Life expectancy gains from dietary modifications: a comparative modeling study in 7 countries.

BACKGROUND: Eating healthier is associated with a range of favorable health outcomes. Our previous model estimated the impact of dietary changes on life expectancy gains but did not consider height, weight, or physical activity. OBJECTIVES: We aimed to estimate the increase in life expectancy resulting from the transition from typical national dietary patterns to longevity-optimizing dietary changes, more feasible dietary modifications, and optimized vegan dietary changes in China, France, Germany, Iran, Norway, the United Kingdom, and the United States. METHODS: Our modeling study used data from meta-analyses presenting dose-response relationships between intake of 15 food groups and mortality. Background mortality data were from the Global Burden of Disease Study. We used national food intake data and adjusted for height, weight, and physical activity level. RESULTS: For 40-y-olds, estimated life expectancy gains ranged from 6.2 y (with uncertainty interval [UI]: 5.7, 7.5 y) for Chinese females to 9.7 y (UI: 8.1, 11.3 y) for United States males following sustained changes from typical country-specific dietary patterns to longevity-optimized dietary changes, and from 5.2 y (UI: 4.0, 6.5 y) for Chinese females to 8.7 y (UI: 7.1, 10.3 y) for United States males following changes to optimized vegan dietary changes. CONCLUSIONS: A sustained change from country-specific typical dietary pattern patterns to longevity-optimized dietary changes, more feasible dietary changes, or optimized vegan dietary changes are all projected to result in substantial life expectancy gains across ages and countries. These changes included more whole grains, legumes, and nuts and less red/processed meats and sugars and sugar-sweetened beverages. The largest gains from dietary changes would be in the United States.

Life expectancy can increase by up to 10 years following sustained shifts towards healthier diets in the United Kingdom.

Adherence to healthy dietary patterns can prevent the development of non-communicable diseases and affect life expectancy. Here, using a prospective population-based cohort data from the UK Biobank, we show that sustained dietary change from unhealthy dietary patterns to the Eatwell Guide dietary recommendations is associated with 8.9 and 8.6 years gain in life expectancy for 40-year-old males and females, respectively. In the same population, sustained dietary change from unhealthy to longevity-associated dietary patterns is associated with 10.8 and 10.4 years gain in life expectancy in males and females, respectively. The largest gains are obtained from consuming more whole grains, nuts and fruits and less sugar-sweetened beverages and processed meats. Understanding the contribution of sustained dietary changes to life expectancy can provide guidance for the development of health policies.

Correction: Estimating impact of food choices on life expectancy: A modeling study.

[This corrects the article DOI: 10.1371/journal.pmed.1003889.].

Estimating impact of food choices on life expectancy: A modeling study.

BACKGROUND: Interpreting and utilizing the findings of nutritional research can be challenging to clinicians, policy makers, and even researchers. To make better decisions about diet, innovative methods that integrate best evidence are needed. We have developed a decision support model that predicts how dietary choices affect life expectancy (LE). METHODS AND FINDINGS: Based on meta-analyses and data from the Global Burden of Disease study (2019), we used life table methodology to estimate how LE changes with sustained changes in the intake of fruits, vegetables, whole grains, refined grains, nuts, legumes, fish, eggs, milk/dairy, red meat, processed meat, and sugar-sweetened beverages. We present estimates (with 95% uncertainty intervals [95% UIs]) for an optimized diet and a feasibility approach diet. An optimal diet had substantially higher intake than a typical diet of whole grains, legumes, fish, fruits, vegetables, and included a handful of nuts, while reducing red and processed meats, sugar-sweetened beverages, and refined grains. A feasibility approach diet was a midpoint between an optimal and a typical Western diet. A sustained change from a typical Western diet to the optimal diet from age 20 years would increase LE by more than a decade for women from the United States (10.7 [95% UI 8.4 to 12.3] years) and men (13.0 [95% UI 9.4 to 14.3] years). The largest gains would be made by eating more legumes (females: 2.2 [95% UI 1.1 to 3.4]; males: 2.5 [95% UI 1.1 to 3.9]), whole grains (females: 2.0 [95% UI 1.3 to 2.7]; males: 2.3 [95% UI 1.6 to 3.0]), and nuts (females: 1.7 [95% UI 1.5 to 2.0]; males: 2.0 [95% UI 1.7 to 2.3]), and less red meat (females: 1.6 [95% UI 1.5 to 1.8]; males: 1.9 [95% UI 1.7 to 2.1]) and processed meat (females: 1.6 [95% UI 1.5 to 1.8]; males: 1.9 [95% UI 1.7 to 2.1]). Changing from a typical diet to the optimized diet at age 60 years would increase LE by 8.0 (95% UI 6.2 to 9.3) years for women and 8.8 (95% UI 6.8 to 10.0) years for men, and 80-year-olds would gain 3.4 years (95% UI females: 2.6 to 3.8/males: 2.7 to 3.9). Change from typical to feasibility approach diet would increase LE by 6.2 (95% UI 3.5 to 8.1) years for 20-year-old women from the United States and 7.3 (95% UI 4.7 to 9.5) years for men. Using NutriGrade, the overall quality of evidence was assessed as moderate. The methodology provides population estimates under given assumptions and is not meant as individualized forecasting, with study limitations that include uncertainty for time to achieve full effects, the effect of eggs, white meat, and oils, individual variation in protective and risk factors, uncertainties for future development of medical treatments; and changes in lifestyle. CONCLUSIONS: A sustained dietary change may give substantial health gains for people of all ages both for optimized and feasible changes. Gains are predicted to be larger the earlier the dietary changes are initiated in life. The Food4HealthyLife calculator that we provide online could be useful for clinicians, policy makers, and laypeople to understand the health impact of dietary choices.

