Estimating the potential health benefits and expenditures of a partially effective HIV vaccine is an important consideration in the debate about whether HIV vaccine research should continue. We developed an epidemic model to estimate HIV prevalence, new infections, and the cost-effectiveness of vaccination strategies in the U.S. Vaccines with modest efficacy could prevent 300,000-700,000 HIV infections and save $30 billion in healthcare expenditures over 20 years. Targeted vaccination of high-risk individuals is economically efficient, but difficulty in reaching these groups may mitigate these benefits. Universal vaccination is cost-effective for vaccines with 50% efficacy and price similar to other infectious disease vaccines.

BACKGROUND: To provide quantitative insight into current U.S. policy choices for cervical cancer prevention, we developed a model of human papillomavirus (HPV) and cervical cancer, explicitly incorporating uncertainty about the natural history of disease. METHODS: We developed a stochastic microsimulation of cervical cancer that distinguishes different HPV types by their incidence, clearance, persistence, and progression. Input parameter sets were sampled randomly from uniform distributions, and simulations undertaken with each set. Through systematic reviews and formal data synthesis, we established multiple epidemiologic targets for model calibration, including age-specific prevalence of HPV by type, age-specific prevalence of cervical intraepithelial neoplasia (CIN), HPV type distribution within CIN and cancer, and age-specific cancer incidence. For each set of sampled input parameters, likelihood-based goodness-of-fit (GOF) scores were computed based on comparisons between model-predicted outcomes and calibration targets. Using 50 randomly resampled, good-fitting parameter sets, we assessed the external consistency and face validity of the model, comparing predicted screening outcomes to independent data. To illustrate the advantage of this approach in reflecting parameter uncertainty, we used the 50 sets to project the distribution of health outcomes in U.S. women under different cervical cancer prevention strategies. RESULTS: Approximately 200 good-fitting parameter sets were identified from 1,000,000 simulated sets. Modeled screening outcomes were externally consistent with results from multiple independent data sources. Based on 50 good-fitting parameter sets, the expected reductions in lifetime risk of cancer with annual or biennial screening were 76% (range across 50 sets: 69-82%) and 69% (60-77%), respectively. The reduction from vaccination alone was 75%, although it ranged from 60% to 88%, reflecting considerable parameter uncertainty about the natural history of type-specific HPV infection. The uncertainty surrounding the model-predicted reduction in cervical cancer incidence narrowed substantially when vaccination was combined with every-5-year screening, with a mean reduction of 89% and range of 83% to 95%. CONCLUSION: We demonstrate an approach to parameterization, calibration and performance evaluation for a U.S. cervical cancer microsimulation model intended to provide qualitative and quantitative inputs into decisions that must be taken before long-term data on vaccination outcomes become available. This approach allows for a rigorous and comprehensive description of policy-relevant uncertainty about health outcomes under alternative cancer prevention strategies. The model provides a tool that can accommodate new information, and can be modified as needed, to iteratively assess the expected benefits, costs, and cost-effectiveness of different policies in the U.S.

Past research has identified social and environmental causes and correlates of behaviors thought to be associated with obesity and weight gain among children and adolescents. Much less research has documented the efficacy of interventions designed to manipulate those presumed causes and correlates. These latter efforts have been inhibited by the predominant biomedical and social science problem-oriented research paradigm, emphasizing reductionist approaches to understanding etiologic mechanisms of diseases and risk factors. The implications of this problem-oriented approach are responsible for leaving many of the most important applied research questions unanswered, and for slowing efforts to prevent obesity and improve individual and population health. An alternative, and complementary, solution-oriented research paradigm is proposed, emphasizing experimental research to identify the causes of improved health. This subtle conceptual shift has significant implications for phrasing research questions, generating hypotheses, designing research studies, and making research results more relevant to policy and practice. The solution-oriented research paradigm encourages research with more immediate relevance to human health and a shortened cycle of discovery from the laboratory to the patient and population. Finally, a "litmus test" for evaluating research studies is proposed, to maximize the efficiency of the research enterprise and contributions to the promotion of health and the prevention and treatment of disease. A research study should only be performed if (1) you know what you will conclude from each possible result (whether positive, negative, or null); and (2) the result may change how you would intervene to address a clinical, policy, or public health problem.