Medicare beneficiaries with congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and type 2 diabetes mellitus (DM) for 12-month episodes of care in 2008 and 2009. Incidence reflects the odds ratio using logistic regression models. Cost models show the change in the continuity of care (COC) index change from 0.4 and 0.5. Error bars represent 95% CIs. ED indicates emergency department.
eTable 1. Number and Percent of Beneficiaries Included After Applying Each Exclusion Criterion
eTable 2. Classification of Specific Complications: Complications Related to the Primary Condition (Type 1), Complications Related to Comorbidities (Type 2), and Complications Related to Patient Safety (Type 3)
eTable 3. Mean Physician Ratings on the Probability That a Complication Is Associated With Care Coordination*
eTable 4. Incidence and Cost of Specific Complications,* Medicare Beneficiaries With CHF, DM, and COPD, 12-Month Episodes of Care, 2008-09
eTable 5. Adjusted Association Between Bice-Boxerman Continuity of Care (COC) Index and Incidence and Costs of Complications, Medicare Beneficiaries With CHF, 12-Month Episodes of Care, 2008-09
eTable 6. Adjusted Association Between Bice-Boxerman Continuity of Care (COC) Index and Incidence and Costs of Complications, Medicare Beneficiaries With COPD, 12-Month Episodes of Care, 2008-09
eTable 7. Adjusted Association Between Bice-Boxerman Continuity of Care (COC) Index and Incidence and Costs of Complications, Medicare Beneficiaries With DM, 12-Month Episodes of Care, 2008-09
eTable 8. Examples of Continuity of Care (COC) Indices for Patients With 7 Total Visits
eTable 9. Evaluation and Management Visit Rate per 30 Days Before and After the First Outpatient Complication
Customize your JAMA Network experience by selecting one or more topics from the list below.
Hussey PS, Schneider EC, Rudin RS, Fox DS, Lai J, Pollack CE. Continuity and the Costs of Care for Chronic Disease. JAMA Intern Med. 2014;174(5):742–748. doi:10.1001/jamainternmed.2014.245
Better continuity of care is expected to improve patient outcomes and reduce health care costs, but patterns of use, costs, and clinical complications associated with the current patterns of care continuity have not been quantified.
To measure the association between care continuity, costs, and rates of hospitalizations, emergency department visits, and complications for Medicare beneficiaries with chronic disease.
Design, Setting, and Participants
Retrospective cohort study of insurance claims data for a 5% sample of Medicare beneficiaries experiencing a 12-month episode of care for congestive heart failure (CHF, n = 53 488), chronic obstructive pulmonary disease (COPD, n = 76 520), or type 2 diabetes mellitus (DM, n = 166 654) in 2008 and 2009.
Main Outcomes and Measures
Hospitalizations, emergency department visits, complications, and costs of care associated with the Bice-Boxerman continuity of care (COC) index, a measure of the outpatient COC related to conditions of interest.
The mean (SD) COC index was 0.55 (0.31) for CHF, 0.60 (0.34) for COPD, and 0.50 (0.32) for DM. After multivariable adjustment, higher levels of continuity were associated with lower odds of inpatient hospitalization (odds ratios for a 0.1-unit increase in COC were 0.94 [95% CI, 0.93-0.95] for CHF, 0.95 [0.94-0.96] for COPD, and 0.95 [0.95-0.96] for DM), lower odds of emergency department visits (0.92 [0.91-0.92] for CHF, 0.93 [0.92-0.93] for COPD, and 0.94 [0.93-0.94] for DM), and lower odds of complications (odds ratio range, 0.92-0.96 across the 3 complication types and 3 conditions; all P < .001). For every 0.1-unit increase in the COC index, episode costs of care were 4.7% lower for CHF (95% CI, 4.4%-5.0%), 6.3% lower for COPD (6.0%-6.5%), and 5.1% lower for DM (5.0%-5.2%) in adjusted analyses.
Conclusions and Relevance
Modest differences in care continuity for Medicare beneficiaries are associated with sizable differences in costs, use, and complications.
