Highly active antiretroviral therapy (HAART) has led to dramatic changes in the clinical course of HIV infection. Well-documented declines in morbidity and mortality caused by opportunistic illness have occurred in the United States and other developed countries in which these drugs have been widely available,1-3 and a decline in hospitalization rates has also been reported.4,5
In addition to changes in HIV therapy, the epidemiology of HIV infection in the United States is changing. African Americans and Latinos are now disproportionately affected by the epidemic.6 HIV is also affecting greater numbers of women and those with a history of substance abuse than in previous years.6 Many studies of patients without HIV infection have demonstrated disparities in health care utilization, health care access, and health status by gender and race/ethnicity.7-10 In view of the changing epidemiologic profile of persons with HIV infection, such disparities assume greater importance for HIV care.
The evidence for racial and ethnic disparities in the receipt of antiretroviral therapy is mixed. Some studies have demonstrated a negative association between nonwhite race and antiretroviral use,11-18 including 2 from a nationally representative sample.19,20 Others, however, have found a positive association,21-23 and even more have found no association.24-34 A review of these studies concluded, however, that the preponderance of evidence across time shows that HIV-infected minorities have had lower rates of antiretroviral medication use than HIV-infected whites.35 Other work has demonstrated that gender disparities exist in access to antiretroviral treatments as well,13,21,36,37 with women less likely to receive antiretroviral therapy than men. Although many of these studies assessed antiretroviral therapy before the use of HAART, studies of utilization of HAART continue to suggest that demographic disparities exist.
One factor that might account for some of the differences in receipt of HAART is the extent to which the patient is integrated into the health care system. Patients who appear regularly for monitoring may be viewed as better candidates for HAART than those who have only intermittent contact with health care providers. One indicator of such integration is the number of outpatient visits made by the patient. A second indirect indicator is the number of CD4 tests the patient received during the year; those with fewer tests may be less well connected to the health care system. This article reports on the use of HAART in calendar year (CY) 2001 and the associations between HAART use and various demographic and clinical characteristics. We also sought to determine if outpatient utilization affected the association of gender and race/ethnicity with HAART utilization.
The HIV Research Network (HIVRN) is a consortium of 19 sites that provide primary and subspecialty care to HIV patients. Fifteen sites collect adult data, and 4 are limited to pediatric patients. To be included, sites had to have a minimum data set available in electronic format or through paper abstraction. The minimum data required were patients' age, sex, race, HIV transmission risk factor, AIDS-defining illnesses, CD4 level, HIV-1 RNA level, and use of antiretroviral medication. Ten of these 15 participating sites also collected data on resource utilization, including outpatient clinic and office visits. Data from these 10 sites were included in this analysis; site locations are as follows: 4 in the eastern United States, 2 in the midwestern United States; 2 in the southern United States, and 2 in the western United States. Eight of these sites have academic affiliations, and 2 are community based. The median sample size per site was 985 patients (range: 22-2980 patients). This analysis was limited to adult patients (≥18 years old) who were engaged in HIV primary care before July 1, 2001 at one of these sites. Primary care was defined by having at least 1 visit to the primary care provider and a CD4 count drawn between January 1, 2001 and July 1, 2001.
The data elements described here were abstracted from electronic or paper records at each site. Abstracted data were sent in electronic format to a data coordinating center after personal identifying information was removed. A uniform format was used for the analytic database. For this analysis, data collection encompassed the time period of January 1 through December 31, 2001. The date of the outpatient encounter (not the date of billing or payment of claim) was used.
Quality assurance protocols at the data coordinating center included review of all electronic data and assessment to ensure that each data element was correctly formatted and all elements were captured. Data elements with incorrect formatting, with unknown or incomplete information, or with other inaccuracies were reviewed with the site and corrected. In addition, all site-specific data were reviewed and compared with the network as a whole. All outlier data were verified independently with the sites. Finally, patients who had an indication for HAART or opportunistic infection (OI) prophylaxis but who were not reported as taking the necessary medication were checked with the site for confirmation. After this verification process, the data were combined across sites to achieve a uniformly constructed multisite database. A variable identifying the site was included in this database.
