- Record: found
- Abstract: found
- Article: found
Informing decision-making for universal access to quality tuberculosis diagnosis in India: an economic-epidemiological model
research-article
Author(s):
Hojoon Sohn
1
,
,
Parastu Kasaie
1 ,
Emily Kendall
2 ,
Gabriela B. Gomez
3 ,
Anna Vassall
3 ,
Madhukar Pai
4
,
5 ,
David Dowdy
1
Publication date (Electronic):
6 August 2019
Journal:
BMC Medicine
Publisher:
BioMed Central
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Abstract
Background
India and many other high-burden countries have committed to providing universal access to high-quality diagnosis and drug susceptibility testing (DST) for tuberculosis (TB), but the most cost-effective approach to achieve this goal remains uncertain. Centralized testing at district-level hub facilities with a supporting sample transport network can generate economies of scale, but decentralization to the peripheral level may provide faster diagnosis and reduce losses to follow-up (LTFU).
Methods
We generated functions to evaluate the costs of centralized and decentralized molecular testing for tuberculosis with Xpert MTB/RIF (Xpert), a WHO-endorsed test which can be performed at centralized and decentralized levels. We merged the cost estimates with an agent-based simulation of TB transmission in a hypothetical representative region in India to assess the impact and cost-effectiveness of each strategy.
Results
Compared against centralized Xpert testing, decentralization was most favorable when testing volume at decentralized facilities and pre-treatment LTFU were high, and specimen transport network was exclusively established for TB. Assuming equal quality of centralized and decentralized testing, decentralization was cost-saving, saving a median $338,000 (interquartile simulation range [IQR] − $222,000; $889,000) per 20 million people over 10 years, in the most cost-favorable scenario. In the most cost-unfavorable scenario, decentralized testing would cost a median $3161 [IQR $2412; $4731] per disability-adjusted life year averted relative to centralized testing.
Conclusions
Decentralization of Xpert testing is likely to be cost-saving or cost-effective in most settings to which these simulation results might generalize. More decentralized testing is more cost-effective in settings with moderate-to-high peripheral testing volumes, high existing clinical LTFU, inability to share specimen transport costs with other disease entities, and ability to ensure high-quality peripheral Xpert testing. Decision-makers should assess these factors when deciding whether to decentralize molecular testing for tuberculosis.
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Most cited references18
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- Article: found
Rapid Diagnosis of Tuberculosis with the Xpert MTB/RIF Assay in High Burden Countries: A Cost-Effectiveness Analysis
Anna Vassall, Sanne C van Kampen, Hojoon Sohn … (2011)
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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Screening for HIV-Associated Tuberculosis and Rifampicin Resistance before Antiretroviral Therapy Using the Xpert MTB/RIF Assay: A Prospective Study
Stephen D Lawn, Sophie V. Brooks, Katharina Kranzer … (2011)
Introduction Tuberculosis is a major challenge for antiretroviral therapy (ART) services in resource-limited settings where patients typically enrol with advanced immunodeficiency [1]. Many patients referred for ART have a current TB diagnosis, and an additional large burden of disease is detected during pre-treatment screening [2]–[4]. Tuberculosis in this population is a major cause of morbidity and mortality [1],[5]–[7] and presents a substantial hazard of nosocomial disease transmission to other patients and health care workers [8]. These risks are heightened when patients have multidrug-resistant TB (MDR-TB) [9]–[11]. To address these challenges, there is a critical need in such settings for rapid, effective screening for TB and detection of drug resistance [1],[12]. Screening for TB in this patient population is difficult, however [12]. The World Health Organization's (WHO) intensified case finding symptom screen has low specificity and misses approximately 10%–20% of cases [13],[14]. Sputum smear microscopy, the mainstay of TB diagnosis in resource-limited settings, detects as few as one in five cases when used as a screening tool pre-ART [4],[12],[15]. Chest radiography is costly and not widely available; interpretation is difficult, and up to one-third of culture-confirmed cases of pulmonary TB diagnosed during screening have a normal radiograph [12],[16]. Availability of culture-based diagnosis is also extremely limited in resource-limited settings because of high cost and technical complexity, and this approach often provides a diagnosis only after several weeks [15],[17]. These challenges are further compounded by the extremely limited laboratory capacity to detect drug resistance [18]. The threat posed by MDR-TB to efforts to control TB worldwide [19] requires urgent improvements in diagnostic capacity. Following a large multi-country evaluation [20], the WHO, in December 2010, endorsed the roll-out of a novel rapid test for the investigation of patients suspected of having TB, especially in settings with a high prevalence of HIV-associated disease and/or MDR-TB [21]. The Xpert MTB/RIF assay (Cepheid) is a fully automated molecular assay in which real-time polymerase chain reaction technology is used to simultaneously detect Mycobacterium tuberculosis and rifampicin resistance mutations in the rpoB gene [22],[23]. The cartridge-based system dispenses with the need for prior sputum processing and requires minimal laboratory expertise, and results are available in less than 2 h, permitting a specific TB diagnosis and rapid detection of rifampicin resistance. Excellent performance characteristics were observed among symptomatic adults with suspected TB in a large multi-country evaluation [20]. These findings have been confirmed in a subsequent multi-country implementation study [24] and in several laboratory-based studies [25]–[29]. The assay has sensitivities of 98%–100% for smear-positive pulmonary TB, 57%–78% for smear-negative pulmonary TB, and 53%–81% for extrapulmonary TB when testing a variety of clinical samples [20],[24]–[29]. Further studies are needed to examine the performance of the assay in different clinical settings, including use as a routine screening test to increase TB case detection in HIV-infected patients. We evaluated the diagnostic accuracy of the Xpert MTB/RIF assay among consecutive patients with advanced immunodeficiency being screened for TB (regardless of symptoms) prior to starting ART in a South African township with a very high burden of TB. Methods Setting The ART cohort was based in Gugulethu township, Cape Town, where the prevalence of HIV and the TB notification rate are both extremely high [5]. Several studies reporting the burden, diagnosis, and complications of TB in this cohort have previously been published [3],[5],[15],[16],[30],[31]. National TB programme guidelines recommend investigating symptomatic adults with suspected pulmonary TB using smear microscopy of two sputum samples; in suspected “retreatment TB” cases only, culture of one sputum sample may be requested in addition [32]. In accordance with the national ART programme guidelines, ART was provided for all patients with WHO stage 4 disease and/or blood CD4 cell counts 200 cells/µl were 28.1% (95% CI, 19.7–36.4), 19.4% (95% CI, 14.7–24.0), and 13.8% (95% CI, 10.2–17.5), respectively. In binomial regression analysis (Table 2), risk of TB was independently associated with low CD4 cell count, low body mass index, high viral load, not previously having received TB treatment, and having a positive WHO symptom screen. However, risk of TB was not associated with chronic cough of ≥2 wk duration. 10.1371/journal.pmed.1001067.t002 Table 2 Binomial regression analysis showing crude and adjusted risk ratios for the associations between risk of sputum culture-positive tuberculosis and patient characteristics. Patient Characteristics Crude Risk Ratio 95% CI p-Value Adjusted Risk Ratio 95% CI p-Value Age ≤30 y 1 Age >30 y 0.90 0.61–1.34 0.62 Male 1 Female 1.06 0.70–1.61 0.79 Body mass index 18–25 kg/m2 1 1 Body mass index 25 kg/m2 0.68 0.42–1.09 0.109 0.70 0.39–1.27 0.243 No history of previous TB treatment 1 1 History of previous TB treatment 0.68 0.41–1.13 0.14 0.50 0.26–0.96 0.036 CD4 ≥100 cells/µl 1 1 CD4 2 wk compared to 56.5% (95% CI, 41.6–71.4) among those with either no cough or cough of shorter duration (p = 0.018). Moreover, sensitivity was substantially greater in patients for whom the time to positivity of sputum samples was less than the median of 16 d (85.7%; 95% CI, 69.4–100) than in those with longer times to positivity (48.5%; 95% CI, 30.4–66.5) (p = 0.005). There was also a weak association between sensitivity and CD4 cell counts: sensitivity was 78.9% (95% CI, 58.8–99.1) in those with CD4 cell counts <100 cells/µl compared to 54.3% (95% CI, 36.9–71.6) in those with higher CD4 cell counts (p = 0.075). However, there was no association with radiographic abnormalities or with a positive WHO symptom screen. There were three patients with apparent false-positive Xpert MTB-RIF assays, giving an assay specificity of over 99.0% in each of the different analyses (Table 3). Review of the study and clinical records of these patients revealed that two of these patients had overt pulmonary and systemic symptoms suggestive of TB, and both had chest radiographs revealing parenchymal consolidation and marked hilar and paratracheal lymphadenopathy highly suggestive