Delivery of HIV-related healthcare in the United States is expensive. Bozzette et al.  estimated that the annual costs of treating a person with HIV infection were US $20 300 per patient in 1996 and US $18 300 in 1998. Estimates of the cost of treating HIV infection appeared early in the epidemic [2,3] and consistently thereafter [1,4–14]. With some exceptions [12–14], most of the estimates were produced prior to the development of HAART or early in the HAART era. Antiretroviral therapy decreases morbidity and reduces inpatient utilization [15–17]. Estimates of costs derived prior to the widespread use of HAART are now primarily of historical interest. Recent data are needed to produce updated estimates of the costs of care for HIV infection.
Costs of care are higher for patients with lower CD4 cell counts [5,12–14,18]. For one provider in Alabama in 2001, medication costs accounted for 71–84% of annual expenses . These results, although informative, are limited by being based on data from a single provider. Cost estimates based on a diverse set of providers and a heterogeneous patient population would have wider applicability. One multisite study provided estimates of inpatient and outpatient costs, but did not include medication costs .
The goal of the present study is to estimate costs of care using relatively recent data on a large sample of patients from multiple providers. We used 2006 data from the HIV Research Network (HIVRN), a multisite consortium of high-volume HIV care providers across the United States.
The HIVRN is a consortium of primary and subspecialty medical care providers to HIV-infected patients. Participating sites abstract specified data elements from patients' medical records; abstracted data are assembled into a uniform database [5,18–20]. Thirteen sites treat adult patients; analyses included 10 sites that collect comprehensive inpatient and outpatient utilization data. Sites are located in the eastern (six), midwestern (one), southern (one), and western United States (two). Nine sites have academic affiliations; one is community-based. Analyses were limited to adult patients (≥18 years old) at these sites who were in HIV primary care, defined by having at least one visit to the primary HIV care provider and a CD4 cell count drawn between 1 January 2006 and 31 December 2006.
Medical record data
Medical records provided information on patients' sex, age, and HIV transmission risk factor. Risk factor was coded as injection drug user (IDU, including IDU in conjunction with other risk factors), MSM, heterosexual (HET), or others. Race/ethnicity categories were white, black, Hispanic, and others. Age and median CD4 cell count were categorized to capture possible nonlinear associations with costs. Age as of 1 July 2006 was categorized as 18–29, 30–39, 40–49, and 50 or older. We categorized the median CD4 cell count recorded in 2006 as less than 50, 51–200, 201–350, 351–500, and more than 500 cells/μl.
All 10 sites provided data on inpatient days, outpatient visits, CD4 and viral load tests, and HIV-related medications. We refer to these as ‘major cost components’. We counted the total numbers of outpatient visits to the HIV primary care provider, inpatient days, CD4 tests, and HIV-1 RNA tests in 2006 for each patient. Medical record data provided detailed information on all prescribed antiretroviral and opportunistic illness (OI) (Pneumocystis jiroveci pneumonia and Mycobacterium avium complex) prophylaxis (OI Px) medications, including start and stop dates. For each patient, we calculated the number of months in 2006 that each medication had been prescribed. In addition, three of the 10 sites provided information on visits to the emergency department (ED), use of non-HIV medications (i.e., other than antiretroviral drugs and OI Px medications), and resistance tests (genotype and phenotype).
Cost calculations were performed from the perspective of a large-scale purchaser of services, such as the Federal government, which can often negotiate discounts from standard charges. Because information on payments was not directly available, we estimated expenditures by multiplying utilization data for inpatient days, outpatient visits, ED visits, and laboratory tests (CD4, resistance, and HIV-1 RNA) by an appropriate unit cost. To estimate a unit cost per inpatient day, we used data on charges and cost-to-charge ratios for HIV-related inpatient admissions from the Healthcare Expenditure and Utilization Project State Inpatient Databases (HCUP/SID) [21,22]. The estimated unit cost for an outpatient visit was based on the 2006 Medicare payment for an outpatient visit involving complex evaluation and management. For antiretroviral and OI Px medications, we multiplied the number of months a medication was prescribed by an estimated monthly cost, based on discounted 2006 Red Book average wholesale price (AWP) for that medication. We performed analogous calculations for non-HIV medications, based on AWP for generic versions where available. Sections below provide detailed description of unit cost estimation; Table 1 reports monthly cost estimates for antiretroviral, OI Px, and other medications.
