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      Diagnosis of tuberculosis from smear-negative presumptive TB cases using Xpert MTB/Rif assay: a cross-sectional study from Nepal

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          Abstract

          Background

          In most developing countries, smear-negative pulmonary TB (SNPT) often gets missed from the diagnosis of consideration, though it accounts 30–65% of total PTB cases, due to deficient or inaccessible molecular diagnostic modalities.

          Methods

          The cross-sectional study enrolled 360 patients with clinical-radiological suspicion of SNPT in Tribhuvan University Teaching Hospital (TUTH). The patient selection was done as per the algorithm of Nepal’s National Tuberculosis Program (NTP) for Xpert MTB/RIF testing. Participants’ demographic and clinical information were collected using a pre-tested questionnaire. The specimens were collected, processed directly for Xpert MTB/RIF test according to the manufacturer’s protocol. The same samples were stained using the Ziehl-Neelsen technique then observed microscopically. Both findings were interpreted; rifampicin-resistant, if obtained, on Xpert testing was confirmed with a Line Probe Assay.

          Result

          Of 360 smear-negative sputum samples analyzed, 85(23.61%) found positive while 3(0.8%) of them were rifampicin resistance. The infection was higher in males, i.e. 60(25.3%) compared to female 25(20.3%). The age group, > 45(nearly 33%) with median age 42 ± 21.5, were prone to the infection. During the study period, 4.6% (515/11048) sputum samples were reported as smear-positive in TUTH. Consequently, with Xpert MTB/RIF assay, the additional case 16.5% ( n = 85/515) from smear-negative presumptive TB cases were detected. Among the most occurring clinical presentations, cough and chest pain were positively associated with SNPT. While upper lobe infiltrates (36.4%) and pleural effusion (40.4%) were the most peculiar radiological impression noted in PTB patient. 94 multi-drug resistant(MDR) suspected cases were enrolled; of total suspects, 29(30.8%) samples were rifampicin sensitive, 1(1.06%) indeterminate, 3(3.19%) rifampicin-resistant while remaining of them were negative. 2(2.2%) MDR cases were recovered from the patient with a previous history of ATT, of total 89 previously treated cases enrolled However, a single rifampicin-resistant from the new suspects.

          Conclusion

          With an application of the assay, the additional cases, missed with smear microscopy, could be sought and exact incidence of the diseases could be revealed.

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          Most cited references14

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          Development of a Standardized Screening Rule for Tuberculosis in People Living with HIV in Resource-Constrained Settings: Individual Participant Data Meta-analysis of Observational Studies

