The cost-effectiveness of screening for latent tuberculosis infection

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1 INT J TUBERC LUNG DIS 4(12):S127 S IUATLD The cost-effectiveness of screening for latent tuberculosis infection Z. Taylor Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA THREE QUESTIONS are routinely asked when evaluating health care interventions: Is the intervention safe? Is it efficacious? Is it effective? These questions can be answered by clinical, epidemiological, and operational studies. Health economists have added a fourth question: Is the intervention efficient? In other words, given that a variety of interventions or therapeutic agents are safe, efficacious, and effective, which alternative provides the best outcome for the resources expended? This question can be answered by a variety of economic analyses, including costbenefit, cost-effectiveness, and cost-utility analyses. What is the relevance of the question of efficiency to tuberculosis (TB) prevention and control? We can answer that as follows: There are three ways that we can reduce the burden of TB disease. First, we can develop better tools, such as more effective treatment regimens, an effective vaccine, or improved diagnostic tests. Second, we can expand the use of existing tools. Either option would require increased financial and human resources. And third, given finite resources and available tools, we can use those tools and resources more efficiently. Economic analyses enable TB control programs to identify more efficient uses of their resources. This question of efficiency is similar to the sort of decision-making people undertake as part of their daily routines. We add up benefits and costs; if the benefits outweigh the costs, we continue. In economic evaluations, the essential differences are that particular rules govern the calculation of costs and benefits, and assumptions are made explicit, which is not done in day-to-day decision making. Having rules govern the calculations imposes a degree of consistency on the analysis; however, this may be more ideal than real. Having rules and explicit assumptions makes it clear what inputs and costs have been included in the analysis and, just as importantly, what has been excluded. Cost-effectiveness analysis (CEA) includes both costs and outcomes. However, the costs and outcomes (or benefits) are not expressed in the same units. For example, the results of a CEA can be expressed as dollars per case of TB prevented. Here, costs are expressed in dollars and outcomes in numbers of TB cases prevented. These results are meaningful only in comparison with other interventions (or no intervention). Comparison is essential in order to establish whether the cost of the TB case prevented is worth spending, as this depends on what else might be bought with the money and what it would cost to prevent a TB case by some other means. Ever-increasing demands for health care services must be met from finite resources. It is this fact that underpins the growing need for systematic economic evaluations of health care, which is especially true for TB control in the United States as well as in many other countries. It is a mistake, however, to infer that health economic studies automatically result in either budgetary cuts or increases in expenditures; decisions about the allocation of resources are ultimately made at a political level. The function of economic evaluation is to ensure that these decisions are based on a rigorous assessment of the available options. These analyses are a means to an end; they are not ends in themselves. It is also a mistake to equate cost-effective with cost-saving. The objective of an economic analysis is to identify the best value for money, not just to save money. To review published decision and cost-effectiveness analyses examining the screening for and treatment of latent TB infection (LTBI), I performed a computerassisted search of MEDLINE files ( ) using the key words decision analysis, cost-effectiveness analysis, and tuberculosis. I selected articles that described original decision or cost-effectiveness analyses of the treatment of LTBI or the screening for and treatment of LTBI in North American populations. I excluded studies comparing BCG vaccination with chemoprophylaxis, those comparing different regimens for treating drug-resistant organism, and studies examining serial testing in occupational settings. Correspondence to: Zachary Taylor, MD, MS, Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, 1600 Clifton Road MS E-10, Atlanta GA, 30333, USA. Tel: ( 1) Fax : ( 1) zxt0@cdc.gov

2 S128 The International Journal of Tuberculosis and Lung Disease REVIEW OF DECISION AND COST-EFFECTIVENESS ANALYSES In 1971, Dr Tom Moulding published an article entitled Chemoprophylaxis of tuberculosis: when is the benefit worth the risk and cost?, 1 in which he calculated the number of persons who would have to be treated to prevent one case of active TB, and the costs of providing preventive therapy to those persons. For example, 14.2 persons with inactive disease would need to be treated to prevent one case, which would cost $824 ($6073 in 1999 dollars). Although this was not a true CEA, it was the first attempt to systematically assess the costs and benefits of treating LTBI, and it brought up many questions that are relevant today: What is the risk of active TB in various populations? Who would benefit most from treatment? What costs should be included? How should the risk of isoniazid hepatotoxicity affect the decision to provide preventive therapy? In 1974, the American Thoracic Society and the Centers for Disease Control recommended that persons younger than 35 years with a positive tuberculin skin test (TST) and no contraindications should be treated with isoniazid (INH) for one year. 2 In response, a series of decision analyses were published concerning the decision to provide INH preventive therapy to low-risk tuberculin reactors. These decision analyses provide the framework for most CEAs regarding TB preventive therapy, and provide some insight into important determinants of the outcomes of the CEAs. Four studies examined the decision to treat lowrisk tuberculin reactors with INH (Table 1). 