Paul G. Barnett, PhD

 To analyze inappropriate use of magnetic resonance imaging (MRI) for patients with low back pain in a healthcare system with no financial incentives for overuse.

 We used administrative data to assess the appropriateness of lumbar spine (LS) MRI in the Veterans Health Administration.

All veterans who received LS MRI in the outpatient setting in fiscal year 2012 were included. We based our assessments of appropriateness on CMS criteria, which have been endorsed by the National Quality Forum. Generalized estimating equations were used to evaluate characteristics of inappropriate scans.

Of the 110,661 LS MRIs performed, 31% were classified as inappropriate. Most scans that were considered appropriate were characterized as such because they were preceded by conservative therapy (53%). "Red flag" conditions were responsible for a much smaller percentage of scans being considered appropriate; 13% of scans were preceded by conservative therapy and were performed in patients with a red flag condition, while only 4% of scans were considered appropriate because of red flag conditions only. Scans ordered in the emergency department and in urgent care, primary care, and internal medicine clinics were most likely to be classified as inappropriate. Resident physicians were significantly less likely than other provider types to order inappropriate LS MRIs (odds ratio, 0.80; 

 <.0001). Approximately 24% of providers ordered 74% of inappropriate scans.

We found that 31% of LS MRIs were inappropriate in a healthcare system largely absent of financial and other incentives for ordering. The problem of inappropriate ordering of LS MRI is concentrated in a small number of providers; any provider-facing interventions to reduce inappropriate order should therefore be targeted, rather than aimed at all providers who order LS MRI.

	Using CMS criteria, we found that 31% of lumbar spine magnetic resonance imaging (LS MRI) performed in fiscal year 2012 was inappropriate. This high level of inappropriate ordering is particularly striking in a system largely free of financial and other incentives for ordering, suggesting that nonpecuniary factors play a role in these inappropriate ordering choices. We found a high concentration of inappropriate ordering among few providers. Results indicate that efforts to reduce inappropriate use of LS MRI should involve cultural change, as well as patient and provider education, rather than financial incentives alone, and that provider-facing efforts should be targeted instead of universal.

Most Americans will experience low back pain (LBP) at some point in their adult lives.

 Many patients with LBP receive imaging to explore the cause of their pain

; advances in imaging technology have revealed unprecedented anatomical detail for LBP patients. The underlying difficulty is that, for many patients, anatomical and functional abnormalities do not correlate well, and thus imaging can fail to guide treatment of the primary complaint.

 In one study of individuals who underwent magnetic resonance imaging (MRI), approximately 90% were detected to have a degenerated or bulging disc, 36% had a herniated disc, and 21% had spinal stenosis—but all patients were asymptomatic.

 For most patients, LBP resolves on its own

; even sophisticated imaging of the lumbar spine (LS) for nonchronic pain does not improve outcomes.

Overuse of LS MRI is suspected to be a large problem; multiple societies have prioritized it as a quality-of-care measure or a measure of prudent stewardship of resources. The American College of Physicians and the American Association of Neurological Surgeons recommend against the use of LS MRI for nonspecific or nonpersistent LBP in their Choosing Wisely campaign.

 The National Quality Forum (NQF), a nonprofit entity that sets national priorities for quality measurement, has endorsed a CMS measure regarding inappropriate use of imaging in LBP.

The reasons for overuse of imaging are not well understood. Fee-for-service financial incentives have been suggested as a potential reason for overuse of lumbar spine imaging,

 In this study, we explored inappropriate use of LS MRI in a US healthcare system—one largely free of financial incentives for overuse, and in which physicians are largely insulated from malpractice concerns&mdash;to understand whether inappropriate ordering occurs in the absence of these financial or legal influences. The federal government accepts liability for any instances of medical negligence or wrongdoing on the part of the Department of Veterans Affairs (VA) providers; concerns over malpractice or incentives to practice defensive medicine are therefore much lower for VA providers compared with providers in the larger US healthcare environment. It is also not well understood whether inappropriate ordering is equally distributed across providers, or concentrated among relatively few high-ordering providers; this information carries important implications for quality improvement activities. Here, we use NQF-endorsed CMS criteria to evaluate inappropriate ordering of LS MRI across the entire VA, including the patient-, provider-, and system-level factors associated with the practice.

To study the appropriateness of this imaging modality, we identified the population of veterans who received an outpatient LS MRI in VA in fiscal year (FY) 2012. If patients received more than 1 LS MRI, we retained information regarding the first MRI. Additionally, we searched VA inpatient and outpatient data from FY 2010 to FY 2012 to evaluate the care and condition of these veterans prior to receipt of LS MRI. CMS criteria pertain to LS MRIs with a diagnosis of LBP on the MRI claim; because VA MRIs do not have associated diagnosis codes, we evaluated all LS MRIs. We considered the clinic visit with the ordering provider that immediately preceded the scan to be the ordering visit.

We evaluated the appropriateness of LS MRI according to the NQF-endorsed CMS measure #0514 (“MRI Lumbar Spine for Low Back Pain”),

). This measure classifies an LS MRI as appropriate if it was: 1) preceded by conservative therapy, or 2) conducted in a patient who has a “red flag” condition. CMS considers physical therapy or chiropractic care in the 60 days prior to the scan, or an evaluation and management visit occurring between 28 to 60 days prior to the scan, to be conservative therapy. Physical therapy, chiropractic care, and evaluation and management visits were identified by Current Procedural Terminology codes specified by the NQF-endorsed CMS measure.

 We classified an evaluation and management (E&M) visit as conservative therapy only if the scan was not ordered during that visit.

We made minor modifications to the CMS criteria regarding red flag conditions. The CMS measure excludes LS MRIs occurring in patients with red flag conditions from the denominator; here, we included them but considered them to be appropriate in order to present a complete view of all LS MRIs in VA. CMS red flag conditions include trauma in the 45 days preceding the scan; LS surgery in the 90 days preceding the scan; or intravenous drug abuse, neurologic impairment, cancer, HIV diagnosis, or diagnosis of unspecified immune deficiency in the 365 days preceding the scan. The measure includes all cancer; we excluded primary skin and primary prostate cancer as these types of neoplasms would not cause lumbar compression and rarely have an impact on spine pain. Additionally, there are concerns regarding a high false-positive rate for prostate cancer due to overuse of prostate-specific antigen (PSA) screening. To the CMS red flag conditions, we added intraspinal abscess or compression fracture in the 30 days prior to the scan, and osteomyelitis in the previous 365 days. All red flag conditions were identified through

 International Classification of Diseases, Ninth Revision, Clinical Modification 

) codes in inpatient and outpatient data sets.

