You are viewing the site in preview mode

Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Cost analysis of rapid diagnostics for drug-resistant tuberculosis

  • 1, 2Email authorView ORCID ID profile,
  • 1,
  • 1,
  • 3,
  • 4,
  • 5,
  • 4,
  • 4,
  • 6,
  • 7,
  • 3 and
  • 4
BMC Infectious DiseasesBMC series – open, inclusive and trusted201818:102

https://doi.org/10.1186/s12879-018-3013-0

  • Received: 1 May 2017
  • Accepted: 30 January 2018
  • Published:
Open Peer Review reports

Abstract

Background

Growth-based drug susceptibility testing (DST) is the reference standard for diagnosing drug-resistant tuberculosis (TB), but standard time to result (TTR) is typically ≥ 3 weeks. Rapid tests can reduce that TTR to days or hours, but accuracy may be lowered.

In addition to the TTR and test accuracy, the cost of a diagnostic test may affect whether it is adopted in clinical settings. We examine the cost-effectiveness of rapid diagnostics for extremely drug-resistant TB (XDR-TB) in three different high-prevalence settings.

Methods

1128 patients with confirmed TB were enrolled at clinics in Mumbai, India; Chisinau, Moldova; and Port Elizabeth, South Africa. Patient sputum samples underwent DST for first and second line TB drugs using 2 growth-based (MGIT, MODS) and 2 molecular (Pyrosequencing [PSQ], line-probe assays [LPA]) assays. TTR was the primary measure of effectiveness. Sensitivity and specificity were also evaluated. The cost to perform each test at each site was recorded and included test-specific materials, personnel, and equipment costs. Incremental cost-effectiveness ratios were calculated in terms of $/day saved. Sensitivity analyses examine the impact of batch size, equipment, and personnel costs.

Results

Our prior results indicated that the LPA and PSQ returned results in a little over 1 day. Mean cost per sample without equipment or overhead was $23, $28, $33, and $41 for the MODS, MGIT, PSQ, and LPA, respectively. For diagnosing XDR-TB, MODS was the most accurate, followed by PSQ, and LPA. MODS was quicker and less costly than MGIT. PSQ and LPA were considerably faster but cost more than MODS. Batch size and personnel costs were the main drivers of cost variation.

Conclusions

Multiple factors must be weighed when selecting a test for diagnosis of XDR-TB. Rapid tests can greatly improve the time required to diagnose drug-resistant TB, potentially improving treatment success, and preventing the spread of XDR-TB. Faster time to result must be weighed against the potential for reduced accuracy, and increased costs.

Trial registration

ClinicalTrials.gov Identifier: NCT02170441.

Keywords

  • Drug-resistant tuberculosis
  • Diagnosis
  • Cost-effectiveness
  • Time to result

Background

Tuberculosis (TB) remains a leading cause of mortality globally [1]. Drug-resistant strains of TB (DR-TB) have emerged, mostly due to inadequate or incomplete treatment [2]. To diagnose and then appropriately treat DR-TB, it is important to conduct drug susceptibility testing (DST), especially in regions known to have high levels of DR-TB [2, 3]. Effectively diagnosing and treating drug-resistant TB is key to reducing transmission [4], improving treatment outcomes and lowering mortality.

In response to the long time to result (TTR) inherent in conventional reference methods for diagnosing DR-TB, novel, rapid DST methods are becoming available [57]. In addition, an international consortium was established to evaluate microbiological and molecular assays for quickly and efficiently detecting DR-TB [8, 9].

In addition to TTR, there are a number of other important characteristics of DR-TB assays that should be considered, including the accuracy and the cost of new tests [10, 11]. A test that is rapid but not accurate may be of questionable value, and an accurate and rapid test may be inaccessible if it is costly [12]. Thus it is important to consider all three of these characteristics (test accuracy, time to result, and cost of the test) when evaluating novel DR-TB diagnostics [13]. The objective of our study was to compare the cost-effectiveness of three currently available, rapid diagnostic tests to MGIT960 DST, a WHO recommended reference standard [1, 14, 15]. The data will allow experts to gauge whether shorter time to result or increased accuracy is worth additional costs for certain new diagnostic tests.

Methods

Study design

The parent study design and methods have been previously described elsewhere [9]. Briefly, the goal of the overall study was to compare the TTR and the sensitivity/specificity of three rapid tests for diagnosis of extremely drug resistant tuberculosis (XDR-TB) to MGIT DST. XDR-TB is defined as resistance to both isoniazid and rifampicin, as well as any one of the fluoroquinolones, and any one of the injectable anti-TB drugs [1]. The diagnostic tests were run in parallel on study participants at three large TB clinics located in areas of elevated XDR-TB prevalence. Participants suspected of having XDR-TB were enrolled (see Additional file 1: Table S1 for inclusion/exclusion criteria) [9]. Biological specimens and interview data were collected at baseline and 52-week follow-up visits. In addition, medical record reviews were conducted at baseline, 30 days post-enrollment and 52 weeks post-enrollment. Enrollment occurred between June 2012 and June 2013. Participants were not compensated for participation, but travel costs were reimbursed when patients traveled an hour or more for research-related visits.

Study sites

The three study sites were located in Mumbai, India; Port Elizabeth, South Africa; and Chisinau, Moldova.

India

The P.D. Hinduja National Hospital (PD-HNH) and Medical Research Centre (MRC), is a tertiary care center in central Mumbai, India. The Pulmonary Department at the PD-HNH is the busiest in Mumbai and is the referral center for MDR and XDR-TB cases from the Mumbai and the state of Maharashtra. In a previous study of the patient population at this clinic, 80% of samples obtained were found to be resistant to one or more standard TB medications, while 51% were resistant to more than one drug [16].

