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The American Journal of Managed Care January 2015
Disease-Modifying Therapy and Hospitalization Risk in Heart Failure Patients
Fadia T. Shaya, PhD, MPH; Ian M. Breunig, PhD; and Mandeep R. Mehra, MD, FACC, FACP, FRCP
Frequency and Costs of Hospital Transfers for Ambulatory Care-Sensitive Conditions
R. Neal Axon, MD, MSCR; Mulugeta Gebregziabher, PhD; Janet Craig, PhD, RN; Jingwen Zhang, MS; Patrick Mauldin, PhD; and William P. Moran, MD, MS
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A. Mark Fendrick, MD, and Michael E. Chernew, PhD Co-Editors-in-Chief, The American Journal of Managed Care
Value-Based Insurance Design: Benefits Beyond Cost and Utilization
Teresa B. Gibson, PhD; J. Ross Maclean, MD; Michael E. Chernew, PhD; A. Mark Fendrick, MD; and Colin Baigel, MBChB
Changing Physician Behavior: What Works?
Fargol Mostofian, BHSc; Cynthiya Ruban, BSc; Nicole Simunovic, MSc; and Mohit Bhandari, MD, PhD, FRCSC
State of Emergency Preparedness for US Health Insurance Plans
Raina M. Merchant, MD, MSHP; Kristen Finne, BA; Barbara Lardy, MPH; German Veselovskiy, MPP; Casey Korba, MS; Gregg S. Margolis, NREMT-P, PhD; and Nicole Lurie, MD, MSPH
Relationship of Diabetes Complications Severity to Healthcare Utilization and Costs Among Medicare Advantage Beneficiaries
Leslie Hazel-Fernandez, PhD, MPH; Yong Li, PhD; Damion Nero, PhD; Chad Moretz, ScD; S. Lane Slabaugh, PharmD, MBA; Yunus Meah, PharmD; Jean Baltz, MMSc, MSW; Nick C. Patel, PharmD, PhD; and Jonathan R
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Mollie Shulan, MD; and Kelly Gao
Medical Homes: Cost Effects of Utilization by Chronically Ill Patients
Jason Neal, MA; Ravi Chawla, MBA; Christine M. Colombo, MBA; Richard L. Snyder, MD; and Somesh Nigam, PhD
Value-Based Insurance Design and Medication Adherence: Opportunities and Challenges
Kevin A. Look, PharmD, PhD
New Start Versus Continuing Users on Aripiprazole: Implications for Policy
Rashid Kazerooni, PharmD, BCPS; Joseph B. Nguyen, PharmD, BCPS; Mark Bounthavong, PharmD, MPH; Michael H. Tran, PharmD, BCPS; and Nermeen Madkour, PharmD, CSP
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Multiple Chronic Conditions in Type 2 Diabetes Mellitus: Prevalence and Consequences
Pei-Jung Lin, PhD; David M. Kent, MD, MSc; Aaron Winn, MPP; Joshua T. Cohen, PhD; and Peter J. Neumann, ScD
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Multiple Chronic Conditions in Type 2 Diabetes Mellitus: Prevalence and Consequences

Pei-Jung Lin, PhD; David M. Kent, MD, MSc; Aaron Winn, MPP; Joshua T. Cohen, PhD; and Peter J. Neumann, ScD
This study's findings showed that diabetes care remained suboptimal among many patients with multiple chronic conditions and that patient outcomes varied by multimorbidity profile.
MCCs were highly prevalent in both nonelderly and elderly patients with diabetes (Figure 2). The most common MCC clusters for both groups included hypertension, hyperlipidemia, and obesity in combination or in isolation. On the other hand, MCC clusters including obesity were far more common among younger adults: among subjects aged <65 years, the leading MCC cluster was hypertension-hyperlipidemia-obesity (23%), whereas among subjects aged ≥65 years, the most common cluster was hypertension and hyperlipidemia only (20%). Further, obesity alone was the fourth-most common MCC cluster among younger patients, whereas this cluster was seventh-most common among older patients. The higher prevalence of obesity among younger patients may reflect the fact that it is an essential risk factor for early onset T2DM.28 In addition, older patients exhibited greater MCC cluster heterogeneity: the top 10 MCC clusters accounted for 66% of older patients, compared with 78% of younger patients. Finally, 14% of younger patients had no diagnosed comorbidities, compared with 11% of older patients (P <.001).

