Cost-effectiveness of Implementation Methods for ELISA ... - CiteSeerX

Am. J. Trop. Med. Hyg., 79(1), 2008, pp. 53–68 Copyright © 2008 by The American Society of Tropical Medicine and Hygiene

Cost-effectiveness of Implementation Methods for ELISA Serology Testing of Trypanosoma cruzi in California Blood Banks Leslie S. Wilson,* Janine M. Ramsey, Yelena B. Koplowicz, Leopoldo Valiente-Banuet, Christi Motter, Stefano M. Bertozzi, and Leslie H. Tobler University of California, San Francisco, California; Area de Estructura Centro de Investigaciones de Paludismo Instituto Nacional de Salud Publica, Calle Poniente Tapachula, Chiapas, Mexico; National Blood Transfusion Center, Gustavo A. Madero Mexico City, Mexico; Division of Health Economics and Policy National Institute of Public Health Cuernavaca, Mexico; Viral Reference Laboratory and Repository Core Blood Systems Research Institute, San Francisco, California

Abstract. The first U.S. ELISA test for T. cruzi antibodies was licensed by the Food and Drug Administration (FDA) on December 13, 2006. Blood banks have begun screening in absence of FDA recommendations for best implementation methods. We surveyed 2,029 blood donors at five California sites with three risk-based Chagas riskscreening questions. Semi-Markov models compared the cost-effectiveness of three testing strategies. 30% of donors screened positively. Screening all dominated doing nothing, being less costly, and saving more lives. The choice to “screen and test” compared with “testing all” varied by Chagas prevalence, “screening and testing” being cost-effective for high (0.004) and low (0.00004) prevalences, and “testing all” cost-effective for moderate risk (0.0004). It is costeffective to screen by ELISA rather than do nothing. The best strategy depends on site-specific risk. Census estimates of Hispanics do not predict donor risk well. We suggest using our screening questions to determine risk level and most cost-effective testing strategy. INTRODUCTION

Latin America and the United States, there has been increasing concern about the risk of undocumented transfusiontransmission of Chagas disease in the current U.S. blood supply. Prior to donation, donors generally have been asked only whether they have Chagas disease, despite its being largely undiagnosed. There is no mandatory serology testing of the U.S. blood supply for Chagas disease, despite FDA licensing on December 13, 2006 of the first U.S. enzyme-linked immunosorbent assay (ELISA) test for detection of T. cruzi antibodies. Nevertheless, many blood banks are beginning some type of testing.26,27 There has been no recommendation of the best method to implement testing, however, and we compare the cost-effectiveness of different implementation methods with this study. Our purpose was first to determine among California blood donors, the characteristics of potential Chagas risk as well as to estimate donor Chagas risk by asking for more detailed state and country specific travel and residence history from those screening positive for Chagas antibodies. Secondly, we developed a semi-Markov model and examined the costeffectiveness of several Chagas screening and serology testing approaches for the U.S. blood banks to assist the implementation of the new Chagas serology testing efforts.

Chagas disease is a parasitic disease found predominantly in Latin America with a prevalence of about 7.6 million, and caused by the protozoan Trypanosoma cruzi (T. cruzi).1,2 The T. cruzi parasite can be transmitted to humans by bloodsucking triatomine insects, blood transfusion, maternal transmission, breastfeeding, organ transplantation, oral contamination, or by laboratory accident.3–8 The seroprevalence of T. cruzi ranges between 0.45% and 19% in Mexico,9,10 and between 0.26% and 4.45%, and 0.0% and 21.5% in Central and South America respectively.11 Although the United States is not endemic for T. cruzi, estimates based on the number of documented immigrants from endemic countries in Latin America combined with Chagas disease rates in those countries lead to estimates of 56,028 to 357,205 persons in the United States that have T. cruzi infection and an additional 33,193 to 336,097 undocumented immigrants in the United States who might also be infected.2,12,13 There are seven documented cases of transfusion transmitted Chagas disease in immunosuppressed patients in the past 20 years in the United States and Canada.14–19 Now that blood banks can test donors for Chagas risk, other cases have been found (32 positive with both ELISA and RIPA in one study)20 and reports that some cases appear to be autochonous. AABB, who collects 65% of the U.S. blood supply, found that at least 317 people in 30 states were confirmed positive for Chagas last year.21 Chagas disease is characterized by an acute, a latent (indeterminate), and a chronic stage.6,22 Current treatment (with benznidazole and nifurtimox) is 60–70% effective in the acute stage and only 8–26% in the chronic stage.23,24 We have described Chagas disease progression and treatment options in greater detail in our previous study.25 With the increases in immigration and migration within

METHODS Sample. We surveyed a convenience sample of 2,029 blood donors at five California blood donation sites with three riskbased screening questions: 1) if they or their birth mother were born in or 2) ever lived in Latin America for more than 2 weeks total or 3) ever traveled in Latin America for more than 2 weeks total. Those answering yes to these screening questions (613 donors) became eligible for a detailed survey asking about birth, travel, and residence history in detail by stage of life and by exact region of Latin America, using maps, if needed as a reminder of places or names, to better specify the locations. The survey was pretested on both nondonors and donors and revised based on feedback. It was a 20-page survey, taking 20 to 45 minutes to complete depending on the extent of the donors contact in endemic areas. The survey was available in both English and Spanish and we had

* Address correspondence to Leslie S. Wilson, Associate Adjunct Professor, University of California, San Francisco, 3333 California Street, Suite 420, Box 0613, San Francisco, CA 94010-0613. E-mail: [email protected]

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nor cohort and their risk were estimated from the detailed survey of blood donors described above, as well as from more current donor serology surveys. Chagas prevalence risk. The risk of Chagas prevalence was estimated first from responses to our survey about residence, travel, and birth history and finally rural or uban setting for Latin American locations visited or lived in. For each donor a weighted average of the highest and lowest prevalence risk by Mexican state or country in either Central or South America that they lived in or traveled to was used to determine risk of having Chagas disease. The published risks at each specific location were assessed and are shown in Table 1. Although previous studies were not able to estimate risk effectively using domicile risk factors, our survey gave much more detailed evidence of exact location of birth, domicile, and travel, which we thought would markedly increase the accuracy of the risk estimates.22,29 Because the literature estimates were from varying time periods and risk locations, we used a local expert in Chagas disease entomology with specific experience in the field to assess which published estimates best represented the states of Mexico and Countries of Central and South America. Since donor risk was likely gained in the previous 30 to 40 years, we primarily used risk estimates across all those years. These literature estimates were used to estimate the blood donors’ mean risk levels by specific location and urban and rural exposure indicated by each donor in the surveys. Those in specific states in Mexico with a mostly rural contact were considered at three times the risk of those that had urban contact.3,30–35 The ratio of urban

