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      Will more sensitive diagnostics identify tuberculosis missed by clinicians? Evaluating Xpert MTB/RIF testing in Guatemala Translated title: ¿Pueden las pruebas diagnósticas más sensibles identificar los casos de tuberculosis que se escapan a los clínicos? Evaluación de Xpert MIB/RIF en Guatemala

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          Abstract Objective To assess the impact of introducing Xpert as a follow-on test after smear microscopy on the total number pulmonary TB notifications. Method Genexpert systems were installed in six departments across Guatemala, and Xpert was indicated as a follow-on test for people with smear-negative results. We analyzed notifications to national tuberculosis (TB) programmes (NTP) and the project's laboratory data to assess coverage of the intervention and case detection yield. Changes in quarterly TB notifications were analyzed using a simple pre/post comparison and a regression model controlling for secular notification trends. Using a mix of project and NTP data we estimated the theoretical yield of the intervention if testing coverage achieved 100%. Results Over 18,000 smear-negative individuals were eligible for Xpert testing during the intervention period. Seven thousand, one hundred and ninety-three people (39.6% of those eligible) were tested on Xpert resulting in the detection of 199 people with smear-negative, Xpert positive results (2.8% positivity rate). In the year before testing began 1098 people with smear positive and 195 people with smear negative results were notified in the six intervention departments. During the intervention, smear-positive notification remained roughly stable (1090 individuals, 0.7%), but smear-negative notifications increased by 167 individuals (85.6%) to an all-time high of 362. After controlling for secular notifications trends over an eight-quarter pre-intervention period, combined pulmonary TB notifications (both smear positive and negative) were 19% higher than trend predictions. If Xpert testing coverage approached 100% of those eligible, we estimate there would have been a + 41% increase in TB notifications. Conclusions We measured a large gain in pulmonary notifications through the introduction of Xpert testing alone. This indicates a large number of people with TB in Guatemala are seeking health care and being tested, yet are not diagnosed or treated because they lack bacteriological confirmation. Wider use of more sensitive TB diagnostics and/or improvements in the number of people clinically diagnosed with TB have the potential to significantly increase TB notifications in this setting, and potentially in other settings where a low proportion of pulmonary notifications are clinically diagnosed.

          Translated abstract

          Resumen Objetivo Evaluar el impacto en la notificación de casos de tuberculosis pulmonar de la introducción de Xpert como prueba de continuación después del análisis microscópico. Método Se instalaron sistemas Genexpert es seis departamentos de Guatemala y se indicó como prueba consecutiva en todos los resultados negativos en la baciloscopia microscópica. Se analizaron los datos del Programa Nacional y los del laboratorio del proyecto para medir la cobertura y la productividad en detección de casos. Las notificaciones trimestrales se compararon con los valores anteriores a la intervención y se adoptó un modelo de regresión para controlar por las tendencias temporales. Se estimó la contribución teórica de la intervención en términos de notificación si se obtuviera una cobertura del 100%. Resultados Durante el período de intervención, más de 18.000 personas con baciloscopia negativa fueron elegibles en los seis departamentos. El esputo de 7193 (36,9%) de ellos fue analizado también por Xpert y se detectaron 199 personas con baciloscopia negativa y Xpert positivo (positividad: 2,8%). En el año anterior a la intervención se notificaron 1098 casos de tuberculosis pulmonar y baciloscopia positiva, y 195 con baciloscopia negativa. Durante la intervención, la notificación de casos con baciloscopia positiva se mantuvo estable (1090 personas, 0,7%), pero las notificaciones con baciloscopia negativa, que incluía los casos con baciloscopia negativa y Xpert positivo, aumentó en 167 casos (85,6%), llegando a los 362 casos. Después de controlar por la tendencia temporal de notificación en los ocho trimestres anteriores, la notificación de tuberculosis pulmonar (con baciloscopia positiva o no) fue un 19% mayor que las predicciones de la tendencia. Si la cobertura de Xpert se acercase al 100%, se estima que se habría producido un incremento del 41% en las notificaciones. Conclusiones Se identifica un importante aumento de las notificaciones de tuberculosis pulmonar solo con la introducción de Xpert. Ello indica que un número importante de personas con tuberculosis en Guatemala son atendidos por los servicios de salud y son sometidos a bacteriología microscópica, pero no son diagnosticados ni tratados porque no disponen de confirmación bacteriológica. La utilización de técnicas diagnósticas más sensibles o la mejora en el diagnóstico clínico tienen potencial para aumentar significativamente las notificaciones de tuberculosis pulmonar en esta zona y en cualquier otro lugar donde exista una proporción baja de diagnósticos clínicos no confirmados por microscopía.

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          Rapid Diagnosis of Tuberculosis with the Xpert MTB/RIF Assay in High Burden Countries: A Cost-Effectiveness Analysis

          Introduction Tuberculosis (TB) control in developing countries is hampered by the inadequate care that can be delivered on the basis of diagnosis by microscopy alone—an issue that is acute in populations with a high prevalence of HIV and/or multidrug resistant (MDR)-TB. It is estimated that 1.7 million people died from TB in 2009, many of them remaining undiagnosed [1]. The Xpert MTB/RIF assay (referred to as Xpert in this article), is a real-time PCR assay that is a design-locked, all-within-cartridge test, and that has demonstrated high performance and could be deployed in a range of low- and middle-income settings [2],[3]. It has recently been endorsed by the World Health Organization (WHO) as a promising new rapid diagnostic technology that has the potential for large-scale roll-out (www.who.int/tb/laboratory). Xpert is commercially produced and sold at concessional prices. However, because the price is considerably higher than that of smear microscopy, there is a concern among TB program managers and policy makers that Xpert may not be cost-effective in low- and middle-income settings. There is little previous research into the cost-effectiveness of TB diagnostics. A study considering a hypothetical TB diagnostic found that cost-effectiveness would be maximized by high-specificity, low-cost tests deployed in regions with poor infrastructure [4]. Other studies have examined the cost-effectiveness of culture, PCR, and novel methods for drug susceptibility testing such as line-probe assays (LPA). These studies all found that these diagnostic tests are potentially cost-effective [5]–[7]. However, because of their technical requirements, mycobacterial culture, PCR, and LPA can only be deployed in specialised laboratories. We present the first (to our knowledge) economic evaluation of the Xpert rapid test for TB. [2]. Methods The aim of this study was to assess whether Xpert is likely to result in an improvement of the cost-effectiveness of TB care in low- and middle-income settings. We did this by estimating the impact of Xpert on the costs and cost-effectiveness of TB care in three countries, using decision analytic modelling, comparing the introduction of Xpert to a base case of sputum microscopy and clinical diagnosis. The model's primary outcome measure is the cost per disability adjusted life year (DALY) averted. Our model followed a cohort of 10,000 individuals suspected of having TB through the diagnostic and treatment pathway, estimating costs and health gains. In the diagnostic pathway, the TB cases among the individuals with suspected TB were either diagnosed as having TB or not, depending on the test sensitivities in the pathway. Similarly, individuals with suspected TB who were not TB cases may have been diagnosed as having TB, depending on the pathway's test specificities. A diagnosis of TB was followed by treatment. Individuals with suspected TB completed the pathway when they were either cured, failed treatment, died, or, if they had no TB from the start, remained without TB. Three different diagnostic scenarios are compared (Figure 1). The base case is defined as two sputum microscopy examinations followed, in smear-negative individuals with suspected TB, by clinical diagnosis that might include chest X-ray and antibiotic trial [8]. The inclusion of an antibiotic trial (empirical treatment with one or more broad-spectrum antibiotics to exclude other infectious causes of pulmonary disease) is no longer part of the WHO diagnostic strategy for HIV-infected patients. However, in the clinics participating in the demonstration study from which the diagnostic performance parameters were sourced [2], an antibiotic trial was still commonly provided during the diagnostic process as an adjunct to the treatment of smear-negative individuals with suspected TB. Antibiotic trial was therefore included in the base case; the model assumed that for each country the use of antibiotic trial and chest X-ray was proportional to the observed use in the demonstration study clinics. In comparison, two alternative pathways involving Xpert were considered: (1) two smear examinations, if negative followed by Xpert on a single sputum specimen (“in addition to”); (2) Xpert instead of smear examination: single sputum specimen tested by Xpert for all individuals with suspected TB (“replacement of”). 10.1371/journal.pmed.1001120.g001 Figure 1 Simplified schematic of model. Each scenario included drug resistance testing of previously treated patients [9], either by conventional drug susceptibility testing (DST) or Xpert. All patients diagnosed with TB were treated using the standard WHO-recommended regimens. Patients awaiting DST results were started on first-line treatment (isoniazid [H], rifampicin [R], pyrazimamide [Z], and ethambutol [E] for 2 mo followed by HR for 4 mo for new patients, and HRZE for 3 mo with streptomycin added during the first 2 mo followed by HRE for 5 mo for patients with a history of previous TB treatment) and switched to second-line treatment when a DST result of rifampicin resistance became available. The second-line treatment regimens differed between the countries but commonly included a fluoroquinolone and an aminoglycoside (kanamycin, amikacin) or capreomycin in addition to one or more first-line drugs and ethionamode, cycloserine, and/or 4-aminosalicylic acid (PAS). If Xpert identified rifampicin resistance, this was confirmed by conventional DST or LPA as practice in the respective countries. LPA, used as a screening test on smear-positive sputum samples in South Africa, detects rifampicin resistance within 24 h by molecular methods. While awaiting this result, the patient was started on second-line treatment, but then switched to first-line treatment if resistance to rifampicin was not confirmed. TB cases that remained undiagnosed were assumed to return to the clinic after 3 mo, with 10% of undiagnosed cases becoming smear-positive within that time. Key model input parameters are shown in Table 1 and further details can be found in Text S1. The model was parameterised for three settings: India (low HIV prevalence, low MDR prevalence), Uganda (high HIV prevalence, low MDR prevalence), and South Africa (high HIV prevalence, high MDR prevalence). In each cohort, TB cases were characterized as: (1) new or previously treated, (2) HIV-negative or HIV-positive, and (3) MDR or drug susceptible. These proportions were sourced from country reports [1],[10],[11]. 