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1
Cost-Effectiveness of Malaria Diagnosis in Sub-Saharan Africa: The
Role of Rapid Diagnostic Tests in Rural Settings with High Plasmodium
falciparum transmission
Additional Web Tables and Figures
Samuel D. Shillcutt1
Chantal M. Morel2
Catherine A. Goodman3
Paul G. Coleman4
David R. Bell5
Christopher J.M. Whitty4
Anne J. Mills6
1. Andean Health and Development/Saludesa, Avenida de La Prensa N 70-78,
Quito, Ecuador
2. Save the Children UK, 1 Saint John's Lane, London EC1M 4AR, UK
3. Health Economics and Financing Programme, London School of Hygiene and
Tropical Medicine and Kenya Medical Research Institute / Wellcome Trust
Research Programme PO Box 43640, Nairobi, 00100 GPO, Kenya
4. Department of Infectious and Tropical Diseases, London School of Hygiene and
Tropical Medicine, Keppel St, London WC1E 7HT, UK
5. World Health Organisation - Regional Office for the Western Pacific, UN
Avenue, Ermita, Manila, Philippines
6. Health Economics and Financing Programme, London School of Hygiene and
Tropical Medicine, Keppel St, London WC1E 7HT, UK
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TABLE OF CONTENTS
Table 1. Definitions of probability parameters
3
Table 2. Definitions of cost parameters
7
Table 3. Definitions of effectiveness parameters
9
Table References
10
Sensitivity Analysis Figures:
Figure 1. Cost of RDTs
12
Figure 2. Cost of an adult dose of ACT
13
Figure 3. Adherence to ACT
14
Figure 4. Adherence to antibiotics
15
Figure 5. Probability Non-Malarial Febrile Illness is bacterial
16
Figure 6. Probability that a patient diagnosed negative for
malaria receives an antibiotic
17
Figure 7. Probability bacterial illness becomes severe
18
Figure 8. Proportion of patients 5 years old or older
19
Figure 9. Valuation of a DALY averted
20
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Web Table 1. Definitions of probability parameters
Probability input variable
P1
Prevalence
Type of
probability
distribution*
Best
estimate
Point estimates

 Sources
Low
estimate
High
estimate
0%
100%
Estimates
50%
70%
Estimates
5%
15%
Demographic parameters
P2
Proportion of febrile outpatients aged ≥5 years
Uniform
Nature of non-malarial febrile illness (NMFIs)
P3
Proportion of NMFI cases that were bacterial
Beta
P4
Probability that a NMFI case received an antibiotic
Point estimate
10%
19.068
167.885
100%
Whitty, Reyburn, Berkley
personal communication
Bell personal communication
(Unofficial WHO assumption
for Africa)
Sensitivity
P5
RDT sensitivity
Beta
P6
Presumptive treatment sensitivity
Point estimate
P7
P8
P9
Microscopy sensitivity (field standard laboratory
conditions)
Specificity
RDT specificity
Presumptive treatment specificity
96%
84%
100%
39.289
2.961
100%
Beta
82%
75%
88%
153.303
34.640
Beta
95%
90%
100%
91.295
5.275
Point estimate
0%
Beadle et al. 1994 (1);
Craig et al. 2002 (2);
Bojang et al. 1999 (3);
Premji et al. 1994 (4);
Bell et al., 2005 (5)
Estimate
Reyburn et al., 2004 (6);
Barat et al., 1999 (7) *****
Bell et al., 2005 (5);
Craig et al., 2002 (2);
Bojang et al., 1999 (3);
WHO, 2000 (8)
Estimate
3
4
P10
Microscopy specificity (field standard laboratory
conditions)
Beta
85%
59%
91%
24.598
6.575
Reyburn et al., 2004 (6);
Barat et al., 1999 (7) *****
40%
91%
10.929
4.427
Depoortere et al., 2004a (9);
Depoortere et al., 2004b (10);
Kachur et al., 2004 (11);
Fogg et al., 2004 (12)
78%
95%
74.101
12.712
60%
90%
32.530
11.167
Adherence and Efficacy
P11
Probability of Adherence - ACT**
Beta
80%
P12
Probability of Adherence - amoxicillin
Beta
P11
P13
ACT efficacy (for malaria)
Beta
85%
P14
Amoxicillin efficacy (for malaria)
Point estimate
0%
P15
Point estimate
Beta
0%
P16
ACT efficacy (for bacterial infection)
Amoxicillin efficacy (for bacterial infection)
75%
P17
ACT efficacy (for viral infection)