Patients, especially those with chronic illnesses, frequently experience a health care system in which care is poorly coordinated.1 They see many different health care providers working across multiple clinical venues.2,3 Communication among these providers is often suboptimal, and poor coordination has been shown to be widespread, with adverse effects on health care costs, patient outcomes, and experiences with care.1
Care coordination has been identified as a priority area by the National Priorities Partnership and the Institute of Medicine.4,5 New models of patient care, coupled with new provider payment mechanisms—including bundled payment, accountable care organizations, and patient-centered medical homes—are expected to achieve reductions in costs and increases in quality through improved care coordination.2,6-9 However, the potential impact of new care models and the areas in which improvements in care coordination are likely to have the largest effects are poorly understood. While previous studies have shown that patients with a close, continuous relationship with a specific physician were more likely to receive recommended care, many programs that aim to improve care coordination have not had the desired effects on costs and quality.10,11
The objective of this study was to measure the difference in costs associated with variations in one aspect of coordination—care continuity—during episodes of care for patients with congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and type 2 diabetes mellitus (DM).12
We conducted a retrospective cohort study of the association between continuity of care (COC) as measured by the Bice-Boxerman COC index and the costs for Medicare beneficiaries with chronic disease. We further examined whether continuity is associated with rates of hospitalizations, emergency department visits, and complications since each may contribute to cost and potential poor outcomes.
The sample included Medicare beneficiaries with a diagnosis of CHF, COPD, and/or DM identified using Medicare claims files for a 5% random sample of Medicare fee-for-service beneficiaries in 2008 and 2009. Beneficiaries were eligible for inclusion in our sample if they were older than 65 years at the start of 2008 and continuously enrolled in fee-for-service parts A and B Medicare coverage for the 2 years. For this sample of beneficiaries, we identified episodes of care for each of the 3 chronic conditions, with every episode triggered by a physician professional service for one set of predefined International Classification of Diseases, Ninth Revision diagnosis codes at any point during 2008.13 Using this approach, we identified 98 850 CHF episodes, 147 708 COPD episodes, and 281 584 DM episodes (eTable 1 in the Supplement). Because our measurement window was limited to 2 years, each person could have only a single episode per condition; however, an individual patient with comorbidities could have up to 3 episodes (1 for each condition).
Patients were excluded if they had an in-hospital death, left the hospital against medical advice, or had a medical exclusion (eg, cardiac arrest, human immunodeficiency virus, cancer, suicide, or end-stage renal disease) during the episode. Claims were excluded from episodes if they were irrelevant to the chronic condition (eg, surgical procedures for which the chronic condition was a comorbidity rather than the primary reason for the procedure). These exclusions accounted for 38.0% of CHF episodes, 36.1% of COPD episodes, and 31.2% of DM episodes. We further excluded an additional 7.9% of CHF episodes, 12.1% of COPD episodes, and 9.6% of DM episodes with more than 2 outpatient evaluation and management visits (as defined below) due to the inability to construct continuity measures for these individuals. After exclusions, the final analytic cohort included 241 722 unique patients, of whom 53 488 had CHF, 76 520 had COPD, and 166 654 had DM.
To measure care continuity, we used the Bice-Boxerman COC index,14 a commonly used measure of continuity.15 The COC index reflects the relative share of all of a patient’s visits during the year that are billed by distinct providers and/or practices; the index ranges from 0 (each visit involved a different provider than all other visits) to 1 (all visits were billed by a single provider).
In constructing the COC index, we included evaluation and management visits that occurred in the outpatient setting, defined as Berenson-Eggers Type of Service codes M1A, M1B, M4A, M4B, M5C, M5D, and M6. Only a single evaluation and management visit per day for each patient-provider dyad was counted, along with visits on the same day to different providers. Visits that were related to complications, hospitalizations, or emergency department visits were excluded from our calculation of the COC index. In addition, we counted only visits to those clinicians most likely to be involved in outpatient management for each of the 3 conditions. For CHF, this included primary care providers (PCPs) (including general practitioners, family practitioners, internal medicine specialists without subspecialty training, physician assistants, and nurse practitioners), cardiologists, and pulmonologists. We included PCPs and pulmonologists for COPD and PCPs, cardiologists, endocrinologists, podiatrists, and ophthalmologists for DM. With the exception of general practitioners, each specialty class of provider accounted for more than 2% of outpatient evaluation and management visits, and the included providers accounted for 90.6% of total visits for CHF, 89.6% for COPD, and 86.0% for DM. We constructed the COC index using counts of visits to unique individual providers in the main analysis and to unique practice groups in sensitivity analyses. We defined practice groups using the tax identification number listed on Medicare claims.
