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Figure 1.
Preventable Acute Diabetes Complication Measures Stratified by Morbidity Group
Preventable Acute Diabetes Complication Measures Stratified by Morbidity Group

Unadjusted plots with adjusted hazard ratios (aHRs) for low-morbidity (A) and high-morbidity (B) members. Total expenditures during acute complication episodes is a proxy indicating utilization in the 7 days after an emergency department (ED) visit for preventable acute diabetes complications. Switch to a high-deductible health plan (HDHP) was considered the index date.

aAdjusted Clinical Group score lower than 2.0 (low morbidity).

bAdjusted Clinical Group score of 3.0 or higher (high morbidity).

Figure 2.
Preventable Acute Diabetes Complication Measures Stratified by Income Group
Preventable Acute Diabetes Complication Measures Stratified by Income Group

Unadjusted plots with adjusted hazard ratios (aHRs) for high-income (A) and low-income (B) members. Total expenditures during acute complication episodes is a proxy indicating utilization in the 7 days after an emergency department (ED) visit for preventable acute diabetes complications. Switch to a high-deductible health plan (HDHP) was considered the index date.

aLiving in neighborhoods with below-poverty levels of less than 5%.

bLiving in neighborhoods with below-poverty levels of 10% or greater.

Table 1.  
Baseline Characteristics of the HDHP Group and the Control Group, Before and After the Propensity Score Match
Baseline Characteristics of the HDHP Group and the Control Group, Before and After the Propensity Score Match
Table 2.  
Rates of High-Priority Outpatient Visits Among HDHP Group Members Before and After an HDHP Switch Compared With Contemporaneous Control Group Members
Rates of High-Priority Outpatient Visits Among HDHP Group Members Before and After an HDHP Switch Compared With Contemporaneous Control Group Members
Table 3.  
Rates of Quality Measures Among HDHP Group Members Before and After an HDHP Switch Compared With Contemporaneous Control Group Members, Stratified by Morbidity and Income
Rates of Quality Measures Among HDHP Group Members Before and After an HDHP Switch Compared With Contemporaneous Control Group Members, Stratified by Morbidity and Income
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Original Investigation
Health Care Reform
January 09, 2017

Diabetes Outpatient Care and Acute Complications Before and After High-Deductible Insurance EnrollmentA Natural Experiment for Translation in Diabetes (NEXT-D) Study

Author Affiliations
  • 1Harvard Medical School and Harvard Pilgrim Health Care Institute, Department of Population Medicine, Boston, Massachusetts
JAMA Intern Med. Published online January 9, 2017. doi:10.1001/jamainternmed.2016.8411
Key Points

Question  How does high-deductible insurance enrollment affect diabetes outpatient care and acute complications?

Findings  In this controlled interrupted-time-series study that included 24 168 patients with diabetes, high-deductible health plan members experienced minimal changes in outpatient visits and disease monitoring. However, low-income and health savings account–eligible high-deductible health plan members experienced statistically significant increases in emergency department visits for preventable acute diabetes complications.

Meaning  Vulnerable patients with diabetes switching to high-deductible insurance experienced major increases in acute diabetes complications and might require protection under improved health insurance designs.

Abstract

Importance  High-deductible health plans (HDHPs) have expanded under the Affordable Care Act and are expected to play a major role in the future of US health policy. The effects of modern HDHPs on chronically ill patients and adverse outcomes are unknown.

Objective  To determine the association of HDHP with high-priority diabetes outpatient care and preventable acute complications.

Design, Setting, and Participants  Controlled interrupted-time-series study using a large national health insurer database from January 1, 2003, to December 31, 2012. A total of 12 084 HDHP members with diabetes, aged 12 to 64 years, who were enrolled for 1 year in a low-deductible (≤$500) plan followed by 2 years in an HDHP (≥$1000) after an employer-mandated switch were included. Patients transitioning to HDHPs were propensity-score matched with contemporaneous patients whose employers offered only low-deductible coverage. Low-income (n = 4121) and health savings account (HSA)–eligible (n = 1899) patients with diabetes were subgroups of interest. Data analysis was performed from February 23, 2015, to September 11, 2016.

Exposures  Employer-mandated HDHP transition.