From these data, we identified which LS MRI scans were considered inappropriate. LS MRIs were considered inappropriate in patients who did not have a red flag condition in the time frame of interest and those who did not receive appropriate conservative therapy.

We also investigated patient-, provider- and facility-level characteristics associated with inappropriate scans, as well as any regional differences in proportion of inappropriate scans. Patient-level characteristics included age, gender, and race. Provider-level variables included ordering provider type (attending physician, resident, physician’s assistant, and advanced practice nurse/nurse practitioner) and the clinic in which the scan was ordered (internal medicine/primary care, physical medicine and rehabilitation; neurology, neurosurgery, orthopedics, pain medicine, “other” medicine services, miscellaneous other medicine services, chiropractic care, and emergency department [ED] and urgent care). Other medicine services refer to various primary and specialty care clinics separate from the other categories of care (eg, geriatrics, oncology, hospice, dementia clinic).  Miscellaneous other services refer to all ancillary and support services (eg, cast clinic, social work, telephone triage).  The facility-level variable included complexity, defined in 5 levels (1a, 1b, 1c, 2, and 3; from most to least complex).

Characteristics of inappropriate scans were evaluated using generalized estimating equations to account for clustering of patients within facilities. Our generalized estimating equations employed a logit link, a binomial distribution family, and an independent correlation structure. We ran sensitivity analyses in which we evaluated facility-level physical therapy penetration—defined as physical therapy visits per 1000 pro-rated patients&mdash;and in which, we evaluated alternative definitions of conservative therapy. Specifically, we required the conservative therapy visits to have an accompanying diagnosis of low back pain. Post estimation commands were used to compare outcomes among different levels of each categorical level; for example, to compare residents with each other provider type, physician’s assistants to each other provider type, etc. All analyses were conducted in SAS version 9.2 (SAS Institute, Inc, Cary, North Carolina). 

In FY 2012, 110,661 LS MRIs were ordered in the outpatient setting by 16,273 unique providers. Most patients received 1 LS MRI; only 3.7% of patients received more than 1. Each provider ordered a median of 3 LS MRIs and a mean of 6.8 LS MRIs. Of the 110,661 total scans, 33,998 (30.7%) were classified as inappropriate; these patients had no red flag conditions coded and did not receive conservative therapy in a VA facility prior to their scan (

). The majority of scans classified as appropriate were done so because the patient was deemed to have received prior conservative therapy (65.3%). Another 16.6% of LS MRIs were classified as appropriate because the patient had a red flag condition. (Note that the numbers add up to more than 100% because patients could have had both a red flag condition and have received conservative therapy.)

describes the demographics of the population, which on average, was male, white, and middle-aged. The greatest proportion of scans (42%) was ordered for patients seen at the most-complex (level 1a) facilities. However, the greatest proportion of inappropriate scans (36%) was ordered for patients seen at the least-complex facilities (level 3) (Table 2). The number of scans ordered varied greatly by clinic setting, with the greatest percentage of scans ordered by the primary care/internal medicine clinic (62%), and the fewest percentage ordered by neurosurgery and orthopedics (6% combined). The greatest percentage of total scans (69%) was ordered by medical doctors or doctors of osteopathy, and the lowest was ordered by physicians’ assistants (8%). We did not find meaningful regional variation in proportion to scans considered inappropriate.

Results from generalized estimating equations revealed that patients aged under 35 years were more likely to have an inappropriate scan than those of all other ages (

). Black patients were less likely to have an inappropriate scan compared with white patients (odds ratio [OR], 0.80; 

 <.0001). Scans ordered at the lowest-complexity facility (level 3) were more likely to be inappropriate compared with those ordered at the highest-complexity facilities (OR, 1.27; 

 = .0096). These multivariate results also show the ED or urgent care clinic to be the settings most strongly associated with an inappropriate scan (OR, 1.42; 

 <.0001, with reference category of primary care/internal medicine clinic). In fact, ED/urgent care providers were more likely than providers practicing in any other clinic to order inappropriate scans (Table 3). Primary care/internal medicine clinics were also associated with a high likelihood of ordering inappropriate scans; all other clinic settings were less likely to order inappropriate scans than the primary care/internal medicine clinic (Table 3). Neurology, neurosurgery, and orthopedic clinics all practiced similarly; after adjusting for covariates, each was equally likely to order an inappropriate scan compared with the others (Table 3). Medical doctors (MDs), doctors of osteopathy (DOs), nurses, and physician’s assistants were also equally likely to order an inappropriate scan; however, residents were less likely to order an inappropriate LS MRI scan compared with MD/DOs (OR, 0.80; 

Results also show that 10.9% of providers ordered 50.1% of inappropriate scans, and 23.6% of providers ordered 74% of inappropriate scans (

). There appeared to be a weak, volume-percent inappropriate relationship; that is, there was little evidence of a relationship between the volume of scans ordered by a provider and the percentage of those scans deemed inappropriate.

We ran sensitivity analyses in which we modified the categorization of E&M codes. CMS considers any E&M visit between 28 and 60 days prior to the MRI to be conservative therapy, as long as the MRI was not ordered during that visit. Using this definition in our base-case, 63% of scans were conducted in patients who had a qualifying E&M visit. In sensitivity analyses, we restricted the E&M visit to have a diagnosis of LBP. Using this more restrictive definition, we found only 34% of LS MRI scans had a qualifying E&M visit, and thus, 53% of LS MRIs were considered inappropriate.

We also ran other sensitivity analyses. We tested a model with a covariate of facility-level physical therapy penetration. This variable was not a significant predictor of inappropriate LS MRI. Additionally, we ran a model on the subset of scans ordered by providers who ordered 5 or more scans per year, to avoid drawing conclusions based on a large number of providers each ordering few scans. Results did not change with respect to direction or significance. Third, we tested the inclusion of all prostate cancer in the red flag conditions, thus considering all LS MRI imaging in patients with a prostate cancer diagnosis in the previous 365 days to be appropriate. This resulted in slightly fewer (28.9%) LS MRIs being considered inappropriate.

We estimated the cost of providing each LS MRI in VA using the Decision Support System, the activity-based cost allocation system used by VA. We estimated that VA spent $44.4 million on LS MRIs in FY 2012, including $13.6 million on inappropriate scans.

The extent of overuse of LS MRI has been the subject of much debate. Here, we find that, despite a strong body of evidence regarding the poor utility of LS MRI in nonchronic LBP, and the identification of LS MRI as a low-value service by multiple specialty societies,

 31% of LS MRIs conducted in VA in FY 2012 were inappropriate.

Estimates of inappropriateness of LBP imaging vary in the literature. The 2000 to 2006 claims-based analysis of Pham and colleagues found that 29% of Medicare beneficiaries received x-ray, computed tomography (CT), or MRI inappropriately.