3 6 Two studies included adults of all ages, and two confined the analysis to adults younger than 35 years of age. The results of two of the studies favored INH treatment for low-risk reactors younger than 35 years, and two did not. Of the two studies that included adults of all ages, the outcome of one favored treatment of low-risk reactors older than 35 years of age, and the other did not. 4,5 On close examination, the actual differences in the outcomes of these studies were small. The different outcomes resulted from the use of different baseline estimates of the probabilities of chance events occurring in the decision models; these were, specifically, the probability of tuberculosis given a positive TST, the probability of hepatitis given administration of INH, the probability of death given hepatitis, and the probability of death given active TB. The probability estimate with the greatest impact on the outcomes of these studies is the probability of death given hepatitis. One other decision analysis examined the decision to provide INH to low-risk tuberculin reactors, with sex and ethnicity considered in addition to age. 7 This analysis explicitly recognized that the risks of developing active TB, hepatitis, and death from hepatitis are different depending on sex and race/ethnicity. It found that INH chemoprophylaxis was preferred for all 20-year-old lowrisk reactors as well as for 35-year-old black men and white men, and white women, but not for 35-year-old black women or for any 50-year-old persons. Four additional decision analyses examined INH chemoprophylaxis in high-risk tuberculin reactors: diabetics, nursing home residents, and persons infected with the human immunodeficiency virus (HIV) These studies found that treatment with INH was minimally effective in diabetics and nursing home residents, but was much more effective in HIV-infected persons. The first CEA of the treatment of LTBI was published in This study examined the cost-effectiveness of 12, 24, and 52 weeks of INH treatment in persons with positive TSTs and fibrotic lesions on chest radiographs. The authors estimated that 52 weeks of therapy prevented more cases of active TB than 12 or 24 weeks; however, the cost per case prevented was $7112 for 24 weeks of therapy compared to $ for 52 weeks of therapy. Thus, for every 1000 persons Table 1 Probability estimates and results of decision analyses studies of chemoprophylaxis for low-risk tuberculin reactors 35 years old Study & ref Taylor et al., Rose et al., Study population Adults, years old White males, years old Tsevat et al., Adults, 20, 35, 50, 65, and 80 years old Colice, year-old white males Annual tuberculosis rate* * For those less than 35 years old e.0569n, where n is the number of years after exposure. INH isoniazid. hepatitis rate* hepatitis case fatality rate* Tuberculosis case fatality rate* Efficacy of INH Results % Did not favor INH treatment % Favored INH treatment (16 extra days of life at 10 years to 1 extra day at 80 years) % Did not favor INH treatment (4 fewer days of life) % Favored INH treatment ( extra days of life)

3 The cost-effectiveness of screening for latent tuberculosis infection S129 treated, each additional case prevented by treating for 52 instead of 24 weeks would cost $ Three other CEAs examined the treatment of latent TB infection The first, published in 1988 by Rose et al. evaluated the policy of only providing INH to persons who are at high risk of activation. 13 This analysis compared the costs and benefits of treating low-risk reactors, defined as 55-year-old white men with a positive TST, and of treating high-risk reactors, defined as 20-year-old white men recently infected. The authors estimated that INH therapy was cost-saving for high-risk reactors, and that treatment of low-risk reactors resulted in a cost of $ per life-year gained, well within the costs of other commonly used medical interventions. From a Canadian perspective, Fitzgerald and Gafni examined the cost-effectiveness of treating 20-, 50- and 70-year-old low-risk reactors; they reported costs per case prevented ranging from $8586 for 20-yearold low-risk reactors to $ for 70-year-old lowrisk reactors (all in Canadian dollars). 14 In 1997, Salpeter et al. compared no treatment to INH treatment for 35-, 50- and 70-year-old low-risk reactors. 15 They estimated that treatment with INH resulted in an increased life expectancy ( days) and cost savings for all age groups. These cost-effectiveness analyses consistently favored the use of INH, in spite of their using somewhat different methods and inputs into the models and reporting the results differently. The base-case probability estimates used in the decision and costeffectiveness analyses of INH chemoprophylaxis for low-risk reactors are presented in Table 2. Of all of these analyses, the cost-effectiveness analyses used input probabilities most favorable for INH treatment. Only one published CEA has included regimens other than INH. Rose published a CEA of treating latent TB infection in HIV-infected, tuberculin positive persons; treating with 12 months of INH or six different short-course regimens was compared to not providing treatment. 