Point estimate
0%
P18
Amoxicillin efficacy (for viral infection)
Point estimate
0%
Assumed equal to malaria
Estimate
IASG, 2004 (13);
Lefevre et al., 2001 (14)
Whitty and Greenwood
personal communication
Treatment seeking patterns
P19
Outpatient visit took place at a health centre
Beta
1-P20
P20
Outpatient visit took place at a hospital
Beta
32%
Beta
48%
Beta
P21
P21
P22
Patient with severe illness went to hospital for
inpatient care after treatment failure
Patient with uncomplicated illness returned to clinic
for outpatient care after treatment failure
Aikins et al., 1995 (15)
19%
88%
5.630
5.471
McCombie, 1996 (16)
Disease Progression***
Illness became severe with first-line treatment failure (TF), non-adherence (NA), not treatment (NT), or treated with an incorrect drug ID)
P23
Malaria not effectively treated led to severe
disease (age ≥5)
P24
Malaria not effectively treated led to severe
disease (age <5)
Beta
Beta
1%
0.01%
5%
2.583
118.540
Whitty and Greenwood
personal communication
7.5%
5%
10%
111.226
1202.634
Whitty and Greenwood
personal communication
4
5
P25
Bacterial illness not effectively treated led to
severe disease (age ≥5)
Beta
15%
10%
25%
20.593
104.104
Whitty, Reyburn, Berkley
personal communication
P26
Bacterial illness not effectively treated led to
severe disease (age <5)
Beta
30%
20%
40%
33.218
76.884
Whitty, Reyburn, Berkley
personal communication
P27
Viral illness not effectively treated led to
severe disease (age ≥5)
Point estimate
0%
Whitty and Greenwood
personal communication
P28
Viral illness not effectively treated led to
severe disease (age <5)
Point estimate
0%
Whitty and Greenwood
personal communication
Neurological Sequelae (Probabilities for both inpatients and severe cases that received no treatment)
Malaria
P29
Severe malaria led to neurological sequelae
(age ≥5)
Beta
1.5%
1%
2%
47.551
3095.926
Whitty and Greenwood
personal communication
P30
Severe malaria led to neurological sequelae
(age <5)
Beta
3.5%
2%
5%
29.670
813.477
Whitty and Greenwood
personal communication
Bacterial
P31
Severe bacterial infection led to neurological
sequelae (age ≥5)
Beta
3.8%
1%
7%
8.696
202.934
Whitty and Greenwood
personal communication
P32
Severe bacterial infection led to neurological
sequelae (age <5)
Beta
2%
1.6%
2.4%
128.777
6288.112
Whitty and Greenwood
personal communication
P33
P34
Inpatient mortality after first line treatment
failure****
Inpatient with severe malaria attending an
Inpatient facility died (all ages)
Inpatient with severe bacterial illness attending
an Inpatient facility died (all ages)
10%
5%
15%
19.068
167.885
Beta
Beta
Whitty, Reyburn, Berkley
personal communication
15%
10%
20%
41.345
232.695
Whitty, Reyburn, Berkley
personal communication
Beta
25%
15%
40%
17.182
47.417
Whitty, Reyburn, Berkley
personal communication
Mortality after no formal secondary care
P35
Patient with severe malaria that did not return
for formal care would die (all ages)
P36
Patient with severe bacterial illness that did not Beta
P35
return for formal care would die (all ages)
*Triangular distributions fitted to low, high, and best estimates were used for most parameters. Parameters representing probabilities within the decision tree were represented by beta
distributions bounded by zero and one, which redistribute probability away from the tails of the distribution towards the best estimate.  and  are moments which described the beta
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6
distributions. Lognormal distributions were used for costs, and uniform distributions where no best estimate could be determined. Point estimates were used where no uncertainty existed
around a parameter estimate, uncertainty was impossible to quantify, or methods were required to be consistent with Global Burden of Disease protocols.