10.1371/journal.pmed.1001120.t001 Table 1 Model inputs: cohort composition and diagnostic parameters, by country. Cohort Proportions and Diagnostic Parameters India South Africa Uganda Distribution Source Cohort proportions Smear-positive TB 0.1 0.1 0.1 Beta Model assumption Smear-positive TB among pulmonary TB cases, HIV-negative 0.723 0.723 0.723 Beta Demonstration study, all sites [2] Smear-positive TB among pulmonary TB cases, HIV-positive 0.446 0.446 0.446 Beta Demonstration study, all sites [2] Previous TB treatment among pulmonary TB cases 0.192 0.168 0.073 Beta WHO [1] Multidrug resistance, among new TB cases 0.023 0.066 0.011 Beta WHO [10] Multidrug resistance, among previously treated TB cases 0.172 0.245 0.117 Beta WHO [10], survey [11] HIV infection, among pulmonary TB cases 0.006 0.588 0.593 Beta WHO [1] Diagnostic parameters Sensitivity for diagnosing pulmonary TB (SEM) Xpert MTB RIF, smear-positive TB cases 0.983 (0.005) 0.983 (0.005) 0.983 (0.005) Beta Demonstration study, all sites [2] Xpert MTB RIF, smear-negative TB cases, HIV-negative 0.793 (0.025) 0.793 (0.025) 0.793 (0.025) Beta Demonstration study, all sites [2] Xpert MTB RIF, smear-negative cases, HIV-positive 0.718 (0.040) 0.718 (0.040) 0.718 (0.040) Beta Demonstration study, all sites [2] Smear microscopy (two slides), HIV-positive 0.723 (0.015) 0.723 (0.015) 0.723 (0.015) Beta Demonstration study, all sites [2] Smear microscopy (two slides), HIV-negative 0.446 (0.036) 0.446 (0.036) 0.446 (0.036) Beta Demonstration study, all sites [2] Mycobacterial culture 1 (—) 1 (—) 1 (—) Model assumption Clinical diagnosis 0.160 (0.073) 0.209 (0.039) 0.444 (0.096) Beta Demonstration study [2] Proportion culture-positive individuals with suspected TB who had chest X-ray 0.032 0.262 0.867 Beta Demonstration study [2] Proportion culture-positive individuals with suspected TB who had antibiotic trial 1 0.051 0.241 Beta Demonstration study [2] Specificity for diagnosing pulmonary TB (SEM) Xpert MTB RIF 0.990 (0.002) 0.990 (0.002) 0.990 (0.002) Beta Demonstration study, all sites [2] Smear microscopy (two slides) 1 (—) 1 (—) 1 (—) Model assumption Mycobacterial culture 1 (—) 1 (—) 1 (—) Model assumption Clinical diagnosis 0.942 (0.009) 0.953 (0.007) 0.869 (0.030) Beta Demonstration study [2] Proportion culture-negative individuals with suspected TB who had chest X-ray 0.037 0.059 0.790 Beta Demonstration study [2] Proportion culture-negative individuals with suspected TB who had antibiotic trial 1 0.009 0.887 Beta Demonstration study [2] Sensitivity for detecting rifampicin-resistance (SEM) Xpert MTB RIF 0.944 (0.015) 0.944 (0.015) 0.944 (0.015) Beta Demonstration study, all sites [2] Conventional drug susceptibility testing 1 (—) — 1 (—) — Model assumption Line-probe assay — 1 (—) — — Model assumption Specificity for detecting rifampicin-resistance (SEM) Xpert MTB RIF 0.983 (0.005) 0.983 (0.005) 0.983 (0.005) Beta Demonstration study, all sites [2] Drug susceptibility testing 1 (—) — 1 (—) — Model assumption Line-probe assay — 1 (—) — — Model assumption Cost parameters US$ 2010 (min, max) First-line category 1 treatment: total 227 (103, 352) 454(306, 602) 185 (146, 224) Triangular WHO-CHOICE [13], literature review [14]–[19] First-line category 2 treatment: total 352 (159, 546) 998 (451, 1546) 287 (130, 445) Triangular WHO-CHOICE [13], literature review [14]–[19] Cotrimoxazol preventive treatment: 1 mo 4, 50 10, 53 3, 25 Triangular WHO-CHOICE [13] Treatment of bacterial infection 3, 66 9, 70 2, 41 Triangular WHO-CHOICE [13] Chest X-ray 11 (9, 13) 16 (14, 18) 3 (2.6, 3.7) Triangular WHO-CHOICE [13], literature review [14]–[19] Second-line treatment total 2,256 (1,463, 3,050) 3,492 (2,068, 4,917) 1,759 (1,285, 2,233) Triangular WHO-CHOICE [13], literature review [14]–[19] DALY parameters: DALYs averted (min, max) HIV positive, sputum smear-negative 9.38 (8.62, 10.39) 10.71 (9.85, 11.90) 11.58 (10.63, 12.90) Triangular See Text S1 HIV negative, sputum smear-negative 13.18 (12.32, 13.96) 13.83 (12.83, 14.72) 18.65 (17.56, 19.61) Triangular See Text S1 HIV positive, sputum smear-positive 9.67 (8.62, 10.39) 11.03 (9.85, 11.90) 11.92 (10.63, 12.90) Triangular See Text S1 HIV negative, sputum smear-positive 16.43 (16.02, 16.79) 17.52 (17.05, 17.93) 22.63 (22.13, 23.07) Triangular See Text S1 The distribution column indicates which probability distribution was specified for each parameter in the Monte Carlo simulations. For triangular distributions the mode, upper and lower limit are given. All beta distributions have boundaries (0, 1). SEM, standard error of the mean. Diagnostic test performance data were sourced from a demonstration study of Xpert use in nine facilities [2]. Sensitivity and specificity parameters for all diagnostic tests and procedures were calculated taking sputum culture as the reference standard. The sensitivity and specificity of Xpert and sputum microscopy (light-emitting diode [LED]) fluorescence microscopy) was based on weighted averages across the sites. Since clinical diagnostic practice of smear negatives in the base case varied considerably between sites, site-specific data were used to estimate performance of the clinical TB diagnosis. A patient was defined as having clinically diagnosed TB if microscopy was negative, but the onset of treatment preceded the availability of the culture result. Estimates of the economic costs of each pathway were made from a health service perspective. All costs were estimated using the ingredient costing approach. This approach identifies all the inputs (and their quantities) required to perform a test or deliver treatment and then values them to arrive at a cost per test/person treated. Diagnostic costs were collected at the demonstration sites. These costs included all building, overhead, staff, equipment and consumables, quality control and maintenance, and calibration inputs. The resource use associated with each test was measured through observations of practice, a review of financial reporting, and interviews with staff in the Xpert demonstration sites. Resource use measurement took into account the allocation of fixed resources between Xpert and any other uses. Estimates of device and test prices, calibration, and training costs were obtained from suppliers. Costs for treatment were estimated using drugs costs from the Global Drug Facility and the MSH International Price Tracker, and unit costs for outpatient visits and hospitalisation sourced from WHO-CHOICE [12]. A review of previous costing studies was used to validate these estimates [13]–[18]. As our constructed estimates were higher than those found in our review, we took the mid-point between our estimate and the lowest estimate found in the literature. All local costs were converted using the average exchange rate for 2010 (imf.statex.imf.org). Where relevant, costs were annualised using a standard discount rate of 3% [19]. All costs are reported in 2010 US$. Treatment outcome probabilities were taken from published meta-analyses of clinical trials, cohort studies, and systematic reviews [20]–[28]. DALYs averted from patients being cured were estimated using the standard formula [19]. Further details can be found in Text S1. Since the Xpert scenarios are most likely to be more costly and more effective than the base case, an incremental cost effectiveness ratio (ICER) was calculated to describe the additional cost for any additional DALYs averted by Xpert over the base case. This ICER was then compared to WHO's suggested country-specific willingness to pay (WTP) threshold, defined as the cost per DALY averted of each country's per capita GDP (US$1,134 for India, US$5,786 for South Africa, and US$490 for Uganda in 2010). If the ICER is below this threshold the intervention is considered cost-effective. In the demonstration study from which our parameter estimates were sourced [2], the probability that an individual with suspected TB was a true TB case varied considerably by location; the proportion with smear-positive TB being 8.9% in India, 14.3% in South Africa, and 32.4% in Uganda. This variation probably reflects the local patterns of (self-) referral, in particular for the extremely high proportion of TB cases among the individuals with suspected TB in Uganda. Therefore to enable generalizability, we assumed a 10% proportion of smear-positive TB in individuals with suspected TB for all three countries as our point estimate with a range of 2.5% to 25% in our uncertainty and sensitivity analyses [29]. A large number of one- and two-way sensitivity analyses were conducted to assess the robustness of our model. These analyses examine the robustness of our results when one or two parameters are varied between the outer limits of their confidence intervals. We examined the sensitivity of our results to the probability that a suspect has TB or MDR-TB or has been previously treated. We examined the impact of varying treatment costs on our results. We tested for different prices of Xpert cartridge. We examined the impact of varying the proportion of individuals with suspected TB who get chest X-ray in addition to Xpert, as physicians may continue clinical diagnosis for smear-negative TB. Similarly we examined the impact of assuming that all HIV-infected individuals with suspected TB who have negative Xpert undergo the clinical diagnosis procedure, with costs based on site-specific use of chest X-rays and antibiotics, and sensitivity and specificity based on site-specific diagnostic performance of clinical diagnosis. We assessed the sensitivity of our results to the performance of the base case in three ways: (1) assuming one instead of two smears; (2) by varying the sensitivity of smear examination; and (3) by replacing the site-specific performance estimates for clinical diagnosis with estimates averaged across the three sites. Recognising that the performance of clinical diagnosis is a trade-off between sensitivity and specificity, we varied the sensitivity and specificity in opposite directions across a plausible range of values. As physicians in the demonstration study were aware that they would receive the results of sputum culture of all individuals with suspected TB, we tested for the effect of deferring treatment decisions until the availability of culture results. For each site culture was costed and assessed on the basis of current practice. We did not include a sensitivity analysis of the use of alternatives to culture such as microscopic observation drug susceptibility test (MODS) [30], as this was not practiced on site, and we found no good source of costing data. We examined the effect of reprogramming Xpert so that no resistance result is obtained. In addition, we conducted a probabilistic sensitivity analysis (Monte Carlo simulation) to explore the effect of uncertainty across our model parameters. This analysis randomly sampled each parameter in our model simultaneously from their probability distribution (Table 1; Text S1), and repeated this 10,000 times to generate confidence intervals around our estimates of incremental cost per DALY averted. The model and the analyses were constructed using TreeAge software. Percentage ranges in the text reflect ranges across countries unless stated otherwise. The demonstration study was endorsed by national TB programmes of participating countries and approved by nine governing institutional review boards (IRBs). The requirement to obtain individual informed consent was waived. The costing and cost-effectiveness assessments were outlined in the study protocol reviewed by the IRBs. Results The cost for the Xpert test (including all costs, such as the cartridge, equipment, salaries) ranges from US$22.63 in India to US$27.55 in Uganda, at an Xpert cartridge price of US$19.40 (including a 25% mark-up for transportation) and US$17,000 per four-module instrument (Tables 2 and 3) [2]. This cost falls to as low as US$14.93 with volume-driven price reductions. As FIND has negotiated a fixed price for Xpert, the difference in costs between sites is primarily determined