Effect of Management Strategies and Clinical Status on Costs of Care for Advanced HIV
Published Online: May 19, 2014
Paul G. Barnett, PhD; Adam Chow, BA; Vilija R. Joyce, MS; Ahmed M. Bayoumi, MD, MSc; Susan C. Griffin, MSc, BSc, PhD; Huiying Sun, PhD; Mark Holodniy, MD; Sheldon T. Brown, MD; D. William Cameron, MD; Mark Sculpher, PhD; Mike Youle, MB, ChB; Aslam H. Anis, PhD; and Douglas K. Owens, MD, MS
Human immunodeficiency virus (HIV) care costs $20,000 per year in industrialized countries.1 Highly active antiretroviral treatment (ART) accounts for approximately half of this expense.2 The annual cost of caring for persons with HIV continues to grow and now exceeds $13 billion worldwide.3
Newer ART medications have markedly improved treatment outcomes.4 Models have been used to estimate the cost effectiveness of these new drugs,5-8 and the effect of patent expiration on the cost effectiveness of older medications.9 Cost-effectiveness models may be hindered by the lack of more contemporary data on the cost of HIV care.10
HIV care is more costly when the disease is more severe, as indicated by a low cluster of differentiation (CD4) level (<50 cells/μL),11-13 a higher level of virus,14 HIV-related symptoms, 15 or an AIDS-defining illness.15,16 Results of other studies of the cost of specific services have showed that hospital costs are lower for patients who receive more ART,18,19 and that the cost of medication for opportunistic infections is lower for patients with higher CD4 count.12
Cost is higher for patients with psychiatric comorbidities, 20-22 among male patients,23 in patients presenting later for antiretroviral treatment,24,25 and for those infected by injection drug use (IDU).14 Other studies have showed baseline characteristics to be unimportant predictors of cost after current health status, as reflected by CD4 count, is considered.12
Multivariate analyses have looked at the effect of Medicaid eligibility21 and compliance with ART guidelines23 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.20,26
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.27 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.28 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.29 Outpatient estimates were based on the Medicare reimbursement for the procedure codes used in visits of patients with HIV.30 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.31 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.32 A log link function was determined appropriate based on a Box-Cox regression.33 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— 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.34
There were 367 participants with cost data (excluding 1 participant with no follow-up data). Table 1 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.
Table 2 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; P <.001).
As previously reported, neither ART interruption nor ART intensification reduced the time to the composite end point of ADE or death.27 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) (P <.001) and $434 additional cost for other medications (P <.001) compared with participants randomized to no ART interruption.
Treatment details appear in Table 3. 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.
Table 4 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 (P <.001). HIV infection from IDU without HCV coinfection was associated with higher outpatient cost (P <.01) and higher cost of ART medications (P <.01).
Higher viral load at baseline was associated with higher ART cost (P <.001). Low CD4 count at baseline was associated with higher cost of medications used to prevent or treat opportunistic infections (P <.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 (P <.001) and with significantly higher concomitant medication cost (P <.05). CD4 below 50 cells/μL was associated with higher inpatient cost (P <.001) and higher concomitant medication cost (P <.001). CD4 in the range of 50 cells/ μL to 200 cells/μL was also associated with greater inpatient cost (P <.05). High viral load was associated with higher concomitant medication cost (P <.01). Death in the quarter was associated with higher inpatient cost (P <.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. Table 5 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 (P <.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 (P <.001), and 67% higher when the CD4 count was between 50 cells/μL and 200 cells/μL (P <.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—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 (P <.001) and 14% lower when CD4 count was between 50 cells/μL and 200 cells/ μL (P <.05). ART cost was 20% lower when viral load was greater than 100,000 cells/mL (P <.01). ART cost was 9% higher during quarters in which there was an AIDS-defining illness (not statistically significant; P = .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.1
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.2 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.2
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.26 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.11,12 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.1 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.20
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.35 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
Author Affiliations: 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).
Source of Funding: US Department of Veterans Affairs Cooperative Studies Program, the United Kingdom Medical Research Council, and the Canadian Institutes for Health Research.
Author Disclosures: 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.
Authorship Information: Concept and design (PGB, AMB, SCG, STB, DWC, MS, MY, DKO); acquisition of data (PGB, VRJ, STB, DWC, MY, DKO); analysis and interpretation of data (PGB, AC, AMB, SCG, DKO); drafting of the manuscript (PGB, MS, DKO); critical revision of the manuscript for important intellectual content (PGB, VRJ, AMB, SCG, MH, STB, DWC, MS, DKO); statistical analysis (PGB, AC); provision of study materials or patients (MH, DWC); obtaining funding (PGB, STB, DWC, MS, DKO); administrative, technical, or logistic support (PGB, AC, VRJ, MH, DWC); supervision (PGB, SCG).
Address correspondence to: Paul G. Barnett, PhD, VA Palo Alto Health Care System, Health Economics Resource Center (HERC), 795 Willow Rd (152), Menlo Park, CA 94025. E-mail: firstname.lastname@example.org.
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