ARTHRITIS & RHEUMATISM
`Vol. 48, No. 10, October 2003, pp 2750–2762
`DOI 10.1002/art.11439
`© 2003, American College of Rheumatology
`
`Direct Medical Costs and Their Predictors in
`Patients With Rheumatoid Arthritis
`
`A Three-Year Study of 7,527 Patients
`
`Kaleb Michaud,1 Jodi Messer,2 Hyon K. Choi,3 and Frederick Wolfe4
`
`Objective. To estimate total direct medical costs
`in persons with rheumatoid arthritis (RA) and to char-
`acterize predictors of these costs.
`Methods. Patients (n ⴝ 7,527) participating in a
`longitudinal study of outcome in RA completed 25,050
`semiannual questionnaires from January 1999 through
`December 2001. From these we determined direct med-
`ical care costs converted to 2001 US dollars using the
`consumer price index. We used generalized estimating
`equations to examine potential predictors of the costs.
`Monte Carlo simulations and sensitivity analyses were
`performed to evaluate the varying prevalence and cost of
`biologic therapy.
`Results. The mean total annual direct medical
`care cost in 2001 for a patient with RA was $9,519. Drug
`costs were $6,324 (66% of the total), while hospitaliza-
`tion costs were only $1,573 (17%). Approximately 25% of
`patients received biologic therapy. The mean total an-
`nual direct cost for patients receiving biologic agents
`was $19,016 per year, while the cost for those not
`receiving biologic therapy was $6,164. RA patients who
`were in the worst quartile of functional status, as
`measured by the Health Assessment Questionnaire,
`
`The National Data Bank for Rheumatic Diseases has received
`grant support from the following pharmaceutical companies: Amgen,
`Aventis, Bristol Myers Squibb, Centocor, Pharmacia, and Pfizer.
`1Kaleb Michaud, MS: Arthritis Research Center Foundation,
`Wichita, Kansas; 2Jodi Messer, PhD: Wichita State University, Wich-
`ita, Kansas; 3Hyon K. Choi, MD, MPH: Massachusetts General
`Hospital, Harvard Medical School, and Harvard School of Public
`Health, Boston, Massachusetts; 4Frederick Wolfe, MD: Arthritis Re-
`search Center Foundation and University of Kansas School of Medi-
`cine, Wichita, Kansas.
`Address correspondence and reprint requests to Frederick
`Wolfe, MD, National Data Bank for Rheumatic Diseases, Arthritis
`Research Center Foundation, 1035 N. Emporia, Suite 230, Wichita,
`KS 67214. E-mail: fwolfe@arthritis-research.org.
`Submitted for publication November 11, 2002; accepted in
`revised form May 22, 2003.
`
`experienced direct medical costs for the subsequent year
`that were $5,022 more than the costs incurred by those
`in the best quartile. Physical status as determined by
`the Short Form 36 physical component scale had a
`similar large effect on RA costs, as did comorbidity.
`Medical insurance type played a more limited role.
`However, those without insurance had substantially
`lower service utilization and costs, and health mainte-
`nance organization patients had lower drug costs and
`total medical costs. Increased years of education, in-
`creased income, and majority ethnic status were all
`associated with increased drug costs but not hospital-
`ization costs. Costs in all categories decreased after age
`65 years.
`Conclusion. Estimates of direct medical costs for
`patients with RA are substantially higher than cost
`estimates before the biologic therapy era, and costs are
`now driven predominantly by the cost of drugs, primar-
`ily biologic agents. RA patients with poor function
`continue to incur substantially higher costs, as do those
`with comorbid conditions, and sociodemographic char-
`acteristics also play an important role in determination
`of costs.
`
`The costs of rheumatoid arthritis (RA) are in-
`creasing because of the introduction and increasing use
`of biologic therapy. Biologic agents are effective but
`expensive, and there are almost no data to measure their
`impact on costs among RA patients in the community.
`In a sense, with the introduction of biologic therapy
`everything is new: RA costs have to be measured all over
`again to account for these agents. Additionally, costs are
`a changing target; if the prevalence of biologic therapy
`use increases, costs estimated today or in the past (1–14)
`may not be valid after a few years.