Spanish-speaking, trained surveyors available to assist donors in survey completion. Surveys were completed on 481 donors. In addition questions about other domicile risk factors including type of housing, domestic and non-domestic animals within and outside their sleeping area, and whether a rural or urban environment was most frequent were asked. Finally, we asked about donors’ knowledge of Chagas disease, and the triatomine bug (using pictures), about their general health symptoms, including symptoms specific to Chagas disease, their general nutrition, and access to medical care. The goal of the survey was to characterize the potential Chagas risk of the donor population, to specify variations in potential risk among individual blood banks and to compare the costeffectiveness of different strategies for implementing Chagas testing in U.S. blood banks. Design. We compared the costs and life expectancies of a cohort of blood donors in California going through a SemiMarkov model and analyzed the cost-effectiveness of three different potential blood donor Chagas testing strategies. 1. T. cruzi serology testing all blood donations 2. Verbal screening with our three questions first and then T. cruzi serology testing only those positive to the verbal screening and 3. No verbal screening or T. cruzi serology testing for Chagas at all We used DATA™ Professional Software.28 Parts of the Markov model were based on the authors’ previously developed Chagas disease model.25 The characteristics of this do-

TABLE 1 Estimated risk by Mexican state and Central/South American country Mexican states

Urban risk

Rural risk

Guanajuato Estado de Mexico Hidalgo Tlaxcala Yucatan Tamaulipas Puebla Campeche Aguascalientes Distrito Federal Sinaloa Michoacan Nayarit Chiapas Zacatecas Morelos Veracruz San Luis Potosi Jalisco Oaxaca Nuevo Leon Tabasco Colima Guerrero Quintana Roo Queretaro Baja California (Norte) Coahuila Sonora Chihuanhua Baja California Sur Durango All Mexico

0.0157 0.01413 0.0139 0.01329 0.01237 0.01161 0.01102 0.01076 0.01074 0.00996 0.00908 0.00887 0.00874 0.00863 0.00776 0.00619 0.00605 0.00549 0.00535 0.00521 0.00442 0.00415 0.00388 0.0037 0.00296 0.00182 0.00171 0.00165 0.00149 0.00148 0.00146 0.00116 0.0123

0.0471 0.04239 0.0417 0.03987 0.03711 0.03483 0.03306 0.03228 0.03222 0.02988 0.02724 0.02661 0.02622 0.02589 0.02328 0.01857 0.01815 0.01647 0.01605 0.01563 0.01326 0.01245 0.01164 0.0111 0.00888 0.00546 0.00513 0.00495 0.00447 0.00444 0.00438 0.00348 0.0369

Refs 30,91 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30,35,106 30 30 30 30,107 30 30 30 30 30 30 30 30 30 30 30 30 3,30

Central/South American country

Guatemala Panama El Salvador Costa Rica Nicaragua Honduras Belize Bolivia Argentina Paraguay Columbia Venezuela Peru Ecuador Brazil Chile Uruguay

Urban/Rural average risk

0.0445 0.0385 0.0375 0.0255 0.023 0.0136 0.0026 0.215 0.051 0.0445 0.03 0.03 0.024 0.013 0.012 0.005 0.0035

Refs 92,93 92,94 4,92 4,92 4,92 4,92 92,95 96,97 98,99 97 100 101,102 97,103 104 4,74,105 4,97,99 4,97

DO WE TEST ALL OR SOME?

to rural risk was estimated from census data on the proportion of the population in each state who reside in urban or rural locations. Those in South and Central American were given an average risk across rural and urban locations to match the published risk found in those countries. If a donor indicated living or traveling in more than one location, the average of the highest and lowest risk was used.4,36–44 Our overall calculated risk across all sites and across all Chagas risk exposures of our blood donors was 0.004. This contrasts with the newly estimated rates for a high-risk U.S. population of between 0.0002 and 0.0003.20,45 The true risk of about 0.0003, is about 10 times lower than our estimated surveybased risk. Therefore we used both the risk estimated from our survey by live/travel/birth and rural or urban experience, as well as our survey-distributed risks but divided by 10 to estimate serology-based risk with our model. In this way we can still estimate the distribution of risk across different categories of blood banks (i.e., high, medium, and low risk). The results of recent donor screening in 46 states indicates an even lower rate of 0.00003605 from 4.33 × 106 donations screened (personal communication, September 2007, Michael Busch, Brian Custer, Blood systems Research Institute, San Francisco, CA). We also used this prevalence risk level in our surveybased models. We therefore ran our model with a low, moderate, and high-risk Chagas prevalence as a sensitivity analysis to see the effect of different plausible risk levels across different blood banks. Semi-Markov model. We developed a semi-Markov model consisting of two parts: a simple decision tree (Figure 1) and a Markov model (Figure 2). Table 2 presents the probabilities in the model and are explained below. First a simple tree was used with separate branches to model the blood donor who is verbally screened and either positive or negative for general potential Chagas risk, and also with a true positive (0.944) and false negative (0.25) rate for screening (Figure 1).46–49 Then the estimated Chagas risk based on our survey data of both where the donor lived, where they traveled, or their birth

FIGURE 1.

55

place and whether or not the risk was from rural or urban visits was estimated on subsequent decision branches (Figure 1). The model was then extended with decision branches to include ELISA and RIPA testing of those screening positive, and a discard of blood (and thus no transmission) for cases which are positive on ELISA testing. True positive (0.988) and false positive (0.012) ELISA tests included RIPA followup testing and resulted in blood being discarded with no subsequent Chagas transmission, but were assessed a cost for lost blood.20 True negative screenings (0.75) were not ELISA tested and did not transmit disease, but false negative screened (0.25) blood was not ELISA tested either and therefore was given to a recipient and did have the opportunity to transmit disease. Only some of the blood transmitted was considered infective with a different infectivity rate for platelets (0.53) and non-platelet (0.33) transfusions, based on literature evidence that most transfusion infective cases have been due to platelets.50 In the “serology testing all” scenario where there was no verbal screening, all blood donations were tested only by ELISA and RIPA and only the false negative (0.0002) units were given to recipients and transmitted disease, again using the same infectivity rate by type of blood transfusion.20 The third cohort had neither survey screening nor serology testing and so the overall Chagas risk from our surveys (0.004), or (0.0004) from the pre-clinical evaluation of Ortho’s Chagas ELISA,20 or the 0.00004 from Chagas screening in 46 states post FDA licensure of the Chagas ELISA in 4.33 × 106 donations screened (personal communication, September 2007, Busch M, Custer B, Blood systems Research Institute, San Francisco, CA) was used in this part of the model. We also included the same infectivity rate to the recipient in this cohort. Table 2 shows all the probabilities in our models. From this starting point, we assumed that one donated unit went into a single recipient and continued with the Markov disease progression model to include a cohort of blood recipients beginning at the average age of U.S. blood recipients

Decision Tree from the Semi-Markov Model of Cost-effectiveness of Chagas testing strategies.

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FIGURE 2.