Inpatient costs per day
Medical record data do not contain expenditure information, and most patients cannot accurately report the costs of their care. Consequently, we obtained inpatient cost information from the Healthcare Expenditure and Utilization Project (HCUP) State Inpatient Database (SID), which contains hospital discharge abstract data covering inpatient stays from all short-term nonfederal community hospitals in participating states. SID data include primary and all secondary diagnoses for each inpatient stay, the length of stay (LOS, calculated as the difference between the admission and discharge date), and the total charges for the hospitalization. We used data for calendar year 2006 from 10 states: California, Colorado, Florida, Iowa, Illinois, Kansas, Maryland, New Jersey, New York, and Washington [23,24].
We identified HIV-related hospitalizations in patients who were more than 18 years old at admission by examining all primary and secondary diagnoses listed in the discharge abstract. All hospitalizations with a primary or secondary International Classification of Diseases, ninth edition (ICD-9 CM) diagnosis codes that included 042.0 through 044.9, inclusive, were selected as HIV-related hospitalizations.
Hospital charges for each admission were converted to costs by multiplying by an inpatient expenditure-to-charge (ICC) ratio . All-payer hospital-specific ICC ratios were based on data from standard accounting files of the Centers for Medicare and Medicaid Services. If a hospital-specific ICC was not available, then a group average ICC was used, where the grouping was based on the hospital's state, ownership, urban or rural location, and size. The group average ICC was used for 19% of the admissions and the hospital-specific ICC was used for the rest.
For 84 906 HIV-related admissions in the SID with data available for total charges and LOS, the mean cost per day was US $2014.66. Total inpatient expenditures were obtained by multiplying the number of inpatient days in 2006 by the mean daily cost.
Price discounts for medications are available to certain entities. For example, AIDS Drug Assistance Programs (ADAPs) can purchase drugs using the 340B Drug Pricing Program, which provides drug price ceilings . The Congressional Budget Office estimated that the 340B ceiling price averaged 51% of the AWP in 2003, although this estimate was not focused on antiretroviral medications . Not all state ADAPs take advantage of 340B prices, however . Prices for specific drugs under the 340B program are not publicly available.
Computation of monthly costs for each medication was based on 2006 Red Book AWP. It is recognized that the AWP overestimates actual pharmaceutical costs. A report by the Office of the Inspector General for the Department of Health and Human Services  compared AWP to the average manufacturer's price (AMP), which is the average unit price paid to manufacturers by wholesalers for retail drugs, calculated from actual sales transactions. For single source brands, the AMP at the median was 23% less than the AWP; for generic drugs, the AMP was 70% less than the AWP at the median. We discounted the published AWP by 23% for all antiretroviral/OI Px medications, as most are not available in generic form. (Zidovudine is available generically, but is rarely prescribed by itself.) We assumed that standard dosages were prescribed, calculated the number of units administered in a month, and used this to derive a monthly cost for each drug. For two drugs available in generic form (sulfamethoxazole/trimethoprim and azithromycin) we used non-discounted monthly costs of US $9.91 and 112.18, respectively.
For non-HIV medications (i.e., other than antiretrovirals and OI Px), we excluded over-the-counter medications, those usually taken for less than 30 days (including antiinfectives and pain medications), and those with an irregular dosing schedule, such as those administered topically, by inhaler, or on an as-needed (PRN) basis. Where generic versions were available, we used the generic price.
The discounted monthly cost was multiplied by the number of months a patient was prescribed the medication in 2006. These products were then summed across drugs for each patient to obtain total antiretroviral and OI Px, and non-HIV medication expenditures.