of TB. One of these patients was reinvestigated during routine clinical follow-up and had two positive sputum smears (2+ and 3+). Both patients received standard treatment for TB and made excellent clinical responses. The third patient had symptoms and an abnormal chest radiograph but was lost to follow-up. Use of Xpert MTB/RIF in Screening Algorithms To further explore the utility of the Xpert MTB/RIF assay, we considered clinical populations with a TB prevalence of 20%, 15%, 10%, or 5%. With an overall sensitivity of 73.3% and specificity of 99.2% (Table 3), the PPVs at these TB prevalence rates would be 95.8%, 94.2%, 91.0%, and 82.8%, respectively, and the NPVs would be 93.7%, 95.5%, 97.1%, and 98.6%, respectively. We next considered the utility of incorporating the Xpert MTB/RIF assay into different screening algorithms, examining the use of smear microscopy, symptom screening, one Xpert assay, two Xpert assays (Xpert done on a second sample if the first was negative), and sequential smear microscopy and Xpert testing (Xpert tests done if smear microscopy was negative). This was simulated for a hypothetical cohort of 1,000 patients with a TB prevalence of 20%, 15%, 10%, or 5% and assuming that 30% of cases were smear-positive. Symptom frequencies and the sensitivity and specificity of the Xpert assay as reported above were used. The yield of TB cases, the number of missed cases, and the number of Xpert tests done for each correct TB diagnosis were compared between these different screening strategies and clinical populations (Table 4). Compared to a base case scenario of smear microscopy of two sputum samples in patients with a positive WHO symptom screen, the sensitivity of algorithms incorporating the Xpert MTB/RIF assay was much greater and the corresponding number of missed diagnoses was far fewer. However, at a TB prevalence of 5%, the number of Xpert tests done per case diagnosed was high (Table 4). A strategy of sequential smear microscopy and then Xpert testing of smear-negative patients yielded the same number of diagnoses, but did not substantially reduce the number of Xpert tests per case diagnosed. 10.1371/journal.pmed.1001067.t004 Table 4 Utility of the Xpert MTB/RIF assay for tuberculosis diagnosis when incorporated into different screening algorithms and when used in hypothetical patient cohorts with a tuberculosis prevalences of 20%, 15% 10%, or 5%. Investigation Strategy Sensitivity (Percent)a Specificity (Percent) TB Prevalence 20% TB Prevalence 15% TB Prevalence 10% TB Prevalence 5% Correct TB Diagnoses Missed TB Cases Xpert Tests per TB Diagnosis Correct TB Diagnoses Missed TB Cases Xpert Tests per TB Diagnosis Correct TB Diagnoses Missed TB Cases Xpert Tests per TB Diagnosis Correct TB Diagnoses Missed TB Cases Xpert Tests per TB Diagnosis Base case screening algorithm Symptom screen + smear ×2 27.6 100.0 55.2 144.8 0 41.4 108.6 0 27.6 72.4 0 13.8 36.2 0 Using one Xpert test in algorithm Symptom screen+Xpert ×1 50.5 99.6 101 99 6.9 75.7 74.3 9.1 50.5 49.5 13.5 25.2 24.8 26.9 Symptom screen+smear ×2+Xpert ×1 50.5 99.6 101 99 6.4 75.7 74.3 8.6 50.5 49.5 13.1 25.2 24.8 26.3 Xpert ×1 for all patients 60.1 99.4 120.2 79.8 8.3 90.2 59.8 11.1 60.1 39.9 16.6 30.1 19.9 33.2 Smear ×2+Xpert ×1 for all patients 60.1 99.4 120.2 79.8 7.8 90.2 59.8 10.6 60.1 39.9 16.1 30.1 19.9 32.7 Using two Xpert tests in algorithm Symptom screen+Xpert ×2 60.6 99.4 121.2 78.8 11.1 90.9 59.1 14.7 60.6 39.4 22.1 30.2 19.8 44.4 Xpert ×2 for all patients 73.4 99.1 146.8 53.2 13.2 110.1 39.9 17.8 73.4 26.6 26.8 36.7 13.3 54.1 a Sensitivity based on the assumption that 30% of cases are sputum smear-positive. Use of symptom pre-screening limited the sensitivity of TB detection. In populations with high TB prevalence, Xpert testing of all patients regardless of symptoms increased sensitivity without substantially increasing the number of Xpert tests done per TB case diagnosed (Table 4). Compared to the strategy of doing an Xpert assay on one sputum sample from patients with a positive symptom screen, a strategy of doing two Xpert tests on all patients was associated with 22.9% higher sensitivity for TB and the fewest missed cases. Although the latter strategy would require a large absolute number of tests, at a TB prevalence of 20%, one extra TB case would be diagnosed for every additional 6.3 tests done. Detection of Rifampicin Resistance Among 81 cases of TB diagnosed, four cases had isolates resistant to rifampicin because of MDR-TB (prevalence, 4.9%; 95% CI, 1.4–12.2). Among the 445 patients (839 samples) with results of culture, drug susceptibility testing, and Xpert MTB/RIF assays all available, there were 84 isolates from 55 patients (including all four cases of MDR-TB) in which rifampicin susceptibility could be compared. Rifampicin resistance was correctly identified in all four cases of MDR-TB