Main Outcomes and Measures  High-priority outpatient visits, disease monitoring tests, and outpatient and emergency department visits for preventable acute diabetes complications.

Results  In the 12 084 HDHP members included after the propensity score match, the mean (SD) age was 50.4 (10.0) years; 5410 of the group (44.8%) were women. The overall, low-income, and HSA-eligible diabetes HDHP groups experienced increases in out-of-pocket medical expenditures of 49.4% (95% CI, 40.3% to 58.4%), 51.7% (95% CI, 38.6% to 64.7%), and 67.8% (95% CI, 47.9% to 87.8%), respectively, compared with controls in the year after transitioning to HDHPs. High-priority primary care visits and disease monitoring tests did not change significantly in the overall HDHP cohort; however, high-priority specialist visits declined by 5.5% (95% CI, −9.6% to −1.5%) in follow-up year 1 and 7.1% (95% CI, −11.5% to −2.7%) in follow-up year 2 vs baseline. Outpatient acute diabetes complication visits were delayed in the overall and low-income HDHP cohorts at follow-up (adjusted hazard ratios, 0.94 [95% CI, 0.88 to 0.99] for the overall cohort and 0.89 [95% CI, 0.81 to 0.98] for the low-income cohort). Annual emergency department acute complication visits among HDHP members increased by 8.0% (95% CI, 4.6% to 11.4%) in the overall group, 21.7% (95% CI, 14.5% to 28.9%) in the low-income group, and 15.5% (95% CI, 10.5% to 20.6%) in the HSA-eligible group.

Conclusions and Relevance  Patients with diabetes experienced minimal changes in outpatient visits and disease monitoring after an HDHP switch, but low-income and HSA-eligible HDHP members experienced major increases in emergency department visits for preventable acute diabetes complications.

Introduction

High-deductible health plans (HDHPs) have recently become the predominant commercial health insurance arrangement in the United States.1 These plans have expanded under the Affordable Care Act and are expected to play a major role in the future of US health policy. High-deductible health plans require potential annual out-of-pocket spending of approximately $1000 to $6000 per person for most nonpreventive services; in 2016, 51% of workers with employer-sponsored insurance had deductibles of $1000 or more and 23% had deductibles of $2000 or more.1

Diabetes is a major cause of morbidity and premature death in the United States.2,3 High cost-sharing might especially affect chronically ill patients who require frequent and expensive services. However, the effects of HDHPs on outpatient care patterns and adverse outcomes among chronically ill patients are unknown.

We hypothesized that some outpatient care, including preventive tests that are inexpensive even under HDHPs, would remain stable among HDHP members with diabetes. We further hypothesized that relatively expensive care, such as specialist visits and outpatient visits for acute complications (typically paid out of pocket until the deductible is met), would decline or be delayed, increasing the frequency and severity of emergency department (ED) visits for acute complications.

Methods
Study Population

We drew our study population from commercially insured members in the Optum database (Eden Prairie, Minnesota) enrolled between January 1, 2003, and December 31, 2012. Data comprised enrollment tables and all medical, pharmacy, and hospitalization claims from members of a large national health plan. We included members in the study based on their employers’ health insurance offerings. We defined employers with low- and high-deductible coverage as those offering exclusively annual deductibles of $500 or less or $1000 or more, respectively (eAppendix in the Supplement). To determine employers’ annual deductibles, we used a benefits variable that was available for most smaller employers (approximately ≤100 employees, representing 57.7% of account years) that included information such as in-network and out-of-network deductible, copayment, and coinsurance amounts. For larger employers (42.4% of account years), we imputed deductible levels using out-of-pocket spending among employees who used health services with an algorithm that had 96.2% sensitivity and 97.0% specificity (eTable 1 in the Supplement). The research protocol, with waiver of informed consent, was approved by the Harvard Pilgrim Health Care institutional review board.

Both low- and high-deductible plans often cover a single annual preventive primary care visit and disease monitoring, such as hemoglobin A1c testing, at low or no out-of-pocket cost.1 In contrast, HDHP members on average must pay substantially higher amounts than low-deductible members for specialist, acute care, and ED visits.

