Al'ona Furmanchuk1, Mei Liu2, Xing Song3, Lemuel R Waitman3, John R Meurer4, Kristen Osinski5, Alexander Stoddard5, Elizabeth Chrischilles6, James C McClay7, Lindsay G Cowell8, Umberto Tachinardi9, Peter J Embi9, Abu Saleh Mohammad Mosa10, Vasanthi Mandhadi10, Raj C Shah11, Diana Garcia12, Francisco Angulo12, Alejandro Patino12, William E Trick13, Talar W Markossian14, Laura J Rasmussen-Torvik15, Abel N Kho16, Bernard S Black17. 1. Division of General Internal Medicine and Geriatrics, Northwestern University, Chicago, Illinois, USA alona.furmanchuk@northwestern.edu. 2. Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA. 3. Division of Health Management and Informatics, University of Missouri, Columbia, Missouri, USA. 4. Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, Wisconsin, USA. 5. Clinical and Translational Science Institute of Southeast Wisconsin, Medical College of Wisconsin, Milwaukee, Wisconsin, USA. 6. Department of Epidemiology, The University of Iowa College of Public Health, Iowa City, Iowa, USA. 7. Department of Emergency Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA. 8. Division of Biomedical Informatics, Department of Population and Data Sciences, Department of Immunology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA. 9. Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA. 10. Department of Health Management and Informatics, University of Missouri School of Medicine, Columbia, Missouri, USA. 11. Department of Family Medicine and Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA. 12. Health Research and Solutions Unit, Cook County Bureau of Health Services, Chicago, Illinois, USA. 13. Department of Medicine, Cook County Bureau of Health Services, Chicago, Illinois, USA. 14. Department of Public Health Sciences, Loyola University Chicago, Maywood, Illinois, USA. 15. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. 16. Division of General Internal Medicine and Geriatrics, Northwestern University, Chicago, Illinois, USA. 17. Pritzker School of Law, Kellogg School of Management, Northwestern University, Chicago, Illinois, USA.
The adoption of the Affordable Care Act (ACA)1 in the USA expanded health insurance for low-income Americans and took two main forms: Medicaid expansion in some states and subsidized private health insurance through insurance exchanges available in all states, with deep subsidies for persons with incomes from 138% to 250% of the federal poverty limit (FPL) in Medicaid expansion states and from 100% to 250% of the FPL in non-expansion states. Prior studies found a statistically significant slightly negative2 effects of the ACA on diabetes diagnoses and controversial (from insignificantly slightly positive3 to significantly positive4) effects on diabetes therapies at county and state levels. We examined the effect of both forms of ACA reform on the improvement of diabetes diagnostics and management in low-income patients who had access to healthcare before the ACA expansion (2011–2013).We used electronic health records (EHR) from 11 major academic health systems in 8 states in the USA (Illinois, Iowa, Wisconsin, Kansas, Nebraska, Missouri, Texas, Indiana). The sample (see table 1 for demographics) was limited to patients aged 55–74 over 2011–2018 who used care (any encounter type) at the study facilities at least once in the pre-expansion period. Due to inconsistent depiction of insurance status in EHR, patient residence in a socially deprived5 census tract (see online supplemental appendix for details) was used as proxy for persons who were more likely to gain insurance under the ACA. Therefore persons aged 55–64 from the socially deprived census tracts were the treatment group. Persons aged 65–74 from socially deprived census tracts were the control group because they had Medicare insurance. For each age group, we studied the per cent of patients of interest with newly detected diabetes6 and the per cent of patients with prevalent diabetes receiving diabetes-related medications before (2011–2013) and during (2014–2018) the ACA expansion. Combined age discontinuity and difference-in-difference research design was employed.