`Lubeck (1) reviewed 10 studies on the costs of
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`COSTS AND COST PREDICTORS IN RA
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`2751
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`RA and noted that hospitalization costs were generally
`ⱖ60% of direct medical costs, with a single exception
`(9), and that drug costs were ⬍25% of total direct
`medical costs. Pugner et al (15) reviewed cost studies
`(2–10) performed between 1978 and1998. They reported
`that the mean annual direct cost of RA was $5,425 per
`patient when expressed in 1998 US dollars. The median
`percentage of costs attributed to hospitalization in their
`review was 47%, and the percentage attributed to drugs
`was 16%. Gabriel and colleagues reported average an-
`nual direct medical charges to be $3,802 in 357 patients
`with RA and $2,654 in 5,730 patients with osteoarthritis
`(13); a random subset of patients was used to estimate
`charges for prescription medications in that study.
`Newhall-Perry et al studied the costs of RA in 150
`seropositive patients during the first 5 years of illness
`and found the average total cost of the disease to be
`$2,400 per year (11). Lanes and colleagues reported on
`RA costs among health maintenance organization
`(HMO) patients from 1993 to 1994 (9). The average
`annual costs were $2,162, and 16% of the costs were for
`hospitalization. The study by Lanes et al is the only
`previous study in which drug costs were found to be the
`predominant cost in RA.
`The study that is perhaps most germane to the
`current report is that by Yelin and Wanke (12). In 1999,
`they reported on 272 patients who were followed up
`continuously in 1995 and 1996. The average annual
`direct medical costs from a societal perspective were
`$8,501. Drugs constituted 18.2% and hospitalization
`accounted for 61.8% of total costs. The authors point
`out that hospital charges in California for that study may
`not be representative of hospital costs generally, and
`they prepared a second set of estimates based on a
`discount of 50% for hospital costs; this was discussed in
`their text though not included in the statistical tables.
`Applying the 50% discount would reduce the total cost
`from $8,501 to $5,876; both numbers are relevant in
`comparing the current report with the data of Yelin and
`Wanke.
`Herein we describe the direct medical costs for
`persons with RA, encompassing costs no matter who
`incurs them (societal perspective), and identify predic-
`tors of these costs. We report that drugs are the pre-
`dominant cost factor in RA, and that total costs are
`considerably greater than in studies performed prior to
`the introduction of biologic agents. In addition, we
`report the quantitative effect of a wide variety of pre-
`dictors on future costs.
`
`PATIENTS AND METHODS
`
`Patient population. This study was performed using
`the National Data Bank for Rheumatic Diseases (NDB). The
`NDB is a rheumatic disease research data bank in which
`patients complete detailed self-report questionnaires at
`6-month intervals. The characteristics of the NDB have been
`reported previously (16–18). Patients in the NDB are recruited
`from 2 sources: 1) nonselected patients from the practices of
`US rheumatologists, and 2) patients enrolled as part of phar-
`maceutical company–sponsored registries. Eligible patients in
`this study were those with RA who had completed at least 2 of
`6 possible semiannual surveys for events between January 1,
`1999 and December 31, 2001. All patients who were recruited
`as part of pharmaceutical company registries were excluded, to
`avoid possible bias. The resultant data set contained 7,527 RA
`patients and 25,050 observations from the 3-year period.
`Patients were referred by 233 rheumatologists dispersed
`throughout the US. More than 90% of the rheumatologists
`were in private practice and were not full-time university
`physicians. The diagnosis of RA was made by the patients’
`rheumatologists.
`Demographic and disease status variables. NDB par-
`ticipants were asked to complete semiannual, detailed 28-page
`questionnaires about all aspects of their illness. At each
`assessment, demographic variables were recorded, including
`sex, age, ethnic origin, education level, current marital status,
`medical history, and total family income. Disease status and
`activity variables collected included the Stanford Health As-
`sessment Questionnaire (HAQ) functional disability index
`(19,20), pain, global disease severity, and fatigue as recorded
`on visual analog scales (VAS) (21), the Arthritis Impact
`Measurement Scales (AIMS) anxiety and depression scales
`(22,23), and the Rheumatoid Arthritis Disease Activity Index
`(RADAI) (24–26). Patients also completed the Medical Out-
`comes Study Short Form 36 (SF-36), from which the physical
`component score (PCS) and the mental component score were
`calculated (27,28). Utilities were mapped from HAQ, anxiety,
`and depression values, based on a regression model derived
`from the simultaneous administration of the EuroQol (29–31),
`HAQ, and anxiety and depression scales to 2,299 RA patients
`(32). We also used the SF-36–derived utility index, the SF-6D
`(33). The comorbidity score represented the sum of 11 comor-
`bid conditions, as reported previously (34).
