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Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Objective

To provide estimates of the growth in out-of-pocket (OOP) medical expenditures for persons with arthritis.

Methods

OOP medical expenditures were estimated for 1998–2004 based on 7 panels of the Medical Expenditures Panel Survey, which provide nationally representative data. A simple simulation then extrapolated the data through 2006, for which the potential effects of Medicare Part D drug coverage were computed.

Results

Median total OOP expenditures for persons with arthritis showed an increase of 52.4% between 1998 and 2004 (7.3% annually beyond inflation). Median OOP expenditures for prescription medication showed larger growth, at 72.0%. Medicare Part D was predicted to lower both total and prescription OOP expenditures and return them close to 2003 levels. Simulation limitations included exclusive use of the standard Medicare Part D benefit structure and the assumption of stable prescribing trends during this period.

Conclusion

High prescription drug expenditures are likely to continue to be an issue, both for individuals faced with increasing OOP burden and for policy makers faced with increasing budgetary shortfalls to fund increasing Medicare expenses.

Individuals with arthritis have higher medical expenditures than persons without arthritis (1–3). Limited insurance coverage of prescription medicines, the aging of the population, and growing reliance on expensive drugs have combined to increase the economic burden faced by individuals with arthritis (4, 5).

Economic burden on individuals is reflected in their overall expenditures for medical care, but is more closely tied to out-of-pocket (OOP) expenditures, that is, how much an individual must pay for care through copayments and deductibles not covered by private or government insurance. The burden of prescription drug costs, in particular, has become a major policy issue due to growing utilization of prescription drugs (6), rising drug prices, and limited availability of good prescription drug coverage (7). Looking at all individuals with arthritis, Yelin et al (8) found that the amount Americans spent on arthritis medications more than doubled between 1998 and 2003. For the population of elderly persons, greater medical expenses combined with limited income results in an increasing OOP burden (9). The Medicare Part D drug benefit was implemented in 2006 in response to these concerns. However, the large copayments associated with the Medicare plan led to questions regarding its ability to meet increasing needs.

This article addresses these concerns by examining the OOP expenditures of persons with arthritis. We assess the recent OOP growth across the expenditure distribution for both total OOP expenditures and OOP expenditures for prescription medication. In addition, we simulate the effect the new Medicare Part D drug program may have had on these expenditures.

Specifically, we use nationally representative medical expenditure data between 1998 and 2004 to address the following questions: 1) did Medicare-eligible individuals age ≥65 years with arthritis experience a substantial growth in total OOP expenses? 2) what was the likely impact of Medicare Part D drug coverage on OOP expenditures?

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Data and study population.

This study uses 7 sequential panels (from the second through the eighth) from 1998 through 2004 of the Medical Expenditures Panel Survey (MEPS). MEPS is a probability sample of the US civilian noninstitutionalized population. The MEPS Household Component (MEPS-HC) uses an overlapping panel design in which data are collected by a series of 5 interviews over a 30-month period (10). Person-level data are collected on medical expenditures with detailed information regarding payment source (OOP, Medicare, Medicaid, etc.). This study focuses on total OOP payments and OOP expenditures for prescription medications. Our analysis sample includes 3,758 individuals with arthritis age ≥65 years at their baseline interview who participated in all 5 interviews of 1 of the 7 1998–2004 MEPS panels. (We exclude 2 observations due to lack of self-reported health status information.)

Identification of arthritis.

Arthritis and other chronic conditions were ascertained from systematic medical interviews from the first year of each panel. Specific conditions were assigned 3-digit diagnosis International Classification of Diseases, Ninth Revision (ICD-9) codes and are maintained in a MEPS condition file. The identification of arthritis is based on the ICD-9 codes used by the National Arthritis Data Workgroup (11) using the MEPS truncated 3-digit ICD-9 codes 710–716, 719–721, and 725–728. Other chronic conditions are identified in a similar manner and include hypertension, diabetes, cancer, pulmonary disease, heart disease, stroke, mental disorder, and neurologic disorder.

OOP expenditures.