Our study groups were drawn from individuals whose employers mandated an HDHP switch (HDHP group) or mandated continuation in low-deductible plans (control group), minimizing self-selection. We required HDHP group members to have 12 baseline months in a low-deductible plan followed by 24 months in an HDHP after the employer-mandated HDHP switch (36 continuous enrollment months per member). We defined the beginning of the month of the low- to high-deductible transition as the index date. We identified all potential control group members whose employers offered only low-deductible plans over at least a 3-year period (n = 1 674 527).

To further minimize potential selection effects, especially at the employer level, we used a 2-level (employer and member level) propensity score–matching approach4,5 (eAppendix in the Supplement) and estimated propensity scores predicting the likelihood of a mandated HDHP switch. After matching at the employer level on multiple characteristics (eAppendix in the Supplement), we identified patients with diabetes aged 12 to 64 years using a standard claims-based algorithm (eTable 2 in the Supplement) (12 854 HDHP members and 69 749 control pool members) (Table 1). We included patients who first met the diabetes diagnosis algorithm criteria at least 6 months before the index date and who had diabetes diagnoses or medication use between 6 months before and 6 months after the beginning of the baseline year.

Within quartiles of the employer propensity score, we matched HDHP members with diabetes at the patient level 1:1 to controls with diabetes based on age, adjusted clinical group (ACG) morbidity score,6,7 month of first diabetes diagnosis, employer size, and index month. We also matched on baseline quarterly numbers of high-priority (defined below) primary care and specialist visits, preventable acute diabetes complication visits (defined below), ED visits, hospitalization days, and total out-of-pocket spending per member.8,9 Compared with the unmatched sample, our propensity score-matching approach increased the similarity of the HDHP and control groups with respect to age, sex, neighborhood poverty level, morbidity score, baseline outpatient copayment, and employer size (Table 1). Our final group included 12 084 HDHP members with diabetes and their 1:1 matched controls.

Our primary subgroups of interest, based on previous evidence of adverse HDHP effects,10,11 included high-morbidity (n = 3640) and low-income (n = 4121) patients with diabetes (defined below). We also assessed HDHP members who were eligible to have health savings accounts (HSAs) because such plans are rapidly increasing in prevalence and have the highest out-of-pocket obligations among commercial insurance plans.1 The HSAs allow pretax contributions from employers or members—funds that can be used to pay for qualified medical expenses.12

Design

We used 3 different study designs depending on the outcome type. We applied a before-after with comparison group design to assess outpatient visits and disease monitoring. To detect changes in the timing of acute, preventable diabetes complication visits, we applied a controlled time-to-event design to the baseline and follow-up periods. A controlled, cumulative, monthly interrupted-time-series design was used to examine rates of and total expenditures for acute complication episodes.

Measures
Utilization and Disease Monitoring Measures

We used standard algorithms for detecting outpatient visits with Current Procedural Terminology evaluation and management codes and a clinician type variable to classify visits as primary care or specialist. We then applied a taxonomy developed by Fenton and colleagues13 (eAppendix in the Supplement) to characterize each office visit as high priority or low priority based on the primary diagnosis. High-priority diagnoses are considered more likely to benefit from medical care, although the measure is not intended to reflect the appropriateness of particular visits. Outpatient disease monitoring measures were captured based on Healthcare Effectiveness Data and Information Set14 specifications, including 1 or more annual primary care visit; hemoglobin A1c, low-density lipoprotein cholesterol, and microalbumin test; and retinal eye examination (eTable 3 in the Supplement).

Health Outcome Measures

To assess whether HDHPs were associated with changes in time-sensitive care, 2 of us (J.F.W and E.M.E.) used a systematic approach to develop a measure of outpatient and ED visits that could indicate a preventable acute diabetes complication, hereinafter termed complication visit (eAppendix and eTable 4 in the Supplement). Acute diabetes complications were defined as symptoms or conditions (when coded by clinicians as the primary diagnosis) that could be associated with delaying recommended or urgent diabetes-related outpatient or ED care (including prescription drug use) for up to 4 months and that require timely care by medical professionals (eFigure 1 in the Supplement). This measure was validated in our population (eTables 5 and 6 in the Supplement) by determining that outpatient and ED visits with these complication diagnoses were associated with odds ratios of 4.10 (95% CI, 3.98-4.23) for outpatient visits and 3.02 (95% CI, 2.96-3.08) for ED visits of subsequent hospitalization compared with other types of outpatient or ED visits. The 5 most common categories of outpatient complication visits at baseline, accounting for 82.0% of such visits, were cellulitis, urinary tract infection, angina and ischemic heart disease, acute cerebrovascular disease, and pneumonia. The 5 most common categories of ED complication visits at baseline, accounting for 62.0% of such visits, were cellulitis, urinary tract infection, hypoglycemia or hyperglycemia and their major acute complications, angina and ischemic heart disease, and pneumonia.