by the intensity of use of the four-module instrument. Other factors also influence costs, but to a lesser extent; these include local wage levels and the room space used. A single sputum smear examination costs between US$1.13 and US$1.63. Unit costs for culture (Löwenstein–Jensen [LJ] or mycobacteria growth indicator tube [MGIT]) range from US$13.56 to US$18.95. Unit costs for tests that diagnose MDR-TB (where relevant for all first-line drugs) range from US$20.23 for LPA only to US$44.88 for MGIT and LPA. 10.1371/journal.pmed.1001120.t002 Table 2 Cost of diagnostic tests at the study sites (2010 US$). Diagnostic Test Type of Laboratory Costs per Test (2010 US$) India South Africa Uganda AFB Smear (one smear) Peripheral/hospital 1.13 1.58 1.63 Xpert (current pricing) US$19.4 including transport Peripheral/hospital 22.63 25.90 27.55 Xpert (volume>1.5 million/y) US$15.5 including transport Peripheral/hospital 18.73 22.00 23.61 Xpert (volume>3.0 million/y) US$11.7 including transport Peripheral/hospital 14.93 18.20 19.85 Culture (LJ) Reference 13.56 — 15.45 Culture (MGIT) Reference — 15.24 18.95 Culture + DST (LJ) Reference 22.33 — 23.98 Culture + DST (MGIT) Reference — 41.17 44.88 DST (MGIT + LPA) Reference — 33.01 38.82 DST (LPA), on sputum Reference — 20.23 21.84 10.1371/journal.pmed.1001120.t003 Table 3 Cost of Xpert (current pricing) by input type (2010 US$). Input Type Costs per Test (2010 US$) India South Africa Uganda Overhead 0.18 0.88 0.40 Building space 0.02 0.08 0.12 Equipment 2.84 3.50 7.00 Staff 0.11 1.82 0.24 Reagents and chemicals 19.40 19.40 19.40 Consumables 0.07 0.22 0.38 Total 22.63 25.90 27.55 The use of Xpert substantially increases TB case finding in all three settings; from 72%–85% to 95%–99% of the TB suspect cohort (Table 4). When Xpert is deployed “as a replacement of” instead of “in addition to” smear microscopy, the number of TB cases detected is similar—while the number of MDR-TB cases detected increases substantially. When undiagnosed TB patients are assumed not to return for diagnosis, TB case detection increases from 62%–76% in the base case to 86%–94% in the Xpert scenarios. 10.1371/journal.pmed.1001120.t004 Table 4 Cohort, cases detected, total cohort costs, and costs per case detected. Country Scenario Cohort n Individuals among the Cohort Who Have TB Total TB Cases Detected Percent of TB Cases Detected Total MDR Cases Detected Percent of MDR Cases Detected Total Diagnostic Costs (2010 US$) Diagnostic Cost per TB Case Detected, Excluding MDR (US$ 2010) Additional Diagnostic Cost per MDR Case Detected (2010 US$) Treatment Costs (2010 US$) Treatment Costs Percent of Total Cohort India Base case Tuberculosis (MDR) 72 59 82 38 52 1,077 — — 89,223 19 Tuberculosis (no MDR) 1,318 1,079 82 — — 8,412 — — 268,122 59 No tuberculosis 8,611 — — — — 46,106 — — 100,759 22 Total 10,000 1,138 82 38 — 55,595 49 165 458,103 100 In addition to smear Tuberculosis (MDR) 72 71 99 49 68 2,335 — — 115,932 25 Tuberculosis (no MDR) 1,318 1,300 99 — — 13,831 — — 325,381 70 No tuberculosis 8,611 — — — — 184,298 — — 22,414 5 Total 10,000 1,371 99 49 200,464 146 116 463,727 100 Replacement of smear Tuberculosis (MDR) 72 71 99 67 93 3,038 — — 151,603 30 Tuberculosis (no MDR) 1,318 1,298 99 — — 28,986 — — 328,669 65 No tuberculosis 8,611 — — — — 174,538 — — 22,414 4 Total 10,000 1,369 99 67 206,562 151 24 502,687 100 South Africa Base case Tuberculosis (MDR) 184 131 72 56 31 2,345 — — 230,989 22 Tuberculosis (no MDR) 1,729 1,237 72 — — 13,772 — — 659,365 63 No tuberculosis 8,087 — — — — 22,014 — — 156,213 15 Total 10,000 1,368 72 56 38,131 28 86 1,046,567 100 In addition to smear Tuberculosis (MDR) 184 175 95 112 61 7,131 — — 423,146 31 Tuberculosis (no MDR) 1,729 1,649 95 — — 30,341 — — 882,010 65 No tuberculosis 8,087 — — — — 205,858 — — 45,788 3 Total 10,000 1,824 95 112 243,331 133 57 1,350,945 100 Replacement of smear Tuberculosis (MDR) 184 175 95 165 90 9,504 — — 583,064 39 Tuberculosis (no MDR) 1,729 1,645 95 — — 46,866 — — 880,190 58 No tuberculosis 8,087 — — — — 193,053 — — 45,788 3 Total 10,000 1,820 95 165 249,423 137 30 1,509,043 100 Uganda Base case Tuberculosis (MDR) 36 30 85 14 38 499 — — 26,422 5 Tuberculosis (no MDR) 1,882 1,594 85 — — 11,282 — — 282,928 59 No tuberculosis 8,082 — — — — 51,565 — — 171,803 36 Total 10,000 1,625 85 14 63,345 39 163 481,154 100 In addition to smear Tuberculosis (MDR) 36 34 95 22 63 1,392 — — 41,123 11 Tuberculosis (no MDR) 1,882 1,794 95 — — 34,694 — — 320,685 85 No tuberculosis 8,082 — — — — 230,369 — — 14,908 4 Total 10,000 1,828 95 22 266,455 146 124 376,717 100 Replacement of smear Tuberculosis (MDR) 36 34 95 32 90 1,849 — — 56,488 14 Tuberculosis (no MDR) 1,882 1,790 95 — — 57,204 — — 322,502 82 No tuberculosis 8,082 — — — — 217,185 — — 14,908 4 Total 10,000 1,824 95 32 276,238 151 27 393,899 100 The diagnostic cost (including the costs of testing all individuals with suspected TB) per TB case detected is US$28–US$49 for the base case and increases significantly to US$133–US$146 and US$137–US$151 when Xpert is used “in addition to” and “as a replacement of” smear microscopy, respectively, depending on the setting (Table 4). The resulting change in treatment costs is more moderate, due to a reduction in the numbers of false positives in the base case from clinical diagnosis. For example, in India, the percentage of treatment costs spent on false-positive diagnoses falls from 22% to 4% when Xpert is used “as a replacement of” smear microscopy in comparison to the base case. ICERs for each Xpert scenario are presented in Table 5. The mean ICER for using Xpert “in addition to” smear microscopy compared to the base case ranges from US$41 to US$110 per DALY averted depending on the setting. The mean ICER for using Xpert “as a replacement of” smear microscopy ranges from US$52 to US$138 per DALY averted. The mean ICER for using Xpert as “a replacement of” smear microscopy compared to using Xpert “in addition to” smear microscopy ranges between US$343 and US$650. This higher ICER is due to the fact that the effectiveness gain from using Xpert as “replacement of smear microscopy” is derived from additional MDR-TB cases detected, and the cost-effectiveness of treating MDR-TB is lower than that for drug-susceptible TB. All the ICERs found are well below the WTP threshold. 10.1371/journal.pmed.1001120.t005 Table 5 Cost per DALY (US$ 2010). Country Scenario Total Cost Total DALYS Cost per DALY ICER Compared to Base Case, Mean Monte Carlo Simulation ICER, Median (2.5, 97.5) ICER Compared to in Addition to, Mean Monte Carlo Simulation ICER, Median (2.5, 97.5) India Base case 513,698 17,133 30 — — — — In addition to smear 664,191 19,887 33 55 54 (40, 70) — — Replacement of smear 709,248 20,019 35 68 68 (51, 87) 343 361 (239, 578) South Africa Base case 1,084,698 15,805 69 — — — — In addition to smear 1,594,276 20,420 78 110 109 (88, 133) — — Replacement of smear 1,758,467 20,702 85 138 136 (105, 172) 582 594 (353, 956) Uganda Base case 544,499 22,182 25 — — — — In addition to smear 643,172 24,570 26 41 34 (−3, 69) — — Replacement of smear 670,137 24,611 27 52 37 (0, 73) 650 276 (−1895, 2,406) The results of the probabilistic sensitivity analysis (Monte Carlo simulation) are also shown in Table 5. Aside from the replacement of smear microscopy in Uganda all estimates remain cost-effective. Figure 2 provides an illustration of the cost-effectiveness of Xpert deployed as “a replacement of” smear microscopy in comparison to the “in addition to” scenario for a range of WTP thresholds. This graph, known as an acceptability curve, shows that if the WTP is US$490 in Uganda, there is around a 75% probability that Xpert as a replacement of smear is cost-effective when compared to the “in addition to” scenario. 10.1371/journal.pmed.1001120.g002 Figure 2 Cost-effectiveness acceptability curves. ICER “replacement of smear” compared with “in addition to smear.” Nearly all of our one- and two-way sensitivity analyses did not increase the ICER compared to the base case of either Xpert scenario above the WTP threshold (Table 6). Figure 3 shows ICER variation when parameters for the suspect population and the performance of the base case change. Varying the true proportion of those with TB and MDR-TB in the cohort has little effect on our results, although Xpert ICERs substantially worsen when the proportion of smear-positive TB cases becomes 5% or less (translating into 7%–9% with any type of TB). Varying assumptions on the performance of the base case alters ICERs substantially. Increasing the sensitivity of smear examination reduces the cost-effectiveness of Xpert, but not below the WTP threshold. If clinical diagnosis has a higher specificity and lower sensitivity than in our study sites, Xpert ICERs worsen, but also remain below the WTP threshold. But, if clinical diagnosis has a lower specificity and higher sensitivity than in our study sites, ICERs for Xpert substantially improve. Adding chest X-ray for 50% of the individuals with suspected TB tested by Xpert has limited impact on the cost-effectiveness of Xpert. Adding clinical diagnosis for all HIV-positive individuals with suspected TB with a negative Xpert result has no or limited effect in India and South Africa, but doubles ICERs for Xpert in Uganda (although not above the WTP threshold). This reflects differences in HIV prevalence as well as relatively high cost and low specificity of clinical diagnosis in Uganda owing to more extensive use of X-ray. Incorporating the cost of culture and increasing the proportion of TB diagnosis based on the culture result, has a mixed effect. Xpert remains cost-effective up until the point where 40%–70% of patients receive a culture-based diagnosis. Above proportions of 50%–90%, the base case becomes more effective. If however, culture performance is less than 100%, the base case does not become more effective than the Xpert-based scenarios until nearly 100% of patients receive a culture-based diagnosis (unpublished data). 10.1371/journal.pmed.1001120.g003 Figure 3 Selected sensitivity analyses. Sensitivity of the model for the prevalence of tuberculosis, for the prevalence of multidrug-resistant tuberculosis, and for the accuracy of clinical diagnosis. Patterns of ICERs in 2010 US$ for varying the proportion of individuals with suspected TB in the cohort who have smear-positive TB (A, D, G); for varying the proportion of new patients with TB who have multidrug-resistant TB (MDR-TB, B, E, H); and for varying the specificity of the clinical diagnosis of TB in the base case (C, F, I). (A, B, and C), South Africa; (E,D, and F), India; (G, H, and I), Uganda. Black lines, Xpert assay in addition to sputum smear examination; grey lines, Xpert assay as replacement of sputum smear examination. The proportion of individuals with suspected TB in the cohort who have smear-negative TB varies along with the proportion of individuals with suspected TB in the cohort who have smear-positive TB in a linear manner, depending on the HIV-infection prevalence (A, D, G; see Table 1 and Text S1). Similarly, the proportion of previously treated patients with TB who have MDR-TB varies linearly with the proportion of new patients with TB who have MDR-TB (B, E, H; see Table 1 and Text S1). The sensitivity of clinical diagnosis in the base case varies inversely with the specificity (range, 8%–80%; C, F, I). 