Moldova

The Phthisiopneumology Institute (PPI) in Chisinau, Moldova is the central unit of the National TB Control Programme. It is a medical consultation, scientific research, and training center that leads all TB patient services across Moldova. Moldova has a high prevalence of drug resistant TB, with 24% of new and 62% of previously treated TB patients having MDR-TB [17].

South Africa

According to the WHO, South Africa has a high number of incident TB cases and a high prevalence of drug-resistant TB [9]. At the Port Elizabeth site, patients were enrolled at six primary health care facilities and one regional hospital. The decentralized enrollment resulted in a lower prevalence of drug resistance at this site [1].

Experience and training of laboratory personnel

Each of the study sites described above had existing clinical laboratories that had been in operation for many years and adhered to international safety standards. The study required each site to identify a laboratory technician with at least one year experience conducting TB testing. Each site had ongoing experience conducting drug-resistance testing for TB using the MGIT and line-probe assay. For these tests, each site was sent a set of samples to test and site results were compared to previously established findings at the coordinating center (UCSD). The lab technician from each site also jointly attended a week-long training for the MODS at the Universidad Peruana Cayetano Heredia in Lima, Peru and a week-long training on Pyrosequencing with Dr. Grace Lin at the California Department of Public Health, Richmond, California. Training consisted of lecture, lab instruction, and hands on training to establish standardized testing procedures across sites.

Inclusion/exclusion criteria

To be eligible for the study, participants had to a) be at least 5 years of age; b) have provided informed consent or had ability and willingness of subject or legal guardian/representative to provide informed consent; c) known to be AFB sputum smear-positive (defined as 1+ or greater within prior 14 days), positive on GeneXpert, or present clinically with high suspicion of active TB and:
  • Had previously received > 1 month of treatment for a prior TB episode or

  • Were failing TB treatment with positive sputum smear or culture after ≥3 months of a standard TB treatment or

  • Had had close contact with a known drug-resistant TB case or

  • Were newly diagnosed with MDR-TB within the last 30 days or

  • Were previously diagnosed with MDR-TB and failed TB treatment with positive sputum smear or culture after ≥3 months of a standard MDR-TB treatment regimen.

  • Exclusion criteria were a) institutionalized; b) unable to provide at least 7.5 ml sputum (1st and 2nd samples combined) or c) had results from second line DST performed within the last 3 months.

Study measures

Effectiveness

The TTR of each assay was the primary effectiveness outcome. TTR was defined as “the number of days from initiation of testing to recording of final results of all seven drugs for each test”. The date was tracked and reported at key steps during assay processing. The sensitivity and specificity of each of the three novel tests (MODS, LPA, PSQ) when compared to the reference standard (MGIT) served as an additional measure of effectiveness. The presence or absence of an interpretable result was also recorded.

Costs

Test-specific materials and personnel costs

The local costs of test-specific laboratory materials and personnel costs were collected at each site using a survey for each diagnostic test using local currency. To ensure accuracy and uniformity of data collection across sites, staff were trained via web- and tele-conferencing on how to complete the surveys. Surveys were completed 3–6 months after enrollment began to allow sites to develop proficiency on any new tests. Costs were listed per batch, and information was gathered on the mean batch size. Site staff were instructed to exclude research-specific costs. When processes were shared across tests, the full cost of that process was counted for each test. Completed surveys were reviewed by investigators at the coordinating site. Missing data or inconsistencies were identified, and sites were asked to clarify the data.

Test-specific equipment costs

Although some equipment was already owned by the study sites, we applied the 2010 purchase price for all required equipment and amortized these costs over a recommended useful life of ten years to be consistent across sites [18].

A per sample cost for test-specific equipment was calculated by dividing the total cost by the projected number of samples over ten years using actual test volume. The cost of test-specific equipment per sample was then calculated. Ranges based on volume from the other study sites and a maximum volume was also estimated.

General lab equipment and overhead costs

In addition to test-specific materials and equipment, each site had existing general laboratory equipment that was used for the study. Examples are standard beakers, storage cabinets, etc. In addition, there were overhead costs related to building space, utilities, and ongoing maintenance. However, it was not possible to accurately separate general lab equipment and overhead costs used for the current study from those used for non-study clinical services. In addition, general equipment and overhead costs were covered through national government health systems at some sites and were not available. Thus, overhead costs were estimated at 69% of the personnel costs required to deliver the intervention. The overhead cost estimate accounts for facilities costs, indirect support personnel, and other typical indirect costs associated with running a medical clinic [19]. This figure is based on data showing that indirect costs are another 69% above medical personnel costs [20]. This method has been used in other cost-effectiveness analyses [21, 22].

Cost analyses

All analyses were conducted from the health care organization perspective. Patient costs were not tracked. All local currency costs were converted to US Dollars using the international currency exchange data reported on XE.com in June 2013. [http://www.xe.com/currencyconverter/] Exchange rates were 1 Dollar = 58.82 Indian Rupees, 12.20 Moldovan Leu, and 9.70 South African Rand. Once converted to US Dollars, personnel costs for India, were $1.52, $1.82, and $4.55/h for an assistant laboratory technician, laboratory technician, and laboratory supervisor, respectively. For Moldova, personnel costs were $1.50, $2.50, or $3.50/h for cleaning personnel, a laboratory technician, and a laboratory supervisor, respectively. For South Africa, all activities were conducted by a laboratory technician at wages of $10.30/h. The mean cost per sample for materials and personnel were calculated separately and then combined in an initial “operations-only” incremental cost-effectiveness analysis. Next, test-specific equipment costs were added to the analysis. A third analysis reflected the addition of overhead costs. Incremental cost-effectiveness ratios (ICER) were calculated for each analysis.

