346


May Ee Png et al Table 3. Base Case Effectiveness and Cost-Effectiveness Analysis Results of Screening Strategies Among Initial Cohort Compared With No Screening Strategy


No screening New


New international þ triennial high risk


New international þ annual high risk


New þ triennial universal


New þ triennial universal þ annual high risk


New þ annual universal


No. of TB cases (per 5,000 HCWs)


19 18 14


13


7 6


6


No. of TB cases averted (per 5,000 HCWs)


1 5


6


12 13


13


Incr. cost (US$) per TB case averted


53,926a 6,745


21,482a


16,298 22,657


26,646 QALYs


(per HCW) 2.91 2.98 3.03


3.07


3.09 3.12


3.13


Incr. QALYs


0.07 0.12


0.15


0.18 0.21


0.22


Note. HCW, healthcare worker; ICER, incremental cost-effectiveness ratio; Incr., incremental; QALYs, quality-adjusted life-years; TB, tuberculosis. aDominated.


and total QALYs experienced by the cohort over the model hori- zon, relative to the benchmark of “no-screening.” We calculated the total direct and indirect costs related to TB control, treatment, and the incremental cost-effectiveness ratio (ICER) for each strat- egy (ie, the difference in total discounted costs over the difference in discounted QALYs), relative to the benchmark of “no screen- ing.” To determine whether an intervention was cost-effective, we compared the cost per QALY gained from each strategy to a locally appropriate willingness-to-pay threshold of US$50,000 per QALY, based on World Health Organization CHOICE guide- lines.18 Interventions below this threshold represent an efficient allocation of healthcare resources. TreeAge software (TreeAge Pro Healthcare Williamstown, MA) was used to conduct the cost-effectiveness analysis.


Budget impact analysis


An intervention can be cost-effective but still unaffordable if the total cost required exceeds available resources. In addition to the CEA, we conducted a budget impact analysis (BIA) to estimate the net cumulative cost of implementing the various strategies including the cost of treating potential adverse events (eg, INH- induced hepatitis) and/or the development of active TB. To capture the budgetary obligations of the hospital at full-scale implementa- tion, we assumed a dynamic cohort with a turnover rate of 10% across all areas and an annual inflow of 500 new HCWs while maintaining the same initial cohort size. With a BIA, costs remain undiscounted to assess the actual dollar impact expected at each time point.19 The BIA was performed using Microsoft Excel (Microsoft, Redmond, WA).


Sensitivity analysis


Because the model incorporates many assumptions, we included sensitivity analyses to evaluate the likely impact of parameter uncertainty.Weconducted one-way sensitivity analysis, determin- ing plausible ranges for the values of all parameters used in the baseline scenario (Table 2) based on the underlying literature or expert opinions. For each parameter individually, holding all others fixed, we then recalculated ICERs for all the strategies at the extreme ends of the range, quantifying the sensitivity of the ICER estimates to the values assumed. Results were presented in


standard tornado diagrams, graphically ranking the model param- eters by their impact on the ICER estimate. Likewise, for the BIA, key characteristics like total number of HCWs, proportion of new HCWs, proportion of international HCWs, proportion of HCWs working in high-risk areas and retention rate were varied (Table 2), and the range of resulting total budget estimates were reported. We also conducted probabilistic sensitivity analysis (PSA),


varying all parameters simultaneously according to an assumed probability distribution for each, using a Monte Carlo simulation with 1,000 runs and calculating the realized ICER for each strategy in each one. A gamma distribution was assumed for the cost parameters, whereas β distributions were assumed for probabilities and utilities. Base case values were used as the mean, and the stan- dard deviation was computed by taking 25% of the difference between the low and high values defined in the one-way sensitivity analysis.26 PSA results were presented as a cost-effectiveness accept- ability curve (CEAC). This curve shows the empirically determined probability that each strategy is cost-effective (horizontal axis) com- pared with “no screening” over a range of possible values of the will- ingness-to-pay thresholds,which is the percentage of simulated runs in which ICER falls below the threshold value (vertical axis). The National Healthcare Group Domain Specific Review Board


exempted this study from full ethics review (reference no.: 2016/01000).


Results


In the “no-screening” benchmark, our model predicted ∼19 casesof activeTBover3yearsamong theHCWs,closetothe 21 cases that were extrapolated from the recorded 7 cases in our hospital in 2015. Table 3 lists the clinical outcomes and cost-effectiveness analy-


ses results. Themost intensive screening strategy (all new hires and annual universal screening) is the most effective in terms of total TB cases averted and QALYs gained but also the most expensive. All screening strategies were found to be cost-effective by local


standards relative to “no screening.” Hence, if any other strategy was implemented, the cost per QALY would be <US$50,000 per QALY. A highly targeted strategy of screening new international


employees and high-risk workers once (“new international þ tri- ennial high-risk”) was the most cost-effective (US$6,745 per TB case averted; US$58/QALY; reduces active TB cases from19 to 14).


Cost (US$ per HCW)


46 55 53


70 86 103 113


Incr. cost (US$)


9 7


ICER (US$/ QALY)


122 58


24 157


40 223 57 275


67 311


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