| Literature DB >> 30372436 |
Karuna D Sagili1, Malaisamy Muniyandi2, Kayzad Soli Nilgiriwala3, Kalpita S Shringarpure4, Srinath Satyanarayana1, Richard Kirubakaran5, Sarabjit S Chadha1, Prathap Tharyan5.
Abstract
BACKGROUND: Early and accurate diagnosis of tuberculosis is a priority for TB programs globally to initiate treatment early and improve treatment outcomes. Currently, Ziehl-Neelsen (ZN) stain-based microscopy, GeneXpert and Light Emitting Diode-Fluorescence Microscopy (LED-FM) are used for diagnosing pulmonary drug sensitive tuberculosis. Published evidence synthesising the cost-effectiveness of these diagnostic tools is scarce.Entities:
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Year: 2018 PMID: 30372436 PMCID: PMC6205591 DOI: 10.1371/journal.pone.0205233
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Flow diagram indicating the process of selecting the studies for this systematic review on cost-effectiveness of tolls to diagnose pulmonary TB.
Characteristics of the 13 studies included in the review.
| Sr. No. | First Author, Year | Country | Setting | Funding Source | Type of Economic Evaluation | Target Population | Comorbidities | Study Perspective | Reporting of ICER | Study Design | Time Horizon (years) | Sensitivity analysis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Whitelaw, 2011 | South Africa | Urban | European Commission & Canadian Institute of Health Research | CEA | Adults | HIV | Health system | NR | Cross-sectional | 1 | NR |
| 2 | Kelly, 2015 | India | Urban | TB Reach Initiative | CUA | Adults | NR | Health system | Yes | Cohort | 1 | One-way PA |
| 3 | Sohn, 2009 | Thailand | NR | USAID & CDC | CEA | NR | NR | Health system | NR | Cross-sectional | 0.25 | NR |
| 4 | Xia, 2013 | China | NR | BMGF | CEA | NR | NR | Health system | No | Cross-sectional | 1 | NR |
| 5 | Mishra, 2012 | Zambia | NR | NR | CEA | NR | HIV | Health system | Yes | Cohort | NR | NR |
| 6 | Pinto, 2016 | Brazil | NR | Bill & Melinda Gates Foundation | CEA | NR | HIV | Health system | Yes | Cohort | NR | Monte Carlo simulation |
| 7 | You, 2015 | Hongkong | Urban | No funding | CUA | Adults | NR | Health system | Yes | Cohort | 10 | Monte Carlo simulation |
| 8 | Jha, 2016 | South Africa | Urban | Frank & Kathleen Polk Assistant Professorship in Epidemiology | CEA | Adults | NR | Health system | Yes | Model | NR | One-way PA |
| 9 | Andrews, 2012 | South Africa | Peri-Urban | NatioNDl Institute of General Medical Sciences | CUA | NR | HIV | Health system | Yes | Cohort | NR | Two-way PA |
| 10 | Millman, 2013 | USA | Urban | American Lung Association, UCSF-GIVI Centre for AIDS Research, National Institutes of Health & NDtioNDl Centre for Research Resources | CBA | NR | NR | Health system | No | Cohort | 1 | Monte Carlo simulation |
| 11 | Menzies, 2012 | Botswana, Lesotho, Namibia, South Africa & Swaziland | NR | UNITAID & MGH Program in Cancer Outcome & Training | CUA | NR | HIV | Health system | Yes | Model | 10 | Monte Carlo simulation |
| 12 | Vassall, 2017 | South Africa | NR | CUA | Adults | HIV | Health system | Yes | cluster-randomised | 1 | One-way PA | |
| 13 | Tesfaye A, 2017 | Ethiopia | Urban | USAID/TB | CUA | NR | HIV | Health system | Yes | observational quantitative modeling | 10 | One-way PA |
NR–Not reported; PA–Probabilistic analysis
Consensus Health Economic Criteria (CHEC) extended checklist for quality assessment of the included studies.
| Sr. No. | Checklist question | Whitelaw | Kelly | Sohn | Xia | Mishra | Pinto | You | Jha | Andrews | Millman | Menzies | Vassall | Tesfaye | Total (% of Yes) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Is the study population | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 38 |
| 2 | Are competing alternatives | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 100 |
| 3 | Is a | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 100 |
| 4 | Is the economic study design appropriate to the stated objective? | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 100 |
| 5 | Are the structural assumptions and the validation methods of the model properly reported? | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 62 |
| 6 | Is the chosen time horizon | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 77 |
| 7 | Is the actual perspective chosen appropriate? | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 85 |
| 8 | Are | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 85 |
| 9 | Are all costs measured | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 69 |
| 10 | Are costs valued | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 62 |
| 11 | Are | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 85 |
| 12 | Are all outcomes measured appropriately? | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 69 |
| 13 | Are outcomes valued | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 46 |
| 14 | Is | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 62 |
| 15 | Are all future costs and outcomes discounted | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 54 |
| 16 | Are all important variables, whose values are uncertain, | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 69 |
| 17 | Do the conclusions follow from the data reported? | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 100 |
| 18 | Does the study discuss the generalizability of the results to other settings and patient/client groups? | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 69 |
| 19 | Does the article/report indicate that there is no potential conflict of interest of study researcher(s) and funder(s)? | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 46 |
| 20 | Are ethical and distributional issues discussed | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 46 |
| 95 | 75 | ||||||||||||||
| Good | Moderate |
* Conflict of interest present
** Not categorised due to lack of information
Description of cost-effectiveness analyses reported in the included studies.
| Sr. No. | First Author, Year | Country | Economic Evaluation Type | Sample size (Test) | Sample size (ZN) | Model Type | Year Cost | Cost Indicator | Cost of Test | Cost of ZN | Effectiveness Indicator | Effectiveness-Test | Effectiveness-ZN | ICER | ICER Threshold | Sensitivity Analysis | Conclusion |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Whitelaw, 2011 | South Africa | CEA | 345 | 345 | NA | 2009–10 | Average cost per smear | 1.63 | 2.1 | Time per slide (min) | 1.8 | 2.5 | 0.67 | NR | NR | LED-FM microscopy is cheaper |
| 2 | Kelly, 2015 | India | CUA | 21,450 | 14,300 | Decision Tree | 2011–12 | Average cost per smear | 0.31 | 0.21 | DALYs | 27.45 | 40.84 | 14.64 | 1489 | One-way PA | LED-FM is cost effective at high load settings |
| 3 | Sohn, 2009 | Thailand | CEA | 30/day | 30/day | NA | 2007 | Average cost per smear | 1.03 | 1.16 | Time per slide (min) | 1 | 2.4 | 0.09 | NR | NR | LED-FM is cost-effective in resource limited settings |
| 4 | Xia, 2013 | China | CEA | 11,276 | 11,276 | NA | 2013 | Average cost per smear | 1.97 | 2.2 | Time per slide (min) | 2 | 3.4 | 0.16 | NR | NR | LED-FM is cost-effective in peripheral laboratories |
| 5 | Mishra, 2012 | Zambia | CEA | NR | NR | Decision Tree | NM | Cost per case detected | 108.9 | 75.74 | TB cases averted | NR | NR | 252 | NR | NR | - |
| 6 | Pinto, 2016 | Brazil | CUA | NR | NR | Decision Tree | 2014 | Average cost per sample | 14.69 | 3.08 | Additional case diagnosed (%) | 3.9 | NR | 643 | 11,000 | Monte Carlo simulation | Single-sample GeneXpert testing can replace 2-sample sputum smear microscopy test |
| 7 | You, 2015 | Hongkong | CUA | NR | NR | Decision Tree | 2014 | Average cost per sample | 128 | 7.5 | QALYs | NR | NR | 99 | 50,000 | Monte Carlo simulation | Single sample GeneXpert testing during initial assessment of hospitalized patients is highly cost-effective |
| 8 | Jha, 2016 | South Africa | CEA | 1,009 | NR | Economic Model | 2015 | Average cost per sample | 14.45 | 1.59 | Additional case diagnosed | NR | NR | 1,927 | 2,000 | One-way PA | GeneXpert is likely to be highly cost-effective where the level of empiric TB diagnosis is low |
| 9 | Andrews, 2012 | South Africa | CUA | NR | NR | CEPAC | 2010 | Average cost per sample | 21.6 | 4.6 | Years of life saved (YLS) | NR | NR | 5,100 | 21,300 | Two-way PA | Two-sample GeneXpert testing is very cost-effective for screening all individuals initiating ART |
| 10 | Millman, 2013 | USA | CBA | 1,358 | 1,381 | Decision Tree | 2011 | Average cost per sample | 218 | 15 | Reduction in hospitalization | NR | NR | 101.5 | NR | Monte Carlo simulation | GeneXpert provides substantial savings to hospitals in high income countries by reducing overall length of stay |
| 11 | Menzies, 2012 | Botswana, Lesotho, Namibia, South Africa & Swaziland | CUA | 8,92,000 | 8,92,000 | Dynamic compartmental model | 2011 | Average cost per sample | 45 | 31 | DALYs | NR | NR | 959 | 1,000 | Monte Carlo simulation | GeneXpert has the potential to produce a substantial reduction in TB morbidity and mortality |
| 12 | Vassall, 2017 | South Africa | CUA | 2324 | 2332 | NA | 2012 | Average cost per participant | 168.79 | 160.64 | DALYs | NR | NR | 16.37 | NR | One-way PA | Xpert introduction in |
| 13 | Tesfaye A, 2017 | Ethiopia | CUA | 54000 | 113000 | discrete-event simulation | 2014 | annualized cost per DALY averted | NR | NR | DALYs | NR | NR | 127 | 690 | One-way PA | Xpert is considered cost effective |
* ICER calculated; NA = Not Applicable; NR = Not Reported
Key Cost components reported by the studies included in the review.
| Sr. No. | First Author, Year | Country | Lab space | Staff | Training | Equipment | Consumables | Overheads | Disposal | Transport | Checkmarks | Cost of Test | Cost of ZN |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Whitelaw, 2011 | South Africa | ✓ | ✓ | ✕ | ✓ | ✓ | ✓ | ✕ | ✕ | 1.63 | 2.1 | |
| 2 | Kelly, 2015 | India | ✕ | ✕ | ✓ | ✓ | ✓ | ✕ | ✕ | ✕ | 0.31 | 0.21 | |
| 3 | Sohn, 2009 | Thailand | ✓ | ✓ | ✕ | ✓ | ✓ | ✓ | ✕ | ✕ | 1.03 | 1.16 | |
| 4 | Xia, 2013 | China | ✓ | ✓ | ✕ | ✓ | ✓ | ✓ | ✓ | ✕ | 1.97 | 2.2 | |
| 5 | Mishra, 2012 | Zambia | - | - | - | - | - | - | - | - | 108.9 | 75.74 | |
| 6 | Pinto, 2016 | Brazil | ✕ | ✓ | ✕ | ✓ | ✓ | ✓ | ✕ | ✕ | 14.69 | 3.08 | |
| 7 | You, 2015 | Hongkong | ✕ | ✓ | ✕ | ✕ | ✓ | ✕ | ✕ | ✕ | 128 | 7.5 | |
| 8 | Jha, 2016 | South Africa | ✓ | ✓ | ✕ | ✓ | ✓ | ✓ | ✕ | ✕ | 14.45 | 1.59 | |
| 9 | Andrews, 2012 | South Africa | ✕ | ✓ | ✕ | ✕ | ✓ | ✕ | ✕ | ✕ | 21.6 | 4.6 | |
| 10 | Millman, 2013 | USA | ✕ | ✓ | ✕ | ✓ | ✓ | ✓ | ✕ | ✕ | 218 | 15 | |
| 11 | Menzies, 2012 | Botswana, Lesotho, Namibia, South Africa & Swazil& | ✕ | ✓ | ✕ | ✓ | ✓ | ✓ | ✕ | ✓ | 45 | 31 | |
| 12 | Vassall, | South Africa | ✕ | ✓ | ✓ | ✓ | ✓ | ✕ | ✕ | ✓ | 168.79 | 160.64 | |
| 13 | Tesfaye, | Ethiopia | ✕ | ✓ | ✓ | ✓ | ✓ | ✓ | ✕ | ✕ | NR | NR |
Additional health system costs per year over 10 years is used for different algorithms, to calculate ICER value, hence cost per test is not reported. NR = Not reported