Sensitivity analyses

Sensitivity analyses were used to explore how the incremental cost-effectiveness analysis results changed when inputs were varied. Inputs that either varied across sites or may vary considerably under other conditions included batch size, hourly wage for laboratory personnel, and lifetime samples processed for test-specific equipment. Thus, it is informative to study the impact of these variables on the study results [23]. Sensitivity analyses were conducted by entering the high and low value from the range of values explained below into the Excel spreadsheets used for calculations. Each cost component (materials, personnel, test-specific equipment, and overhead) was then recalculated and combined into total cost/sample. Each 1-way analysis examined the individual sensitivity of results when batch, hourly wage, and equipment costs were varied separately. Next, the high and low values for two of the three variables were added together. Two-way sensitivity analyses were run for paired variables of batch size/mean hourly wage and batch size/mean equipment cost. The first two-way pair is of interest because two study sites had both low mean hourly wage and higher batch sizes. The second two-way pair is of interest because high volume sites are likely to maximize batch size and also have a reduced per sample equipment cost while the opposite is true of sites with low testing volumes. Finally, a three-way sensitivity analysis was conducted, co-varying the range of values for laboratory batch size, mean hourly wage, and mean equipment cost. The sensitivity tornado chart was produced using Microsoft Excel add-on software.

Batch sizes varied across the sites depending on patient volumes at each site. Batches of PSQ were limited to a maximum of 12 per batch. Batches of the MGIT test were limited to eight samples per well. Each sample was tested with all four tests, so batches remained in the range of 5–12 per batch so that one test was not lagging behind. We varied batch sizes from the minimum to the maximum reported in our study. We also varied personnel costs by the ranges reported in our study.

As described above, hourly wages ranged from $1.50 per hour to $10.30 per hour. However, personnel costs were also affected by time spent on each task, and sites with lower wages tended to use multiple levels of staffing.

Because the actual volume of tests performed with our study equipment may have significantly underestimated total potential volume per machine, we varied the volume from the lowest seen at our study sites, to a maximum of 2000 DST tests per year. We estimated that the PSQ could use its 96 wells to test 12 samples x seven drugs and one control approximately every two days, providing DST on about 2000 samples annually. While more than 2000 samples could be tested with the other diagnostic tools, we used the 2000 tests maximum to standardize the comparison across tests.

Results

Operations cost by test

Demographic information for the sample have been published previously [8], and clinical characteristics are available as Additional file 1: Table S1. Operations costs for each test at the three study sites are presented in Table 1. Materials costs varied considerably between sites for the various diagnostic tests. With the exception of the MODS test, India reported higher expenses for consumable laboratory materials. Personnel effort also varied across sites. For the MGIT culture and DST, Moldova spent twice as many hours completing each batch. For all tests, South Africa had the highest cost/sample, mostly because of higher wages as described above. However, South Africa also reported testing the smallest number of samples per batch for most tests, which increased the per sample cost.
Table 1

Operations costs for XDR-TB diagnostic tests by study site (in US $ unless otherwise noted)a

 

MGIT (culture and DST)

LPA (plus and sl)

MODS

PSQ

 

Moldova

India

South Africa

Moldova

India

South Africa

Moldova

India

South Africa

Moldova

India

South Africa

Materials in $

126

274

187

372

525

338

114

42

161

296

353

325

Personnel (in Hours)

8.7

5.7

4.0

12.2

17.0

16.0

6.0

5.5

5.5

11.3

8.5

12.0

Personnel cost $/batch

23

11

41

33

47

165

18

20

57

30

28

124

Total cost$/ batch

149

285

228

405

572

503

132

62

218

326

381

449

# samples/batch

8

8

5

12

12

5

4

6

5

11

12

9

Cost/sample in $

18.56

35.65

45.61

33.73

47.69

100.50

32.88

10.35

43.56

29.64

31.76

49.90

aActual costs were tracked once sites had developed test proficiency (performing a test for at least 3 months)

DST Drug susceptibility testing, LPA Line-probe assay, MGIT Mycobacteria Growth Indicator Tube, MODS Microscopic-observation drug-susceptibility assay, PSQ Pyrosequencing, US United States, XDR-TB Extremely drug-resistant tuberculosis

Test-specific equipment costs

Equipment costs are presented in Table 2. It is noted if equipment was used for more than one diagnostic test. The MGIT and PSQ had large equipment costs because they require expensive machinery while MODS had minimal equipment costs.
Table 2

Test-specific equipment costs (US$)

GCDD Equipment

use 1

use 2

MGIT

LPAs

MODS

PSQ

MGIT machine BD

MGIT

 

47,500

   

Inverted microscope

MODS

   

2888

 

Pyromark 96 ID

PSQ

    

81,086

Pyro Vacuum Workstation

PSQ

    

5053

Pyro Assay Design software

PSQ

    

943

Pyro IdentiFire software

PSQ

    

1734

PCR Workstation

LPA

PSQ

 

3368

 

3367

Ultrasonic waterbath

LPA

PSQ

 

2790

 

2790

Plate shaker 230 V

PSQ

    

734

Block Heater Digital-230 V

LPA

PSQ

 

1323

 

1323

Water bath - heat

LPA

PSQ

 

593

 

593

Uninterruptible power supply

PSQ

    