For each 365-day episode, we examined whether the patient had at least 1 inpatient hospitalization and/or emergency department visit. We also measured the incidence of complications that were categorized as related to the primary condition (CHF, COPD, or DM), comorbidities, or patient safety (eTable 2 in the Supplement). For each clinical condition, complications were defined based on clinical classifications from the Agency for Healthcare Research and Quality as implied in Evidence-Informed Case Rates.13 Three clinicians independently reviewed and rated the extent to which each complication was likely to be sensitive to care continuity for each clinical condition (see eTable 3 in the Supplement for ratings and details). Complications rated as potentially sensitive to continuity and observed in at least 1% of patients were included in our analyses. We excluded complications that occurred during inpatient stays or emergency department visits from complication rates to create mutually exclusive study outcomes.
We measured the total Medicare part A and B costs of care associated with each episode by summing the Medicare and beneficiary payment amounts from all episode-related claims. We similarly calculated costs of hospitalizations, emergency department visits, and each type of complication.
Patient age, sex, and census region were determined from beneficiary enrollment files. Risk adjustment was performed using the 2008 hierarchical condition categories (HCC) from the Centers for Medicare & Medicaid Services, which calculate a beneficiary’s expected Medicare expenditure on the basis of diagnosis codes in claims, age, sex, Medicaid status, and reason for Medicare entitlement.16 Zip code median income was used as a proxy for socioeconomic status. Medicaid enrollment was included as a measure of socioeconomic status and/or disability. We adjusted for the number of visits to adjust for any residual association between use (possibly due to unmeasured severity) and the COC index that may persist, although the index also accounts for the volume of visits. Because having a PCP has been associated with higher quality and lower costs, we created a dummy variable indicating whether the patient had at least 1 visit to a PCP during the episode.17
For each condition, we calculated descriptive statistics summarizing patient and episode characteristics, the COC index, and complications among patients. Bivariate analyses were used to examine the association between patient characteristics and the COC index. We constructed separate multivariable logistic regression models for each condition and with each type of event as the dependent variable (hospitalization, emergency department visit, category of complication, and specific complication) and the COC index as an independent variable, adjusting for the relevant covariates. To test the association between the COC index and total episode costs, we used generalized linear regression models with a γ variance distribution and log link function.18 We used two-part models to test the association between the COC index and the costs of hospitalizations, emergency department visits, and complications. Two-part models were chosen due to the high concentration of beneficiaries with zero costs in these categories in the study population.18 The first part of the model was a logistic regression model predicting the incidence of each type of event, as described earlier. The second part of the model was a generalized linear regression model with a γ variance distribution and log link function estimating complication costs, with the model estimated only for the population of patients with each type of event. We used the results of the two models to calculate predicted costs for each beneficiary, including an estimate that the beneficiary had nonzero cost and, conditional on nonzero cost, the predicted amount. Specifically, we used recycled predictions from the two-part model to estimate the costs associated with a 0.1-unit difference in the COC index. We multiplied the predicted probability that the event occurred by the predicted costs given that the event occurred, assuming a COC index of 0.4 and 0.5. We then calculated the mean predicted cost across all patients in the sample at each level of COC. We generated bootstrapped 95% CIs for the estimated differences in costs using 1000 bootstrap samples taken from the study population with replacement.
In sensitivity analyses, we tested whether our findings were similar when we used COC calculated at the practice group level. To test whether complications lead to an increased visit rate and potentially lowered continuity, we compared the monthly rate of evaluation and management visits before and after a patient’s first complication. This calculation was limited to patients who had their first complication between months 3 and 6 of their 12-month episode.
Statistical analyses were performed using SAS version 9.3 (SAS Institute Inc). This study was approved by the RAND Human Subjects Protection Committee and the Johns Hopkins Institutional Review Board.
Most study patients were 75 years or older and female (Table 1). The population was predominantly white (82.6%-89.7% of patients with each condition). The median number of visits to a clinician during a yearlong episode ranged from 5 (COPD) to 7 (CHF). Patients with DM had the lowest mean (SD) COC index (0.50 [0.32]) compared with patients with CHF (0.55 [0.31]) and COPD (0.60 [0.34]).
Between 3.5% of patients with DM and 10.5% of patients with CHF had at least 1 hospitalization during the episode of care (Table 2). Emergency department use was common (range, 26.6%-44.6% of beneficiaries across the 3 conditions). More than half of all patients with each condition had a complication related to a comorbidity (range, 50.4%-67.3%). Complications related to the primary condition (range, 8.7%-40.8%) and patient safety issues (range, 14.9%-24.7%) were less common but still substantial among patients. The incidence and costs of specific complications varied by condition (eTable 4 in the Supplement).
