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1. Estimated number of eligible Part B beneficiaries for the medicare diabetes prevention program at the county level and by urban-rural classification.

Author

Boon Peng Ng, Yiling J Cheng, Stephanie Rutledge, Michael J Cannon, Ping Zhang, and Bryce D Smith

Subjects
Medicine, Science
Abstract

IntroductionDiabetes imposes large health and financial burdens on Medicare beneficiaries. Type 2 diabetes can be prevented or delayed through lifestyle modification programs. In 2018, Medicare began to offer the Medicare Diabetes Prevention Program (MDPP), a lifestyle intervention, to eligible beneficiaries nationwide. The number of MDPP-eligible beneficiaries is not known, but this information is essential in efforts to expand the program and increase enrollment. This study aimed to estimate the number and spatial variation of MDPP-eligible Part B beneficiaries at the county level and by urban-rural classification.MethodsData from 2011-2016 National Health and Nutrition Examination Surveys and a survey-weighted logistic regression model were used to estimate proportions of prediabetes in the United States by sex, age, and race/ethnicity based on the MDPP eligibility criteria. The results from the predictive model were applied to 2015 Medicare Part B beneficiaries to estimate the number of MDPP-eligible beneficiaries. The National Center for Health Statistics' Urban-Rural Classification Scheme for Counties from 2013 were used to define urban and rural categories.ResultsAn estimated 5.2 million (95% CI = 3.5-7.0 million) Part B beneficiaries were eligible for the MDPP. By state, estimates ranged from 13,000 (95% CI = 8,500-18,000) in Alaska to 469,000 (95% CI = 296,000-641,000) in California. There were 2,149 counties with ≤1,000 eligible beneficiaries and 11 with >25,000. Consistent with demographic patterns, urban counties had more eligible beneficiaries than rural counties.ConclusionsThese estimates could be used to plan locations for new MDPPs and reach eligible Part B beneficiaries for enrollment.

Published
2020
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5. Returning integrated genomic risk and clinical recommendations: the eMERGE study