by the Xpert MTB/RIF assay (100% sensitivity) (Table 5). However, the Xpert MTB/RIF assay also reported rifampicin resistance in three samples from three further patients in which the isolates were reported as rifampicin susceptible using comparator assays (Table 5). A paired sputum sample was available from two of these patients and rifampicin-susceptible M. tuberculosis was reported by Xpert MTB/RIF assay in both. To resolve these discrepancies, the rpoB regions of all five isolates from these three patients were sequenced. All were found to be wild-type, confirming absence of genotypic rifampicin resistance and indicating that the three Xpert MTB/RIF assay results were false positives. All remaining patients with susceptible isolates were correctly identified as such by the assay. Thus, in a per-patient analysis, the PPV of the Xpert MTB/RIF assay for detecting rifampicin resistance was 4/7 (57%) and the specificity was 48/51 (94.1%; 95% CI, 84.8–98.8). 10.1371/journal.pmed.1001067.t005 Table 5 Comparison of results regarding drug susceptibility testing for rifampicin among paired samples from patients (n = 6) in whom rifampicin resistance was detected using one or more assays. Patient Number Sputum Smear Xpert MTB/RIF MTBDRplus on Sputum MTBDRplus on Culture Isolate MGIT Phenotypic DST rpoB Gene Sequencing Final Rifampicin Susceptibility Overall Susceptibility Pattern Concordant susceptibility results #020 NEG/NEG −/R −/− −/R −/R − Resistant MDR-TB #099 POS/POS R/R −/R R/R −/− − Resistant MDR-TB #208 NEG/NEG R/− −/− R/R R/R − Resistant MDR-TB #292 NEG/POS R/R R/− R/R R/− − Resistant MDR-TB Discordant susceptibility results #039 NEG/NEG R/S S/− S/S S/S WT/WT Susceptible Pan-susceptible #157 POS/POS R/S S/S S/S S/S WT/WT Susceptible Pan-susceptible #322 POS R − S S WT/WT Susceptible Pan-susceptible DST, drug susceptibility testing; NEG, smear-negative; POS, smear-positive; R, resistant; S, susceptible; WT, genotypically wild-type. Time to Diagnosis The median delays between sputum collection and results being available to the clinic for smear microscopy and Xpert MTB/RIF assays and positive liquid cultures were 3 d (IQR, 2–5) and 4 d (IQR, 3–6), respectively. The median delays for culture results were 12 d (IQR, 10–14) and 20 d (IQR, 17–27) for smear-positive and smear-negative disease, respectively. Cultures were incubated for 42 d before being declared negative for M. tuberculosis, with a median time to reporting of 43 d (IQR, 43–45). For the patients with confirmed MDR-TB (n = 4), the mean time to TB diagnosis and detection of rifampicin resistance was 2 d using Xpert MTB/RIF assay, 21 d using the MTBDRplus assay on a positive culture isolate, and 40 d using phenotypic drug susceptibility testing in liquid culture. Discussion A high prevalence (17.3%) of culture-proven pulmonary TB was diagnosed in this patient population, but conventional diagnostic tools widely used in resource-limited settings performed poorly. Smear microscopy detected just 28% of cases, and chest radiology was of low discriminatory value. Even using automated liquid culture as the diagnostic gold standard, diagnosis was slow, with a median delay of almost 3 wk among those with smear-negative disease. In contrast, the Xpert MTB/RIF assay was able to diagnose with extremely high specificity all cases of smear-positive TB and almost two-thirds of smear-negative cases and three-quarters of cases overall when testing two samples. Only 0.6% of test results were indeterminate. The assay also rapidly detected rifampicin resistance in all four cases of confirmed MDR-TB. However, false-positive rifampicin resistance results were also observed. The TB prevalence and associated risk factors detected in this clinical setting were similar to those previously reported from this and another ART clinic in South Africa [3],[4],[15]. Almost 30% of patients with CD4 cell counts <100 cells/µl had culture-proven TB, and rapid diagnosis is needed since such patients have high mortality risk [5],[34]. Only one-quarter of all TB patients reported a cough lasting ≥2 wk—a symptom screen widely used for many years to define suspected TB cases. Use of the new WHO symptom screening tool [13],[14] had higher sensitivity but still would have missed 13 of the 81 TB diagnoses made in this study, suggesting the need for routine microbiological screening of all patients in this setting. We evaluated the utility of the Xpert MTB/RIF assay as a screening tool in consecutive HIV-infected adult patients enrolling for ART, excluding those who already had a TB diagnosis (approximately one-third of referrals to this cohort [35]). Since patients were screened regardless of the presence or absence of symptoms, our study is likely to have diagnosed TB cases at an earlier stage in the disease course than studies in which symptomatic patients were tested. In