Table 3 shows the unadjusted association between patient characteristics and the COC index.
The differences in the COC index by age group, sex, race/ethnicity, census region, median household income, Medicaid enrollment, and HCC score were generally small but statistically significant. Overall, patients with the highest HCC scores tended to have lower COC indexes; the difference in the COC index between patients in the highest quartile of HCC scores (indicating highest health risk) vs the lowest quartile was –0.03 for CHF and –0.07 for COPD and DM (all P < .001). There were larger differences in the COC index between patients with higher numbers of PCP visits; the difference between the highest and lowest quartiles of visits was –0.14 for CHF, –0.17 for COPD, and –0.21 for DM (all P < .001). The small number of patients without a PCP visit (<10%) had a higher mean COC index than those with a PCP visit.
The Figure shows the association between the COC index and health care use and outcomes after adjustment. For each condition, higher levels of COC were associated with lower odds of inpatient hospitalization (odds ratios were 0.94 [95% CI, 0.93-0.95] for CHF, 0.95 [0.94-0.96] for COPD, and 0.95 [0.95-0.96] for DM) and lower odds of emergency department visits (0.92 [0.91-0.92] for CHF, 0.93 [0.92-0.93] for COPD, and 0.94 [0.93-0.94] for DM). Higher levels of COC were also associated with lower odds of complications in 3 categories relating to the primary condition, comorbidities, and patient safety (odds ratio range, 0.92-0.96 across the 3 complication types and 3 conditions; all P < .001). For each condition, some of the specific complications were statistically significantly associated with the COC index, with odds ratios ranging between 0.87 and 0.99 (eTables 5-7 in the Supplement).
For total episode costs, a 0.1-unit increase in the COC index was associated with 4.7% lower costs for CHF (95% CI, 4.4%-5.0%), 6.3% lower costs for COPD (6.0%-6.5%), and 5.1% lower costs for DM (5.0%-5.2%). For a patient with CHF with median total costs of $1437, this translates into a $66 decrease (95% CI, $62-$70). With median costs of $1062 for patients with COPD, a 0.1-unit increase in the COC index was associated with a $64 decrease (95% CI, $62-$67); with $1047 in median costs for DM, costs were $52 lower (95% CI, $51-$53). In estimates from two-part models (Figure), a 0.1-unit increase in COC was associated with statistically significantly lower costs for hospitalizations (4.6%-6.1% lower across the 3 conditions), emergency department visits (5.8%-6.2% lower), and complications (4.1%-9.8% lower).
In sensitivity analyses, using continuity calculated at the practice group level revealed qualitatively similar results with respect to the main outcomes (eTables 5-7 in the Supplement). In tests of differences in use following a patient’s first complication during an episode, the mean rate of visits per 30 days was slightly lower in the post–complication period compared with the pre–complication period (0.7 vs 0.9 for CHF, 0.5 vs 0.7 for COPD, and 0.7 vs 0.8 for DM; see eTable 8 in the Supplement).
For Medicare beneficiaries with each of 3 chronic diseases (DM, COPD, and CHF), we found a consistent association between higher levels of care continuity, lower rates of hospital and emergency department visits, lower complication rates, and lower episode costs. A 0.1-unit increase in the COC index (which ranges from 0-1) was associated with a difference of between 4.7% and 6.3% lower costs across the 3 conditions.
Although it has been used frequently in health services research, the Bice-Boxerman COC index may be unfamiliar to many readers and difficult to interpret. Assuming the number of visits in a year is constant, an increase in the COC index can be achieved by either involving fewer providers in a patient’s care or concentrating the visits among fewer providers. For example, among patients with 7 total visits—the median in the CHF sample—moving from 3 to 2 providers or increasing the number of visits with a PCP from 4 to 5 visits can increase the COC index by 0.1 units. All potential COC indexes possible for a patient with 7 visits are listed in eTable 9 in the Supplement. In our sample, a difference of 0.1 units in the COC index corresponds to an SD of 0.3 units, a variation that generally would be interpreted as a “small” effect size.
The finding that a higher continuity score is associated with lower complication rates may also be difficult to interpret given the cross-sectional design. It is possible that underlying processes of care that may be affected by continuity (eg, the flow of information across providers and care settings) may be relevant for many different types of complications.19 However, it is also plausible that patients with complications see more PCPs. We tried to exclude this possibility by examining the mean number of visits before and after a patient’s initial complication. Measuring processes of care that may be sensitive to information continuity—for example, repeated laboratory or radiologic testing by multiple providers—may be an important next step in examining potential mechanisms as well as drivers of cost.