The MEPS-HC provides total and OOP expenditures for each individual for the following type of services: ambulatory care visits, physician office visits, emergency room visits, hospital inpatient stays, prescription medications, and other medical expenditures related to home health care, dental care, ambulance services, orthopedic items, hearing devices, prostheses, bathroom aids, medical equipment, disposable supplies, alterations/modifications, and miscellaneous items or services that were obtained, purchased, or rented during the monitored year. We focus on total OOP expenditure and OOP expenditure for prescription medication because prescription costs represent on average more than half of all OOP expenditures. We use the first year of medical data to identify persons with arthritis-related diagnoses and the second year of each 2-year panel to ascertain expenses. Those data are collected by the MEPS for each sampled person and are then summarized to provide annual expenditures data. All expenditures are adjusted for inflation using the annual Consumer Price Index not seasonally adjusted and are in 2004 dollars.

Data analysis.

All analyses on total OOP and prescription OOP medication expenditures use a sample limited to those adults age ≥65 years having a MEPS arthritis-designated ICD-9 code with population weights to provide inferences regarding the older US adult population with arthritis. Median, 75th percentile, and 90th percentile OOP expenditures are estimated using quantile regression. Quantile regression, which models median (or 75th or 90th percentile) OOP expenditures, is analogous to least squares regression that models the mean outcome. Since quantile regression is robust to outliers and does not require assumptions regarding the underlying distribution of the outcome to obtain valid inference tests, the method is advantageous for modeling outcomes which are not normally distributed (12). The variance of the quantile regression coefficients is estimated using bootstrapping to account for potential correlation among outcomes due to the complex sampling design (13).

The quantile regression estimates adjust for age, age squared, race/ethnicity, marital status, education, income, self-reported health status, and number of chronic conditions. We calculated income based on whether the person with arthritis is single or married. For single individuals, we used their reported income. For an individual who is married, we calculated the average per-person income given the income of the respondent and his/her spouse. Indicator variables were used to track each of the 5 self-reported health status categories (excellent, very good, good, fair, and poor). Results of both total OOP and prescription medication expenditures are estimated in reference to a white single female age 75 years with up to 12 years of education, income of $23,585 in 2004 dollars, in good health, and with one other chronic condition besides arthritis. The difference between the adjusted 1998 and 2004 OOP expenditure estimates and the associated confidence interval (CI) are also estimated. A 95% CI that excludes zero indicates a statistically significant difference.

Simple simulation of effects of Medicare Part D on OOP expenditures.

Medicare Part D can potentially change the OOP expenditures that individuals experience (14–16). To investigate the maximum possible effect of Medicare Part D, we used the 2004 MEPS sample of Medicare-age adults (≥65 years), the last year of data available from MEPS, to simulate OOP expenditure in the years 2005 and 2006. We projected total OOP and prescription medication OOP expenditures for the years 2005 to 2006 by assuming that the average growth rates in prescription and nonprescription medical OOP expenditures between 1998 and 2004 would continue from 2004 to 2006. To estimate the maximum likely benefit, the simulation of 2006 OOP expenditures makes a number of simplifying assumptions. First, it assumes that individuals choose the standard drug benefit under Medicare Part D rather than keep their existing drug insurance if and only if their OOP prescription medication expenditures under the standard Medicare plan would be lower than their projected 2006 OOP prescription medication expenditures, which are in turn based on extrapolated 2004 OOP expenditures. Because we assume that the trend in OOP prescription drug expenditures continues through 2006, we are implicitly assuming that there was no sudden change in private drug insurance coverage given the implementation of Medicare Part D in 2006 and also that physician prescribing behavior did not change. Finally, the simulation assumes that all expenditures for medication are included in the Medicare formulary.

The standard Medicare Part D plan in 2006 had the following schedule of OOP payments: 1) the patient paid first $250 and then paid 25% of cost beyond $250 until total prescription drug payments reached $2,250, 2) the patient was responsible for all drug expenses between $2,250 and $5,850, and 3) the patient then also paid 5% OOP of any drug expenses beyond $5,850.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Our sample from the MEPS of 3,758 respondents age ≥65 years represents 9.5 million older Americans with arthritis in 1998 and grows to 12.7 million in 2004. Table 1 shows the sample characteristics of the population with arthritis in 1998 and 2004. This population consists largely of white women. Consistent with demographic trends, this older age group over time included more individuals with higher education (26.7–39.2%) (17). Also consistent with population trends, inflation-adjusted median income dropped between 1998 and 2004 (18).