Markov Model from the Semi-Markov Model of Cost-effectiveness of Chagas testing strategies.

of 60 years,51 and with our estimated risks of Chagas disease prevalence and infectivity rates (Figure 1). Markov model. A steady-state Markov cohort simulation model was used to model blood recipients (Figure 2). Markov models consider a patient to be in one of a finite number of discrete health states. All clinically important events are modeled as transitions from one state to another using transition probabilities.52 These models are particularly useful when determining prognosis for a medical problem that involves a risk over time. Each health state is assigned a utility (year of life expectancy in this case), and this utility contributes to the overall prognosis by adding to a total length of time spent in each state. The time horizon of the analysis is divided up into equal cycle lengths (1 year with half cycle corrections in this case) and a transition can be made from one state to another during each cycle. Persons are absorbed into the dead state, where they remain. Our Markov model starts with the cohort of blood recipients beginning the process with some probability distribution among the starting health states. Those infected entered the model as blood recipients who either were immunosuppressed and had severe acute illness, just as the immunosuppressed cases reported in the literature16 or who were acutely ill though not immunosuppressed at the same rate as those

getting acute illness from a vector, or the remaining cases who entered at the latent Chagas disease state. All others were not infected and entered the model in the “no disease” health state and were subject only to the normal life expectancy of those receiving blood. For each annual Markov cycle the recipients are newly distributed among the health states according to the transition probabilities specified. At the same time a utility (life expectancy) is summed for all the health states for each cycle to arrive at a cumulative utility. We have eight health states in our model: No Disease, Immunosuppressed/ Acute Stage, Indeterminate Stage, General Chronic Stage, a stage for GI abnormalities, one for cardiac abnormalities, and two Death states, one for death due to Chagas disease and one for death due to both general transfusion and all other causes. Disease stages. Branch and Transition Probabilities are shown in Table 2. Acute disease. Only immunosuppressed recipients (0.02) and 2% of infected recipients enter in the acute health state. Approximately 2.2% of total recipients are immunosuppressed based on the proportion of all blood recipients who undergo transplants53,54 and have an immediate acute disease presentation of 3 years duration.50,55 An additional 2% of recipients (the same number that are reported to get severely

57


TABLE 2 Probability variables Variables (moderate Chagas Risk Models)

p_General Chagas Risk p_Chagas Risk in_negative verbally screened donors p_Chagas Risk in verbally unscreened donors p_ChagasRisk of verbally screened donors living in rural high-risk areas p_ChagasRisk of verbally screened donors traveling in rural high-risk areas p_Chagas Risk on average across all high-risk areas p_Chagas Risk of verbally screened after ELISA/RIPA serology testing who live in urban high-risk areas p_VS_ELISA urbanTravelAllChagas Risk of verbally screened after ELISA/RIPA serology testing who travel in urban high-risk areas p_of CHF annually given you have chronic disease p_platelets given p_you will enter Latent Disease stage if infected by platelets P_of Infectivity if given platelets p_Red Blood Cells given P_you will enter Latent Disease if infected by red blood cells p_of Infectivity if given red blood cells p__Sympt_Immunosup Acute Disease

Mean

Low

High

SD

Ref 20

0.000396 0.000015

0.00003 0

0.000636 0.0001514 0.00003 0.0000075

& Survey Survey

0.000015 0.00235

0 0.002174

0.00003 0.0000075 0.002525 0.00008767

Survey Survey

0.001665

0.000777

0.002554 0.00044421

Survey

0.001892 0.001487

0.000749 0.013566

0.003034 0.000571263 Survey 0.016176 0.0006525 Survey

0.001059

0.00372

0.017449 0.0034323

Survey

0.042 0.33 0.97837

0.0378 0.297 0.880533

0.0462 0.0021 0.363 0.0165 1.076207 0.0489185

60

0.5 0.5 0.97837

0.45 45 0.880533

0.55 0.025 0.55 0.025 1.076207 0.0489185

50,63,88,108 (calculations) calculated calculated

0.33 0.02163

0.297 0.00163

0.363 0.12163

p_of being true negative (TN) after an 0.99998 ELISA/RIPA serology test in those verbally screening positive p_of being true positive (TP) after an ELISA/RIPA 0.9988 serology test in those verbally screening positive p_of a FalseNegative Verbal Screen 0.025 P_that positive screened donor lived in high-risk 0.1983 area p_of positive screened donor who lived in high-risk 0.254 area was mostly rural p_that a donor verbally Screened positively for risk 0.302 p_that a positive verbally screened donor was a 0.944 True Positive screen p_that a positively screened donor who traveled in 0.439 high-risk areas was mostly traveling in rural areas Average age of blood donors 60

0.99996

1

0.89892

1

0.02527

0.02 0.17847

0.031 0.21813

0.00275 0.009915

Survey

0.2286

0.2794

0.0127

Survey

0.2718 0.8496

0.3322 1.0384

0.0151 0.0472

Survey Survey

0.3951

0.4829

0.02195

Survey

7.5

51

59

65

0.0165 0.03 1E-05

108

50,88

Calc from 54,64,109 www.optn.org/latestdata/rptData.asp, 1/ds 20

20

46,47,49

p ⳱ probability; high-risk areas ⳱ Mexico or Central or South America; CHF ⳱ congestive heart failure; Sympt_immunosup⳱ patients immunosuppressed and/or symptomatic with Chagas disease; SD ⳱ standard deviation.

acute symptoms when contracting the disease directly from the vector) are also estimated to be symptomatic in the acute disease stage.56,57 For the rest of the population we assumed an infectivity rate (discussed earlier) and then latent disease for those infected. Indeterminate stage. Recipients stayed a minimum of 15 years in the latent or indeterminate stage before progressing to the chronic stage, but were allowed to die of other causes during this stage. Some recipients (40%) may remain in the indeterminate stage for life, but our model assumes that eventually everyone will move to the chronic stage at a rate of 1% per year with either mild or severe symptoms, or eventually will die either of Chagas-related or other causes.56 Since we run the model for 50 years, however, those that eventually die of other causes represent the population who never become ill with chronic Chagas disease and remain indeterminate. We did not allow deaths in the indeterminate stage except from normal life table deaths plus deaths from transfusion related causes.58 Deaths from sudden death that might occur in the indeterminate stage were attributed to the chronic stage (as asymptomatic heart disease; EKG changes).59,60

General chronic disease. As soon as symptoms or any heart changes without symptoms occur, it was assumed that a transition into the general chronic stage had occurred. Beginning at year 15 (age 75) after contracting the disease, recipients entered the chronic stage at about 1% per year.61,62 Cardiac disease. Depending on the type of symptoms, we then modeled increasing heart symptoms including some EKG changes and cardiomyopathy but no CHF, and finally progression to cardiomyopathy with CHF and death.60 Megaviscera. Those with gastrointestinal (GI)/esophageal symptoms were moved from the general chronic disease stage to the megaviscera stage, where we assumed that about 20% would have palliative surgery at some point and either improve or die. Death from megaviscera was assumed to occur as a surgical or post surgical death only at a rate of 2%.63 Blood recipient mortality. We allowed deaths from natural causes using the mortality from U.S. life tables for our Markov model and we also included the added risk of death for those without Chagas but who are at heightened risk due to being an individual who is receiving a blood transfusion. The estimated mortality of blood recipients is high, with 24%