All health care expenditures during the 7 days after an acute complication visit to the ED were summed as a health outcome measure to assess the intensity of and need for diagnostic and therapeutic services. That is, we included this as a proxy to indicate level of “sickness” at presentation to the ED. Health care expenditures were from a data vendor-provided variable that was standardized across geography and time and represents combined health plan and patient payments.

Covariates

We applied the Johns Hopkins ACG System comorbidity score (version 10.0.1) algorithm, a validated measure that predicts mortality,6,15 to members’ baseline year to estimate comorbidity and defined high and low morbidity as ACG scores of 3.0 or higher and lower than 2.0, respectively. Using 2000 US Census block group data and validated methods,16,17 members were considered high and low income based on living in neighborhoods with below-poverty levels of less than 5% and 10% or higher, respectively, and a similar approach to categorizing educational levels was used.1621 Members were classified as white, black, Hispanic, Asian, or other based on a combination of geocoding and surname analysis (eAppendix in the Supplement).22,23 Other covariates included age category (12-25, 26-45, and 46-64 years), sex, and US region (West, Midwest, South, and Northeast).

Statistical Analysis

Baseline characteristics of our study groups were compared using χ2 tests; unpaired, 2-tailed t tests; and nonparametric tests. Continuous measures are reported as mean (SD), and categorical variables are reported as count (percentage).24 In all statistical models estimating HDHP effects, we removed from analyses the month before and after the index date to reduce bias due to anticipatory increases in utilization before the HDHP switch (and consequent reductions in the month after the switch).

For the high-priority primary care and specialist visit outcomes, we first fit interrupted-time-series models25 to both visually display monthly trends and confirm that the study groups did not have differential baseline trends—a key assumption of difference-in-differences analysis. We then used difference-in-differences analysis to examine changes in annual high-priority outpatient visits and disease monitoring measures. Generalized estimating equation26,27 models with a negative binomial distribution for outpatient visits and a binary distribution for disease monitoring measures were applied, controlling for age, sex, race/ethnicity, educational level, poverty level, US region, ACG score, employer size, and index date.

To examine time to first outpatient and ED visits for acute complications, we used separate Cox proportional hazards regression models for the baseline and follow-up periods, adjusting for the same covariates as listed above. To analyze annual changes in complication visits and subsequent 7-day total expenditures, aggregate-level segmented regression was applied to cumulative rates that had been adjusted for the above covariates (eAppendix in the Supplement).

Using the same methods and outcomes as described above, subgroup analyses stratified by low and high income and morbidity were performed and HSA-eligible members and their matched pairs were examined. We also assessed several other subgroups of interest, including those defined by different income cutoff levels (residing in neighborhoods with <10%, >5%, and >20% of households below the federal poverty level) and residents of predominantly white and nonwhite race neighborhoods. As a sensitivity analysis, we restricted the sample to members aged 18 to 64 years, rematched, and then analyzed all primary outcomes. Data analysis was performed from February 23, 2015, to September 11, 2016. Statistical analysis was performed using SAS, version 9.4 (SAS Institute Inc) and Stata, version 12.1 (StataCorp).

Results
Baseline Characteristics

The mean (SD) age of HDHP and control members was 50.4 (10.0) and 50.5 (10.3) years, respectively; women composed 44.8% and 44.9% of the groups, respectively (Table 1). Thirty-four percent to 35% of members lived in low-income neighborhoods; 25% lived in low-education neighborhoods; 10.7% and 11.5% were Hispanic members, respectively; and the mean (SD) ACG morbidity score was 2.8 (3.5) for both groups. At baseline, HDHP and control members had unadjusted mean, high-priority, primary care visit rates of 2.04 and 2.05 (P = .69) per member per year, respectively, and corresponding high-priority specialist visit rates of 1.38 and 1.38 (P = .94), respectively.