 A 2004 to 2006 claims-based analysis of privately insured patients found 9% of patients with LBP had an MRI within 14 days of initial diagnosis, and approximately 60% of patients had MRI, CT, or x-ray on the same day as the first diagnosis of LBP.

 These variations in the ordering of imaging could be due to increasing supply of MRI scanners from 2000 to 2012,

 different ratios of acute and chronic LBP patients in study samples, and varying definitions of inappropriate.

We found inappropriate use of MRI in VA, even though its providers have little to no financial incentive to order more scans and have less malpractice liability than physicians in private practice—2 factors often cited as reasons for overuse of imaging.

 This suggests that nonpecuniary factors play an important role in ordering behavior in VA. Thus, it also suggests that changes in financial incentives alone will not be sufficient to reduce inappropriate ordering of LS MRI, and that provider and patient education may be necessary. Further research to explore the reasons for inappropriate ordering in this healthcare system, including the relative importance of lack of awareness of guidelines and concerns about patient satisfaction or reassurance

 will be informative for quality improvement. Reducing inappropriate ordering of LS MRI also may be a question of educating patients, as well as providers. One randomized controlled trial found that patients receiving MRI were more satisfied than those receiving x-ray, independent of functional outcome.

 This tactic may be increasingly important as organizations move to include patient satisfaction metrics in their measures of quality of care.

This is, to the best or our knowledge, the first analysis of NQF-endorsed CMS criteria to evaluate inappropriate ordering of LS MRI. Our analysis indicated that providers in primary care and internal medicine clinics, EDs, and urgent care clinics were most likely to order inappropriate LS MRIs. MDs, DOs, nurses, and physician’s assistants were equally likely to order inappropriately. Of note, we found that residents were significantly less likely than all other provider types to order inappropriately. Although resident behavior has been relatively understudied in the literature, Pitts and colleagues found that ED visits with residents were more likely to have resulted in advanced imaging orders compared with ED visits without residents.

 Our dissimilar results may be due to questions of appropriateness; Pitts and colleagues did not evaluate the appropriateness of the imaging. We also found a weak relationship between the volume of scans ordered by a provider and the percentage of those scans considered inappropriate; high-ordering providers were not necessarily inappropriately ordering providers. Rather, we found that inappropriate LS MRIs were concentrated among relatively few providers. Approximately a quarter of providers (24%) ordered roughly three-fourths of all inappropriate scans (74%). Most providers (58%) ordered 3 or fewer scans per year, accounting for 60% of all LS MRIs ordered in this year. This suggests that the majority of providers do not need to be involved in interventions to reduce inappropriate use.

Our results indicate that interventions to reduce inappropriate ordering will be most appropriately directed at providers who order 18 or more scans per year, at providers and managers at the lowest-complexity facilities, and at providers in primary care/internal medicine clinics. Interventions aimed at providers who order 18 or more scans per year represent an opportunity to have a substantive impact on reducing the absolute number of inappropriate MRIs ordered. Although ED and urgent care services have the greatest percentage of inappropriate scans (41% in univariate analysis), intervening in these clinics will have modest effects, as they account for only 4% of all scans. Primary care/internal medicine clinics accounted for 62% of all scans. Scans ordered in these settings were significantly more likely to be inappropriate, making them likely targets for intervention.

It is possible that scans are being ordered in these primary care/internal medicine clinics to meet the scheduling requirements of specialists; specialty clinics may require an LS MRI before seeing a patient. We found that orthopedics and neurosurgery, together, ordered only 6% of all LS MRIs in VA, suggesting this may be a factor. Further research is needed to determine if scans attributed to primary care/internal medicine providers were ordered to secure referral to specialty clinics; this research should inform any interventions to reduce inappropriate ordering.

Our sensitivity analyses showed that changes in the definition of evaluation and management—which are a major component of what constitutes conservative therapy&mdash;have a large influence on estimates of appropriateness. Using a more restrictive definition of evaluation and management reduced the number of scans with conservative therapy and almost halved the percentage of scans regarded as appropriate. We used the most generous definition of evaluation and management in our base-case analyses. Our choice of a generous definition was two-fold: We wished to be consistent with the definition developed by CMS, and we prioritized identifying sensitivity (true appropriateness) over specificity (true inappropriateness). Maximizing sensitivity represents a cautious approach that avoids erroneously categorizing scans as inappropriate.

Use of imaging that provides no benefit to patients, and may in fact cause harm through catalyzing medical interventions for common and asymptomatic abnormalities, represents waste within the healthcare system. With healthcare expenditures of $2.6 trillion in 2010 and a consistently positive rate of growth in healthcare costs,

 waste is something the US healthcare system and US economy can ill afford. Our analysis indicates an excess annual cost of $13.7 million for LS MRI in VA. Outside of VA, it has been estimated that if all US adults were treated in accordance with imaging guidelines for LBP, the healthcare system would save at least $300 million annually (in 2009 dollars),

 with even greater cost savings achieved if accounting for costs associated with treating asymptomatic disease.

This study was a retrospective analysis of administrative VA data and is subject to certain limitations. We used administrative data to assess the appropriateness of LS MRI. Administrative data do not contain the complete details of the content of care; there may be data captured through history and physical (eg, regarding duration of treatment) that was not assigned an 

code. For example, it is possible that patients received some conservative therapy in a non-VA setting prior to their VA MRI, or that physicians had some rationale for ordering the MRI that was not captured in the coding of care such as progressively worsening symptoms; in this case, our analysis would overestimate the proportion of scans that were inappropriate. However, we employed CMS-generated and NQF-endorsed administrative data-based algorithms that were generous in their categorization of appropriate scans in order to mitigate the likelihood of this. Additionally, MRIs conducted at VA do not contain diagnosis codes; our analysis thus evaluated all LS MRIs. It is possible that we included LS MRIs that were conducted for reasons other than low back pain. This may overestimate the proportion of LS MRIs considered inappropriate. However, the appropriateness of LS MRIs is a function of both the reason for the LS MRI (eg, low back pain), as well as the reasons for which the LS MRI may be warranted (ie, prior failed conservative therapy or a patient with a red flag condition). As we used the least restrictive definition of conservative therapy (that which did not require the conservative therapy visit to have a diagnosis of low back pain) to evaluate care prior to the scan within VA, it is more likely that our analysis actually underestimates the proportion of scans that were inappropriate.

VA is a unique healthcare setting; our results may differ from those of studies of other US providers. While our analysis has limited generalizability outside of VA, analyzing this population provides important insight, by illuminating that even in a healthcare system largely absent of financial and other incentives for overuse, inappropriate ordering of LS MRI is still a substantial problem. Furthermore, our results may be of particular interest to accountable care organizations, whose financial incentives and organizational structure more closely resemble those of VA.