          Introduction By the end of 2009, an estimated 33 million people were living with HIV, the vast majority in sub-Saharan Africa and Asia. Tuberculosis (TB) remains the most common cause of death in people living with HIV. Compared to people without HIV, people living with HIV have a more than 20-fold increased risk of developing TB [1]. TB disease may occur at any stage of HIV disease and is frequently the first recognized presentation of underlying HIV infection [2],[3]. Without antiretroviral treatment (ART), up to 50% of people living with HIV who are diagnosed with TB die during the 6–8 mo of TB treatment [4]–[6]. This risk increases to 72%–98% among those with multi-drug (MDR) or extensively drug-resistant (XDR) TB [7],[8]. Although ART can reduce the incidence of TB both at individual [9] and population [10] levels, people living with HIV on ART still have higher TB incidence rates and a higher risk of dying from TB [11]. Routine TB screening offers the opportunity to diagnose and promptly treat TB disease, and to identify those without TB disease who may be eligible for TB preventive therapy [12]. The use of TB preventive therapy can reduce TB incidence and is therefore of considerable benefit to patients [13]. For these reasons, the World Health Organization (WHO) recommends regular screening for active TB disease of all people living with HIV and providing either treatment for active disease or isoniazid preventive therapy (IPT) to mitigate TB morbidity, mortality, and transmission [14]. However, in 2009, of the estimated 33 million people living with HIV, only 1.7 million (5%) were screened for TB, and about 85,000 (0.2%) were offered IPT [15]. Currently there is no internationally accepted evidence-based tool to screen for TB in people living with HIV. Several studies have shown that the presenting signs and symptoms of TB in people living with HIV are different from those in people without HIV to diagnose TB; for example, many people living with HIV who have culture-confirmed TB do not report having a prolonged cough, which is one of the standard TB screening questions used by national TB control programs globally [16]. Moreover, the most widely available TB diagnostic tests such as smear microscopy and chest radiography perform poorly among people living with HIV; because most people living with HIV and TB have either sputum acid-fast bacilli (AFB) smear negative pulmonary or extrapulmonary TB [17]. We conducted an individual participant data meta-analysis of published and unpublished studies to develop a simple, standardized TB screening rule for resource-constrained settings that will adequately separate patients into two groups: (1) those for whom TB is reliably excluded, and IPT and ART, if indicated, can be initiated; and (2) those who require further investigation for TB disease. We describe the results of this meta-analysis and propose an algorithm for TB screening among people living with HIV in resource-constrained settings. Methods We proceeded through several steps. First, we prospectively enumerated criteria for studies to be included in our meta-analysis. Second, we searched for and selected studies that met these criteria. Third, we sought primary data from investigators and mapped individual-level data to common symptoms. Fourth, we identified five symptoms available in most studies and, within each study, computed the sensitivity and specificity of 23 screening rules derived from these five symptoms. Finally, we used meta-analysis methods to estimate the performance of all 23 rules, as well as the association of study-level and individual-level correlates with performance. Inclusion of Studies We defined studies as being eligible for inclusion in this analysis if they met the following criteria: (1) collected sputum specimens from people living with HIV regardless of signs or symptoms; (2) used mycobacterial culture of at least one specimen to diagnose TB; and (3) collected data about signs and symptoms. Search Strategy and Selection of Studies To identify studies eligible for the meta-analysis, we conducted a systematic literature review of studies related to TB screening among people living with HIV in June 2008 using PubMed and various combinations of the following keywords: “HIV,” “Tuberculosis,” “TB screening,” “Smear-negative TB,” “Sputum negative TB,” “TB case finding,” “Intensified TB case finding,” “Isoniazid prevention treatment, trial or therapy.” We also searched for abstracts presented at conferences organized by the International Union Against TB and Lung Diseases and the International AIDS Society between 2000–2008. No language restriction was placed on the search. We reviewed all retrieved titles and abstracts for relevance to the topic. The reference lists of retrieved studies were also reviewed to identify further studies that meet the eligibility criteria. In addition, recognized experts in the field were contacted to identify studies that were not available (e.g., unpublished) in the initial electronic search. Studies that involve concomitant HIV testing and mycobacterial culture on all patients are resource intensive and challenging to implement in countries with a high burden of TB and HIV. Therefore, we believe it is unlikely that eligible studies would have been completed but missed by our search strategy. We found 2,119 publications and reviewed all their abstracts. Using the criteria above, we selected 53 for review of the full text. Twenty-one articles included information on signs and symptoms for TB screening in people living with HIV. A total of 14 studies (six published and eight unpublished at the time of the search) met the inclusion criteria of our meta-analysis (Figure 1). The corresponding authors or principal investigators were contacted for all 14 studies to confirm that their studies met all the eligibility criteria. One unpublished dataset was excluded for not meeting the inclusion criteria after verification with the principal investigator, and another one was excluded because the investigators could not submit the data within the agreed timeframe. A total of 12 investigators (representing six published and six unpublished studies at the time of the search) provided de-identified individual patient data for inclusion in the primary meta-analysis within an agreed time framework (Table 1) [18]–[29]. In November 2010, immediately preceding manuscript publication, we re-ran the search strategy again to look for additional studies that were reported since the initial search and should have been included in the meta-analysis. The search found seven eligible studies, of which all except one [30] were included in our meta-analysis as unpublished datasets. 10.1371/journal.pmed.1000391.g001 Figure 1 Search strategy and studies included in the meta-analysis (PRISMA flow diagram). 10.1371/journal.pmed.1000391.t001 Table 1 Summary of studies included in the meta-analysis. Reference Study Population Characteristics (Setting, Continent, n Samples, Culture Method Used) Sample Size PLTB/PLHIVa (%) PLTB/PLHIV (%) with Data on the Five Symptoms Ayles et al. 2009 Adults of more than 15 y of age sampled from one rural and one urban communities in Zambia (community, sub-Saharan Africa, 1 LJ and MGIT). 8,044 43/2,253 (1.9) 41/2,145 (1.9) Corbett et al. 2010b Random sample of adults in 46 previously enumerated neighbourhoods in the high density suburbs of Harare, Zimbabwe (community, sub-Saharan Africa, 3 LJ). 10,079 31/1,841 (1.7) 31/1,834 (1.7) Cain et al. 2010 PLHIV from 8 outpatient clinics in Cambodia, Thailand, and Vietnam who were enrolled regardless of signs or symptoms suggestive of TB (clinical, Southeast Asia, 3 MGIT and LJ). 1,748 267/1,724 (15.5) 267/1,721 (15.5) Day et al. 2006 Employees of a gold mining company first attending the TB preventive therapy clinic in South Africa (miners, sub-Saharan Africa, 2 LJ). 1,093 32/991 (3.2) 0/0 (–) Corbett et al. 2007b Employees of 22 small and medium-sized enterprises in Zimbabwe (community, sub-Saharan Africa, 3 LJ). 4,668 3/797 (0.4) 3/797 (0.4) Lewis et al. 2009b All consenting employees of a gold mining indusry who undergo annual medical examinations in an occupational health centre in South Africa (miners, sub-Saharan Africa, 2 LJ). 1,955 18/560 (3.2) 18/560 (3.2) Shah et al. 2009 All newly diagnosed HIV-positive clients of at least 18 y old from the VCT Clinic in a large referral hospital in Addis Ababa, Ethiopia (clinical, sub-Saharan Africa, 1 LJ). 453 27/427 (6.3) 22/357 (6.2) Kimerling et al. 2002 PLHIV of at least 15 y of age and enrolled in an HIV home-based care service in Phnom Penh, Cambodia (community, Southeast Asia, 1 LJ). 441 36/393 (9.2) 36/393 (9.2) Lawn et al. 2009 PLHIV with more than 18 y of age who were referred to a community-based ART service in South Africa (clinical, sub-Saharan Africa, 2–4 MGIT). 235 58/226 (25.7) 57/218 (26.1) Wood et al. 2007 Randomly sampled and consenting adults living in shacks in a high-density residential area in South Africa (community, Sub-Saharan Africa, 4 MGIT). 