Because they are derived from health insurance claims data, the measures of care continuity and complications used in this study may be useful in tracking aspects of care that are sensitive to continuity involving large populations.20 Physician organizations and insurers may use such measures to target specific patients at increased risk for complications and high costs. Disadvantages of the COC index include the lack of detailed information about communication across providers and the absence of direct guidance about the optimal modifications to current care delivery that can improve coordination. Care coordination is a multidimensional construct, and the COC index reflects only one aspect of coordination.20
Episodes of care may be an important framework for studying the role of care coordination in health care delivery.2 Yearlong episodes of care for chronic conditions help standardize comparisons across patients, suggest specific providers and encounters that are associated with the chronic condition and may be used to measure coordination, and define aspects of health care use and complications that may be most sensitive to differences in coordination.
Our results are subject to limitations. First, the analyses focus on adults older than 65 years enrolled in fee-for-service Medicare and may not be generalizable to younger populations and those with other types of health insurance. Second, like all claims-based analyses, the risk adjustment model lacks clinical detail that may be associated with both our measures of continuity and study outcomes. Unmeasured severity of illness could be a confounder. Third, our analyses are cross-sectional and therefore do not address causality. Adverse health outcomes may lead to patterns of care, such as increased visit rates, that reduce the COC index. However, mean visit rates decreased following complications in our sample. Fourth, we used the COC index at the provider and practice group levels as our measure of continuity. We have discovered that other commonly used measures of continuity (eg, Sequential Continuity Index21 and usual provider of care22) are highly correlated with one another and thus unlikely to significantly alter our findings (C. E. Pollack, MD, P. S. Hussey, PhD, R. S. Rudin, PhD, D. S. Fox, PhD, J. Lai, MPH, and E. C. Schneider, MD, unpublished data, 2013). However, as discussed earlier, all claims-based continuity measures share certain limitations. Fifth, our results showed a counterintuitive finding that the group of patients without a PCP had a lower COC index score on average. This group without a PCP comprises a relatively small proportion of each sample (<10%). Patients with no PCP and CHF had higher HCC scores (1.1 vs 1.0) but not among the populations with DM and COPD. Patients with no PCP visit had a lower mean total visit count compared with patients with at least 1 PCP visit (CHF, 8.9 vs 5.2; COPD, 6.7 vs 4.2; and DM, 7.7 vs 5.2). Consistent with the previous literature, patients with a PCP tended to have lower costs after adjustment for other factors. We speculate that not having a PCP is a marker for other differences between patients with and without PCPs that we were unable to adjust for using claims data. Our findings of the association between the COC index and costs, hospital and ED use, and complications were not sensitive to exclusion of the PCP indicator from the model. Sixth, complications were classified into different types, but these categories are unlikely to be mutually exclusive. Seventh, our analysis excluded patients with less than 2 outpatient evaluation and management visits. Eighth, our analysis did not include pharmaceutical use and costs.
While improving care continuity and realizing the associated benefits has, in practice, proved challenging,10 our results suggest the potential importance of care continuity and underscore the potential benefits that may be achievable through programs that improve continuity. With changes in health care delivery and payment, it will be necessary to measure whether these reforms have an effect on continuity and, in turn, reduce health care use, the rate of complications, and costs of care.
Accepted for Publication: November 28, 2013.
Corresponding Author: Peter S. Hussey, PhD, RAND Corporation, 20 Park Plaza, Ste 920, Boston, MA 02116 (email@example.com).
Published Online: March 17, 2014. doi:10.1001/jamainternmed.2014.245.
Author Contributions: Dr Hussey had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Hussey, Schneider, Rudin, Fox, Pollack.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Hussey, Schneider, Lai, Pollack.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Hussey, Schneider, Rudin, Fox, Lai.
Obtained funding: Hussey, Schneider.
Administrative, technical, or material support: Rudin.
Study supervision: Hussey, Schneider.
Conflict of Interest Disclosures: None reported.
Funding/Support: This study was supported by the Aetna Foundation, the National Cancer Institute (Dr Pollack), and grant K07 CA151910 from the Office of Behavioral and Social Sciences (K07 CA151910).
Role of the Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Previous Presentation: This study was presented at the Society for General Internal Medicine Annual Meeting; April 25, 2013; Denver, Colorado.
Additional Contributions: Claude Setodji, PhD, RAND Corporation, provided statistical advice.
Create a personal account or sign in to: