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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.
Multiple chronic comorbidities (MCCs) are an issue of growing significance in diabetes because they are highly prevalent and can increase disease burden and costs. We examined MCC patterns among patients with type 2 diabetes mellitus and identified specific comorbidity clusters associated with poor patient outcomes.

Study Design and Methods
We conducted a cross-sectional analysis of 161,174 patients with diabetes using electronic health record data supplied by US providers in the 2008 to 2012 Humedica data sets. We examined prevalence of MCC clusters in younger and older patients. For each of the 15 most common MCC clusters, we reported predicted probabilities for diabetes face-to-face visits, reaching glycated hemoglobin <8%; emergency department (ED) visits; and 30-day hospital readmissions, based on logistic regression results.

The leading MCC combination was the presence of hypertension-hyperlipidemia-obesity and no other diagnosed comorbidities (19% of the sample). The most notable difference, by age, was a higher prevalence of obesity in the younger cohort. MCC clusters were more diverse among the older population: the top 10 MCC clusters accounted for 66% of older patients, compared with 78% of younger patients. Patients with certain comorbidity profiles, such as those with obesity only, were less likely to have diabetes-related face-to-face visits and to meet A1C treatment goals, and more likely to have ED visits and 30-day readmissions.

Patients with diabetes have substantial comorbidities, but the patterns vary considerably across patients and by age. Diabetes care remained suboptimal among many types of MCC patients, and patient outcomes varied by MCC profile. Specific management strategies should be developed for common MCC clusters, such as hypertension-hyperlipidemia-obesity.

Am J Manag Care. 2015;21(1):e23-e34
This study is the first of which we are aware to examine a large number of the most common multiple chronic comorbidity (MCC) combinations for diabetes and to analyze specific, mutually exclusive MCC clusters associated with poor patient outcomes.
  • MCC patterns varied considerably across patients and by age.
  • Patients with certain MCC profiles, such as those with obesity only, were less likely to have diabetes-related face-to-face visits and to meet A1C treatment goals, and were more likely to have emergency department visits and 30-day readmissions.
  • Specific management strategies should be developed for common MCC clusters, such as hypertension-hyperlipidemia-obesity.
Most adults with diabetes have at least 1 coexisting chronic condition,1 and approximately 40% have 3 or more.2 As the number of comorbidities increases, the risks of poor patient outcomes (eg, unnecessary hospitalizations, adverse drug events, mortality) and healthcare costs also increase.2-6 Further, the types of comorbidities impact diabetes care.7,8 In a retrospective cohort study of 42,826 veterans with new-onset diabetes, individuals with “concordant conditions” (illnesses that overlap with diabetes in their pathogenesis or management plans; eg, hypertension and coronary artery disease) were more likely to receive recommended diabetes care, including glycated hemoglobin (A1C) testing, low-density lipoprotein cholesterol (LDL-C) testing, and diabetes-related visits, compared with individuals with no concordant comorbidities.9 In contrast, “discordant conditions” (illness with unrelated pathogenesis or management plans [eg, musculoskeletal diseases]) were associated with decreased diabetes care.9

Although previous studies have shown that the type and severity of comorbidities matter, not just the number of conditions,7-13 less attention has been paid to multiple chronic comorbidities (MCCs) and how they impact diabetes care. Patients with MCCs constitute a majority of the diabetes population, and are known to require high levels of healthcare and to account for a significant proportion of healthcare costs.2,14-16 However, it is unclear which MCC clusters in diabetes are most prevalent, or how MCC patterns vary by age.

To address these gaps, this study sought to identify specific MCC combinations associated with high morbidity. Unlike previous research that focused on 2-way17,18 or 3-way19 combinations between and among comorbidities, this is the first study to our knowledge to examine a large number of the most common MCC combinations for diabetes and to compare these clusters in younger and older patients. Additionally, most diabetes outcomes research that considered MCCs focused on the impact on disease-specific measures (eg, A1C, cholesterol, blood pressure [BP]), but ignored how MCCs affect broader health outcomes. This study identified specific comorbidity profiles associated with emergency department (ED) visits and 30-day hospital readmissions in addition to diabetes-specific outcomes.

This fresh look at comorbidities is important for 2 reasons. First, MCC outcomes exhibit substantial heterogeneity. Focusing on a limited set of “pairs” (or “triplets”) misses many combinations, including those consisting of more than 3 conditions. Second, to date, disease-focused guidelines (including those for diabetes) tend to underplay MCCs, and more importantly, do not describe how comorbidities may affect treatment plans and patient outcomes.15,19-21 Our analysis of mutually exclusive clusters may suggest a more useful set of patient subgroup definitions for use in clinical guidelines.


Data and Sample

We used the 2008 to 2012 data sets from the healthcare

informatics company Optum Humedica (, which link de-identified electronic health records (EHRs), encounter files, prescribed medications, and lab values to provide clinical details typically not available in administrative claims files. These data files were supplied by US providers, including ambulatory groups, hospital systems, and integrated delivery networks (IDNs). Information for services acquired from outside of Humedica’s provider networks was not available.

From the 2008 to 2012 Humedica data sets (n = 7,247,143), we retained “integrated patients” (n = 4,025,581), defined as those with both ambulatory and institutional data available from IDN providers (Figure 1). After excluding individuals younger than 18 years (n = 655,638), we identified 398,377 subjects with any evidence of diabetes, including: type 1 (n = 12,778), type 2 (n = 212,160), prediabetes (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] = 790.2X for “abnormal glucose”; n = 60,563), other diabetes (eg, gestational diabetes and secondary diabetes, n = 7249), and individuals with an unknown diabetes type (n = 105,627). Unknown type was defined as subjects who had lab evidence of diabetes or received medications for diabetes, but no diabetes diagnosis or abnormal glucose diagnosis.