174 12/163 (7.4) 0/0 (–) Mohammed et al. 2004 PLHIV with WHO clinical stage 3 or 4 disease referred for possible participation in a trial of TB-preventive therapy in 3 hospital-based adult HIV clinics in South Africa (clinical, sub-Saharan Africa, 1 BACTEC). 129 10/128 (7.8) 0/0 (–) Chheng et al. 2008 All consenting participants of more than 19 y old who were tested for HIV in a Voluntary Counseling Center and referred for TB screening in Cambodia (clinical, Southeast Asia, 3 LJ). 504 20/123 (16.3) 20/123 (16.3) Total 29,523 557/9,626 (5.8) 495/8,148 (6.1) a The following patients were excluded: (1) patients who were on TB treatment or prophylaxis at enrolment; (2) patients who were smear positive, but culture grew non- tuberculosis mycobacterium (NTM); and (3) patients who were smear positive, but culture negative. b Patients were previously screened for TB before enrolment into the study. LJ, Lowenstein-Jensen culture medium; MGIT, Mycobacterial Growth Indicator Tube culture system; PLHIV, persons living with human immunodeficiency virus; PLTB, persons with tuberculosis disease; VCT, voluntary counselling and testing for HIV. Investigators for all included studies signed a data sharing and confidentiality agreement, and agreed to a data management, analysis, and publication plan. During design and analysis phases of the meta-analysis, the investigators of the studies and data managers of the included studies held multiple discussions by email, by teleconference, and in person in Geneva, Switzerland and Atlanta, Georgia, United States. Data Abstraction and Management The list of variables from the most comprehensive dataset [20] was used to construct an initial master list of variables. All the variables from each study included in the meta-analysis were mapped to this master list. Principal investigators and data managers for the 12 studies worked with the meta-analysis investigators to ensure accurate mapping of data from the primary studies to the master variable list. In the end, the final dataset of the meta-analysis included 159 variables that appeared in at least two of the studies. We identified five symptoms common to most studies and limited the meta-analysis to the nine studies with substantially complete information for those five symptoms. Case Definitions We defined a TB patient as any person living with HIV and at least one specimen culture positive for Mycobacterium tuberculosis (MTB). We defined participants as having no TB if cultures were negative for MTB and participants were judged not to have TB on the basis of the original study definition of the investigators. We excluded from the analysis: (1) patients who were receiving treatment for TB (infection or disease) at enrolment; (2) patients who were AFB smear positive, but whose culture grew non-tuberculous mycobacteria; and (3) patients who were AFB smear positive or scanty, but sputum culture negative. Sources of Study Heterogeneity All studies were reviewed to identify study-level characteristics that could substantially impact the findings of the meta-analysis. Two studies were conducted exclusively among gold miners living in South Africa [21],[23], a population that may not be broadly generalizable, because of its demographics, its high prevalence of non-TB illnesses (e.g., silicosis) that can produce cough, and the practice of annual TB radiological screening. Five studies [18],[19],[22],[25],[27] were conducted among individuals drawn from a community setting through prevalence surveys, which may lead to enrolment of patients with a different spectrum of TB and HIV disease than would be found among patients seeking care. Three studies [19],[22],[23] involved participants who were previously screened for TB or who had had access to routine TB screening before being enrolled into the studies. Finally, three studies exclusively used liquid media to culture specimens [26]–[28], two studies used both solid and liquid media [18],[20], and seven studies exclusively used solid media (Table 1). Liquid media have substantially increased sensitivity for growing MTB, particularly in patients with low levels of TB bacilli, as would be expected in a population of people living with HIV being screened for TB [31]. Studies that used liquid media, therefore, would have improved ability to classify patients correctly into those who have TB and those who do not. We explored the impact of these factors on the performance of the screening algorithms and analyzed subsets of the final dataset grouped according to these characteristics. Data Analysis We compared characteristics of patients with TB to those of patients without TB to derive a standardized rule for TB screening among people living with HIV. The goal of TB screening is to divide the population of people living with HIV into two groups: (1) those who do not have TB; and (2) those who need further evaluation for the diagnosis of TB (i.e., TB suspects). We restricted our analysis to clinical symptoms that could be readily assessed at any level of the health system and that were asked about in all studies: current cough (C), haemoptysis (H), fever (F), night sweats (S), and weight loss (W). Using the four studies that included chest radiography data [20],[23],[24],[26], we also evaluated the impact of adding abnormal chest radiography findings to the TB screening rule. Only observations with no missing data for the symptoms of interest were included in the analysis. We considered “1-of-n” rules as candidates for screening for TB that could best classify patients into two groups (not TB and suspected TB) with high sensitivity [20]. The “1” represents the minimum number of symptoms that must be present in an individual to be classified as a suspected TB patient and the “n” represents the number of symptom(s) in a given rule. For example, a “1-of-3” rule could be a set of symptoms such as current cough, fever, and weight loss, abbreviated here as CFW. An individual with at least one symptom specified in this particular rule would be classified as a TB suspect; an individual without any of these symptoms would be classified as not having TB. We considered all combinations of the five candidate symptoms except for combinations that include both current cough and haemoptysis, yielding a total of 23 candidate rules: two 1-of-4 rules (CFSW, HFSW), seven 1-of-3 rules (CFS, CFW, CSW, HFS, HFW, HSW, FSW), nine 1-of-2 rules (CF, CS, CW, HF, HS, HW, FS, FW, SW), and five 1-of-1 rules (C, H, F, S, W) (see also Table 3.) The analysis with abnormal chest radiographic findings (X) considered these 23 rules, each augmented with this additional sign (e.g., CFSWX). Other analyses have considered m-of-n rules with m>1 [20]. These rules cannot exceed the sensitivity of 1-of-n rules. Suppose that a positive screen requires the presence of at least two symptoms out of current cough, fever, and night sweats, the number of true positives for this 2-of-3 rule will not be greater than the number of true positives from the corresponding 1-of-3 rules. Because our aim in this analysis was to identify the most sensitive rule, we did not include rules of this kind in our analysis. We applied two closely related methods for cross-study analysis of sensitivity and specificity of these 23 candidate screening rules: bivariate random-effects meta-analysis (BREMA) and the hierarchical summary relative operating characteristic (HSROC) curve [32],[33]. BREMA jointly models sensitivity and specificity while accommodating between-study heterogeneity, and HSROC models tradeoffs between sensitivity and specificity across study populations. Both methods can be unified in the same model. In addition to sensitivity and specificity, we calculated predictive value negative and likelihood ratio negative of each candidate rule [34]. Our goal was to identify a combination of symptoms that achieved the highest possible sensitivity and the lowest possible negative likelihood ratio for ruling out TB disease (without any predetermined cut-off points). To further understand between-study heterogeneity and other factors associated with the diagnostic performance of the most sensitive rule, we analyzed several study-level predictors (setting, prior screening of study participants, culture medium used, and geographic region) and participant-level predictors (age, gender, CD4 T-lymphocyte cell count [CD4], and abnormal chest radiographic findings). Our analytic methods produced odds ratios that reflect the magnitude of association between each factor and the probability of correctly identifying persons with TB (sensitivity) or without TB (specificity). For a range of TB prevalence values, we calculated the negative predictive values at levels of each covariate. We calculated the ratio of the number of patients that screen positive but who actually have no TB (false positives) and hence unnecessarily require additional TB diagnostic evaluation (e.g., culture) to the number of patients that screen positive and actually have TB (true positives), which is referred to as the number needed to screen. We calculated this ratio for different