Cardiometabolic disease costs associated with suboptimal diet in the United States: A cost analysis based on a microsimulation model

English version

Autoři: Thiago Veiga Jardim ^aff001; Dariush Mozaffarian ^aff003; Shafika Abrahams-Gessel ^aff002; Stephen Sy ^aff002; Yujin Lee ^aff003; Junxiu Liu ^aff003; Yue Huang ^aff003; Colin Rehm ^aff004; Parke Wilde ^aff003; Renata Micha ^aff003; Thomas A. Gaziano ^aff001
Působiště autorů: Department of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America ^aff001; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America ^aff002; Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts, United States of America ^aff003; Office of Community and Population Health, Montefiore Medical Center, Bronx, New York, United States of America ^aff004
Vyšlo v časopise: Cardiometabolic disease costs associated with suboptimal diet in the United States: A cost analysis based on a microsimulation model. PLoS Med 16(12): e32767. doi:10.1371/journal.pmed.1002981
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pmed.1002981

Souhrn

Background

Poor diet is a leading risk factor for cardiometabolic disease (CMD) in the United States, but its economic costs are unknown. We sought to estimate the cost associated with suboptimal diet in the US.

Methods and findings

A validated microsimulation model (Cardiovascular Disease Policy Model for Risk, Events, Detection, Interventions, Costs, and Trends [CVD PREDICT]) was used to estimate annual cardiovascular disease (fatal and nonfatal myocardial infarction, angina, and stroke) and type 2 diabetes costs associated with suboptimal intake of 10 food groups (fruits, vegetables, nuts/seeds, whole grains, unprocessed red meats, processed meats, sugar-sweetened beverages, polyunsaturated fats, seafood omega-3 fats, sodium). A representative US population sample of individuals aged 35–85 years was created using weighted sampling from National Health And Nutrition Examination Surveys (NHANES) 2009–2012 cycles. Estimates were stratified by cost type (acute, chronic, drug), sex, age, race, education, BMI, and health insurance. Annual diet-related CMD costs were $301/person (95% CI $287–$316). This translates to $50.4 billion in CMD costs (18.2% of total) for the whole population, of which 84.3% are attributed to acute care ($42.6 billion). The largest annual per capita costs are attributed to low consumption of nuts/seeds ($81; 95% CI $74–$86) and seafood omega-3 fats ($76; 95% CI $70–$83), and the lowest are attributed to high consumption of red meat ($3; 95% CI $2.8–$3.5) and polyunsaturated fats ($20; 95% CI $19–$22). Individual costs are highest for men ($380), those aged ≥65 years ($408), blacks ($320), the less educated ($392), and those with Medicare ($481) or dual-eligible ($536) insurance coverage. A limitation of our study is that dietary intake data were assessed from 24-hour dietary recall, which may not fully capture a diet over a person's life span and is subject to measurement errors.

Conclusions

Suboptimal diet of 10 dietary factors accounts for 18.2% of all ischemic heart disease, stroke, and type 2 diabetes costs in the US, highlighting that timely implementation of diet policies could address these health and economic burdens.

Klíčová slova:

Cardiovascular diseases – Diet and type 2 diabetes – Fats – Health economics – Health insurance – Meat – Medicare – Schools

Zdroje

1. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet. 2012;380(9859):2224–60.

2. Murray CJ, Atkinson C, Bhalla K, Birbeck G, Burstein R, Chou D, et al. The state of US health, 1990–2010: burden of diseases, injuries, and risk factors. Jama. 2013;310(6):591–608. doi: 10.1001/jama.2013.13805 23842577

3. Micha R, Peñalvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D. Association Between Dietary Factors and Mortality From Heart Disease, Stroke, and Type 2 Diabetes in the United States. JAMA. 2017;317(9):912–24. doi: 10.1001/jama.2017.0947 28267855