HIV transmission risk factors included injection drug use, men who have sex with men (MSM), and heterosexual transmission (HET), which was defined as either heterosexual activity with a partner at high risk for HIV or sex with an HIV-infected individual. Risk factor assignment was not mutually exclusive; multiple HIV risk factors were recorded. For purposes of analysis, we classified patients as an injection drug user (IDU) or non-IDU; the IDU category included patients with other risk factors (ie, MSM, HET) in addition to injection drug use. Adequate outpatient utilization was defined as 4 or more visits in a CY, consistent with the International AIDS Society-United States of American (IAS-USA) Panel guide-lines, which recommend at least quarterly visits for HIV-infected patients.38,39 For this analysis, outpatient visits were limited to non-emergency department visits to a primary health care provider and did not include administrative visits, laboratory testing, or other encounter visits in which a health care provider was not seen.
HAART was defined as receipt of 3 or more nucleosides or any use of 1 or more protease inhibitors (PIs) or a non-nucleoside reverse transcriptase inhibitor (NNRTI) in combination with 2 or more nucleoside reverse transcriptase inhibitor (NRTI), or as receipt of a PI, NNRTI, NRTI combination. This definition includes triple-nucleoside regimens, which were standard of care in 2001. We opted to be as inclusive as possible in our definition of HAART to maximize the sensitivity of the analysis; this definition is unlikely to exclude any preferred drug combinations. Insurance was categorized into private, Medicaid, Medicare, self-pay/Ryan White, and other. A small number of patients with dual Medicare/Medicaid coverage were classified as Medicare, because Medicare is the primary payer in these patients.
During 2000, the IAS-USA Panel recommended antiretroviral therapy for all patients with CD4 counts less than 350 cells/mm3.38 We constructed a variable indicating the number of CD4 counts less than 350 cells/mm3 during CY 2001. One possible indicator for eligibility for HAART would be the presence of a single CD4 test result below 350 cells/mm3. Some clinicians, however, might wait for a second CD4 count below 350 cells/mm3 to initiate HAART. To capture these different decision rules, we included in the analyses indicators of exactly 1 CD4 count less than 350 cells/mm3 and 2 or more CD4 counts less than 350 cells/mm3 during 2001. No CD4 count less than 350 cells/mm3 was the reference group. Because patients may differ in the number of CD4 tests they receive, we also controlled for this variable in the analyses. To further adjust for disease stage, multivariate analyses included the value of the first reported CD4 test in 2001, categorized as <50, 51-200, 201-350, 351-500 and >500 cells/mm3.
To examine bivariate associations between individual demographic and clinical variables and receipt of HAART, we performed logistic regression analyses. We next examined the association of outpatient utilization with receipt of HAART, adjusting for demographic and clinical factors associated with receipt of HAART in the previous regression. Finally, we determined whether controlling for outpatient utilization altered the associations of demographic factors with HAART.
Analyses were conducted using STATA 8.0 (College Station, TX). In all regressions, adjustment was made for the site of care to account for variations in practice patterns and demographic differences across sites.
Table 1 describes the demographic, clinical, and insurance characteristics of the 10,905 persons enrolled in the cohort and involved in primary care during 2001. The sample was predominantly male (71.1%) and of minority race/ethnicity. The modes of HIV transmission were predominately MSM (41.3%), HET (37.3%), and IDU (22.1%). The median age was 41 years, with a range of 18 to 89 years. The median CD4 count was 329 cells/mm3, and the median HIV-1 RNA level was log10 2.97 copies/mL. Thirty-seven percent of the sample had Medicaid, 30.7% were uninsured or enrolled in Ryan White, 20.8% had Medicare, and 11.3% had private/commercial insurance. A total of 4644 (42.6%) had at least 2 CD4 counts less than 350 cells/mm3, and 19% had exactly 1 CD4 count below 350 cells/mm3.