`Patients also completed several instruments measuring
`productivity, the number of days they were unable to perform
`their usual activities in the last 30 days, the number of days they
`were unable to work in the last 180 days, and the Work
`Limitations Questionnaire (35,36). In addition, patients re-
`ported on the number of persons they depended on for help
`and whether help was needed none, a little, some, most, or all
`of the time.
`Direct medical costs. Direct medical costs in this study
`include expenditures for physician and health care worker
`visits, medications, diagnostic tests and procedures, and hos-
`pitalizations. In the study surveys, patients reported all drug
`use, hospitalizations, medical visits, procedures, and laboratory
`testing. Medical costs reflected both RA and non-RA direct
`costs. Drug costs were assigned using Center for Medicare and
`Medicaid Services (CMS; the organization succeeding the
`Health Care Financing Administration) (37), Federal Upper
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`2752
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`MICHAUD ET AL
`
`Limit, or wholesale rates according to Drug Topics Red Book
`(38). We requested copies of hospital and procedure records
`for all hospitalizations, and obtained diagnosis-related group
`(DRG) and procedure codes from the records. In the event
`records could not be obtained, we imputed DRG and proce-
`dure codes based on patients’ reported events. Hospitaliza-
`tions were assigned costs according to their DRG classification
`using national values from CMS’s Medicare Provider Analysis
`and Review (37) and were adjusted by the number of days of
`inpatient care. In addition, average hospitalization physician
`fees were added depending on whether the stay was for
`medical ($500) or surgical ($2,000) services. Laboratory costs
`were derived from Medicare utilization tapes for patients with
`RA and applied to study patients with laboratory usage, since
`we could not always accurately determine the number and
`specific kinds of laboratory tests from a patient’s self-report.
`Cost data for procedures, medical visits, and labora-
`tory services were obtained from the Medicare Physician Fee
`Schedule, with outpatient procedure costs modified by the
`national Medicare utilization rates. For example, typical cost
`estimates used in this report for events in the year 2000 were as
`follows: average physician visit codes (CPT 99211–99215)
`$49.50, hand and wrist radiograph (CPT 73100) $27.54, hip
`radiograph (CPT 73500) $27.19, gall bladder procedures (in-
`cludes 52 CPTs) $688, and hospitalization for conditions
`involving major joints of the lower extremity, 5.2-day stay
`(DRG 219) $9,254 and 3.2 day stay (DRG 209) $4,083.
`All costs were initially calculated using the above
`resources for the appropriate year of patient observation.
`Costs were then inflation-adjusted to 2001 US dollar rates
`using the consumer price index from the Bureau of Labor
`Statistics (www.bls.gov). Costs in this study are reported per 6
`months, reflecting the semiannual survey data, except as
`specifically described. A time-trend dummy variable (calendar
`half-year) was included in the analyses to reflect the particular
`6-month survey period.
`In calculating infliximab costs, we assumed that inflix-
`imab was being administered at a dose of 3 mg/kg (227.7 mg for
`an average measured weight of 75.9 kg per infliximab user), but
`we rounded up the dose to make use of the full vial of
`infliximab. The average dose/kg that made use of 3 vials (300
`mg), therefore, was 3.96 mg/kg. This is closely consistent with
`postmarketing data supplied to the authors by Centocor, Inc.,
`after this study was completed, that indicated that the mean
`infliximab dose in 150 patients was 3.98 mg/kg during 2001 and
`2002. At a dose of 5 mg/kg (379.5 mg), the number of vials
`required would be 4 (400 mg). This would result in an increase
`in infliximab costs of 25%.