Outpatient visit expenditures
The estimated unit cost for an outpatient visit with the HIV primary care provider was based on the Medicare National Physician Fee Schedule for 2006 . The facility unit cost for an outpatient visit was based on CPT-4 code 99215, for the most complex level of patient evaluation and management. Unit costs ranged from US $91.72 to 107.97 per visit, depending on the geographic location of the clinic. We multiplied the number of visits by the cost per visit to obtain total outpatient visit expenditures between enrollment and 2007.
Emergency department expenditures
ED visits were based on estimates from the HIV Cost and Services Utilization Study , which were inflation-adjusted to 2006 dollars using the gross domestic product (GDP) price deflator. The GDP price deflator is preferred to using the medical component of the Consumer Price Index (CPI), as the CPI covers only 60% of the economy, omitting, for example, government purchases. We used US $430.98 as the cost of an ED visit.
We used US $38 as the cost of a CD4 test, US $90 as the cost of an HIV-1 RNA test, US $380 as the cost of a genotype resistance test, and US $800 as the cost of a phenotype resistance test (J. Keruly, Johns Hopkins Medical Institutions, personal communication, 2008).
We summed outpatient, inpatient, antiretroviral, OI Px, CD4, HIV-1 RNA test, ED visit, non-HIV medications, and phenotype and genotype test expenditures to obtain total expenditures in 2006 for each patient. The full array of expenditure components was available from three HIVRN sites. Seven other sites provided data on all components except ED, non-HIV medications, and resistance tests. The unit costs are themselves estimates and may only approximate true opportunity costs. Variations in the intensity of inpatient treatment or the length of outpatient visits, which could affect costs, were not incorporated into the analyses.
We estimated mean expenditures as a function of median CD4 cell count, for total expenditures and for each separate cost component. Using data from three sites, we estimated total expenditures, which included major cost components and expenditures for ED visits, non-HIV medications, and resistance tests. To maximize sample size, we also used data from all 10 sites to calculate total major expenditures (i.e., the sum of outpatient, inpatient, antiretroviral, OI Px, CD4, and HIV-1 RNA expenditures) in 2006 for each patient.
We identified 15 064 adult patients in the 10 sites who had at least one outpatient visit and one CD4 test in 2006. We removed from analyses nine patients with death dates prior to dates of service use, seven missing CD4 test results, 15 with enrollment date after 2006, 11 missing demographic data, three missing all medication start dates, and 328 whose medication data were not abstracted at one site, resulting in an analytic sample of 14 691.
Of these, 4258 were from three sites that provided data on all cost components, while 10 433 came from seven sites that provided data on only major cost components. Initial analyses focused on data from the three sites with all cost components; to provide corroborating data from a larger sample, parallel analyses were conducted using data from the seven other sites.
Most patients (88%) had enrolled in their respective clinics prior to 2006; 1809 enrolled during 2006 and 234 died in 2006. We annualized costs for patients who enrolled in 2006, as they had partial-year data; we did not annualize costs for patients who died during 2006, as this would represent extrapolating beyond death. As in other analyses of medical care costs , we compensated for annualizing costs by using weights for partial-year patients in analyses, where the weight was the proportion of months in 2006 that the patient provided data. Patients with full-year data (and decedents) had analytic weights equal to 1.
To examine demographic variations in costs of care, we estimated multiple regression models that included sex, race/ethnicity, HIV risk factor, age, and CD4 cell count category. Ordinary least-squares (OLS) regression can perform suboptimally when used to analyze data that are skewed or have heavy tails, a feature characteristic of expenditure data [30,31]. Generalized linear models have been recommended as an alternative mode of estimation for such data [31,32]. We used a generalized linear model with a log link and a gamma distribution. Because the coefficients of such models are on a log scale and difficult to interpret, we present predicted values on the original scale. To calculate predictions, the indicator variable for a specific category was set to ‘1’ for all observations, other variables remained unchanged, and predicted values were averaged over the sample. Finally, because costs may be higher for decedents, we compared costs for patients known to have died in 2006 (n = 234) with those for patients not known to have died (n = 14 164), and also with those for patients who died in 2007 (n = 240). Analyses were conducted using Stata 9.0 (StataCorp., College Station, Texas, USA).