**assumed to be a middle estimate between regimen that was co-administered and one that was co-formulated
*** assumed that 10% of patients over 5 were HIV positive
**** mortality rates for children with severe bacterial illness were based on children over 60 days: rates for younger children would be expected to be significantly higher (Berkley, pers.
Comm.)
*****recent evidence suggests that field microscopy sensitivity and specificity may be even lower than estimated here (17)
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Web Table 2. Definitions of cost parameters
Cost input variable
Diagnostic costs
Type of
probability
distribution
C1
Presumptive Treatment
Point estimate
C2
Lateral-Flow RDTs
Uniform
C3
Field-Standard Microscopy
Lognormal
Best
estimate
Low
estimate
High
estimate
Mean
SD
Source
$0.00
$0.53
Estimate
$0.60
$1.00
$0.32
$1.27
$1.00
$2.40
Bell, 2005(5); WHO, 2004 (18)
0.676
1.380
Goodman, 1999 (19);
Goodman et al., 2000 (20)
Drug costs (Adult Doses)
C4
Artemisinin-Based Combination Therapy (ACT)
Uniform
Arrow, 2004 (21)
C5
Antibiotic (Amoxicillin)
Lognormal
$0.71
$0.61
$0.93
0.742
1.100
MSH, 2005 (22); BNF, 2005 (23)
C6
Oral Quinine (10 mg/kg every 8 hours for 7 days)
Lognormal
$3.12
$2.75
$3.67
3.168
1.066
C7
Intravenous Quinine (initial dose - 20 mg/kg over 4 hours)
Lognormal
$0.55
$0.45
$0.67
0.556
1.090
C8
Intravenous Quinine (per day after - 10 mg/kg every 8 hours)
Lognormal
$0.82
$0.68
$1.01
0.834
1.091
C9
Drugs for severe bacterial infection
Point estimate
2*C5
MSH, 1996 (24); WHO, 1995
(25)
MSH, 1996 (24); WHO, 1995
(25)
MSH, 1996 (24); WHO, 1995
(25)
BNF, 2005 (23)
Cost weightings
C10
Child drug dosage as a percentage of adult drug dose
Point estimate
50%
C11
Cost of transport/logistics, insurance and wastage as a % of drug price
RDT training, additional staff time, and quality control as a % of cost
Point estimate
30%
Goodman et al., 2000 (20); BNF,
2005 (23)
Goodman et al., 2000 (20)
Point estimate
10%
Goodman et al., 2000 (20)
C12
Outpatient
C13
C14
Patient cost: Cost of attending an outpatient facility (including
transport but excluding fees and cost of patient and caretaker time)
Proportion of outpatient facility costs that were fixed
Lognormal
Uniform
$0.74
$0.18
$1.68
25%
40%
0.871
1.505
Asenso-Okyere et al., 1997 (26);
Louis et al., 1992 (27) ;
Sauerborn et al., 1991 (28);
Litvack et al., 1993 (29)
Gilson 1992 (30)
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C15
Proportion of outpatient costs that were drugs
Point estimate
C16
Provider cost: Health centre outpatient facility costs per visit
Lognormal
37%
$0.72
Ettling et al., 1992 (31)
$0.34
$1.35
0.798
1.335
Ettling et al., 1992 (31);
Gilson, 1992 (30);
Hanson et al. 1992 (32);
Mills, 1991 (33)
C17
Provider cost: Hospital outpatient facility costs per visit
Lognormal
$3.90
$0.91
$6.08
3.832
1.376
Lognormal
$3.84
$1.11
$9.37
4.724
1.514
Gilson, 1992 (30);
Barnum & Kutzin, 1993 (34);
Kirigia et al., 1998 (35)
Inpatient
C18
Patient cost: Cost of attending inpatient facility (including transport
but excluding fees and cost of patient and caretaker time)
Asenso-Okyere et al., 1997 (27);
CNLP, 1994 (36);
Louis et al., 1992 (27);
Sauerborn et al., 1991 (28);
Litvack et al., 1993 (29)
C19
Proportion of inpatient costs that were drugs
Point estimate
17%
C20
Proportion of inpatient facility costs that were fixed
Uniform
C21
Provider cost: Cost of inpatient facility per day
Lognormal
C22
Average length of stay as an inpatient when died (all illnesses)
Point estimate
2
$14.15
C23
Average length of stay as an inpatient when had severe malaria and
recovered
Point estimate
4.50
C24
Average length of stay as an inpatient when had severe bacterial
infection and recovered
Point estimate
C23
Gilson, 1992 (30)
50%
75%
$4.57
$24.48
Gilson, 1992 (30);
Kirigia et al., 1998 (35)
14.750
1.368
Nelson et al.1995 (37);
Kirigia et al., 1998 (35);
Barnum & Kutzin 1993 (34)
Nelson et al., 1995 (37);
Faye et al., 1996 (38);
Brewster et al., 1989 (39)
Nelson et al. 1995 (37);
Faye et al. 1996 (38)
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Web Table 3. Definitions of effectiveness parameters
Type of
Effectiveness input variable
distribution
Disability weights (viral illness can only be uncomplicated followed by cure)
Best estimate
Low
estimate
High
estimate
Source
E1
Severe illness (age ≥5)
Point estimate
0.25
Estimate
E2
Severe illness (age <5)
Point estimate
0.25
Estimate
E3
Neurological sequelae (age ≥5)
Point estimate
0.473
E4
Neurological sequelae (age <5)
Point estimate
0.473
E5
Uncomplicated illness (age ≥5)
Point estimate
0.172
E6
Uncomplicated illness (age <5)
Point estimate
0.211
E7
Discounted years of life lost (DYLL)
Death at Age 27
Point estimate
24.83
Point estimate
27.47
E8
Death at Age 2
Lopez et al., 2006 (40), based on malaria
neurological sequelae
Lopez et al., 2006 (40), based on malaria
neurological sequelae
Lopez et al., 2006 (40), based on malaria
episode
Lopez et al., 2006 (40), based on malaria
episode
Calculated according to a West African life
table (41)
Calculated according to a West African life
table (41)
Durations of illness (viral illness could only be uncomplicated followed by cure)
E9
Between first and second visits (days)
Point estimate
4
Estimate
E10
Neurological sequelae (years)
Point estimate
21.81
E11
Uncomplicated illness cured after first-line treatment (days)
Point estimate
2
E12
Uncomplicated illness post-treatment failure (days)
Uniform
E13
Uncomplicated illness untreated (days)
E14
Inpatient stay when had severe illness (recovered)
Uniform
Point estimate
C23
E15
Inpatient stay when recovered with NS (days)
Point estimate
10
Brewster et al., 1990 (39)
E16
Inpatient stay when had severe illness (days) (died)
Point estimate
2
Goodman et al., 2000 (20)
E17
Illness severe no second visit (recovered)
Point estimate
2*E20
Estimate
E18
Illness severe no second visit (recovered with NS)
Point estimate
2*E20
Estimate
E19
Malaria severe no second visit (days) (died)
Point estimate
Lopez et al, 2006 (40)
Estimate
14
21
Estimate
E20
2
Estimate
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Probability planes showing sensitivity analyses of the cost-effectiveness of RDTs
compared to PT at different malaria prevalences.