Overall, 84% of patients received HAART in 2001. Of those with 2 or more CD4 counts below 350 cells/mm3 in 2001, 91% received HAART; 82% of those with 1 CD4 test result below 350 cells/mm3 received HAART; and 77% of those with no CD4 counts below 350 cells/mm3 received HAART.
In bivariate analyses (Table 2), factors associated with HAART usage included male gender (odds ratio [OR] = 1.39, 95% confidence interval [CI]: 1.25, 1.56), age of 40 years or greater (OR = 1.31, 95% CI: 1.19, 1.46), 1 or more CD4 counts, 350 cells/mm3 (P < 0.001), Medicare coverage (OR = 2.25, 95% CI: 1.91, 2.66), Medicaid coverage (OR = 1.26, 95% CI: 1.10, 1.44), and private coverage (OR = 1.24, 95% CI: 1.02, 1.50). African-American race (OR = 0.79, 95% CI: 0.69, 0.90) and injection drug use as an HIV risk factor (OR = 0.85, 95% CI: 0.75, 0.96) were negatively associated with receiving HAART.
In multivariate analysis, excluding outpatient visits from the model, demographic and clinical factors associated with HAART included male gender (adjusted odds ratio [AOR] = 1.21), age of 40 years or greater (AOR = 1.14), and 1 or more CD4 counts <350 cells/mm3 (P < 0.01). Patients with Medicare or Medicaid were more likely to receive HAART than those without insurance (AOR = 1.75 and AOR = 1.16, respectively); the difference between those uninsured and those with private insurance was not significant. Patients who were African-American (AOR = 0.83) or IDUs (AOR = 0.87) were less likely to receive HAART. ORs for demographic and clinical variables did not change appreciably from bivariate to multivariate analyses.
In bivariate and multivariate analyses, patients with one CD4 test result less than 350 cells/mm3 were more likely to receive HAART than those with no CD4 counts below this threshold. Patients with 2 or more CD4 counts below 350 cells/mm3 were even more likely to receive HAART. Initial CD4 count in 2001 provided additional explanatory power, because the likelihood of receiving HAART was greater for those with lower CD4 counts. Despite controlling for these CD4-related variables, the number of CD4 tests recorded in 2001 also had a significant positive effect on HAART receipt.
Outpatient utilization, defined as 4 or more visits in a year, was significantly associated with receipt of HAART (OR = 2.44, 95% CI: 2.18, 2.72) in bivariate analysis. In multivariate logistic regression analyses, when a dichotomous variable indicating 4 or more (vs. 3 or fewer) outpatient visits was added to the model, age of 40 years or older (AOR = 1.13), male gender (AOR = 1.23), African-American race (AOR = 0.84), IDU (AOR = 0.86), 1 or more CD4 counts less than 350 cells/mm3 (P < 0.01), Medicare coverage (AOR = 1.73), and Medicaid coverage (AOR = 1.16) remained significant. Having 4 or more outpatient visits (AOR = 1.34) continued to be strongly associated with receipt of HAART.
This study from a multistate, multisite patient sample has several important findings. First, 84% of patients in primary care in our cohort received HAART. Using a more stringent criterion of eligibility (having 2 or more CD4 counts less than 350 cells/mm3 during the year), 91% of eligible patients received HAART. This is significantly higher than has been reported in other studies, which have demonstrated rates of 57%,36 71%, and 53%.19 Heightened knowledge by HIV providers or increased access by patients since the earlier studies were conducted may have contributed to this higher estimate. This finding may also reflect the selection of high-volume HIV specialty providers into the HIVRN. It should also be noted that the 71% figure, based on the nationally representative HIV Cost and Services Utilization (HCSUS) sample in 1997, reflects cumulative incidence (any use since inception of HAART) and the 53% estimate reflects use at one point in time, whereas the current estimate reflects any receipt of HAART during a 1-year period. The 1-year time frame in the present study should contribute to a somewhat higher estimate than the HCSUS point prevalence estimate. Comparison across studies should be made with caution and should take into account differences in time periods used to evaluate rates of use.