Table 2 presents the distributions of demographic and clinical characteristics for 14 691 patients receiving primary care for HIV infection in 2006. We present the overall distribution, as well as the distributions within the two groups of sites (three that had all cost components and seven that reported major cost components).
The two groups of sites differed significantly in the distribution of demographic and clinical characteristics. Two of the three sites providing all cost components were located on the West Coast, whereas five of the seven other sites were on the East Coast. Consistent with epidemiological trends, the proportions of female patients, minorities, and those with an IDU risk factor were greater in the latter group.
Three sites with all cost components
Averaging over all CD4 cell count categories and summing all cost types, the mean total cost per person for HIV care in 2006 in the three sites with all cost components was US $19 912, with an interquartile range from US $11 045 to 22 626. The full range was US $317 to 513 202. Total costs were substantially lower for patients with less advanced HIV disease (Table 3). For patients with a median CD4 cell count of 50 cells/μl or lower in 2006, total costs averaged US $40 678 [95% confidence interval (CI) US $33 566–47 789]. In contrast, total costs were considerably lower for those with median CD4 cell count between 351 and 500 cells/μl [US $16 859 (15 798–17 920)] or higher than 500 cells/μl [US $16 614 (16 052–17 177)]. Nevertheless, even among those with the highest CD4 cell counts, annual medical care costs were substantial.
Table 3 also reports mean costs (95% CIs) for each cost category, stratified by median CD4 cell count in 2006. For all but one cost category, overall differences in mean costs across CD4 cell count categories were statistically significant, as assessed by one-way analyses of variance. Inpatient costs were considerably higher for patients with median CD4 cell count 50 cells/μl or less, compared with patients with higher CD4 cell counts. Eight to 18% of those with CD4 cell counts more than 200 cells/μl had any inpatient expenditures, compared with 49% of those with CD4 cell counts 50 cells/μl or less. Mean outpatient costs declined as median CD4 cell count increased.
Although statistically significant, variation in costs of CD4 and HIV-1 RNA tests was minimal across CD4 cell count categories. Only 15.1% of patients had resistance tests and the percentage of patients with a resistance test varied by CD4 cell count category, from 40.7% for those with CD4 cell counts 50 cells/μl or less to 5.7% for those with CD4 cell counts 500 cells/μl or higher.
Costs for OI Px medications were minimal for patients with CD4 cell count more than 200 cells/μl. Non-HIV medication costs were similar across CD4 cell count categories; one-way analysis of variance was not significant (P = 0.13). Costs for ED visits were twice as high for patients with CD4 cell count less than 50 cells/μl, compared with those with CD4 counts between 50 and 200 cells/μl; overall, ED costs declined as CD4 cell count increased. The percentage of patients with any ED cost dropped from 43% among those with CD4 cell count less than 50 cells/μl to 18% among those in the highest CD4 cell count category.
It is notable that costs for antiretroviral drugs were lower for patients with CD4 cell counts 50 cells/μl or less compared with those for patients with CD4 cell counts between 51 and 200 cells/μl. The lower mean costs for the most severely immunosuppressed patients derives in part from the lower proportion taking these medications. The percentages of patients with zero antiretroviral drug costs were 15, 10, 15, 24, and 16% in the respective CD4 cell count categories, from lowest to highest. Excluding patients with zero antiretroviral drug costs, the mean antiretroviral drug costs for those taking medications were US $10 775 for patients with CD4 cell counts 50 cells/μl or less and US $13 140, 13 783, 14 437, and 14 430 for the other respective CD4 cell count categories; only the difference between the two lowest CD4 categories was significant. In absolute terms, antiretroviral medication costs remained substantial for all patients, regardless of CD4 cell count.