10.1371/journal.pmed.1001120.t006 Table 6 Costs per DALY 2010 US$: sensitivity analyses. Assumption ICER Compared to: India South Africa Uganda Base Case In Addition to Smear Replacement of Smear Base Case In Addition to Smear Replacement of Smear Base Case In Addition to Smear Replacement of Smear Primary estimate Base case — 55 68 — 110 138 — 41 52 In addition to smear — 343 — — 582 — — 650 Reprogrammed test: no signal MDR Base case — 50 51 — 87 86 — 37 40 In addition to smear — 107 — — NA — — 289 Clinical diagnosis performance pooled across countries Base case — 62 78 — 89 121 — 53 58 In addition to smear — 342 — — 582 — — 650 Proportion retreatment doubles Base case — 115 119 — 209 220 — 67 73 In addition to smear — 170 — — 334 — — 200 Cartridge cost reduces to US$11.70 Base case — 42 54 — 102 129 — 26 36 In addition to smear — — 318 — 570 — — 561 50% of individuals with suspected TB have X-ray added to Xpert Base case — 73 87 — 126 154 — 47 58 In addition to smear — — 378 — — 606 — — 686 HIV-infected individuals with suspected TB have clinical diagnosis when Xpert is negative Base case — 55 68 — 132 157 — 82 90 In addition to smear — 343 — 610 — 706 Undiagnosed patients with TB do not return for diagnosis Base case — 50 67 — 109 138 — 33 43 In addition to smear — — Dominated by in addition to scenario — — 1,442 — — Dominated by in addition to scenario Single smear examination in base case Base case — 48 58 — 105 130 — 40 48 In addition to smear — — 343 — — 582 — — 650 60% of case receive culture diagnosis Base case — Dominates base case Dominates base case — 67 311 — Base case more cost-effective Base case more cost-effective In addition to smear — — 343 — — 582 — — 650 Sensitivity of smear examination increase by 15% Base case — 106 130 — 131 165 — 59 74 In addition to smear — — 343 — — 582 — — 650 NA, not available. Discussion Our results suggest that Xpert is likely to be more cost-effective than a base case of smear microscopy and clinical diagnosis of smear-negative TB. The extent and type of cost-effectiveness gain from deploying Xpert is dependent on a number of different setting-specific factors. First and foremost of these factors is the performance of current TB diagnostic practice. Where the sensitivity of current practice is low, but specificity high, Xpert has a substantial impact on effectiveness. Where the sensitivity of current practice is high, but specificity low, Xpert will lower treatment costs by reducing the number of false positives. This latter effect is illustrated by the case of Uganda, where the model predicts a reduction in the treatment costs of false positives from US$171,803 to US$14,908, contributing to the overall reduction in treatment costs. Other factors that are likely to determine the extent of cost-effectiveness gain include the proportion of those co-infected with HIV and the proportion of those with MDR-TB, and the numbers of true TB cases in the suspect population. However, our results show that increasing proportions of HIV in the suspect population will not always reduce the ICER of Xpert (Figure 3). This finding is counter-intuitive. One would expect the cost-effectiveness of a diagnostic test that diagnoses smear-negative TB to improve with increases in HIV prevalence. However, as the proportion of individuals co-infected with HIV in the suspect population increases, so the sensitivity of Xpert decreases. Depending on the relative costs and performance of the base case, this counter-effect means that the relationship between HIV prevalence and Xpert's cost-effectiveness is weaker than anticipated. Nor can we conclude on the direction of the relationship between cost-effectiveness gain and the level of prevalence of MDR-TB in the suspect population at this time. Our model demonstrates that when transmission effects are excluded, the ICER of Xpert increases as the MDR-TB prevalence increases (Figure 3). This result occurs because although the effectiveness of Xpert increases with a higher MDR-TB prevalence, the ICER of treating MDR-TB is higher than that of drug susceptible TB, thus countering the gain from increased effectiveness. Unsurprisingly, we also find that higher proportions of TB cases in the suspect population improve the cost-effectiveness of Xpert. The cost per TB case detected will also decrease with increases in TB prevalence. As TB programmes already fund elements of the base case, cost-effectiveness may therefore be initially improved by using existing diagnostic tools, such as X-ray and clinical scores, to screen the TB suspect population prior to Xpert. In the longer run, however, the expansion of X-ray as a permanent approach for suspect screening is unlikely to be cost-effective, and further work examining alternative screening approaches may be required. Moreover, different approaches are likely to be adopted for specific suspect populations, most notably those already known to be HIV infected, those who have already failed treatment, and those at a high risk of MDR-TB. We therefore recommend that further work is conducted to explore the impact on cost-effectiveness of different algorithms when Xpert is applied to more limited suspect groups. A number of factors limit our analysis. Firstly, the assumption of no transmission effects or additional mortality benefit from early diagnosis is a conservative approach and will underestimate the cost-effectiveness of Xpert—particularly where the introduction of Xpert is likely to increase the numbers of drug-resistant patients who are appropriately and rapidly treated. Likewise, we do not factor in patient costs. A full societal evaluation would make all options less cost-effective, but Xpert is likely to fare better than alternatives, as it requires fewer patient visits. In addition, if Xpert can achieve earlier diagnosis, substantial reductions in patient costs prior to treatment may be achieved [31]. The reference standard for the test performance parameters in our model did not include culture-negative TB based on response to treatment, because this diagnostic category will include cases with no TB or extra-pulmonary TB that cannot be diagnosed by sputum-based tests. This situation may have lead to overestimation of the sensitivity and underestimation of the specificity of Xpert. Owing to lack of evidence, we only included one repeat visit for false negatives in our model, to capture those who quickly progress to smear-positive TB. This number may be insufficient and miss both the additional costs and effectiveness of further repeated visits. On the other hand, our assumption that 100% of false negatives still alive and with TB after 3 mo have a repeat visit may be an overestimation, thereby inflating ICERs for the Xpert scenarios. We assumed that a negative Xpert result does not lead to further TB diagnostic procedures. This assumption may not be true in practice, in particular not for HIV-infected individuals with suspected TB [32]. Our sensitivity analyses show that adding clinical diagnostic procedures for this group can substantially reduce cost-effectiveness of Xpert when HIV prevalence is high and X-ray is used extensively. Also because of the lack of data, we have not included a high MDR-TB, but low HIV-prevalence setting. This lack of data restricts our ability to generalise findings to all low- and middle-income settings, particularly the former Soviet states, where this epidemiological pattern is common in suspect populations. Finally, our sensitivity analysis demonstrates that Xpert may not be cost-effective when compared to a base case in which a high proportion of smear-negative TB cases are diagnosed by culture. However, this result is based on our assumption that culture performs at 100% sensitivity and specificity. In addition, we did not include costs of specimen transport, increased risk of false-negative cultures or contamination, reduced sensitivity when only one specimen is cultured, and possible delay effects on mortality and patient drop out. All these simplifications will inflate the cost-effectiveness of a base case that includes culture. As is standard practice, we determine cost-effectiveness in comparison to the WHO WTP threshold. Unfortunately, achieving this threshold does not mean that the resources are available in low- and middle-income countries, merely that Xpert should be afforded [33]. In reality, resourcing for tuberculosis services in low- and middle-income countries is extremely constrained. Countries may therefore need to prioritise. In terms of priorities, suspect populations with a high likelihood of TB, particularly in settings with high HIV and MDR-TB prevalence, are an obvious choice. However, our findings illustrate that it is also important to balance these factors with the current performance of the existing diagnostic pathway. Countries or areas that have the weakest performance in terms of diagnosing smear-negative cases may benefit the most, even when they have relatively low levels of MDR-TB and HIV; although additional investment may be required to strengthen aspects of the health system to ensure that Xpert can be implemented successfully. Funding Xpert may also mean that scarce resources are not made available to other equally deserving areas. Care must therefore be exercised to take into account the broader tuberculosis control and health system needs of any particular setting when funding Xpert. Our model is robust given the current evidence and data available. However, key data in this area—particularly on the characteristics of TB suspect populations, the feasibility of implementing Xpert at scale, and the extent to which clinicians allow diagnostic test results to influence treatment decisions—remain scarce. Moreover, it is likely that there will be costs associated with Xpert scale-up that we cannot predict at this point. Although our model strongly suggests that Xpert will be cost-effective in a wide variety of settings, Xpert scale-up will substantially increase TB diagnostic costs. Given this increase, and the current data paucity, we recommend careful monitoring and evaluation of initial roll-out before full scale-up. Funding should be provided for implementation studies, including pragmatic trials, in a number of countries to accelerate this process. As we did not assess cost-effectiveness in a setting with high MDR but low HIV prevalence, we also recommend additional economic modelling studies before embarking on roll-out in these settings, taking into consideration operational factors that may affect outcomes such as patient drop-out and physician behavior [34]. Finally, although Xpert is a highly promising technology, there is still room for improvement in TB diagnostics. Xpert has incomplete sensitivity for smear-negative TB and rifampicin resistance and does not detect resistance to isoniazid and other drugs. Other promising tests, such as microscopic observation drug susceptibility test (MODS) [35], should be evaluated for their cost-effectiveness, including comparisons with Xpert. Our finding should not discourage investment in other promising new TB diagnostic technologies, particularly those that further improve the diagnostic sensitivity and detection of wider forms of drug resistance and can be implemented at peripheral health care level at low cost. Conclusion Despite the fact that there is considerable concern from policy makers about the costs and affordability of new diagnostic technologies in low- and middle-income countries, our results suggest that Xpert is likely to be a highly cost-effective investment. If demonstrated test performance is maintained at scale, Xpert has the potential to substantially increase TB case detection. Moreover, in the settings modelled, TB treatment costs are not predicted to substantially increase with the introduction of Xpert; instead, treatment is likely to be switched from those who do not benefit from treatment, to those who do. Our results suggest that funding should be provided to initiate the roll-out of Xpert in low- and middle-income countries, as a promising means of enabling access to effective treatment for all those with the disease. We recommend, however, that this roll-out is carefully evaluated to validate our results before full scale-up—to ensure that Xpert implementation is done in a way that does not negatively impact TB programmes, their funding, and the health systems that support them. Supporting Information Text S1 Details of model assumptions, test turnaround times (Table S[A]), treatment outcome probabilities (Table S[B]), probabilities of death and spontaneous recovery with false-negative tuberculosis diagnosis (Table S[C]), and variables used in the DALY calculations (Table S[D]). (DOC) Click here for additional data file.