Changes in Out-of-Pocket Expenditures

Mean out-of-pocket medical expenditures increased by 49.4% (95% CI, 40.3%-58.4%; absolute, $374.6) in the overall HDHP cohort, by 56.8% (95% CI, 45.8%-67.8%; absolute, $292.0) in the low-morbidity group, by 40.9% (95% CI, 31.5%-50.4%; absolute, $448.8) in the high-morbidity group, by 48.4% (95% CI, 37.2%-59.6%; absolute, $361.8) in the high-income group, by 51.7% (95% CI, 38.6%-64.7%; absolute, $400.4) in the low-income group, and by 67.8% (95% CI, 47.9%-87.8%; absolute, $463.0) in the HSA HDHP group compared with controls in the year after transitioning to HDHPs (eTable 7 in the Supplement). Out-of-pocket obligations for hemoglobin A1c, low-density lipoprotein cholesterol, and microalbumin tests increased from a mean of $1.2 to $1.4 at baseline among HDHP members to $2.2 to $4.8 at follow-up (eTable 8 in the Supplement). Primary care visit costs increased from $15.4 to $23.3 to $26.8 from baseline to follow-up among HDHP members, and the mean cost of specialist visits was $23.3 at baseline and approximately $42.0 at follow-up.

Utilization and Disease Monitoring Measures

Interrupted-time-series analyses demonstrated no statistically significant baseline trend differences between the HDHP and control groups in high-priority outpatient visits for all subgroups (implying validity of difference-in-differences estimates; eTable 9 in the Supplement) except primary care visits among low-morbidity members. In adjusted difference-in-differences analyses, relative changes in high-priority primary care visits occurred only in the low-morbidity (−5.1%; 95% CI, −8.6% to −1.6%), high-income (−5.7%; 95% CI, −10.5% to −0.9%), and low-income (−5.2%; 95% CI, −9.8% to −0.7%) subgroups from baseline to follow-up year 2 (Table 2; eFigure 2 in the Supplement).

High-priority specialist visits declined in the overall HDHP cohort by 5.5% (95% CI, −9.6% to −1.5%) in follow-up year 1 and 7.1% (95% CI, −11.5% to −2.7%) in follow-up year 2 vs baseline. Among the low- and high-morbidity HDHP subgroups compared with controls, year 2 vs baseline changes in high-priority specialist visits were −7.9% (95% CI, −14.4% to −1.4%) for the low-morbidity subgroup and −12.2% (95% CI, −17.9% to −6.5%) for the high-morbidity subgroup. Corresponding changes among high- and low-income HDHP members were −10.7% (95% CI, −17.1% to −4.3%) and −7.6% (95% CI, −15.9% to 0.7%).

By follow-up year 2, we did not detect any relative changes in disease monitoring measures, including annual primary care visits (−0.2%; 95% CI, −1.4% to 0.9%), hemoglobin A1c testing (−0.8%; 95% CI, −2.6% to 1.0%), low-density lipoprotein cholesterol testing (−1.6%; 95% CI, −3.6% to 0.5%), microalbumin testing (−0.7%; 95% CI, −4.7% to 3.4%), and retinal eye examinations (0.9%; 95% CI, −3.4% to 5.1%). These patterns were similar across all subgroups (Table 3).