544

Twincubator (Hain)

LPA

  

2441

  

Ultrapure water filtration

PSQ

    

1167

Mini-Plate Spinner Centrifuge

PSQ

    

1220

Mini Centrifuge

LPA

PSQ

 

771

 

771

Mini Vortex

LPA

PSQ

 

722

 

722

Totals

  

$47,500

$12,008

$2888

$102,048

Mean samples/batch (range)

  

7.0 (5–8)

9.67 (5–12)

5.0 (4–6)

10.67 (9–12)

Samples in equipment lifetime (520 weeks)

  

3640

(2600–4160)

5028

(2600–6240)

2600

(2080–3120)

5547

(4680–6240)

Mean cost/sample

(range by batch size)

  

$13.05

(11.42–18.27)

$2.39

(1.86–4.47)

$1.11

(0.93–1.39)

$18.40 ($16.35–21.81)

Minimum cost/sample

(2000 tests/year)

  

$2.38

$0.60

$0.14

$5.10

LPA Line-probe assay, MGIT Mycobacteria Growth Indicator Tube, MODS Microscopic-observation drug-susceptibility assay, PSQ Pyrosequencing, US United States

Total cost per sample with overhead

Table 3 presents the total costs per sample for each diagnostic test. MODS was cheaper than the MGIT (culture + and DST), with the difference driven by MGIT having a larger equipment cost. The PSQ total costs were slightly higher than MGIT, but considerably less than the LPA tests. The LPA test required little equipment but was more costly due to higher materials and personnel costs associated with conducting both the MTBDRsl and MTBDRplus assays for XDR-TB diagnosis.
Table 3

Mean costs /sample adding various components (US$)

Diagnostic Test

Mean Operating Costs

Test-Specific Equipment Costs

Direct Cost Subtotal

General Equipment and Overhead per sample

Total Cost/sample

MGIT

$33.27

$13.05

$46.32

$4.00

$50.32

LPAs

$60.64

$2.39

$63.03

$9.13

$72.16

MODS

$28.93

$1.11

$30.04

$4.35

$34.39

PSQ

$37.10

$18.40

$55.50

$3.90

$59.40

LPA Line-probe assay, MGIT Mycobacteria Growth Indicator Tube, MODS Microscopic-observation drug-susceptibility assay, PSQ Pyrosequencing, US United States

Incremental cost-effectiveness

The direct cost subtotal (operations plus equipment costs) was chosen for the final cost-effectiveness analysis because it was more accurately measured than overhead costs which were estimated. Table 4 presents the main incremental cost effectiveness analysis and showed that MODS had a lower cost and a shorter TTR than MGIT, thus dominating that comparison. Ranking third in cost per sample, the PSQ was compared to the previous best choice; MODS. PSQ costs $25.46 more per sample than MODS but was 13.2 days faster, producing an ICER of $25.46/13.2 = $1.93/day saved. Finally, the combined LPA tests were more costly than the previous best choice (PSQ), but did not provide additional effectiveness (time to result) for calculating an ICER.
Table 4

Incremental cost-effectiveness of diagnostic tests for XDR-TB

Diagnostic Test

Mean cost /sample ($)

Effectiveness (days to XDR diagnosis)

Incremental cost/sample ($US)

Incremental effectiveness

Incremental cost effectiveness ($/day saved)

MODS

$30.04

14.3 days

MGIT

$46.32

24.7 days

$16.28

dominated

dominated

PSQ

$55.50

1.1 days

$25.46

13.2

$1.93/day saved

LPA plus and sl

$63.03

1.1 days

$7.53

dominated

dominated

LPA Line-probe assay, MDR-TB Multi-Drug Resistant Tuberculosis, MGIT Mycobacteria Growth Indicator Tube, MODS Microscopic-observation drug-susceptibility assay, PSQ Pyrosequencing, US United States, XDR-TB Extremely-Drug Resistant Tuberculosis

Test accuracy

Sensitivity and specificity results for the three rapid tests compared to MGIT DST from sputum for the diagnosis of XDR-TB and MDR-TB are shown in Tables 5 and 6, respectively. Sensitivity and specificity results for the three rapid tests compared to MGIT DST for individual drugs have been previously published [24] but are also available in Additional file 1: Table S2.
Table 5

Agreement between three rapid tests and MGIT for detection of XDR-TB

 

Sensitivity (95% CI)

Specificity (95% CI)

PPV (95% CI)

NPV (95% CI)

LR+ (95% CI)

LR- (95% CI)

% Agreement (95% CI)

XDR

LPA (n = 656)

0.49 (0.36, 0.61)

1.00 (0.99, 1.00)

1.00 (0.87, 1.00)

0.94 (0.92, 0.96)

0.51 (0.41, 0.65)

0.95 (0.93, 0.96)

MODS (n = 674)

0.83 (0.72, 0.91)

1.00 (0.99, 1.00)

0.97 (0.88, 0.99)

0.98 (0.97, 0.99)

251 (63, 1003)

0.17 (0.10, 0.28)

0.98 (0.96, 0.99)

PSQ (n = 538)

0.69 (0.56, 0.79)

1.00 (0.98, 1.00)

0.96 (0.85, 0.99)

0.96 (0.93, 0.97)

162 (40, 651)

0.32 (0.22, 0.45)

0.96 (0.94, 0.97)

CI Confidence Interval, LPA Line-probe assay, LR- Negative Likelihood Ratio, LR+ Positive Likelihood Ratio, MGIT Mycobacteria Growth Indicator Tube, MODS Microscopic-observation drug-susceptibility assay, NPV Negative Predictive Value, PPV Positive Predictive Value, PSQ Pyrosequencing, XDR-TB Extremely-Drug Resistant Tuberculosis