Second, in 2001, demographic disparities continue to exist in the receipt of antiretroviral therapy by HIV-infected patients. In particular, patients younger than 40 years of age, women, African-Americans, IDUs, and those who were uninsured or had private insurance were less likely to receive clinically indicated HAART than older patients, men, whites, Hispanics, those with risk factors other than injection drug use, or those who had Medicare coverage.
The lower rate of HAART receipt among African-Americans compared with whites is consistent with past studies that have demonstrated racial disparities19,20 in receipt of HAART. Even with the advent of newer and more effective therapies for HIV, racial disparities have persisted from the early HAART era. This association remained significant after adjustment for outpatient utilization. Lack of access to medical care does not explain the current results, because the odds of receiving HAART remained lower for African-Americans even after controlling for outpatient utilization and number of CD4 tests. This suggests that even African-Americans with advanced HIV who were engaged in HIV care are less likely to be prescribed antiretroviral treatment than whites. Clearly, such persistent disparities require interventions to improve access to these life-saving therapies.
In addition, IDUs were less likely to receive HAART than non-IDUs. This is consistent with other studies that have shown IDUs have been less likely to start HAART than MSM40 and slower to use HAART compared with MSM.41,42 In our analysis, this association remained after adjustment for outpatient utilization.
Several studies in the pre-HAART37 and early HAART36,37 eras reported that men were more likely to receive HAART (or any antiretroviral) than women. In the nationally representative HCSUS study,19 men were more likely than women to receive HAART in bivariate analyses; however, this difference was not significant in multivariate analyses. Analyses of New Jersey Medicaid data from 1996 through 1998 showed no gender difference in time to initiation of HAART.43 Data from 1 clinic in 1998, in contrast, revealed that women were less likely to receive HAART.44 In our study, the gender effect remained significant after adjustment for other demographic factors. The statistical significance of gender differences in HAART receipt may depend, in part, on whether the analysis adjusts for other indicators of socioeconomic status, such as education or income, that are strongly correlated with gender among patients with HIV disease.
In analyses of nationally representative data from 1997,19 lower odds of current HAART use among African-Americans and patients with an injection drug use risk factor, which were obtained in bivariate analyses, became nonsignificant when education, region, and insurance were controlled.
Unfortunately, the current analyses could not control for indicators of socioeconomic status, such as education or income, which are often not reported in medical records. Gender, race, and injection drug use may be surrogate markers for other aspects of socioeconomic status, and it is possible that our obtained effects for race and injection drug use would diminish had we been able to control for education and income. Minority race/ethnicity, low income, and relative lack of education are components of a general constellation of disadvantage, and distinguishing their independent effects may be difficult.
In the full multivariate model, patients with Medicare were more likely to receive HAART than those who were uninsured (including those with services funded by Ryan White) or had private insurance. Medicare does not typically include pharmacy coverage; thus, the positive association between Medicare and HAART was surprising. Because Medicare eligibility requires a 2-year waiting period for persons less than 65 years of age receiving Social Security Disability Insurance (SSDI), patients on Medicare may have more advanced HIV disease than others. Despite adjusting for CD4 count, unmeasured variation in disease severity may partially explain the association between Medicaid, Medicare, and HAART. Other research has found that patients with both Medicaid and Medicare coverage initiated HAART more rapidly and persisted on HAART regimens longer than those with only Medicaid coverage.43 On the other hand, the lack of a significant difference in receipt of HAART in uninsured persons is contrary to prior findings.19,32 In addition, the association of HAART and private insurance was no longer significant after adjustment for other clinical and demographic variables. There are several potential reasons for this finding. Some private insurance programs do not have prescription coverage, and patients who have employer-based private insurance often exceed income requirements for public pharmacy assistance programs. In addition, other studies have demonstrated that as copayments increase, patients may be less likely to continue expensive medications.45 Finally, our model included AIDS Drug Assistance Program (ADAP) programs and Ryan White as uninsured. Expansions in ADAP between 1997 and 2001 may have improved access to medications for patients without insurance.