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the model.75 One-way, two-way, and tornado analyses on all costs and all probabilities were run. Monte Carlo secondorder simulations were performed with a random sampling of 1,000 samples of probabilities and outcomes from discrete and continuous distributions during expected value calculations to examine the models behavior under more realistic probabilistic conditions. Acceptability curves were determined to compare the ICER to a variety of threshold values by showing the probability that the ICER falls below a range of WTP or acceptable maximum ceiling ratio values, and acts as a summary measure of joint uncertainty in the ICER.72,73 We calculated net health benefits (NHB) and incremental net health and monetary benefits (IHNB, IMNB) as well as incremental cost-effectiveness ratios (ICER). Net health benefits (NHB ⳱ Effectiveness-(Cost/WTP)) were calculated as an alternative to ICER analyses. Given the same WTP, the intervention with the greater average NHB is more costeffective. The INHB is in units of health-effectiveness and is “the average health gained per patient who takes the new treatment, adjusted for cost”.76–81

mortality in the first year, 6% in the second year, 4% in the third year and 3% in the next 4 years, and 2% in the next 3 years.64–66 Then in the 11th year the cohort moved to a mortality rate using the distribution from the U.S. life tables corresponding to the aging of the cohort from age 71 to 100.58 Chagas-related mortality. Those who were infected with Chagas who were immunosuppressed died within the first 3 years based on published cases in the United States.19,50 Those who entered the latent stage were subject to nonChagas mortality and moved to chronic disease in the Markov model. Recipients died from Chagas disease in the chronic stage from either cardiomyopathy with or without congestive heart failure, or megaviscera surgery.63,67–71 Life expectancy utilities. Our model output was life expectancy which is calculated within the Markov model from the annual mortality rates as the population cycles through the model and enters the two dead states described earlier. For each cycle one year of life expectancy is recorded for the proportion of donors still living. We did not adjust life expectancy for quality of life in this model because it is unclear what the quality of life utilities are for Chagas disease contracted at this later age, by this mode of transmission in the United States. In addition, given the need to make a strict decision of the value of different modes of patient screening and testing, with some uncertainty still about true risk, it was more accurate to exclude quality adjustment for now. Later models may be able to test for utility levels in older Americans and include this in the models.72–74 Cost-effectiveness analysis. The cost and life expectancy estimates from running the semi-Markov model were used to calculate the cost-effectiveness of the three Chagas serology implementation policies at three different levels of risk. Costeffectiveness is calculated by subtracting the difference in costs of two alternatives and dividing by the difference in life expectancy of the two alternatives. This number results in the incremental cost-effectiveness ratio (ICER) or the average additional costs of choosing one alternative over the other for each additional unit of life expectancy [Cost1-Cost2/Effect1Effect 2 ⳱ ICER]. Sensitivity analysis. We performed univariate and probabilistic sensitivity analysis to vary the cost and effect parameters in the model to see which variables were most sensitive within

DIRECT COSTS There is very little data on the utilization of health care and their costs for Chagas disease and most is country specific. Bosombrio’s estimates from Argentina were selected for use in our previous Chagas model82 but did not adequately reflect either disease contracted from blood transfusion rather than from vectors, or disease treatment in the United States. There is no U.S. source for determining the costs of transfusiontransmitted Chagas disease, with only seven cases described in the United States and Canada, and all occurring in immunosuppressed recipients. Therefore we estimated Chagas disease costs by modeling the utilization of health services by stages of the disease, reviewing clinical treatment guidelines for each symptom and then consulting experts in the management of each particular symptoms found in the disease stage and how they would manage the diagnosis and treatment of these symptoms for a patient with Chagas disease (Table 3)6,82,83 (personal communication for treatment management, Stephen Kayser, University of California San Francisco). The

TABLE 3 Variables of cost Cost variables in US $

Mean

c_Annual Acute Chagas Treatment c_Annual Cardiac care Non-CHF in Chronic stage c_Chagas treatment in year of death c_Annual treatment of CHF in chronic stage c_ELISA/RIPA Testing

29888 17614 33620 33620 7.5

c_Annual Gen chronic stage Chagas care c_Last year of latent Chagas care c_Latent stage annual care c_Megacolon care in Later years c_Megacolon diagnosis & care in 1st year c_Platelets lost due to discarding c_GI surgery due to Chagas c_Care in year of Death due to transfusion itself c_Verbal screening c_Red blood cells lost due to discarding

17614 6492 2914 3234 9532 525.67 12363 13659 3.25 426.74

Low

14944 8807 16810 16810 3.75 8807 3246 1457 1617 4766 262.835 6181.5 6829.5 1.625 213.37

High

59776 35228 67240 67240 15 35228 12984 5828 6468 19064 1051.34 24726 27318 6.5 853.48

SD

11208 6605.25 12607.5 12607.5 2.8125 6605.25 2434.5 1092.75 1212.75 3574.5 197.12625 4636.125 5122.125 1.21875 160.0275

C ⳱ Cost in US $; CHF ⳱ Congestive heart failure; Gen ⳱ general; GI ⳱ gastrointestinal; SD ⳱ standard deviation.

Reference 110,111 86,110,112–115 86,110,112–115 86,110,112–115 84

& personal communication Brian Custer Ph.D., Blood Systems Research Institute, 1/2007, SF, CA

86,110,112–115 86,110,111,113–116 86,110,111,113,114,116 86,113,114,116–121 86,113,114,116–121 86

(includes unit & transfusion $)