Table 1. Characteristics of populations of persons with arthritis in 1998 and 2004*
Characteristic1998 (n = 465)2004 (n = 671)
Age, mean ± SD years74.9 ± 6.6475.0 ± 6.31
Individual income in 2004 dollars, mean ± SD27,454 ± 36,60223,585 ± 19,585
More than high school education, %26.739.2
Male, %34.032.5
Married, %51.446.9
White, %90.490.1
Number of chronic diseases, mean ± SD1.49 ± 1.101.68 ± 1.13
Health status, %  
 Excellent11.613.4
 Very good23.226.4
 Good37.532.0
 Fair18.020.2
 Poor9.88.0

Table 2 presents the adjusted median, 75th percentile, and 90th percentile of OOP prescription medication expenditures and total OOP expenditures for persons with arthritis in 1998 and 2004. Table 2 shows that the median adjusted expenditure for prescription medication rose significantly between 1998 and 2004, from $415 to $714 (in 2004 dollars). This increase represents a growth of 72.0% (∼9.5% annually) beyond inflation during a time when inflation-adjusted income was declining. High OOP prescription medication expenditures, represented by the 75th and 90th percentiles, also significantly increased during this period, with 64% growth for the 75th percentile and 77% growth for the 90th percentile (in 2004 dollars). Similar results are shown for total OOP expenditures. The adjusted median total OOP expenditure increased significantly by $397 between 1998 and 2004 (from $757 in 1998 to $1,154 in 2004). This represents an average annual growth of 7.3% beyond inflation for persons age ≥65 years with arthritis. For comparison purposes we repeated these analyses for diabetes and heart disease and found average annual growth rates in total OOP expenditures of 5.8% and 8.5%, respectively.

Table 2. Adjusted total out-of-pocket expenditures and out-of-pocket expenditures for prescription drugs in 1998 compared with 2004 for adults age ≥65 years with arthritis*
 19982004Difference (95% CI)
  • *

    Expenditures are in 2004 dollars adjusted for age, sex, self-reported health status, marital status, education, and income. Bootstrapped 95% confidence intervals (95% CIs) are shown. Expenditures are estimated in reference to a white single female age 75 years with up to 12 years of education, income of $23,585 in 2004 dollars, in good health, and with one other chronic condition besides arthritis.

Out-of-pocket expenditures for  prescription drugs   
 Median415714299 (200–435)
 75th percentile9061,482576 (347–791)
 90th percentile1,6022,8301,228 (523–1,638)
Total out-of-pocket expenditures   
 Median7571,154397 (256–581)
 75th percentile1,3342,014680 (419–1,080)
 90th percentile2,4524,1961,744 (855–2,396)

Figure 1 displays the distribution of adjusted total OOP expenditures for the years 1998 through 2004 and simulated projections of OOP expenditures for the years 2005 and 2006, based on these inflation-adjusted growth rates observed from 1998 to 2004. The 2006 OOP expenditures simulate the implementation of Medicare Part D. The simulation projects that median OOP expenditures could fall by as much as $212 (in 2004 dollars) from the 2005 level (from $1,258 in 2005 to $1,046 in 2006) after controlling for age, sex, marital status, education, health status, and income. Individuals at the 75th percentile of total OOP expenditures show a projected decline of $201; for the 90th percentile, the decline is $865. Figure 2 shows the distribution of adjusted OOP prescription medication expenditures. Although OOP prescription expenditures consistently grow from 1998 to 2004, the simulated effect of Medicare Part D indicates the maximum extent to which it could constrain those expenditures if people make rational Medicare Part D decisions, their prescription drugs are included in the Medicare formulary, and growth trends in prescribing behavior remain unchanged. Medicare Part D is projected to impede OOP prescription medication expenditures by 2006, but would not bring them below 2003 levels, for each of the median, 75th percentile, and 90th percentile levels.

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Figure 1. Distribution of total out-of-pocket expenditures for adults age ≥65 years with arthritis, adjusted for age, sex, marital status, education, and income.

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Figure 2. Distribution of out-of-pocket expenditures for prescription drugs for adults age ≥65 years with arthritis, adjusted for age, sex, marital status, education, and income.