`For this report we chose to include all medical costs,
`not just RA costs, because it is not always clear what is an RA
`cost. Over the last few years cardiovascular disease and other
`illnesses such as infections and gastrointestinal ulcers have
`been recognized as potential consequences of RA (39–42). In
`addition, many patients receive their RA and non-RA care
`from general physicians, and it is not possible to disaggregate
`such costs into RA and non-RA components. Another issue of
`importance is the term “costs,” as opposed to the term
`“charges.” In the current report we have used the term “costs”
`because we relied on cost payment figures from Medicare
`sources and used minimum cost estimates for drugs. This
`difference between costs and charges is the reason we used the
`
`Table 1. Clinical and demographic characteristics of the 7,527 RA
`patients at their most recent survey*
`
`Age, years
`Sex, % male
`Education, years
`Highest year of education, %
`0–8
`8–11
`12
`13–15
`ⱖ16
`Ethnicity, %
`Non-Hispanic white
`African American
`Asian American
`Native American
`Mexican/Mexican American
`Puerto Rican
`Other
`Total income, US dollars ⫻ 10,000
`Lifetime comorbidity score, 0–11
`Disease duration, years
`HAQ score, 0–3
`RADAI score, 0–10
`Pain score, 0–10
`Global severity score, 0–10
`Fatigue score, 0–10
`Depression score, 0–10
`SF-36 physical component score
`SF-36 mental component score
`VAS QOL scale, 0–100
`EuroQol utility, 0–1
`SF-6D utility, 0–1
`
`61.7 ⫾ 13.1 (62.6)
`23.2
`13.5 ⫾ 2.3 (13)
`
`2.3
`7.6
`36.7
`25.7
`27.6
`
`92.4
`3.2
`1.1
`0.9
`1.9
`0.1
`0.4
`4.5 ⫾ 2.9 (3.5)
`2.7 ⫾ 1.9 (2)
`15.0 ⫾ 11.1 (11.9)
`1.05 ⫾ 0.74 (1)
`3.3 ⫾ 2.1 (3.1)
`3.7 ⫾ 2.7 (3)
`3.4 ⫾ 2.5 (3)
`4.2 ⫾ 2.9 (4)
`2.3 ⫾ 1.7 (2.0)
`32.4 ⫾ 10.4 (31.4)
`44.4 ⫾ 14.1 (47.3)
`69.0 ⫾ 20.3 (75)
`0.64 ⫾ 0.21 (0.67)
`0.63 ⫾ 0.10 (0.61)
`
`* Except where indicated otherwise, values are the mean ⫾ SD
`(median). RA ⫽ rheumatoid arthritis; HAQ ⫽ Health Assessment
`Questionnaire; RADAI ⫽ Rheumatoid Arthritis Disease Activity
`Index; SF-36 ⫽ Short Form 36; VAS ⫽ visual analog scale; QOL ⫽
`quality of life; SF-6D ⫽ SF-36–derived utility index.
`
`50% discount for the Yelin and Wanke study (12), so relatively
`comparable estimates would be available.
`To understand the relationship between drug therapy
`and medical insurance coverage, we added a question to the
`last survey of 2001, asking about the extent to which RA
`patients have to pay for their medications out of pocket, as
`opposed to having insurance pay for the medications. We then
`organized patient responses according to whether they had to
`pay ⱖ25%, as opposed to having to pay ⬍25%, of their drug
`costs; 20.1% of the participants did not answer this question.
`Statistical methods. To determine the effect of previ-
`ous disease status and activity on current medical costs,
`“lagged” predictor variables were created for the HAQ, RA-
`DAI, depression, fatigue, comorbidity, utilities, and PCS. A
`lagged variable represents the value of the study variable (e.g.,
`HAQ) in the assessment 6 months prior to the current
`assessment.
`Graphic analysis of the effect of age on total direct
`costs indicated an inverted V-shaped nonlinear relationship,
`with a relatively linear positive component from age 15 years
`through age 65 years and a linear negative component after
`that age. To model these separate components of age, linear
`splines were created. Linear splines allow estimation of the
`
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`COSTS AND COST PREDICTORS IN RA
`
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`Table 2. 2001 direct annual medical costs for 7,527 RA patients, by cost type*
`
`Cost type
`
`Outpatient costs, total
`Physician and health professional
`Radiographs
`MRI, CT scans
`Endoscopies
`Other tests†
`Outpatient surgery
`Drug costs, total
`DMARDs
`Biologic agents
`NSAIDs
`GI medications and analgesics
`Non-RA medications
`Hospitalization costs, total
`
`Total costs
`
`Cost, $ (95% CI)
`
`1,541 (1,501, 1,581)
`674 (662, 686)
`329 (311, 347)
`199 (185, 212)
`93 (86, 99)
`130 (126, 134)
`114 (106, 123)
`6,324 (6,172, 6,477)
`643 (619, 667)
`3,307 (3,164, 3,451)
`591 (573, 610)
`518 (496, 540)
`1,247 (1,224, 1,270)
`1,573 (1,450, 1,697)
`
`9,519 (9,301, 9,737)
`
`% (95% CI)
`
`16.2 (15.4, 17.0)
`7.1 (6.8, 7.4)
`3.5 (3.2, 3.7)
`2.1 (1.9, 2.3)
`1.0 (0.9, 1.1)
`1.4 (1.3, 1.4)
`1.2 (1.1, 1.3)
`66.4 (63.4, 69.6)
`6.8 (6.4, 7.2)
`34.7 (32.5, 37.1)
`6.2 (5.9, 6.6)
`5.4 (5.1, 5.8)
`13.1 (12.6, 13.7)
`16.5 (14.9, 18.2)
`
`100
`
`* Adjusted for age, sex, and calendar half-year. RA ⫽ rheumatoid arthritis; 95% CI ⫽ 95% confidence
`interval; MRI ⫽ magnetic resonance imaging; CT ⫽ computed tomography; DMARDs ⫽ disease-
`modifying antirheumatic drugs; NSAIDs ⫽ nonsteroidal antiinflammatory drugs; GI ⫽ gastrointestinal.