86,113,114,116–121 122 85 86


health care utilization was divided by stage of Chagas disease and whether or not the patients were immunosupressed. Acute disease in immunosuppressed blood recipients included drug treatment with benznidazole and congestive heart failure (CHF) care, which included physician consultations, hospitalizations, diagnostic procedures, general laboratory tests, CHF drug treatment, and parasitologic and conventional serologic tests for T. cruzi infection. Acute disease in non-immunosuppressed recipients included drug treatment with benznidazole, physician consultations, and costs for possible sudden cardiac death. The latent indeterminate stage included physician consultations, periodic medical visits, diagnostic and laboratory tests, and hospitalizations. The chronic cardiac manifestations with CHF included physician consultations, periodic medical visits, diagnostic procedures, general laboratory tests, CHF drug treatment, and parasitologic and conventional serologic tests for T. cruzi infection. The GI-related costs included surgical procedures, lab and diagnostic imaging, MD visits and consultations, procedures and drug costs (Table 3). Costs were applied to these estimates of health care utilization from national health care sources. We used CPT codes and Medicare fees for physician visits and procedures, Healthcare Cost and Utilization Project (HCUP) national all-payer hospital inpatient costs for hospitalizations, the Red Book national average wholesale price (AWP) estimates for drug costs, and local or internet sources for costs of laboratory testing. COSTS OF CHAGAS DISEASE SCREENING AND TESTING Preliminary cost estimates for ELISA testing in the blood bank are from $5–9 per donation so we used $7.50 84 (personal communication, February 2007, Brian Custer, Blood Systems Research Institute). To this cost we added the cost of verbal screening, which we estimated would take 10 minutes and determined the time cost using the wages and salaries of a blood worker ($3.15 per donor screened).85 Additionally, the cost of blood units discarded due to either verbal screening or ELISA testing of the blood was estimated at $427 for red blood cells and $526 for platelets.86 All costs were reported in 2005 U.S. dollars. We discounted costs and effects by 3% to account for time preference. The 2002 hospitalization and procedure costs were inflated to 2005 U.S. dollars using the consumer price index for in-patient hospital services for a non-seasonally adjusted U.S. city average.75 We excluded costs of work days lost because most subjects were contracting the disease after age 65 and were probably retired and not working. RESULTS Donors at risk. One of our major findings is that about 30.2% (range 16–37%) of the blood donor population across three diverse regions in California verbally screen positively for our very broad screening category of potential risk. Given the relatively short time period of required contact with the risk location (2 weeks or more) on our screening questions to be considered a screen positive donor, we likely have captured most people of potential risk. The accuracy of donor screening responses however is not perfect and despite the fact that the screening questions were quite easy to under-

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stand and non threatening in nature, we still determined that 5.6% (TPS ⳱ 0.944) of respondents gave false positive information based on our sample who screened ELISA positive and had no risk factors on the longer survey. Furthermore, we estimate 25% (FNS) false negative responses.47,49,87 In addition, we found that there was variation across sites in the percentage of the donor population that verbally screened positive and that this percentage was not the same as the prevalence of the Hispanic population surrounding the blood banks. For example for Delta Blood Bank, which has the highest census of Hispanics eligible to donate blood (ⱖ 18 years) only 15% verbally screened positive, while in San Francisco, with the census showing only 13% eligible Hispanics, more, 22% of donors, screened positive. Finally, in San Diego, only 29% of donors verbally screened positive despite a higher population prevalence of Hispanics eligible to donate. These data indicate that we are unable to predict high and low lifetime risk of blood donors using existing known Hispanic population estimates from the U.S. Census surrounding a blood bank because this indicator does not correlate with the potential risk of those donating blood. Further characteristics of the risk potential of the donor population will be discussed in a separate paper. Our verbal screening results demonstrate a range of screening risk that can be used to estimate the value of different ELISA testing strategies across blood centers. Estimated Chagas risk. Another finding is that even a very careful determination of risk from donor surveys such as ours which included a much more detailed location of donor travel and residence history than previous surveys, still obtains a Chagas risk level that is about 12 times higher than the current serology based estimate in a high-risk area (0.004 rather than 0.0003). Although more serology testing is now being performed and higher risk numbers in some areas near the Mexican border have been established while lower estimates (0.00003605) are obtained when test results from 46 states are evaluated (personal communication, September 2007, Busch M, Custer B, Blood Systems Research Institute), it appears that verbal screening greatly overestimates risk level. When we calculate risk only for those who have lived in rural areas or were born in Latin America (excluding all travel risk and urban living) however, our calculated risk level lowers to 0.000214, which is closer to the current published risk level in a medium- to high-risk area.20 However, our risk survey gives us a good characterization of the maximum potential risk in our current blood donors. The screening questions we used will be even more helpful currently to better identify those needing serology testing for Chagas if universal testing is not adopted. In addition, more serology results across sites of varying risk will be determined in the next year and better data of true risk will emerge. Therefore we have conducted our cost-effectiveness studies with both our survey-based Chagas prevalence which can be considered a maximum risk and with the currently published moderate serology risk of 0.0003 from a high-risk population,20 and also with the even lower national risk of 0.00003605. Cost-effectiveness. We compare the cost-effectiveness results of three Chagas testing implementation strategies. ELISA test all versus none. Our major finding is that it is cost effective to serology test all donors compared with serology testing none of them, across a wide range of Chagas risk levels. ELIAS serology “testing all” is dominant using our

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TABLE 4 Cost-effectiveness of “ELISA Test All” versus “Testing None” across three Chagas prevalence rates: high-risk case Strategy: (high-risk case) (0.004)

Cost ($)

Incr. cost

Effect (LYs)*

Incr. effect (LYS)**

Incr. C/E (ICER)***

ELISA Test All No Screen/Test

$13,662.45 $13,695.79

$33.34

14.7093769 14.69492761

−0.014449289

(Dominated)

* LY ⳱ Life years expected per person; ** LYS ⳱ Life years saved per person; *** ICER ⳱ Incremental Cost-effectiveness ratio; Incr. ⳱ incremental.

higher survey based risk levels (0.004), is very cost-effective ($689.24/LYS) at the published moderate serology-based risk in a high-risk donor area, and also still quite cost-effective ($5,632.58/LYS) when using the very low risk level of (0.00004) (Tables 4–6). For the highest risk estimates, serology ELISA testing all donors both costs slightly less and saved about 1.4/100 more life years per person than does no testing at all. Using the current high serology risk levels (0.0004), ELISA serology testing all donors cost only slightly more but had an average additional life expectancy of only 0.002 or about one day per person (Tables 4–6). Verbal screening + ELISA testing positives only versus no screening or testing. We also compared a strategy of verbal screening first and then ELISA serology testing only those screening positive with no ELISA testing at all across the three risk levels. At the currently published medium Chagas risk level (0.0004) and at the higher risk level the “screening and testing” strategy was dominant compared with the “no testing” strategy (Tables 7 and 8). Even at our very low risk levels, it is still cost effective ($787/LYS) to screen and test those positive compared to not ELISA testing donors at all, being only slightly more expensive by risking 0.0009 more life years/person (Table 9). Screen first then ELISA test only positives versus ELISA test all donations. We also found that it both cost more but also was more effective to “test all” compared with testing only those verbally screening positive at all levels of risk. The “verbal screening first” strategy is cost-effective at all risk levels except the moderate Chagas risk level (0.0004). At both higher and lower Chagas risk prevalence levels it is not costeffective to “test all” compared with “testing some”, costing almost $400,000/LYS (Tables 10–12). At these levels of risk it is preferable from a cost-effectiveness perspective to verbally screen and then ELISA test. At the higher Chagas risk of 0.004, the per person cost of testing all was $400 more with an increase of 1/1,000 life year/person and at the lower risk level (0.00004) the test all strategy cost only $4.00 more and saved 1/100,000 more life years/person (Tables 10–12). UNIVARIATE SENSITIVITY ANALYSES One-way and two-way analyses. We conducted univariate sensitivity analysis on all factors of cost and outcomes. Transmission and/or infectivity of a recipient of blood from a T. cruzi positive donor are reported higher for platelets than for red blood cells. However, literature based estimates report a

