The clinical course of HIV-infected patients changed with the advent of highly active antiretroviral therapy (HAART). Using ≥3 active HIV drugs in combination results in higher CD4 cell counts,1 lower plasma HIV RNA levels,2 better health status,2,3 and a more chronic condition with prolonged survival.4,5 Clinical pharmacists with specialization in HIV disease have many roles and can contribute to HIV chronic disease management.6,7 They can counsel patients on optimal medication adherence,8 manage side effects and drug interactions,9 promote timely refills,10 and reduce antiretroviral (ARV)-related medication errors.11 Some pharmacists provide active case management, arrange follow-up appointments, and order laboratory tests per predetermined protocols. Many assist with clinical trials,12 enabling access to newer medications. Two thirds of staff model health maintenance organizations (HMOs) employ clinical pharmacists in the management of various chronic conditions, including HIV.13
Adherence is a critical aspect of HAART,14,15 with a number of patient characteristics associated with levels of adherence, some of which are modifiable.14,16-20 Optimal adherence is challenging, with reports noting that ≥90% adherence to a complex regimen is necessary for achieving and sustaining satisfactory viral control.3,15 Small prospective studies in public health care settings suggest that HIV pharmacists contribute to better adherence and viral control for HIV-infected patients, especially for directly observed therapy.21-24 Small studies from private health care clinics report similar results,12 with the greatest benefit found in ensuring timely refills for monthly prescriptions.8 Other small studies failed to demonstrate improved adherence associated with a pharmacist, however.6,13 A Veterans Affairs (VA) study found that pharmacist intervention did not improve adherence, but better adverse effects management was reported.25 Pharmacist telephone calls improved self-reported adherence but did not improve virologic outcome in another study.26 Further, few studies have evaluated the effect of the clinical pharmacist on resource utilization, as measured by hospital days and emergency department (ED) or office visits.
Studying the effect of the clinical pharmacist in larger health care settings is warranted. We investigated the impact of the clinical pharmacist on clinical outcomes among HIV-infected health plan members of a large HMO, Kaiser Permanente Northern California (KPNC). We hypothesized that the use of the clinical pharmacist would result in better clinical and utilization parameters in this managed care setting.
METHODS
Study Design
We performed an analysis of ARV-naive HIV-infected patients starting HAART among KPNC HIV-infected patients from 1997 through 2002. We compared clinical and utilization measures in patients receiving care at clinics with a clinical pharmacist (all of whom had a pharmacy degree with specialization in HIV disease) with those in patients receiving care at clinics without a clinical pharmacist over a 24-month period after initiation of HAART.
Although other disease entities have disease-specific clinical pharmacists at each medical center, for HIV disease, each medical center has made its own decision based on perceived need, budgetary concerns, and availability of a qualified clinical pharmacist. At those medical centers with HIV clinical pharmacists, the standard of practice is that HIV-infected patients are closely followed during consultative visits with the clinical pharmacist at HAART initiation and with each regimen change. Specific duties of the pharmacist include regimen counseling, adverse effects management, arranging appointments, and case management. In medical centers in which there is no clinical pharmacist, case managers or the HIV provider is expected to perform these duties. In some medical centers, HIV clinical pharmacists were hired after 1997; thus, follow-up for these medical centers was divided accordingly.
Subjects
KPNC is an integrated health care system composed of 14 medical centers (synonymous with clinics here). Patients are eligible for outpatient and inpatient care, including pharmacy, medical, and hospital care. KPNC maintains electronic databases that include patients' diagnoses, laboratory values, pharmacy prescriptions, hospitalizations, office visits, and demographics. Separately, a confidential registry of all HIV-infected patients receiving care at KPNC has been maintained since 1988 and includes patients' age, gender, race/ethnicity, HIV risk behavior, and other HIV specific data. Linking the HIV registry to other KPNC electronic databases, we can examine diagnoses (inpatient and outpatient); office and hospital visits; pharmacy utilization, including prescriptions dispensed and refills; and pertinent laboratory values. Most HIV-infected patients receive their medications through the KPNC pharmacy system.
