High-Deductible Insurance and Delay in Care for the Macrovascular Complications of Diabetes

Patients with diabetes are at risk for macrovascular disease, including coronary heart disease, cerebrovascular disease, and peripheral artery disease (15). Macrovascular disease causes 70% of deaths and can profoundly affect patient well-being (617). Access to primary care, acute care, diagnostic tests, preventive medications, and advanced interventions can help prevent or delay macrovascular complications, such as myocardial infarction, stroke, and amputation (3, 1826). The RAND Health Insurance Experiment (27) and a study of a single employer (28) found that high levels of cost sharing reduce use of many health services, but other studies have found that such reductions do not occur in all clinical situations (27, 2932) or subgroups of people (27, 31, 32). High-deductible plans, which require potential out-of-pocket spending of approximately $1000 to $7000 per person per year for most nonpreventive care, have become an increasingly common feature of U.S. commercial health insurance. In 2018, 58% of workers with individual plans had deductibles of $1000 or more, and 26% had deductibles of $2000 or more (33). Recent research has found that low-income (but not high-income) patients with diabetes in high-deductible health insurance plans have short-term increases in emergency department visits for acute complications of diabetes (32) and high-severity conditions (31). We hypothesized that patients with diabetes might also experience changes over a longer period after an employer-mandated switch from a low-deductible to a high-deductible plan. Methods Study Population Our study population comprised commercially insured persons in the Optum database who were enrolled between 1 January 2003 and 31 December 2012. This database includes enrollment information and all medical, pharmacy, and hospitalization claims for approximately 43 million members of 1 large national health insurance plan. We included only members with employer-sponsored insurance; we excluded those with individually purchased insurance because of concerns about selection. We considered an insurance plan to have a low deductible if the annual amount was $500 or less and a high deductible if the annual amount was $1000 or more. For smaller employers, we determined the deductible from a benefits table obtained from the health insurer. This table mostly included employers with fewer than 100 employees but also included a modest number of larger employers. For employers not represented in the benefits table (mostly large employers), we used an algorithm with a sensitivity and specificity greater than 96% to impute deductible amounts from actual out-of-pocket spending by persons who used health services (Table 1 of the Supplement). Supplement. Supplementary Material Persons in this study were not able to choose a low- versus a high-deductible plan because each employer provided only 1 level of deductible each year. Some employers offered a low-deductible plan throughout the study, and others that offered a low-deductible plan early in the study switched to a high-deductible plan for the rest of the study. We defined the index date for employers who switched to high-deductible plans as the first day of the month when the switch occurred. We defined the index date for employers who did not switch plans as the first day of the month when their yearly account was renewed. If an employer had multiple potential index dates (for example, 5 continuous years with a low-deductible plan or 4 years with a low-deductible plan followed by 1 year with a high-deductible plan), we randomly selected 1 index date. Persons entered the study at different times because their employers had different index dates. Therefore, for each person, we defined time zero as 12 months before the employer's index date and treated the interval between time zero and the index date as the baseline period. We used the employer's index date as the beginning of follow-up (Figure 1). For each person, we measured the number of months from time zero to the first outcome measure in the baseline period and from the index date to the first outcome measure during follow-up. Figure 1. Study design showing example members of the intervention group (top) and the matched control group (bottom). A person was eligible for the study if their employer was present in the database for at least 1 year before and 1 year after the index date (20344218 persons from 192458 employers [Figure 1 of the Supplement]), they were aged 12 to 64 years and met criteria for diabetes (Table 2 of the Supplement) (716715 persons from 61099 employers), their first diabetes diagnosis occurred before the index date (486208 persons from 51585 employers), and they were continuously enrolled for at least 1 year before and 1 month after the index date (353337 persons from 44457 employers). These criteria yielded 34744 persons among 11808 employers who switched to high-deductible plans and 