Projecting the cost-effectiveness of adherence interventions in persons with human immunodeficiency virus infection☆

Article Outline

• Abstract
• Methods
  • Data
• Results
  • Cohort with early disease
  • Cohort with late disease
  • Urban cohort
  • Sensitivity analysis
• Discussion
• Acknowledgements
• References
• Copyright

Abstract 

Purpose

To explore the cost-effectiveness of interventions to improve adherence to combination antiretroviral therapy in patients with human immunodeficiency virus (HIV) infection.

Methods

A simulation model of HIV infection, incorporating CD4 cell count and HIV ribonucleic acid levels as predictors of disease progression, was used to estimate the lifetime costs and quality-adjusted life expectancy associated with clinical interventions to improve adherence to antiretroviral therapy (e.g., directly observed therapy, automatic medication dispensers, beepers, portable alarms) in a clinical trial cohort with early disease (mean CD4 count, 350 cells/μL), a clinical trial cohort with advanced disease (mean CD4 count, 87 cells/μL), and an urban cohort (mean CD4 count, 217 cells/μL). Data were from clinical trials, national databases, and published literature.

Results

For relatively healthy patients with early disease, interventions that reduced virologic failure rates by 10% increased quality-adjusted life expectancy by 3.2 months, whereas those that reduced failure by 80% increased quality-adjusted life expectancy by 34.8 months, as compared with standard care. The cost-effectiveness ratio was below $50,000 per quality-adjusted life-year (QALY) for interventions costing $100 per month provided that failure rates were reduced by at least 10%, and for those costing $500 per month provided that failure rates were reduced by more than 50%. For both patients with advanced disease and those from an urban cohort, adherence interventions costing about $500 per month (e.g., directly observed therapy) had to reduce failure by about 25% to have cost-effectiveness ratios below $50,000 per QALY.

Conclusion

In patients with lower baseline levels of adherence or advanced disease, even very expensive, moderately effective adherence interventions are likely to confer cost-effectiveness benefits that compare favorably with other interventions.

The use of combination antiretroviral therapy has greatly reduced morbidity and mortality associated with human immunodeficiency virus (HIV) disease 1, 2. Among patients who are able to adhere to potent antiretroviral drug regimens, dramatic reductions in HIV ribonucleic acid (RNA) level and concomitant increases in CD4 cell count have been sustained for more than 3 years of follow-up (3). Nonadherence, however, compromises overall therapeutic success by promoting the development of drug-resistant virus, accelerating virologic failure, and reducing subsequent options for second- and third-line combination antiretroviral therapy 4, 5, 6, 7, 8.

Considerable effort has been devoted to establishing the association of adherence to HIV medications and virologic outcomes 9, 10. Historically, it has been difficult to conduct randomized controlled trials of interventions to improve adherence to prescribed medications, partly because of the lack of consensus on the definition of adherence, the difficulty of measuring adherence, and the lack of any consistent predictors to identify patients at greatest risk of nonadherence 11, 12. Although clinical studies involving HIV-infected patients face similar challenges, numerous investigations are currently under way to examine the short-term effect of interventions to promote greater adherence to combination therapy 13, 14, 15, 16, 17, 18. How such changes will ultimately translate into long-term viral suppression and improvements in quality-adjusted life expectancy remains uncertain.

This analysis was motivated by questions about how the information that emerges from new adherence intervention trials should be used to inform health policy (19). Several factors prompted us to adopt a model-based approach. First, most adherence studies are designed with surrogate endpoints (e.g., HIV RNA suppression) and will not be able to describe long-term clinical and cost implications of different strategies to improve adherence. A model, conversely, permits the extrapolation of costs and health effects beyond the time horizon of a single clinical study. Second, a model-based approach can be used to anticipate the results of new clinical investigations and to guide clinicians and policymakers in judging the quality and interpreting the policy relevance of the outcomes. In addition to relating biological and clinical information, a model can provide quantitative insight into the relative importance of different components of a treatment strategy and investigate how results will change if values of key parameters are affected. By identifying the most important sources of uncertainty, a model can also be used to help prioritize and guide data collection efforts. Our objective was to explore the associations among the effectiveness of interventions to improve adherence, the monthly cost of such interventions, and the long-term implications of improvements in measurable intermediate outcomes such as HIV RNA suppression.