16 The six short-course regimens were 6 months of daily INH, 6 months of twiceweekly INH, 3 months of daily INH (H) and rifampin (R-HR), 3 months of daily HR and pyrazinamide (HRZ), 2 months of daily RZ, and 2 months of twiceweekly RZ. Compared to no prophylaxis, all of the regimens resulted in dramatic increases in life-expectancy and all but one, 3 months of daily HRZ, resulted in cost savings. None of the analyses discussed so far have included the costs of screening. However, my search did yield four published studies of the cost-effectiveness of screening for and treatment of latent TB infection The first study estimated the cost-effectiveness of screening all school children vs. screening only children at high risk for infection, defined as children born in high-prevalence countries. 17 Two studies estimated the cost-effectiveness of screening in drug treatment programs, 18,19 and the fourth study compared the cost-effectiveness of screening to case-finding approaches in long-term care facilities for the elderly. 20 The two studies in the drug treatment centers used data from existing screening programs, both of which also used directly observed preventive therapy. The school-based study also used data from existing screening programs. Targeted screening of high-risk school children also resulted in cost savings. 17 Screening injecting drug users in treatment programs was also found to be cost saving in both studies. 18,19 Screening residents of a long-term care facility for the elderly resulted in positive health outcomes, including increases in life expectancy, and resulted in a cost of $3800 to $7500 per case of TB prevented. 20 The important variables in these studies appeared to be the risk of active disease given infection and the prevalence of latent TB Table 2 Probability estimates for decision and cost-effectiveness analyses of chemoprophylaxis for low-risk tuberculin reactors Decision analyses not favoring INH Decision analyses favoring INH Cost-effectiveness analyses Probability estimate Taylor et al., Tsevat et al., Rose et al., Colice, Rose et al., Fitzgerald and Gafni, Salpeter et al., Annual tuberculosis rate hepatitis rate Probability of fatal hepatitis while receiving INH e n where n is the number of years after exposure e n where n is the number of years after exposure Not available Tuberculosis case fatality rate Efficacy of INH 50% 50% 70% 50% 70% 70% 85% INH isoniazid.

4 S130 The International Journal of Tuberculosis and Lung Disease Figure 1 Decision tree: cost-effectiveness analysis of screening for latent TB infection (LTBI). infection. Only one of the studies included the risk of fatal INH hepatitis in its model, that being the study of screening residents of a long-term care facility. The school children were not considered to be at risk, and there were apparently no fatal cases among the participants at the drug treatment centers. To examine the interaction of the variables I identified as important for an analysis that included the costs of screening, I constructed a decision tree model to evaluate the effectiveness and cost-effectiveness of screening persons with a high prevalence of latent TB infection who are not at increased risk for TB disease if infected. METHODS I constructed a decision tree model of screening for and treatment of LTBI using DATA 3.0 (Figure 1, Table 3). Beginning at the left in Figure 1, subjects are screened or not screened. If not screened, they either have LTBI or they do not; if infected, they have a risk of TB disease. If screened and if found to be TST positive, they either begin treatment or not. If they begin treatment, they either complete, do not complete, do not complete because of INH hepatitis, or die from fatal INH hepatitis. I estimated the outcomes of screening vs. not screening in a hypothetical cohort of persons 35 years old who have a 20% prevalence of infection and a low risk of reactivation. I used the equation developed by Tsevat et al. 5 to estimate the probability of active TB if latently infected. I used a moderate risk of fatal hepatitis, 1 per INH recipients, and an estimated effectiveness of a 68% reduction in TB for persons completing 9 months of INH. Quality-adjusted life-year (QALY) estimates were derived from a survey of 51 TB clinicians using the Quality-of-Well-Being Scale. 21,22 Costs included only direct medical costs. Future costs and benefits were discounted at a 3% annual rate and reported in 1999 US dollars. A Markov model was developed to trace the health of this hypothetical cohort over a 20-year period (Figure 2). After either completing treatment, taking an incomplete course or not taking any treatment, patients move from one health state to another. Ini- Table 3 Probability inputs in decision tree model Variable Base-case value Range Reference for base-case value Probability of latent TB infection Assumed Probability of starting treatment Assumed Probability of completing treatment Assumed Probability of fatal hepatitis Taylor et al. 3 Probability of active TB given e n Tsevat et al. 5 latent TB infection where n is the number of years after exposure Probability of death given active TB disease Salpeter et al. 15 Efficacy of a complete course of INH Snider et al. 12 Efficacy of an incomplete course of INH Salpeter et al. 15 INH isoniazid.

