Protocol for the analysis of a natural experiment on the impact of the Affordable Care Act on diabetes care in community health centers

Nathalie Huguet, Heather Angier, Miguel Marino, K John McConnell, Megan J Hoopes, Jean P O'Malley, Lewis A Raynor, Sonja Likumahuwa-Ackman, Heather Holderness, Jennifer E DeVoe, Nathalie Huguet, Heather Angier, Miguel Marino, K John McConnell, Megan J Hoopes, Jean P O'Malley, Lewis A Raynor, Sonja Likumahuwa-Ackman, Heather Holderness, Jennifer E DeVoe

Abstract

Background: It is hypothesized that Affordable Care Act (ACA) Medicaid expansions could substantially improve access to health insurance and healthcare services for patients at risk for diabetes mellitus (DM), with pre-DM, or already diagnosed with DM. The ACA called for every state to expand Medicaid coverage by 2014. In a 2012 legal challenge, the US Supreme Court ruled that states were not required to implement Medicaid expansions. This 'natural experiment' presents a unique opportunity to learn whether and to what extent Medicaid expansion can affect healthcare access and services for patients with DM risk, pre-DM, or DM.

Methods/design: Data from electronic health records (EHRs) from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) clinical data research network, which has data from >700 community health centers (CHCs), was included in the study. EHR data will be linked to Oregon Medicaid claims data. Data collection will include information on changes in health insurance, service receipt, and health outcomes, spanning 9 years (pre- and post-expansion), comparing states that expanded Medicaid, and those that did not. Patients included in this study will be diagnosed with DM, be at risk for DM, or have pre-DM, between the ages of 19 and 64, with ≥1 ambulatory visit. Sample size is estimated to be roughly 275,000 patients. Biostatistical analyses will include the difference-in-differences (DID) methodology and a generalized linear mixed model. Econometric analyses will include a DID two-part method to calculate the difference in Medicaid expenditures in Oregon among newly insured CHC patients.

Discussion: Findings will have national relevance on DM health services and outcomes and will be shared through national conferences and publications. The findings will provide information needed to impact the policy as it is related to access to health insurance and receipt of healthcare among a vulnerable population.

Trial registration: This project is registered with ClinicalTrials.gov ( NCT02685384 ). Registered 18 May 2016.

Keywords: Affordable Care Act; Community health center; Diabetes; Natural experiment.

