Previously Unrecognized Trends in Diabetes Consumption Clusters in Medicare
Published Online: July 16, 2013
A. Enrique Caballero, MD; Jaime Davidson, MD; Angelo Elmi, PhD; James Gavin, MD, PhD; Kenyatta Lee, MD; Gail L. Nunlee-Bland, MD; Farhad Zangeneh, MD; and Gary A. Puckrein, PhD
Previous studies have demonstrated that a relatively small proportion of Medicare beneficiaries are the principal consumers of Medicare benefits.1-6 By comparing healthcare expenditures over a number of years, Berk and Monheit2 noted that there is a large amount of stability in the spending distribution over time. Because of the stable, skewed distribution of healthcare expenditures, the prevailing approach to reducing healthcare spending has been to focus on those who are receiving large amounts of care.
However, it has recently become clear that disease management programs that focus on those receiving the largest amounts of care have not been successful. A recent report from the Congressional Budget Office describes an evaluation of Medicare’s demonstration projects on disease management.7 Six major disease management and care coordination demonstrations were evaluated by independent researchers. The results showed that most programs did not reduce Medicare spending, despite the fact that the programs specifically targeted beneficiaries who were high users of healthcare services.7
Our study extends the previous work by reviewing consumption and hospitalization patterns for fee-for-service Medicare beneficiaries living with diabetes. Consistent with previous findings, the proportions of patients in the various spending clusters were stable over time. However, when the behavior of individual beneficiaries was studied further it became clear that the populations in the spending clusters were dynamic, reconstituted each year while retaining the same proportional dimensions, as beneficiaries migrated from 1 cluster to another within definable parameters. These findings show that there is a previously unreported trend for beneficiaries in the lowest-spending clusters to transition to the highest-spending clusters in subsequent years. We discuss the potential implications these findings for future approaches to reducing healthcare spending.
RESEARCH DESIGN AND METHODS
Retrospective expenditure data were collected from Medicare records. Medicare patients with diabetes were identified with an algorithm that required at least 1 inpatient, skilled nursing facility, or home health agency claim, or 2 Health Options Program or carrier (defined as Medicare administrative contractors or carriers that oversee the administration of both Medicare part A and part B policies) claims with the following diagnosis codes (any diagnosis on the claim) during the 2-year period: 249.00, 249.01, 249.10, 249.11, 249.20, 249.21, 249.30, 249.31, 249.40, 249.41, 249.50, 249.51, 249.60, 249.61, 249.70, 249.71, 249.80, 249.81, 249.90, 249.91, 250.00, 250.01, 250.02, 250.03, 250.10, 250.11, 250.12, 250.13, 250.20, 250.21, 250.22, 250.23, 250.30, 250.31, 250.32, 250.33, 250.40, 250.41, 250.42, 250.43, 250.50, 250.51, 250.52, 250.53, 250.60, 250.61, 250.62, 250.63, 250.70, 250.71, 250.72, 250.73, 250.80, 250.81, 250.82, 250.83, 250.90, 250.91, 250.92, 250.93, 357.2, 362.01, 362.02, and 366.41.
An annual cost profile for each Medicare fee-for-service beneficiary with diabetes for the years 2000 through 2006 was constructed using a unique beneficiary identifier to link the Beneficiary Annual Summary Files to Medicare’s Chronic Condition Data Warehouse flags, which identified beneficiaries who received Medicare reimbursements for diabetes.8 Total annual Medicare expenditures for each beneficiary with diabetes were calculated as the sum of all reimbursements made for inpatient and outpatient care, skilled-nursing facilities, carriers, durable medical goods, and home health and hospice care during a calendar year. In 2000, there were 106,995 (2.1%) diabetes beneficiaries whose primary payer for their medical expenses was not Medicare. Medicare total reimbursements therefore did not reflect their real insured healthcare expenditures. We removed such beneficiaries from study for the years 2000 through 2006. In 2000, there were 1376 (0.03%) diabetes beneficiaries with negative Medicare reimbursement values. Among these beneficiaries, 141 were removed because Medicare was not the primary payer of their medical expenses. For each year, the study included beneficiaries enrolled in Medicare part A and part B for at least 10 months and excluded all beneficiaries enrolled in Medicare Advantage because of the lack of complete reimbursement data. Beneficiaries were only removed from the study when they had at least 1 month of managed care for the year. The date of the death of a beneficiary was determined by the date given in the summary file.
All analyses were conducted with SAS software, version 9.1 (SAS Institute Inc, Cary, North Carolina). On the basis of their annual cost profiles, beneficiaries with diabetes were grouped into 5 consumption clusters. Simple percentages were used to describe patterns of Medicare expenditures for the clusters: (1) crisis consumers (beneficiaries accounting for the 99th percentile [top 1%] of aggregate Medicare payments); (2) heavy consumers (90th through 98th percentiles); (3) moderate consumers (75th through 89th percentiles); (4) light consumers (50th through 74th percentiles); and (5) low consumers (1st through 49th percentiles).
An analysis was performed to determine whether the annual repopulation of a cluster followed any discernible pattern. Percentages were calculated to ascertain what proportions of the beneficiaries in a cluster migrated from one of the prior year’s consumption clusters. For example, what percentages of crisis consumers in 2001 were crisis, heavy, moderate, light, or low consumers in 2000? These migration studies were conducted for each of the clusters for the years 2001 through 2006. Once these calculations were completed, a comparison was made to determine whether the migration patterns among clusters were stable, with similar proportions from one year to the next.
The influence that expenditures for inpatient care had on migration patterns was examined by calculating reimbursements for hospitalization for the 5 consumption clusters from 2000 through 2006. Percentages were derived by dividing total expenditures for inpatient care by the total of all expenditures in a given year.
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