We used 4 visits per year to identify persons with appropriate outpatient utilization, because standards suggest that HIV patients should be seen and evaluated at least on a quarterly basis.38,39 Consistent with another study that found outpatient utilization to be associated with HAART prescription,36 we found that HAART usage was associated with 4 or more visits per year. More outpatient utilization could be associated with an increased likelihood of HAART receipt for several reasons, including greater access to health care providers, more regular visits to assess adherence, and providers' improved understanding of the patient's social situation and potential barriers to adherence.
The inclusion of outpatient visits in the logistic regression model implies a causal structure in which visits affect the likelihood of receiving HAART. It is possible that the reverse causal sequence could operate: patients receiving HAART may be asked to return more frequently for monitoring of side effects and therapeutic efficacy. Alternatively, it is possible that patients who were more symptomatic or in a more advanced stage of disease were likely to have more visits and to receive HAART; unmeasured disease severity could give rise to an association. We reported results of a logistic regression that included outpatient visits to compare our results with those of McNaghten et al,36 who reported a similar analysis. Neverthe-less, the study design does not permit an unambiguous causal inference regarding outpatient visits; the coefficient for outpatient visits cannot be given a causal interpretation.
This study has several important potential limitations. First, the sample is not nationally representative and does not generalize to all HIV care sites. The sites in the sample do encompass a broad geographic distribution, and multisite studies afford greater generalizability than single-site studies. Moreover, the sites in the HIVRN were all highly experienced in the treatment of HIV; results may differ at sites with less provider experience with HIV or a smaller caseload of patients with HIV. In addition, not all the sites in the HIVRN collect comprehensive HAART or outpatient utilization data; therefore, we were only able to analyze data from 10 of the 15 adult sites in the network. Second, in identifying patients who were not receiving HAART, we were unable to assess whether they refused it or whether other complex medical decision-making by them and their providers resulted in their not being on HAART. Third, we did not have access to socioeconomic variables, such as income and education, which have been shown to be associated with adherence to HAART. Also, we were unable to assess whether patients were benefiting from HAART by decreases in HIV-1 RNA levels, hospitalization rates, or mortality; future studies will address the long-term benefits of treatment in these patients.
Although 84% of the persons in our cohort received HAART, important disparities persist. HAART was less likely to be received by African Americans, IDUs, women, and those who came to the clinic less frequently, despite controlling for frequency of outpatient visits. Such disparities potentially increase the risk of morbidity and mortality in these subgroups. Policies that attempt to increase access to care and enhance patients' integration into the care system are essential to improve the quality of HIV care and increase access to HAART.
1. Palella FJ, Jr, Delaney KM, Moorman AC, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med.
2. Ledergerber B, Egger M, Erard V, et al. AIDS-related opportunistic illnesses occurring after initiation of potent antiretroviral therapy: the Swiss HIV Cohort Study. JAMA.
3. Moore RD, Chaisson RE. Natural history of HIV infection in the era of combination antiretroviral therapy. AIDS.
4. Mouton Y, Alfandari S, Valette M, et al. Impact of protease inhibitors on AIDS-defining events and hospitalizations in 10 French AIDS reference centres. Federation National des Centres de Lutte contre le SIDA. AIDS.
5. Torres RA, Barr M. Impact of combination therapy for HIV infection on inpatient census. N Engl J Med.
6. Centers for Disease Control and Prevention. HIV/AIDS Surveillance Supplemental Report, vol. 7. Atlanta: Centers for Disease Control and Prevention; 2001.
7. Hargraves JL, Cunningham PJ, Hughes RG. Racial and ethnic differences in access to medical care in managed care plans. Health Serv Res.
8. Kressin NR, Petersen LA. Racial differences in the use of invasive cardiovascular procedures: review of the literature and prescription for future research. Ann Intern Med.
9. Schneider EC, Cleary PD, Zaslavsky AM, et al. Racial disparity in influenza vaccination: does managed care narrow the gap between African Americans and whites? JAMA.
10. Garg PP, Diener-West M, Powe NR. Reducing racial disparities in transplant activation: whom should we target? Am J Kidney Dis.
