Cost-Effectiveness of a Peer and Practice Staff Support Intervention | Page 2
Published Online: March 20, 2014
Christopher S. Hollenbeak, PhD; Mark G. Weiner, MD; and Barbara J. Turner, MD, MSED
Two cost-effectiveness analyses were performed: a withintrial stochastic cost-effectiveness analysis that focused on cost-effectiveness during the 6-month trial period, and longterm cost-effectiveness based on a Markov model that modeled the longer-term benefits of BP reduction.6,7 For the within-trial stochastic cost-effectiveness analysis, we estimated 2 incremental cost-effectiveness ratios (ICERs): incremental cost per predicted CHD event avoided within 6 months, and incremental cost per mm Hg in SBP reduced. To characterize the uncertainty of the within-trial cost-effectiveness results, we used bootstrapping to estimate a 95% confidence ellipse around the ICER.8,9 The bootstrap method resampled the data 10,000 times with replacement, and computed the ICER for each replicate. From the bootstrap samples, we estimated the probability that one treatment was cost-effective compared with the other for a given willingness to pay (WTP). In addition, we computed the cost-effectiveness acceptability curve (CEAC) and plotted the probability that the behavioral health intervention was cost-effective over a reasonable range of levels of WTP.10
We performed a subgroup analysis for patients who were compliant with the intervention. We defined an effective “dose” as at least 2 peer coach calls and 1 practice visit. We then estimated the ICER for the intervention in this intervention subgroup relative to the control group. All stochastic cost-effectiveness analyses were performed using R statistical software (version 2.10.1, http://www.r-project.org).
Long Term Cost-Effectiveness
To estimate the long-term costs and benefits of BP reduction observed in the trial, data on costs and effectiveness from the clinical trial were entered into a Markov model of CHD risk in order to extrapolate trial results to a 10-year lifetime horizon. The Markov model was designed to study the impact of antihypertensive medications and was adapted to this setting. The model has a 10-year time horizon with yearly cycles. All costs begin in year 2010 US dollars and were discounted at a rate of 3%. Utility values for health states were drawn from Sullivan et al and Currie et al.13,14 As effectiveness measures, the model estimates YLS and QALYs. Additional details of the model are provided in Baker et al.11 Because the trial only lasted 6 months, it was necessary to make assumptions about how the intervention would be provided over the 10-year time horizon of the Markov model. We assumed that yearly reinforcements of the intervention would be required in order to sustain improvements.
The 280 intervention (N = 136) and control (N = 144) subjects were well balanced on demographics and clinical conditions (Table 2). Complete data to estimate CHD risk were available for 212 (94 intervention and 118 control)subjects and complete end point data were available for 247 (116 intervention and 131 control) subjects. Sixty-eight percent of intervention subjects (N = 79) were compliant with an effective “dose” intervention (ie, at least 2 peer coach calls and 1 practice visit) and included in the compliance subgroup analysis.
Baseline cost-effectiveness results (Table 3) show that the average cost over 6 months of the intervention was $435 for intervention subjects and $74 for control subjects. The intervention was successful in reducing both SBP and CHD risk. SBP fell by 7.2 mm Hg among intervention subjects, compared with only 0.77 mm Hg for control subjects (P = .0011). The average difference in CHD risk among intervention subjects fell by 0.046% but rose by 0.034% among control subjects (P = .07). The ICERs were $453,419 per predicted CHD event avoided over 6 months and $55 per mm Hg reduced in 6 months.
The uncertainty analysis for these ICERs (Figures 1A and 1B) shows a high probability that the intervention would rensult in a reduction in systolic blood pressure. The probability that the intervention is cost-effective is 25%, 50%, and 75% if the decision maker is willing to pay $45.20, $55.40, and $70.80, respectively, to reduce SBP by 1 mm Hg for at least 6 months (Figure 1B). For CHD risk, the CEAC suggests that the probability that the intervention is cost-effective for reducing CHD risk is 25%, 50%, and 75% if the decision maker is willing to pay $324,000, $449,000, and $674,000, respectively, to avoid 1 CHD event over 6 months (Figures 1C and 1D).
Similar results were observed for the subgroup of patients who were compliant with the intervention (Table 3). The average cost of the intervention over 6 months was $442 for compliant intervention subjects and $74 for control subjects. SBP fell 8.5 mm Hg among intervention subjects, compared with only 0.77 mm Hg for control subjects. The average difference in CHD risk among compliant subjects decreased by 0.07% and increased by 0.03% among control subjects. The ICERs were $48 per mm Hg reduced in 6 months and $350,134 per predicted CHD event avoided over 6 months. The CEACs for the compliant subgroup, presented in Figure 1 (G and H), suggest that the probability that the intervention is cost-effective in reducing SBP is 25%, 50%, and 75% if the decision maker is willing to pay $34, $40, and $48, respectively, to reduce SBP by 1 mm Hg for at least 6 months. The probability that the intervention is cost-effective in reducing CHD risk is 25%, 50%, and 75% if the decision maker is willing to pay $274,000, $351,000, and $476,000, respectively, to avoid 1 additional CHD event in 6 months (Figures 1E and 1F).
Long-term cost-effectiveness results are presented in Table 4. Assuming that the intervention was given every year over the patient’s lifetime (10-year horizon), the intervention was more costly ($7324 vs $5584), but also more effective, both in terms of YLS (8 vs 7) and QALYs (6.3 vs 6.2). This yields ICERs for the intervention of $12,373 per incremental YLS and $10,866 per incremental QALY saved.
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