`† Includes laboratory tests, Doppler examinations, treadmill tests, mammograms, bone density tests, and
`other examinations.
`
`relationship between y and x variables as a piecewise linear
`function in which one segment represents (in this instance) the
`values below age 65 years and the other segment the values
`above age 65 years (43). A nonlinear relationship was also
`noted for disease duration, with turning points at 10 years and
`40 years. Splines were formed to describe this relationship.
`Subsequent analyses indicated that the relationship between
`the third spline (⬎40 years) and costs was not significant.
`Because of nonsignificance and the relatively small number of
`patients with disease duration ⬎40 years, we reverted to a
`2-spline basis with a single cut point (knot) at 10 years.
`The relationships between costs and predictor vari-
`ables were analyzed with a generalized estimating equation
`(GEE) procedure. Stata’s implementation of the GEE proce-
`dure (XTGEE) is an extension of generalized linear models
`that properly handle panel data (43). In the analyses used, we
`specified the robust Huber/White/sandwich estimator of vari-
`ance. This estimator produces consistent standard errors even
`if within-group correlations are not hypothesized by the spec-
`ified correlation structure (43). All analyses used an identity
`link so coefficients could be expressed in an easily understand-
`able form. However, we first conducted GEE analyses using a
`log link in order to be sure the identity link adequately
`represented the data. The significance level of all analyses was
`set at 0.05, and all tests were 2-tailed. Statistical computations
`were performed using Stata, version 7.0 (43).
`Biologic therapy was defined as treatment with inflix-
`imab, etanercept, or anakinra. Total costs as a function of the
`percent of patients receiving biologic therapy were estimated
`using 2001 data.
`We performed various sensitivity analyses using Monte
`Carlo simulations with 1,000 repetitions. We simulated total costs,
`assuming that use of biologic therapy occurs in 0% to 100% of
`patients in 10% steps, and costs of drug therapy increase or
`decrease in 10% steps. Monte Carlo modeling was performed
`using Stata (43) and Tomz et al’s Clarify programs (44).
`
`RESULTS
`
`Baseline clinical and demographic characteris-
`tics. Table 1 presents the demographic and disease
`status variables for the 7,527 study patients at their last
`questionnaire assessment. The mean age of the patients
`was 62 years, and the median duration of RA was 11.9
`years. The median income was $35,000. Twenty-three
`percent of the patients were male, 8% were from
`minority ethnic groups, and 10% had not graduated
`from high school.
`Among disease-related variables, 3 measures of
`quality of life (QOL) were available. The mean utility as
`measured by the SF-6D was 0.63, a number very similar
`to the value of 69.0 obtained with the VAS for QOL
`(0–100 scale). On the EuroQol, mapped from the HAQ,
`anxiety and depression scales, the mean utility was 0.64.
`The average HAQ score was 1.05, the RADAI score was
`3.34, and the PCS from the SF-36 was 32.4.
`Components of RA costs. Three primary compo-
`nents of costs (drugs, hospitalization, and outpatient
`procedures) and their subcomponents are summarized
`in Table 2. The mean total direct medical cost in 2001
`was $9,519. Drug expenses represented 66% of total
`costs. Hospital costs and outpatient and procedure costs
`amounted to 17% and 16% of total costs, respectively.