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Methods 

We used a computer-based mathematical model of HIV infection to simulate the effect of alternative adherence interventions and to explore their likely effects on life expectancy, quality-adjusted life expectancy, and lifetime costs. The comparative attractiveness of alternative adherence programs was expressed in terms of the ratio of additional costs to additional quality-adjusted life expectancy, relative to no adherence (i.e., the incremental cost-effectiveness ratio). We adopted a societal perspective and discounted all costs and clinical benefits at a rate of 3% per year (20).

Specific details of the simulation model have been published elsewhere 21, 22, 23. In brief, we employed a state-transition framework, wherein disease progression in a patient was characterized as a sequence of monthly transitions from one health state to another. The analysis was implemented as a first-order, Monte Carlo simulation, meaning that a random number generator and a set of estimated probabilities were used to determine the sequence of state-to-state, clinical pathways that a given patient would follow until death. A running tally was maintained of all acute clinical events, the length of time spent in each health state, and the cost and quality-of-life effects associated with each health state. Upon the patient's death, summary statistics (e.g., quality-adjusted survival) were recorded. This process was repeated until each of 1 million patients in the hypothetical cohort had passed through the model, at which point overall performance measures (e.g., average life expectancy, quality-adjusted life expectancy, cost) were computed.

Health states were chosen to be descriptive of the patient's current health and relevant history as well as predictive of clinical prognosis and future use of resources. Health states were defined by the current and maximum HIV RNA level (viral load setpoint), current and lowest CD4 lymphocyte count (CD4 cell nadir), current and prior opportunistic infections, history of prior antiretroviral therapy, and prophylaxis against opportunistic infections. Viral load and CD4 counts were each classified into six strata (HIV RNA: >100,000 copies/mL, 30,001 to 100,000 copies/mL, 10,001 to 3000 copies/mL, 3001 to 10,000 copies/mL, 501 to 3000 copies/mL, and ≤500 copies/mL; CD4 count: >500 cells/μL, 301 to 500 cells/μL, 201 to 300 cells/μL, 101 to 200 cells/μL, 51 to 100 cells/μL, and ≤50 cells/μL). Pneumocystis carinii pneumonia, toxoplasmosis, Mycobacterium avium complex infection, disseminated fungal infections, and cytomegalovirus infection were specified as distinct opportunistic infections. Patients could die of an acute clinical event, chronic acquired immune deficiency syndrome (AIDS) (e.g., wasting), or non–HIV-related causes.

The progression of underlying HIV disease was modeled as a function of both HIV RNA level and CD4 lymphocyte count 24, 25. Individual patient characteristics (e.g., age, sex, CD4 cell count, HIV RNA level) were randomly drawn from distributions derived from a specific clinical trial (e.g., the DuPont 006 trial [26]). Upon entry into the model, patients were eligible to receive up to four sequential antiretroviral regimens. CD4 and HIV RNA testing were performed every 3 months in stable patients, and decisions about prophylaxis for opportunistic infections and antiretroviral therapy were made based on observed CD4 cell counts and HIV RNA levels in accordance with national guidelines 27, 28.

With successful HIV RNA suppression, CD4 cell counts rose, reducing the risk of acute opportunistic infections and AIDS-related deaths. Virologic failure was defined as a 0.5-log increase in HIV RNA level in each of 2 consecutive months while on combination antiretroviral therapy. For this analysis, an adherence intervention was defined as a clinical intervention (e.g., beeper, counseling, directly observed therapy) that was provided in conjunction with antiretroviral medication to reduce the likelihood of nonadherence and, in turn, increase the likelihood of HIV RNA suppression.

To enhance the generalizability of the analysis, we considered three different target patient samples: a clinical trial cohort with patients similar to those in the DuPont 006 trial (mean CD4 count, 350 cells/μL; median log₁₀ HIV RNA, 4.8) (26)—hereafter referred to as the clinical trial cohort with early disease; a clinical trial cohort with patients similar to those in the AIDS Clinical Trials Group Protocol 320 study (mean CD4 count, 87 cells/μL; median log₁₀ HIV RNA, 5.0) (29)—hereafter referred to as the clinical trial cohort with late disease; and an urban cohort with patients similar to those in the Johns Hopkins Clinic Cohort trial (mean CD4 count, 217 cells/μL; median log₁₀ HIV RNA, 4.6) (6)—hereafter referred to as the urban cohort.

Data 

A summary of key data elements, base case assumptions, plausible ranges for sensitivity analysis, and their sources is shown in Table 1. The methods employed to produce input data in a format that could be used in the model have been described elsewhere 21, 22, 23.

Table 1. Model Variables

AIDS = acquired immune deficiency syndrome; HIV RNA = human immunodeficiency virus ribonucleic acid.

We employed the Multicenter AIDS Cohort Study to estimate monthly CD4 cell count declines, the incidence of primary opportunistic infection, acute mortality from opportunistic infections, and chronic mortality in the absence of treatment (25). The decline in viral load and increase in CD4 cell count in the presence of effective therapy were modeled using data from several clinical trials of initial or subsequent antiretroviral therapy 3, 6, 26, 29, 44, 45, 46. To model the effectiveness of specific combination antiretroviral therapy using data from different clinical trials, we adjusted (up or down) the monthly failure rate associated with a particular regimen. The baseline failure rate was based on the three-drug (zidovudine, lamivudine, and efavirenz) arm of the DuPont 006 trial, in which 70% of patients had HIV RNA suppression at 48 weeks. For other regimens, the monthly failure rate was chosen such that the probability of HIV RNA suppression in the model matched the reported suppression at the corresponding follow-up time in the clinical trial.

We modeled the effectiveness of an adherence intervention as a reduction in the virologic failure rate, and a resultant increase in the proportion of the cohort with HIV RNA suppression. Figure 1 shows the relation between the percentage reduction in failure and percentage suppressed for each target group. This relation differed depending on the baseline efficacy of the antiretroviral regimen and the characteristics of the cohort. For example, in the urban cohort, the baseline level of HIV RNA suppression was lower (44% at 24 weeks) than in the cohort with early disease (70% at 48 weeks). Given the same percentage reduction in failure (e.g., 30%), the increase in percentage HIV RNA suppression was greater in the urban cohort (increase from 44% at baseline to 60%) than in the clinical trial cohort with early disease (increase from 70% at baseline to 79%).

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• Figure 1. 

  Relation between the percentage reduction in failure and percentage of viral suppression in three cohorts.

There are few published data on the costs of interventions to improve adherence to combination antiretroviral therapy in HIV-infected patients. We therefore explored a wide range of costs (e.g., $25 to $1500 per person per month) to reflect the spectrum of interventions used to improve adherence (e.g., counseling, pagers and beepers, automatic dispenser systems, directly observed therapy) 40, 41, 42, 43, 47, 48, 49, 50, 51, 52. In addition to direct medical costs, these estimates included the foregone value of the extra time patients spend as a result of the intervention.

We used microcosting techniques to assess the direct medical and time costs associated with directly observed therapy in different settings (e.g., home-based vs. clinic-based) largely based on data from the tuberculosis literature 40, 41, 42, 43. Costs for directly observed therapy ranged from $557 per month for a clinic-based program once daily for 5 days each week, to $780 per month for a clinic-based program 7 days each week. Clinic-based programs based on supervised medication administered twice daily were more expensive, ranging from $914 to $1560 per month. Home-based directly observed therapy programs were estimated to cost from $470 to $690 per month, depending on the frequency of observed doses. We also estimated a range of costs for adherence tools, such as pager and beeper systems, electronic reminder systems, and more sophisticated dispenser systems with monthly fees. The monthly costs of beepers and alarms (including equipment, service, and batteries) ranged from $34 to $58, and the monthly costs of automated medication dispensers and monitors (including equipment, monthly service, utilities, and batteries, and either home-based or clinic-based costs) ranged from $82 to $133 50, 51, 52. Other costs have been previously published (Table 2) and included the direct medical costs associated with the chronic care of HIV disease, laboratory monitoring, antiretroviral drugs, opportunistic infection prophylaxis, office visits, and hospitalizations associated with acute clinical events 20, 21, 22, 23.

Table 2. Quality-Adjusted Life Expectancy, Cost, and Cost-effectiveness of Different Adherence Interventions

QALY = quality-adjusted life-year.

The consequences of both morbidity and mortality were incorporated in a single outcome measure by adjusting life expectancy (53) for quality of life on a scale from 0.0 (death) to 1.0 (perfect health) 20, 21, 22, 23. We used recently reported health-related quality weights (54), in which health state utilities were derived from community preferences using the Short Form-6D health state classification based on data from the HIV Cost and Services Utilization Study 54, 55, 56.

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Results 

Cohort with early disease 

In relatively healthy patients in early stages of HIV disease, the average projected discounted quality-adjusted life expectancy was 116.4 months in the absence of an adherence intervention (Table 2). Providing patients with an intervention that reduced the probability of virologic failure by 10% was associated with quality-adjusted life expectancy gains of 3.2 months, whereas an 80% reduction in virologic failure was associated with quality-adjusted life expectancy gains of 34.8 months. Discounted total lifetime costs increased with more effective and expensive adherence interventions. For example, for interventions costing $100 per month, the total lifetime costs ranged from $144,300 (with a 10% reduction in failure) to $238,900 (with an 80% reduction in failure). For interventions costing $500 per month, the total lifetime costs ranged from $167,800 (with a 10% reduction in failure) to $295,000 (with an 80% reduction in failure).

To achieve a cost-effectiveness ratio below $50,000 per quality-adjusted life-year (QALY), more expensive interventions required increasing levels of effectiveness (Table 2). For example, adherence interventions costing $100 per month required a reduction in failure rate of at least 10%, whereas those costing $250 per month would require failure rates to be reduced by more than 20%. In contrast, the cost-effectiveness ratios associated with interventions costing $500 per month approached $50,000 per QALY only when failure rates were reduced by more than 50%. Finally, the cost-effectiveness of an adherence intervention costing $1000 per month never fell below $50,000 per QALY. Therefore, in this group of relatively motivated patients with early-stage disease, a clinic-based, directly observed therapy program (costing between $560 and $780 per month), regardless of effectiveness, would not cost less than $50,000 per QALY. Such an intervention would need to reduce failure rates by more than 40% to confer QALYs at a cost below $100,000.

Cohort with late disease 

In HIV-infected patients in late stages of disease, the average quality-adjusted life expectancy was 55.5 months in the absence of an adherence intervention (Table 2). Providing patients with interventions that reduced the probability of virologic failure by 10% to 80% was associated with quality-adjusted life expectancy gains of 5.5 months to more than 42.7 months, as compared with no intervention. In these patients with relatively advanced disease, even expensive adherence interventions had cost-effectiveness ratios below $50,000 per QALY. For example, interventions costing $500 per month only had to reduce virologic failure by 25% to have cost-effectiveness ratios below $50,000 per QALY. Interventions costing $1000 per month had cost-effectiveness ratios below $50,000 per QALY provided that virologic failure was reduced by at least 50%. Thus, in these patients with advanced disease, even intensive interventions such as home-based directly observed therapy would represent an efficient use of resources if failure rates could be halved.

Urban cohort 

In the urban cohort, the average projected discounted quality-adjusted life expectancy was 73.80 months in the absence of an adherence intervention. Quality-adjusted life expectancy gains ranged from 9.8 months with an intervention that reduced virologic failure by 20% to 33.4 months with an intervention that reduced failure by 80%. In general, adherence interventions in these patients with intermediate disease were associated with favorable incremental cost-effectiveness ratios even for expensive interventions.

This cohort is perhaps the most generalizable to patients living with HIV disease in the United States, because the majority of infected patients in the country are not enrolled in clinical trials. Figure 2 shows the association among the effectiveness of an intervention, the incremental cost-effectiveness ratio (compared with no intervention), and four types of interventions with monthly costs ranging from $50 to $1000 in a cohort similar to the Johns Hopkins Clinic cohort.. Even minimally effective interventions that cost less than $100 per month (e.g., beepers, portable alarms and watches, automated medication dispensers and monitors) had cost-effectiveness ratios below $50,000 per QALY. Failure rates needed to be reduced by at least 20% (i.e., increase in HIV RNA suppression in 44% to 55% of the cohort) for interventions such as once-daily home-based directly observed therapy ($473 per month) or once-daily clinic-based directly observed therapy ($510 per month) to have a similar incremental cost-effectiveness ratio. More intensive directly observed therapy programs (services provided 7 days per week or twice daily) had to reduce failure rates by at least 50% to approximate a $50,000 per QALY threshold.

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• Figure 2. 

  Cost-effectiveness of interventions to improve adherence in an urban cohort. The sensitivity analysis shows the relation among the effectiveness of an intervention, the incremental cost-effectiveness ratio (compared with standard care), and four hypothetical adherence interventions with monthly costs ranging from $50 to $1000.

Sensitivity analysis 

When an adherence intervention accompanied only the first antiretroviral regimen, the quality-adjusted life-expectancy benefits were reduced by nearly 50%, compared with an equally effective and costly intervention that accompanied every regimen. In contrast, when an adherence intervention accompanied only later regimens, the quality-adjusted life expectancy benefits were greater because the relative influence of a fixed percentage reduction in the failure rate was greater in the setting of less effective, or later, regimens. The cost-effectiveness ratios associated with interventions to improve adherence to antiretroviral therapy remained stable over a wide range of assumptions, including increasing the lag time before CD4 cell decline following virologic failure, extending the duration of the efficacy observed in clinical trials, and increasing the direct medical costs associated with routine HIV care.