11. Stein MD, Piette J, Mor V, et al. Differences in access to zidovudine (AZT) among symptomatic HIV-infected persons. J Gen Intern Med.
12. McLaughlin TJ, Soumerai SB, Weinrib D, et al. The association between primary source of ambulatory care and access to outcomes of treatment among AIDS patients. Int J Qual Health Care.
13. Crystal S, Sambamoorthi U, Merzel C. The diffusion of innovation in AIDS treatment: zidovudine use in two New Jersey cohorts. Health Serv Res.
14. Graham NM, Jacobson LP, Kuo V, et al. Access to therapy in the Multi-center AIDS Cohort Study, 1989-1992. J Clin Epidemiol.
15. Moore RD, Stanton D, Gopalan R, et al. Racial differences in the use of drug therapy for HIV disease in an urban community. N Engl J Med.
16. Jeffe DB, Meredith KL, Mundy LM, et al. Factors associated with HIV-infected patients' recognition and use of HIV medications. J Acquir Immune Defic Syndr Hum Retrovirol.
17. Sambamoorthi U, Moynihan PJ, McSpiritt E, et al. Use of protease inhibitors and non-nucleoside reverse transcriptase inhibitors among Medicaid beneficiaries with AIDS. Am J Public Health.
18. Lucas GM, Cheever LW, Chaisson RE, et al. Detrimental effects of continued illicit drug use on the treatment of HIV-1 infection. J Acquir Immune Defic Syndr.
19. Cunningham WE, Markson LE, Andersen RM, et al. Prevalence and predictors of highly active antiretroviral therapy use in patients with HIV infection in the United States. HCSUS Consortium. HIV Cost and Services Utilization. J Acquir Immune Defic Syndr.
20. Shapiro MF, Morton SC, McCaffrey DF, et al. Variations in the care of HIV-infected adults in the United States: results from the HIV Cost and Services Utilization Study. JAMA.
21. Smith SR, Kirking DM. Access and use of medications in HIV disease. Health Serv Res.
22. Turner BJ, Newschaffer CJ, Zhang D, et al. Translating clinical trial results into practice: the effect of an AIDS clinical trial on prescribed antiretroviral therapy for HIV-infected pregnant women. Ann Intern Med.
23. Anderson KH, Mitchell JM. Differential access in the receipt of antiretroviral drugs for the treatment of AIDS and its implications for survival. Arch Intern Med.
24. Weissman JS, Makadon HJ, Seage GR III, et al. Changes in insurance status and access to care for persons with AIDS in the Boston Health Study. Am J Public Health.
25. Katz MH, Chang SW, Buchbinder SP, et al. Health insurance and use of medical services by men infected with HIV. J Acquir Immune Defic Syndr Hum Retrovirol.
26. Hellinger FJ. The use of health services by women with HIV infection. Health Serv Res.
27. Solomon L, Stein M, Flynn C, et al. Health services use by urban women with or at risk for HIV-1 infection: the HIV Epidemiology Research Study (HERS). J Acquir Immune Defic Syndr Hum Retrovirol.
28. Sambamoorthi U, Warner LA, Crystal S, et al. Drug abuse, methadone treatment, and health services use among injection drug users with AIDS. Drug Alcohol Depend.
29. Kass N, Flynn C, Jacobson L, et al. Effect of race on insurance coverage and health service use for HIV-infected gay men. J Acquir Immune Defic Syndr.
30. Turner BJ, Newschaffer CJ, Zhang D, et al. Antiretroviral use and pharmacy-based measurement of adherence in postpartum HIV-infected women. Med Care.
31. Sorvillo F, Kerndt P, Odem S, et al. Use of protease inhibitors among persons with AIDS in Los Angeles County. AIDS Care.
32. Smith MY, Rapkin BD, Winkel G, et al. Housing status and health care service utilization among low-income persons with HIV/AIDS. J Gen Intern Med.
33. Jacobson LP, Gore ME, Strathdee SA, et al. Therapy naivety in the era of potent antiretroviral therapy. J Clin Epidemiol.