`The largest component of total costs was drug
`costs as indicated above, and these were largely deter-
`mined by the cost of biologic therapy. In the study cohort
`the total annual direct cost for patients receiving biologic
`
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`2754
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`Table 3. Univariate effect of demographic and clinical variables on total semiannual direct medical costs in RA: age- and sex-adjusted analysis*
`
`Variable
`
`Beta coefficient
`
`Z score
`
`P
`
`95% CI
`
`4th vs. 1st quartile
`
`Clinical variables
`SF-36 PCS (0–100)
`HAQ (0–3)
`SF-6D utility (0–10)†
`RADAI (0–10)
`Fatigue (0–10)
`How often depend on others (0–4)‡
`Comorbidity (0–11)
`VAS QOL scale (0–100)
`Days unable to perform usual activities (0–30)‡
`Depression (0–10)
`No. of people depended on (0–7)
`Work limitations (0–100)
`Days lost from work (0–180)‡
`Demographic variables
`RA duration (0–10 years)
`RA duration (⬎10 years)
`Age (⬎65 years)
`Age (0–65 years)
`Majority ethnic group
`Total income
`Education (years)
`
`⫺901
`1,447
`⫺66
`328
`204
`1,031
`427
`⫺25
`130
`312
`372
`23
`5
`
`71
`18
`⫺40
`18
`257
`⫺24
`⫺15
`
`2,351
`2,511
`1,343
`1,585
`1,489
`
`1,849
`1,404
`
`1,262
`805
`1,204
`
`⫺26
`25
`⫺20
`19
`18
`17
`17
`⫺16
`16
`14
`10
`7
`6
`
`8
`6
`⫺6
`4
`2
`⫺2
`⫺1
`
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`
`⬍0.001
`⬍0.001
`⬍0.001
`⬍0.001
`0.075
`0.058
`0.381
`
`⫺98, ⫺84
`1,335, 1,559
`⫺72, ⫺59
`2,934, 361
`184, 227
`9,134, 1,148
`379, 476
`28, 22
`114, 146
`268, 357
`295, 448
`16, 30
`3, 7
`
`54, 88
`12, 25
`⫺52, ⫺26
`9, 27
`⫺26, 541
`⫺49, 1
`⫺47, 18
`
`* Beta coefficients represent the difference in costs for a 1-unit difference in the predictor variable. Clinical variables are lagged and therefore
`represent costs that occur in the 6 months following the clinical assessment. 95% CI ⫽ 95% confidence interval; PCS ⫽ physical component score
`(see Table 1 for other definitions).
`† Multiplied by 10 to increase scale, since a 1-unit difference in a 0–1 variable is not useful.
`‡ Difference in 4th versus 1st quartile not calculated for categorical variables treated as continuous variables in these analyses or for those with
`markedly skewed distributions (days lost from work and days unable to perform usual activities).
`
`agents was $19,016, and the cost for those not receiving
`biologic agents was $6,164 (adjusted for age, sex, and
`calendar half-year); 24.7% of the patients had received
`biologic therapy at some time while they were enrolled
`in the data bank, and 26.1% were receiving biologic
`agents during the last 6 months of 2001.
`Predictors of direct medical costs. Table 3 pre-
`sents predictors of total costs among clinical and demo-
`graphic variables ranked by Z score. In these analyses
`the clinical predictors were measured first, and the costs
`were those accrued over the following 6-month period.
`The importance of a predictor can be judged best by 2
`variables in this table. The Z score is related to the P
`value and is a measure of the probability that the
`relationship between cost and the predictor variable
`occurred by chance. Because most variables in this table
`were statistically significant at the ⬍0.001 level, the Z
`score, and not the P value, is better able to describe the
`cost–predictor relationship. Thus, the greater the abso-
`lute Z score, the more reliable or accurate is the
`measure. The first-versus-fourth–quartile difference
`measures how well the variable can predict the breadth
`of cost differences. The larger the first-versus-fourth–
`quartile difference, the more useful the variable is
`clinically. The first-versus-fourth–quartile difference is a
`
`method that standardizes the effect of predictor vari-
`ables independent of units, and allows direct comparison
`among continuous predictors.
