Quantifying the population impact of a prophylactic Helicobacter pylori vaccine.

BACKGROUND: Helicobacter pylori vaccines, which have been suggested as promising interventions to control infection, are under development. We sought to quantify the potential population impact of a prophylactic H. pylori vaccine. METHODS: We developed a mathematical model that compartmentalized the population according to age, infection status and clinical state. A proportion of individuals was assumed to acquire infection and develop gastritis, duodenal ulcer (DU), chronic atrophic gastritis and gastric cancer (GC). We first simulated the model without vaccine intervention, to obtain estimates of H. pylori prevalence, and GC and DU incidences based on intrinsic dynamics. We then incorporated a prophylactic vaccine (80% efficacy, lifetime protection, 80% coverage) targeting all infants. We tested vaccination programs over unlimited as well as limited time spans. Analyses were performed for the US, Japan and a prototypical developing country. RESULTS: In the US, our model predicted a decrease in H. pylori prevalence from 12.0% in 2010 to 4.2% in 2100 without intervention. With 10 years of vaccination beginning in 2010, prevalence would decrease to 0.7% by year 2100. In the same period, incidence of H. pylori-attributable GC would decrease from 4.5 to 0.4 per 100,000 with vaccine (compared to 1.3 per 100,000 without vaccine). Incidence of H. pylori-attributable DU would decrease from 33.3 to 2.5 per 100,000 with vaccine (compared to 12.2 per 100,000 without vaccine). In Japan, incidence of H. pylori-attributable GC would decrease from 17.6 to 1.0 per 100,000 after 10 years of vaccination (compared to 3.0 per 100,000 without vaccine). In a prototypical developing country, after 10 years of vaccination, H. pylori-attributable GC would decrease from 31.8 to 22.5 per 100,000 by 2090, returning to the original level by mid-2100s. Under continuous vaccination, it would decrease to 5.8 per 100,000 by 2100. INTERPRETATION: In the US and Japan, a 10-year vaccination program would confer almost the same reduction in H. pylori and associated diseases as a vaccination effort that extends beyond 10 years. In developing countries, a continuous vaccination effort would be required to eliminate the pathogen and its associated diseases.

A dynamic transmission model for predicting trends in Helicobacter pylori and associated diseases in the United States.

To assess the benefits of intervention programs against Helicobacter pylori infection, we estimated the baseline curves of its incidence and prevalence. We developed a mathematical (compartmental) model of the intrinsic dynamics of H. pylori, which represents the natural history of infection and disease progression. Our model divided the population according to age, infection status, and clinical state. Case-patients were followed from birth to death. A proportion of the population acquired H. pylori infection and became ill with gastritis, duodenal ulcer, chronic atrophic gastritis, or gastric cancer. We simulated the change in transmissibility consistent with the incidence of gastric cancer and duodenal ulcer over time, as well as current H. pylori prevalence. In the United States, transmissibility of H. pylori has decreased to values so low that, should this trend continue, the organism will disappear from the population without targeted intervention; this process, however, will take more than a century.

Population effects of preventive and therapeutic HIV vaccines in early- and late-stage epidemics.