34. Schwarz DF, Henry-Reid L, Houser J, et al. The association of perceived health, clinical status, and initiation of HAART (highly active antiretro-viral therapy) in adolescents. J Adolesc Health.
35. Palacio H, Kahn JG, Richards TA, et al. Effect of race and/or ethnicity in use of antiretrovirals and prophylaxis for opportunistic infection: a review of the literature. Public Health Rep.
36. McNaghten AD, Hanson DL, Dworkin MS, et al. Differences in prescription of antiretroviral therapy in a large cohort of HIV-infected patients. J Acquir Immune Defic Syndr.
37. Andersen R, Bozzette S, Shapiro M, et al. Access of vulnerable groups to antiretroviral therapy among persons in care for HIV disease in the United States. HCSUS Consortium. HIV Cost and Services Utilization Study. Health Serv Res.
38. Carpenter CCJ, Cooper DA, Fischl MA, et al. Antiretroviral therapy in adults: updated recommendations of the International AIDS Society-USA Panel. JAMA.
39. Yeni PG, Hammer SM, Carpenter CC, et al. Antiretroviral treatment for adult HIV infection in 2002: updated recommendations of the International AIDS Society-USA Panel. JAMA.
40. Mocroft A, Madge S, Johnson AM, et al. A comparison of exposure groups in the EuroSIDA study: starting highly active antiretroviral therapy (HAART), response to HAART, and survival. J Acquir Immune Defic Syndr.
41. Junghans C, Low N, Chan P, et al. Uniform risk of clinical progression despite differences in utilization of highly active antiretroviral therapy: Swiss HIV Cohort Study. AIDS.
42. Fairfield KM, Libman H, Davis RB, et al. Delays in protease inhibitor use in clinical practice. J Gen Intern Med.
43. Crystal S, Sambamoorthi U, Moynihan PJ, et al. Initiation and continuation of newer antiretroviral treatments among medicaid recipients with AIDS. J Gen Intern Med.
44. Giordano TP, White AC, Sajja P, et al. Factors associated with the use of highly active antiretroviral therapy in patients newly entering care in an urban clinic. J Acquir Immune Defic Syndr.
45. Huskamp HA, Deverka PA, Epstein AM, et al. The effect of incentive-based formularies on prescription-drug utilization and spending. N Engl J Med.
Montefiore Medical Group, Bronx, NY (Robert Beil, MD)
Alameda County Medical Center, Oakland, CA (Kathleen Clanon, MD, PhD)
Wayne State University, Detroit, MI (Lawrence Crane, MD)
Community Health Network, Rochester, NY (Steven Fine, MD)
St. Jude's Children's Hospital and University of Tennessee, Memphis, TN (Patricia Flynn, MD)
Montefiore Medical Group, Bronx, NY (Marc Gourevitch, MD)
Montefiore Medical Center, Bronx, NY (Lawrence Hanau, MD)
Community Medical Alliance, Boston, MA (James Hellinger, MD)
Henry Ford Hospital Detroit, MI (John Jovanovich, MD)
Parkland Health and Hospital System, Dallas, TX (Philip Keiser, MD)
Oregon Health and Science University, Portland, OR (P. Todd Korthuis, MD, MPH)
University of California, San Diego, CA (W. Christopher Mathews, MD, MSPH)
Johns Hopkins University, Baltimore, MD (Richard D. Moore, MD)
Tampa General Health Care, Tampa, FL (Jeffrey Nadler, MD)
Nemechek Health Renewal, Kansas City, MO (Patrick Nemechek, MD)
Children's Hospital of Philadelphia, Philadelphia, PA (Richard Rutstein, MD)
St. Luke's Roosevelt Hospital Center, New York, NY (Victoria Sharp, MD)
Drexel University, Philadelphia, PA (Peter Sklar, MD, MPH)
University of California, San Diego, La Jolla, CA (Stephen Spector, MD)
Drexel University, Philadelphia, PA (James Witek, MD)