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The American Journal of Managed Care June 2011
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Better Continuity of Care Reduces Costs for Diabetic Patients
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Better Continuity of Care Reduces Costs for Diabetic Patients

Chi-Chen Chen, PhD; and Shou-Hsia Cheng, PhD
This longitudinal analysis of diabetes patients found that better continuity of care was associated with reduced healthcare utilization and decreased pharmaceutical and total healthcare expenses.

Objective: To examine the effects of continuity of care on healthcare utilization and expenses for patients with diabetes mellitus.


Study Design: Longitudinal study based on claims data.


Methods: Data on healthcare utilization and expenses from a 7-year period (2000-2006) were gathered from claims data of the Taiwanese universal health insurance system. The continuity of care index (COCI) was analyzed, and the values were classified into 3 levels. Outcome variables included the likelihood of hospitalization and emergency department visit, pharmaceutical expenses for diabetes-related conditions, and total healthcare expenses for diabetes-related conditions. A generalized estimating equation that considered the effects of repeated measures for the same patients was applied to examine the effects of continuity of care on healthcare utilization and expenses.


Results: Compared with patients who had low COCI scores, patients with high or medium COCI scores were less likely to be hospitalized for diabetes-related conditions (odds ratio [OR] 0.26, 95% confidence interval [CI] 0.25, 0.27, and OR 0.58, 95% CI 0.56, 0.59, respectively) or to have diabetes-related emergency department visits (OR 0.34, 95% CI 0.33, 0.36, and OR 0.64, 95% CI 0.62, 0.66, respectively). Patients with low COCI scores incurred $126 more in pharmaceutical expenses than patients with high COCI scores. Furthermore, patients with high COCI scores had greater savings ($737) in total healthcare expenses for diabetes-related conditions than patients with low COCI scores.


Conclusion: Better continuity of care was associated with less healthcare utilization and lower healthcare expenses for diabetic patients. Improving continuity of care might benefit diabetic patients.

(Am J Manag Care. 2011;17(6):420-427)

This study used a longitudinal analysis of a large, representative data set from Taiwan to examine the effects of continuity of care on healthcare utilization and expense for patients with diabetes mellitus (DM).

  • Diabetic patients with better continuity of care had a significantly lower likelihood of hospitalization or an emergency department visit for diabetes-related conditions.
  • Patients with better continuity of care for diabetes had significantly lower pharmaceutical and total healthcare expenses for diabetes-related conditions.
  • Improving the continuity of care might be beneficial to patients with DM.
Diabetes mellitus (DM) is a prevalent condition with significant complications and serious consequences.1 In many countries, soaring healthcare expenses for diabetes related conditions have become a major concern of health authorities. In the United States, healthcare expenditures for diabetes reached an estimated $116 billion in= 2007.2 The total diabetes-related healthcare expenses in Taiwan increased from $207 million in 1998 to $466 million in 2008.3,4 Finding ways to contain the soaring expenses for diabetes care is a major issue for healthcare policy makers worldwide.

Long-term relationships between physicians and patients are an important determinant of mutual trust and better communication. A commonly used indicator for measuring the relationship between patients and their physicians is the continuity of care. Numerous studies in the United States have shown that patients with greater continuity of care were more likely to have better healthcare outcomes.5 Similar results have also been found in Taiwan.6,7

Previous research suggests that continuity of care may be particularly beneficial to patients with chronic diseases such as diabetes.8 With a high degree of continuity of care, the ongoing relationship allows the physician to be familiar with a patient’s history and current condition. Thus, physicians can continually monitor the glycemic status and detect minor conditions at earlier stages to reduce the incidence of diabetes complications. Additionally, the ongoing relationship may increase the level of mutual trust between physicians and patients, generate greater consensus during decision making, and result in better adherence to guidelines.9 Empirical studies on continuity of care and healthcare outcomes for diabetic patients indicate that better continuity of care may lead to earlier diagnosis of diabetes,10 higher patient satisfaction or quality of life,11,12 and fewer hospital admissions.13,14 Yet the association between continuity of care and healthcare expenses for diabetic patients has not been reported.

Continuity of care is closely associated with the healthcare system.The use of managed care models has expanded in the United States, and the integrated delivery system of managed care might improve continuity of the physician-patient relationship.8 However, the policy of managed competition that encourages patients to change health plans or physicians15 may reduce the continuity of care for patients. Therefore, some states have passed legislation to improve the continuity of care by promoting the physician-patient relationship as a component of managed care.8 On the other hand, more than 99% of all residents are enrolled in Taiwan’s compulsory national health insurance (NHI), and about 95% of hospitals and 90% of community clinics nationwide were contracted with NHI in 2008. There is no required referral arrangement under the NHI system, and patients are free to choose any physician based on their preferences. Facilitated by the ease of accessibility, patients in Taiwan are often criticized for their doctor-shopping behavior, such as changing doctors or making unnecessary visits.16,17 This phenomenon may hamper the communication or trust between patients and physicians, resulting in the deterioration of continuity of care for patients. This study used longitudinal analysis of a large, representative data set from Taiwan to examine the effects of continuity of care on healthcare utilization and healthcare expense.