`The data in Table 3 indicate that the HAQ and
`SF-36 PCS were the most important predictors of cost,
`as determined using the Z score and first-versus-fourth–
`quartile difference. The difference between these vari-
`ables as predictors was not statistically significant, al-
`though the first-versus-fourth–quartile cost difference
`was greater for the HAQ. The HAQ predicted a wide
`range of future costs. The usefulness of the HAQ as a
`predictor of costs is illustrated in Figure 1. Of interest,
`the RADAI and the SF-6D were also useful predictors
`of costs, ranking just below the HAQ and PCS. How-
`ever, because of the compressed scale of the SF-6D, it
`identified the breadth of costs slightly less effectively
`than the RADAI. In addition, comorbidity identified the
`breadth of costs well, ahead of the SF-6D and RADAI.
`In general, demographic variables provided less infor-
`mation about costs than clinical variables, and education
`was not a significant variable in these univariable analyses.
`In addition to the relative predictive power of the
`variables, examination of the key variables in their
`original units provides important quantitative informa-
`tion. A 1-unit difference (higher or lower) in the HAQ
`
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`

`
`COSTS AND COST PREDICTORS IN RA
`
`2755
`
`and dependence on others provides insight into such
`limitations and costs. As shown in Figure 2, persons
`losing time from work (Figure 2a) or from their usual
`activities (Figure 2b) had direct medical costs that were
`proportionate to the time lost. A similar finding related
`to work activities came from the Work Limitations
`Questionnaire (35,36). This questionnaire assesses diffi-
`culties at work for employed persons. As seen in Figure
`2d, there was a linear relationship between costs and
`work limitations. Finally, persons who depend on others
`for help also had increased medical costs (Figure 2c).
`This latter, 5-choice question regarding dependence on
`others was simple but relatively powerful in identifying
`costs, as shown in Table 3. Days lost from work, days
`unable to work, and the Work Limitations Question-
`naire were less useful and important as predictor vari-
`ables (Table 3), while still demonstrating a relationship
`between limitations and future costs.
`Because health insurance or the lack thereof is
`thought to influence costs, we also examined different
`types of insurance. Table 4 presents information on total
`direct medical costs by insurance type. Unadjusted costs
`and costs adjusted for demographic and clinical severity
`variables had the same ranking. In adjusted analyses,
`
`Figure 1. Predictive effect of the Health Assessment Questionnaire
`(HAQ) disability index on total direct medical costs in the 6 months
`following HAQ measurement, adjusted for age, sex, and calendar
`half-year. Lines represent predicted values and 95% confidence inter-
`vals.
`
`score was associated with a cost difference of $1,447 in
`the next 6 months. Similar values for the PCS and
`RADAI were $901 and $328, respectively. A 10% dif-
`ference in the quality of life as measured by the SF-6D
`resulted in a cost difference of $656.
`A series of variables related to work limitations
`
`Figure 2. Relationship between direct medical costs for patients with rheumatoid arthritis and limitations in
`activities, as assessed by a, number of days unable to work in the last 6 months, b, number of days unable to
`perform usual activities in the last 30 days, c, amount of time the patient depends on others, and d, total score
`on the Work Limitations Questionnaire. Lines represent predicted values and 95% confidence intervals.
`
`Page 00006
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`

`
`2756
`
`MICHAUD ET AL
`
`Table 4. Effect of medical insurance type on total semiannual direct medical costs in RA, ranked by
`decreasing costs*
`
`Costs, $, mean (95% CI)
`
`Insurance type
`
`% of patients
`
`Age- and sex-adjusted
`analysis†
`
`Multivariable-adjusted
`analysis‡
`
`Medicaid
`Medicare disability
`Medicare
`Medicare HMO
`Private
`PPO
`HMO
`No insurance
`
`5.3
`2.7
`38.3
`5.1
`27.8
`7.3
`11.6
`1.9
`
`5,500 (5,178, 5,823)
`5,372 (5,036, 5,708)
`5,103 (4,950, 5,256)
`4,735 (4,497, 4,973)
`4,461 (4,311, 4,612)
`4,412 (4,218, 4,607)
`4,172 (3,992, 4,352)
`3,519 (3,186, 3,851)
`
`5,133 (4,765, 5,501)
`4,958 (4,578, 5,338)
`4,798 (4,638, 4,957)
`4,623 (4,352, 4,893)
`4,379 (4,214, 4,545)
`4,323 (4,113, 4,532)
`4,126 (3,931, 4,322)
`2,984 (2,598, 3,371)
`
`* 95% CI ⫽ 95% confidence interval; HMO ⫽ health maintenance organization; PPO ⫽ preferred
`provider organization (see Table 1 for other definitions).