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            Population Health Impact and Cost-Effectiveness of Tuberculosis Diagnosis with Xpert MTB/RIF: A Dynamic Simulation and Economic Evaluation

            Introduction Tuberculosis (TB) remains a leading cause of global mortality and morbidity, with an estimated 9 million new TB cases and 1.5 million TB-related deaths in 2010 [1]. Although significant advances have been made in improving TB outcomes under the DOTS approach championed by the World Health Organization (WHO) and its partners in the Stop TB Partnership [2], continued progress is threatened by the inadequacy of existing diagnostic tools [3]. In most high-burden settings, TB diagnosis relies principally on sputum smear microscopy, which has limited sensitivity, especially among HIV-infected patients [4]–[6]. Traditional culture-based diagnosis and evaluation of drug sensitivity is relatively costly and slow [7],[8], and many resource-limited settings lack the laboratory capacity to perform culture and sensitivity testing at high volume [9],[10]. Lack of prompt diagnosis and appropriate treatment of TB increases the risks of transmission, drug resistance, and case fatality [11]–[13]. Recently, the Xpert MTB/RIF automated DNA test has been shown to provide rapid and sensitive detection of TB and rifampicin (RIF) resistance [14]–[17]. The Xpert test uses a cartridge-based system that integrates sample processing and real-time PCR, accommodates use by relatively unskilled healthcare workers, and provides results in 98% of patients with smear-positive TB and >70% of patients with smear-negative TB [14],[15]. Sensitivity and specificity for RIF resistance were above 94% and 98%, respectively. More recent analyses have suggested that Xpert can greatly reduce the delay until treatment initiation for individuals with active TB [19]. In December 2010, WHO recommended that Xpert be used for initial diagnosis in patients suspected of having multidrug-resistant TB (MDR-TB) or HIV-associated TB disease [20]. By the end of May 2012, 66 of 145 countries eligible to purchase Xpert equipment at reduced prices had already done so [21]. A volume-dependent price mechanism is being used for purchase of test cartridges [22], such that by August 2012 the ex-works price of Xpert cartridges had dropped to less than US$10 for eligible countries [23]. Whereas the global TB control community has moved quickly to embrace the new technology, several studies and commentaries have sounded important notes of caution concerning the cost of the technology, the demand it will place on existing infrastructure, and the challenge of addressing false positive indications of RIF resistance [24]–[30]. As implementation advances, evidence on the epidemiologic impact and cost-effectiveness of Xpert is urgently needed, particularly as the consequences of Xpert introduction may vary across epidemiologic settings and may depend on the specific diagnostic algorithms that are considered [31],[32]. In this study we used a calibrated, dynamic mathematical model of TB to quantify the potential health and economic consequences of introducing Xpert in five southern African countries characterized by high prevalence of HIV infection and extant multidrug resistance. Comparing a diagnostic strategy based on Xpert to the status quo, we predicted changes in TB incidence, prevalence, mortality, and drug resistance; estimated health system costs; and assessed the incremental cost-effectiveness of Xpert adoption. Methods Overview We evaluated the population health outcomes and health system costs associated with two alternative strategies for diagnosing TB, the first based on current diagnostic algorithms and the second based on implementing Xpert in accordance with current WHO recommendations. Comparisons between these two strategies were made using a calibrated mathematical model of TB, reflecting key features of TB transmission dynamics and natural history, interactions with HIV infection, and patterns and trends in TB control interventions and treatment for HIV/AIDS. Model simulations were undertaken for five southern African countries: Botswana, Lesotho, Namibia, South Africa, and Swaziland. We assessed changes in epidemiological outcomes and health system costs over 10-y and 20-y time horizons, as well as the incremental cost-effectiveness ratio (ICER) of the Xpert strategy compared to the current algorithm. Diagnostic Strategies A “status quo scenario” was created to represent the current diagnostic approach. Under this approach, all patients with suspected TB receive an initial sputum smear, and those diagnosed as smear-positive are directed to treatment. Sputum culture is indicated for patients with suspected TB who test smear-negative but who have a history of TB treatment or in whom there is a strong suspicion of TB. Drug sensitivity testing (DST) is indicated for treatment-experienced patients diagnosed with TB. Those who receive DST are initiated on a treatment regimen appropriate to their drug resistance profile, while those who do not receive DST are initiated on the standard first-line regimen. In the main analysis we assumed that the coverage of culture testing would be 20% (range 10%–30%) among smear-negative, treatment-naïve patients, and 80% (range 70%–90%) among smear-negative, treatment-experienced patients. We assumed further that 80% (range 70%–90%) of treatment-experienced patients diagnosed with TB would go on to receive DST. Given limited empirical data on country-specific coverage of culture and DST, these values were all varied across wide ranges in sensitivity analyses. An “Xpert scenario” was constructed based on the diagnostic algorithms suggested for high HIV prevalence settings in the May 2011 WHO recommendations for Xpert implementation [33]. These recommendations suggest the use of Xpert as an initial diagnostic for all individuals of HIV-positive or unknown status. Given the high prevalence of HIV among patients with suspected TB in southern Africa and the low number of individuals with a recent HIV test result [34], we modeled an algorithm in which Xpert was used as the initial diagnostic for all patients with suspected TB. According to this algorithm, such patients are first tested with a single Xpert assay, and no sputum smear is performed. Those testing TB-positive but negative for RIF resistance are initiated on a standard first-line regimen. Those testing positive for RIF resistance go on to receive DST. If the DST result indicates drug resistance, the individual is treated with a drug regimen tailored to the observed resistance profile. Under this scenario we assumed that scale-up to full coverage of Xpert within the national TB program would occur over the 3-y period starting in 2012. A diagram of the two alternative diagnostic algorithms is shown in Figure S1. Modeling Approach We developed a dynamic compartmental model of TB following the conventions of earlier models [35]–[41], with additional detail to accommodate evaluation of alternative diagnostic strategies. The model structure (Figure 1) is defined by a set of core TB states, and these states are further subdivided to account for (1) aspects of HIV infection, progression, and treatment relevant to TB epidemiology; (2) multiple circulating TB strains, with different drug resistance profiles; and (3) tracking of TB treatment history. 10.1371/journal.pmed.1001347.g001 Figure 1 Model states, subdivisions, and transitions. Core TB states The core TB model simulates the movements of individuals between states that capture important features of TB transmission, natural history, and treatment. Individuals enter the model in the susceptible state, where they face a risk of TB infection. The risk of infection is modeled as a time-dependent variable that reflects contact rates between infected and uninfected individuals, and transmission probabilities that allow for varying infectivity across different categories of active disease. Upon infection, individuals progress either directly to active disease or to latent infection. Individuals with latent infection may subsequently progress to active TB or be superinfected by a different TB strain. Active disease is categorized as smear-positive or smear-negative. Smear-negative cases may progress to smear-positive, and all individuals with active disease may spontaneously self-cure, which returns them to the latent/recovered state. An individual with active disease can be diagnosed as a TB case, according to the characteristics of the diagnostic algorithm, and initiated on treatment (as described in detail below). All individuals in the model are subject to a background mortality rate and to TB-related mortality specific to each active disease state. HIV subdivisions HIV coinfection can alter the natural history of TB, with HIV-infected individuals having a higher probability of primary progressive TB upon initial infection [42],[43], a higher rate of breakdown from latent infection to active TB [44], a lower probability of smear-positivity amongst those with active disease [4]–[6], and higher mortality rates [4],[45],[46]. The HIV sub-model draws on model structure and key parameters from an array of published HIV models [47]–[50]. Seven HIV subdivisions were created, defined by CD4 cell count (>350 cells/µl, 200–350 cells/µl, and 350 cells/µl category, with time-varying incidence rates defined as exogenous model parameters. HIV-positive individuals not on ART progress over time to subdivisions with lower CD4 counts. Untreated individuals transition onto ART at rates specific to their CD4 category. These rates are allowed to vary over time to capture changing eligibility criteria and coverage of testing and referral. HIV-related mortality occurs at rates specific to each subdivision. Drug resistance subdivisions Model states are further subdivided to account for differences in drug resistance among circulating TB strains, including (1) pan-sensitive TB, (2) isoniazid (INH) mono-resistant TB, (3) RIF mono-resistant TB, (4) TB resistant to both INH and RIF (MDR-TB), and (5) TB resistant to INH and RIF plus one or more second-line drugs (MDR+/XDR-TB). An individual in the susceptible state who is newly infected with TB transitions to the subdivision of the infecting strain. An individual with latent TB who is superinfected by a different strain transitions to the subdivision of the superinfecting strain. Individuals may also develop acquired drug resistance as a result of TB treatment, transitioning to subdivisions with broader resistance profiles. Treatment history subdivisions A final subdivision of model states distinguishes treatment-naïve from treatment-experienced individuals, as diagnostic algorithms may dictate different confirmatory tests depending on an individual's history of prior treatment. Individuals enter the model in the treatment-naïve subdivision, and all individuals exiting their first course of TB treatment (through default, failure, or cure) transition to the treatment-experienced subdivision. The model is implemented as a series of difference equations with a monthly time step. A full description of model structure and equations is given in Text S1. TB Diagnosis and Treatment The model allows for TB diagnosis and treatment through the national TB DOTS program, or through non-DOTS providers functioning outside the national program. Uptake into treatment programs requires that individuals (1) present to a health facility and are identified as patients with suspected TB, (2) are diagnosed as active cases, and (3) are initiated on regimens determined by their background characteristics and information on drug sensitivity, if available. The model accounts for differences in test performance and information provided by each diagnostic algorithm, and for attrition between diagnosis and treatment, which varies depending on the delay to test results [51]. Individuals with false negative diagnoses for active TB will remain in the pool of undiagnosed active TB cases, with the possibility of presenting for diagnosis again. Individuals without active TB who attend with TB symptoms and are incorrectly diagnosed with active TB are assumed to undergo TB treatment, incurring costs but no positive or negative health effects. Algorithms for diagnosis and treatment in non-DOTS programs are assumed to be the same in both the status quo and Xpert scenarios, i.e., independent of the choice of diagnostic algorithm in the national DOTS program. Individuals on TB treatment may successfully complete treatment, fail, default (become lost to follow-up), or die. Those who successfully complete treatment return to the latent/recovered state. A percentage of individuals failing therapy are identified as failures by the treatment program and reinitiate treatment, while all others return to active disease. Individuals who fail or default from treatment may acquire resistance to the drugs they have received. The model allows individuals with pan-sensitive TB to develop mono-INH-resistant TB, mono-RIF-resistant TB, or MDR-TB directly. Individuals with mono-INH- or mono-RIF-resistant TB can develop MDR-TB, and individuals with MDR-TB can develop MDR+/XDR-TB, with the rates of acquiring drug resistance dependent on a patient's TB drug regimen and current drug resistance profile (see details in Text S1). Impact of Diagnostic Algorithms on TB Epidemiology Any change in diagnostic algorithm is assumed to impact TB epidemiology through two channels. The first major effect is via changes in the overall sensitivity and specificity of TB diagnosis. For the population with undiagnosed active TB, an improvement in diagnostic sensitivity results in improved case detection and reduced delay to treatment initiation and, consequently, increases survival and decreases the duration of infectiousness. The second major effect is via changes in the distribution of regimens received by newly diagnosed TB cases. Drug-resistant TB cases identified by an algorithm with better sensitivity for diagnosing resistance have a higher probability of being initiated on a more effective treatment regimen, which in turn improves cure rates, increases survival, and reduces the probability that a patient will return to an infectious state. Estimation Approach We used a Bayesian estimation approach developed by Raftery and colleagues [52],[53] and recently adopted by the Joint United Nations Programme on HIV/AIDS for HIV epidemic projections [54]–[56]. This approach provides a method for calibrating complex