Cost-effectiveness of azithromycin for preventing Mycobacterium avium complex infection in HIV-positive patients in the era of highly active antiretroviral therapy

Peter P. Sendia, Bruce A. Craigb, Gabriela Meiera, Dominik Pflugerc, Amiram Gafnid, Milos Opravile, Manuel Battegaya, Heiner C. Buchera,* and the Swiss HIV Cohort Study

a Internal Medicine Outpatient Department, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland b Depatment of Statistics, Purdue University, West Lafayette, IN, USA c Department of Social and Preventive Medicine, University of Bern, Switzerland d Centre for Health Economics and Policy Analysis, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada e Division of Infectious Diseases, University Hospital Zurich, Switzerland


    Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
We conducted a cost-effectiveness analysis to determine the clinical and economic consequences of Mycobacterium avium complex (MAC) prophylaxis in HIV-infected patients in the era of highly active antiretroviral therapy (HAART) in a health care system with access unrestricted by financial barriers. The analysis was performed from a health care perspective and compared azithromycin (1200 mg/week) with no prophylaxis over a period of 10 years based on data from the Swiss HIV Cohort Study (SHCS) and randomized controlled trials. The main outcome measures were: expected survival; average health care costs; and cost-effectiveness in 1997 Swiss francs (£1 corresponds to about 2.3 CHF) per life-year saved. In patients with an initial CD4 count <50 cells/mm3 and no AIDS, azithromycin increased expected survival by 4 months. In patients with AIDS, HAART durability had a major impact on expected survival and costs. Incremental survival increased from 2 to 4 months if we assumed a 10 year, instead of a 3 year, HAART effect. The cost-effectiveness of azithromycin relative to no prophylaxis in patients without AIDS was between 47,000 CHF (3-year HAART effect) and 60,000 CHF (10-year HAART effect) per life-year saved. The cost-effectiveness ratio increased to 118,000 CHF per life-year saved in patients with symptomatic AIDS. In conclusion, in the era of HAART, MAC prophylaxis with azithromycin increases expected survival and reduces health care costs substantially. Starting MAC prophylaxis in patients without AIDS is more effective and cost-effective than in patients with AIDS.


    Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Disseminated Mycobacterium avium complex (MAC) infection in severely immunosuppressed HIV-infected patients is associated with increased morbidity and mortality.1 Randomized controlled trials have documented the benefit of MAC prophylaxis with macrolides in HIV-infected patients with advanced immunosuppression.2,3,4 Economic studies, based on models with clinical data gathered before5,6 and after7 highly active antiretroviral therapy (HAART) became widely available, have investigated the policy implications of MAC prophylaxis in the USA. However, the cost-effectiveness of MAC prophylaxis within a European setting has not yet been evaluated. The Swiss health care system, as an example of a Western European-type health care system, provides unrestricted access to all approved therapies for patients with HIV infection. The majority (66.2%) of the Swiss health care resources are funded by consumers either directly (out-of-pocket payment) or indirectly (insurance companies).8 Official federal organizations fund most of the remainder.8

We conducted a cost-effectiveness analysis from the health care perspective to address the clinical and economic implications of MAC prophylaxis with azithromycin in the era of HAART. Our analysis is based on the Swiss HIV Cohort Study (SHCS).9 This study offers additional insights about the consequences of MAC prophylaxis in HIV-infected individuals receiving care in a system with access unrestricted by financial barriers. In addition, we applied Bayesian analytical techniques to reflect overall parameter uncertainty.10


    Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
The model

We developed a Markov (or state-transition) model11 that describes the disease history of HIV-infected subjects as a sequence of monthly health states. We defined four broad categories of health: No AIDS (asymptomatic HIV-positive), AIDS (non-MAC), MAC and death. We used the European classification for AIDS.12 The No AIDS and AIDS states were further stratified into three CD4-cell count categories: 0–49 cells/mm3, 50–74 cells/mm3 and >=75 cells/mm3. A patient currently in a No AIDS state is at risk of dying from non-AIDS-related causes, developing MAC or developing an AIDS-defining disease other than MAC. A patient with AIDS is at risk of dying from AIDS or developing MAC. A patient with MAC is at risk of dying from MAC. Prophylactic drug efficacy is expressed as a reduction in MAC incidence. A low adherence in turn is modelled as a low drug efficacy. Severe drug toxicity results in discontinuation of MAC prophylaxis. The model was implemented in DATA 3.0 (TreeAge Software, Williamstown, MA, USA) and self-written FORTRAN 90 code. The validation procedures applied to our model are described in detail elsewhere.13 Expected survival and average costs were computed over a 10-year period using matrix multiplication. We felt that 10 years would be the maximum realistic time-horizon for extrapolation in this highly dynamic and changing field where far-reaching predictions are rarely possible.