Association Between MCCs and Diabetes Care

Figure 3 shows the predicted probabilities for receiving recommended diabetes care based on the fully adjusted logistic regression models. Predicted diabetes visit probabilities ranged from 18% to 52% among the 15 most common MCC clusters, and exceeded 50% for only 5 clusters: hypertension-hyperlipidemia-obesity only (52%), hypertension-hyperlipidemia-obesity plus depression (51%), arthritis (51%), CKD (51%), and hypertension-hyperlipidemia only (50%). Notably, subjects with no documented comorbidities (18%) and patients with only obesity (24%) were less likely to have a diabetes visit.

Predicted probabilities for achieving the A1C treatment goal ranged from 54% to 76% among the 15 most common MCC clusters and were most likely among patients with co-existing hypertension-hyperlipidemia plus either COPD/asthma (76%), CAD (75%), obesity and COPD/asthma (75%), or obesity and arthritis (75%) (Figure 2). Subjects with no documented comorbidities (54%) and subjects with obesity only (60%) were less likely to meet the A1C goal. Sensitivity analysis using each patient’s average A1C values instead of the patient’s last A1C test showed similar patterns (results not shown).

Association Between MCCs and ED Visits and 30-Day Hospital Readmissions

Overall, 18.2% of the diabetes sample had at least 1 ED visit. Adjusted probabilities for ED visits ranged from 13% to 22% in the top 15 MCC clusters (Figure 4). Patients with only obesity were most likely to have ED visits (22%), followed by subjects with no comorbidities (20%). Patients with hypertension-hyperlipidemia only (13%) and hypertension-hyperlipidemia plus obesity and CKD (13%) were less likely to have ED visits.

Among the 21,765 hospitalized patients, 14.5% were readmitted within 30 days of discharge. Adjusted probabilities for readmissions were lower than probabilities for ED visits, ranging from 9% to 15% among the top 15 MCC clusters (Figure 4). Subjects with only obesity (15%) and those with no recorded comorbidities (15%) were more likely to have readmissions, as were patients with hypertension-hyperlipidemia-cancer (15%). Patients with hypertension-hyperlipidemia plus obesity and CAD were less likely to have readmissions (9%).


Our analysis of MCCs showed that 1 in 5 patients with T2DM had the combination of hypertensionhyperlipidemia-obesity and no other diagnosed comorbidities. The top 10 MCC clusters accounted for roughly 70% of all patients with T2DM. However, MCC cluster patterns exhibit substantial heterogeneity across patients and by age. Older patients more frequently had hypertension and hyperlipidemia only, whereas younger patients had hypertension and hyperlipidemia plus obesity, or had obesity and no other conditions. We also found greater MCC cluster heterogeneity among older diabetes patients, reflecting the more complex health needs of this group. In addition, patients with certain comorbidity profiles, such as those with obesity only, had poorer diabetes outcomes and more ED visits and 30-day readmissions.

MCCs are an issue of growing significance not only because of their prevalence, but because they can complicate treatment and increase disease burden and costs.15,21,29,30 Previous studies have suggested aggressive multifactorial management of hypertension-hyperlipidemia-obesity (commonly referred to as metabolic syndrome31,32) in diabetes33,34—the leading MCC combination in our data—but less attention has been paid to other comorbidity clusters. In applying the Piette and Kerr framework for understanding the impact of comorbidity on patients with diabetes,8 we found that diabetes-concordant comorbidities (eg, hypertension, hyperlipidemia, obesity, CAD) co-occurred more frequently than discordant (eg, COPD/asthma, arthritis) or dominant (eg, cancer) conditions. Our findings suggest that diabetes guidelines should explicitly address the cooccurrence of multiple concordant comorbidities and the co-occurrence of concordant and discordant/dominant conditions. Explicit consideration of MCC clusters is important because appropriate management of individual diseases in isolation may not be optimal for patients with MCCs.21,29

It should be noted that examining distinct MCC combinations as we have done is only feasible using very large data sets. Even the consideration of 14 comorbid conditions defined more than 16,000 subgroups (214). As a result, many of the most common clusters comprised <1% of the overall population, and many patients had completely unique MCC combinations. While combinations may have unique disease-disease, disease-treatment, and treatment-treatment interactions, the vast combinatorics suggest the need for frameworks and strong hypotheses regarding the most relevant interactions to help reduce the dimensionality of analyses addressing challenges with managing MCCs.35

Previous studies showed that certain comorbidities were associated with poor diabetes outcomes (eg, A1C control9) and lower self-management abilities.7 Our study further demonstrated that diabetes care outcomes remained suboptimal among many types of MCC patients. For example, probabilities for having diabetes-related visits ranged from 18% to 52%, and probabilities for meeting the treatment goal for A1C ranged from 54% to 76% (depending on the MCC cluster).