Study Population
We identified all HIV-infected patients initiating HAART with no record of any prior ARV medication (ie, ARV naive). We defined a new HAART regimen as ≥3 ARV drugs used in combination, with ritonavir at doses ≤400 mg/d not considered an active drug in the regimen. To ensure the identification of ARV-naive patients, we required ≥12 months of health plan membership before starting medications. Additional inclusion criteria were being ≥18 years of age at regimen initiation and evidence of filling HIV medications through the KPNC pharmacy system. We excluded 62 patients without a KPNC pharmacy benefit because we could not reliably capture all prescriptions dispensed for this group.
Measurements
We obtained the following demographic characteristics from the HIV registry and KPNC databases (measured at the start of HAART for time-sensitive data): gender, race/ethnicity (classified as white, black, Latino, or other), HIV risk behavior (recorded as men having sex with men [MSM], injection drug use, heterosexual sex, or other/unknown), address (for assigning 2000 US census data poverty level and calculated geographic code poverty level index), comorbidities (measured by the Charlson Comorbidity Index),27,28 and years known to be HIV infected. We also recorded the medical clinic and HIV provider (to calculate and assign the provider HIV patient panel size dichotomized to ≤50 or >50 patients as a surrogate for provider experience29).
The pharmacy database provided details of ARVs used, including regimen composition and other medications prescribed, year of first ARV prescription fill, and all refill data for these patients during study follow-up. Twelve and 24 months of adherence to a HAART regimen were calculated using established methods developed for administrative pharmacy databases, which account for all the component medicines of an individual patient's HAART regimen.15,30,31 This measure is computed the same across all ARV medications as the number of doses in an interval bounded by first and last fill dates of a drug (at least 2 fills required per drug) for which the patient has the drug in possession (based on quantity supplied and doses in the interval between fills) as a percentage of total doses in the span between the first and last fills.
The following measures were determined: plasma HIV RNA level (log10 copies/mL) before HAART initiation and at 6, 12, and 24 months after initiation; CD4 T-cell counts (cells/μL) before HAART initiation and all measurements within 24 months after HAART initiation; and the numbers of hospital days, ED visits, and office visits over 24 months from the start of HAART. Plasma HIV RNA level measures lower than the limits of quantification were recorded as the lowest measurable quantity.
Statistical Methods
The primary predictor variable of interest was attending a clinic at which a clinical pharmacist was stationed. The principal outcomes were the plasma HIV RNA level changes at 12 and 24 months (as a continuous outcome and as the odds of achieving <500 copies/mL), CD4+ T-cell count changes over 12 and 24 months, and utilization measures over 24 months. Changes from pretreatment levels for plasma HIV RNA levels at 12 and 24 months were determined using multiple linear regression. Conditional logistic regression was used for the outcome of achieving <500 copies/mL, with clustering by medical center treated as a fixed effect (to account for any variability between medical centers). We chose 500 copies/mL as the plasma HIV RNA level odds cutoff, because assays before 2000 used this as the lower limit of quantification.
Utilizing all available measurements during the 2-year follow-up period, we used linear mixed models to examine changes over time in CD4 T-cell counts longitudinally incorporating random intercepts and slopes. A 2-piece segmented linear model was used to allow for changing slopes over time, with time segments from 0 to 6 months and from 6 to 24 months, consistent with previous studies.1 This analysis required that we cluster by patient and time and treat medical center as a potential covariate.
We used conditional fixed effects Poisson regression analysis for comparison of hospital days, ED visits, and office visits over 24 months, with clustering by medical center. Utilization results are reported as days or visits per patient year and as rate ratios. We report all analyses with and without adjustment for covariates.
Other patient predictors considered were gender, race/ethnicity, age at the start of a regimen, HIV risk behavior, regimen type (nonnucleoside reverse transcriptase inhibitor [NNRTI] based, protease inhibitor [PI] based, nucleoside reverse transcriptase inhibitor [NRTI] only, or PI + NNRTI mixed), total number of ARV pills per day, total number of all pills per day, HIV provider panel size (≤50 or >50 HIV-infected patients), medical center at which patient received care (to account for practice differences), AIDS status (Centers for Disease Control and Prevention [CDC] 1993 definition), baseline plasma HIV RNA level and CD4 T-cell count, Charlson Comorbidity Index, geographic poverty level percent index (surrogate for socioeconomic status and stratified as ≤15% and >15% geographic poverty level), year a regimen began (to account for secular trends), and years of known HIV infection. We used a backward selection approach to model building, with a predictor included if the regression coefficient associated with the primary predictor variable changed 10% or more.