Table 6

Agreement between three rapid tests and MGIT for detection of MDR-TB

  

Sensitivity

(95% CI)

Specificity

(95% CI)

PPV

(95% CI)

NPV

(95% CI)

LR+

(95% CI)

LR-

(95% CI)

% Agreement

(95% CI)

MDR

LPA (n = 656)

0.99 (0.97, 0.99)

1.00 (0.97, 1.00)

1.00 (0.99, 1.00)

0.97 (0.94, 0.99)

217 (31, 1534)

0.01 (0.01, 0.03)

0.99 (0.98, 1.00)

MODS (n = 674)

1.00 (0.98, 1.00)

1.00 (0.97, 1.00)

1.00 (0.99, 1.00)

0.99 (0.96, 1.00)

216 (31, 1527)

0.00 (0.00, 0.02)

1.00 (0.99, 1.00)

PSQ (n = 538)

0.99 (0.97, 0.99)

0.99 (0.95, 1.00)

1.00 (0.98, 1.00)

0.96 (0.91, 0.98)

135 (19, 951)

0.02 (0.01, 0.03)

0.99 (0.97, 0.99)

CI Confidence Interval, LPA Line-probe assay, LR- Negative Likelihood Ratio, LR+ Positive Likelihood Ratio, MDR-TB Multi-Drug Resistant Tuberculosis, MGIT Mycobacteria Growth Indicator Tube, MODS Microscopic-observation drug-susceptibility assay, NPV Negative Predictive Value, PPV Positive Predictive Value, PSQ Pyrosequencing

Sensitivity results

Analysis inputs (batch size, hourly wage, equipment cost) were varied individually and in combination (Fig. 1). When batch size was maximized using our study site data, LPA became less expensive than PSQ and returns the lowest ICER. In almost all scenarios, MODS remains the least expensive test and is faster than MGIT (Table 5). When hourly wage was varied between $2 and $10 per hour, the ICERs changed very little, ranging from $1.88 to $1.81, respectively. Equipment costs play a significant role in PSQ and MGIT costs, and are affected by equipment lifetime volume. In a low volume site, equipment costs for PSQ increase the ICER to $2.19 per day saved. However, if a site conducted 2000 tests per year over the life of the machines, the ICER for the PSQ compared to MODS is reduced to $0.99.
Fig. 1
Fig. 1

Sensitivity Analyses varying main components of the ICER. a LPA ICER ratio relative to the optimal choice of MODS. b PSQ ICER ratio relative to the optimal choice of MODS. c MODS ICER ratio relative to the optimal choice of MGIT

Figure 1 Sensitivity Analysis Graph of the incremental cost/day saved in diagnosis time by laboratory batch size, mean hourly wage, and mean equipment cost (as a result of testing volume). Solid line reflects decreases, and striped line increases, in the incremental cost-effectiveness ratios as analytic components along the y-axis are varied across the range of values observed in the study.

In two-way sensitivity analyses, varying batch size and equipment from high to low volume provides ICERs of $1.14 to $2.19 for PSQ versus MODS. Varying hourly wage and batch size together produces larger swings in the predicted ICERs. Smaller batch sizes and higher wages ($10) result in an ICER of $2.61 per day saved for PSQ versus MODS. With larger batch sizes and lower wages, the LPA tests again becomes less expensive than PSQ, and provides an ICER of $0.70 per day saved (LPA vs. MODS). Finally, in three-way analysis, small batch size, high wages, and low volume equipment use result in the highest cost per day saved ($2.85). while large batches, low wages, and a high sample volume provide the lowest ICER of $0.22, with MGIT becoming less expensive than MODS.

Discussion

Using actual study data, MODS is the least expensive test per sample, and is ten days faster than MGIT DST, with good sensitivity/specificity for MDR-TB, but lower accuracy for XDR-TB. The LPA tests (MTBDRsl and MTBDRplus) and the PSQ provide results in one day, shaving 13 more days off the TTR, but with increased costs. Like MODS, these tests had high sensitivity/specificity for MDR-TB diagnosis, but accuracy drops for XDR-TB.

Despite significantly higher equipment costs per sample, PSQ costs less than LPA overall, especially when volume if high, making it a leading choice for a rapid diagnostic test that can provide a result within one day. Once the PSQ equipment is purchased, the operating costs for this test were about $37/sample in our study, only a few dollars more than MGIT DST with culture and about $9 more per sample than MODS. For clinical sites than cannot afford the PSQ machine, the MODS assay may be a viable and scalable option for detecting XDR-TB in clinical samples. However, MODS may require more intensive biohazard control measures than the molecular assays which do not require growth of TB cultures. However, the difference may be of little impact because most sites would already have safety measures for culture-based testing in place.

When accounting for tests that had to be re-run because of indeterminate or failed tests, we incorporated the proportion of interpretable results for each test by dividing the cost per sample for each test by this ratio, providing a cost per valid result. The PSQ was able to provide a result 84% of the time within 1.1 days, while both the MODS and LPA delivered interpretable results approximately 80% of the time. (Additional file 1: Table S3) Thus, while the rate of indeterminate tests varied by the drug being tested, the PSQ provided significantly more interpretable results overall. This slightly improved the cost per interpretable result for the PSQ relative to the other two tests, potentially enhancing it as a cost-effective choice in some contexts.