large range, 12–53% for red blood cells50 and in Mexico a rate of 10–40% is reported.88 We used an average 33%50 for our base case for red blood cells and used a higher rate for platelets (53%). However, a two-way sensitivity analysis of infectivity rates for red blood cells and platelets showed that even at an infectivity of 10% for red blood cells and 20% for platelets, it was still cost-effective to “test all” compared with “no testing” ($7,575/LYS). Figure 3 compares the sensitivity of the ICER to a range of Chagas risk prevalence levels, another variable of uncertainty, demonstrating that “ELISA testing all” compared to “none” at a medium risk is either cost-effective or dominates. Tornado analysis. For the “ELISA test all” versus “none” comparison, moderate risk case, the major sensitive factors of cost are the cost of transfusion mortality which in our tornado analysis of all cost variables accounted for 99% of the variability in the model. When these costs are less than $2,732, then “no ELISA testing” dominates the model, but at amounts greater than this cost “ELISA testing all” becomes cost-effective at all higher costs tested (up to 2 times base costs). When that cost variable was removed from the tornado cost analysis, the cost of the ELISA testing accounted for 99% of the variability and next is the cost of dying from Chagas disease. The cost of treating latent disease accounted for an addition 4% and 2% respectively. The costs of red blood cells and platelets lost also accounted for 2% and 1% of the variability in the model, with all other variables of cost contributing less (Figure 4). A tornado analysis of all the probability variables demonstrated that 99.9% of the variability was accounted for by the starting age of the blood recipient. The only other sensitive variable accounting for 0.3% of the variability was the probability of CHF given that you have chronic Chagas disease. If we remove age from the tornado analysis, however, the prevalence risk of Chagas disease accounts for the most variability in the model (81%), with the true negative rate on the ELISA tests accounting for almost all the rest (18.6%). Looking at Chagas prevalence risk alone, the “testing all” strategy remains cost-effective at all levels of risk and begins to dominate the “no testing” strategy at a level of 0.000552. Changing the true negative rate of Chagas ELISA testing does not affect the cost-effectiveness of the “ELISA test all” strategy compared with “no testing”, but at a true negative rate of 0.9996 testing all is no longer dominant but remains CE at $7,920 per LYS. This model is robust to all the other variables of cost and effect.

TABLE 5 Cost-effectiveness of “ELISA Test All” versus “Testing None” across three Chagas prevalence rates: moderate base risk case Strategy: (moderate base risk case) (0.0004)

Cost ($)

No Screen/Test ELISA Test All

$13,657.40 $13,658.85

Incr. cost

Effect (LYs)

Incr. effect (LYS)

Incr. C/E (ICER)

$1.45

14.70792934 14.71003877

0.002109429

$689.24


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TABLE 6 Cost-effectiveness of “ELISA Test All” versus “Testing None” across three Chagas prevalence rates: very low-risk case Strategy (very low-risk case) (0.00004)

Cost ($)

Incr. cost

Effect(LYs)

Incr. effect (LYS)

Incr. C/E (ICER)

No Screen/Test ELISA Test All

$13,653.56 $13,658.49

$4.93

14.70922951 14.71010522

0.000875703

$5,632.58/LYS


TABLE 7 Cost-effectiveness of “Verbal Screen and Test only Positives” versus “Testing None” across three Chagas prevalence rates: high-risk case Strategy (high-risk case (0.004)

Cost ($)

Incr. cost

Effect (LYs)*


Incr. C/E (ICER)***

Screen & Test + No ELISA Testing

$13,245.48 $13,695.79

$450.30

14.7082962 14.6949276

−0.013368571

Dominated


TABLE 8 Cost-effectiveness of “Verbal Screen and Test only Positives” versus “Testing None” across three Chagas prevalence rates: moderate base risk case Strategy (moderate base risk case) (0.0004)

Cost ($)


$13,654.89 $13,657.40

Incr. cost

Effect (LYs)

Incr. effect (LYS)

Incr. C/E (ICER)

$2.51

14.70993069 14.70792934

−0.002001354

Dominated

TABLE 9 Cost-effectiveness of “Verbal Screen and Test only Positives” versus “Testing None” across three Chagas prevalence rates: very low-risk case Strategy (very low-risk case) (0.00004)

Cost ($)


$13,654.24 $13,653.56

Incr. cost

Effect (LYs)

Incr. effect (LYS)

Incr. C/E (ICER)

−$0.68

14.71009441 14.70922951

0.0008649

$787.20/LYS


TABLE 10 Cost-effectiveness of “Verbal Screen and Test only Positives” versus “ELISA Testing All” across three Chagas prevalence rates: high-risk case Strategy: (high-risk case) (0.004)

Cost ($)

Incr. cost

Effect (LYs)*


Incr. C/E (ICER)***

Screen & Test ELISA Test All

$13,245.48 $13,662.45

$416.97

14.70829618 14.7093769

0.001080718

$385,829.58/LYS


TABLE 11 Cost-effectiveness of “Verbal Screen and Test only Positives” versus ‘ELISA Testing All’ across three Chagas prevalence rates: moderate risk case Strategy (moderate risk case) (0.0004)

Cost ($)

Incr. cost

Effect (LYs)

Incr. effect (LYS)

Incr. C/E (ICER)

Screen & Test ELISA Test All

$13,654.89 $13,658.85

$3.96

14.70993069 14.71003877

0.000108075

$36,668.39/LYS


TABLE 12 Cost-effectiveness of “Verbal Screen and Test only Positives” versus “ELISA Testing All” across three Chagas prevalence rates: very lowrisk case Strategy (very low-risk case) (0.00004)

Cost ($)

Screen & Test + ELISA Test All

$13,654.24 $13,658.49

Incr. cost

Effect (LYs)

Incr. effect (LYS)

Incr. C/E (ICER)

$4.25

14.71009441 14.71010522

0.000010807

$393,413.85/LYS


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FIGURE 3. ELISA Test All versus ELISA Test None (0.0004 Prevalence Risk).