Potential interactions between a clinical pharmacist and provider panel size were explored, and results were stratified by the provider panel size if P < 0.10. No other potential interactions achieved a statistical significance of P < 0.20 and are not reported. We performed analyses comparing these results with the final adjusted outcome measures for the following subpopulations: MSM with injection drug user (IDU) with heterosexual HIV acquisition risk behavior, patients with a geographic poverty level percent equal to or greater than compared with less than the 15% level, patients with a Charlson Comorbidity Index score less than compared with greater than or equal to 1, and number of years patients known to be HIV infected less than or equal to compared with greater than the median value of 1.5 years. We compared all final statistical models with and without the adherence covariate measure to ascertain the magnitude of which type of adherence accounted for the effects of a clinical pharmacist. STATA version 9SE (Stata Corporation, College Station, TX) was used for all analyses, using the commands “xtreg,” “xtlogit,” “xtmixed,” and “xtpoisson.”
We obtained approval from the KPNC Institutional Review Board with waiver of informed consent for protection of human subjects before the start of data collection.
RESULTS
Six of the 14 KPNC medical centers have HIV clinical pharmacists. A total of 1571 patients met entry criteria (of 5384 on HAART in the registry), of whom 733 received their HIV care at sites employing an HIV pharmacist and 838 were followed at clinics without a pharmacist. The patient characteristics at initiation of the ARV regimen are shown in Table 1. The groups were similar in age, type of ARV regimen used, total number of pills per day, and baseline laboratory values. Patients at clinics with pharmacists were much more likely to be male (P < 0.001), white (P < 0.001), MSM (P < 0.001), less poor (P = 0.006), and infected with HIV longer (P < 0.001), as well as to have physicians with greater experience (P < 0.001).
Table 2 displays the primary outcome measures for individual medical centers with and without a clinical pharmacist. Overall, patients exposed to a clinical pharmacist were more likely to achieve HIV RNA levels <500 copies/mL at both time points and had lower mean utilization measures. CD4 counts seemed to be similar among most medical centers. Within each cohort, the mean values for each medical center were generally consistent, with more variance seen among the clinical pharmacist-associated clinics. Of note, the medical centers that obtained a clinical pharmacist after 1996 (see clinics C, D, and E in Table 2) had generally improved statistics after the acquisition of the clinical pharmacist.
Plasma HIV RNA level changes are shown in Table 3. Achieving a plasma HIV RNA level <500 copies/mL differed by time point. The 12-month adjusted data showed a doubling of the odds of achieving an HIV RNA level <500 copies/mL (odds ratio [OR] = 2.01; P = 0.06). At 24 months, however, the OR was dependent on the provider panel size (P = 0.09 for the interaction), with a greater association observed for patients cared for by a provider with an HIV panel size ≤50 patients. On a continuous scale, the decline in HIV RNA levels was dependent on the provider HIV patient panel size at 12 months (P = 0.06 for interaction), with the adjusted log10 decline greater in the patients cared for by a provider with an HIV panel size >50 patients (−0.77 log10/mL; P < 0.001). The greater decline in HIV RNA levels with the clinical pharmacist group persisted at 24 months (−0.28 log10/mL; P = 0.02), but there was no interaction with the provider HIV panel size.
As depicted in Figure 1, patients exposed to a clinical pharmacist had higher mean CD4 T-cell counts at HAART initiation, at 6 months, and at 12 months but similar levels at 24 months. After adjustment for other predictors (see Table 3), the clinical pharmacist group had a nonsignificant but larger increase in CD4 T-cell counts at each time point (6 months: 32 cells/μL, P = 0.24; 12 months: 24 cells/μL, P = 0.38; and 24 months: 7 cells/μL, P = 0.80). Provider HIV panel size had no impact on these results.
Table 4 demonstrates that the effect of a clinical pharmacist on utilization measures depended on the provider panel size, with a statistically significant interaction for all 3 outcome measures. The clinical pharmacist was associated with a mean 64% (95% confidence interval [CI]: 30% to 108%) increase in hospital days for the clinical pharmacist group compared with the nonclinical pharmacist group for patients whose provider cared for ≤50 patients but was associated with only a 9% (95% CI: −11% to 33%) mean increase for patients whose provider cared for >50 HIV-infected patients. For patients cared for by providers with panel sizes ≤50 HIV-infected patients, the clinical pharmacist was associated with a 19% decrease (95% CI: −13% to −24%) in office visits over 24 months after adjustment, but this savings was only 2% (95% CI: −7% to 3%) for patients cared for by providers with HIV panel sizes >50 patients. For ED visits, the clinical pharmacist group was associated with a 19% (95% CI: −40% to 8%) decrease compared with the nonclinical pharmacist group for providers with panel size ≤50 but with a 10% increase (95% CI: −16% to 43%) for a panel size >50 patients.