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Discussion 

The clinical effectiveness of antiretroviral therapy for HIV infection depends heavily on patients' ability to adhere closely to complicated drug regimens. Our objective was to employ a simulation model, assembling the best available data, to explore the relation among the effectiveness of adherence interventions, the monthly cost of such interventions, and the long-term implications of improvements in measurable intermediate outcomes (e.g., percentage of the cohort achieving HIV RNA suppression).

In order to enhance the generalizability of our results, we chose three target groups to represent patients with different stages of disease, treatment efficacy, and likelihood of adherence. It is important to realize that the differences in response to treatment may also be attributable to differences in the potency of regimens, differences in serum concentrations of drugs, and other unaccounted for differences in these studies. However, in all three of the patient groups assessed, adding an effective adherence intervention to every antiretroviral regimen appeared to be associated with substantial gains in quality-adjusted life expectancy. In fact, these were far greater in magnitude than the gains attributable to opportunistic infection prophylaxis 21, 22.

There is no consensus that defines the cost per QALY that represents acceptable value for money; however, cost-effectiveness ratios are often placed in context by comparisons with interventions that are widely utilized, such as screening for colorectal cancer, hemodialysis, and cholesterol-lowering drugs for men with cardiovascular risk factors. Accordingly, interventions costing less than a commonly suggested threshold of $50,000 per QALY are often considered reasonable value for money (20). The incremental cost-effectiveness ratios associated with different strategies to improve adherence depended on the associations among the cost of the intervention, its effectiveness, and the specific characteristics of the target patient group. In clinical trial patients with early disease, interventions that cost up to $500 per month can be expected to return cost-effectiveness ratios below $50,000 per QALY provided that HIV RNA suppression is increased from a baseline of 70% of the cohort to at least 88%. Among urban cohorts, even more favorable cost-effectiveness results can be expected, and at any specific monthly cost and intervention effectiveness. Similarly, in clinical trial patients with late-stage disease, even costly interventions such as intensive directly observed therapy (e.g., $1000 per month) that provide modest improvement in HIV RNA suppression (e.g., from 60% to 78% of the cohort) are associated with cost-effectiveness ratios below $50,000 per QALY. This represents a plausible scenario with the use of clinic-based or home-based directly observed therapy 7 days per week, both in terms of costs and benefits.

This study has several limitations. Although we used data from clinical trials, we made several assumptions about the mechanism of HIV disease progression (57). We assumed that combination antiretroviral therapy leads to HIV RNA suppression with subsequent immune reconstitution (increase in CD4 cell count), leading to fewer opportunistic infections and death. This reliance on surrogate markers (HIV RNA level and CD4 cell count) is unavoidable because clinical trials, including those of adherence interventions, do not follow patients long enough to observe survival effects. When we use percentage decrease in virologic failure rate as the surrogate marker or outcome of adherence interventions, we implicitly assume a uniform percentage reduction in failure or increase in virologic suppression rates. However, given a heterogeneous patient sample we may have overestimated the value of these interventions (58). Although nonadherence is one cause of virologic failure, other factors such as viral resistance and fitness, potency of antiretroviral agents, and HIV RNA level at baseline can influence the efficacy of antiretroviral therapy (59). Limited data were available for patients with prior extensive suboptimal treatment. We conservatively assumed that an intervention was only effective during the time the patient was receiving the intervention, but this is not known. As better information becomes available for specific subgroups, these data may be incorporated and their effect on results assessed.

Our analysis suggests that interventions that improve adherence to combination antiretroviral therapy, such that failure rates are reduced by at least 10% to 20%, will provide quality-adjusted life expectancy gains of a similar magnitude to opportunistic infection prophylaxis. In patients with advanced disease and those with lower levels of baseline adherence, even very expensive interventions, if moderately effective, would yield cost-effectiveness estimates that compare favorably with other interventions in HIV disease.

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Acknowledgements 

We would like to thank our Cost-Effectiveness of Preventing AIDS Complications (CEPAC) colleagues, including Calvin Cohen, MD, Runa Islam, Bruce Schackman, PhD, and Hong Zhang, SM, for their helpful contributions to the analysis.

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