Reach and Use of Diabetes Prevention Services in the United States, 2016-2017

Mohammed K Ali, Kai McKeever Bullard, Giuseppina Imperatore, Stephen R Benoit, Deborah B Rolka, Ann L Albright, Edward W Gregg, Mohammed K Ali, Kai McKeever Bullard, Giuseppina Imperatore, Stephen R Benoit, Deborah B Rolka, Ann L Albright, Edward W Gregg

Abstract

Importance: Coordinated efforts by national organizations in the United States to implement evidence-based lifestyle modification programs are under way to reduce type 2 diabetes (hereinafter referred to as diabetes) and cardiovascular risks.

Objective: To provide a status report on the reach and use of diabetes prevention services nationally.

Design, setting, and participants: This nationally representative, population-based cross-sectional analysis of 2016 and 2017 National Health Interview Survey data was conducted from August 3, 2017, through November 15, 2018. Nonpregnant, noninstitutionalized, civilian respondents 18 years or older at high risk for diabetes, defined as those with no self-reported diabetes diagnosis but with diagnosed prediabetes or an elevated American Diabetes Association (ADA) risk score (>5), were included in the analysis. Analyses were conducted for adults with (and in sensitivity analyses, for those without) elevated body mass index.

Main outcomes and measures: Absolute numbers and proportions of adults at high risk with elevated body mass index receiving advice about diet, physical activity guidance, referral to weight loss programs, referral to diabetes prevention programs, or any of these, and those affirming engagement in each (or any) activity in the past year were estimated. To identify where gaps exist, a prevention continuum diagram plotted existing vs desired goal achievement. Variation in risk-reducing activities by age, sex, race/ethnicity, educational attainment, insurance status, history of gestational diabetes mellitus, hypertension, or body mass index was also examined.

Results: This analysis included 50 912 respondents (representing 223.0 million adults nationally) 18 years or older (mean [SE] age, 46.1 [0.2] years; 48.1% [0.3%] male) with complete data and no self-reported diabetes diagnosis by their health care professional. Of the represented population, 36.0% (80.0 million) had either a physician diagnosis of prediabetes (17.9 million), an elevated ADA risk score (73.3 million), or both (11.3 million). Among those with diagnosed prediabetes, 73.5% (95% CI, 71.6%-75.3%) reported receiving advice and/or referrals for diabetes risk reduction from their health care professional, and, of those, 35.0% (95% CI, 30.5%-39.8%) to 75.8% (95% CI, 73.2%-78.3%) reported engaging in the respective activity or program in the past year. Half of adults with elevated ADA risk scores but no diagnosed prediabetes (50.6%; 95% CI, 49.5%-51.8%) reported receiving risk-reduction advice and/or referral, of whom 33.5% (95% CI, 30.1%-37.0%) to 75.2% (95% CI, 73.4%-76.9%) reported engaging in activities and/or programs. Participation in diabetes prevention programs was exceedingly low. Advice from a health care professional, age range from 45 to 64 years, higher educational attainment, health insurance status, gestational diabetes mellitus, hypertension, and obesity were associated with higher engagement in risk-reducing activities and/or programs.

Conclusions and relevance: Among adults at high risk for diabetes, major gaps in receiving advice and/or referrals and engaging in diabetes risk-reduction activities and/or programs were noted. These results suggest that risk perception, health care professional referral and communication, and insurance coverage may be key levers to increase risk-reducing behaviors in US adults. These findings provide a benchmark from which to monitor future program availability and coverage, identification of prediabetes, and referral to and retention in programs.

Conflict of interest statement

Conflict of Interest Disclosures: None reported.

Figures

Figure.. The Diabetes Prevention Continuum
Figure.. The Diabetes Prevention Continuum
Graphs depict the numbers of US adults with elevated body mass index (calculated as weight in kilograms divided by height in meters squared). Total bars represent those eligible for lifestyle modification programs. Subsequeent bar heights depict eligible adults who reported receiving screening, referral, or advice regarding diabetes prevention behaviors; these bars were divided further to illustrate the number of those who did and did not engage among those advised or referred. Data are from the National Health Interview Survey, 2016 to 2017. Overweight was defined as a body mass index from 23.0 to 29.9 for Asian adults and 25.0 to 29.9 for all other adults; obesity, body mass index of 30.0 or higher. Output in eTable 4 in the Supplement was used for figure development. DPP indicates Diabetes Prevention Program; PA, physical activity; WLP, weight loss program.

References

    1. American Diabetes Association. Economic costs of diabetes in the US in 2017 Diabetes Care. ;41(5):917-928.
    1. Centers for Disease Control and Prevention National diabetes statistics report, 2017. . Accessed July 15, 2018.
    1. Knowler WC, Barrett-Connor E, Fowler SE, et al. ; Diabetes Prevention Program Research Group . Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-. doi:10.1056/NEJMoa012512
    1. Pan XR, Li GW, Hu YH, et al. . Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20(4):537-544. doi:10.2337/diacare.20.4.537
    1. Tuomilehto J, Lindström J, Eriksson JG, et al. ; Finnish Diabetes Prevention Study Group . Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343-1350. doi:10.1056/NEJM200105033441801
    1. Haw JS, Galaviz KI, Straus AN, et al. . Long-term sustainability of diabetes prevention approaches: a systematic review and meta-analysis of randomized clinical trials. JAMA Intern Med. 2017;177(12):1808-1817. doi:10.1001/jamainternmed.2017.6040
    1. Diabetes Prevention Program Research Group Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the Diabetes Prevention Program Outcomes Study. Lancet Diabetes Endocrinol. 2015;3(11):866-875. doi:10.1016/S2213-8587(15)00291-0
    1. Diabetes Prevention Program Research Group The 10-year cost-effectiveness of lifestyle intervention or metformin for diabetes prevention: an intent-to-treat analysis of the DPP/DPPOS. Diabetes Care. 2012;35(4):723-730. doi:10.2337/dc11-1468
    1. Orchard TJ, Temprosa M, Barrett-Connor E, et al. ; Diabetes Prevention Program Outcomes Study Research Group . Long-term effects of the Diabetes Prevention Program interventions on cardiovascular risk factors: a report from the DPP Outcomes Study. Diabet Med. 2013;30(1):46-55. doi:10.1111/j.1464-5491.2012.03750.x
    1. Brown JS, Wing R, Barrett-Connor E, et al. ; Diabetes Prevention Program Research Group . Lifestyle intervention is associated with lower prevalence of urinary incontinence: the Diabetes Prevention Program. Diabetes Care. 2006;29(2):385-390. doi:10.2337/diacare.29.02.06.dc05-1781
    1. Curry SJ, Krist AH, Owens DK, et al. ; US Preventive Services Task Force . Behavioral weight loss interventions to prevent obesity-related morbidity and mortality in adults: US Preventive Services Task Force recommendation statement. JAMA. 2018;320(11):1163-1171. doi:10.1001/jama.2018.13022
    1. American Diabetes Association 5. prevention or delay of type 2 diabetes: Standards of Medical Care in Diabetes–2018. Diabetes Care. 2018;41(suppl 1):S51-S54. doi:10.2337/dc18-S005
    1. Ali MK, Echouffo-Tcheugui J, Williamson DF. How effective were lifestyle interventions in real-world settings that were modeled on the Diabetes Prevention Program? Health Aff (Millwood). 2012;31(1):67-75. doi:10.1377/hlthaff.2011.1009
    1. Galaviz KI, Weber MB, Straus A, Haw JS, Narayan KMV, Ali MK. Global diabetes prevention interventions: a systematic review and network meta-analysis of the real-world impact on incidence, weight, and glucose. Diabetes Care. 2018;41(7):1526-1534. doi:10.2337/dc17-2222
    1. Mudaliar U, Zabetian A, Goodman M, et al. . Cardiometabolic risk factor changes observed in diabetes prevention programs in US settings: a systematic review and meta-analysis. PLoS Med. 2016;13(7):e1002095. doi:10.1371/journal.pmed.1002095
    1. Herman WH, Hoerger TJ, Brandle M, et al. ; Diabetes Prevention Program Research Group . The cost-effectiveness of lifestyle modification or metformin in preventing type 2 diabetes in adults with impaired glucose tolerance. Ann Intern Med. 2005;142(5):323-332. doi:10.7326/0003-4819-142-5-200503010-00007
    1. Li R, Qu S, Zhang P, et al. . Economic evaluation of combined diet and physical activity promotion programs to prevent type 2 diabetes among persons at increased risk: a systematic review for the Community Preventive Services Task Force. Ann Intern Med. 2015;163(6):452-460. doi:10.7326/M15-0469
    1. Zhuo X, Zhang P, Selvin E, et al. . Alternative HbA1c cutoffs to identify high-risk adults for diabetes prevention: a cost-effectiveness perspective. Am J Prev Med. 2012;42(4):374-381. doi:10.1016/j.amepre.2012.01.003
    1. Centers for Disease Control and Prevention, National Center for Health Statistics. National Health Interview Survey. . Updated March 20, 2019. Accessed July 15, 2018.
    1. National Center for Health Statistics Survey Description, National Health Interview Survey, 2016. Hyattsville, MD: National Center for Health Statistics; 2017.
    1. National Center for Health Statistics Survey Description, National Health Interview Survey, 2017. Hyattsville, MD: National Center for Health Statistics; 2018.
    1. Bang H, Edwards AM, Bomback AS, et al. . A patient self-assessment diabetes screening score: development, validation, and comparison to other diabetes risk assessment scores. Ann Intern Med. 2009;151(11):775-783. doi:10.7326/0003-4819-151-11-200912010-00005
    1. Bullard KM, Ali MK, Imperatore G, et al. . Receipt of glucose testing and performance of two US diabetes screening guidelines, 2007-2012. PLoS One. 2015;10(4):e0125249. doi:10.1371/journal.pone.0125249
    1. Centers for Disease Control and Prevention National Diabetes Prevention Program. . Published 2018. Accessed September 15, 2018.
    1. Ali MK, Bullard KM, Gregg EW, Del Rio C. A cascade of care for diabetes in the United States: visualizing the gaps. Ann Intern Med. 2014;161(10):681-689. doi:10.7326/M14-0019
    1. Thorpe KE. Analysis & commentary: the Affordable Care Act lays the groundwork for a national diabetes prevention and treatment strategy. Health Aff (Millwood). 2012;31(1):61-66. doi:10.1377/hlthaff.2011.1023
    1. Albright AL, Gregg EW. Preventing type 2 diabetes in communities across the US: the National Diabetes Prevention Program. Am J Prev Med. 2013;44(4)(suppl 4):S346-S351. doi:10.1016/j.amepre.2012.12.009
    1. Torjesen I. NHS England rolls out world’s first national diabetes prevention programme. BMJ. 2016;352:i1669. doi:10.1136/bmj.i1669
    1. Nhim K, Khan T, Gruss SM, et al. . Primary care providers’ prediabetes screening, testing, and referral behaviors. Am J Prev Med. 2018;55(2):e39-e47. doi:10.1016/j.amepre.2018.04.017
    1. Tseng E, Greer RC, O’Rourke P, et al. . Survey of primary care providers’ knowledge of screening for, diagnosing and managing prediabetes. J Gen Intern Med. 2017;32(11):1172-1178. doi:10.1007/s11606-017-4103-1
    1. Cefalu WT, Petersen MP, Ratner RE. The alarming and rising costs of diabetes and prediabetes: a call for action! Diabetes Care. 2014;37(12):3137-3138. doi:10.2337/dc14-2329
    1. Dall TM, Yang W, Halder P, et al. . The economic burden of elevated blood glucose levels in 2012: diagnosed and undiagnosed diabetes, gestational diabetes mellitus, and prediabetes. Diabetes Care. 2014;37(12):3172-3179. doi:10.2337/dc14-1036
    1. Ali MK, Bullard KM, Saydah S, Imperatore G, Gregg EW. Cardiovascular and renal burdens of prediabetes in the USA: analysis of data from serial cross-sectional surveys, 1988-2014. Lancet Diabetes Endocrinol. 2018;6(5):392-403. doi:10.1016/S2213-8587(18)30027-5
    1. Messina J, Campbell S, Morris R, Eyles E, Sanders C. A narrative systematic review of factors affecting diabetes prevention in primary care settings. PLoS One. 2017;12(5):e0177699-e0177699. doi:10.1371/journal.pone.0177699
    1. Ad Council Type 2 Diabetes Prevention. . Accessed April 4, 2019.
    1. Berg S. 7 steps to get prediabetic patients the preventive help they need. . Published October 4, 2018. Accessed April 4, 2019.
    1. Loewenstein G, Asch DA, Volpp KG. Behavioral economics holds potential to deliver better results for patients, insurers, and employers. Health Aff (Millwood). 2013;32(7):1244-1250. doi:10.1377/hlthaff.2012.1163
    1. Volpp KG, Asch DA. Make the healthy choice the easy choice: using behavioral economics to advance a culture of health. QJM. 2017;110(5):271-275.
    1. Albu JB, Sohler N, Li R, et al. . An interrupted time series analysis to determine the effect of an electronic health record-based intervention on appropriate screening for type 2 diabetes in urban primary care clinics in New York City. Diabetes Care. 2017;40(8):1058-1064. doi:10.2337/dc16-2133
    1. Grant RW, Schmittdiel JA, Neugebauer RS, Uratsu CS, Sternfeld B. Exercise as a vital sign: a quasi-experimental analysis of a health system intervention to collect patient-reported exercise levels. J Gen Intern Med. 2014;29(2):341-348. doi:10.1007/s11606-013-2693-9
    1. Ali M, Wharam F, Kenrik Duru O, et al. ; NEXT-D Study Group . Advancing health policy and program research in diabetes: findings from the Natural Experiments for Translation in Diabetes (NEXT-D). Curr Diabetes Rep. 2018;18(12):146. doi:10.1007/s11892-018-1112-3
    1. Volpp KG, John LK, Troxel AB, Norton L, Fassbender J, Loewenstein G. Financial incentive-based approaches for weight loss: a randomized trial. JAMA. 2008;300(22):2631-2637. doi:10.1001/jama.2008.804
    1. Yancy WS Jr, Shaw PA, Wesby L, et al. . Financial incentive strategies for maintenance of weight loss: results from an internet-based randomized controlled trial. Nutr Diabetes. 2018;8(1):33. doi:10.1038/s41387-018-0036-y
    1. Moin T, Li J, Duru OK, et al. . Metformin prescription for insured adults with prediabetes from 2010 to 2012: a retrospective cohort study. Ann Intern Med. 2015;162(8):542-548. doi:10.7326/M14-1773
    1. Saaddine JB, Engelgau MM, Beckles GL, Gregg EW, Thompson TJ, Narayan KM. A diabetes report card for the United States: quality of care in the 1990s. Ann Intern Med. 2002;136(8):565-574. doi:10.7326/0003-4819-136-8-200204160-00005
    1. Saydah SH, Fradkin J, Cowie CC. Poor control of risk factors for vascular disease among adults with previously diagnosed diabetes. JAMA. 2004;291(3):335-342. doi:10.1001/jama.291.3.335
    1. Ali MK, Bullard KM, Saaddine JB, Cowie CC, Imperatore G, Gregg EW. Achievement of goals in US diabetes care, 1999-2010. N Engl J Med. 2013;368(17):1613-1624. doi:10.1056/NEJMsa1213829
    1. American Medical Association Prediabetes Quality Measures Technical Expert Panel Prediabetes quality measures. . Published 2018. Accessed September 15, 2018.
    1. Bullard KM, Saydah SH, Imperatore G, et al. . Secular changes in US prediabetes prevalence defined by hemoglobin A1c and fasting plasma glucose: National Health and Nutrition Examination Surveys, 1999-2010. Diabetes Care. 2013;36(8):2286-2293. doi:10.2337/dc12-2563
    1. Hill JO, Galloway JM, Goley A, et al. . Scientific statement: socioecological determinants of prediabetes and type 2 diabetes. Diabetes Care. 2013;36(8):2430-2439. doi:10.2337/dc13-1161

Source: PubMed

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