Despite recommendations from specialty societies that LS imaging should be performed only in patients who have severe or progressive neurological deficits or signs and symptoms of serious underlying conditions, we found that 31% of LS MRIs performed in a US healthcare system largely absent financial incentives for overuse—were inappropriate. This work reveals 2 important insights that will be informative for quality improvement purposes. First, our work suggests that nonpecuniary factors have a role to play in inappropriate ordering of imaging for low back pain; efforts to reduce inappropriate ordering cannot rely on financial incentives alone. Second, we found a high concentration of inappropriate ordering among few providers, indicating that provider-focused efforts to reduce inappropriate orders should be targeted rather than universal. Further work should be conducted to assess the proportion of inappropriate LS MRI in environments where defensive medicine or fee-for-service incentives may be at play. 

 VA Health Economics Resource Center (RG, PS, JL, PGB) and VA Center for Innovation to Implementation (RG, TA, SMA), VA Palo Alto Health Care System, Menlo Park, CA; Division of General Medical Disciplines (SMA) and Department of Health Research and Policy (PGB), Stanford University School of Medicine, Stanford, CA.

 This work was supported by VA QUERI Grant: RRP 12-442 Decision Support to Decrease Overuse of Imaging: Usability and Measurement; the funding source played no role in study design or interpretation of results. Dr Gidwani had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (RG, PS, SMA, PGB); acquisition of data (RG, JL, PGB); analysis and interpretation of data (RG, PS, TA, JL, SMA, PGB); drafting of the manuscript (RG, PGB); critical revision of the manuscript for important intellectual content (RG, PS, TA, SMA, PGB); statistical analysis (RG, JL, PGB); obtaining funding (SMA, PGB); administrative, technical, or logistic support (TA); and supervision (RG, SMA, PGB).

 Risha Gidwani, DrPH, VA Health Economics Resource Center, VA Center for Innovation to Implementation, 795 Willow Rd, 152 MPD, Menlo Park, CA 94025. E-mail: Risha.Gidwani@va.gov.

1. Deyo RA, Mirza SK, Martin BI. Back pain prevalence and visit rates: estimates from U.S. national surveys, 2002. 

2. Di Iorio D, Henley E, Doughty A. A survey of primary care physician practice patterns and adherence to acute low back problem guidelines. 

Articles

This article analyzes use of lumbar spine magnetic resonance imaging in a national sample of patients with low back pain.

Inappropriate Ordering of Lumbar Spine Magnetic Resonance Imaging: Are Providers Choosing Wisely?

To determine the association between preexisting characteristics and current health and the cost of different types of advanced human immunodeficiency virus (HIV) care.

Treatment-experienced patients failing highly active antiretroviral treatment (ART) in the United States, Canada, and the United Kingdom were factorial randomized to an antiretroviral-free period and ART intensification. Cost was estimated by multiplying patient-reported utilization by a unit cost.

A total of 367 participants were followed for a mean of 15.3 quarters (range 1-26). Medication accounted for most (61.8%) of the $26,832 annual cost. Cost averaged $4147 per quarter for ART, $1981 for inpatient care, $580 for outpatient care, and $346 for other medications. Cost for inpatient stays, outpatient visits, and other medications was 171% higher (

 <.01) when cluster of differentiation 4 (CD4) count was <50 cells/μL compared with periods when CD4 count was >200 cells/μL. Some baseline characteristics, including low CD4 count, high viral load, and HIV from injection drug use with hepatitis C coinfection, had a sustained effect on cost.

The association between health status and cost depended on the type of care. Indicators of poor health were associated with higher inpatient and concomitant medication costs and lower cost for ART medication. Although ART has supplanted hospitalization as the most important cost in HIV care, some patients continue to incur high hospitalization costs in periods when they are using less ART. The cost of interventions to improve the use of ART might be offset by the reduction of other costs.

Healthcare costs were measured in a clinical trial of alternatives for advanced human immunodeficiency virus (HIV):

Antiretroviral drugs accounted for most (61.8%) of the $26,832 annual cost of care for advanced HIV.

Costs were higher during AIDS-defining illnesses.

Costs were especially high in patients infected with HIV from injection drug use and in patients coinfected with hepatitis C.

Antiretroviral drugs have replaced hospital stays as the most costly component of HIV care, but hospitalization remains the predominant cost in especially advanced disease. The cost of interventions to improve the use of antiretroviral treatments could be offset by reduced use of other services.

Human immunodeficiency virus (HIV) care costs $20,000 per year in industrialized countries.

 Highly active antiretroviral treatment (ART) accounts for approximately half of this expense.

 The annual cost of caring for persons with HIV continues to grow and now exceeds $13 billion worldwide.

Newer ART medications have markedly improved treatment outcomes.

 Models have been used to estimate the cost effectiveness of these new drugs,

 and the effect of patent expiration on the cost effectiveness of older medications.

 Cost-effectiveness models may be hindered by the lack of more contemporary data on the cost of HIV care.

HIV care is more costly when the disease is more severe, as indicated by a low cluster of differentiation (CD4) level (<50 cells/μL),

 Results of other studies of the cost of specific services have showed that hospital costs are lower for patients who receive more ART,

 and that the cost of medication for opportunistic infections is lower for patients with higher CD4 count.

Cost is higher for patients with psychiatric comorbidities, 

 in patients presenting later for antiretroviral treatment,

 and for those infected by injection drug use (IDU).

 Other studies have showed baseline characteristics to be unimportant predictors of cost after current health status, as reflected by CD4 count, is considered.

Multivariate analyses have looked at the effect of Medicaid eligibility

 on total cost while controlling for disease severity. These studies employed ordinary least squares regression, a method that requires assumptions that are usually inappropriate for cost data, which have a highly skewed distribution. The more robust general linear models have been used only in the most recent studies of HIV cost.

We report the cost of care received by participants enrolled in OPTIMA (OPTions In Management with Antiretrovirals), the largest randomized clinical trial of treatment strategies in advanced HIV. We applied advanced multivariate methods to address gaps in the knowledge about the cost of advanced HIV care: whether preexisting factors have a sustained effect on cost, and the effect of these factors and current health status on the different types of cost, including hospital services, combination ART medication, medications for opportunistic infections, and outpatient care.

The OPTIMA trial studied 2 interventions: an intended 12-week ART-free period and intensification of ART to a target of 5 or more drugs.

 A 2-by-2 factorial design evaluated both interventions simultaneously. Participants were randomized to ART interruption, ART intensification, both interventions, or neither intervention.