nonlinear models to reported data on disease burden, and for characterizing uncertainty in analysis results using Bayesian posterior intervals and similar metrics. These features are particularly important for our analysis, given the substantial uncertainty around many of the parameters describing TB epidemiology. We used this approach to calibrate the model to independent WHO estimates of TB incidence and prevalence in each of the five countries [57], and to data from drug resistance surveys available for all countries except Namibia [58]. The analysis was implemented using a sampling/importance resampling algorithm [52],[55],[59]. First, a large number of parameter sets were drawn from the joint prior distribution of the input parameters. For each of these parameter sets the model was run and a likelihood statistic calculated by comparing model outcomes to the corresponding calibration data. The likelihood for each parameter set was then used as the probability weight in a second-stage resample of the parameter sets, which yielded draws representing the posterior parameter distribution, reflecting the information available on both model inputs and calibration data. The results of this simulation are similar to those produced by traditional Monte Carlo simulation and probabilistic sensitivity analyses, with the additional benefit of being constrained to be consistent with independent estimates on TB outcomes for each country. For each modeled outcome, uncertainty intervals were calculated by taking the 2.5th and 97.5th percentiles of the distribution for this outcome generated by the resampled parameter sets, and the point estimate was calculated by taking the arithmetic mean of this distribution (see Text S1 for further detail). Model Parameter Values We parameterized the model using historical demographic and epidemiologic data available for each country. Parameter values relating to population demographics were derived from United Nations Population Division estimates and projections. Parameter values relating to TB transmission dynamics were chosen to be consistent with data and assumptions used in earlier TB models [35]–[41]. Parameter values relating to TB program coverage and treatment outcomes were derived from published reporting data [57]. Key parameter values relating to TB diagnosis and treatment are summarized in Table 1. Estimates for HIV incidence and ART access between 1983 and 2010 were derived from unpublished data provided by the Joint United Nations Programme on HIV/AIDS. Future ART access was assumed to increase from current levels to the WHO universal access target of 80% coverage [60] over the course of 10 y. For Botswana, which was providing ART to an estimated 83% of those in need by 2009, coverage was maintained at current levels. ART eligibility was initially limited to individuals with CD4 count 350 cells/µl, no ART 0. 008 (0.005–0.012) CD4 200–350 cells/µl, no ART 0.030 (0.018–0.048) CD4 350 cells/µl 0.008 (0.005–0.012) On ART initiated at CD4 200–350 cells/µl 0.023 (0.014–0.037) On ART initiated at CD4 350 cells/µl, no ART 0.135 (0.078–0.213) HIV-positive, CD4 200–350 cells/µl, no ART 0.320 (0.176–0.496) HIV-positive, CD4 350 cells/µl 0.135 (0.078–0.213) HIV-positive, on ART initiated at CD4 200–350 cells/µl 0.151 (0.087–0.238) HIV-positive, on ART initiated at CD4<200 cells/µl 0.167 (0.096–0.262) All costs are given in 2011 US dollars. a As smear status is tracked in the model, the sensitivity of sputum smear for individuals classed as smear-negative and smear-positive is 0% and 100% (respectively) by construction. b As sputum culture is the gold standard for TB detection, the sensitivity is assumed to be 100%. c As the per-test cost of Xpert is of key interest to policy-makers (and potentially subject to price negotiation), the results of the analyses are presented for three separate values for the Xpert cost. Measurement of Resource Use and Costs Costs were assessed from a health system perspective and expressed in 2011 US dollars. Costs reflected resources used to deliver TB diagnosis and treatment, as provided by both public and private providers, and resources used in providing ART to HIV-infected individuals. An ingredients approach to costing was used, by which the total cost to provide a particular diagnostic procedure or a course of treatment was calculated by estimating the number of units of each specific type of resource input needed to deliver the service, multiplying each quantity by the corresponding unit cost of that resource input, and summing across all inputs. Average costs for each type of service are shown in Table 1. Cost estimates extrapolated from the literature were adjusted for inflation, currency conversions, and price levels, where relevant. Treatment costs for TB and HIV included drugs, clinic visits, and monitoring tests, including regular smear examinations during TB treatment. Drug costs were derived from the WHO price reporting mechanism [63]. Costs for laboratory tests (excluding Xpert) were derived from the literature. Numbers of treatment monitoring visits and laboratory tests followed a previous global analysis [35]. For Xpert, estimates in WHO implementation guidelines [33] suggest an economic cost of US$25–US$35 per test in southern Africa (including consumables, equipment, personnel, transport, facilities, and managerial overheads). This range of estimates is consistent with the results from a cost analysis conducted for the South African national program, which found a cost range of US$25–US$33 [64], as well as an analysis of potential implementation strategies that reported costs of US$27 per patient with suspected TB for placement of equipment at central laboratories and US$39 for placement of equipment at point of care [65]. Costs of Xpert may continue to change as volume increases, through reductions in the prices of equipment and consumables [22],[23], economies of scale, and accumulated implementation experience; we therefore conducted analyses using Xpert per-test costs of US$20, US$30, and US$40. Outcomes We estimated trends in population-level epidemiological outcomes including TB prevalence, incidence, mortality, and resistance to anti-TB drugs, prior to Xpert introduction in 2012, and over the subsequent 20-y period. Summary outcome measures computed based on population survivorship in the model included life-years and disability-adjusted life-years (DALYs), the latter incorporating disability weights from the Global Burden of Disease study [66],[67]. We evaluated the cost-effectiveness of introducing Xpert in terms of the ICER, expressed as the difference in total costs between the Xpert and status quo scenarios, divided by the difference in life-years or DALYs between the two scenarios. Cost-effectiveness ratios were computed over both 10-y and 20-y time horizons following Xpert introduction, in each case based only on the costs and health outcomes accrued during that period. Costs and health benefits were discounted at an annual rate of 3% [68],[69]. Following standard benchmarks proposed in international work on cost-effectiveness, we compared the ICER to thresholds for cost-effectiveness defined in reference to the annual gross domestic product (GDP) per capita in each country. Interventions are considered to be highly cost-effective when they have ICERs that fall below the annual per-capita GDP, and are regarded as being potentially cost-effective if they have ICERs between one and three times annual per-capita GDP [70]. Sensitivity Analysis The sensitivity of the model to changes in individual parameters was investigated through traditional one-way sensitivity analyses as well as by computing partial rank correlation coefficients across the set of simulation results produced by the Bayesian uncertainty analysis [38],[71],[72]. For the one-way sensitivity analyses, we computed the change in the ICER (calculated over a 10-y time horizon) that would occur when we changed one parameter value by ±1 standard deviation from its posterior mean value while holding all other parameter values at their posterior means. We also conducted an array of additional sensitivity analyses that varied assumptions regarding the diagnostic algorithms being compared, the use of inpatient care as part of MDR-TB treatment, future ART coverage decisions, and trends in antiretroviral drug prices. Finally, we conducted a probabilistic sensitivity analysis to assess the uncertainty around the optimal choice of diagnostic strategy resulting from the joint effects of uncertainty around all input parameters simultaneously, and these results are presented as posterior intervals around key model outcomes and as cost-effectiveness acceptability curves. Results Epidemiological Projections under the Current Diagnostic Algorithm Figure 2 shows estimates and projections for TB prevalence and incidence in the southern Africa region from 1990 through the end of 2032, under the assumption that the current (status quo) diagnostic algorithm is used over the whole period. The results for individual countries followed the general trend seen in the regional results, with historical declines in TB prevalence and incidence reversed over the period 1995–2010 as a consequence of concurrent HIV epidemics. The magnitude of the TB epidemic differed across individual countries, with Lesotho having the lowest prevalence and incidence and Swaziland the highest. 10.1371/journal.pmed.1001347.g002 Figure 2 Estimated and projected TB prevalence, TB incidence, and multidrug-resistant TB prevalence in southern Africa under status quo diagnostic algorithm, 1990–2032. Performance of Diagnostic Algorithms Based on our model simulations, the positive predictive value for Xpert diagnosis of active TB, at full coverage by 2014, would be 96.9% (95% CI: 93.4–98.7), compared to 88.4% (81.5–93.1) for the status quo algorithm. The negative predictive values for Xpert and the status quo would be 93.9% (88.8–97.2) and 79.3% (67.6–87.9), respectively. We estimate the positive predictive value for the diagnosis of RIF resistance by Xpert to be 67.3% (51.3–82.0) and the negative predictive value 99.9% (99.8–100.0). The relatively low positive predictive value indicates that Xpert is expected to produce a number of false positive diagnoses of RIF resistance, with relatively modest implications for treatment outcomes, as we assume that a subsequent DST is required before individuals receive an MDR-TB diagnosis. Under the Xpert algorithm, 5.8 (95% CI: 3.8–9.2) patients are tested for TB for each active case starting treatment, compared to 7.5 (4.9–12.1) under the status quo, a consequence of improved sensitivity in the Xpert algorithm. The average duration of infectiousness is 9.9 mo (95% CI: 6.7–14.0) under the Xpert algorithm compared to 12.8 mo (9.6–14.0) under the status quo. The benefit of the reduced duration of infectiousness is primarily accrued among individuals with smear-negative TB, for whom the duration of infectiousness is reduced from 19.3 mo (13.8–24.6) under the status quo to 12.1 mo (7.8–18.0) under the Xpert scenario. Results for those with smear-positive disease are comparable under both scenarios. Treatment effectiveness (the probability of cure for individuals starting treatment) rises only marginally under the Xpert scenario, with the probability of cure 2.7 (95% CI: 1.6–4.4) percentage points higher than in the status quo scenario. Table 2 presents estimates for the average cost per programmatic outcome for the status quo and Xpert strategies, summed over the first 10 y of Xpert implementation (2012–2022). These results show that adopting the Xpert algorithm increases the cost of achieving various diagnostic and treatment outcomes. 10.1371/journal.pmed.1001347.t002 Table 2 Average programmatic outcomes and costs over 10 y following choice of strategy. Outcome Status Quo Strategy Xpert Strategy Programmatic measures for DOTS diagnosis Average annual DOTS diagnosis costs $27 million (15–46 million) $37 million (21–61 million) Average annual number of patients receiving TB testing 892,000 (519,000–1,508,000) 829,000 (487,000–1,400,000) Average annual number of true positive diagnoses 151,000 (100,000–215,000) 175,000 (120,000–245,000) Average diagnosis cost per patient with suspected TB $31 (25–38) $45 (40–50) Average diagnosis cost per true positive diagnosis $181 (117–287) $211 (136–334) Programmatic measures for DOTS treatment Average annual DOTS treatment costs $57 million (30–102 million) $81 million (42–137 million) Average treatment volume 57,000 (38,000–85,000) 69,000 (48,000–100,000) Average annual number of true positive treatment initiations 122,000 (81,000–175,000) 147,000 (103,000–206,000) Average number of annual cures 100,000 (66,000–146,000) 121,000 (84,000–172,000) Average treatment cost per month $84 (59–135) $98 (67–147) Average treatment cost per TB case initiated $469 (321–761) $556 (371–861) Average treatment cost per TB case cured $575 (396–914) $675 (461–1,008) All costs are given in 2011 US dollars. Results are based on US$30 Xpert per-test cost. Range in parentheses represents the 95% posterior interval for each estimate. Population Health Impact of Introducing Xpert Introduction of Xpert is projected to produce immediate and sustained changes in TB epidemiology (Figure 3). Within 10 y after the introduction of Xpert, prevalence would be lower by 186 (95% CI: 86–350) per 100,000 (28% [95% CI: 14–40]), incidence by 35 (13–79) per 100,000 (6% [2]–[13]), and annual TB mortality by 50 (23–89) per 100,000 (21% [10]–[32]), compared to status quo projections. The absolute number of MDR-TB cases after 10 y would be lower by 25% (6–44) in the Xpert scenario compared to the status quo scenario. The decline in MDR-TB cases parallels the overall decline in TB prevalence in these projections. There is no significant change expected in MDR-TB as a percentage of all TB under the Xpert scenario (4.3% [−17.5 to 34.6] greater after 10 y). Figure S2 shows the incremental differences between Xpert and the status quo for these health outcomes, including uncertainty intervals around these differences. 