rules using a theoretical population of 1,000 people living with HIV with different levels of TB prevalence. This ratio provides proxy information similar to a marginal cost-effectiveness analysis for different screening rules and it helps quantify how much a health program would need to invest (as measured by additional diagnostic tests) for every patient with TB identified through the screening rule [35]. Each observation with a missing covariate value was omitted from analysis of that covariate. BREMA models were fitted using SAS procedure Glimmix (SAS 9.22, SAS Institute), and further calculations were performed in R (R 2.10.1, R Development Core Team). Ethical Review All data collection included in the meta-analysis was approved by institutional ethical review boards at the respective institutions during the original study; if necessary, principal investigators requested additional approval from institutional review boards for the inclusion of the primary dataset in the meta-analysis. All data for the meta-analysis were provided completely de-identified. In the meta-analysis dataset, investigators were not able to link case records to individuals. Results Investigators provided data about 29,523 participants, of whom 10,057 were people living with HIV. The dataset included 9,626 people living with HIV who had TB screening and sputum culture performed, of whom 8,148 could be evaluated on the five symptoms of interest from nine of 12 studies (Figure 2). 10.1371/journal.pmed.1000391.g002 Figure 2 Flow chart of study participants included in the individual patient data meta-analysis. Most patients (77% [7,386/9,626]) were from sub-Saharan Africa; the rest were from Southeast Asian countries. The median age was 34 y (interquartile range [IQR], 27–41 y). Of the 9,626 patients with HIV in the 12 studies, CD4 cell count information was available for 3,489 (36%) and chest radiography information for 3,903 (41%). The median CD4 count was 248 cells/µl (IQR, 107–409). The overall prevalence of TB disease was 5.8% (557/9,626), ranging across studies from 0.4% to 25.7% (Table 1). More than half of TB patients (52% [288/557]) had sputum smear negative pulmonary TB, whereas 39% (218/557) had sputum smear positive pulmonary, and 5% (28/557) had exclusively extrapulmonary TB. The anatomic site of TB was not specified in 4% (23/557) of patients. Table 2 summarizes the distribution of common variables, and Table S1 summarizes how each question was actually asked in each study. Because duration of cough was included in many studies but was asked about in different ways, we were able to analyze data using three different cough variables: cough in the past 4 wk (information available for 39.3% of participants); cough lasting for 2 wk or more (information available for 47.1%); and cough present in the last 24 h, which is referred to as “current cough” (information available for 89.6%). 10.1371/journal.pmed.1000391.t002 Table 2 Characteristics of participants with and without TB for variables included in the analysis. Characteristic All PLHIV (n = 9,626) PLHIV with Data on the Five Symptoms (n = 8,148) No TB Disease (n = 9,069), n (%) TB Disease (n = 557), n (%) TB Disease (n = 7,653), n (%) TB Disease (n = 495) n (%) Origin of patient Sub-Saharan Africa 7,152 (78.9) 234 (42.0) 5,739 (75.0) 172 (34.8) Southeast Asia 1,917 (21.1) 323 (58.0) 1,914 (25.0) 323 (65.2) Setting Clinical 2,246 (24.8) 382 (68.5) 2,053 (26.8) 366 (73.9) Community 5,322 (58.7) 125 (22.4) 5,058 (66.1) 111 (22.4) Miners 1,501 (16.5) 50 (9.0) 542 (7.1) 18 (3.6) Sex Male 4,957 (54.7) 356 (63.9) 3,811 (49.8) 309 (62.4) Female 4,111 (45.3) 201 (36.1) 3,841 (50.2) 186 (37.6) Missing or not recorded 1 (0.0) 0 (0.0) 1 (0.0) 0 (0.0) Median age (IQR), n =  8,633 (7,286) 34 (27–41) 33 (28–40) 33 (27–40) 32 (28–39) Median CD4+ count (IQR), n =  3,489 (2,409) 268 (126–427) 106 (38–241) 229 (94–391) 94 (33–215) Cough in the past 4 wk Yes 1,439 (15.9) 303 (54.4) 1,270 (16.6) 278 (56.2) No 1,909 (21.0) 129 (23.2) 1,067 (13.9) 110 (22.2) Missing or not recorded 5,721 (63.1) 125 (22.4) 5,316 (69.5) 107 (21.6) Cough lasting for 2 wk or more Yes 957 (10.6) 197 (35.4) 848 (11.1) 177 (35.8) No 3,093 (34.1) 288 (51.7) 2,046 (26.7) 260 (52.5) Missing or not recorded 5,019 (55.3) 72 (12.9) 4,759 (62.2) 58 (11.7) Haemoptysis Yes 543 (6.0) 60 (10.8) 523 (6.8) 58 (11.7) No 8,509 (71.4) 495 (88.9) 7,130 (93.2) 437 (88.3) Missing or not recorded 17 (0.2) 2 (0.4) 0 (0.0) 0 (0.0) Current cough or cough in the past 24 h Yes 1,625 (17.9) 274 (49.2) 1,530 (20.0) 260 (52.5) No 6,474 (71.4) 250 (44.9) 6,123 (80.0) 235 (47.5) Missing or not recorded 970 (10.7) 33 (5.9) 0 (0.0) 0 (0.0) Current fever or fever in the past 4 wk Yes 1,801 (19.9) 294 (52.8) 1,669 (21.8) 280 (56.6) No 7,002 (77.2) 246 (44.2) 5,984 (78.2) 215 (43.4) Missing or not recorded 266 (2.9) 17 (3.0) 0 (0.0) 0 (0.0) Current night sweats or night sweats in the past 4 wk Yes 1,710 (18.9) 242 (43.4) 1,497 (19.6) 225 (45.4) No 7,329 (80.8) 313 (56.2) 6,156 (80.4) 270 (54.6) Missing or not recorded 30 (0.3) 2 (0.4) 0 (0.0) 0 (0.0) Current weight loss or weight loss in the past 4 wk Yes 2,434 (26.8) 333 (59.8) 2,258 (29.5) 308 (62.2) No 6,478 (71.4) 218 (39.1) 5,395 (70.5) 187 (37.8) Missing or not recorded 157 (1.7) 6 (1.1) 0 (0.0) 0 (0.0) Abnormal chest radiography Yes 581 (6.4) 271 (48.7) 294 (3.8) 239 (48.3) No 2,900 (32.0) 151 (27.1) 2,155 (28.1) 145 (29.3) Missing or not recorded 5,588 (61.6) 135 (24.2) 5,204 (68.0) 111 (22.4) Abnormal chest radiography consistent with TB Yes 377 (4.2) 227 (40.8) 261 (3.4) 209 (42.2) No 2,589 (28.5) 144 (25.8) 1,641 (21.4) 129 (26.1) Missing or not recorded 6,103 (67.3) 186 (33.4) 5,751 (75.2) 157 (31.7) Any one of current cough, fever, night sweats, or weight loss Yes 3,591 (39.6) 425 (76.3) 3,563 (46.6) 418 (84.4) No 4,090 (45.1) 77 (13.8) 4,090 (53.4) 77 (15.6) Not evaluable 1,388 (15.3) 55 (9.9) 0 (0.0) 0 (0.0) We analyzed the performance of individual and combinations of symptoms as screening rules using data from the 8,148 participants who could be evaluated based on the five candidate symptoms. Table 3 shows the diagnostic performance characteristics for the 23 candidate combinations of symptoms, sorted from highest sensitivity to lowest. The most sensitive rule was the presence of any one of the following symptoms: current cough, fever, night sweats, and weight loss (CFSW). The population-average sensitivity of this symptom combination was 78.9% (95% confidence interval [CI] 58.3%–90.9%) with the negative likelihood ratio of 0.426 (95% CI 0.349–0.520), which corresponds to a postscreening reduction in the probability of TB by 15%–20% [34]. 10.1371/journal.pmed.1000391.t003 Table 3 Diagnostic performance of 23 candidate 1-of-n rules. Rule Sensitivity (95% CI) Specificity (95% CI) LRN (95% CI) CFSW 78.9 (58.3–90.9) 49.6 (29.2–70.1) 0.426 (0.349–0.520) HFSW 75.7 (53.9–89.2)a 52.7 (31.8–72.7) 0.461 (0.391–0.544) CFW 74.0 (51.7–88.3)a 53.8 (32.8–73.6) 0.483 (0.416–0.561) CSW 73.4 (51.0–88.0) 53.8 (32.8–73.5) 0.494 (0.428–0.570) CFS 73.1 (50.6–87.9) 61.1 (39.7–79.0) 0.440 (0.382–0.506) HFW 70.6 (47.5–86.4) 57.5 (36.2–76.4) 0.511 (0.454–0.576) FSW 69.2 (45.9–85.6) 55.7 (34.5–75.0) 0.554 (0.497–0.617) HSW 68.1 (44.6–85.0) 58.7 (37.3–77.2) 0.544 (0.492–0.602) CW 65.3 (41.6–83.3) 60.3 (38.8–78.4) 0.576 (0.530–0.625) CF 65.0 (41.3–83.1) 68.6 (47.7–83.9) 0.510 (0.470–0.553) HFS 63.7 (39.9–82.3) 66.3 (45.2–82.4) 0.548 (0.509–0.589) FW 63.1 (39.3–81.9) 61.4 (40.0–79.1) 0.601 (0.560–0.644) SW 61.0 (37.2–80.5) 61.9 (40.5–79.5) 0.630 (0.594–0.669) CS 59.7 (35.9–79.6) 69.4 (48.7–84.4) 0.581 (0.551–0.613) HW 56.8 (33.3–77.6) 66.8 (45.7–82.8) 0.647 (0.620–0.675) FS 56.3 (32.8–77.3) 70.1 (49.6–84.9) 0.623 (0.598–0.649) HF 52.0 (29.2–74.1) 75.0 (55.6–87.7) 0.640 (0.620–0.660) W 49.3 (27.0–71.9) 71.1 (50.8–85.5) 0.712 (0.693–0.733) F 42.8 (22.2–66.3) 79.8 (62.4–90.4) 0.716 (0.695–0.738) HS 38.9 (19.5–62.6) 78.1 (59.9–89.5) 0.782 (0.753–0.813) C 38.5 (19.2–62.2) 81.8 (65.3–91.5) 0.753 (0.724–0.783) S 31.4 (14.8–54.6) 82.2 (65.9–91.7) 0.835 (0.780–0.893) H 5.9 (2.3–14.5) 94.4 (87.6–97.6) 0.996 (0.735–1.351) Sensitivity, specificity, and likelihood ratio negative (LRN) from the bivariate random-effects meta-analysis model. Rule is at least one of the indicated symptoms. C, current cough; H, haemoptysis; F, fever; S, sweats; W, weight loss. a p-Value >0.05 for the same sensitivity of the CFSW rule and the indicated rule. All specificities are significantly different from that of the CFSW rule. LRN, likelihood ratio negative. The nine included studies demonstrated significant between-study heterogeneity on both sensitivity (p<0.001) and specificity (p<0.001) of the rule CFSW (see also Figure 3). The bivariate graphic shows that six studies have study-level specificities below and three above the population average specificity. Furthermore, this rule has the highest-ranking sensitivity in eight of the nine included studies (Table S2). The hierarchical summary relative operating characteristic curves (Figure S1) show slightly better overall diagnostic performance