5 The cost-effectiveness of screening for latent tuberculosis infection S131 Figure 2 Markov process subtree; INH isoniazid. tially well patients may develop active TB, remain well, or die from another cause. Patients with active TB can be cured or die from TB. RESULTS The results reported per persons screened are presented in Table 4. The no screen strategy would result in 125 active TB cases over the 20-year time period and the estimated costs would be $ The screen strategy would result in 68 cases and estimated costs of $ Therefore, the screening strategy would prevent 57 cases of active TB and result in cost savings. In addition, there would be a slight gain in QALYs using the screen strategy, which amounts to less than a day for the 35-year-old persons in this hypothetical cohort. The impact of using different estimates of the risk of fatal INH hepatitis is presented in Table 5. Salpeter et al. used a probability of one fatality per INH recipients. 15 Using that value in the decision tree model resulted in a gain of four QALYs per persons screened. The estimated probability of fatal INH hepatitis used by Tsevat et al. in their decision analysis was 91.2 per INH recipients, which would result in a loss of 16 QALYs per persons screened, if used in my model. The other important variables in this model are the following: 1 The probability of active TB given latent TB infection. With an increasing risk of active TB, more cases are prevented at a given level of INH efficacy. These cases are prevented at a lower cost, or greater cost savings, and there is a more favorable risk/benefit ratio for using INH. 2 The probability of death given active TB. The probability of death given active TB can also affect the QALYs or life expectancy, but does not have a great effect on costs. 3 The prevalence of latent TB infection. The prevalence of latent TB infection has an obvious effect on total cost and numbers of active TB cases prevented. However, the higher the prevalence of infection, the more favorable the risk/benefit ratio, and there is minimal effect on the cost-effectiveness ratio. 4 The efficacy of INH. An increase in the estimated efficacy of INH has a favorable effect on the costeffectiveness ratio. Even if the estimated efficacy of INH is 50%, screening remains cost-saving, although fewer cases of active TB are prevented. 5 The cost of treating active TB. In the base case, if the average cost of treating active TB disease is as low as $6600 dollars, then screening incurs costs. Table 4 Results of base-case analysis, per persons screened Strategy Total costs ($US) Active TB cases Incremental costs (savings), $US Active TB cases prevented QALYs gained Cost per active TB case prevented, $ Cost per QALY gained, $ No screen $ Screen $ ($ ) 57 3 Savings Savings QALY quality-adjusted life-year.

6 S132 The International Journal of Tuberculosis and Lung Disease Table 5 CONCLUSIONS Impact of fatal INH hepatitis on QALYs Probability of fatal INH hepatitis QALYs gained (lost)* (Salpeter et al.) (Taylor et al.) (Tsevat et al.) 5 (16) * Per persons screened. QALY quality-adjusted life-year. Three questions remain to be answered after this review of and brief exercise in cost-effectiveness analysis. First, how did these various decision and costeffectiveness analyses impact medical practice and public health policy? Second, how should TB control programs use these study results to guide their use of resources? Third, what analyses should be undertaken in the future? Ultimately, the decision analyses did not provide a definitive answer regarding the treatment of low-risk reactors. It is also not clear whether these studies had any impact on policies regarding the treatment of latent TB infection. However, the findings may have influenced the practice of physicians in prescribing INH and may have resulted in less use of INH, even in persons at higher risk of disease. The results of the decision analyses in high risk reactors were clear: those persons at higher risk of reactivation greatly benefited from INH preventive therapy. Finally, these models identified those important variables that contributed to the decision tree model outcomes: the TB activation rate, the TB case fatality rate, and the INH hepatitis case fatality rate. The CEA examining the duration of therapy contributed to changes in policy and practice, which resulted in the widespread adoption of 6 months of therapy with INH. 12 It is not clear if the other CEAs resulted in policy changes or changes in published recommendations. Although they may have influenced some physicians practices, there is no evidence of large-scale changes in practice resulting from these studies. CEAs of screening for and treating LTBI may be most useful to TB control programs in making decisions regarding which populations in their jurisdictions should be targeted for screening. These populations should be locally