In Switzerland, HAART was introduced in 1996.14 There is a paucity of data on the long-term efficacy of HAART. Therefore, we analysed this model under three scenarios assuming different levels of HAART durability: a continuous time effect scenario (CTES), a 5 year effect scenario (5-YES) and a 3 year effect scenario (3-Yes). We used Swiss HIV Cohort Study (SHCS) data from 1996–1997 to estimate the transition probabilities in the era of HAART. We used probability estimates derived from the SHCS 1993–1995 data set, before those therapies became widely available, to reflect the loss of HAART effect.

Costs in Swiss francs (£1 corresponds to about 2.3 CHF) and survival were discounted at an annual rate of 4% to correspond with the discounting practice of major Swiss insurance companies (Schweizerische Unfallversicherung sanstalt, personal communication, 1998).15

Clinical data

The SHCS,9 a multi-centre, prospective study, started in 1988, has enrolled more than 9000 patients over a period of 10 years. We used the SHCS data set to obtain maximum likelihood estimates of transition probabilities describing the natural history of HIV disease. Formation of posterior distributions for the Bayesian analysis is available on request from the authors.

For estimating transition probabilities among CD4 count strata, we included only patients with a CD4 count <75 cells/mm3 after 1 January 1993 and at least one follow-up. Individuals contributed to the model from the first data characterized by a CD4-cell count <75/mm3. A rise in CD4-cell count above 75/mm3 after that date was accepted. We pooled follow-up data recorded before and after 31 December 1995 separately, to estimate the probabilities for the two periods. We converted these 6 month transition matrices into 1 month transition matrices applying matrix decomposition (Table I).16


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Table I. Monthly probabilities of transitions among CD4-cell count strata in two time periods in the SHCS
 
Probability estimates for developing an AIDS-defining disease (MAC/non-MAC) and dying from AIDS given a specified CD4-cell stratum were also extracted from the SHCS (Table II). The monthly risk of dying from MAC was derived from an analysis by Low and colleagues.17 Pre-AIDS mortality may vary among different populations.18,19 We integrated this assumption into our model using SHCS data and Swiss life-table estimates. The base case was a 33-year-old HIV-infected individual to coincide with the mean age of SHCS participants.


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Table II. Monthly probabilities of Mycobacterium avium complex infection and other opportunistic diseases stratified by CD4-cell count in two time periods in the SHCS
 
Efficacy and toxicity estimates for azithromycin were extracted from a randomized controlled trial.4 We computed for azithromycin (1200 mg once weekly) an efficacy of 0.6561 and a monthly risk of severe toxicity of 0.006. We did not include clarithromycin in our analysis. Its efficacy and toxicity are comparable to azithromycin but it is much more expensive in Switzerland.2,4,20Furthermore, the dosing schedule with azithromycin (once weekly) is favourable since it reduces the pill burden of patients. Rifabutin was excluded from our analysis because it is less effective, more toxic and more expensive than azithromycin.3,20

Cost data

Since we chose the viewpoint of the health care system, only treatment costs are considered. We reviewed a random sample of charts of HIV-infected patients enrolled in the SHCS to estimate use of health care resources. These patients receive their main ambulatory care at the internal medicine outpatient services in four university hospitals (Basel, Bern, Geneva and Zurich). We included 46 patients with MAC infection and 62 patients with an AIDS-defining disease other than MAC.

Quantities of resource use were abstracted on a per patient basis. We estimated each component of resource use (micro-costing).21 As protease inhibitors were not available at the time of cost data collection (1993–1995), the central cost for protease inhibitors was derived from an estimated 70% of patients taking protease inhibitors22 at a daily cost of 21 CHF.23 The ‘hotel component’ of hospital expenditure in case of stationary health care was estimated on the basis of patient-days (assuming average daily hotel costs). We assumed that patients who were asymptomatic had regular check-up visits, routine laboratory examinations, Pneumocystis carinii prophylaxis and antiretroviral drugs. Costing details are described elsewhere.24

Monthly cost estimates were averaged for each institution. We then linked these average resource use estimates to the three states: No AIDS (pre-AIDS), AIDS with MAC and AIDS without MAC (Table III). For patients receiving MAC prophylaxis we added the wholesale price of azithromycin (200 CHF per month).20 Azithromycin toxicity was assumed not to lead to additional costs, since these are captured by the regular physician visits and laboratory tests for check-up examinations. All costs were inflated to 1997 CHF using the Swiss consumer price index for health care (Swiss Federal Statistics Office, Bern Switzerland).


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Table III. Monthly costs (CHF)a of care for AIDS-free patients, patients with MAC and patients with other AIDS-defining diseases in the SHCS
 
Bayesian analysis

The expected survival and average costs are both functions of the model parameters. Therefore, it is important to account for the joint uncertainty in the parameters when comparing prophylactic strategies.25 Bayesian analysis relies on the idea that uncertainty can be described by a distribution. We took a Bayesian approach thereby creating a joint distribution of model parameters (posterior) which is conditional on the model, prior opinion and the data. In turn, 5000 samples from this joint distribution were used to approximate distributions of expected survival and average costs. For each set of sampled model parameters, we computed the expected survival and average costs of the two strategies using matrix multiplication. These results were then combined to form the approximate distributions of each summary. The resulting distributions reflect our joint uncertainty of the model parameters. A detailed description of the Bayesian methodology is available on request from the authors.


    Results
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Baseline analysis

The survival curves in patients with a CD4 count <50 cells/mm3 and without MAC prophylaxis are shown in Figure 1. In all scenarios with the exception of the CTES the survival probability was about 10% or below at year 10. Expected survival and costs are shown in Table IV. Starting prophylaxis at a CD4 cell level of 50–74 cells/mm3 instead of 0–49 cells/mm3 was always less cost effective. All calculations were also done undiscounted and at an annual discount rate of 8% but no major effect on the incremental cost-effectiveness ratios was observed.



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Figure 1 Survival probability in patients with an initial CD4 count <50 cells/mm3. We used different scenarios concerning highly active antiretroval therapy durability. CTES denotes continuous-time effect scenario, 5-YES denotes 5 year effect scenario, 3 YES denotes 3-year effect scenario, A denotes AIDS (patients with AIDS initially), NA denotes No AIDS (patients without AIDS initially). {blacktriangledown}, NA, CTES; {blacksquare}, NA, 5-YES; , NA, 3-YES; {triangledown}, A, CTES; {square}, A, 5-YEs; {circ}, A, 3-YES.

 

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Table IV. Costs, life-expectancy and cost-effectiveness of preventing M. avium complex infection with azithromycin in patients with an initial CD4 count <50 cells/mm3
 
Bayesian analysis

The 95% confidence intervals for incremental costs, life-expectancy and cost-effectiveness ratios are shown in Table V. We present for azithromycin (Figure 2) the joint posterior distribution of incremental costs and incremental effectiveness as iso-probability contour plots.26 The size of the contour plot reflects the level of uncertainty in the estimates. The central contour circumscribes the area where the joint posterior for incremental costs and effectiveness will lie with highest probability. Average costs and life-expectancy are positively correlated leading to the depicted characteristic shape of the contour plots. This correlation is positive because only treatment costs were included. The higher the expected survival, the more resources are needed to treat a chronic disease condition. This is shown by the contour plots shifting upward and to the right when we move from the 3-YES to the CTES. When MAC prophylaxis is started in asymptomatic HIV-infected patients, this shift is less pronounced. The slopes of the two lines in each cell reflect the upper and lower limits of the 95% confidence interval of the incremental cost-effectiveness ratio. These slopes suggest that starting antibiotic prophylaxis in patients with AIDS is less cost effective than in patients without AIDS.


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Table V. 95% credible intervals for incremental costs, life-expectancy and cost-effectiveness ratios of preventing M. avium complex infection with azithromycin in patients with an initial CD4 count <50 cells/mm3
 


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Figure 2 Iso-probability contour plots of the joint distribution of the mean incremental costs and effectiveness for starting azithromycin prophylaxis in patients with and without AIDS. CHF, Swiss Francs, rounded to nearest 1000. LE, Life-expectancy (months). We used different scenarios concerning highly active antiretroval therapy durability. CTES denotes continuous-time effect scenario, 5-YES denotes 5 year effect scenario, 3-YES denotes 3 year effect scenario.

 

    Discussion
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
In the era of HAART, MAC infection is still one of the most frequent opportunistic infections among HIV-positive individuals in Switzerland and parts of Europe.27 None the less, the incidence of AIDS-defining diseases, including MAC, has declined since the introduction of HAART,14,28 which disfavours antibiotic prophylaxis.29 However, the expected survival of patients with advanced CD4 cell depletion has increased remarkably. This, in contrast, makes MAC prophylaxis more beneficial.29 Although HAART lowers the monthly risk of acquiring MAC at a given CD4 cell level and may also partially restore immune function, our model shows that these effects are by far outweighed by a higher cumulative risk of MAC infection associated with a higher life-expectancy. This is documented by an increasing gain in survival with azithromycin in the more optimistic scenarios of HAART durability. Azithromycin was only discontinued when severe toxic reactions were experienced, but not when HAART increased the CD4 cell count above the cut-off point (<=50 CD4 cells/mm3) for MAC prophylaxis.30 We found an average gain in survival comparable to the estimates reported by Bayoumi and Redelmeier7 for a North American setting.

MAC prophylaxis leads to a substantial amount of resource consumption within the Swiss health care sector. Antibiotic prophylaxis in patients with AIDS increases direct medical costs more than in AIDS-free patients and results in a lower gain in survival. This translates into a cost-effectiveness ratio that is higher. It would not be uncommon for the cost-effectiveness ratio for azithromycin in patients with AIDS to exceed 150,000 CHF per life-year saved. If MAC prophylaxis is started in AIDS-free patients, there is a negligible chance that this would cost 100,000 CHF per life-year saved. If the health care system wants to contain costs, it needs to cancel programmes where the loss in health terms is lower than the health gains from the azithromycin programme.31 Alternatively, policy decision-makers should find ways to increase the health care budget.


    Acknowledgments
 
This study took advantage of the infrastructure of the Swiss HIV Cohort Study (SHCS). The members of the Swiss HIV Cohort Study are Battegay, M. (Co-Chairman of the Scientific Board), Bernasconi, E., Bürgisser, Ph., Egger, M., Erb, P. (Chairman of the Laboratories Group), Fierz, W., Flepp, M. (Chairman of the Clinics Group), Francioli, P. (President of the SHCS, Centre Hospitalier Universitaire Vaudois, CH-1011 Lausanne), Furrer, H. J., Grob, P., Hirschel, B., Kaiser, L., Ledergerber, B., Malinverni, R., Matter, L., Opravil, M., Paccaud, F., Pantaleo, G., Perrin, L., Pichler, W., Piffaretti, J.-C., Rickenbach, M. (Head of Data Center), Sudre, P., Schupbach, J., Telenti, A., Vernazza, P. & Weber, R. We thank Dr R. Malinverni and Dr O. Rutschman for their support during the chart review and data abstraction process at the University hospitals of Berne and Geneva. This study was supported in part by Abbott Switzerland, Pharmacia & Upjohn Switzerland, Pfizer Switzerland and the Swiss HIV Cohort Study. Dr P. Sendi was supported by an unlimited research fellow grant from Merck Sharp & Dohme-Chibret, Switzerland.


    Notes
 
* Corresponding author. Fax: +41-61-265-46-04; E-mail: bucher{at}ubaclu.unibas.ch Back


    References
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
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Received 25 January 1999; accepted 11 August 1999