OBJECTIVE: To evaluate the population effects of potential preventive and therapeutic vaccines in early- and late-stage epidemics in a population of homosexual men. METHODS: An epidemic model was used that simulated the course of the epidemic for a population of homosexual men in San Francisco, California. Vaccine programs were evaluated by the number of cases of HIV averted, the effect on the prevalence of HIV, and by the gain in quality-adjusted life years (QALY) for the total population. RESULTS: In the model, a preventive vaccine prevented 3877 cases of HIV infection during a 20-year period, reduced the projected prevalence of HIV infection from 12 to 7% in a late-stage epidemic, and gained 15,908 QALY. A therapeutic vaccine that did not affect the infectivity of vaccine recipients increased the number of cases of HIV infection by 210, resulted in a slight increase in the prevalence of HIV infection from 12 to 15% in a late-stage epidemic, and gained 8854 QALY. If therapeutic vaccines reduced infectivity, their use could produce net gains of QALY in the population that were similar to gains from the use of preventive vaccines. In an early-stage epidemic, the advantage of a preventive vaccine program relative to a therapeutic vaccine program was markedly enhanced. CONCLUSIONS: Both preventive and therapeutic vaccine programs provided substantial benefit, but their relative merit depended on which outcome measures were assessed. Evaluation of HIV vaccine programs based solely on cases averted or on prevalence of HIV in the population underestimates the benefit associated with therapeutic vaccine programs. The effect of a therapeutic HIV vaccine on the epidemic outcomes depended markedly on whether the therapeutic vaccine reduced the infectivity of the vaccine recipient. The relative merits of preventive and therapeutic vaccines depend on the stage of the epidemic. Field vaccine trials should evaluate correlates of infectivity, such as HIV viral load. HIV vaccine implementation strategies should be tailored to the dynamics of the epidemic in specific populations.

Representation and analysis of medical decision problems with influence diagrams.

Influence diagrams are a powerful graphic representation for decision models, complementary to decision trees. Influence diagrams and decision trees are different graphic representations for the same underlying mathematical model and operations. This article describes the elements of an influence diagram, and shows several familiar decision problems represented as decision trees and as influence diagrams. The authors also contrast the information highlighted in each graphic representation, demonstrate how to calculate the expected utilities of decision alternatives modeled with an influence diagram, provide an overview of the conceptual basis of the solution algorithms that have been developed for influence diagrams, discuss the strengths and limitations of influence diagrams relative to decision trees, and describe the mathematical operations that are used to evaluate both decision trees and influence diagrams. They use clinical examples to illustrate the mathematical operations of the influence-diagram-evaluation algorithm; these operations are arc reversal, chance node removal by averaging, and decision node removal by policy determination. Influence diagrams may be helpful when problems have a high degree of conditional independence, when large models are needed, when communication of the probabilistic relationships is important, or when the analysis requires extensive Bayesian updating. The choice of graphic representation should be governed by convenience, and will depend on the problem being analyzed, on the experience of the analyst, and on the background of the consumers of the analysis.

A physician-based architecture for the construction and use of statistical models.

Physicians need specially tailored computer tools to take advantage of published research results. We present a knowledge-based computer framework--the physician-based (PB) architecture--for constructing such tools, and we use the problem of physicians' interpretation of two-arm parallel randomized clinical trials (TAPRCT) as a working example. Statistical models are represented by influence diagrams. The interpretation of influence-diagram elements are mapped into users' language in a domain-specific, physician-based user interface, called a patient-flow diagram. Statistical-model transformations that maintain the semantic relationships of the model and that embody clinical-epidemiological knowledge are encoded in a mediating structure called the cohort-state diagram. The algorithm that coordinates the interactions among the knowledge representations uses modular actions called construction steps. This architecture has been implemented in a Bayesian system, called THOMAS, that supports physician decision making in light of TAPRCT data. This support entails assessing clinical significance, prior beliefs, and methodological concerns. We suggest that the PB architecture applies to a wide range of statistical tools and users.

Representation of preferences in decision-support systems.

The recommendations of computer-based decision-support systems depend on the preferences of the expert who is responsible for the decisions. Often, these preferences are only represented implicitly, rather than explicitly, in the system. Decision-theoretic preference models that explicitly represent the preferences of the decision maker provide numerous advantages for decision-support systems. In this paper, we describe these advantages. The creation and refinement of decision-theoretic preference models, however, is a difficult task. We describe an accurate and efficient method for determining the preferences of domain experts and refining the model that captures those preferences. In this preference assessment method, we simulate familiar decisions in the expert's area of expertise. We then infer the preferences of the expert from the choices that the expert makes on the simulated decisions, and use the preference information to refine the model automatically.