Author

Jodell E. Linder, Aimee Allworth, Sarah T. Bland, Pedro J. Caraballo, Rex L. Chisholm, Ellen Wright Clayton, David R. Crosslin, Ozan Dikilitas, Alanna DiVietro, Edward D. Esplin, Sophie Forman, Robert R. Freimuth, Adam S. Gordon, Richard Green, Maegan V. Harden, Ingrid A. Holm, Gail P. Jarvik, Elizabeth W. Karlson, Sofia Labrecque, Niall J. Lennon, Nita A. Limdi, Kathleen F. Mittendorf, Shawn N. Murphy, Lori Orlando, Cynthia A. Prows, Luke V. Rasmussen, Laura Rasmussen-Torvik, Robb Rowley, Konrad Teodor Sawicki, Tara Schmidlen, Shannon Terek, David Veenstra, Digna R. Velez Edwards, Devin Absher, Noura S. Abul-Husn, Jorge Alsip, Hana Bangash, Mark Beasley, Jennifer E. Below, Eta S. Berner, James Booth, Wendy K. Chung, James J. Cimino, John Connolly, Patrick Davis, Beth Devine, Stephanie M. Fullerton, Candace Guiducci, Melissa L. Habrat, Heather Hain, Hakon Hakonarson, Margaret Harr, Eden Haverfield, Valentina Hernandez, Christin Hoell, Martha Horike-Pyne, George Hripcsak, Marguerite R. Irvin, Christopher Kachulis, Dean Karavite, Eimear E. Kenny, Atlas Khan, Krzysztof Kiryluk, Bruce Korf, Leah Kottyan, Iftikhar J. Kullo, Katie Larkin, Cong Liu, Edyta Malolepsza, Teri A. Manolio, Thomas May, Elizabeth M. McNally, Frank Mentch, Alexandra Miller, Sean D. Mooney, Priyanka Murali, Brenda Mutai, Naveen Muthu, Bahram Namjou, Emma F. Perez, Megan J. Puckelwartz, Tejinder Rakhra-Burris, Dan M. Roden, Elisabeth A. Rosenthal, Seyedmohammad Saadatagah, Maya Sabatello, Dan J. Schaid, Baergen Schultz, Lynn Seabolt, Gabriel Q. Shaibi, Richard R. Sharp, Brian Shirts, Maureen E. Smith, Jordan W. Smoller, Rene Sterling, Sabrina A. Suckiel, Jeritt Thayer, Hemant K. Tiwari, Susan B. Trinidad, Theresa Walunas, Wei-Qi Wei, Quinn S. Wells, Chunhua Weng, Georgia L. Wiesner, Ken Wiley, Josh F. Peterson, Adam Gordon, Agboade Sobowale, Akshar Patel, Alanna Strong, Alborz Sherafati, Alborz Sherfati, Alex Bick, Alka Chandel, Alyssa Rosenthal, Amit Khera, Amy Kontorovich, Andrew Beck, Andy Beck, Angelica Espinoza, Anna Lewis, Anya Prince, Ayuko Iverson, Bahram Namjou Khales, Barbara Benoit, Becca Hernan, Ben Kallman, Ben Kerman, Ben Shoemaker, Benjamin Satterfield, Bethany Etheridge, Blake Goff, Bob Freimuth, Bob Grundmeier, Brenae Collier, Brett Harnett, Brian Chang, Brian Piening, Brittney Davis, Candace Patterson, Carmen Demetriou, Casey Ta, Catherine Hammack, Catrina Nelson, Caytie Gascoigne, Chad Dorn, Chad Moretz, Chris Kachulis, Christie Hoell, Christine Cowles, Christoph Lange, Cindy Prows, Cole Brokamp, Courtney Scherr, Crystal Gonzalez, Cynthia Ramirez, Daichi Shimbo, Dan Roden, Daniel Schaid, Dave Kaufman, David Crosslin, David Kochan, Davinder Singh, Debbie Abrams, Digna Velez Edwards, Eduardo Morales, Edward Esplin, Ehsan Alipour, Eimear Kenny, Elisabeth Rosenthal, Eliza Duvall, Elizabeth McNally, Elizabeth Bhoj, Elizabeth Cohn, Elizabeth Hibler, Elizabeth Karlson, Ellen Clayton, Emily Chesnut, Emily DeFranco, Emily Gallagher, Emily Soper, Emma Perez, Erin Cash, Eta Berner, Fei Wang, Firas Wehbe, Francisco Ricci, Gabriel Shaibi, Gail Jarvik, George Hahn, Georgia Wiesner, Gillian Belbin, Gio Davogustto, Girish Nadkarni, Haijun Qiu, Hannah Beasley, Hao Liu, Heide Aungst, Hemant Tiwari, Hillary Duckham, Hope Thomas, Iftikhar Kullo, Ingrid Holm, Isabelle Allen, Iuliana Ionita-Laza, Jacklyn Hellwege, Jacob Petrzelka, Jacqueline Odgis, Jahnavi Narula, Jake Petrzelka, Jalpa Patel, James Cimino, James Meigs, James Snyder, Janet Olson, Janet Zahner, Jeff Pennington, Jen Pacheco, Jennifer Allen Pacheco, Jennifer Morse, Jeremy Corsmo, Jim Cimino, Jingheng Chen, Jocelyn Fournier, Jodell Jackson, Joe Glessner, Joel Pacyna, Johanna Smith, John Lynch, John Shelley, Jonathan Mosley, Jordan Nestor, Jordan Smoller, Joseph Kannry, Joseph Sutton, Josh Peterson, Joshua Smith, Julia Galasso, Julia Smith, Julia Wynn, Justin Gundelach, Justin Starren, Karmel Choi, Kate Mittendorf, Katherine Anderson, Katherine Bonini, Kathleen Leppig, Kathleen Muenzen, Kelsey Stuttgen, Kenny Nguyen, Kevin Dufendach, Kiley Atkins, Konrad Sawicki, Kristjan Norland, Laura Beskow, Li Hsu, Lifeng Tian, Lisa Mahanta, Lisa Martin, Lisa Wang, Lizbeth Gomez, Lorenzo Thompson, Lucas Richter, Luke Rasmussen, Lynn Petukhova, Madison O’Brien, Maegan Harden, Malia Fullerton, Marta Guindo, Martha Horike, Marwah Abdalla, Marwan Hamed, Mary Beth Terry, Mary Maradik, Matt Wyatt, Matthew Davis, Matthew Lebo, Maureen Smith, Maya del Rosario, Meckenzie Behr, Meg Roy-Puckelwartz, Mel Habrat, Melanie Myers, Meliha Yetisgen, Merve Iris, Michael DaSilva, Michael Preuss, Michelle McGowan, Mingjian Shi, Minoli Perera, Minta Thomas, Mitch Elkind, Mohammad Abbass, Mohammad Saadatagah, Molly Hess, Molly Maradik, Nataraja 'RJ' Vaitinadin, Nataraja Vaitinadin, Neil Netherly, Niall Lennon, Ning Shang, Nita Limdi, Noah Forrest, Noheli Romero, Nora Robinson, Noura Abul-Husn, Omar Elsekaily, Patricia Kovatch, Paul Appelbaum, Paul Francaviglia, Paul O’Reilly, Paulette Chandler, Pedro Caraballo, Peter Tarczy-Hornoch, Pierre Shum, Priya Marathe, Qiping Feng, Quinn Wells, Rachel Atchley, Radhika Narla, Rene Barton, Rex Chisholm, Richard Sharp, Riki Peters, Rita Kukafka, Robert Freimuth, Robert Green, Robert Winter, Roger Mueller, Ruth Loos, Ryan Irvin, Sabrina Suckiel, Sajjad Hussain, Samer Sharba, Sandy Aronson, Sarah Jones, Sarah Knerr, Scott Nigbur, Scott Weiss, Sean Mooney, Sharon Aufox, Sharon Nirenberg, Shawn Murphy, Sheila O’Byrne, Shing Wang (Sam) Choi, Sienna Aguilar, S.T. Bland, Stefanie Rodrigues, Stephanie Ledbetter, Stephanie Rutledge, Stuart James Booth, Su Xian, Susan Brown Trinidad, Suzanne Bakken, Teri Manolio, Tesfaye Mersha, Thevaa Chandereng, Tian Ge, Todd Edwards, Tom Kaszemacher, Valerie Willis, Vemi Desai, Vimi Desai, Virginia Lorenzi, Vivian Gainer, Wendy Chung, Wu-Chen Su, Xiao Chang, Yiqing Zhao, Yuan Luo, and Yufeng Shen

