective cohort studies of heart failure patients.
Result: Our model suggests that the predicted mode of death changes across survival time and SHFM scores. We have integrated this procedure within the TEAMHF costeffectiveness model that generates virtual cohorts of patients by sampling sets of patient characteristics from a multivariate distribution, wherein each characteristic is defined in terms of its mean and standard deviation, and the global correlation structure is derived from a known target population. For a particular SHFM score, the model calculates the expected survival time, as well as the conditional and unconditional probabilities of death associated with each cause of death. For simulated patients with a particular SHFM score in the costeffectiveness model, their mode of death is probabilistically sampled conditional on their randomly sampled survival time within a Monte Carlo framework.
Conclusion: The integration of this survival modeling procedure within the TEAMHF costeffectiveness model allows it to more accurately make cost and survival predictions for various heart failure interventions (e.g. implantable cardioverter defibrillators) that may differentially impact a patient’s mortality risk and their mode of death.
Purpose: Bayesian mixed treatment comparison (MTC) metaanalysis is becoming a popular method for use in comparative effectiveness reviews when headtohead data are limited. The aim of this research was to examine how findings of Bayesian MTC metaanalyses compare when there are different numbers of studies available and for different network patterns.
Method: We used simulated data to examine the Bayesian MTC method’s ability to produce valid results for two data scenarios. Each data scenario included four drugs and was constructed by random draws from a binomial distribution, with predetermined response rates for each drug in the evidence network. Within each data scenario, we sampled a subset of studies to create analysis datasets with a varying number of studies, representing networks where there are one, two, three, five, or ten studies available for each drug comparison. These analysis datasets were created for four common types of network patterns: star, loop, one closed loop, and ladder. We compiled results from 40,000 analyses to generate a distribution of the probability of best treatment under each sample size and network pattern scenario. We compared these distributions to the predetermined response rates to assess the validity of findings.
Result: Our simulations supported the validity of Bayesian MTC methods for star and ladder network patterns but raised some concerns about one closed loop, and possibly loop, network patterns. Simulations generally found similar results for scenarios when only one study was available for each comparison and those when more studies (two, three, five, or ten) were available. However, in certain cases, small but statistically significant changes occurred between results when only one study was available for each comparison and those when two or more studies were available.
Conclusion: Our findings raise some concerns about the validity of the results of Bayesian MTC methods for one closed loop, and possibly loop, network patterns. For star and ladder network patterns, our findings support validity. Analyses based on one study for each comparison were usually similar to those based on two or more studies, supporting the use of Bayesian MTC metaanalysis even when data are relatively sparse. Further research is needed to explore additional simulations to determine if our findings are generalizable and to better understand the validity of Bayesian MTC methods under different scenarios.