10.1371/journal.pmed.1001347.g003 Figure 3 Epidemiologic outcomes in Xpert and status quo scenarios, 2012–2032. Summing the health effects of Xpert introduction over the first 10 y of implementation, this strategy is estimated to prevent 132,000 (95% CI: 55,000–284,000) of the estimated 2.6 million (1.7–4.3 million) new TB cases and 182,000 (97,000–302,000) of the estimated 1.2 million (0.6–2.0 million) TB deaths projected for southern Africa under the status quo. Health System Costs of Introducing Xpert Figure 4 shows the additional annual costs associated with the Xpert scenario compared to the status quo, subdivided by type of cost. TB program costs rise rapidly as Xpert scales up to full coverage over 2012–2015. While implementation of Xpert requires increased spending on TB diagnosis and treatment, the major financial impact of Xpert introduction in this region is on HIV treatment programs. This is because prompt TB treatment extends survival among TB/HIV-coinfected individuals, leading to increases in HIV treatment demand. The model predicts that at 10 y after Xpert introduction, HIV treatment costs will comprise 58% (95% CI: 40–72) of the total incremental costs associated with the Xpert strategy (assuming an Xpert per-test cost of US$30). Considering only the additional costs incurred by national DOTS programs, almost three-quarters (71% [47]–[87]) of these will be due to growth in TB treatment costs, with almost all of this increase coming from a higher volume of MDR-TB treatment. 10.1371/journal.pmed.1001347.g004 Figure 4 Incremental costs of Xpert strategy (based on US$30 Xpert per-test cost) compared to status quo strategy, by cost category, 2012–2032 (2011 US dollars). Cost-Effectiveness of Xpert Strategy versus the Status Quo Table 3 shows ICERs for the Xpert strategy versus the status quo strategy under 10-y and 20-y analytic horizons and a range of Xpert costs. Assuming an Xpert cost of US$30 per test, the Xpert scenario is expected to avert approximately half a million DALYs during the first 10 y following introduction, at a cost of US$959 (95% CI: 633–1,485) per DALY averted. 10.1371/journal.pmed.1001347.t003 Table 3 Cost-effectiveness results for Xpert algorithm compared to status quo algorithm in southern Africa. Outcome Xpert Cost US$20 US$30 US$40 10-y analytic horizon (costs and benefits summed over 2012–2022) Incremental costs, health system $401 million (248–623 million) $460 million (294–699 million) $520 million (333–772 million) Incremental costs, DOTS program only $225 million (119–378 million) $284 million (166–448 million) $344 million (209–522 million) Incremental life-years saved 421,000 (234,000–679,000) 421,000 (234,000–679,000) 421,000 (234,000–679,000) Incremental DALYs averted 480,000 (261,000–809,000) 480,000 (261,000–809,000) 480,000 (261,000–809,000) Incremental cost per life-year saveda $952 (606–1,326) $1,093 (746–1,592) $1,234 (836–1,872) Incremental cost per DALY averteda $836 (531–1,223) $959 (633–1,485) $1,083 (716–1,760) 20-y analytic horizon (costs and benefits summed over 2012–2032) Incremental costs, health system $1,103 million (594–1,979 million) $1,217 million (691–2,093 million) 1,330 (784–2,205) Incremental costs, DOTS program only $481 million (205–993 million) $594 million (295–1,125 million) 707 (379–1,262) Incremental life-years saved 1,500,000 (800,000–2,570,000) 1,500,000 (800,000–2,570,000) 1,500,000 (800,000–2,570,000) Incremental DALYs averted 1,550,000 (800,000–2,770,000) 1,550,000 (800,000–2,770,000) 1,550,000 (800,000–2,770,000) Incremental cost per life-year saveda $734 (459–1,173) $810 (504–1,311) $885 (557–1,467) Incremental cost per DALY averteda $711 (422–1,187) $784 (476–1,345) $857 (523–1,534) All costs are given in 2011 US dollars. a ICERs calculated using health system costs (including DOTS costs). Both costs and health outcomes discounted at 3%. Range in parentheses represents the 95% posterior interval for each estimate. Figure 5 presents the costs per DALY averted through implementation of Xpert in each of the five southern African countries. In almost all cases, the cost-effectiveness ratios fall below the standard benchmarks for cost-effectiveness suggested by WHO, whereby interventions with cost-effectiveness ratios less than three-times annual per-capita GDP are regarded as potentially cost-effective, and interventions with cost-effectiveness ratios less than annual per-capita GDP are deemed very cost-effective. Among these five countries, per-capita GDP in 2010 ranged from above US$7,000 in South Africa and Botswana down to US$982 in Lesotho [73]. 10.1371/journal.pmed.1001347.g005 Figure 5 Cost-effectiveness of Xpert strategy compared to status quo strategy in five southern African countries (2011 US dollars). For each ratio, the diamond indicates the point estimate (mean incremental costs divided by mean incremental DALYs averted), and the bar indicates the width of the 95% posterior interval. Results based on US$30 Xpert per-test cost. Sensitivity Analyses We conducted one-way sensitivity analyses for all model inputs. Figure 6 shows the results for South Africa for the ten parameters producing the greatest variation in the cost-effectiveness ratio when varied by ±1 standard deviation from their posterior means. While the overall uncertainty in model results—as expressed in the posterior intervals and in the cost-effectiveness acceptability curves described below—is not small, the uncertainty generated by any individual parameter is relatively small, and does not change the general conclusions of the study. Complete results, by country, for the one-way sensitivity analyses on all parameters are reported in Text S1. Partial rank correlation coefficients, which reflect a probabilistic approach to identifying influential parameters, were calculated for all model inputs based on the simulation results, and yielded conclusions that were largely consistent with those based on the one-way sensitivity analyses (results for South Africa presented in Figure S3). 10.1371/journal.pmed.1001347.g006 Figure 6 Results from univariate sensitivity analyses, showing the ten parameters with the greatest influence on the cost-effectiveness of Xpert compared to status quo, South Africa. Sensitivity analyses on the incremental cost per DALY averted (2011 US dollars) over a 10-y analytic horizon, assuming a US$30 Xpert per-test cost. In each one-way analysis, one parameter was varied ±1 standard deviation from its posterior mean, with all other variables fixed at their posterior means. The cost-effectiveness ratios presented in Table 3 and Figure 5 attempt to capture the major changes in health system resource use and health outcomes resulting from the adoption of the Xpert algorithm, including increases in TB treatment and HIV treatment volume. The increase in TB treatment volume is a direct consequence of better case-finding under the Xpert algorithm. The increase in ART volume is an indirect consequence of Xpert introduction, resulting from improved survival of TB/HIV-coinfected individuals who are currently receiving ART or who will go on to receive ART in the future. As shown in Figure 4, the increase in health system costs due to increased ART volume is substantial. In order to disentangle the direct effect of Xpert from this secondary effect through HIV survival, we constructed a scenario in which access to ART under a scaled-up Xpert approach was constrained to be the same as in the status quo scenario (as might be the case if the future HIV treatment budget were fixed and did not increase as a function of HIV treatment need). While artificial, this scenario allowed us to estimate the cost-effectiveness of Xpert adoption separate from the effects on HIV treatment. In this scenario, incremental costs and DALYs averted dropped by 35%–40% and 10%–15%, respectively, compared to the main analysis, and the cost per DALY averted (assuming a US$30 per-test cost for Xpert) dropped to US$656 (95% CI: 386–1,115) over a 10-y analytic horizon. Further sensitivity analyses (described in Text S1) tested the robustness of the cost-effectiveness results to the use of clinical diagnosis as part of the status quo algorithm, to the removal of inpatient care from MDR-TB treatment, to the provision of empiric MDR-TB treatment while awaiting results from DST for all patients diagnosed with RIF resistance by Xpert, and to a revised assumption about ART cost trends, in which ART prices drop 50% over 10 y. Each of these changes produced a change in the 10-y ICER of <20% and did not change the qualitative conclusions about Xpert cost-effectiveness. Detailed three-way sensitivity analyses were conducted to understand how current coverage of culture (among treatment-naïve and treatment-experienced patients) and DST affected the incremental costs, health benefits, and cost-effectiveness of Xpert in each country. These analyses (Figure S4) show that if use of culture under the status quo algorithm is higher than the value used in the main analysis, this reduces the incremental costs and health benefits produced by adopting Xpert and results in a less favorable cost-effectiveness ratio. In some countries, very high values of culture use would result in the status quo strategy dominating the Xpert strategy, i.e., having lower costs and greater health benefits. The coverage levels that produce such a result (80% of all treatment- naïve and treatment-experienced TB patients diagnosed via culture), however, are unlikely to be in place at present, given current infrastructure and program constraints. Higher than expected DST access under the status quo would produce modest reductions in incremental costs and minimal changes in cost-effectiveness ratios. We also considered an alternative Xpert algorithm that requires more aggressive investigation (via culture, chest X-ray, and antibiotic trial) of Xpert-determined TB-negative individuals with HIV-positive or unknown status, as described in recent South African Xpert guidelines [74]. The ICER for this aggressive Xpert algorithm, compared to the base-case Xpert algorithm evaluated in the main analysis, was US$2,128 (95% CI: 1,215–3,954) per DALY averted, suggesting that while this more aggressive algorithm may be cost-effective in some settings, limited programmatic resources might yield higher benefits by expanding access to a simplified Xpert algorithm. Finally, we constructed cost-effectiveness acceptability curves to consider the likelihood that Xpert would be cost-effective under different thresholds for societal willingness to pay for an additional year of healthy life (Figure S5). If society were willing to pay up to the average per-capita GDP (US$6,850 for the region) for each averted DALY, our results suggest essentially no uncertainty in the conclusion that Xpert would be cost-effective. At a threshold of only US$1,000 (representing <15% of per-capita GDP in the region), the probability that Xpert would be regarded as cost-effective was 85%, when we considered the benefits that would accumulate over 20 y, or 55%, over a 10-y horizon. Discussion In this study, we used a dynamic, calibrated mathematical model of TB to evaluate the potential health and economic consequences associated with scaling up the new Xpert MTB/RIF test in settings with high TB burden, prevalent MDR-TB, and high concurrent prevalence of HIV. Our modeling approach enables quantification of the population-level health effects of alternative diagnostic strategies, projections of impact over the short term and longer time horizons, and assessment of the economic impact and cost-effectiveness of scaling up Xpert compared to continuation of the status quo diagnostic approach. Our results indicate that the introduction of the Xpert MTB/RIF diagnostic has the potential to produce a substantial reduction in TB morbidity and mortality in southern Africa. For individuals with smear-negative TB, the benefits of Xpert implementation would be immediate, leading to the diagnosis and early treatment of many individuals who would be missed by the conventional diagnostic algorithm. Over a longer time frame, the introduction of Xpert would reduce transmission and reduce the reservoir of latent TB infection in the population, but these secondary effects are smaller than might have been anticipated. Even accounting for indirect transmission benefits, we project that TB incidence will remain substantial after three decades of Xpert use, in the absence of other modifications to the status quo TB control strategy. This is due to the large existing pool of latently infected individuals whose progression to active disease would be unmitigated by improved diagnostics, and to the fact that a substantial fraction of the additional cases diagnosed using Xpert will be smear-negative cases, who are less likely to transmit infection than smear-positive cases. Along with the projected health benefits of scaling up Xpert will come significantly increased demands on healthcare resources. The large increase in funding required under the Xpert scenario raises the question of affordability. Although our cost-effectiveness results suggest that the introduction of Xpert represents good value for money according to typical international benchmarks, it does not automatically follow that TB program budgets will be able to absorb these changes. Whereas current debate about the costs of Xpert roll-out focuses largely on equipment and consumables connected directly to the assay, our results show that the indirect cost consequences associated with improved case-finding overshadow the direct costs of diagnosis. If current guidelines are followed, the adoption of Xpert places three key demands on a health system that are additional to the direct costs of diagnosis: providing first-line TB treatment to the large number of additional pan-sensitive TB cases that will be identified, providing additional HIV treatment to coinfected individuals who will live longer as a result of better TB care, and providing second-line TB treatment to the limited number of individuals diagnosed with drug-resistant TB. While our analysis accounts for all three demands, we recognize that response to each of these demands could be evaluated as a separate policy question. Such analyses are beyond the scope of our present study, but it is nevertheless important to note how the economics of Xpert are dependent on the additional interventions triggered by Xpert introduction—which are sensitive to both epidemiologic context and policy decisions. It is likely that existing resources and infrastructure will be called upon to support the introduction of Xpert and the cascade of complementary services this will trigger, and our findings underscore the concern raised by other commentators regarding the possible pitfalls of introducing Xpert into health systems that are already facing capacity constraints [26],[29]. An important observation in this study is that substantial increases in HIV treatment