Antiretroviral medications accounted for 61–74% of costs for those with CD4 cell counts more than 200 cells/μl, 45% of costs for those with CD4 cell counts between 51 and 200 cells/μl, and 23% of costs for those with CD4 cell counts 50 cells/μl or less. For those with CD4 cell counts 50 cells/μl or less, inpatient services accounted for the greatest proportion of total costs. Overall, costs for laboratory tests and for ED use were relatively small proportions of total costs. Costs for non-HIV medications exceeded those for OI Px medications, even among patients with CD4 cell counts 200 cells/μl or less.
Seven sites with major cost components
Table 4 reports additional cost estimates from seven sites without the data on resistance tests, ED use, and non-HIV medications. As noted above, these cost components were minor contributors to total costs. For comparison, mean total major costs (i.e., the sum of inpatient, outpatient, antiretroviral medications, OI Px medication, and laboratory tests) are also presented for the three sites with full data. Analysis of variance of total major costs revealed significant main effects for CD4 cell count category and for site group (three versus seven), but the interaction of CD4 cell count category and site group was not significant (P = 0.07); total major costs followed the same pattern across CD4 cell count categories for both groups of sites. Total major costs were higher for the group of three sites than that for the group of seven sites, especially for patients with CD4 cell counts 50 cells/μl or less. For each major cost component, results from seven sites were generally similar to those from three sites; however, mean antiretroviral costs among patients with median CD4 cell counts more than 500 cells/μl were lower (US $9082) than those among patients from other three sites (US $12 313).
Analyses of demographic variations examined total costs (for three sites) and total major costs (for 10 sites). Table 5 shows the results of generalized linear model regression analyses of these cost variables. The ratio of the deviance to the degrees of freedom was 1.15, indicating some overdispersion. However, we used robust estimates of standard errors. Examining a scatterplot of deviance residuals by predicted values indicated no obvious areas of poor fit. Removing 72 cases with deviance residuals, more than three produced only minor changes in results.
The general pattern of results of both analyses was similar. After adjusting for median CD4 cell count, total costs were significantly higher for IDUs than for those with an MSM risk factor. Total costs were higher in older age categories. Differences in costs were not statistically significant by sex or risk group. Although black patients did not differ significantly from white patients, Hispanic patients incurred higher costs than whites.
Costs for decedents
Table 6 presents mean total major costs in 2006 by decedent status and median CD4 cell count category. To maximize sample size, data from all 10 sites were used. Total major costs were substantially higher for patients who died in 2006 (mean = US $44 331, 95% CI 37 667–50 994) than for nondecedents (mean = US $14 932, 95% CI 14 645 15 219). Patients who died in 2007 had mean total major costs in 2006 that were higher than nondecedents' costs, but lower than the costs for those who died in 2006 (mean = US $31 201, 95% CI 26 142–36 260). For all groups, however, mean total costs rose as median CD4 cell count dropped. For decedents, differences between means for adjacent CD4 cell count categories were not statistically significant, reflecting the small samples in each cell.
This study provides the most recent estimates of the cost of treating HIV infection, using data from a large sample of patients from multiple provider sites. HIV-infected individuals in this cohort reported high utilization of inpatient and outpatient care and antiretroviral medications, resulting in high costs of HIV care at all CD4 strata. For 10 sites, mean annual total ‘major’ costs were US $15 693 (95% CI 15 377–16 009). Overall costs of care increased as patients became more immunosuppressed. A substantial proportion of costs was attributable to antiretroviral medication. In patients with severe immunosuppression, inpatient services were the most expensive cost category.
The cost of HAART has been previously estimated to exceed US $10 000 per year [12,33]. Our antiretroviral medication cost estimate, across CD4 cell count categories (n = 14 691) was US $10 138 (95% CI 10 009–10 267). However, the mean includes people who were not taking antiretroviral medications. Among those with nonzero antiretroviral costs, the mean was US $13 024 (95% CI 12 901–13 146), consistent with prior estimates. Our analyses are based on prescribed medications, not actual purchases, to the extent that patients did not purchase all the medications prescribed for them, our estimates would overstate costs.