identified; however, this review and analysis demonstrates that to maximize the impact of targeted testing programs, TB control programs should screen populations with higher risks of developing active TB disease, populations that have a greater prevalence of LTBI, or populations who have increased costs of treatment for active TB disease. TB control programs should also keep in mind that CEAs are simply tools to aid decision-making, since these analyses cannot incorporate all relevant social and political values. Finally, what studies should be undertaken in the future? CEAs of targeted testing programs should be undertaken, with comparisons made with alternative testing strategies. Disease-specific outcomes, such as TB cases prevented, will be more useful for decision makers, but the overall benefit for patients should also be considered. Therefore, these analyses should include patient costs and productivity losses incurred by persons treated for LTBI and persons with active TB disease. These analyses should consider the different perspectives of society and patients as well as the more limited perspective of the TB control program. Lastly, the costs and benefits of the new short course treatment regimens should also be considered in future analyses. References 1 Moulding T. Chemoprophylaxis of tuberculosis: when is the benefit worth the risk and cost? Ann Intern Med 1971; 74: American Thoracic Society. Preventive therapy of tuberculous infection. Am Rev Respir Dis 1974; 110: Taylor W C, Aronson M D, Delbanco T L. Should young adults with a positive tuberculin test take isoniazid? Ann Intern Med 1981; 94: Rose D N, Schecter C B, Silver A L. The age threshold for isoniazid chemoprophylaxis. A decision analysis for low-risk tuberculin reactors. JAMA 1986; 256: Tsevat J, Taylor W C, Wong J B, Pauker S G. Isoniazid for the tuberculin reactor: take it or leave it. Am Rev Respir Dis 1988; 137: Colice G L. Decision analysis, public health policy, and isoniazid chemoprophylaxis for young adult tuberculin skin reactors. Arch Intern Med 1990; 150: Jordan T J, Lewit E M, Reichman L B. Isoniazid preventive therapy for tuberculosis. Decision analysis considering ethnicity and gender. Am Rev Respir Dis 1991; 144: Rose D N, Silver A L, Schechter C B. Tuberculosis chemoprophylaxis for diabetics: are the benefits of isoniazid worth the risk? Mt Sinai J Med 1985; 52: Cooper J K. Decision analysis for tuberculosis preventive treatment in nursing homes. J Am Geriatr Soc 1986; 34: Jordan T J, Lewit E M, Montgomery R L, Reichman L B. Isoniazid as preventive therapy in HIV-infected intravenous drug abusers. JAMA 1991; 265: Rose D N, Schechter C B, Sacks H S. Preventive medicine for HIV-infected patients: an analysis of isoniazid prophylaxis for tuberculin reactors and for anergic patients. J Gen Intern Med 1992; 7: Snider D E, Caras G J, Koplan J P. Preventive therapy with isoniazid. Cost-effectiveness of different durations of therapy. JAMA 1986; 255: Rose D N, Schechter C B, Fahs M C, Silver A L. Tuberculosis prevention: cost-effectiveness analysis of isoniazid chemoprophylaxis. Am J Prev Med 1988; 4: Fitzgerald J M, Gafni A. A cost-effectiveness analysis of the routine use of isoniazid prophylaxis in patients with a positive Mantoux skin test. Am Rev Respir Dis 1990; 142: Salpeter S R, Sanders G D, Salpeter E E, Owens D K. Monitored isoniazid prophylaxis for low-risk tuberculin reactors older than 35 years of age: a risk-benefit and cost-effectiveness analysis. Ann Intern Med 1997; 127: Rose D N. Short-course prophylaxis against tuberculosis in HIV-infected persons. 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7 The cost-effectiveness of screening for latent tuberculosis infection S Mohle-Boetani J C, Miller B, Halpern M, et al. School-based screening for tuberculosis infection. A cost-benefit analysis. JAMA 1995; 274: Gourevitch M N, Alcabes P, Wasserman W C, Arno P S. Costeffectiveness of directly observed chemoprophylaxis of tuberculosis among drug users at high risk for tuberculosis. Int J Tuberc Lung Dis 1998; 2: Snyder D C, Paz E A, Mohle-Boetani J C, Fallstad R, Black R L, Chin D P. Tuberculosis prevention in methadone maintenance clinics. Effectiveness and cost-effectiveness. Am J Respir Crit Care Med 1999; 160: Marchand R, Tousignant P, Chang H. Cost-effectiveness of screening compared to case-finding approaches to tuberculosis in long-term care facilities for the elderly. Int J Epidemiol 1999; 28: Kaplan R M, Anderson J P. The general health policy model: an integrated approach. In: Spilker B, ed. Quality of life and pharmacoeconomics in clinical trials. 2nd ed. Philadelphia: Lippincott-Raven Publishers 1996: Nguyen C, Taylor Z, Qualls N. Quality-of-life estimates for tuberculosis. 30th International Union Against Tuberculosis & Lung Disease World Conference on Lung Health. Madrid, Spain. September [Abstract] Int J Tuberc Lung Dis 1999; 3 (Suppl 1): S140.