`† Also adjusted for calendar half-year.
`‡ Adjusted for age, sex, HAQ, RADAI, depression, fatigue, ethnic origin,
`duration, comorbidity, and calendar half-year.
`
`income, education, RA
`
`patients with no insurance had low direct medical costs
`per 6 months (mean $2,984). The next lowest costs were
`for HMO members ($4,126). However, confidence limits
`overlapped between HMO, preferred provider organi-
`zation (PPO), and private insurance patients. Costs were
`greatest for patients covered by Medicaid ($5,133),
`Medicare disability ($4,958), and Medicare ($4,798).
`Costs for persons on Medicaid, Medicare, and Medicare
`disability were, respectively, 1.7, 1.7, and 1.6 times
`greater than those for persons without insurance.
`Medical insurance, however, exerts its strongest
`effect on drug costs in circumstances where insurance
`coverage is incomplete or absent. As noted in Table 5,
`insurance coverage for medication costs varied with
`insurance type. The highest rate of out-of-pocket drug
`costs occurred in the Medicare group and in those
`without insurance; 77% of those without insurance paid
`
`⬎25% of their drug costs, as did 47% of patients
`receiving Medicare. As a percentage, the least out-of-
`pocket costs occurred for those with private, PPO, or
`HMO insurance.
`Actual drug costs, however, did not parallel the
`extremes in out-of-pocket costs noted above. Drug costs
`per 6 months were lowest among those without insur-
`ance ($2,104), followed by the 2 HMO groups ($2,344
`and $2,377). Costs were highest among Medicaid pa-
`tients ($2,711) and intermediate (⬃$2,400–2,500) in the
`other groups. The clearest effect of insurance type was
`demonstrated in the percent of patients receiving bio-
`logic therapy. After adjustment for disease severity and
`demographic characteristics, the no-insurance group had
`the lowest usage of anti–tumor necrosis factor (anti-
`TNF) therapy (10.5%), followed by the 2 HMO groups
`(19.6% and 19.9%). The greatest usage was found
`
`Table 5. Association between medical insurance type and semiannual drug costs in RA*
`
`Insurance type
`
`Medicaid
`Medicare disability
`Medicare
`Medicare HMO
`Private
`PPO
`HMO
`No insurance
`
`% of patients
`paying ⱖ25%
`of drug costs
`
`Cost, $, mean (95% CI)
`(multivariable adjusted
`analysis)†
`
`Receiving biologic therapy,
`% (95% CI)
`
`17.4
`42.1
`47.1
`41.4
`19.1
`15.7
`12.6
`77.1
`
`2,711 (2,553, 2,870)
`2,549 (2,374, 2,724)
`2,527 (2,448, 2,606)
`2,377 (2,264, 2,491)
`2,439 (2,358, 2,519)
`2,509 (2,386, 2,633)
`2,344 (2,241, 2,447)
`2,104 (1,903, 2,306)
`
`27.1 (21.9, 33.0)
`31.4 (24.5, 39.2)
`23.6 (21.7, 25.7)
`19.6 (15.4, 24.6)
`28.0 (25.5, 30.7)
`27.6 (23.7, 31.9)
`19.9 (16.3, 24.0)
`10.5 (5.5, 19.0)
`
`* 95% CI ⫽ 95% confidence interval; HMO ⫽ health maintenance organization; PPO ⫽ preferred provider organization (see
`Table 1 for other definitions).
`† Adjusted for age, sex, HAQ, RADAI, depression, fatigue, ethnic origin, income, education, RA duration, comorbidity, and
`calendar half-year.
`
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`COSTS AND COST PREDICTORS IN RA
`
`2757
`
`Table 6. Multivariable analysis of the effect of demographic and clinical variables on total semiannual
`direct medical costs in RA*
`
`Variable
`
`Beta coefficient
`
`Z score
`
`HAQ (0–3)
`RADAI (0–10)
`Depression (0–10)
`Fatigue (0–10)
`Comorbidity (0–11)
`Medical insurance
`Private
`HMO
`Medicare disability
`Medicare HMO
`Medicare
`PPO
`Medicaid
`No insurance
`Age (0–65 years)
`Age (⬎65 years)
`RA duration (0–10 years)
`RA duration (⬎10 years)
`Majority ethnic group
`Education (yea