Mean antiretroviral drug costs were lower for patients with CD4 cell counts less than or equal to 50 cells/μl than those for those with CD4 cell counts between 51 and 200 cells/μl. In the former CD4 cell count category, 78% had some antiretroviral medications prescribed, compared with 86% in the latter CD4 cell count category, for all 10 sites. People with severe immunosuppression may have extensive resistance, with few available HAART options, or may not be able to tolerate these medications.
Antiretroviral drug costs remained substantial for patients with CD4 cell counts more than 350 cells/μl. Presumably, the higher antiretroviral costs in the higher CD4 strata in this study, compared with the 1998 estimates, are due to the effectiveness of maintenance HAART in increasing the CD4 cell count.
Our overall per-person annual cost estimate (US $19 912) is slightly higher than the overall mean of US $18 300 in 1998 obtained in HCSUS . Estimated HIV care costs from HCSUS adjusted to 2006 dollars ranged from US $33 987 for those with CD4 cell counts less than 50 to US $19 734 for those with CD4 cell counts between 50 and 200, and US $7714 for those with CD4 cell counts more than 500 cells/μl . The first estimate is broadly similar to current results, but the last two are lower than current estimates. Differences in antiretroviral regimens between 1998 and 2006 may contribute to differences between the current estimates and those derived from HCSUS. In HCSUS, the proportion of costs due to medications was under 20% for all CD4 strata, except for those with CD4 cell count more than 500 cells/μl. In contrast, the proportion of costs due to medications was greater in the current analyses.
On the basis of the data from 635 patients in one clinic in Alabama in 2001, Chen et al.  estimated a mean total cost of US $18 640 per patient per year, ranging from US $36 532 for those with CD4 cell counts 50 cells/μl or less to US $13 885 for those with CD4 cell counts 350 cells/μl or higher. Our estimates were higher in all CD4 strata. Inpatient expenditure estimates were considerably higher in our study. Our results point to variation in average expenditures from site to site, which highlights the importance of basing estimates on data from multiple sites.
In all CD4 strata, some of the costs are likely due to treatment of non-HIV comorbidities. Rates of hospitalizations for liver-related complications, comorbid psychiatric disease, and substance abuse disorders have increased in HIV-infected populations [34–39]. It is likely that costs will continue to increase in the next decade due to non-HIV-related complications, including age-related conditions such as cardiovascular disease, cerebrovascular disease, and malignancies [40–43]. The higher costs for persons in older age groups in this study may arise from their having more comorbid conditions.
Of note, the cost estimates in this study do not include expenditures for other services, such as treatment for alcohol or substance abuse, mental healthcare (beyond the costs of psychotropic medications), and nonreimbursable costs for services provided by case managers, adherence counselors, nutritionists, expanded access nurses, and other social service providers.
Although our study is one of the most comprehensive assessments of healthcare costs among HIV-infected patients in the United States, our sample is not nationally representative and may not generalize to all HIV patients. However, the sites from which patients were sampled do encompass a broad geographic distribution, and multisite studies afford greater generalizeability than single-site studies. The sites in the HIVRN were all highly experienced in the treatment of HIV; results may differ for patients at sites with less provider experience with HIV or smaller caseloads of HIV patients. It is possible that patients received medical care from multiple providers, and data from one provider might not capture all services used. Provider staff believed that most of their patients received all their HIV care at their site. Nevertheless, our cost estimates are lower bounds to the extent that patients received services from multiple providers.
In conclusion, the annual per-person costs of care for HIV-infected patients in the United States are high. It is misleading to focus on a single number as representing ‘the’ cost of treating HIV infection. Costs estimates varied greatly, depending on severity of illness. Within each CD4 stratum, CIs for total costs could cover a wide range. Such variation should be considered in resource allocation decisions. Antiretroviral regimens containing ‘boosted’ protease inhibitors are now increasingly prevalent and may also be more costly. Given the potential increases in costs of therapeutic agents, toxicities and comorbidities due to HAART, and aging-related comorbidities, it is likely that the aggregate costs of HIV care will continue to increase for the foreseeable future.