Probabilistic sensitivity analysis. The Monte Carlo simulations at the medium Chagas risk level (0.0004) comparing “serology testing all” versus “none” demonstrated from the scatterplot iso-contours that all of the points fall below the $100,000 WTP line with 67% of the points lying where the comparator (testing all) is more costly but still cost-effective and 33% of the points lying where the comparator is dominant (Figure 5). The distribution of the ICERs in the Monte Carlo simulation had a mean of $882/LYS (SD ⳱ $1,969/ LYS). The Monte Carlo simulations using sampling distributions comparing the strategies “to screen prior to Elisa testing” compared with “Elisa testing all” (again using medium [0.0004] Chagas risks) demonstrated a mean value for costs of $13,541 (SD ⳱ $5,107) and a mean of 14.7099 life years (SD ⳱ 0.0000456). The mean of the distributions of ICERs for this comparison is $62,829 (SD ⳱ 815,153). The ICER scatterplot for the comparison “screen and test” compared with “testing all donors” demonstrated that 90% of the points were in quadrant 2 where the comparator (test all) is more costly and effective but its ICER is less than the WTP ($100,000) and so is cost-effective; 1% of the time, the “testing all” strategy dominates “verbal screening and testing” at this Chagas prevalence. However 8% of the time the comparator (test all) is more costly and effective, but above the

WTP line, so not cost-effective. And only 1.4% of the time screening and testing dominates (Figure 6). Acceptability curves. The acceptability curves for the “screen and test” versus “ELISA test all” strategies at the medium Chagas prevalence demonstrate that the verbal screen and test strategy is cost-effective 87% of the time at a maximum acceptable WTP of $15,000/LYS, 37% of the time at $45,000, and less than 10% at WTP of $100,000, indicating that the alternative strategy to “test all” has a higher chance of acceptability at most of the acceptable WTP ranges ($50,000 to $100,000/LYS) at medium Chagas risk prevalence (Figure 7). Using the acceptability curve we also obtained the confidence interval on cost-effectiveness. For example, the 95% upper bound is as high as $150,000/LYS, and the 95% lower bound is equal to $7,000/LYS for the “ELISA test all” option compared with the “screen and test” option. There is no acceptability curve for the “test all” versus “test none” strategy because the “test all” strategy is dominant across all the WTP levels. Net benefits. The NHB was calculated for the medium prevalence risk of the “screen and test” strategy versus the “test all” strategy because this strategy was most variable across risk levels with the “screen and test” strategy not costeffective only at this moderate risk prevalence. The NHB of the “test all” strategy is higher (14.575, SD 0.051) than that of the “screen and test” strategy (14.574, SD 0.051) indicating that the “test all” strategy is the preferred strategy at the medium Chagas risk prevalence (Figures 8 and 9). Net benefits shown in Figure 9 demonstrate that the “test all” strategy is optimal over the “verbal screen and test” strategy about 99% of the time at a WTP of $100,000/LYS at the medium risk level and also is optimal across a range of WTP rates (Figure 10). The mean INHB value is 0.00007 (SD ⳱ 0.00006) and the 95% CI is −0.00004–0.00019, so the “test all” option does not always have a positive INHB, indicating that “screening first” has some probability of being optimal at a medium Chagas risk prevalence (Figure 11). The mean INMB (which is the difference in the NMB of two alternative strategies) is $210 (SD ⳱ $59.4) for the “test all” versus “test none” comparison and a WTP of $100,000 and Chagas risk of 0.0004. The INMB is positive at a wide range of WTP thresholds, indicating that “testing all” is superior to “testing no one”. The INMB of the comparison

FIGURE 4. Tornado Diagram: ELISA Test All versus ELISA Test None (0.0004 Prevalence Risk). This figure appears in color at www.ajtmh.org.


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FIGURE 7. Acceptability Curve Stratified by Strategy: Model Screen and Test versus Elisa Test All (0.0004, Moderate risk).

between “verbal screening and then ELISA testing” versus “ELISA testing all” (at medium Chagas risk) also demonstrates that “testing all” is positive for all but the very lowest range of WTP thresholds. Limitations. There are limitations in our model due to a lack of empirical estimates since Chagas testing in United States is just beginning. We only surveyed in five blood banks and these may not be representative of the rest of California nor the United States. However we did attempt to be representative of different Hispanic risk areas, based on the California Department of Social Services recommended group-

ings of counties for regional studies, and with one site located near the border with Mexico and another representative of the central valley farm areas, and another representative of a large city with mixed ethnic groups.89 In addition, we only used American Blood Centers and no Red Cross Blood Banks, although we are not aware that the two differ substantially in Chagas risk. We also did not include U.S. autochthonous T. cruzi-infected donors in this model since no data is available on this. However, since submission of this manuscript, there is some indication that some positive donors by serology testing may have become T. cruzi positive from vectors in the border areas of the United States and Mexico. We have included a variety of Chagas risk levels in our analysis, some based on estimated risk, and some based on actual serology risk, making the model robust to this factor. There also is no data on the ELISA sensitivity and specificity changes with different Chagas prevalence risks which

FIGURE 6. lence Risk).


FIGURE 5.

ICER Scatterplot (0.0004 Prevalence Risk).

Elisa Test All versus Screen and Test (0.0004 Preva-

Screen and Test versus Elisa Test All (0.0004 Preva-

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Screen and Test versus Elisa Test All (0.0004 Preva-

would have some effect, especially for the false negative rate which provides most of the T. cruzi transmission in our model. Yet the false negative rate was not identified as a sensitive variable in our model. Also some newer data since submission of this manuscript shows that RIPA follow-up testing may show more false positives than the research represented here.90 These Markov models only addressed single unit testing. Although we are aware that blood pooling is becoming a more common practice, we thought a simple model would address the basic questions of testing approach first and then a more complex model including some of the multiple methods of pooled blood testing could follow when more empirical data is available. In addition, we did not address questions about ELISA Chagas testing requirements for repeat donors, especially if universal testing is not implemented. Donors testing negative and not entering high-risk areas within the period between donations may not require additional serology testing, and verbal screening may become a more important consideration for handling repeat donors.

FIGURE 10. ELISA Test All versus Screen and Test: (0.0004 Prevalence Risk).

FIGURE 11. Risk).

Incremental Net Health Benefit (0.0004 Prevalence

CONCLUSION We found that it is highly cost-effective to both “verbally screen first and ELISA test only those positive” and also to conduct “universal ELISA testing,” compared to a “do nothing” strategy at all Chagas risk prevalence levels. These comparisons were robust across a variety of sensitivity analyses and Chagas prevalence rates, and using both ICER analyses and INHB analyses. Based on this study then, we would recommend either testing strategy over doing nothing. However the “verbal screening first” strategy compared with the “universal ELISA testing” strategy was not costeffective at all levels of risk, specifically not at the moderate Chagas risk level we tested (0.0004), where it is more costeffective to ELISA test all donors. At higher and lower Chagas risk levels, which may exist in many areas of the United States, it is cost-effective to screen first and universal testing is not optimal. The newer donor risk estimates across 46 states indicate a risk similar to our lower Chagas risk estimates, and therefore our model demonstrates for this risk it is costeffective to test only those verbally screening positive instead of implementing universal testing. In addition, in those areas with very high Chagas prevalence in the donor population, we would also recommend from our model, testing only those screening positive, remembering that this includes accepting an additional loss of 1/1,000 life years per person. For cases of more moderate risk, however, we did not demonstrate an advantage of verbally screening first and our extensive sensitivity analysis shows that it was cost-effective across a wide range of assumptions to implement universal screening. The difference across risk levels may partially be affected by our use of the same estimate for the proportion of donors screening positive that we determined by screening all donors at our 5 blood banks at all levels of risk. Although this variable was not identified as sensitive in our models, further study might demonstrate that the number of donors verbally screening positive is much lower and make verbally screening first even