costs are expected following introduction of Xpert. This critical insight has a large influence on the cost-effectiveness of Xpert that would be missed in simpler models that do not capture the concurrent dynamics of TB and HIV, and is consistent with other analyses pointing to the importance of HIV and ART access for TB outcomes in this setting [27],[75]. Sensitivity analyses show that if future HIV treatment access were limited by a hard budget constraint, this would actually result in a more attractive cost-effectiveness ratio for Xpert adoption (reducing the ICER to less than US$700 per DALY over a 10-y analytic horizon), with the subtraction of ART costs from the numerator of the ICER outweighing the reduction in health benefits in the denominator. Note that this finding provides no evidence about the appropriate level of ART access in the future, but does provide a clear illustration of the interlinked nature of TB and HIV policy in settings with dual epidemics. Although the absolute increase in HIV treatment spending would eventually be larger than the increase in TB program costs, the relative effects on total budgets for HIV and TB control are reversed; we estimate that introduction of Xpert would result in a 2% increase in HIV treatment costs after 10 y, but a 40% increase in the costs of TB control. Providing treatment to additional cases diagnosed with MDR-TB represents another major component of the incremental costs of Xpert adoption. In our base-case analysis, we assumed that second-line TB treatment would be available for diagnosed MDR-TB cases, which resulted in an estimated 2- to 3-fold increase in the volume of MDR-TB treatment under an Xpert scale-up scenario. If second-line therapy were less available than we assumed, the cost-effectiveness of Xpert would actually improve in the short term (at the cost of faster growth in drug resistance), as the reduction in treatment costs would outweigh the reduction in survival among MDR-TB patients receiving ineffective first-line regimens. Recent empirical cost analyses suggest that MDR-TB care costs may be even higher than estimated in our analysis, with a South African study estimating per-patient costs of over US$17,000 during the inpatient phase of therapy alone, more than 40 times the cost of treating drug-sensitive TB [76]. While this might motivate the development of more efficient approaches to MDR-TB treatment, it also highlights the trade-offs involved in Xpert introduction. Although the scenarios considered in this analysis assumed that DST would be used prior to the initiation of patients on second-line regimens, the availability of DST remains limited in some settings. Of note, the 67% positive predictive value of the Xpert test for RIF resistance in this setting suggests that a positive result on the Xpert RIF test would be insufficient evidence to initiate individuals on second-line regimens, and further screening would be necessary. Further, the benefits achieved through better detection and treatment of drug-resistant TB would be offset by increases in the number of cases developing resistance, resulting from Xpert's better case detection and the resulting increase in treatment volume. Consequently, the percentage of all TB cases with MDR-TB after 10 and 20 y is projected to be higher under the Xpert scenario, although this result is not statistically significant, and—given the overall reduction in TB prevalence produced by Xpert—the absolute number of MDR-TB cases would be lower than under the status quo. A recent modeling study on Xpert introduction in three countries [77] reported an ICER of US$138 per DALY in South Africa for Xpert versus the status quo, which is around 5–8 times lower than the estimated ratios in our study. Because the prior study used a cohort model of patients with suspected TB, its results pertained only to the direct effects of diagnosis and treatment in a defined cohort, rather than reflecting the population-level health and economic consequences. The higher ratios in our study relate in part to our inclusion of HIV treatment costs, which are relevant to a health system or societal perspective. Exclusion of these costs from the prior analysis resulted in a more favorable assessment of Xpert, since the survival benefits of antiretroviral treatment were credited to Xpert when estimating DALYs averted, but at an implicit zero cost. An additional point of difference is that this prior study assumed no access to culture as part of the status quo algorithm, which also contributed to a lower cost-effectiveness ratio for Xpert when compared to the base-case assumptions about culture access used in our analysis. Another recent analysis looked at the use of Xpert for TB screening prior to ART initiation in South Africa. This analysis included ART costs in the cost-effectiveness ratio, and reported a cost-effectiveness ratio of US$5,100 per life-year saved for the Xpert algorithm compared to current diagnostics [78]. This analysis considered only the health benefits for the individual being screened, rather than counting the cases averted by reducing transmission, and focused on a population in which ART costs would dominate the cost-effectiveness ratio, and so it is understandable that the cost-effectiveness ratio was considerably higher than the cost per life-year saved estimated in our study. Our analysis has several limitations. The application of any mathematical model of TB is inevitably limited by uncertainty regarding the true values of epidemiologic and programmatic parameters. Our approach aims to reduce this parameter uncertainty through calibration, and to provide a valid quantitative expression of what parameter uncertainty remains based on Bayesian statistical inference; however, the uncertainty associated with model structure is impossible to quantify without building and assessing the whole range of possible model structures that might be adopted. For example, the results of this analysis would be different if the interdependency of TB and HIV epidemics were not considered, or if the indirect effect of Xpert on TB transmission were not captured. It will therefore be important to undertake continued empirical research evaluating the impact of Xpert as it is rolled out in practice, with the information generated by these evaluation efforts used to progressively refine the mathematical models used to estimate long-term intervention effects. In the results reported here, we constrained estimates on costs and health outcomes to account only for those that would accrue during either the first 10 y or the first 20 y following introduction of Xpert. While the choice of a limited time horizon acknowledges our increasing uncertainty about the distant future and reflects the immediacy of policy decisions, it also makes our results somewhat conservative. This is particularly true for the 10-y results, which truncate the full streams of future benefits that will be enjoyed by those patients who avert TB mortality or infection during the 10-y analysis period. Likewise, we observe that cost-effectiveness ratios are more attractive over the 20-y horizon than the 10-y horizon, reflecting the compounding benefits of interrupting transmission dynamics through better diagnosis and treatment. Moreover, the restriction of our study to adult populations will underestimate the total burden of disease that might be averted, with Xpert adoption likely to reduce pediatric TB through reduced exposure to actively infected adults as well as the direct application of the test for pediatric diagnosis [79],[80]. Finally, we note that the results of the present analysis emphasize the importance of interactions between TB and HIV epidemiology in settings where both are highly prevalent, but we caution against generalizing these results to regions where HIV rates are meaningfully different from those in southern Africa. Additional analyses are urgently needed to assess the consequences of introducing Xpert elsewhere, particularly regions of low HIV prevalence or with different TB drug resistance patterns. Similarly, this study focused on the relative benefits of the status quo algorithm and the Xpert algorithm suggested by WHO for diagnosis of patients with suspected TB in settings with high HIV burden. While this is an important comparison to make, there is abundant scope for considering a wide array of alternatives, for example, considering different potential roles for sputum smear microscopy or chest X-ray within diagnostic algorithms designed around Xpert, or use of Xpert for different purposes, such as prior to provision of INH preventive therapy for individuals with HIV, or as part of active case-finding efforts [81]. Because the model developed for this analysis reflects detailed structure relating both to HIV and to patterns of resistance to major anti-TB drugs, it offers substantial flexibility to accommodate adaptation to other settings. In view of these features, and our statistical approach to calibrate this model to available epidemiologic data, we envision that the model can provide a durable platform for evaluating an array of different diagnostic strategies in diverse settings in the future. Supporting Information Figure S1 Status quo and Xpert diagnostic algorithms. (PDF) Click here for additional data file. Figure S2 Incremental difference in epidemiologic outcomes between Xpert and status quo scenarios, 2012–2032. (PDF) Click here for additional data file. Figure S3 Partial rank correlation coefficients for ten parameters with greatest influence on the cost-effectiveness of Xpert compared to status quo, South Africa, 10-y time horizon. (PDF) Click here for additional data file. Figure S4 Three-way sensitivity analyses showing effects of changes in culture and DST coverage on major study outcomes, by country. (A) Botswana; (B) Lesotho; (C) Namibia; (D) South Africa; (E) Swaziland. Costs, DALYs, and ICERs assessed over a 10-y analytic horizon with a US$30 Xpert unit cost. All other parameters held at their mean posterior values. (PDF) Click here for additional data file. Figure S5 Cost-effectiveness acceptability curves showing probability that Xpert strategy is cost-effective as a function of willingness to pay for health benefits. (PDF) Click here for additional data file. Text S1 Technical appendix. Figures S1, S2, S3, S4, S5 are available as individual files, but are also included here for ease of access. (PDF) Click here for additional data file.
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              Results from early programmatic implementation of Xpert MTB/RIF testing in nine countries

              Background The Xpert MTB/RIF assay has garnered significant interest as a sensitive and rapid diagnostic tool to improve detection of sensitive and drug resistant tuberculosis. However, most existing literature has described the performance of MTB/RIF testing only in study conditions; little information is available on its use in routine case finding. TB REACH is a multi-country initiative focusing on innovative ways to improve case notification. Methods We selected a convenience sample of nine TB REACH projects for inclusion to cover a range of implementers, regions and approaches. Standard quarterly reports and machine data from the first 12 months of MTB/RIF implementation in each project were utilized to analyze patient yields, rifampicin resistance, and failed tests. Data was collected from September 2011 to March 2013. A questionnaire was implemented and semi-structured interviews with project staff were conducted to gather information on user experiences and challenges. Results All projects used MTB/RIF testing for people with suspected TB, as opposed to testing for drug resistance among already diagnosed patients. The projects placed 65 machines (196 modules) in a variety of facilities and employed numerous case-finding strategies and testing algorithms. The projects consumed 47,973 MTB/RIF tests. Of valid tests, 7,195 (16.8%) were positive for MTB. A total of 982 rifampicin resistant results were found (13.6% of positive tests). Of all tests conducted, 10.6% failed. The need for continuous power supply was noted by all projects and most used locally procured solutions. There was considerable heterogeneity in how results were reported and recorded, reflecting the lack of standardized guidance in some countries. Conclusions The findings of this study begin to fill the gaps among guidelines, research findings, and real-world implementation of MTB/RIF testing. Testing with Xpert MTB/RIF detected a large number of people with TB that routine services failed to detect. The study demonstrates the versatility and impact of the technology, but also outlines various surmountable barriers to implementation. The study is not representative of all early implementer experiences with MTB/RIF testing but rather provides an overview of the shared issues as well as the many different approaches to programmatic MTB/RIF implementation.
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                Author and article information

                Journal
                gs
                Gaceta Sanitaria
                Gac Sanit
                Ediciones Doyma, S.L. (Barcelona, Barcelona, Spain )
                0213-9111
                April 2020
                : 34
                : 2
                : 127-132
                Affiliations
                [1] orgnameStop TB Partnership Switzerland
                [2] orgnameThe Task Force for Global Health/TEPHINET Guatemala Branch Office Guatemala
                [3] orgnameMinistry of Health of Guatemala orgdiv1National Tuberculosis Program Guatemala
                [4] orgnameIndependent M&E TB REACH Team, Epirus Spain
                Article
                S0213-91112020000200127 S0213-9111(20)03400200127
                10.1016/j.gaceta.2019.02.010
                e2ec4cf1-e7a4-4d73-9c72-c1d469aefa18

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 29 October 2018
                : 26 February 2019
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                Figures: 0, Tables: 0, Equations: 0, References: 27, Pages: 6
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                SciELO Spain

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                Original Articles

                Tuberculosis,Xpert MTB/RIF,Laboratory,Clinical diagnosis,Laboratorio,Diagnóstico clínico,Case detection,Detección de casos

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