The effect of all predictors is presented in Table 5 for all models involving each outcome measure. For most outcome measures, other predictor variables had a significant effect on the results independent of the clinical pharmacist. Age and years known to be HIV infected had the greatest consistent association with viral control. For CD4 T-cell count changes, the predictors with the most significant associations were negative. The clinical pharmacist had a significant association with office visit and hospital day rates, even in comparison to other predictors. No one predictor was consistently statistically significant for all the outcome measures.
Subpopulation analyses (Table 6) did not reveal any significant impact on our final adjusted outcome measures for Charlson Comorbidity Index scores or for years known to be HIV infected. The effect of a clinical pharmacist on the odds of achieving an HIV RNA level <500 copies/mL was twice as great for patients with a geographic poverty level index >15% (OR: 3.32 vs. 1.64). The clinical pharmacist group caring for patients with a poverty level index >15% had greater than 70% lower hospital day rates and greater than 30% lower office visit rates compared with the final adjusted outcomes for the entire cohort. For HIV acquisition risk behavior, the clinical pharmacist was associated with a trend toward lower ED visit rates for MSM but with a trend toward higher ED visit rates for IDUs.
We also considered the contribution of adherence to our outcome measures in Table 7. After adjustment, the clinical pharmacist was associated with a 7.1% greater adherence at 12 months (81.1% vs. 74.0%; P = 0.04) and with a 7.8% higher adherence at 24 months (76.7% vs. 68.9%; P = 0.02). Results of adjusted analyses of plasma HIV RNA level with adherence added to the multivariate models show a muting of the associations with the clinical pharmacist at 12 and 24 months, reducing point estimates by 13% to 28%. The statistically significant decrease in plasma HIV RNA level at 24 months was no longer statistically significant with adherence in the model, suggesting that the effect of the clinical pharmacist was partially mediated through improved adherence. The reduction in magnitude of ORs for achieving a plasma HIV RNA level <500 copies/mL after adjusting for adherence was small (range: 6%-15%). Adherence seemed to have a minimal effect on measures of CD4+ T-cell count and utilization outcomes, with these differences changing by ≤6%.
DISCUSSION
To our knowledge, this study is the largest analysis of the contribution of a clinical pharmacist to clinical outcome and utilization measures in untreated HIV-infected patients initiating HAART. The study's significance relates to its size, quantifiable outcome measures, and correlation with utilization outcomes. As noted earlier, there is a paucity of data on the impact of the clinical pharmacist on clinical outcomes and even less data on the clinical pharmacist's impact on resource utilization among HIV-infected patients on HAART. Being able to quantify the benefit of these professionals should enable health systems to direct resources in the care of these patients better. Here, the clinical pharmacist was beneficially associated with greater decreases in plasma HIV RNA levels and higher odds of achieving plasma HIV RNA levels <500 copies/mL. The HIV pharmacist working with providers with smaller HIV patient panels was associated with a significant decrease in total office visits over 24 months but also with a significant increase in the total number of hospital days.
The results suggest that the clinical pharmacist has a strong impact on promoting plasma HIV RNA reduction, especially at 12 months, where the result approaches a 1-log10/mL decline with larger provider panel sizes, a clinically significant difference,32 and odds of achieving HIV RNA levels <500 copies/mL. We determined that improved therapy adherence accounted for some of the benefit of a clinical pharmacist. We postulate that better adverse effect management and better counseling on HAART consumption also contributed to this finding. We also found a stronger association with smaller panel sizes, which is potentially attributable to a greater influence of the pharmacist on the individual patient when the provider is less experienced and potential synergies with more experienced clinicians.9,12,15,29
The effect of the clinical pharmacist was not observed for CD4 T-cell count changes. Usually, greater improvement in plasma HIV RNA levels is associated with greater improvement in CD4 T-cell counts; thus, this lack of effect is puzzling. Equally puzzling is the lack of contribution of improved adherence to the T-cell count effects. Male gender, younger age, and fewer total pills per day were associated with elevated CD4 cell counts, however, and these results are also seen in other studies.1,33-35
The clinical pharmacist seemed to have a significant impact on utilization outcomes, particularly for smaller provider panel sizes. Again, we postulate that providers with larger panel sizes likely drive the utilization results for their patients.29 The increase in hospital days for patients exposed to a pharmacist has been described elsewhere36 and may reflect less confidence in outpatient care for serious complications or opportunistic infections by less experienced providers.29 The savings are less for ED visit rates and dependent on provider panel size, implying that the drivers of ED utilization do not necessarily include the clinical pharmacist. In the office visit setting, the clinical pharmacists may provide savings in time for larger panel providers if they assist with prescription refills and adverse effects management9,10,37 outside of regular appointments, but these activities could not be measured in our study.