65


more cost-effective. However, at high-risk blood centers, more donors may screen positive and it may become more cost-effective to implement universal testing. Therefore a strategy based on estimated Chagas prevalence at each blood center may be required to reach the most cost-effective strategy. Another important study finding is that the proportion of all donors screening positive for Chagas risk varied across our five blood centers from 16% to 30.2%. In addition, we identified that this proportion could not be accurately predicted from population based estimates of Hispanics living in the area around the blood centers. Therefore, we suggest that blood centers not rely on population based estimates to determine their need for or strategy for Chagas testing, but collect their own information of Chagas risk using our verbal screening questions to determine their likely Chagas risk levels and following this with ELISA testing. The best Chagas testing strategy across the United States requires different strategies at different levels of potential risk based on verbal Chagas risk screening questions such as those presented here. Our screening questions include birth, domicile, and travel of both the donor and donor’s birth mother, to include Chagas transmission risk from mother to child. In addition, our questions include risk behavior for a very short period (two weeks) which will be less likely to miss identifying Chagas positive donors than risk questions covering longer time periods of months and therefore are preferable if that strategy is used. We suggest the introduction of these screening questions in blood banks, because their use even with universal ELISA testing will provide evidence needed to determine the best strategy for each blood center’s particular risk level both for single and repeat donors. Blood donor testing policies are informed by costeffectiveness analyses such as this one which demonstrate value when weighing both costs and benefits. However, individual blood centers also want to know, given their likely donor Chagas risk, what the aggregate costs and benefits are based on these studies. The average annual number of total blood donations in the United States is about 14,990,055. Assuming one donation to one recipient and our moderate Chagas prevalence of 0.0004, nationally it will cost an additional $21.7 million to ELISA test all donors compared with testing no one for T. cruzi, but 31,615 additional life years are saved from the first year of testing as well. If the Chagas prevalence was our lower estimate (0.00004) then the additional costs of testing everyone is $73.9 million while saving only 13,127 more life years. Assuming at age 60 there are 15 more life years per person, this is 875 lives saved from the first year of ELISA testing all. Verbally screening all United States donors prior to ELISA testing at the moderate Chagas prevalence of 0.004 will save the United States $59.4 million but will also incur an additional loss of 1,620 life years or about 108 lives compared to universal testing and so is not recommended. At higher and lower Chagas risk prevalence levels we demonstrated that the additional savings were worth the extra lives lost. For example, at the lower Chagas prevalence risk (0.00004), ELISA testing only those screening positive saves $63.7 million at an additional loss of only 162 life years or about 11 more lives. In this case, it may be desirable from a cost-effectiveness view point to test only those verbally screening positive for Chagas risk.

We show, that it is definitely cost-effective to either “test all” or to “screen and test” compared with no testing at all, at all risk levels, but the extent of the value of verbal screening first compared with universal ELISA testing is very dependent on the risk prevalence of Chagas disease of those donating blood at a particular blood center. It is likely too that after the first year of ELISA testing many of the positive donors will be eliminated from donation and the value of testing all donors will decrease, since the Chagas donor prevalence will also decrease. Therefore, how donor follow-up screening and testing is handled will also be important, with screening questions likely becoming increasingly important. Received October 1, 2007. Accepted for publication April 8, 2008. Acknowledgments: The authors very gratefully acknowledge Dr. David Oh, Dr. Kim-Ahn Nguyen, Dr. Nora Hirschler, and Dr. Patricia Kopko at the American Blood Bank Sites who assisted us with survey data collection and also all blood donors answering our survey. We also thank Dr. Michael Busch and Dr. Brian Custer from the Blood Systems Research Institute for their helpful suggestions, expert advice, and data estimates. Financial support was received from UC-MEXUS Programma de Investigacion en Migracion y Salud (PIMSA). Authors’ addresses: Leslie S. Wilson, Associate Adjunct Professor, University of California, San Francisco, 3333 California Street, Suite 420, Box 0613, San Francisco, CA 94010-0613, Tel: 415-502-5092, Fax: 415-502-0792, E-mail: [email protected]. Janine M. Ramsey, Director de Area de Estructura Centro de Investigaciones de Paludismo Instituto Nacional de Salud Publica, 4ta Av. Nte esq 19 Calle Poniente Tapachula, Chiapas, 30700 Mexico, Tel: 52-962-6250800, Fax: 52-962-628-5782, E-mail: [email protected]. Yelena B. Koplowicz, Resident, University of California, San Francisco, 3333 California Street, San Francisco, CA 94010, Tel: 415-502-5092, Fax: 415502-0792. Leopoldo Valiente-Banuet, MC Medical Sciences Scientist B, CNTS, National Blood Transfusion Center, Av. Othon de Mendizabal 195 Col Zacatenco Del Gustavo A. Madero, Mexico City, Mexico DF 07360, Tel: 001-52-51194620 – 28 ext. 1408, E-mail: [email protected]. Christi Motter, Candidate, University of California, San Francisco, 3333 California Street, San Francisco, CA 94010, Tel: 415-502-5092, Fax: 415-502-0792. Stefano M. Bertozzi, Director, Division of Health Economics and Policy, National Institute of Public Health, Cuernavaca, Mexico, E-mail: sbertozzi@ correo.insp.mx. Leslie H. Tobler, Senior Scientist, Viral Reference Laboratory and Repository Core Blood Systems Research Institute, 270 Masonic Ave., San Francisco, CA 94118, Tel: 415-749-6609, Fax: 415-775-3859, E-mail: [email protected].

REFERENCES 1. Organizacion Panamericana de la Salud, 2006. Estimacion cuantitativa de la enfermedad de Chagas en las Americas. Organizacion Panamericana de la Salud POS/.HDM/CD/425-06. 2. Schmunis Gabriel A, 2007. Epidemiology of Chagas disease in non endemic countries: the role of international migration. Mem Inst Oswaldo Cruz 102: 1–16. 3. Guzman-Bracho C, 2001. Epidemiology of Chagas disease in Mexico: an update. Trends Parasitol 17: 372–376. 4. Moncayo A, 2003. Chagas disease: current epidemiological trends after the interruption of vectoral and transfusional transmission in the Southern Cone countries. Mem Inst Oswaldo Cruz, Rio de Janeiro 98: 577–591. 5. Murray CHL, Lopez AD, 1996. Global Health Statistics. Geneva. 6. Punukollu G, Gowda RM, Khan IA, Nararro VS, Vasavada BC, 2007. Clinical aspects of the Chagas’ heart disease. Int J Cardiol 115: 279–283. 7. Torrico F, Alonso-Vega C, Suarez E, Rodriguez P, Torrico MC, Dramaix M, Truyens C, Carlier Y, 2004. Maternal trypanosome cruzi infection, pregnancy outcome, morbidity and mortality of congenitally infected and non-infected newborns in Bolivia. Am J Trop Med Hyg 70: 201–209.

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