To our knowledge, no previous studies have examined the costs of different rapid diagnostic tests for both MDR-TB and XDR-TB, making it hard to interpret our ICER results. However, our cost/sample for the tests are quite similar to those found in other studies. For example, our MGIT DST costs are quite similar to previous figures of about $37 (operations only) [25] and $56 (overhead) [14] found in previous cost studies. The $36 per sample (half of $72) we found was still higher than those found for either LPA test in previous studies ($23–26) [26, 27], primarily because of high costs at our South Africa site (lower volume and batch size with high wages). When omitting South Africa site data, costs become very close to previous results. While previous studies conclude that MODS is a low-cost method for detecting active TB, almost all use only materials costs and do not report a detailed cost analysis [28, 29]. One study estimated the overall costs of using MODS for 1st line drugs and reported costs in Peru around $5 per sample [30]. Our study is the only known study to report a comprehensive cost analysis of MODS for obtaining an XDR diagnosis. Thus, good comparisons were not available for the cost of MODS in our study.

Costs varied considerably across our three study sites. Materials costs were subject to fluctuation because of availability, delivery costs, and currency conversion rates. Personnel costs were about four to five times higher in Port Elizabeth, South Africa than in Moldova or India, partially because they employed a single higher level laboratory technician for the study while the other sites employed multiple levels of staff, including lab assistants. It is notable that the higher paid personnel in South Africa required less time to accomplish most diagnostic tests. Batch sizes also tended to be the smallest in South Africa, due to a lower volume of participants recruited per week, which may be unique to the research environment. The higher costs in South Africa did not result in better test performance on the PSQ [31].

In sensitivity analyses, varying these basic values obtained from our sites, our results were insensitive to most of the assumptions tested, providing a similar result in most cases. A few exceptions worth noting are that, when batch size was maximized by itself or in addition to low hourly wages the LPA became less expensive than PSQ, independent of indeterminate/failed test rates. However, when equipment costs were also minimized through high volume use in addition to max batch size and low hourly wages, the MGIT is the least expensive test and PSQ becomes less expensive than LPA once again. This scenario is not unrealistic for clinical sites in many countries where DR-TB is prevalent and wages are low.

Our study was limited to using TTR and accuracy of the three rapid diagnostic tests as a measure of effectiveness because to properly compare the tests, it was important to conduct all four tests with every study sample. This means treatment decisions and treatment outcomes could not be assigned to a given test. In addition, the sites varied in their familiarity with some tests and thus would vary on which test they used to make treatment decisions. Therefore, we were unable to study the down-stream impact of shorter TTR and/or reductions in sensitivity or specificity on treatment and health outcomes of study patients while comparing the tests. However, a shorter TTR is of obvious importance. In all three of the countries studied, many patient travel significant distances to receive health care from remote locations. With many people traveling 6–12 h or more to receive care, keeping them at the facility or nearby for 1–2 days while DR-TB presence or absence is confirmed may have a major impact on both the spread of the infection as well as on the length and quality of life of the patient that presented.

Other limitations

As part of our study, sites were not eligible for discounts on equipment or materials for conducting certain tests. These discounts can be sizable for developing countries [14], and should be considered when making decisions on diagnostic tools for detecting DR-TB.

The current study did not quantify and report the amount of time it took to train laboratory personnel on each of the four tests because not all tests were new. Thus sites differed in their familiarity with the tests making it difficult to provide an accurate and comparable summary of training time and experience.

While our inclusion of three different sites is a strength, sites varied considerably in the costs they paid for materials, in their personnel structure, and in wages. Thus, using the mean cost/sample across the three sites may not always provide the best comparison when generalizing the results to other clinical settings. Costs by site are presented, allowing readers to make the most appropriate comparisons for their needs. However, even site specific costs are affected by currency exchange rates and changing availability of materials. The site in South Africa was a high volume site but could only conduct all four research tests on a more limited set of samples. Thus, test costs were likely inflated because of the limited volume of samples studied.

Finally, the accuracy of the rapid tests is based on the assumption that MGIT DST is 100% accurate, which is likely not the case. It is possible that one or more of the rapid tests may be more accurate, which would change the results and conclusions. Further research in this area is needed to determine this.

Conclusion

In conclusion, our analysis presents the costs associated with three rapid diagnostic tests with good accuracy for the detection of XDR-TB. MODS typically provided a quicker time to result and was less expensive than MGIT DST. The PSQ and LPA tests both provided results much more rapidly and had similar sensitivity and specificity. However, testing volume, the upfront cost of expensive equipment, and potential discounts for developing countries should be considered when deciding which diagnostic test to use.

Our study demonstrates that there are many different factors that affect the actual cost of conducting rapid tests for XDR-TB in clinical practice. Equipment costs, laboratory materials costs, testing volume, and monetary exchange rates are all very important, as are levels of existing laboratory infrastructure. The estimated costs to conduct each test in our study were very similar to those found in previous studies, confirming the relevance of our results. The results allow clinical sites and organizations to roughly estimate their own costs based on characteristics of the three clinical sites in our study. The rapid diagnostic tests studied offer TB clinics and health organizations a variety of options for improving their time to result for XDR diagnosis, depending on their economic options. Our study forms a solid comparator for future cost-effectiveness studies of XDR-TB diagnostic technologies.

Abbreviations

DR-TB: 

Drug-resistant tuberculosis

DST: 

Drug susceptibility testing

GCDD: 

Global Consortium for Drug-resistant TB Diagnostics

ICER: 

Incremental cost-effectiveness ratios

LPA: 

Line-probe assay

MDR-TB: 

Multi drug-resistant TB

MGIT: 

Mycobacteria Growth Indicator Tube

MODS: 

Microscopic-observation drug-susceptibility assay

PSQ: 

Pyrosequencing

TB: 

Tuberculosis (TB)

TTR: 

Time to result

XDR-TB: 

Extremely drug-resistant TB

Declarations

Acknowledgements

Not applicable.