of the rules CFS and CF, but our application requires the highest sensitivity possible, allowing for some tradeoffs with lower specificity. Figure 3 shows that three studies are outliers, and they represent studies of patients who were previously screened for TB or studies in which much of the population likely had previous TB screening (e.g., miners); this can modify the performance characteristics of the screening rule. 10.1371/journal.pmed.1000391.g003 Figure 3 Diagnostic performance of CFSW rule in the included studies. BREMA, bivariate random-effects meta-analysis; HSROC, hierarchical summary relative operating characteristic. The CFSW rule has sensitivity of 90.1% (95% CI 76.3%–96.2%) and 67.1% (95% CI 41.7%–85.3%) among participants selected from clinical and community settings, respectively. Similarly the sensitivity of the rule among those who had not been previously screened for TB was higher at 88.0% (95% CI 76.1%–94.4%) compared to those who had been screened for TB at 40.5% (95% CI 16.6%–69.9%). At the 95% confidence level, the sensitivity of this rule could not be statistically distinguished from the sensitivity of the rule that substitutes haemoptysis for current cough (HFSW, 75.7% sensitive [95% CI 53.9–89.2%]) or the rule that drops night sweats (CFW, 74.0% sensitive [51.7–88.3%]). All other rules had lower sensitivity. Regression analysis of study-level predictors revealed that studies in which TB screening was performed in clinical settings had 4.5 times the odds for a true-positive screening result compared to studies in which TB screening was performed in a community setting (95% CI 1.0–19.5). Studies of participants who had not previously been screened for TB had 10.8 times the odds for a true-positive screen (95% CI 2.4–47.8) compared with studies in which participants had previously been screened for TB. Participants with CD4 cell count <200 cells/µl had 6.4 times the odds of a true-positive screen (95% CI 2.9–14.2). Statistically significant predictors of true-negative results include prescreening, geographic region, participant age ≥33 y, CD4 cell count <200 cells/ml, and abnormal result on chest radiograph (Table 4). 10.1371/journal.pmed.1000391.t004 Table 4 Association of study-level and individual-level predictors with the diagnostic performance of CFSW rule. Predictors Sensitivity (95% CI) Specificity (95% CI) Study level Setting Community 1.0 Clinical 4.45 (1.02, 19.46)a 0.25 (0.06–1.01) Miners 0.25 (0.02–2.51) 4.07 (0.44–37.68) Screening Prescreened for TB 1.0 Not screened for TB 10.82 (2.45–47.78)a 0.08 (0.06–0.12)a Culture medium Solid 1.0 Liquid 3.41 (0.57–20.30) 0.33 (0.06–1.97) Region Sub-Saharan Africa 1.0 Southeast Asia 4.03 (0.65–24.84) 0.20 (0.04–1.00)a Individual level Ageb <33 y 1.0 ≥33 y 1.43 (0.81–2.52) 0.74 (0.66–0.84)a Gender Female 1.0 Male 1.26 (0.71–2.24) 1.04 (0.93–1.16) CD4 cell countc ≥200 cells/µl 1.0 <200 cells/µl 6.38 (2.87–14.17)a 0.46 (0.38–0.57)a Abnormal chest radiographd No 1.0 Yes 1.36 (0.68–2.73) 0.41 (0.30–0.57)a Values in each cell indicate the odds ratio for sensitivity or specificity compared with a referent group. a p-value <0.05 for null hypothesis that odds ratio  = 1. b Excludes Shah et al. [24]. c Includes only studies Cain et al. [20], Shah et al. [24], Lawn et al. [26], and Chheng et al. [29]. d Includes only studies Cain et al. [20], Lewis et al. [23], Shah et al. [24], and Lawn et al. [26]. Table 5 shows the negative predictive value and the numbers needed to screen for the CFSW rule adjusted for individual- and study-level covariates. In a setting with 5% TB prevalence among people living with HIV, the rule has a negative predictive value of 98.3% (95% CI 97.5%–98.8%) for patients screened in a clinical setting and 97.3% (95% CI 96.9%–97.7%) for patients screened in a community setting. The numbers needed to screen at the same prevalence of TB are 15 and 11 for clinical and community setting, respectively. The negative predictive value was similar in those having high (≥200) and low (<200) CD4 count at 96.9% (95% CI 95.1%–98.0%) and 98.9% (95% CI 97.5%–99.5%), respectively (see also Table S3). 10.1371/journal.pmed.1000391.t005 Table 5 Negative predictive value (NPV) and number needed to screen (NNS) using rule CFSW in a hypothetical population of 1,000 people living with HIV stratified by study and individual level predictors. Participants 1% TB Prevalence 5% TB Prevalence 10% TB Prevalence 20% TB Prevalence NPV 95% CI NNS NPV 95% CI NNS NPV 95% CI NNS NPV 95% CI NNS All study participants 99.6 (99.5–99.6) 62 97.7 (97.4–98.0) 12 95.3 (94.6–95.9) 6 90.0 (88.6–91.3) 3 All study participants excluding miners 99.6 (99.5–99.7) 67 97.9 (97.5–98.2) 13 95.6 (94.8–96.3) 7 90.6 (89.0–92.1) 3 Setting Clinical 99.7 (99.5–99.8) 78 98.3 (97.5–98.8) 15 96.4 (94.8–97.5) 8 92.3 (89.0–94.6) 4 Community 99.5 (99.4–99.5) 55 97.3 (96.9–97.7) 11 94.5 (93.7–95.2) 5 88.5 (86.9–89.9) 3 Miners 99.2 (98.7–99.5) 38 96.1 (93.8–97.6) 8 92.2 (87.8–95.1) 4 84.0 (76.1–89.6) 2 Screening Nonscreened for TB 99.6 (99.5–99.7) 77 98.1 (97.5–98.5) 15 96.0 (94.9–96.9) 7 91.5 (89.2–93.3) 4 Prescreened for TB 99.3 (99.3–99.3) 36 96.5 (96.2–96.7) 7 92.8 (92.4–93.2) 4 85.1 (84.3–86.0) 2 Culture medium Liquid 99.6 (99.3–99.8) 75 98.2 (96.7–99.0) 15 96.2 (93.2–98.0) 7 91.9 (85.9–95.5) 3 Solid 99.5 (99.4–99.5) 57 97.3 (97.0–97.7) 11 94.6 (93.8–95.2) 6 88.5 (87.1–89.8) 3 Geography Southeast Asia 99.6 (99.2–99.8) 81 98,0 (95.9–99.0) 16 95,9 (91.6–98.0) 8 91.2 (83.0–95.6) 4 Sub-Saharan Africa 99.5 (99.4–99.6) 52 97.4 (97.1–97.8) 10 94.8 (94.0–95.4) 5 88.9 (87.5–90.2) 3 Age ≥33 y 99.6 (99.5–99.7) 63 97.8 (97.2–98.2) 12 95.4 (94.3–96.4) 6 90.3 (88.0–92.1) 3 <33 y 99.5 (99.4–99.6) 59 97.5 (97.0–97.9) 12 94.8 (94.0–95.6) 6 89.1 (87.4–90.6) 3 Gender Male 99.5 (99.4–99.6) 64 97.5 (97.2–97.9) 13 95.0 (94.2–95.6) 6 89.3 (87.8–90.6) 3 Female 99.6 (99.5–99.7) 60 98.0 (97.5–98.4) 12 95.8 (94.9,96.6) 6 91.0 (89.2–92.6) 3 CD4 cell count ≥200 cells/µl 99.4 (99.0–99.6) 80 96.9 (95.1–98.0) 16 93.6 (90.2–95.9) 8 86.7 (80.4–91.2) 4 <200 cells/µl 99.8 (99.5–99.9) 81 98.9 (97.5–99.5) 16 97.8 (94.8–99.1) 8 95.1 (89.1–97.9) 4 Abnormal chest radiograph Yes 99.4 (99.0–99.6) 83 97.0 (95.2–98.2) 16 93.9 (90.3–96.2) 8 87.2 (80.6–91.8) 4 No 99.5 (99.3–99.7) 61 97.7 (96.7–98.4) 12 95.2 (93.2–96.7) 6 89.9 (85.9–92.9) 3 Four studies [20],[23],[24],[26] consistently recorded information on chest radiograph, allowing screening rules with this sign to be evaluated using data from 2,805 participants The addition of abnormal chest radiographic findings into the CFSW rule increases the sensitivity to 90.6% (95% CI 66.7%–97.9%) with a specificity of 38.9% (95% CI 12.8%–73.3%), and a likelihood ratio negative of 0.242 (95% CI 0.102–0.571). Fifteen of the 23 rules included in our analysis outperform the symptom-based CFSW rule when abnormal chest radiographic findings are added (Table S4). On the basis of our meta-analysis findings and incorporating current WHO recommendations on provision of IPT, we developed a simple TB screening algorithm for public health programmes to screen people living with HIV, and, depending on the outcome of screening, to either provide IPT or evaluate patients further for TB or other diseases (Figure 4). 10.1371/journal.pmed.1000391.g004 Figure 4 Algorithm for TB screening in person living with HIV in HIV prevalent and resource-constrained settings. * Every person living with HIV needs to be evaluated for ART eligibility, and all settings providing care should reduce TB transmission through proper measures. ** Chest radiography is not required to classify patients into the TB and not-TB groups, but can be done, if available, to increase the sensitivity of screening. *** Assess for contraindications, including active hepatitis (acute or chronic), regular and heavy alcohol consumption, and symptoms of peripheral neuropathy, is required prior to initiating IPT. Past history of TB is not a contraindication for starting IPT. Tuberculin skin test may be performed as part of eligibility screening in some settings. **** Investigations for TB should be done in accordance with existing national guidelines. Discussion We found that the absence of all of current cough, fever, night sweats, and weight loss can identify a subset of people living with HIV who have low probability of having TB disease. This screening rule was superior over other candidate rules in eight of the nine studies included and had an overall favourable performance over competing rules in the hierarchical summary relative operating characteristic (HSROC) analysis. We also demonstrated that the negative predictive value of the rule was high across a range of TB disease prevalence estimates and across different population subsets, including those with low and high CD4 count, and those drawn from clinical and community settings and South African miners. We believe that these screening questions are likely to be acceptable to practitioners, because they are symptoms classically associated with TB disease. Underdiagnosis and delayed diagnosis of TB contribute to excess mortality