Subjects
Genetics (clinical)
Abstract

Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk.To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores (PRS), monogenic risks, family history, and clinical risk assessments via a Genome Informed Risk Assessment (GIRA) report and will assess uptake of care recommendations after return of results.GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022.Return of a novel report for communicating monogenic, polygenic, and family history based risk factors will inform the benefits of integrated genetic risk assessment for routine health care.

Published
2022

9. Diabetes Self-Management Education Programs in Nonmetropolitan Counties — United States, 2016

Author

Stephanie Rutledge, Magon M Saunders, Svetlana Masalovich, and Rachel J Blacher

Subjects
Program evaluation, Gerontology, medicine.medical_specialty, Health (social science), Epidemiology, Health, Toxicology and Mutagenesis, Vital signs, Medically Underserved Area, 03 medical and health sciences, 0302 clinical medicine, Patient Education as Topic, Health Information Management, Diabetes Mellitus, medicine, Humans, 030212 general & internal medicine, Surveillance Summaries, business.industry, 030503 health policy & services, Public health, Incidence (epidemiology), Odds ratio, Census, Metropolitan area, United States, Audience measurement, Self Care, Rural Health Services, 0305 other medical science, business, Demography
Abstract

Problem/condition Diabetes self-management education (DSME) is a clinical practice intended to improve preventive practices and behaviors with a focus on decision-making, problem-solving, and self-care. The distribution and correlates of established DSME programs in nonmetropolitan counties across the United States have not been previously described, nor have the characteristics of the nonmetropolitan counties with DSME programs. Reporting period July 2016. Description of systems DSME programs recognized by the American Diabetes Association or accredited by the American Association of Diabetes Educators (i.e., active programs) as of July 2016 were shared with CDC by both organizations. The U.S. Census Bureau's census geocoder was used to identify the county of each DSME program site using documented addresses. County characteristic data originated from the U.S. Census Bureau, compiled by the U.S. Department of Agriculture's Economic Research Service into the 2013 Atlas of Rural and Small-Town America data set. County levels of diagnosed diabetes prevalence and incidence, as well as the number of persons with diagnosed diabetes, were previously estimated by CDC. This report defined nonmetropolitan counties using the rural-urban continuum code from the 2013 Atlas of Rural and Small-Town America data set. This code included six nonmetropolitan categories of 1,976 urban and rural counties (62% of counties) adjacent to and nonadjacent to metropolitan counties. Results In 2016, a total of 1,065 DSME programs were located in 38% of the 1,976 nonmetropolitan counties; 62% of nonmetropolitan counties did not have a DSME program. The total number of DSME programs for nonmetropolitan counties with at least one DSME program ranged from 1 to 8, with an average of 1.4 programs. After adjusting for county-level characteristics, the odds of a nonmetropolitan county having at least one DSME program increased as the percentage insured increased (adjusted odds ratio [AOR] = 1.10, 95% confidence interval [CI] = 1.08-1.13), the percentage with a high school education or less decreased (AOR = 1.06, 95% CI = 1.04-1.07), the unemployment rate decreased (AOR = 1.19, 95% CI = 1.11-1.23), and the natural logarithm of the number of persons with diabetes increased (AOR = 3.63, 95% CI = 3.15-4.19). Interpretation In 2016, there were few DMSE programs in nonmetropolitan, socially disadvantaged counties in the United States. The number of persons with diabetes, percentage insured, percentage with a high school education or less, and the percentage unemployed were significantly associated with whether a DSME program was located in a nonmetropolitan county. Public health action Monitoring the distribution of DSME programs at the county level provides insight needed to strategically address rural disparities in diabetes care and outcomes. These findings provide information needed to assess lack of availability of DSME programs and to explore evidence-based strategies and innovative technologies to deliver DSME programs in underserved rural communities.