Table 1
Sample characteristics used to measure healthcare utilization outcomes
Sample demographic characteristics for outcome measures
2011–2013
2014–2018
55–64
65–74
55–64
65–74
(A) Total newly detected diabetes (305 726 patients aged 55–74 years old during 2011–2018)
73 479
56 371
90 948
84 928
% from socially deprived census tracts
41.9
34.3
34.7
28.3
Sex: % female
50.2
50.2
47.6
48.0
Race: % white
65.2
71.9
69.8
75.7
Race: % black
22.0
15.7
16.9
11.4
Race: % Asian
3.2
3.3
2.6
2.8
Race: % mixed
0.0
0.0
0.0
0.0
Race: % missing
9.6
9.1
10.7
10.1
Ethnicity: % Hispanic
11.9
8.1
9.5
6.9
Ethnicity: % missing
18.1
20.3
20.8
21.7
(B) Total with prevalent diabetes and relevant medical prescriptions (67 083 patients aged 55–74 years old during 2011–2018)
34 831
32 252
% from socially deprived census tracts
44.1
37.6
Sex: % female
51.0
49.9
Race: % white
62.0
59.9
Race: % black
15.7
11
Race: % Asian
1.7
1.6
Race: % mixed
0.3
0.3
Race: % missing
6.1
5.2
Ethnicity: % Hispanic
5.4
4.1
Ethnicity: % missing
21.5
19.7
The pre-ACA period is 2011–2013; the ACA period is 2014–2018. For medical management of diabetes, patients with prevalent diabetes were studied (sample is the same before and during the ACA period).
ACA, Affordable Care Act.
Sample characteristics used to measure healthcare utilization outcomesThe pre-ACA period is 2011–2013; the ACA period is 2014–2018. For medical management of diabetes, patients with prevalent diabetes were studied (sample is the same before and during the ACA period).ACA, Affordable Care Act.Different from individuals who had no access to healthcare2 before the ACA, our sample of patients from socially deprived tracts shows no increase in rates of newly diagnosed diabetes (figure 1). An insignificant drop of −0.72 (95% CI −3.22 to 1.77) in newly diagnosed diabetes for the treated group was detected. We have to note the identification of diabetes in the sample was not limited to ambulatory settings. This makes us conclude that the study centers may have already been using all available resources to accurately diagnose diabetes before 2014, including for low-income patients. Therefore, the ACA did not lead to an improvement in diagnostics for our sample. The decline in new diabetes cases may be a positive effect of the improved access to other preventive care7 services and medications during the ACA.
Figure 1
Annual trends in healthcare utilization outcomes before and during the Affordable Care Act Medicaid expansion (dotted vertical line). States are equally weighted. Bars are 95% CI. (A) Disadvantaged patients as per cent of all patients with newly detected diabetes. (B) Disadvantaged patients (with low socioeconomic status (SES)) as per cent of individuals with prevalent diabetes who received medical management at partnering health systems.
Annual trends in healthcare utilization outcomes before and during the Affordable Care Act Medicaid expansion (dotted vertical line). States are equally weighted. Bars are 95% CI. (A) Disadvantaged patients as per cent of all patients with newly detected diabetes. (B) Disadvantaged patients (with low socioeconomic status (SES)) as per cent of individuals with prevalent diabetes who received medical management at partnering health systems.We also assessed whether the ACA led to low-income persons with prevalent diabetes having better access to diabetes medications. We detected an insignificant increase of 0.21 (95% CI −2.10 to 2.52) in the prescription for diabetes medications in the treatment group. The observed trend for the prescribed diabetes medications matched the 2010–2016 dispensed medication trend detected with the Medicaid State Drug Utilization Data.3 Overall, the reported increase in diabetes medication due to the ACA tended to be modest if a ‘per enrollee’-like measure was selected as opposed to an ‘all prescriptions’4 one.In summary, we would like to stress that selected health outcomes are not doing the ACA justice and, as a result, underestimating the presumed improvement in the health services for low-income patients-clients of the academic centers before the ACA implementation. Such patients would face a different level of improvement in access to care comparing with ones who were completely isolated from the healthcare system before the policy took place.
Authors: Gregory A Nichols; Jay Desai; Jennifer Elston Lafata; Jean M Lawrence; Patrick J O'Connor; Ram D Pathak; Marsha A Raebel; Robert J Reid; Joseph V Selby; Barbara G Silverman; John F Steiner; W F Stewart; Suma Vupputuri; Beth Waitzfelder Journal: Prev Chronic Dis Date: 2012-06-07 Impact factor: 2.830
Authors: Karen R Siegel; Edward W Gregg; Obidiugwu Kenrik Duru; Lizheng Shi; Carol M Mangione; Pamela L Thornton; Steve Clauser; Mohammed K Ali Journal: BMJ Open Diabetes Res Care Date: 2021-12