4. Mozaffarian D. Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity. A Comprehensive Review. 2016;133(2):187–225.

5. Gase LN, Kuo T, Dunet D, Schmidt SM, Simon PA, Fielding JE. Estimating the Potential Health Impact and Costs of Implementing a Local Policy for Food Procurement to Reduce the Consumption of Sodium in the County of Los Angeles. American Journal of Public Health. 2011;101(8):1501–7. doi: 10.2105/AJPH.2011.300138 21680933

6. Akanni O, Smith M, Ory M. Cost-Effectiveness of a Community Exercise and Nutrition Program for Older Adults: Texercise Select. International Journal of Environmental Research and Public Health. 2017;14(5):545.

7. Palar K, Sturm R. Potential societal savings from reduced sodium consumption in the U.S. adult population. American journal of health promotion. 2009;24(1):49–57. doi: 10.4278/ajhp.080826-QUAN-164 19750962

8. Doll TM, Fulgoni VL, Zhang Y, Reimers KJ, Packard PT, Astwood JD. Potential Health Benefits and Medical Cost Savings from Calorie, Sodium, and Saturated Fat Reductions in the American Diet. American Journal of Health Promotion. 2009;23(6):412–22. doi: 10.4278/ajhp.080930-QUAN-226 19601481

9. Smith-Spangler CM, Juusola JL, Enns EA, Owens DK, Garber AM. Population strategies to decrease sodium intake and the burden of cardiovascular disease: a cost-effectiveness analysis. Ann Intern Med. 2010;152(8):481–7. doi: 10.7326/0003-4819-152-8-201004200-00212 20194225

10. Pandya A, Sy S, Cho S, Alam S, Weinstein MC, Gaziano TA. Validation of a Cardiovascular Disease Policy Micro-Simulation Model using Both Survival and Receiver Operating Characteristic Curves. Med Decis Making. 2017;37(7):802–14. doi: 10.1177/0272989X17706081 28490271

11. Food-PRICE (Food Policy Review and Intervention Cost-Effectiveness) Project. 2018 [cited 2019 Jun 13]. Available from: https://www.food-price.org

12. Micha R, Shulkin ML, Penalvo JL, Khatibzadeh S, Singh GM, Rao M, et al. Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: Systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE). PLoS ONE. 2017;12(4):e0175149. doi: 10.1371/journal.pone.0175149 28448503

13. Bureau of Labor Statistics. CPI Inflation Calculator. Available from: https://www.bls.gov/data/inflation_calculator.htm. [cited 2018 Mar 4]

14. Husereau D, Drummond M, Petrou S, Carswell C, Moher D, Greenberg D, et al. Consolidated health economic evaluation reporting standards (CHEERS) statement. Cost Effectiveness and Resource Allocation. 2013;11(1):6. doi: 10.1186/1478-7547-11-6 23531194

15. US Department of Health and Human Services and US Department of Agriculture. 2015–2020 Dietary Guidelines for Americans. 8th ed. 2015 [cited 2018 Jul 16]. Available from: http://health.gov/dietaryguidelines/2015/guidelines/.

16. Micha R, Kalantarian S, Wirojratana P, Byers T, Danaei G, Elmadfa I, et al. Estimating the global and regional burden of suboptimal nutrition on chronic disease: methods and inputs to the analysis. Eur J Clin Nutr. 2012;66(1):119–29. doi: 10.1038/ejcn.2011.147 21915137

17. Agency for Healthcare Research and Quality. Total Health Services-Mean and Median Expenses per Person With Expense and Distribution of Expenses by Source of Payment: United States, 2014. [cited 2019 Oct 29]. Available from: https://meps.ahrq.gov/mepsweb/data_stats/tables_compendia_hh_interactive.jsp?_SERVICE=MEPSSocket0&_PROGRAM=MEPSPGM.TC.SAS&File=HCFY2014&Table=HCFY2014_PLEXP_%40&VAR1=AGE&VAR2=SEX&VAR3=RACETH5C&VAR4=INSURCOV&VAR5=POVCAT14&VAR6=REGION&VAR7=HEALTH&VARO1=4+17+44+64&VARO2=1&VARO3=1&VARO4=1&VARO5=1&VARO6=1&VARO7=1&_Debug=.