Figures

Fig. 1
Fig. 1
Medicaid expansion status timeline

References

    1. American DA. Economic costs of diabetes in the U.S. In 2007. Diabetes Care. 2008;31(3):596–615. doi: 10.2337/dc08-9017.
    1. Centers for Disease Control and Prevention. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2011.
    1. National Center for Chronic Disease Prevention and Health Promotion . National diabetes statistics report, 2014. Atlanta, GA: Centers for Disease Control and Prevention; 2014.
    1. Zhang X, Geiss LS, Cheng YJ, Beckles GL, Gregg EW, Kahn HS. The missed patient with diabetes: how access to health care affects the detection of diabetes. Diabetes Care. 2008;31(9):1748–1753. doi: 10.2337/dc08-0527.
    1. Bailey SR, O'Malley JP, Gold R, Heintzman J, Marino M, DeVoe JE. Receipt of diabetes preventive services differs by insurance status at visit. Am J Prev Med. 2015;48(2):229–233. doi: 10.1016/j.amepre.2014.08.035.
    1. Gold R, DeVoe J, Shah A, Chauvie S. Insurance continuity and receipt of diabetes preventive care in a network of federally qualified health centers. Med Care. 2009;47(4):431–439. doi: 10.1097/MLR.0b013e318190ccac.
    1. Nelson KM, Chapko MK, Reiber G, Boyko EJ. The association between health insurance coverage and diabetes care; data from the 2000 Behavioral Risk Factor Surveillance System. Health Serv Res. 2005;40(2):361–372. doi: 10.1111/j.1475-6773.2005.0d362.x.
    1. Zhang JX, Huang ES, Drum ML, Kirchhoff AC, Schlichting JA, Schaefer CT, et al. Insurance status and quality of diabetes care in community health centers. Am J Public Health. 2009;99(4):742–747. doi: 10.2105/AJPH.2007.125534.
    1. Levine DA, Allison JJ, Cherrington A, Richman J, Scarinci IC, Houston TK. Disparities in self-monitoring of blood glucose among low-income ethnic minority populations with diabetes. United States Ethn Dis. 2009;19(2):97–103.
    1. Robbins JM, Thatcher GE, Webb DA, Valdmanis VG. Nutritionist visits, diabetes classes, and hospitalization rates and charges: the Urban Diabetes Study. Diabetes Care. 2008;31(4):655–660. doi: 10.2337/dc07-1871.
    1. Rhee MK, Cook CB, El-Kebbi I, Lyles RH, Dunbar VG, Panayioto RM, et al. Barriers to diabetes education in urban patients: perceptions, patterns, and associated factors. Diabetes Educ. 2005;31(3):410–417. doi: 10.1177/0145721705277022.
    1. Vest BM, Kahn LS, Danzo A, Tumiel-Berhalter L, Schuster RC, Karl R, et al. Diabetes self-management in a low-income population: impacts of social support and relationships with the health care system. Chronic Illn. 2013;9(2):145–155. doi: 10.1177/1742395313475674.
    1. Wallia A, Molitch ME. Insulin therapy for type 2 diabetes mellitus. JAMA. 2014;311(22):2315–2325. doi: 10.1001/jama.2014.5951.
    1. The Henry J . Kaiser Family Foundation. Menlo Park, CA: Summary of the Affordable Care Act; 2013.
    1. Supreme Court of the United States. National Federation of Independent Business v Sebelius. 2012. . Accessed September 27 2016.
    1. The Henry J Kaiser Family Foundation. Status of state action on the Medicaid expansion decision. 2015. . Accessed September 27 2016.
    1. Wachino V, Artiga S, Rudowitz R. How is the ACA Impacting Medicaid Enrollment? 2014. . Accessed January 15 2015.
    1. DeVoe JE, Gold R, Cottrell E, Bauer V, Brickman A, Puro J, et al. The ADVANCE network: accelerating data value across a national community health center network. J Am Med Inform Assoc. 2014;21(4):591–595. doi: 10.1136/amiajnl-2014-002744.
    1. Davis MM, Keller S, DeVoe JE, Cohen J. Characteristics and lessons learned from practice-based research networks (PBRNs) in the United States. J Health Care Leadership. 2012;4:107–116.
    1. Mayer K, Appelbaum J, Rogers T, Lo W, Bradford J, Boswell S. The evolution of the Fenway Community Health model. Am J Public Health. 2001;91(6):892–894. doi: 10.2105/AJPH.91.6.892.
    1. Likumahuwa S, Song H, Singal R, Weir RC, Crane H, Muench J, et al. Building research infrastructure in community health centers: a Community Health Applied Research Network (CHARN) report. J Am Board Fam Med. 2013;26(5):579–587. doi: 10.3122/jabfm.2013.05.130025.
    1. The Fenway Institute. The National LGBT Health Education Center. Boston, MA. 2013. . Accessed September 27 2016.
    1. Angier H, Gold R, Gallia C, Casciato A, Tillotson CJ, Marino M, et al. Variation in outcomes of quality measurement by data source. Pediatrics. 2014;133(6):e1676–e1682. doi: 10.1542/peds.2013-4277.