Funding

The Global Consortium for Drug-resistant TB Diagnostics (GCDD; http://gcdd.ucsd.edu) was funded by the National Institute of Allergy and Infectious Diseases (NIAID) grant U01-AI082229.

Availability of data and materials

In the conduct of this project, a rich collection of specimens and data have been collected. We are in the process of analyzing and reporting this information. Inquiries for information or collaboration should be directed the Principle Investigator (Dr Antonino Catanzaro) using the e mail address, gcdd@ucsd.edu.

Authors’ contributions

Conceived and designed the experiments: EJG TGG TCR DGC RG AC. Performed the experiments: AT CR VC TCV. Analyzed the data: EJG TGG NH AT RLJ DGC. Contributed reagents/materials/analysis tools: EJG TGG NH AC TCR DGC RLJ AT. Wrote the paper: EJG TGG NH DGC AC. Provided oversight to GCDD: TGG AC TCR RG. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study was registered with ClinicalTrials.gov (#NCT02170441) and was reviewed and approved by an academic Human Research Protection Program and each of the study sites. The primary IRB was approved at University of California San Diego Human Research Protection Program. Participation did not include any changes to the standard of care. All study participants or their legal guardian/representative provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This 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.

Authors’ Affiliations

(1)
Department of Family Medicine and Public Health, University of California San Diego, 9500 Gilman Dr, #0994, San Diego, CA, USA
(2)
VA San Diego Healthcare System, San Diego, CA, USA
(3)
Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
(4)
Department of Medicine, University of California, San Diego, CA, USA
(5)
University of Arkansas, Little Rock, USA
(6)
Hinduja National Hospital, Mumbai, India
(7)
Microbiology and Morphology Laboratory, Institute of Phthisiopneumology, Chisinau, Moldova

References

  1. World Health Organization (WHO): Global tuberculosis report 2013. 2013.Google Scholar
  2. Blumberg HM, Burman WJ, Chaisson RE, Daley CL, Etkind SC, Friedman LN, Fujiwara P, Grzemska M, Hopewell PC, Iseman MD, et al. American Thoracic Society/Centers for Disease Control and Prevention/Infectious Diseases Society of America: treatment of tuberculosis. Am J Respir Crit Care Med. 2003;167(4):603–62.View ArticlePubMedGoogle Scholar
  3. Andrews JR, Shah NS, Weissman D, Moll AP, Friedland G, Gandhi NR. Predictors of multidrug- and extensively drug-resistant tuberculosis in a high HIV prevalence community. PLoS One. 2010;5(12):e15735.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Dharmadhikari AS, Mphahlele M, Venter K, Stoltz A, Mathebula R, Masotla T, van der Walt M, Pagano M, Jensen P, Nardell E. Rapid impact of effective treatment on transmission of multidrug-resistant tuberculosis. Int J Tuberc Lung Dis. 2014;18(9):1019–25.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Engstrom A, Morcillo N, Imperiale B, Hoffner SE, Jureen P. Detection of first- and second-line drug resistance in mycobacterium tuberculosis clinical isolates by pyrosequencing. J Clin Microbiol. 2012;50(6):2026–33.View ArticlePubMedPubMed CentralGoogle Scholar
  6. McNerney R, Maeurer M, Abubakar I, Marais B, McHugh TD, Ford N, Weyer K, Lawn S, Grobusch MP, Memish Z, et al. Tuberculosis diagnostics and biomarkers: needs, challenges, recent advances, and opportunities. J Infect Dis. 2012;205(Suppl 2):S147–58.View ArticlePubMedGoogle Scholar
  7. Toosky M, Javid B. Novel diagnostics and therapeutics for drug-resistant tuberculosis. Br Med Bull. 2014;110(1):129–40.View ArticlePubMedGoogle Scholar
  8. Catanzaro A, Rodwell TC, Catanzaro DG, Garfein RS, Jackson RL, Seifert M, Georghiou SB, Trollip A, Groessl E, Hillery N, et al. Performance comparison of three rapid tests for the diagnosis of drug-resistant tuberculosis. PLoS One. 2015;10(8):e0136861.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Hillery N, Groessl EJ, Trollip A, Catanzaro D, Jackson L, Rodwell TC, Garfein RS, Lin SY, Eisenach K, Ganiats TG, et al. The global consortium for drug-resistant tuberculosis diagnostics (GCDD): design of a multi-site, head-to-head study of three rapid tests to detect extensively drug-resistant tuberculosis. Trials. 2014;15(1):434.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Boehme CC, Nicol MP, Nabeta P, Michael JS, Gotuzzo E, Tahirli R, Gler MT, Blakemore R, Worodria W, Gray C, et al. Feasibility, diagnostic accuracy, and effectiveness of decentralised use of the Xpert MTB/RIF test for diagnosis of tuberculosis and multidrug resistance: a multicentre implementation study. Lancet. 2011;377(9776):1495–505.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Pang Y, Li Q, Ou X, Sohn H, Zhang Z, Li J, Xia H, Kam KM, O'Brien RJ, Chi J, et al. Cost-effectiveness comparison of Genechip and conventional drug susceptibility test for detecting multidrug-resistant tuberculosis in China. PLoS One. 2013;8(7):e69267.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Pantoja A, Fitzpatrick C, Vassall A, Weyer K, Floyd K. Xpert MTB/RIF for diagnosis of tuberculosis and drug-resistant tuberculosis: a cost and affordability analysis. Eur Respir J. 2013;42(3):708–20.View ArticlePubMedGoogle Scholar
  13. Dowdy DW, van't Hoog A, Shah M, Cobelens F. Cost-effectiveness of rapid susceptibility testing against second-line drugs for tuberculosis. Int J Tuberc Lung Dis. 2014;18(6):647–54.View ArticlePubMedGoogle Scholar
  14. Balabanova Y, Drobniewski F, Nikolayevskyy V, Kruuner A, Malomanova N, Simak T, Ilyina N, Zakharova S, Lebedeva N, Alexander HL, et al. An integrated approach to rapid diagnosis of tuberculosis and multidrug resistance using liquid culture and molecular methods in Russia. PLoS One. 2009;4(9):e7129.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Rodrigues C, Jani J, Shenai S, Thakkar P, Siddiqi S, Mehta A. Drug susceptibility testing of mycobacterium tuberculosis against second-line drugs using the Bactec MGIT 960 system. Int J Tuberc Lung Dis. 2008;12(12):1449–55.PubMedGoogle Scholar
  16. Almeida D, Rodrigues C, Udwadia ZF, Lalvani A, Gothi GD, Mehta P, Mehta A. Incidence of multidrug-resistant tuberculosis in urban and rural India and implications for prevention. Clin Infect Dis. 2003;36(12):e152–4.View ArticlePubMedGoogle Scholar
  17. Jenkins HE, Plesca V, Ciobanu A, Crudu V, Galusca I, Soltan V, Serbulenco A, Zignol M, Dadu A, Dara M, et al. Assessing spatial heterogeneity of multidrug-resistant tuberculosis in a high-burden country. Eur Respir J. 2013;42(5):1291–301.View ArticlePubMedGoogle Scholar
  18. Useful Lives Table. Office of the Controller. Chicago: Northwestern University; 2013. http://www.northwestern.edu/controller/accounting-services/equipment-inventory/docs/useful-lives-table.xls. Accessed July 2013.
  19. Latimer EA, Becker ER. Incorporating practice costs into the resource-based relative value scale. Med Care. 1992;30(11 Suppl):NS50–60.PubMedGoogle Scholar
  20. Physician Payment Review Commission. Practice expenses under the Medicare fee schedule: a resource-based approach. Washington, DC: Physician Payment Review Commission; 1992.Google Scholar
  21. Groessl EJ, Kaplan RM, Blair SN, Rejeski WJ, Katula JA, King AC, Fielding RA, Glynn NW, Pahor M. A cost analysis of a physical activity intervention for older adults. J Phys Act Health. 2009;6(6):767–74.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Herman WH, Brandle M, Zhang P, Williamson DF, Matulik MJ, Ratner RE, Lachin JM, Engelgau MM. Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program. Diabetes Care. 2003;26(1):36–47.View ArticleGoogle Scholar
  23. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in health and medicine. New York, NY: Oxford University Press; 1996.Google Scholar
  24. Catanzaro A, Rodwell TC, Catanzaro DG, Garfein RS, Jackson RL, Seifert M, Georghiou SB, Trollip A, Groessl EG, Hillery N, et al. Performance comparison of three rapid tests for the diagnosis of drug-resistant tuberculosis. PLoS One. in press;Google Scholar
  25. Ogwang S, Asiimwe BB, Traore H, Mumbowa F, Okwera A, Eisenach KD, Kayes S, Jones-Lopez EC, McNerney R, Worodria W, et al. Comparison of rapid tests for detection of rifampicin-resistant mycobacterium tuberculosis in Kampala, Uganda. BMC Infect Dis. 2009;9:139.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Pooran A, Pieterson E, Davids M, Theron G, Dheda K. What is the cost of diagnosis and management of drug resistant tuberculosis in South Africa? PLoS One. 2013;8(1):e54587.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Shah M, Chihota V, Coetzee G, Churchyard G, Dorman SE. Comparison of laboratory costs of rapid molecular tests and conventional diagnostics for detection of tuberculosis and drug-resistant tuberculosis in South Africa. BMC Infect Dis. 2013;13:352.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Leung E, Minion J, Benedetti A, Pai M, Menzies D. Microcolony culture techniques for tuberculosis diagnosis: a systematic review. Int J Tuberc Lung Dis. 2012;16(1):16–23. i-iiiView ArticlePubMedGoogle Scholar
  29. Moore DA, Evans CA, Gilman RH, Caviedes L, Coronel J, Vivar A, Sanchez E, Pinedo Y, Saravia JC, Salazar C, et al. Microscopic-observation drug-susceptibility assay for the diagnosis of TB. N Engl J Med. 2006;355(15):1539–50.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Solari L, Gutierrez A, Suarez C, Jave O, Castillo E, Yale G, Ascencios L, Quispe N, Valencia E, Suarez V. Cost analysis of rapid methods for diagnosis of multidrug resistant tuberculosis in different epidemiologic groups in Peru. Revista peruana de medicina experimental y salud publica. 2011;28(3):426–31.View ArticlePubMedGoogle Scholar
  31. Georghiou SB, Seifert M, Lin SY, Catanzaro D, Garfein RS, Jackson RL, Crudu V, Rodrigues C, Victor TC, Catanzaro A, et al. Shedding light on the performance of a pyrosequencing assay for drug-resistant tuberculosis diagnosis. BMC Infect Dis. 2016;16:458.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement