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
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Abstract |
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Introduction |
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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
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Methods |
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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: 049 cells/mm3, 5074 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 19961997 to estimate the transition probabilities in the era of HAART. We used probability estimates derived from the SHCS 19931995 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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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 (19931995), 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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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.
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Results |
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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 5074 cells/mm3 instead of 049 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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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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Discussion |
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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.
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Acknowledgments |
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Notes |
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References |
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Received 25 January 1999; accepted 11 August 1999