The trial enrolled patients who had been taking their current ART regimen for at least 3 months, had failed 2 or more prior regimens involving all 3 available ART classes in use at the time, and had evidence of current treatment failure (HIV viral load ≥2500 copies/mL and CD4 count of ≤300 cells/μL), while excluding those with an ongoing opportunistic infection.

 Consent was obtained according to the human subjects regulations of the 3 countries where participants were enrolled: the United States, Canada, and the United Kingdom.

Participants were assessed every 12 weeks and a case report form was used to record inpatient stays, outpatient visits, and changes in antiretroviral medication regimens. Unit costs for HIV care were derived from analysis of costs of HIVpositive patients at the US Veterans Health Administration (VHA), where 78% of study participants were enrolled. Inpatient costs were derived from a regression method.

 Outpatient estimates were based on the Medicare reimbursement for the procedure codes used in visits of patients with HIV.

 Cost for all participants was determined by multiplying the utilization recorded on case report forms by these unit costs: $5004 per day of hospital stay in intensive care, $1782 per day of other medical-surgical care, $768 per day of nursing home care, $201 per visit to an infectious disease clinic, and $254 per visit to other medical clinics. All costs were adjusted to 2007 US dollars using the consumer price index.

The dates that each ART medication was initiated and stopped were recorded on a case report form. Estimates of medication use assumed the guideline-recommended dosage and were adjusted by participant-reported adherence. We characterize ART medications by categories defined by the year in which the drug was approved by the US Food and Drug Administration (FDA). We identified periods in which participants received 1 of the 5 new ART medications: etravirine, tipranavir, darunavir, maraviroc, or raltegravir.

The unit costs of medications were estimated as 64% of the average wholesale price, which is the net cost of brand name medications to Medicaid.

 Medicaid is an important sponsor of US HIV care, with costs that are similar to those of the US AIDS Drug Assistance Program, another important sponsor. Since we wanted study findings to be generalizable to the US healthcare system, we did not use the lower cost paid by VHA. Data on prescription medications used to prevent or treat opportunistic infections were obtained from the pharmacy benefit management system of VHA. Although data on ART medications was available for participants who enrolled at sites in Canada and the United Kingdom, information on these concomitant medications was not available at these sites. Cost and utilization were assigned to each 3-month period following randomization. These quarterly observations were matched to clinical data indicating if the patient had an AIDS-defining event (ADE).

We further characterized health using the mean CD4 count and viral load tests conducted during the quarter. If no laboratory test was conducted in the quarter, we used the weighted average of tests that preceded and followed the quarter. Weights were inversely proportional to the number of days the test preceded the beginning of the quarter and the number of days the test followed the end of the quarter.

The association of patient characteristics with cost was assessed with Generalized Linear Model (GLM). These Take-Away Points Healthcare costs were measured in a clinical trial of alternatives for advanced human immunodeficiency virus (HIV): n Antiretroviral drugs accounted for most (61.8%) of the $26,832 annual cost of care for advanced HIV. n Costs were higher during AIDS-defining illnesses. n Costs were especially high in patients infected with HIV from injection drug use and in patients coinfected with hepatitis C. n Antiretroviral drugs have replaced hospital stays as the most costly component of HIV care, but hospitalization remains the predominant cost in especially advanced disease. The cost of interventions to improve the use of antiretroviral treatments could be offset by reduced use of other services. regressions avoid the inappropriate assumption of normal distribution and homoscedastic errors.

 A log link function was determined appropriate based on a Box-Cox regression.

 The gamma distribution was selected based on a modified Park test. Standard errors were corrected for the correlation between multiple observations from each trial participant. Independent regressors were chosen based on significance in univariate tests. Final models were chosen based on predictive fit, with attention to the extremes of the distribution. To express the effect of current clinical status in the original units of analysis—dollars cost&mdash; we used the regression parameters to predict log cost for each observation given the participant’s baseline characteristics and a set of current status variables (indicators of CD4 count, viral load, and presence of AIDS-defining illness). We exponentiated these predicted log values without correction for retransformation bias. This correction is not needed with GLM regressions. We reported the mean cost in dollars of predicted values for each set of clinical status variables, and the associated standard error.

Participants were enrolled between 2001 and 2006 and followed until 2007. This maximized study power, but there were fewer observations representing periods more distant from randomization. Bias from this design was corrected by applying an inverse probability weight, the Kaplan-Meier estimator for the probability of administrative censoring.

There were 367 participants with cost data (excluding 1 participant with no follow-up data). 

 describes participant characteristics at randomization. Participants were immunologically compromised, with 32.2% having baseline CD4 counts below 50 cells/μL. Most (78%) participants enrolled at VA sites, with the balance enrolling at sites in Canada (11%) and the United Kingdom (11%). HIV infection was attributed to men having sex with men (MSM) (47.6%), heterosexual transmission (23.4%), IDU (14.1%), and infection from blood products (9.5%). There were 7 participants (1.9%) who had both MSM and IDU risk factors.

Participants were randomized to care as usual (n = 106), structured treatment interruption followed by treatment intensification (n = 78), structured treatment interruption without treatment intensification (n = 85), or treatment intensification without treatment interruption (n = 98). Participants from the United Kingdom were not randomized to structured treatment interruption. Baseline characteristics did not differ by treatment group.

 provides the unweighted means of 5612 quarterly observations of cost, utilization, and clinical status. Participants were followed for a mean of 15.3 quarters (range 1-26). Quarterly cost averaged $4147 for ART, $1981 for inpatient care, $580 for outpatient care, and $346 for concomitant medications (medications used for prophylaxis and treatment of opportunistic infections). Information on concomitant medications was available only for US participants. Total cost was $6708 per quarter. Participant CD4 count was below 50 cells/μL during 22.7% of follow-up quarters, and viral load exceeded 100,000 copies/mL during 22.1% of follow-up quarters. There was modest correlation between the costs in all categories of care, with 1 exception. Inpatient costs were negatively correlated with the cost of ART medication (r = −.068; 

As previously reported, neither ART interruption nor ART intensification reduced the time to the composite end point of ADE or death.

Neither intervention had a significant effect on cost, except a transitory difference during the first 3 months of the trial when participants randomized to ART interruption had a $4216 lower cost of highly active antiretroviral treatment (HAART) (

 <.001) and $434 additional cost for other medications (P <.001) compared with participants randomized to no ART interruption.