contrast, the previous Foundation for Innovative New Diagnostics multi-country evaluation [20] enrolled only patients with overt TB symptoms; all had a chronic cough of at least 2 wk duration and were able to produce three 1.5-ml sputum specimens. Early disease in our study would tend to be associated with lower bacillary numbers in sputum samples, as indicated by the observations that almost 70% of cases were sputum smear-negative and the prolonged median time to positivity of liquid cultures. This patient population therefore represents a major challenge for any diagnostic assay [17]. The limits of detection of the Xpert MTB/RIF assay (95% sensitivity) defined by in vitro experiments is 131 bacilli/ml of sputum, which approaches than that of liquid culture, which falls within the range 10–100 bacilli/ml [17],[23]. In contrast, smear microscopy is able to detect only samples with more than approximately 10,000 organisms per millilitre [17],[23]. Testing a single sputum sample using Xpert MTB/RIF allowed diagnosis of all smear-positive cases regardless of smear grade; these cases pose the greatest infectious hazard within the community and health care settings. As anticipated [17], the sensitivity for smear-negative disease was lower than that reported in the previous multi-country evaluation [20] (43.3% versus 72.5% using one sputum sample; 63.3% versus 85.1% using two samples). Presence of cough of ≥2 wk was associated with much higher sensitivity for smear-negative TB, as was shorter time to culture positivity. The latter observation suggests that sensitivity was likely to have been limited by very low numbers of bacilli in sputum samples. Three patients had false-positive TB diagnoses using Xpert MTB/RIF compared to the predefined laboratory gold standard of liquid culture. However, the clinical and radiological features in these cases were highly suggestive of TB; one was confirmed as having smear-positive TB on reinvestigation, two exhibited excellent responses to TB treatment, and the third patient was lost to follow-up. These follow-up data suggest that some or all of these false-positive Xpert MTB/RIF assays may actually have been correct. The proportion of cultures lost to contamination was very low (3.1%), highlighting possible over-decontamination in the laboratory and loss of sensitivity in the culture gold standard. If this was the case, the PPV of the assay would be higher, which would increase assay utility, especially in clinical populations with lower disease prevalence. Few Xpert MTB/RIF assays were indeterminate, but the observation that three out of five of these were in culture-positive cases suggests that indeterminate results should be followed up by a repeat test. Despite only moderate sensitivity for smear-negative disease, Xpert MTB/RIF nevertheless increased overall case detection by 36% when testing one sample and by 45% when testing two samples, compared to smear microscopy. Used for baseline screening evaluation of patients enrolling in this ART service, Xpert MTB/RIF testing of a single sputum sample would detect TB in approximately 10% of the cohort, and testing two samples would detect TB in 12.5%. Thus, the assay would detect approximately one TB case for every eight patients screened, compared to one in 18 patients screened using sputum microscopy. We explored the potential impact of incorporating the assay in several screening algorithms applied to clinical populations with a range of TB prevalence rates. The likely benefits (increased TB yield) and assay costs (tests done per case diagnosed) were highly dependent on TB prevalence, and at a prevalence rate of 5%, the number of tests done per case diagnosed was high (4-fold higher than for a population with a prevalence of 20%). A strategy of screening with sputum microscopy and then testing smear-negative samples with Xpert MTB/RIF assay would result in minimal savings with regard to the number of Xpert tests done but would also result in failure to diagnose MDR-TB in highly infectious smear-positive cases. Symptom pre-screening restricted sensitivity and, at higher TB prevalence rates, did not substantially reduce the number of Xpert MTB/RIF tests done to identify one case of TB when compared to a strategy of testing all patients regardless of symptoms. Screening two samples with Xpert MTB/RIF would substantially increase the absolute number of tests done, but at high TB prevalence rates the high incremental yield may justify this approach. The number of Xpert MTB/RIF assays done might logically be stratified by CD4 cell count since this is a strong predictor of TB prevalence. For example, in high-burden settings such as South Africa, two tests might be done for those with CD4 cell count <200 cells/µl and just one test for those with higher counts. These strategies need to be evaluated by detailed cost-effectiveness analyses that take into account not simply the costs of testing but also the downstream impact on clinical outcomes and associated costs. Since the Xpert MTB/RIF instrument was based in a centralised laboratory service, with results reported via the routine laboratory system, the median time to diagnosis was similar to that of smear microscopy (4 d versus 3 d, respectively). The time to diagnosis of smear-negative disease, however, was shortened by a median of 2 wk compared to culture. Time to diagnosis and treatment would potentially be further shortened by location of the instrument in the ART clinic [24]. The assay also has the potential to shorten the time to exclude a diagnosis of TB; this normally takes 6 wk or more via negative cultures and may lead to inappropriate delays in ART initiation. In view of the high NPV of the Xpert MTB/RIF assay in this cohort (94.8%), a negative result at baseline evaluation could provide a useful indication of a low probability of TB, increasing clinical confidence to start ART without undue delay. In cohorts with a lower prevalence of TB, the NPV would be higher, further increasing its utility in this regard. HIV-associated MDR-TB carries a high mortality risk, and nosocomial outbreaks in HIV care and treatment centres pose a grave threat to patients accessing these services [9],[10],[36]. Many patients with HIV-associated MDR-TB die before a diagnosis can be made [9],[36]. In this study, the Xpert MTB/RIF assay identified four patients with rifampicin-resistant isolates who had MDR-TB, greatly reducing the mean time to detection (2 d) compared to using conventional culture-based susceptibility testing (40 d) or using line probe assays on culture isolates (20 d). By accelerating diagnosis, the Xpert MTB/RIF assay has the potential to substantially reduce the risks of nosocomial transmission of MDR-TB and improve the prognosis of affected individuals. The Xpert MTB/RIF assay reported three false-positive rifampicin resistance results. The finding of discordant rifampicin susceptibility results from paired samples using the Xpert MTB/RIF assay suggests that specificity might be increased by requiring confirmation of resistance in more than one sample. While such false positives were not found in the initial multi-country evaluation [20], another ongoing field study sponsored by the Foundation for Innovative New Diagnostics has also detected cases, leading the manufacturer to modify the instrument software and cartridge specifications [24],[37]. With WHO approval of roll-out of this assay in December 2010, confirmation of successful reconfiguration of the test platform is urgently required. Strengths of the study include the use of a quality-assured laboratory that participated in the previous multi-country evaluation [20]. Whereas all previously published studies have evaluated use of the assay among individuals with suspected TB [20],[24]–[29], this study evaluated the assay as a screening tool in unselected consecutive patients regardless of symptoms in a high-burden setting. The TB status of all patients was clearly defined based on a rigorous laboratory gold standard. Weaknesses include the fact that a small number of tests were not done because of a laboratory clerical error and that there were few cases of MDR-TB. While a similar burden of disease has been reported from an ART clinic elsewhere in South Africa [4], the prevalence of TB may differ in other countries, and we therefore explored utility at a range of prevalence rates. The impact of the sputum concentration procedure and of dividing the sputum pellet between three assays rather than testing unprocessed sputum was not investigated in this study, but these methods were not found to impact assay sensitivity in a previous large-scale multi-country evaluation [20]. The usefulness of the assay as a point-of-care test was not evaluated. Further studies are needed to assess the impact of Xpert MTB/RIF screening on subsequent patient outcomes, the operational feasibility of using the assay within the clinic, and cost-effectiveness. In conclusion, when used as a routine screening test among patients with advanced immunodeficiency and high TB risk, rapid screening using the Xpert MTB/RIF assay substantially increased case detection, supporting replacement of microscopy as the initial diagnostic tool. The assay also greatly decreased the time to diagnosis of MDR-TB. Use of Xpert MTB/RIF as a screening tool might effectively reduce the risk of nosocomial MDR-TB outbreaks in HIV care and treatment settings and improve the prognosis of affected patients. However, the specificity of the assay for detecting rifampicin resistance needs to be improved to prevent overdiagnosis of rifampicin-resistant disease. Supporting Information Text S1 STARD checklist. (PDF) Click here for additional data file.
