How Would a Better Diet Affect Health and Economic Outcomes in the United States?
Poor diet quality is a risk factor for such conditions as cardiovascular disease, diabetes, and some cancers. Worldwide, dietary risks accounted for 11 million deaths among adults 25 or older in 2017. In the United States, dietary risks are the leading cause of death and third leading cause of morbidity, with 46% of adults having a poor-quality diet, according to the Federal Nutrition Research Advisory Group.
Diets that are more closely aligned with national nutritional guidelines could lower the risk of illness. This reduction in risk may consequently improve economic outcomes, such as the proportion of people in paid employment, government spending on assistance programs, and health care costs.
This tool shows the potential impact of improving diet quality in the United States on health and economic outcomes, such as the prevalence of diet-related illnesses, health care spending, and labor force participation, over a 30-year period. This information would be useful to researchers, policymakers, community leaders, and others working in nutrition and public health who are interested in the potential scope and timing of the impacts of programs to improve diet quality.
Simulating Long-Term Health and Economic Outcomes from Improved Diet
To project the population-level impacts of an improved diet, RAND researchers used a simulation model, the Future Americans Model (FAM). FAM is an economic-demographic microsimulation that projects individual trajectories of health outcomes, health care expenditures, and economic outcomes for the U.S. population ages 25 and older. It uses the Panel Study of Income Dynamics as its host data set.
The tool allows the user to compare the effects of two scenarios over a 30-year-period:
- Status Quo: no changes in dietary quality
- Diet Improvement: a two-quintile improvement in dietary quality under the Alternate Healthy Eating Index.
The Diet Improvement scenario is based on a hypothetical improvement in dietary quality that improves the average U.S. score on the Alternate Healthy Eating Index, which is informed by the current body of knowledge of foods and nutrients predictive of chronic disease risk. In creating this scenario, we assumed that the current average diet in the United States is represented by the third quintile of Alternate Healthy Eating Index scores (scale of 0 to 110) in a large observational study. We coded the simulation to assume an improvement to the fifth quintile, which takes effect immediately starting in 2019. This improvement is an increase in diet quality of 14–15 points on a scale from 0 to 110 and could be achieved through any combination of more fruit, vegetables, whole grains, nuts, and good fats; less sugar (particularly in the form of drinks); red and processed meat; trans fats; and salt.
This dietary improvement results in reductions in relative risks for various diet-related chronic conditions (diabetes, heart disease, cancer, and stroke), estimates of which are taken from the literature. Finally, a simulation approach is used to predict how such changes in disease progression correspond to population-level changes in health and economic outcomes over 30 years.
Use the tool below to explore our projections. For example, improvements in diet today could result in 4 million fewer people with heart disease (Outcome: Heart Disease; Metric: Number of individuals; Subgroup: All; Chart View: Difference). This reduction in heart disease would accrue more to men, who saw an average reduction of roughly 2 percentage points in prevalence; prevalence in women dropped by more than 1 percentage point (Outcome: Heart Disease; Metric: Prevalence; Subgroup: Sex; Chart View: Difference). In terms of economic outcomes, improvements in diet could result in 700,000 more people having paid work (Outcome: Labor force participation; Metric: Number of individuals; Subgroup: All; Chart View: Difference) and save $144 billion in health care costs nationwide (Outcome: Medical spending; Metric: Total; Subgroup: All; Chart View: Difference) in year 30.
This work was funded by Pharmavite.
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