Our analysis focused on patients with type 2 diabetes mellitus (T2DM) and therefore excluded individuals with type 1, prediabetes, and other diabetes. We classified subjects as having T2DM if they had any ICD-9-CM diagnosis codes of 250.X0 or 250.X2 on their ambulatory evaluation and management and/or procedure encounters (n = 212,160). Patients without a last visit at least 2 years apart from their first visit were excluded (n = 50,986). Finally, our analytic sample included 161,174 patients with T2DM with at least 24 months of data available to ensure that they maintained contact with the provider networks. In sensitivity analyses, we included patients with less than 24 months of data and patients with an unknown type of diabetes to examine the robustness of our results.

Comorbidities of Interest

We derived a list of comorbidities of interest by reviewing previous studies8,18,22,23 and the 2012 American Diabetes Association (ADA) guidelines,24 and identified the following conditions that have been determined to be important—clinically or economically—for adults with T2DM: hyperlipidemia, hypertension, obesity, depression, chronic obstructive pulmonary disease (COPD)/asthma, coronary artery disease (CAD), chronic kidney disease (CKD), arthritis, cancers, neuropathy, heart failure, fractures, peripheral arterial disease, and retinopathy. We identified individuals with these conditions by using the Clinical Classifications Software, a tool developed at the Agency for Healthcare Research and Quality for clustering ICD-9-CM diagnosis codes of conditions into clinically meaningful categories.19,25

We also used prescription records, lab values, and vital signs data to identify certain comorbidities in order to minimize potential underdiagnosis and undercoding. Specifically, individuals were classified as having hypertension if they had 2 or more BP readings on different days in which their diastolic BP reading was over 80, if their systolic BP was over 130 during the study period, or if they received any antihypertensive medications (see eAppendix [available at] for list of drugs). Individuals were classified as having hyperlipidemia if they had 2 or more readings on different days in which their total cholesterol levels were over 200, LDL-C levels were over 130, triglyceride levels were over 150, or highdensity lipoprotein cholesterol levels were under 60; or if they received any lipid-lowering medications (eAppendix). Finally, individuals were classified as obese if they had at least 1 obesity diagnosis (ICD-9-CM = 278.XX) or had a body mass index of ≥30 kg/m2.24

Patient Outcome Measures

We assessed 2 guideline-recommended diabetes care measures and 2 broader health outcomes. Diabetes care measures included a diabetes-related face-to-face visit at least once every 6 months and the attainment of A1C <8%—the treatment goal. These measures were chosen based on the 2012 ADA guidelines24 and the Diabetes Quality Improvement Project.9,26 We used each patient’s last A1C test results in primary analysis and each patient’s average A1C values in sensitivity analysis. The analysis of A1C was limited to a subset of 135,357 diabetes patients with available glucose data (84% of the overall sample). For broader health outcomes, we assessed all-cause ED visits and all-cause 30-day hospital readmissions during a year because these measures may signal poor patient outcomes. Readmissions were defined as inpatient hospital admissions that occurred within 30 days of discharge from a previous hospital admission. The analysis of 30-day readmissions was limited to a subset of 21,765 patients with at least 1 admission to the hospital (13.5% of the sample).

Statistical Analysis

We constructed a 2-year panel of 161,174 adults with T2DM, examined their comorbidity profiles and patient characteristics at the baseline year, and analyzed the 4 patient outcomes at the subsequent year. First, we examined the prevalence rate of each comorbid condition by itself and used t tests to compare the prevalence between patients aged <65 years and patients aged ≥65 years. Second, for analysis of MCCs, we limited our attention to the most prevalent comorbidities that affected ≥5% of the sample; these were hyperlipidemia, hypertension, obesity, CAD, COPD/asthma, CKD, arthritis, depression, cancers, and heart failure. Each MCC cluster was mutually exclusive. We analyzed the frequency and clustering of MCCs, and used χ2 tests to compare overall difference in MCC prevalence rates among older and younger patients.

Using logistic regression, we examined associations between MCC clusters and the 4 binary patient outcomes: at least 1 diabetes face-to-face visit every 6 months, A1C <8%, any ED visits, and any 30-day hospital readmissions. We included binary indicators for each of the top 15 most common MCC clusters, and classified the rest of the comorbidity combinations as “other” in our regression models. We started with a parsimonious model, adjusting for age, sex, race, neighborhood income, and insurance status. The fully adjusted model incorporated 3 additional covariates: Diabetes Complications Severity Index,27 number of evaluation and management visits, and number of medications prescribed to the patient (measured as unique classes of drugs). Finally, we used the model to estimate probabilities for each patient outcome.


The Table summarizes the characteristics of our sample. Eighty-eight percent of the patients with diabetes had at least 1 of the 14 comorbidities of interest, while 51% had 3 or more. Compared with younger adults, older adults had more comorbidities (mean = 2.3 vs 2.7, P <.001) and were more likely to have hyperlipidemia, hypertension, CAD, COPD/asthma, CKD, arthritis, cancer, and heart failure, and less likely to have obesity and depression.

Patterns of MCCs

Figure 2 displays the 15 most common MCC clusters, representing 75% of patients in our sample. The most common pattern was the presence of hypertension, hyperlipidemia, and obesity—roughly 1 in 5 patients (19%) had this combination. Five of the 6 most common clusters, corresponding to 51% of the sample, included some combination of hypertension, hyperlipidemia, and obesity. CAD plus hypertension and hyperlipidemia, either with or without obesity, was also common (5%). Other common clusters included the combination of hypertension-hyperlipidemia-obesity plus 1 other condition, such as COPD/asthma, arthritis, or depression.

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