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Supplementary appendix

This appendix formed part of the original submission and has been peer reviewed.We post it as supplied by the authors.

Supplement to: Arinaminpathy N, Cordier-Lassalle T, Vijay A, Dye C. The Global DrugFacility and its role in the market for tuberculosis drugs.Lancet2013; published onlineMay 29. http://dx.doi.org/10.1016/S0140-6736(13)60896-X.


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Web Appendix

[NB: citations refer to references provided at the end of this web appendix]

1. Data and methods: further details

For the denominator of Equ.(1) in the main text, TB case notification and burden estimatesfor years 2001 2011 and for 217 countries worldwide were taken from WHO data, available

from: http://www.who.int/tb/country/data/download/en/index1.html (accessed 20 Feb 2013).

Owing to ongoing updates sent by individual countries to WHO, there may be slight

differences between these numbers and those stated in the Global Tuberculosis Control

Report.

For the numerator of Equ.(1) in the main text, GDF procurement data in terms of type and

quantities of drugs were translated to numbers of treatments delivered annually per country,

using standard quantification methodologies based on approved regimens. In brief, for first-

line drugs, a single patient treatment was counted when a specific combination of two or

more core drugs (the latter drawn from the fixed-dose combinations RHZE, RHE, RH, EH)

has been effectively confirmed as delivered in the GDF Order Management System. For

second-line drugs, patient treatments were enumerated using supplied volumes of three

injectable drugs alone: amikacin, kanamycin and capreomycin. These calculations are

performed consistently with the recommended minimum duration of the intensive phase of

second-line treatment, which was increased in 2012 from 6 to 8 months; we adopt the former

in patient treatment calculations upto and including 2011. Where we refer to 2012 numbers in

the main text, we instead adopt the latter. Overall, representing comparatively restrictive

inclusion criteria for patient treatments, these approaches yield conservative estimates for

numbers of treatments, and thus also of GDF market share.

Our analysis concentrates on drugs for treatment, not prophylaxis, and for the purpose of

broad estimates does not distinguish adult and paediatric cases. While TB/HIV coinfection

is a serious epidemiological concern, current clinical guidelines do not distinguish TB

treatment for HIV-positive or negative patients 1. Accordingly we also aggregate these

patients in our analysis; in the case of revisions in these guidelines, however, future work

may explore these distinctions in more detail.

As described below, estimates of MDR-TB burden require separate notification data for new

and retreatment cases, due to differing rates of MDR-TB amongst these groups. Accordingly,

we neglect those notifications classified in the WHO data as other cases not included in new

and re-treatment case numbers, with marginal impact on overall calculations: in 2011, for

example, such cases accounted for 0.7% of all notifications.


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Particular strengths and limitations of the data are as follows: WHO-sourced data and

estimations are publicly available. By convention, however, WHO estimates for incident

cases are quoted as aggregate new and relapse cases: for our purposes this necessitates a

calculation of estimated new and relapse cases separately, as well as of other previously

treated cases (see Estimating new and previously treated cases, below). Second, we also

use WHO estimates for rates of MDR-TB amongst new and previously treated cases (see

below, Potential scale-up scenarios). A limitation is that these estimates were taken at

different years for different countries, and in most cases a single estimate is available for the

period from 2001-2011. Accordingly, for each country we assume a constant rate of MDR-TB

during this time and use the most recent estimate available.

Data for GDF patient treatments is also publicly available, in aggregate form, in the annual

GDF report (see e.g.

http://www.stoptb.org/assets/documents/gdf/whatis/GDF_Annual_Report_2011_web_lowres.pdf, accessed 20 Feb 2013). In quantifying annual patient treatments, however, a limiting

feature of this data is that while drug delivery to a given country may be staggered over

several successive shipments, to optimize the overall shelf life of the delivery the date of

delivery is recorded as the date of the first shipment. Additionally, in capsule form, FLDs can

have a shelf life of up to three years2. Overall then, the GDF supply data can show year-to-

year intermittency, which does not necessarily reflect the actual standing availability of drugs,

at a given time. This intermittency is illustrated in Fig.S2 (dashed lines).

To address this issue, and as a simple proxy for standing availability, we use a moving

average to smooth the GDF supply data. We employ a three-year time window, this being

consistent with maximum FLD shelf-life. Fig.S2 (solid line) illustrates the effect of this

smoothing on the national, regional and global levels.

2. Estimating new and previously treated cases

For calculations in both Fig.5 and Fig.S1, it is required to reformulate WHO estimates (of new

and relapse cases aggregated) in terms of new cases on the one hand, and all previously

treated cases (including relapse) on the other. For clarity, we adopt the following symbols for

estimated cases:

New (not previously treated) cases: N

Relapse cases: R

Other previously treated cases: P


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Thus, estimates for incident cases in WHO data essentially represent the quantity I= (N+ R).

We take the following steps: (i) To estimate N, Rseparately, we follow ref.3and partition the

estimate Iin proportion to the relative notifications for new and relapse cases respectively. (ii)

To estimate P, we scale the estimate for Nusing the ratio of notified, previously-treated, non-

relapse cases, to notified, new cases. (iii) Finally, we take estimates of new cases as N, and

those of previously treated cases as R+ P.

3. Potential scale-up scenarios

Having estimated N, R, Pas above, we next structure by drug-resistant status, writing n for

the total number of new cases that are drug sensitive, and n for the total number having

multi-drug-resistant (MDR) TB. Thus we have n+ n = N. We define r,p, r, pcorrespondingly.

To find these estimates from N, R, P, we use WHO estimates for rates of MDR-TB amongst

new and previously treated cases1

, respectively.

To characterise a scale-up scenario, we include three further parameters: the case detection

rate (d), the proportion of new notified cases tested for drug sensitivity (!N) and the

proportion of previously-treated, notified cases tested for drug sensitivity (!RP).

We then have simply:

Estimated number of first-line treatments required = d(n+ r+p)

Estimated number of second-line treatments required = d[!N

n+ !RP

(r +p)]

Here, we do not count those first-line treatments that would go to misdiagnosed drug-

resistant cases.

On a global level, estimates for detection and diagnostic rates in 2011 are: d = 0.66;

!N = 0.038; !RP = 0.060. As some guide to what scale-ups may be achievable, we borrow

from projections for by the Global Plan to Stop TB 4, and from the DOTS Expansion Working

Group Strategic Plan 5: on the global scale, this scenario involves d = 0.84; !N = 0.2; and

!RP = 1. Although these scenarios are quoted for smear-positive cases, for simplicity we

assume the same rates for smear-negative cases; while this is not currently the case in

practice, it could become feasible with, for example, optimized smear microscopy (eg ref. 6),

or indeed the same molecular diagnostic tools permitting expanded drug sensitivity testing 2,7.


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Figure S1.Relative amounts of case detection and drug sensitivity testing globally, as

of 2011. WHO notifications are shown with a solid line border, while dotted line borders

indicate numbers drawn from WHO estimates and authors own calculations. In particular, for

numbers marked * see calculation described under Web Appendix section 2, Estimating new

and previously treated cases. For numbers marked we take global estimates for MDR-TB

rates amongst new and previously treated cases (3.7% and 20% respectively), and apply

these to the numbers estimated in *. Numbers are indicative only of relative scales, and are

thus given to nearest millions, or thousands as appropriate.

Overall, the figure illustrates that for new and previously treated cases alike, not all estimated

cases are currently notified through public health systems (comparing blue with red areas).

Moreover, notification of multi-drug resistant cases (intersection, orange and green areas) is

limited by case detection rates as well as by low rates of drug sensitivity testing.

New (not previously notified) cases, 2011 Cases previously treated

(relapse + post-default/failure)

All estimated

8.2m*; 1.0m*

All notified

5.5m; 703,000

Tested for sensitivity to

rifampicin and isoniazid130,000; 48,000

All estimated MDR

303,000; 210,000

(All areas shown to scale)


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Figure S2. The effect of smoothing on the national, regional and global levels. Dashed lines

indicate raw numbers for GDF first-line patient treatments supplied, while solid, dot-lines

indicate the effect of centered smoothing using a three-year time window. The upper two

rows illustrate the intermittencies in drug supply; while the spikes indicate the initial entry ofGDF drugs into national TB programmes, they are not directly informative of the number of

drugs actually available at any given time. The smoothing approach seeks to address this:

while it does not perform ideally in all cases (eg Rwanda), it appears to perform somewhat

better from the regional level upwards (bottom row).

2000 2002 2004 2006 2008 20100

2

4

6

8

10

12x 10

5 India

2000 2002 2004 2006 2008 20100

1

2

3

4x 10

4 Afghanistan

2000 2002 2004 2006 2008 20100

0.5

1

1.5

2x 10

5 Nigeria

TotalnumberofpatienttreatmentssuppliedbyGDF

2000 2002 2004 2006 2008 20100

0.5

1

1.5

2x 10

4 Rwanda

2000 2002 2004 2006 2008 20100

5

10

15x 10

5 Regional level

Year

SEA

AFR

EMR

2000 2002 2004 2006 2008 20100

1

2

3x 10

6 Global level

Year

Raw data

Smoothed data


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Figure S3.A breakdown of supplies in GDF first-line drugs, according to whether they were

supplied through grants, or direct procurements (DP). Dashed lines show raw data; solid

lines show smoothed numbers. The figure illustrates an apparent shift from Grants to DP,

from 2007 onwards. Nonetheless, many countries remain dependent on external funding for

quality-assured drugs, with the above trend largely reflecting a transition from grant to Global

Fund support. In particular, in 2011 the Global Fund accounted for the majority (83% by order

value) of direct procurements by individual countries, and the majority (56%) of all drug

orders, including those supplied through grants.

2002 2004 2006 2008 20100

0.5

1

1.5

2

2.5

3

Year

GDFtreatmentssupplied(mil.)

Total

Grant

DP


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Table S2. Summary estimates for TB in 2011, all new and retreatment cases.

Notes

-- Retreatment cases are estimated as in ref.3

-- Estimated incidence of MDR-TB calculated using method described in Web Appendix

section 2, Estimating new and previously treated cases, together with estimated rates of

MDR-TB.

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Web Appendix references

1. World Health Organization (2012). Global Tuberculosis Control 2012.Available from:

http://www.who.int/tb/publications/global_report/en/

2. Agrawal S, Panchagnula R. In vitro evaluation of fixed dose combination tablets of anti-

tuberculosis drugs after real time storage at ambient conditions. Pharmazie2004;59:782-785.

3. Zignol M, Hosseini MS, Wright A et al. Global incidence of multidrug-resistant

tuberculosis. Journal of Infectious Diseases2006; 194:479-485.

4. Stop TB Partnership (2006). The Global Plan to Stop TB 2006 - 2015.Available from:

http://www.stoptb.org/global/plan/main/part2.asp

5. Stop TB Partnership (2006). DOTS Expansion Working Group Strategic Plan.Available

from: http://whqlibdoc.who.int/hq/2006/WHO_HTM_TB_2006.370_eng.pdf

6. Cattamanchi A, Huang L, Worodria W et al. Integrated strategies to optimize sputum

smear microscopy: a prospective observational study.Am J Respir Crit Care Med2011;

183:547-551.7. Boehme CC, Nabeta P, Hillemann D et al. Rapid molecular detection of tuberculosis

and rifampin resistance. N Engl J Med2010; 363:1005-1015.

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