Health Outcome Measures

The overall HDHP diabetes cohort experienced a follow-up period delay in the time to first outpatient complication visit compared with controls (adjusted hazard ratio [aHR], 0.94; 95% CI, 0.88-0.99) (eTable 10 and eFigure 3 in the Supplement) that was not present at baseline (aHR, 1.01; 95% CI, 0.93 to 1.09). Total annual ED complication visits and complication episode expenditures increased by 8.0% (95% CI, 4.6%-11.4%) and 5.6% (95% CI, 3.8%-7.3%), respectively, in the overall HDHP group compared with the controls (eTable 11 and eFigure 3 in the Supplement). Among key HDHP subgroups, high-morbidity HDHP members experienced a follow-up period delay in first outpatient complication visits (aHR, 0.89; 95% CI, 0.82-0.97) (Figure 1 and eTable 10 in the Supplement) and increased annual total expenditures for ED complication episodes (adjusted relative change, 12.1%; 95% CI, 7.2%-17.0%) (eTable 11 in the Supplement). Low-income HDHP members also delayed first outpatient complication visits at follow-up (aHR, 0.89; 95% CI, 0.81-0.98) (Figure 2 and eTable 10 in the Supplement), experienced an acceleration in first ED complication visits that approached statistical significance (aHR, 1.14; 95% CI, 0.98-1.33), and had increased total ED complication visits (21.7%; 95% CI, 14.5%- 28.9%) and increased total expenditures for ED episodes (9.5%; 95% CI, 6.5%-12.5%). Corresponding changes in these ED outcomes among HSA HDHP members were 1.17 (95% CI, 0.92-1.51), 15.5% (95% CI, 10.5%-20.6%) and 29.6% (95% CI, 19.0%-40.1%) (eFigure 4 and eTable 11 in the Supplement).

Sensitivity analyses (eAppendix in the Supplement) did not change the interpretation of our main findings. These results are included in eTables 10-18 and eFigure 5 in the Supplement).

Discussion

In this study, HDHP members with diabetes experienced minimal changes in high-priority outpatient visits and disease monitoring measures, but they delayed presenting for first outpatient complication visits and experienced 5.6% to 8.0% increases in ED complication measures. Low-income and high-morbidity HDHP members also experienced delays in presenting for outpatient complication visits after the HDHP switch, and these groups, as well as HSA HDHP members, experienced moderate to large increases in ED acute complication visits or expenditures.

The results are generally consistent with our hypotheses. Reductions in specialist visits were smaller than expected, but the decline in this rate might have been tempered by an increased need for specialist care because of increased severity of diabetes complications. Although we cannot directly determine whether delayed outpatient complication visits caused increased morbidity among vulnerable HDHP members, the large increases in ED complication episode costs that were detected seem suggestive. Despite some uncertainty about causal mechanisms and morbidity impact, increased acute diabetes complications and associated expenditures are almost certainly unintended consequences that all stakeholders wish to avoid. Another potential implication of this study is that adverse HDHP effects in diabetes would have gone undetected if assessed using traditional Healthcare Effectiveness Data and Information Set disease monitoring metrics, suggesting that health systems could benefit from adopting acute preventable diabetes complication measures, such as the one we created and validated.

Reasons for the acute complication findings may be clarified by considering the effects in the key patient subgroups. It is likely that high-morbidity, low-income, and HSA-eligible patients with diabetes (who experienced the largest cost-sharing increases) had significantly greater concerns about HDHP-related out-of-pocket spending than did their less vulnerable HDHP counterparts. These patients might therefore attempt to minimize health expenditures by forgoing expensive scheduled and acute visits or by shifting care to less expensive but potentially less appropriate settings. Such effects might lead to more severe disease by the time of presentation for acute complications. Adverse outcomes among HSA HDHP members might imply that HSA funding levels were low, that patients were unaware of this resource, or that they engaged in inappropriate attempts to preserve HSA funds. These findings among subgroups suggest that a bifurcation of outpatient care could be occurring among HDHP members with diabetes, with less vulnerable patients largely unaffected but more vulnerable patients facing access limitations that ultimately increase acute care utilization. Future studies could directly assess the causal association between care delays and acute complication visits and determine whether other factors, such as medication nonadherence, play any role.

To our knowledge, no previous research has examined outpatient visits or complications among HDHP patients with diabetes. The landmark RAND Health Insurance Experiment from 40 years ago predicted that the “poor and sick” would have increased long-term mortality under high-level cost sharing due to worsened hypertension control.11 Our study, which occurs in a different health care era and was able to enroll a far larger sample of chronically ill patients, is, to our knowledge, the first to examine acute complication measures among chronically ill HDHP members. The study adds the key finding that concerning utilization patterns increase soon after an HDHP switch among similarly vulnerable populations. Other chronically ill HDHP patients who require time-sensitive care, such as those with coronary heart disease, heart failure, or cancer, might be at risk, but further research is warranted.