318593 persons among 32431 employers who kept low-deductible plans (Table 1). Table 1. Baseline Characteristics of the Intervention and Control Groups Before and After Coarsened Exact Matching* Study Design We conducted an observational, longitudinal, beforeafter study by comparing matched groups. Longitudinal study designs are less subject to bias than cross-sectional designs (34). The intervention group comprised persons who were in low-deductible insurance plans for 1 year and then were switched to high-deductible plans for an additional 1 month to 4 years. The control group consisted of matched persons who remained in low-deductible plans throughout the study (Figure 1). We matched participants on the propensity of the employer to mandate high-deductible insurance and the propensity of persons to work for such employers (divided into tertiles) (35, 36) (section I.c. of the Supplement), employer size (0 to 99, 100 to 999, or 1000 employees), baseline tertile of mean out-of-pocket expenditure per person at the employer, mean out-of-pocket expenditures per person at baseline ($0 to $500, $501 to $999, $1000 to $2499, or $2500), months of follow-up, and presence of a study outcome at baseline. We used coarsened exact matching to match participants (3739) (sections I.d. and I.e. of the Supplement), which is similar to exact matching but differs in that it uses categories instead of exact values (for example, 5-year age groups rather than age in years). The software for this type of matching creates weights for each stratum that adjust for differences between study groups in the proportion of persons in the stratum. Our final study groups were an intervention group of 33957 persons from 11575 employers matched with a control group of 294942 persons from 31443 employers (Table 1). Person-Level Outcome Measures Our principal outcome measures were the differences between groups in the time to the first major symptom, the first major diagnostic test, and the first procedure-based treatment for aggregated coronary heart disease, cerebrovascular disease, and peripheral artery disease (Tables 3 and 4 of the Supplement). We also analyzed 9 secondary measures that disaggregated the primary measures by disease type. The major symptoms measure was intended to include conditions that represent recognizable macrovascular disease at a stage where intervention can prevent subsequent major complications and that patients can identify themselves so that they can decide whether to delay care. We did not include myocardial infarction, stroke, and amputation in this measure because patients have much less discretion in decisions to present for care. We defined major symptoms as angina and acute and subacute forms of ischemic heart disease for coronary heart disease; transient ischemic attack for cerebrovascular disease; and intermittent claudication, resting ischemic pain, extremity thrombosis or embolism, lower-limb ulcer, cellulitis, extremity abscess, and acute osteomyelitis for peripheral artery disease. Major diagnostic tests were defined as electrocardiographic exercise tolerance test, stress echocardiography, cardiac angiography, cardiac perfusion imaging, computed tomography angiography of coronary vessels, and cardiac magnetic resonance imaging for coronary heart disease; brain and neck vessel angiography, brain imaging, and ambulatory cardiac monitoring and echocardiography (to detect atrial fibrillation and clot) for cerebrovascular disease; and magnetic resonance angiography, arteriography, and intravascular ultrasonography for peripheral artery disease. Finally, we defined procedure-based treatment as percutaneous coronary intervention and coronary artery bypass grafting for coronary heart disease; cerebrovascular endarterectomy and stenting for cerebrovascular disease; and peripheral artery angioplasty, stenting, endarterectomy, bypass, and thrombectomy for peripheral vascular disease. Population-Level Outcome Measure To estimate the difference between groups in the time to each outcome (Figure 2; section I.e. of the Supplement), we estimated the interval during follow-up between the index date and the date when the control group reached half its event rate at the end of follow-up, estimated the interval during follow-up between the index date and the date when the intervention group reached half the event rate that the control group achieved at the end of follow-up, and calculated the difference between these intervals. We believe this difference provides an intuitive measure of any delays that an average patient with diabetes in our sample might experience after a mandated switch to high-deductible insurance. We used the same approach to assess potential delays during the baseline year. Figure 2. Steps in estimating the delay for the intervention group to reach half the final event rate of the control group during follow-up. Covariates We used version 10 of the Johns Hopkins ACG System (40, 41) to calculate participants' baseline morbidity score (section I.