among people living with HIV [17]. Similarly, concerns about the ability to reliably rule out active TB before initiating IPT have been a major barrier for wider use of this intervention. In the absence of a rapid and effective TB diagnostic tool available at the point-of-care, simple clinical algorithms must be used to screen people living with HIV for TB, dividing them into those in whom active TB is excluded and those who require further evaluation. This meta-analysis synthesizes the best available evidence for how to do this by relying on individual patient data of culture-confirmed TB cases from people living with HIV in the two regions of the world with the most severe burden of the TB and HIV dual epidemic. The major change to existing practice would be the replacement of chronic cough with current cough as a screening question and the addition of other symptoms to standard screening. National TB programs have traditionally defined a TB “suspect” as someone with cough lasting greater than 2 or 3 wk, and designed case-finding activities to investigate up to ten TB suspects for every TB case detected [36]. However, studies included in this analysis have shown that chronic cough is highly insensitive for TB disease in people living with HIV; using this symptom as a screening rule would miss cases and contribute to diagnostic delays [16],[20]. Using the combination of symptoms that we propose, in a population of people living with HIV with a 5% TB prevalence (excluding miners), requires the investigation of 13 extra patients for every TB case detected, a ratio of TB suspects to a TB case not much different from what national TB control programmes target in the general population. There has been ongoing debate about the importance of chest radiography in screening people living with HIV for IPT eligibility [37],[38]. Our analysis showed that the addition of abnormal chest radiography findings into the screening rule of CFSW increases the sensitivity of the rule by 11.7% (90.6% versus 78.9%) with a reduction of specificity by 10.7% (49.6% versus 38.9%). However, for example at a 5% TB prevalence rate among people living with HIV, augmenting the CFSW rule with abnormal chest radiographic findings increases the negative predictive value by a margin of only 1% (98.7% versus 97.8%), albeit with the same number of cases needed to be screened. On the other hand, the addition of abnormal chest radiographic findings to the rule at TB prevalence of 20% among people living with HIV increases the negative predictive value by almost 4% (94.3% versus 90.4%) without additional cases needed to be screened. It is also worth noting that the CFSW screening rule has higher sensitivity among those who presented into a clinical setting (90%) and among those who have not been previously screened for TB (88%). Our findings show that the utility of the proposed symptom-based screening rule have excellent performance in most settings with TB and HIV burden. However, the negative predictive value will fall in those settings with higher TB prevalence when symptom screening alone is used, as it depends on prevalence of disease. In particular settings (e.g., antiretroviral clinics with a very high TB burden [39]), consideration must be given to use of an algorithm that contains chest radiography, or even adding additional sensitive investigations (e.g., culture) while screening people living with HIV for TB [30],[40]. People living with HIV and receiving IPT should also be regularly screened for TB during their visit to a health facility or contact with health care provider so as to promptly detect active TB, if it develops. Programme managers need to weigh the financial, technical, and logistic difficulties, and patient cost and inconvenience associated with performing chest radiography or other additional sensitive investigations on all people living with HIV as part of a screening program compared with an approach that relies only on symptomatic screening. When interpreting our results, one must bear in mind that only a few variables were common to all studies included in the meta-analysis. It is possible that the addition of one or more symptoms not included in our list of common symptoms could have improved the performance of our proposed screening rule. However, at least one study included in our meta-analysis explored over 80 million combinations of about 100 signs and symptoms and found a symptom combination (cough and fever of any duration and night sweats for 3 weeks or longer), which was similar to the one we propose as the best performing one [20]. Furthermore, questions were not asked in a uniform manner across all studies, and the reporting of symptoms can be highly dependent on factors such as the quality of the interview and interviewer, the circumstances under which questions are asked, and the social and cultural factors that shape individual perceptions of symptoms and disease [41]. We reviewed all questions carefully with principal investigators and data managers to ensure accurate mapping of differently phrased questions to common variables. Our study relied on patients drawn from multiple countries and multiple settings, and the variation in the performance of the proposed screening rule across these different settings suggests that variation in patient self-report of symptoms is unlikely to have major impact, at least at the population level. In some studies, only one sputum specimen was collected for culture, while multiple cultures are required to maximize sensitivity. Some patients with TB may have been incorrectly classified as not having TB. Extrapulmonary TB is an important cause of morbidity and mortality in people living with HIV, but most studies included in the meta-analysis focused on screening for pulmonary TB. Young children were not included in the studies. We did not specifically look into the role of tuberculin skin test in the proposed screening rule. Ideally, the utility of the algorithm we propose, based on the screening rule from our meta-analysis, should be studied prospectively using a standardized protocol in multiple diverse sites; this is particularly important as the studies included in our analysis came from only two geographical regions of the world. Similarly, because of the time required for the data aggregation, statistical analysis, manuscript preparation, and publication, there was one potentially eligible study that was not included in our analysis [30]. We believe that the exclusion of this single study from South Africa, a country from which we have included similar studies already, will not affect the interpretation of our data and conclusions. In the future, as more studies are reported, particularly from other regions, it will be important to repeat the meta-analysis. Greatly improving TB screening, diagnosis, and treatment in people living with HIV will require deployment of a rapid, accurate, point-of-care TB diagnostic test. In the absence of such a test, we believe that a standardized algorithm employing symptoms, as we propose here, can improve the diagnosis and treatment of TB for people living with HIV, and by doing so would save many lives. Reliable exclusion of TB disease will facilitate safer initiation of antiretroviral therapy and will allow for broader use of IPT, which can substantially reduce TB incidence. Earlier and accurate HIV and TB screening and treatment may also help identify infectious cases earlier, thereby reducing both HIV and TB transmission. Evidence-based and internationally recommended guidelines should be used to expedite the diagnosis and treatment of TB in people living with HIV [42]. Supporting Information Figure S1 Hierarchical summary relative operating characteristic (HSROC) curves for the 23 candidate 1-of-n rules. (0.08 MB DOC) Click here for additional data file. Table S1 Phrasing of questions that were used in all studies to ask about five common symptoms. (0.09 MB DOC) Click here for additional data file. Table S2 Study-specific values and rankings of the sensitivity of each candidate screening rule in the nine studies included. (0.14 MB DOC) Click here for additional data file. Table S3 Diagnostic performance of all 23 candidate rules and number needed to screen in a hypothetical population of 1,000 people living with HIV stratified by TB prevalence among people living with HIV. (0.15 MB DOC) Click here for additional data file. Table S4 Diagnostic performance of 23 candidate rules that include abnormal chest radiograph and number needed to screen in a hypothetical population of 1,000 people living with HIV stratified by TB prevalence among people living with HIV. (0.15 MB DOC) Click here for additional data file.
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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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              Association between health information, use of protective devices and occurrence of acute health problems in the Prestige oil spill clean-up in Asturias and Cantabria (Spain): a cross-sectional study

              Background This paper examines the association between use of protective devices, frequency of acute health problems and health-protection information received by participants engaged in the Prestige oil spill clean-up in Asturias and Cantabria, Spain. Methods We studied 133 seamen, 135 bird cleaners, 266 volunteers and 265 paid workers selected by random sampling, stratified by type of worker and number of working days. Information was collected by telephone interview conducted in June 2003. The association of interest was summarized, using odds ratios (OR) obtained from logistic regression. Results Health-protection briefing was associated with use of protective devices and clothing. Uninformed subjects registered a significant excess risk of itchy eyes (OR:2.89; 95%CI:1.21–6.90), nausea/vomiting/dizziness (OR:2.25; 95%CI:1.17–4.32) and throat and respiratory problems (OR:2.30; 95%CI:1.15–4.61). There was a noteworthy significant excess risk of headaches (OR:3.86: 95%CI:1.74–8.54) and respiratory problems (OR:2.43; 95%CI:1.02–5.79) among uninformed paid workers. Seamen, the group most exposed to the fuel-oil, were the worst informed and registered the highest frequency of toxicological problems. Conclusion Proper health-protection briefing was associated with greater use of protective devices and lower frequency of health problems. Among seamen, however, the results indicate poorer dissemination of information and the need of specific guidelines for removing fuel-oil at sea.
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                Author and article information

                Contributors
                khadka.priyatam@gmail.com
                januka_thapaliya@yahoo.com
                rbahadurbasnet440@gmail.com
                gokarnarghimire@gmail.com
                j_amatya@hotmail.com
                basistaprijal@gmail.com
                Journal
                BMC Infect Dis
                BMC Infect. Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                30 December 2019
                30 December 2019
                2019
                : 19
                : 1090
                Affiliations
                [1 ]ISNI 0000 0001 2114 6728, GRID grid.80817.36, Medical Microbiology, , Tri-Chandra Multiple Campus, ; Kathmandu, Nepal
                [2 ]ISNI 0000 0004 0635 3456, GRID grid.412809.6, Tribhuvan University Teaching Hospital, ; Kathmandu, Nepal
                [3 ]National Tuberculosis Center (NTC), Bhaktapur, Nepal
                Author information
                http://orcid.org/0000-0002-1525-8130
                Article
                4728
                10.1186/s12879-019-4728-2
                6937953
                31888522
                be37bc54-9931-4d78-a743-5f0362c4439d
                © The Author(s). 2019

                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
                : 11 December 2019
                : 23 December 2019
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Infectious disease & Microbiology
                xpert mtb/rif assay,mycobacterium tuberculosis,line probe assay,mdr-tb,smear-negative

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