On the path towards universal coverage of hepatitis C treatment among people receiving opioid agonist therapy (OAT) in Norway: a prospective cohort study from 2013 to 2017.

OBJECTIVES: We aimed to calculate cumulative hepatitis C virus (HCV) treatment coverage among individuals enrolled in opioid agonist therapy (OAT) in Norway between 2013 and 2017 and to document the treatment transition to direct-acting antiviral (DAA) agents. Moreover, we aimed to describe adherence to DAAs in the same cohort. DESIGN: Prospective cohort, registry data. SETTING: Specialist healthcare service (secondary) PARTICIPANTS AND OUTCOMES: This observational study was based on data from The Norwegian Prescription Database. We studied dispensed OAT and HCV treatment annually to calculate the cumulative frequency, and employed secondary sources to calculate prevalence, incidence and HCV treatment coverage from 2013 to 2017, among the OAT population. Factors associated with adherence to DAAs were identified a priori and subject to logistic regression. RESULTS: 10 371 individuals were identified with dispensed OAT, 1475 individuals of these were identified with dispensed HCV treatment. Annual HCV treatment coverage increased from 3.5% (95% CI: 3.2 to 4.4) in 2013 to 17% (95% CI: 17 to 20) in 2017, giving a cumulative HCV coverage among OAT patients in Norway of 38.5%. A complete shift to interferon-free treatment regimens occurred, where DAAs accounting for 32% of HCV treatments in 2013 and 99% in 2017. About two-thirds of OAT patients were considered adherent to their DAA regimens across all genotypes. High level of OAT continuity was associated with improved adherence to DAAs (adjusted OR 1.4, 95% CI: 1 to 2, p=0.035). CONCLUSIONS: A large increase in HCV treatment coverage attributed by a complete shift to interferon-free regimens among the Norwegian OAT population has been demonstrated. However, treatment coverage is inadmissibly too low and a further substantial scale-up in HCV treatment is required to reach the universal targets of controlling and eliminating the HCV endemic.

Estimating Health Adjusted Age at Death (HAAD).

OBJECTIVES: At any point in time, a person's lifetime health is the number of healthy life years they are expected to experience during their lifetime. In this article we propose an equity-relevant health metric, Health Adjusted Age at Death (HAAD), that facilitates comparison of lifetime health for individuals at the onset of different medical conditions, and allows for the assessment of which patient groups are worse off. A method for estimating HAAD is presented, and we use this method to rank four conditions in six countries according to several criteria of "worse off" as a proof of concept. METHODS: For individuals with specific conditions HAAD consists of two components: past health (before disease onset) and future expected health (after disease onset). Four conditions (acute myeloid leukemia (AML), acute lymphoid leukemia (ALL), schizophrenia, and epilepsy) are analysed in six countries (Ethiopia, Haiti, China, Mexico, United States and Japan). Data from 2017 for all countries and for all diseases were obtained from the Global Burden of Disease Study database. In order to assess who are the worse off, we focus on four measures: the proportion of affected individuals who are expected to have HAAD<20 (T20), the 25th and 75th percentiles of HAAD for affected individuals (Q1 and Q3, respectively), and the average HAAD (aHAAD) across all affected individuals. RESULTS: Even in settings where aHAAD is similar for two conditions, other measures may vary. One example is AML (aHAAD = 59.3, T20 = 2.0%, Q3-Q1 = 14.8) and ALL (58.4, T20 = 4.6%, Q3-Q1 = 21.8) in the US. Many illnesses, such as epilepsy, are associated with more lifetime health in high-income settings (Q1 in Japan = 59.2) than in low-income settings (Q1 in Ethiopia = 26.3). CONCLUSION: Using HAAD we may estimate the distribution of lifetime health of all individuals in a population, and this distribution can be incorporated as an equity consideration in setting priorities for health interventions.