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Tuberculosis diagnostics in 2015: landscape, priorities, needs, and prospects.
Madhukar Pai, Marco Schito (2015)
In 2015, tuberculosis remains a major global health problem, and drug-resistant tuberculosis is a growing threat. Although tuberculosis diagnosis in many countries is still reliant on older tools, new diagnostics are changing the landscape. Stimulated, in part, by the success and roll out of Xpert MTB/RIF, there is now considerable interest in new technologies. The landscape looks promising, with a robust pipeline of new tools, particularly molecular diagnostics, and well over 50 companies actively engaged in product development. However, new diagnostics are yet to reach scale, and there needs to be greater convergence between diagnostics development and development of shorter-duration tuberculosis drug regimens. Another concern is the relative absence of non-sputum-based diagnostics in the pipeline for children and of biomarker tests for triage, cure, and progression of latent Mycobacterium tuberculosis infection. Several initiatives, described in this supplement, have been launched to further stimulate product development and policy, including assessment of needs and priorities, development of target product profiles, compilation of data on resistance-associated mutations, and assessment of market size and potential for new diagnostics. Advocacy is needed to increase funding for tuberculosis research and development, and governments in high-burden countries must invest more in tuberculosis control to meet post-2015 targets for care, control, and prevention.
Author and article information
Contributors
Hojoon Sohn:
ORCID: http://orcid.org/0000-0001-5837-3844
hsohn6@jhu.edu
Parastu Kasaie: pkasaie@jhu.edu
Emily Kendall: ekendall@jhmi.edu
Gabriela B. Gomez: Gabriela.Gomez@lshtm.ac.uk
Anna Vassall: Anna.Vassall@lshtm.ac.uk
Madhukar Pai: madhukar.pai@mcgill.ca
David Dowdy: ddowdy1@jhmi.edu
Journal
Journal ID (nlm-ta): BMC Med
Journal ID (iso-abbrev): BMC Med
Title:
BMC Medicine
Publisher:
BioMed Central
(London
)
ISSN
(Electronic):
1741-7015
Publication date
(Electronic):
6
August
2019
Publication date PMC-release: 6
August
2019
Publication date Collection: 2019
Volume: 17
Electronic Location Identifier: 155
Affiliations
[1
]ISNI 0000 0001 2171 9311, GRID grid.21107.35, Department of Epidemiology, , Johns Hopkins Bloomberg School of Public Health, ; 615 N. Wolfe St., E6529, Baltimore, MD 21205 USA
[2
]ISNI 0000 0001 2171 9311, GRID grid.21107.35, Division of Infectious Disease, , Johns Hopkins University School of Medicine, ; Baltimore, MD 21205 USA
[3
]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Department of Global Health and Development, , London School of Hygiene and Tropical Medicine, ; London, WC1E 7HT UK
Author information
Hojoon Sohn
http://orcid.org/0000-0001-5837-3844
Article
Publisher ID: 1384DOI: 10.1186/s12916-019-1384-8
PMC ID: 6683370
PubMed ID: 31382959
SO-VID: 215d2444-d4c8-4e6e-b3aa-622bf8f53581
Copyright © © The Author(s). 2019
License:
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
History
Date
received
: 22
March
2019
Date
accepted
: 5
July
2019
Funding
Funded by: FundRef http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;
Award ID: OPP1083276
Award Recipient
:
David Dowdy
Funded by: FundRef http://dx.doi.org/10.13039/501100000156, Fonds de Recherche du Québec - Santé;
Award ID: 36095
Award Recipient
:
Hojoon Sohn
Categories
Subject:
Research Article
Custom metadata
issue-copyright-statement © The Author(s) 2019
ScienceOpen disciplines: Medicine
Data availability:
ScienceOpen disciplines: Medicine