Diabetes mellitus affects about 23.6 million individuals in the United States and is the seventh leading cause of death.¹ Because of the complexity of the disease and its management, patients with diabetes are more likely to use healthcare services than the general population,¹ with a total direct medical cost of about $116 billion and a mean cost that is 2.3 times higher than that of individuals without diabetes.^1,2 Moreover, almost 6.3 million hospital stays in 2004 were by patients with diabetes as a principal or coexisting condition, costing almost $57.8 billion, or about 20% of total hospital costs, while hospitalizations principally for diabetes cost about $3.9 billion.²

Many of these hospital stays included readmissions of the same patients, and hospital readmission has been a major quality concern in diabetes care. However, there are few population-based studies on readmission among patients with diabetes and its risk factors. Jiang et al³ reported that more than 30% of older patients with diabetes were readmitted during the same year as their index hospitalization in 5 states in 1999. Readmissions accounted for 55.2% of total hospital stays, and the mean total hospital cost for patients with readmissions was 2.5 times higher than that for patients without readmissions. They also reported a high risk of readmission among racial/ethnic minority groups and variations in readmission rates by insurance type.^4,5 Robbins et al6 and Robbins and Webb^7,8 observed racial/ethnic disparities among hospital readmissions in Pennsylvania hospitals between 1994 and 2001 and noted the positive effect of public health clinics in reducing hospital readmissions among low-income patients with diabetes.

A gap in the literature on hospital readmissions among patients with diabetes has been the lack of differentiation between scheduled and unscheduled readmissions.^3-5 Readmissions are often assumed to be unscheduled, but this may not be true, especially for patients with existing complex conditions such as diabetes. The need for attention to the type of readmission has been raised,⁹ but few studies^6,7 have explicitly focused on unscheduled readmissions only. No study seems to have assessed what the reasons for and the factors associated with scheduled readmissions are, and whether they are similar to those associated with unscheduled readmissions.

Another gap in the literature is that studies have often focused on readmissions within 1 month of hospital discharge^6,7,10 or on readmissions among Medicare patients.^10,11 This short observation window is because hospital readmission has been widely studied in terms of its relationship to hospital performance.^7,12 However, this may be too narrow an approach to evaluate readmission risk from the perspective of patients, for whom avoiding a later readmission is as important as avoiding an early readmission. Investigations focusing on patient risk for readmission have often used a 3-month window following hospital discharge,⁵ which we adopted.

The objectives of this study were to examine and compare the demographic, socioeconomic, and clinical factors associated with scheduled and unscheduled readmissions in midlife and older patients with diabetes using a population-based data set from California. We also examined common reasons for scheduled and unscheduled readmissions and estimated the cost and extent of potentially preventable readmissions using Prevention Quality Indicators (PQIs) of the Agency for Healthcare Research and Quality (AHRQ).¹³

METHODS

We analyzed the 2006 California State Inpatient Dataset (SID), developed as part of the Healthcare Cost and Utilization Project (HCUP) sponsored by the AHRQ.¹⁴ The publicly available California SID includes hospital discharge abstract data (such as demographic characteristics, clinical information, resource use, and payers) from short-term, nonfederal, general, and specialty hospitals. California was selected for the analysis because it has a large and ethnically/racially diverse population¹⁵ and provides in its SID an encrypted unique patient identifier (UPI) that is used to link hospital readmissions to a distinct patient. The California Office of Statewide Health Planning and Development (OSHPD)¹⁶ routinely monitors the quality of hospital inpatient data and corrects or edits the data if necessary. They also ask reporting institutions to make corrections when their reports do not meet the error tolerance level (now 2%) that the state has established.

Our sample included patients 50 years or older with diabetes as the principal or a secondary diagnosis, identified using the AHRQ’s Clinical Classifications Software (CCS 45 [diabetes mellitus without complications] and CCS 50 [diabetes mellitus with complications]),¹⁷ all of whom were admitted to California hospitals at least once between April and September 2006. We defined the first admission as the index hospitalization and identified readmissions within 3 months of the index hospitalization using the encrypted UPI. As did Jencks et al,¹⁰ we counted no more than 1 readmission for each patient and focused on the characteristics of the first readmission (ie, scheduled or unscheduled) in the analysis. Similar to the approach by Jiang et al,³ we excluded patients with a missing UPI, patients with the same UPI but with an inconsistent age (differing by >1 year) or sex, and patients with a missing admission or discharge month.

The outcome variables of this study were scheduled and unscheduled readmissions. We used the SCHED variable in the California SID,¹⁸ which refers to whether an admission is scheduled at least 24 hours in advance. Therefore, we defined unscheduled readmissions as readmissions that were not scheduled at least 24 hours in advance and scheduled readmissions as those that were scheduled at least 24 hours in advance. The SCHED variable is derived from the type of admission in the California hospital inpatient data report collected by the California OSHPD, from which the California SID is developed for the HCUP. The California OSHPD¹⁶ provides detailed guidelines for each variable in the report, including the type of admission, and conducts multistep validation checks and edits.