Published
2017
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10. Implementing Key Drivers for Diabetes Self-Management Education and Support Programs: Early Outcomes, Activities, Facilitators, and Barriers

Author

Shelly-Ann Bowen, Yvonne Mensa-Wilmot, Gia E. Rutledge, Stephanie Rutledge, Jennifer Murphy Morgan, Timethia Bonner, and Monica D. Murphy

Subjects
Essay, MEDLINE, 030209 endocrinology & metabolism, Diabetes self management, Health Promotion, 03 medical and health sciences, 0302 clinical medicine, Nursing, Diabetes mellitus, Health care, Medicine, Humans, 030212 general & internal medicine, Self-management, business.industry, Health Policy, Self-Management, Public Health, Environmental and Occupational Health, medicine.disease, United States, Health promotion, Outcome and Process Assessment, Health Care, Diabetes Mellitus, Type 2, Key (cryptography), Centers for Disease Control and Prevention, U.S, business
Published
2018

11. Evaluating Cross-Cutting Approaches to Chronic Disease Prevention and Management: Developing a Comprehensive Evaluation

Author

Seraphine Pitt Barnes, Marla Vaughan, Stephanie Rutledge, Pat Shea, Jan Jernigan, and Rachel Davis

Subjects
Program evaluation, medicine.medical_specialty, Health Planning Guidelines, Quality Assurance, Health Care, media_common.quotation_subject, Control (management), MEDLINE, Public policy, Public Policy, 01 natural sciences, 03 medical and health sciences, 0302 clinical medicine, Health care, Diabetes Mellitus, Humans, Medicine, Special Topic, Quality (business), Obesity, 030212 general & internal medicine, 0101 mathematics, Health policy, media_common, business.industry, Health Policy, Public health, 010102 general mathematics, Health Plan Implementation, Public Health, Environmental and Occupational Health, United States, Health Planning, Risk analysis (engineering), Public Health, Centers for Disease Control and Prevention, U.S, business, Program Evaluation, State Government
Abstract

We provide an overview of the comprehensive evaluation of State Public Health Actions to Prevent and Control Diabetes, Heart Disease, Obesity and Associated Risk Factors and Promote School Health (State Public Health Actions). State Public Health Actions is a program funded by the Centers for Disease Control and Prevention to support the statewide implementation of cross-cutting approaches to promote health and prevent and control chronic diseases. The evaluation addresses the relevance, quality, and impact of the program by using 4 components: a national evaluation, performance measures, state evaluations, and evaluation technical assistance to states. Challenges of the evaluation included assessing the extent to which the program contributed to changes in the outcomes of interest and the variability in the states’ capacity to conduct evaluations and track performance measures. Given the investment in implementing collaborative approaches at both the state and national level, achieving meaningful findings from the evaluation is critical.

Published
2017
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12. Early Results of States’ Efforts to Support, Scale, and Sustain the National Diabetes Prevention Program

Author

Kimberly D. Farris, Stephanie Rutledge, Rachel J Blacher, Yvonne Mensa-Wilmot, Shelly-Ann Bowen, Jennifer Murphy Morgan, Gia E. Rutledge, and Timethia Bonner

Subjects
medicine.medical_specialty, National Health Programs, MEDLINE, 030209 endocrinology & metabolism, Type 2 diabetes, 03 medical and health sciences, 0302 clinical medicine, Diabetes mellitus, medicine, Humans, Special Topic, 030212 general & internal medicine, business.industry, Health Policy, Public Health, Environmental and Occupational Health, medicine.disease, Disease control, United States, Health Planning, Chronic disease, Early results, Diabetes Mellitus, Type 2, Family medicine, Scale (social sciences), Lifestyle change, Centers for Disease Control and Prevention, U.S, business
Abstract

The Centers for Disease Control and Prevention (CDC) developed a cooperative agreement with health departments in all 50 states and the District of Columbia to strengthen chronic disease prevention and management efforts through the implementation of evidence-based strategies, such as CDC's National Diabetes Prevention Program. The National Diabetes Prevention Program supports organizations to deliver the year-long lifestyle change program that has been proven to prevent or delay the onset of type 2 diabetes among those at high risk. This article describes activities, barriers, and facilitators reported by funded states during the first 3 years (2013-2015) of a 5-year funding cycle.

Published
2017

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