Two previous studies found minimal or no changes in several diabetes disease monitoring metrics28,29; similarly, we detected no changes in such measures, likely related to low out-of-pocket costs (eTable 8 in the Supplement) and perhaps the perceived nondiscretionary nature of these tests (eg, retinal eye examinations). These findings should be reassuring to primary care physicians both because they might presage unchanged long-term disease control under HDHPs and because the rates of such tests are increasingly being used to measure clinician performance. Our disease monitoring results also confirm a growing body of literature demonstrating that excluding high-value services, such as secondary preventive tests, from cost-sharing under HDHPs might help to preserve their use.3033

Limitations

This study has several potential limitations. We did not have exact benefit coverage details for large employers, but we used a highly sensitive and specific algorithm for detecting their deductible levels. Furthermore, analyses of actual out-of-pocket expenditures showed that, at the population level, the HDHP group experienced increased out-of-pocket medical expenditures of approximately 50%, indicating the validity of our plan type classification. The measure of acute complication visits is novel, but we created the measure rigorously and validated it extensively, as described above and in the eAppendix in the Supplement. Furthermore, the top 5 diagnosis clusters (eg, cellulitis, urinary tract infection, hypoglycemia or hyperglycemia and their major acute complications, angina and ischemic heart disease, and pneumonia) have face validity, and analyses of this subset revealed similar patterns (eTable 18 in the Supplement). Nevertheless, measurement error is still possible given the lack of consensus regarding which diagnoses are considered “acute preventable diabetes complications” and the imprecision of International Classification of Diseases, Ninth Revision diagnoses. We did not have information about HSA contributions, which could permit determining whether such funds modify the adverse outcomes that were detected. We also did not report changes in laboratory values (because of a high degree of missing values) or medication use. We did not have access to health insurance premiums and therefore could not estimate the total member expenditures (premiums plus out of pocket). Finally, this study is not representative of people with nonemployer-sponsored insurance, very low socioeconomic status, very high deductibles, or those whose first exposure to insurance is under HDHPs.

Conclusions

This study found that patients with diabetes experienced minimal changes in outpatient visits and disease monitoring after an HDHP switch, but low-income, high-morbidity, and HSA HDHP subgroups experienced major increases in ED visits or expenditures for preventable acute diabetes complications. These subgroups might be especially at risk in the increasingly HDHP-centric private US health system, and our results support a strategy of minimizing the enrollment of vulnerable diabetes subpopulations in HDHPs or targeting cost-sharing reductions, such as HSA contributions, to such patients.34

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Article Information

Corresponding Author: J. Frank Wharam, MB, BCh, BAO, MPH, Harvard Medical School and Harvard Pilgrim Health Care Institute, Department of Population Medicine, 401 Park St, Ste 401, Boston, MA 02215 (jwharam@post.harvard.edu).

Accepted for Publication: October 3, 2016.

Published Online: January 9, 2017. doi:10.1001/jamainternmed.2016.8411

Author Contributions: Drs Wharam, Zhang, and Ross-Degnan had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Wharam, Eggleston, Soumerai, Ross-Degnan.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Wharam.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Zhang, Soumerai.

Obtained funding: Wharam, Soumerai, Ross-Degnan.

Administrative, technical, or material support: Wharam.

Study supervision: Wharam, Ross-Degnan.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by grant 1U58DP002719 from the Centers for Disease Control and Prevention/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and 1P30-DK092924 from the NIDDK Health Delivery Systems Center for Diabetes Translational Research.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We thank the Natural Experiment for Translation in Diabetes (NEXT-D) colleagues Kenrik Duru, MD, MSHS (UCLA [University of California, Los Angeles] School of Medicine), Carol Mangione, MD, MSPH (UCLA School of Medicine), Jeanine Albu, MD (Mount Sinai Hospital), Tannaz Moin, MD, MBA, MS (UCLA School of Medicine), and Ron Ackerman, MD, MPH (Northwestern University School of Medicine), for assistance with early development of an acute diabetes complication measure and valuable insights regarding approaches to construction. We are grateful to Xin Xu, MS (Harvard Pilgrim Health Care Institute), for assistance with running analyses. We thank Joshua Fenton, MD, MPH (UC Davis), for providing access to an outpatient visit high- and low-priority classification algorithm. No compensation was provided.

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