18. Centers for Medicaid and Medicare Services. National Health Expenditure Data. Available from: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/index.html. [cited 2018 Apr 17]

19. Neumann PJ, Sanders GD, Russell LB, Siegel JE, Ganiats TG, editors. Cost-effectiveness in health and medicine. New York: Oxford University Press; 2016.

20. Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, et al. Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association. Circulation. 2018;137(12):e67. doi: 10.1161/CIR.0000000000000558 29386200

21. Bernstein AM, Bloom DE, Rosner BA, Franz M, Willett WC. Relation of food cost to healthfulness of diet among US women. Am J Clin Nutr. 2010;92(5):1197–203. doi: 10.3945/ajcn.2010.29854 20810972

22. Jetter KM, Cassady DL. The availability and cost of healthier food alternatives. Am J Prev Med. 2006;30(1):38–44. doi: 10.1016/j.amepre.2005.08.039 16414422

23. McDermott AJ, Stephens MB. Cost of eating: whole foods versus convenience foods in a low-income model. Family medicine. 2010;42(4):280–4. 20373171

24. Ryden PJ, Hagfors L. Diet cost, diet quality and socio-economic position: how are they related and what contributes to differences in diet costs? Public Health Nutr. 2011;14(9):1680–92. doi: 10.1017/S1368980010003642 21255480

25. Rao M, Afshin A, Singh G, Mozaffarian D. Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis. BMJ Open. 2013;3(12).

26. Lenoir-Wijnkoop I, Jones PJ, Uauy R, Segal L, Milner J. Nutrition economics–food as an ally of public health. The British Journal of Nutrition. 2013;109(5):777–84. doi: 10.1017/S0007114512005107 23339933

27. Dalziel K, Segal L. Time to give nutrition interventions a higher profile: cost-effectiveness of 10 nutrition interventions. Health Promot Int. 2007;22(4):271–83. doi: 10.1093/heapro/dam027 17916604

28. Incentivizing Healthy Food Choices for Better Health: The John Hancock Vitality HealthyFood Program John Hancock Insurance. 2016.

29. Wharam JF, Soumerai S, Trinacty C, Eggleston E, Zhang F, LeCates R, et al. Impact of Emerging Health Insurance Arrangements on Diabetes Outcomes and Disparities: Rationale and Study Design. Preventing Chronic Disease. 2013;10:E11. doi: 10.5888/pcd10.120147 23369764

30. Rehm CD, Monsivais P, Drewnowski A. The quality and monetary value of diets consumed by adults in the United States. Am J Clin Nutr. 2011;94(5):1333–9. doi: 10.3945/ajcn.111.015560 21918223

31. Rehm CD, Penalvo JL, Afshin A, Mozaffarian D. Dietary Intake Among US Adults, 1999–2012. Jama. 2016;315(23):2542–53. doi: 10.1001/jama.2016.7491 27327801

32. Lee Y, Mozaffarian D, Sy S, Huang Y, Liu J, Wilde PE, et al. Cost-effectiveness of financial incentives for improving diet and health through Medicare and Medicaid: A microsimulation study. PLoS Med. 2019;16(3):e1002761. doi: 10.1371/journal.pmed.1002761 30889188

33. Mozaffarian D, Liu J, Sy S, Huang Y, Rehm C, Lee Y, et al. Cost-effectiveness of financial incentives and disincentives for improving food purchases and health through the US Supplemental Nutrition Assistance Program (SNAP): A microsimulation study. PLoS Med. 2018;15(10):e1002661. doi: 10.1371/journal.pmed.1002661 30278053