We also found that many patients with diabetes had ED visits and 30-day readmissions. “Ballpark” estimates suggest that reducing ED visits and 30-day readmissions in the diabetes population could yield substantial savings nationally.36 To illustrate, costs average $2168 per ED visit37 and $9700 per hospital stay,38 and nationwide there are roughly 26 million adults with T2DM. Applying the 18.2% ED admission rate for our sample and assuming only 1 ED visit per person per year yields a national cost of roughly $10 billion annually. Similarly, the total cost of 30-day readmissions is roughly $5 billion based on our sample’s 14.5% readmission rate among hospitalized patients.

In contrast with previous studies reporting that patients with diabetes with more comorbidities had poorer outcomes,3-7 our analysis showed that subjects with obesity alone and individuals with no documented comorbidities performed more poorly across the 4 outcomes we examined. Several factors may contribute to these findings. First, our results may reflect the fact that many patients with diabetes had undiagnosed comorbidities.24,39 In fact, among patients in our sample without any documented comorbidities in the baseline year, 27.7% had at least 1 recorded comorbidity during the follow-up year. Such underlying conditions could lead to poor patient outcomes such as uncontrolled chronic illnesses,40 which could trigger more hospital use.

Second, it is possible that patients sought care from out-of-network providers and thus their healthcare utilization records were not fully captured in the Humedica data sets. We found that patients who did not have A1C values in their records were more likely to have other clinical values missing, were less likely to have any diagnosed comorbidities, and had fewer evaluation and management visits, compared with patients with an A1C value recorded. Third, patients with more diagnosed comorbidities may have more frequent contact with their physicians, which may lead to stronger provider-patient relationships, and thus higher adherence to treatment plans and better follow-up.10,41 Indeed, certain comorbidities have been found to be associated with more resource utilization and better diabetes care.9,10,18,42 Additionally, providers may be more likely to treat comorbidities in complex patients to reduce adverse outcomes, such as enrolling them in disease management programs that aim to improve overall health.10


Our analysis has several limitations. First, we may have underestimated MCC prevalence rates and care outcomes due to unavailable out-of-network utilization data. Second, patients with <24 months of data were excluded (n = 50,986); however, sensitivity analysis including such patients suggested similar results. Third, T2DM and comorbidities may have been under-identified due to coding and practice differences within and across providers that submit data to Humedica. Nevertheless, sensitivity analysis suggested that including the 105,627 patients with an unknown type of diabetes yielded similar results. For comorbidities, we attempted to capture a broader comorbidity profile by utilizing ICD-9-CM diagnosis codes, lab data, and prescription records. Future validation study will be helpful in determining if patients classified as having “no comorbidities” in fact have any undiagnosed conditions, and whether they have more access barriers. Fourth, although the Humedica data sets contain detailed utilization and EHR data, they do not contain cost information. Future research should identify the most expensive MCC clusters in order to better target high-cost patients for disease management. Finally, future research into areas related to comorbidity management, such as medication adherence, would help explain why certain MCC clusters are associated with poorer patient outcomes and would highlight areas for quality improvement.


Despite these limitations, the current study extended prior work by using considerably more detailed and extensive information about MCCs in T2DM patients. Our findings highlighted important comorbidity clusters, such as co-existing hypertension-hyperlipidemia-obesity, that need to be addressed by diabetes guidelines and disease management programs. Our analysis also showed that many types of MCC patients had poor diabetes outcomes as well as excess ED visits and 30-day hospital readmissions. Determining specific MCC subgroups at increased risk of universal, rather than solely diabetes-specific, outcomes has important policy implications and provides targets for tailored prevention. In addition to improving clinical decisions, such information can be used to refine diabetes risk adjustment measures.3,43 The results can help guide payment reforms and improve cost prediction for diabetes patients with MCCs.


Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
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