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DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Increased OOP medical expenses have substantially outpaced inflation for persons age ≥65 years with arthritis, a group that largely lives on fixed incomes. From 1998 to 2004, median OOP medical expenditures increased at an average annual rate of 7.3% after adjustment for general inflation. This increase is similar to the rates of growth in OOP expenditures among persons with diabetes and heart disease. The median OOP expenditure for prescription drugs increased even more, at an annual rate of ∼9.5%, or 72.0% growth over the entire period compared with 52.4% growth for all OOP medical expenditures. These increases in inflation-adjusted median medical expenditures can be contrasted with inflation-adjusted median income, which did not increase over this period.

Given the increased OOP burden on the elderly and the major contribution of prescription drugs to this increase, the implementation of Medicare Part D drug coverage would be expected to impede this increasing burden. Indeed, our simulation of its effects shows a substantial decrease in total and prescription OOP expenditures, with a tendency for OOP expenditures to move back toward the 2003 levels.

Limitations inherent in the MEPS data and our simulation assumptions may limit the accuracy of these findings. It was not possible to reliably attribute increased OOP expenditures to specific rheumatic disorders (e.g., rheumatoid arthritis). Simulated Medicare Part D estimates are based on some very simple but generous assumptions regarding the plan and who would switch from current to Part D insurance. We assume that all prescription medications would be included in the Medicare Part D formulary, that people would be able to determine whether Medicare Part D would provide better coverage than their current plan, and that people are rational in their decisions. If current insurance coverage (extrapolated to 2006 from the observed years) pays more than Medicare Part D, we assume that current coverage is maintained, even though it is possible that drug coverage has deteriorated over time given that Medicare Part D has become a viable alternative.

In addition, we used the standard Medicare Part D benefit structure described in Materials and Methods when calculating the OOP expenditures under the Medicare plan. In actuality, there are a host of alternative Medicare plans offered that may provide coverage more tailored to individual circumstances, and thus the burden under the Medicare plan could be lower than our simulation suggests. However, since the standard Part D plan is the basis for payment of insurance intermediaries, these plans are not likely to be overly generous relative to the standard plan. For example, lower copayments are likely to be accompanied by more restrictions on which types of drugs are covered or to be directed at populations with lower prescription drug needs.

Finally, we assumed that the growth in prescription expenditures was the same from 2004 to 2006 as in the prior period. This may not be the case. For example, a large part of the increase in cost of medications for osteoarthritis was coxibs through 2004, but many older patients have converted to nonselective nonsteroidal antiinflammatory drugs. It is also unlikely that the growth in expenditures on biologics has been constant over this period.

While the maximum impact of Medicare Part D coverage would clearly have a substantial effect on constraining overall OOP burden, it would not be sufficient to offset the increasing burden faced by individuals over time. The OOP expenses in 2006 are still estimated to be greater than those for 2003, even under generous assumptions regarding the people who would use those benefits. As individual drug expenditures move into ranges where copayments drastically increase under Medicare plans, large increases in OOP expenditures can again be expected.

Medicare Part D coverage places a large responsibility on persons with arthritis to choose a plan that maximizes their benefits. The use of new expensive drugs by many persons with arthritis makes it important to make sure that such drugs are on the formulary of the drug plan chosen, and the use of expensive drugs such as biologic agents also requires beneficiaries to calculate the relative merits of plans with different copayment structures. Because one may change drugs frequently over time in response to changes in medical condition, changes in our overall knowledge of drug effects, and the introduction of new drugs on the market, it may be necessary for many arthritis patients to reevaluate their coverage with their physician and pharmacist annually.

The rising effectiveness and costliness of drugs is likely to continue despite Part D coverage. The continuing growth of prescription medication expenditures coupled with the overall decline in income for persons age ≥65 years will result in a growing financial burden over time both for the Medicare population and for policymakers faced with increasing budgetary shortfalls to fund increasing Medicare expenditures. The availability of data on actual expenditures for 2006 and beyond will provide further information on the growth of OOP burden, the impact of Medicare Part D, and potential improvements in medical care coverage and design that may be more effective in reducing OOP burden.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES

Dr. Manheim had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study design. Lurie, Dunlop, Manheim.

Acquisition of data. Lurie.

Analysis and interpretation of data. Lurie, Dunlop, Manheim.

Manuscript preparation. Lurie, Dunlop, Manheim.

Statistical analysis. Lurie, Dunlop.

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. REFERENCES