    1. DeVoe JE, Gold R, McIntire P, Puro J, Chauvie S, Gallia CA. Electronic health records vs Medicaid claims: completeness of diabetes preventive care data in community health centers. Ann Fam Med. 2011;9(4):351–358. doi: 10.1370/afm.1279.
    1. DeVoe JE, Marino M, Angier H, O’Malley J, Crawford C, Nelson C, et al. Effect of expanding Medicaid for parents on children’s health insurance coverage: lessons from the Oregon experiment. JAMA Peds. 2015;169(1):e143145. doi: 10.1001/jamapediatrics.2014.3145.
    1. Centers for Disease Control and Prevention. Preventing diabetes. n.d. . Accessed February 16 2015.
    1. Mooy JM, Grootenhuis PA, de Vries H, Kostense PJ, Popp-Snijders C, Bouter LM, et al. Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the Hoorn Study. Diabetologia. 1996;39(3):298–305. doi: 10.1007/BF00418345.
    1. Klompas M, Eggleston E, McVetta J, Lazarus R, Li L, Platt R. Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data. Diabetes Care. 2013;36(4):914–921. doi: 10.2337/dc12-0964.
    1. Wilke RA, Berg RL, Peissig P, Kitchner T, Sijercic B, McCarty CA, et al. Use of an electronic medical record for the identification of research subjects with diabetes mellitus. Clin Med Res. 2007;5(1):1–7. doi: 10.3121/cmr.2007.726.
    1. American Diabetes Assocation Standards of Medical Care-2013. Diabetes Care. 2013;36(Suppl 1):S11–S66. doi: 10.2337/dc13-S011.
    1. Heintzman J, Marino M, Hoopes M, Bailey SR, Gold R, O'Malley J, et al. Supporting health insurance expansion: do electronic health records have valid insurance verification and enrollment data? J Am Med Inform Assoc. 2015;22(4):909-13. doi:10.1093/jamia/ocv033.
    1. Expert Panel on Detection E, and Treatment of High Blood Cholesterol in Adults Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol in ddults (Adult Treatment Panel III) JAMA. 2001;285(19):2486–2497. doi: 10.1001/jama.285.19.2486.
    1. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, Jr, et al. The seventh report of the joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–2572. doi: 10.1001/jama.289.19.2560.
    1. American Diabetes Association (8) Cardiovascular disease and risk management. Diabetes Care. 2015;38(Suppl):S49–S57. doi: 10.2337/dc15-S011.
    1. McConnell KJ, Wallace NT, Gallia CA, Smith JA. Effect of eliminating behavioral health benefits for selected medicaid enrollees. Health Serv Res. 2008;43(4):1348–1365. doi: 10.1111/j.1475-6773.2008.00844.x.
    1. Bertrand M, Duflo E, Mullainathan S. How much should we trust differences-in-differences estimates? Q J Econ. 2004;119(1):249–275. doi: 10.1162/003355304772839588.
    1. Friedberg MW, Schneider EC, Rosenthal MB, Volpp KG, Werner RM. Association between participation in a multipayer medical home intervention and changes in quality, utilization, and costs of care. JAMA. 2014;311(8):815–825. doi: 10.1001/jama.2014.353.
    1. Higgins S, Chawla R, Colombo C, Snyder R, Nigam S. Medical homes and cost and utilization among high-risk patients. Am J Manag Care. 2014;20(3):e61–e71.
    1. Guo S, Fraser MW. Propensity score analysis: statistical methods and applications advanced quantitative techniques in the social sciences series. Thousand Oaks, CA: Sage Publications, Inc; 2014.
    1. Duan NH, Manning WG, Morris CN, Newhouse JP. A comparison of alternative models for demand for medical care. J Bus Econ Stat. 1983;1(2):115–126.
    1. Manning WG. The logged dependent variable, heteroscedasticity, and the retransformation problem. J Health Econ. 1998;17(3):283–295. doi: 10.1016/S0167-6296(98)00025-3.
    1. Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461–494. doi: 10.1016/S0167-6296(01)00086-8.
    1. Gold R, Muench J, Hill C, Turner A, Mital M, Milano C, et al. Collaborative development of a randomized study to adapt a diabetes quality improvement initiative for federally qualified health centers. J Health Care Poor Underserved. 2012;23(Suppl 3):236–246. doi: 10.1353/hpu.2012.0132.
    1. Cowburn S, Carlson MJ, Lapidus JA, DeVoe JE. The association between insurance status and cervical cancer screening in community health centers: exploring the potential of electronic health records for population-level surveillance, 2008-2010. Prev Chronic Dis. 2013;10:E173. doi: 10.5888/pcd10.130034.
    1. Little TD, Schnabel KU, Baumert J. Modeling longitudinal and multilevel data 2000. New Jersey: Lawrence Earlbaum Associates; 2000.
    1. Little RJA, Rubin DB. Statistical analysis with missing data. Hoboken, NJ: John Wiley & Sons; 2002.

Source: PubMed

Sponsorzy i współpracownicy

Warunki medyczne

Interwencje lekowe

3
Subskrybuj