. Medications approved by the FDA after 2001 (the year the trial began) accounted for 36% of the duration and 49% of the costs of ART. Participants reported at least 90% adherence during 78.6% of the quarters of follow-up. They received an average of 3 or more ART active ingredients during 79.7% of the quarters. At least 1 of the newest ART medications (which includes those approved after 2005) was received during 19.7% of the quarters.

presents regression results. Each column represents a regression with a different cost as the dependent variable. Independent regressors were all indicator variables. The parameters represent the effect of the characteristic relative to its absence (eg, quarters in which an AIDS-defining illness was present compared with quarters in which the illness was not present) controlling for all other factors in the regression. Baseline characteristics were associated with higher cost. Patients who were coinfected with hepatitis C virus (HCV) whose HIV was a result of IDU had significantly higher hospital cost (

 <.001). HIV infection from IDU without HCV coinfection was associated with higher outpatient cost (

 <.01) and higher cost of ART medications (

Higher viral load at baseline was associated with higher ART cost (

 <.001). Low CD4 count at baseline was associated with higher cost of medications used to prevent or treat opportunistic infections (

 <.001). In multivariate analyses, other baseline characteristics including age, employment status, and diagnosis of coronary artery disease were not associated with higher cost in any category of care, and those variables were not included in the models.

Several indicators of poor health status in the quarter were associated with higher cost. An AIDS-defining event was associated with significantly higher inpatient cost (

 <.001) and with significantly higher concomitant medication cost (

<.05). CD4 below 50 cells/μL was associated with higher inpatient cost (

 <.001) and higher concomitant medication cost (

<.001). CD4 in the range of 50 cells/ μL to 200 cells/μL was also associated with greater inpatient cost (

 <.05). High viral load was associated with higher concomitant medication cost (

 <.01). Death in the quarter was associated with higher inpatient cost (

 <.001) and lower cost of ART and concomitant medications; the individual did not survive to fill all prescriptions.

The results of log regressions are parameters that are not easily interpreted. To provide a result in dollars of cost, we used these parameters to estimate the cost of different health states. 

presents the mean and standard error of the quarterly cost of patients in health states defined by CD4 count, viral load, and the presence of an AIDS-defining illness, given the range patient character istics at baseline. Although some scenarios have a low probability of occurring (eg, a period with an ADE, high CD4 count, and low viral load), all of these combinations occurred during the trial.

The cost of inpatient stays, outpatient visits, and concomitant medications was greater when health was poor. These costs were 327% higher when there was an AIDSdefining illness than they were when one was not present (

<.001). Compared with periods in which CD4 count was below 200 cells/μL, these costs were 171% higher when CD4 count was below 50 cells/μL (

 <.001), and 67% higher when the CD4 count was between 50 cells/μL and 200 cells/μL (

 <.05). Costs were 31% higher when viral load was greater than 100,000 cells/mL compared with periods when viral load was below this threshold (a difference that was not statistically significant; P = .07). When health was the most compromised—when there was an AIDS-defining illness, a CD4 count of less than 50 cells/μL, and viral load greater than 100,000 cells/mL&mdash;quarterly cost for care, excluding ART medications, was $20,494. This was 15 times higher than the $1353 cost when health level was good; or when the participant did not have an AIDS-defining illness, had a low CD4 count, or had a high viral load.

The cost of ART was lower when health was poor. Compared with periods during which CD4 count was below 200 cells/μL, ART cost was 32% lower when CD4 count was below 50 cells/μL (

 <.001) and 14% lower when CD4 count was between 50 cells/μL and 200 cells/ μL (

 <.05). ART cost was 20% lower when viral load was greater than 100,000 cells/mL (

 <.01). ART cost was 9% higher during quarters in which there was an AIDS-defining illness (not statistically significant;

 = .35). ART costs were $4664 per quarter during periods in which health level was good, 59% greater than the $2774 cost of ART in when health was the most compromised. ART accounted for 77% of the total cost during the periods in which health level was good and for 12% of the total cost of during periods in which health was the most compromised.

Participants in this clinical trial incurred $6708 in quarterly costs. This represents $26,832 in annual healthcare costs, 34% more than the $20,000 mean annual cost of HIV care in industrialized countries which was reported in a recent review.

OPTIMA participants differed in important ways. First, they were all receiving ART. Other HIV cohorts include patients not yet receiving treatment. A study of privately insured HIV patients found that 63% were receiving ART.

 OPTIMA participants had a history of treatment failure, and may have progressed to more expensive medications. Antiretroviral drugs and concomitant medications accounted for 61% of the total healthcare cost for OPTIMA participants. In the United States, pharmacy accounted for 54% of the cost of HIV care in 1997 and for 52% of the cost of care for privately insured patients with HIV.

We found that ART made up a smaller percentage of total costs during periods in which health was poor. ART accounted for 12% of total costs during periods in which health was the most compromised (when patients had an AIDS-defining illness, low CD4 count, and high viral load) and for 77% of total costs during the periods when none of these factors were present. These findings are consistent with those of other studies. A recent study of patients in HIV clinics found that ART made up 23% of total costs in patients with CD4 counts below 50 cells/μL, and two-thirds of total costs for those with CD4 counts above 200 cells/μL.

 A model of HIV costs estimated that ART accounted for 38% of total costs during months when CD4 counts were below 50 cells/μL and 77% of total costs during months when CD4 counts were above 300 cells/μL.36 The findings from these studies are not exactly comparable, as each used a different definition of poor health. We found that the difference in the proportion of costs attributable to ART was not only due to the higher cost of non-ART care in patients in poor health, but also because of their lower utilization of ART.

Previous studies of the cost of HIV care have found that individuals with CD4 counts below 50 cells/μL incurred 3 times the annual cost of individuals with CD4 counts above 350 cells/μL.

 Our findings were similar. We found that the cost of inpatient stays, outpatient visits, and concomitant medications was 171% higher when CD4 counts were below 50 cells/μL than they were when CD4 counts were above 200 cells/μL. At the same time, we found that patients with low CD4 counts had 32% lower ART cost. One limitation of some HIV cost studies is the failure to distinguish the different types of cost, such as medication, hospitalization, and ambulatory care.

 Our findings illustrate why this distinction can be important: clinical status may have a different effect on ART cost than on others costs. We found that AIDS-defining illness was associated with higher costs, even after controlling for CD4 count. The cost of inpatient stays, outpatient visits, and concomitant medications was 327% greater during periods with such an illness. Future cost studies may want to include this health status measure.

We found that some baseline characteristics, including low CD4 count, high viral load, and HIV from IDU with HCV coinfection, had a sustained effect on cost.

We acknowledge study limitations. The use of standard inpatient and outpatient unit cost understates cost variation. The use of US unit costs reduces the applicability outside the United States, especially with respect to medication cost. Data on concomitant medications were available only for US sites. Also, our sample included few women.

The clinical trial protocol may have affected utilization, but the trial interventions had little effect on cost and no effect on outcomes. The protocol specified that a follow-up interval of 3 months is not excessive for patients with advanced HIV. OPTIMA participants were similar to other patients with advanced HIV in the VA healthcare system.

Medications approved prior to 2001, when this trial began, accounted for 61% of ART utilization and 51% of the cost of ART incurred by study participants. The patents on many of those medications have expired or will soon be expiring, and the costs for those medications has already or can be expected to decrease. It has been argued that reduced medication costs from patent expiration should be incorporated into cost-effectiveness studies.

 This effect may be of less concern when new branded medications are adopted to respond to viral resistance to older agents.

Although antiretroviral drugs have replaced hospital stays as the most costly component of HIV care, we found that this is not true over all periods for patients with especially advanced HIV disease. Indicators of poor health were associated with lower ART costs and higher costs for inpatient stays and concomitant medications. Treatments that lead to improved use of ART, either by improving adherence or reducing adverse effects of treatment, would at least partially offset their cost by reducing the need

VA Palo Alto Healthcare System, VA Cooperative Studies Program, VA HSR&D Health Economics Resource Center, Menlo Park, CA (PGB, AC, VRJ); Centre for Research on Inner City Health, The Keenan Research Centre in the Li Ka Shing Knowledge Institute and Division of General Internal Medicine, St. Michael’s Hospital, Toronto, Ontario, Canada (AMB); Departments of Medicine and Health Policy, Management and Evaluation, University of Toronto, Ontario, Canada (AMB); Centre for Health Economics, University of York, York, United Kingdom (SCG, MS); VA Palo Alto Healthcare System, Palo Alto, CA (MH, DKO); Center for Primary Care and Outcomes Research, and Center for Health Policy, Stanford University, Stanford, CA (DKO); James J. Peters VA Medical Center, Bronx, NY (STB); Division of Infectious Diseases, University of Ottawa at The Ottawa Hospital, Ottawa, Ontario, Canada (DWC); Royal Free Hospital, London, United Kingdom (MY); CIHR Canadian HIV Trials Network, Vancouver, British Columbia, Canada (HS, AHA); School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada (AHA).

Antiretroviral drugs have replaced hospitalization and other services as the most costly component of HIV care, except in patients with especially advanced HIV.

Effect of Management Strategies and Clinical Status on Costs of Care for Advanced HIV

To learn how age and chronic illness affect costs inthe Veterans Affairs healthcare system.

Veterans Affairs patients 65 years or older wereidentified from administrative data. We noted their healthcare utilization,cost, and diagnosis of any of 29 common chronic conditions(CCs). We examined how those 80 years or older differedfrom the younger patients.

The Department of Veterans Affairs spent $8.5 billionto treat 1.6 million older patients in fiscal year 2000. Age was lessimportant than chronic illness in explaining cost differences. Theoldest patients incurred a mean of $1295 greater costs than theyounger patients, primarily because they were more likely to havea high-cost CC. The oldest patients incurred higher total costs thanthe younger patients in only 14 of 29 groups defined by CC. Long-termcare accounted for most of the extra cost of the oldestpatients. When this cost was excluded, the oldest patients incurredonly $266 more cost than the younger patients.

Growth in the population of the oldest patientswill increase the number of individuals with CCs requiring long-termcare. With its limited long-term care benefit, Medicare willavoid much of this financial consequence. In contrast, the financialrisk of acute and long-term care gives the Department of VeteransAffairs an incentive to develop strategies to prevent CCs associatedwith long-term care.

The Department of Veterans Affairs (VA) healthcare system is   one of the largest integrated healthcare systems in the United States,   comprising 163 hospitals, 137 nursing homes, 43 domiciliaries, and 913   outpatient clinics.1 In fiscal year (FY) 2002, the VA spent $23   billion to provide medical care to 4.7 million veterans.2 A unique   feature of the veteran population is that the age distribution closely relates   to the timing of wars and can change sharply in a short time. As the veterans of   World War II and the Korean War have aged, the number of veterans 85 years or   older has increased at a mean rate of 11% per year, from 156 000 in 1990 to 510   000 in 2000; it is projected to reach 1.3 million by 2010.3,4

The rapid aging among veterans has increased the demand for VA   healthcare services, especially long-term care among the oldest veterans. A   population-based survey showed that the percentage of older persons who ever   used nursing homes increased from 22% in the 65- to 74-year-old cohort to 58% in   the 85-to 94-year-old cohort.5 The VA has to respond to these changes   because the 1999 Veterans Millennium Healthcare and Benefits Act, Public Law   106-117, mandates that the VA must provide nursing home care to any veterans in   need of such care for a service-connected disability.

Although the oldest patients use more long-term care, they are   also less likely to receive aggressive treatment. Two studies6,7   found that Medicare expenditures decreased with increasing age because   hospital and intensive care unit use by the oldest beneficiaries   declined.8-10 This research suggests that there are 2   competing effects. Long-term care is expensive, but because treatment is less   aggressive, the net cost effect of the increasing numbers of the oldest veterans   on the VA healthcare costs is ambiguous.

To accurately predict demand, we must understand what care   older adults are using and the independent effects of age and chronic conditions   (CCs) on costs. Studies7,11-13 have analyzed the   relationship between age, CCs, and healthcare costs. The commonly used Medicare   claims data include information on medical treatments and diagnoses, but these   data offer limited insight on long-term care because of Medicare's restrictive   long-term care benefits. Medicaid data are also incomplete because people must   spend down their wealth before they become eligible. Hence, studies based on   Medicare or Medicaid data do not provide complete information on the   relationships among age, CCs, and use of long-term care. A study of the VA   system can help VA policy makers make projections and can offer insight on   expected trends in total healthcare costs of the non-VA population.

Although we focus on veterans, understanding changes in costs   incurred by older veterans will increase our understanding of how the aging of   the population will affect the larger US healthcare system. The aging of the   veteran population is a harbinger of changes in the US population as a whole.   The number of individuals 85 years or older increased from 3.0 million in 1990   to 4.2 million in 2000; it will reach 6.1 million in 2010, when they will   account for 1.2% of the population.4

We emphasize age effects, the effects of having more older   people in the population on the cost of healthcare. These are different from the   effects of aging, that is, the effects of increased life expectancy on   healthcare cost.14,15 In this study, we analyzed healthcare costs   incurred by 1.6 million older (≥65 years) VA patients. We identified 29 common   CCs. Then, controlling for each CC, we compared healthcare costs of the younger   (65-79 years) patients with those of the oldest (≥80 years) patients.

To understand patterns of resource use for treating chronic   illness, we grouped patients by their most expensive condition. With veterans   assigned to mutually exclusive groups, we then estimated costs by type of   medical services actually used. We analyzed the differences in resource use   between the younger patients and the oldest patients, particularly in long-term   care.

We collected electronic medical records on all veterans aged 65   years or older who received VA care in federal FY 2000, ending September 30,   2000. We excluded 82 713 people who used the VA system for only pharmacy   benefits. The study population comprised 1 596 789 veterans, of whom 1 283 542   were younger patients and 313 247 were the oldest patients.

We did not adjust costs for patients who died during the study   year. The higher mortality rates of the oldest patients have 2 conflicting   effects on annual healthcare costs. As a consequence of a higher mortality rate,   the oldest patients had fewer days of life during the study year and thus fewer   days in which they could incur costs. On the other hand, there are high costs   associated with illness sufficiently severe to result in death. In the   preliminary study, we calculated the costs excluding patients who died during   the study year. We found that mean costs were lower for all 29 CC groups;   however, the cost differences between the oldest patients and the younger   patients remained. Because including people who died during the study year   reflects actual costs for a cohort in any period, we retained these subjects in   this analysis.

The VA uses contract providers for those services it cannot   render conveniently. In FY 2000, 7% of VA costs were paid for contracted   services. Because contract care is not completely described by VA data sets, we   excluded it from analysis.

We chose the 29 CCs because they were included in related   studies,12,16 because they are given high priority in VA   research17 or quality enhancement programs,18 or because   they are known to be especially common among VA patients. We identified which   patients had these CCs using the 

Diseases, Ninth Revision, Clinical Modification 

diagnostic codes recorded   in VA healthcare utilization files in FY 2000. We included in our review all   diagnostic codes in the inpatient and outpatient files. For 28 conditions, we   classified patients with a CC based on 1 or more diagnoses. For depression, we   required 2 or more outpatient diagnoses or a single diagnosis from a psychiatric   clinic.

We reviewed the classification methods from a Kaiser Permanente   study12 and the Clinical Classifications Software developed by the   Agency for Healthcare Research and Quality.19 We also reviewed other   published studies,20,21 noting when methods differed from the Kaiser   Permanente and Clinical Classifications Software methods. We conducted a   sensitivity analysis of these classification methods. In a conservative   approach, we excluded those codes identified by our physician consultants that   did not clearly specify a CC.22

We determined the cost of all VA-provided medical care in FY   2000. For inpatient stays that spanned multiple FYs, we allocated inpatient   costs in proportion to the number of days in FY 2000. Inpatient and outpatient   costs were obtained from the Health Economics Resource Center's average cost   database. We allocated costs for medical/surgical hospitalizations to each   hospital stay using a clinical cost function that we developed from Medicare   data through a regression of cost against the diagnosis related group relative   value weights, length of stay, and use of an intensive care unit.23   We adjusted nursing home costs for case mix using the resource utilization   group score.24 For inpatient rehabilitation and specialty mental   healthcare, costs were measured by a mean daily rate.25 Outpatient   healthcare costs were based on the 

codes recorded in the database. The relative weights were   based on the Medicare Resource-Based Relative Value Scale,26 the   relative value units developed by Ingenix Health Intelligence,27   surveys of provider fees and fee schedules, 28-30   and the 1999 survey of the American Dental Association.31 We   used these relative weights to allocate VA outpatient costs to each   encounter.32 Costs for outpatient pharmacy services were obtained   from the VA Decision Support System national extract, which includes overhead   costs proportionally distributed to the outpatient pharmacy   departments.33

Many veterans have multiple CCs. Because we could not clearly   separate medical treatments by CC, we faced the challenging question of how to   group the patients such that their resource use would be closely related to   their CC.

We developed a hierarchical classification method that grouped   patients who had any of the 29 CCs into 29 mutually exclusive CC groups.   Patients were classified into only 1 group according to their most expensive CC.   For example, a patient who had lung cancer and hypertension was assigned to the   lung cancer group. This method was preferable to defining groups solely by CC,   as this would have assigned patients with several conditions to several groups.   Under that approach, a group's mean cost would be difficult to interpret because   it would be affected by the inclusion of patients with other, higher-cost   conditions.

We first calculated the mean costs of all 29 CCs. In group 1,   we placed patients who had the most expensive condition. We removed patients who   were in group 1 from the analysis and then recalculated the mean costs for the   remaining 28 CCs, placing those patients who had the most expensive CC in group   2. We repeated this process to rank all 29 conditions. Patients who did not have   any diagnosis of the 29 CCs were in a single "other"group, group 30. Except for   the most expensive CC group, the number of people in each CC group does not   reflect the prevalence of that condition, because each CC group excludes members   who also have a more expensive CC. For the same reason, the mean annual cost of   each CC group (other than the first) is the lower bound of the mean cost   incurred by patients who have that condition; excluded from the calculation are   patients who have that condition and a more expensive one.

We categorized inpatient care into 4 groups: (1) medical/   surgical, (2) rehabilitation, (3) long-term care, and (4) specialized mental   health or substance abuse treatment. For outpatient care, we separated pharmacy   services from all other care. To determine whether the oldest patients had more   CCs than the younger patients, we calculated the proportion of patients who had   0, 1, 2, and 3 or more of the 29 CCs. To evaluate costs, we compared mean costs   for both age groups for each of the 30 CC groups. We compared the long-term care   costs incurred by each age group, the proportion of individuals who used any   long-term care during the study year, and the proportion of total cost accounted   for by long-term care. We tested statistical differences in mean costs between   age groups using 2-tailed 

In FY 2000, VA facilities provided $8.5 billion worth of care   to the 1.6 million older veterans. Diagnoses of CCs were equally common in both   age groups. Use of long-term care was the major difference in demand for   healthcare between the 2 age groups. The patterns of resource use, however,   depended heavily on CC group.

Among the 1.6 million older veterans who received services from   the VA in FY 2000, 85.5% had at least 1 of the 29 CCs; 40.5% had 3 or more CCs   (

). The number of CCs (1, 2, or = 3) was similar in the 2 age   groups. The oldest group had a slightly higher percentage (16.5% vs 14.5%) of   patients who had none of the CCs.

gives the mean costs of the hierarchically   classified CC groups and is sorted by the mean cost of each CC for all ages. The   mean costs of the oldest patients were higher than those of the younger patients   in 14 of the 29 conditions. The mean costs of the oldest patients were 8% lower   in renal failure and 11% lower in lung cancer than those of the younger   patients. The differences in mean costs between the 2 age groups were not   statistically different for the remaining 13 conditions. In both age groups,   patients who had at least 1 of the 29 CCs incurred costs that were 3 to 4 times   higher than those incurred by patients who had none of the 29 CCs.

Paul G. Barnett, PhD

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Effect of Management Strategies and Clinical Status on Costs of Care for Advanced HIV

The Relationships Among Age, Chronic Conditions, and Healthcare Costs