We are grateful to all patients, physicians, investigators, and staff involved in the HIVRN. K.A.G and J.A.F. contributed to the study design and analysis, and writing and revisions to the manuscript. RDM contributed to the data collection, study design and analysis, and critical revisions of the manuscript. R.C., J.H., F.H., J.S.J., P.K., and P.G. contributed to the critical revisions of the manuscript.
This work was supported by the Agency for Healthcare Research and Quality (290–01–0012) and National Institutes of Aging (R01 AG026250) and Drug Abuse, NIH (K23-DA00523, and K24-DA00432). K.A.G. was also supported by the Johns Hopkins University Richard Ross Clinician Scientist Award. These agencies had no role in the collection, analysis, or interpretation of the data or in the decision to submit the paper for publication.
The authors would like to acknowledge the state data organizations that participate in the HCUP: California Office of Statewide Health Planning and Development; Colorado Health and Hospital Association; Florida Agency for Healthcare Administration; Iowa Hospital Association; Illinois Healthcare Cost Containment Council; Kansas Hospital Association; Maryland Health Services Cost Review Commission; New Jersey Department of Health and Senior Services; New York State Department of Health; Washington State Department of Health. HCUP is sponsored by AHRQ.
The views expressed in this article are those of the authors. No official endorsement by DHHS, the National Institutes of Health, or the Agency for Healthcare Research and Quality is intended or should be inferred.
Participating sites: Alameda County Medical Center, Oakland, California (Howard Edelstein, M.D.) Children's Hospital of Philadelphia, Philadelphia, Pennsylvania (Richard Rutstein, M.D.) Community Health Network, Rochester, New York (Roberto Corales, D.O.).
Community Medical Alliance, Boston, Massachusetts (James Hellinger, M.D.) Drexel University, Philadelphia, Pennsylvania (Sara Allen, C.R.N.P., Peter Sklar, M.D.).
Henry Ford Hospital Detroit, Michigan (Norman Markowitz, M.D.) Johns Hopkins University, Baltimore, Maryland (Kelly Gebo, M.D., Richard Moore, M.D) Montefiore Medical Group, Bronx, New York (Robert Beil, M.D.) Montefiore Medical Center, Bronx, New York (Lawrence Hanau, M.D.) Nemechek Health Renewal, Kansas City, Missouri (Patrick Nemechek, D.O.) Oregon Health and Science University, Portland, Oregon (P. Todd Korthuis, M.D.) Parkland Health and Hospital System, Dallas, Texas (Laura Armas-Kolostroubis, M.D.) St. Jude's Children's Hospital and University of Tennessee, Memphis, Tennessee (Aditya Gaur, M.D.) St. Luke's Roosevelt Hospital Center, New York, New York (Victoria Sharp, M.D.) Tampa General Healthcare, Tampa, Florida (Charurut Somboonwit, M.D.) University of California, San Diego, La Jolla, California (Stephen Spector, M.D.) University of California, San Diego, California (W. Christopher Mathews, M.D.) Wayne State University, Detroit, Michigan (Jonathan Cohn, M.D.) Sponsoring Agencies Agency for Healthcare Research and Quality, Rockville, Maryland (Fred Hellinger, Ph.D., John Fleishman, Ph.D., Irene Fraser, Ph.D.) Health Resources and Services Administration, Rockville, Maryland (Alice Kroliczak, Ph.D., Robert Mills, Ph.D.).
Data Coordinating Center: Johns Hopkins University (Richard Moore, M.D., Jeanne Keruly, C.R.N.P., Kelly Gebo, M.D., Perrin Hicks, M.P.H., Michelande Ridoré, B.A., Cindy Voss, M.S., Bonnie Cameron, M.S.).
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