Data Source

Data for this study came from the NHI claim data set obtained from the National Health Research Institute. This data set is a nationwide, population-based claim database that contains detailed records on every visit for each patient, including outpatient visits and hospital admission. The data set includes principal and secondary diagnosis codes, prescription orders, claimed expense, and copayment. Multiple year data were used in this study.

Study Design

Most of the previous studies examining continuity of care and healthcare utilization used a cross-sectional study design.18-23 Compared with a cross-sectional design, longitudinal design possesses significant strengths. First, a longitudinal design not only accounts for variation between individuals with healthcare utilization but also accounts for unobserved time-invariant characteristics of patients (like health-seeking behavior). Second, the longitudinal design allows for estimation due to changes in continuity of care and healthcare utilization over time, whereas a cross-sectional study can only obtain information about the relationship between continuity of care and healthcare utilization at 1 given point in time.24,25 Given the availability of the multiple-year data, the present study was conducted using a longitudinal design to strengthen the inference of the findings.

Study Population

We identified study subjects who had either a type 1 or type 2 DM diagnosis (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] code 250 or A181) and who had diabetes-specific prescriptions from the NHI claim data set in 2000-2006. We then conducted systematic sampling to select 100,000 subjects from this nationwide diabetes-related claims database. Using the data, we composed a 7-year panel (2000-2006) of claims for healthcare utilization and expenses analysis.

We applied several exclusion criteria to select appropriate study subjects. First, we excluded patients with diabetes who were younger than 18 years to ensure homogeneity of the sample. Second, because the continuity-of-care indicators are not applicable to those with very few visits, we excluded subjects who had fewer than 3 diabetes related visits in any of the years between 2000 and 2006. This exclusion criterion has been used by previous studies.18,20,21 Finally, to increase the comparability of continuity of care among patients with diabetes, we excluded the following treatment categories: outpatient surgery, dental care, traditional Chinese medicine, and specific services such as long-term care or home health services. As a result, 48,107 subjects were included in the study. We compiled baseline information in 2000 and then collected followup information for each patient over the subsequent 6 years.

Measures of Study Variables

Continuity of Care. From the several indicators available that measure continuity of care,26 we elected to use the continuity of care index (COCI) developed by Bice and Boxerman.27 The COCI is composed of the number of different physicians seen and the number of visits to each physician.The equation for this index is as follows:


COCI = Σnj2-N / N(N-1)
where N represents the total number of physician visits, nj is the number of visits to the same physician, j, j represents a given physician, and M is the number of physicians. This index measures the degree to which patient visits are dispersed among different physicians, and the corresponding values (0 and 1) indicate the degree of continuity of care. A higher value corresponds with better continuity of care.

The COCI score is usually calculated by physician visits for any condition of a patient. However, in this study, we calculated the COCI score based on diabetes-related visits only. We considered that the specific COCI is more sensitive to detect the association between continuity of care and healthcare utilization for diabetic patients due to the lack of referral arrangements and the very high number of physician visits in Taiwan. Because the score of the COCI cannot be interpreted intuitively, it was divided into 3 groups based on the tertiles of the distribution for analysis. We chose COCI instead of other commonly used indicators such as the usual provider continuity index28 because the COCI score is independent from the number of physician visits.29

Outcome Measures. The main outcomes measured in this study were healthcare utilization and healthcare expense. We defined diabetes-related healthcare services and expenses as claims indicating DM as the principal or secondary diagnostic ICD-9-CM code. The healthcare utilization variables included the likelihood of diabetes-related hospitalizations and emergency department (ED) visits. In addition, we excluded hospitalizations or ED visits for diagnosis of an injury, poisoning (ICD-9-CM 850-995), and all supplementary classifications (V-codes) like chemotherapy, because these specific services were not associated with regular healthcare-seeking behavior of diabetic patients.

The healthcare expenses were calculated using NHI claim data and included diabetes-related pharmaceutical expenses and diabetes-related total healthcare expenses (including outpatient visits, hospitalizations, and ED visits) incurred by patients. The healthcare expenses during 2001 and 2005 were adjusted for inflation to allow for comparison with figures from 2006 in the regression models. Additionally, we computed the predicted mean expense for each patient, holding patient characteristics constant, and compared differences in diabetes-related pharmaceutical and total healthcare expense for each category of COCI.

Other Variables. Several confounding factors were controlled for in the regression models. These variables include patient’s age and sex, low-income status, the total number of physician visits in the previous year, the likelihood of hospitalization in the previous year, and diabetes complication severity index score in the previous year.30 Enrollment in the NHI diabetes pay-for-performance program was also included because a previous study reported significant effects of the program on healthcare utilization.31

Statistical Analysis

Descriptive statistics, including frequency, percentage, mean, and standard deviation, were calculated for this study. We used generalized estimating equations (GEEs) to create an extended, generalized, linear model to account for correlated data from the longitudinal data analysis.25 Based on the characteristics of the variables used herein, the likelihoods of diabetes-related hospitalization and ED visit were analyzed by using a logit link with a binomial distribution. Additionally, values from the healthcare expenses were right skewed; therefore, we used the GEE model with a logarithmic link function with a Gamma distribution. A similar approach has been used in the econometric literature to assess healthcare expenses.32,33

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