Comparative and Cost-Effectiveness of Population Strategies to Improve Diet and Reduce Cancer

项目摘要 在美国，癌症是主要的死亡原因。研究表明，不良的饮食习惯是一种 癌症的主要可预防原因。美国人背负着次优饮食摄入量的负担。改善饮食是 显然，这是减轻美国癌症负担的优先事项。不幸的是，改善饮食的最佳策略 和降低癌症尚不清楚。虽然各种个人层面的行为改变方法可能是有效的 对一些人来说，总体利益和长期坚持可能是适度的，而总体利益很差。 反对有效。相比之下，基于人口的战略可以更强大和更可持续，并实现 更广泛的影响。我们系统地回顾和综合了各种不同作用的证据基础。 关于饮食摄入量的人口干预措施，并确定了具有最强证据基础的几项战略。 然而，这些策略在减轻癌症负担方面的有效性还没有得到量化。此外, 实施这些战略的成本在很大程度上是未知的；以及实施的成本效益 这些干预措施尚未确立。为了解决这些知识上的重大差距，我们选择了 以人群为基础的策略，以改善对以下方面有最有力证据的特定饮食目标 癌症发病率：(1)媒体/教育，(2)食品标签/信息，(3)税收/补贴， (4)规章制度/质量标准。我们将开发两个基于人口的模型：(A)比较风险 评估(CRA)模型；以及(B)具有蒙特卡洛模拟的健康策略状态转移马尔可夫模型， 饮食癌症结局模型(DICOM)，通过以下方式量化和比较这些干预措施的有效性 估计减少的癌症发病率和死亡人数、残疾调整生命年(DALY) 美国人口的避免寿命年(QALY)和质量调整寿命年(QALY)有所增加，预计超过5、10、15、 还有20年。其次，我们将制定成本计算协议，以估计实施特定方案的成本 从社会角度进行以人群为基础的饮食干预，并随后评估成本- 通过计算每QALY的增量成本-效果比(ICER)来计算干预的有效性 收获了。我们将进一步评估实施以人群为基础的饮食干预措施的影响，以减少 作为对弱势群体癌症差异的探索性分析。估计的稳健性将是 通过对模型参数和假设的各种假设进行广泛的敏感性分析进行评估。 这项研究将首次提供明确界定的人口战略的有效性估计，以 在美国，改善有针对性的饮食因素和减少癌症负担，这是告知饮食的关键 健康的政策制定和循证的癌症预防努力。

Project Summary Cancer is a leading cause of death in the US. Research has demonstrated that poor dietary habits are a major preventable cause of cancer. Americans are burdened by suboptimal dietary intake. Improving diet is clearly a priority for reducing burdens of cancer in the US. Unfortunately, the optimal strategies to improve diet and reduce cancer are not clear. While various individual-level behavioral change approaches can be effective for some people, overall benefits and long-term adherence may be modest and overall benefits poorly sustained. In contrast, population-based strategies can be more powerful and sustainable, and achieve broader impact. We have systematically reviewed and synthesized the evidence-base for effects of various population interventions on dietary intake and identified several strategies with the strongest evidence-base. However, the effectiveness of these strategies on reducing cancer burden has not been quantified. In addition, the cost for implementing these strategies is largely unknown; and the cost-effectiveness of implementing these interventions has not been established. To address these major gaps in knowledge, we have selected population-based strategies to improve specific dietary targets that have the strongest evidence for effects on cancer incidence, in the realms of (1) media/education, (2) food labeling/information, (3) taxation/subsidies, and (4) regulations/quality standards. We will develop two population-based models: (a) a Comparative Risk Assessment (CRA) model; and (b) a health policy state transition Markov model with Monte Carlo simulation, the Diet Cancer Outcome Model (DiCOM), to quantify and compare the effectiveness of these interventions by estimating the numbers of cancer incidence and deaths reduced, disability-adjusted life years (DALYs) averted, and quality-adjusted life years (QALYs) gained in the US population, with projections over 5, 10, 15, and 20 years. Second, we will develop costing protocols to estimate the cost of implementing specific population-based dietary interventions from a societal perspective, and subsequently assess the cost- effectiveness of the interventions by calculating the incremental cost-effectiveness ratio (ICER) per QALY gained. We will further evaluate the impact of implementing population-based dietary interventions to reduce cancer disparities in disadvantaged groups as exploratory analyses. The robustness of estimates will be assessed with extensive sensitivity analyses to varying assumptions on model parameters and assumptions. This study will provide, for the first time, the effectiveness estimates of well-defined population strategies to improve targeted dietary factors and reduce cancer burden in the US, which are crucial to informing dietary healthy policy making and evidence-based cancer prevention efforts.