Although the adjusted means per patient difference for office visits is small, the reduction in office visits could be substantial (approximately 393-4085 visits saved per year) for the entire study population. The implementation of a clinical pharmacist is most cost-effective if the office visits savings tend toward the upper limit. If we assume an average office visit cost of $150, each clinical pharmacist (with a mean salary of approximately $120,000 per year [internal data]) needs to save nearly 800 visits per year. A greater potential cost benefit could be actualized, however, if the cost of ancillary services are considered (but not measured in this study). A more in-depth cost-effectiveness study on this topic is warranted.
The associations seen for most parameters were not solely attributable to adherence, suggesting that adherence counseling may not be the only impact that the pharmacist has on the clinical outcome. Studies have found that increased outpatient care in advanced disease decreases ED utilization.38 Adverse effects of ARV medication have been associated previously with worse outcomes.39,40 Greater adverse effect control and increased access to the pharmacist41 could improve utilization, as could case management by the pharmacist.
Our subpopulation analyses revealed an apparent benefit of the clinical pharmacist for patients living in higher geographic poverty level areas, especially for utilization outcomes. Prior studies have shown that case management can increase access to HIV care and retain patients in care,42 and patient education targeted to lower income communities has been found to increase adherence.43 Also, studies indicate that the clinical pharmacist spends more time discussing HIV medication management with patients than physicians,12,24,44 which may benefit patients with more life stressors. Thus, clinics that care for more poverty-stricken patients could benefit from clinical pharmacists more than clinics that care for patients less affected by socioeconomic status.
Our other subpopulation analyses revealed a negative association between the clinical pharmacist and IDUs for resource utilization, implying that the clinical pharmacist identified a need for more intensive management that could not be achieved without direct patient contact by physicians. By contrast, the clinical pharmacist's association among MSM with lower ED and office visit rates implies a greater amenability to manage these patients without direct physician contact. These differences should guide the implementation of clinical pharmacists to clinics where the patient population mix would maximize the benefit of the clinical pharmacist.
There are limitations to this study. Because this was an ecologic study, we could not confirm that every patient at sites with a clinical pharmacist actually saw a pharmacist. KPNC membership for at least 12 months was used to ensure no prior ARV medication, but we cannot guarantee distant prior ARV use. Missing data were also a concern; 125 patients (8%) did not have plasma HIV RNA level data at 12 months, and 139 patients (9%) did not have such data at 24 months. Nevertheless, the proportion of patients with missing data was small and was evenly distributed between patients at clinics with and without a pharmacist. The CD4 T-cell count analysis accounts for potential missing data. Because 24 months of membership was required for inclusion, there were no data lost to follow-up for utilization for these patients. A final potential limitation was the use of the timing of ARV refill data to measure therapy adherence.
In conclusion, we demonstrate a positive association between exposure to the clinical pharmacist and plasma HIV RNA control. We demonstrate a modest decline in office visit rates but a modest increase in total hospital day rates over a 24-month period for patients exposed to an HIV pharmacist, with the results stratified by the provider HIV patient panel size. These results are achieved at least in part through improved HAART adherence. This study provides additional evidence that a clinical pharmacist should be incorporated into a multidisciplinary HIV care management model, which may have a greater impact in certain clinic settings. Further, this study raises the possibility of determining “benchmarks” by which to measure and compare the impact of various health care personnel in the management of HIV-infected patients.
ACKNOWLEDGMENTS
The authors thank Drs. Joseph Selby, Jeffrey Martin, and David Glidden for their advice and guidance throughout this study.
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