Cost Effectiveness of Nutrition Policies on Processed Meat: Implications for Cancer Burden in the U.S

Abstract

Introduction: Processed meats are associated with increased risk of colorectal and stomach cancers, but health and economic impacts of policies to discourage processed meats are not well established. This paper aims to evaluate the cost effectiveness of implementing tax and warning labels on processed meats.

Methods: A probabilistic cohort-state transition model was developed in 2018, including lifetime and short-term horizons, healthcare, and societal perspectives, and 3% discount rates for costs and health outcomes. The model simulated 32 subgroups by age, gender, and race/ethnicity from the U.S. adult population and integrated nationally representative 2011-2014 data on processed meat consumption, with etiologic effects of processed meat consumption on cancer incidence, medical and indirect societal costs, and policy costs.

Results: Over a lifetime, the 10% excise tax would prevent 77,000 cases of colorectal cancer (95% uncertainty interval=56,800, 107,000) and 12,500 cases of stomach cancer (95% uncertainty interval=6,880, 23,900), add 593,000 quality-adjusted life years (95% uncertainty interval=419,000, 827,000), and generate net savings of $2.7 billion from a societal perspective, including $1.1 billion healthcare costs saved. The warning label policy would avert 85,400 cases of colorectal cancer (95% uncertainty interval=56,600, 141,000) and 15,000 cases of stomach cancer (95% uncertainty interval=6,860, 34,500), and add 660,000 quality-adjusted life years (95% uncertainty interval=418,000, 1,070,000), with net savings of $4.5 billion from a societal perspective, including $1.3 billion healthcare costs saved. In subgroup analyses, greater health and economic benefits accrued to (1) younger subpopulations, (2) subpopulations with greater cancer risk, and (3) those with higher baseline processed meat consumption.

Conclusions: The model shows that implementing tax or warning labels on processed meats would be a cost-saving strategy with substantial health and economic benefits. The findings should encourage policy makers to consider nutrition-related policies to reduce cancer burden.

Figure 1.

Impact of nutrition policies under different scenarios. Note: Under the scenario analyses, three modeling choices were varied: (1) the latency period, (2) approaches to project secular trends in cancer incidence, and (3) the policy effect size. For the 10% tax policy, (1) the conservative scenario: 3% effect size (uncertain range: 1%–5%), a 10-year latency period, and a historical trend from 1999–2013; (2) the base-case scenario: 9% effect size (5%–15%), a 5-year latency period, and a historical trend from 1999–2013; and (3) the optimistic scenario: 13% effect size (10%–15%), no latency period, and a constant trend as of 2013. For the warning label policy, (1) the conservative scenario: 4% effect size (2%–8%), a 10-year latency period, and a historical trend from 1999–2013; (2) the base-case scenario: 12.5% effect size (2%–23%), a 5-year latency period, and a historical trend from 1999–2013; and (3) the optimistic scenario: 20% effect size (15%–25%), no latency period, and a constant trend as of 2013. From a societal perspective, societal costs included savings from both healthcare costs and non-healthcare costs, including time costs associated with receiving medical care and productivity. Appendix Table 7 provides full results of the scenario analyses. QALYs, quality-adjusted life years.

Figure 2.

Impact of analytic time horizons on cost effectiveness of nutrition policies. Note: Even for a 10-year time horizon (rather than lifetime), both policies were net cost-savings from healthcare and societal perspectives, compared with the status-quo. Appendix Table 9 provides full results from the sensitivity analysis of the analytic time horizon. QALYs, quality-adjusted life years.