Areas shaded black represent conditions where one strategy was cost-effective relative
to the comparator with 99% certainty. Areas shaded dark grey represent 95%
confidence intervals. Light grey areas represent conditions of uncertainty, where it was
not 95% certain which strategy was cost-effective. The black line through the light grey
region represents conditions of indifference, where it was equally likely that each
strategy was cost-effective.
Figure 1. Cost of RDTs
RDTs became less cost-effective as their cost increased. RDTs were expected to cost
$0.80 in reference case conditions, and the line of 50% certainty corresponded with
reference case results at 81% malaria prevalence.
PT was cost-effective
relative to RDT
$4
$2
RDT was cost-effective
relative to PT
$1
0%
$0
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Malaria Prevalence Among Febrile Outpatients (%)
Cost of RDT (US$)
$3
.
> 99% CostEffective
> 95% CostEffective
< 95% CostEffective
50% CostEffective
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13
Figure 2. Cost of an adult dose of ACT
RDTs became more cost-effective as the cost of an adult dose of ACT increased. This
trend was not uniform because the incremental difference in proportion of patients who
received ACTs between diagnostic strategies became smaller with increasing malaria
prevalence. At low malaria prevalence, everyone received ACT under PT, but the
specificity of RDTs screened out 95% of patients with NMFI, who went on to receive
antibiotics. At high prevalence, almost every patient received ACT with both PT and
RDT diagnosis. Therefore, raising the cost of ACT affected both sides of the decision
tree equally at high prevalence, and the cost-effectiveness outcome was not strongly
affected. ACTs were expected to cost $1.70 in reference case simulations, and the line
of 50% certainty corresponded with reference case results at 81% malaria prevalence.
$4
$3
$2
$1
Cost of ACT (US$)
RDT was cost-effective
relative to PT
.
> 99% CostEffective
> 95% CostEffective
< 95% CostEffective
50% Cost-
0%
$0
Effective
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Malaria Prevalence Among Febrile Outpatients (%)
PT was cost-effective
relative to RDT
13
14
Figure 3. Adherence to ACT
RDTs became less cost-effective as adherence to ACT increased. The main impact of
better adherence to ACTs was that treatment failures were avoided, particularly relevant
in areas of high malaria prevalence. All strategies became less costly as secondary
treatment costs were avoided, and more effective as unnecessary mortality was avoided.
These factors impacted PT more strongly than RDT diagnosis because PT had higher
sensitivity. The trend in cost-effectiveness was not uniform (i.e. contours became more
vertical at higher malaria prevalence) because there was a greater difference in the
proportion of patients that receive ACT as their first line drug at low malaria prevalence
than at high prevalence. An average of 71% of patients adhered to ACT in reference
case simulations, and the line of 50% certainty corresponded with reference case results
at 81% malaria prevalence.
100%
RDT was cost-effective
relative to PT
75%
50%
25%
0%
0%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Malaria Prevalence Among Febrile Outpatients (%)
Adherence to ACT (%)
PT was cost-effective
relative to RDT
.
> 99% CostEffective
> 95% CostEffective
< 95% CostEffective
50% CostEffective
14
15
Figure 4. Adherence to antibiotics
RDTs became more cost-effective as adherence to antibiotics increased. The main
impact of better adherence was that first-line therapy for patients diagnosed with NMFI
was less likely to fail, particularly relevant in areas of low malaria prevalence. This
change had very little impact on costs, highlighting the fact that second-line treatment
of bacterial infection was much less costly than that for malaria (C6-C9). Fewer DALYs
were incurred at low malaria prevalence as adherence improved. The trend in costeffectiveness was not uniform (i.e. contours became more vertical at higher malaria
prevalence) because there was a greater difference in the proportion of patients that
received antibiotics as their first line drug at low malaria prevalence than at high
prevalence. An average of 71% of patients adhered to antibiotics in reference case
simulations, and the line of 50% certainty corresponded with reference case results at
81% malaria prevalence.
100%
75%
50%
25%
0%
Adherence to
Antibiotic (%)
RDT was cost-effective
relative to PT
.
0%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Malaria Prevalence Among Febrile Outpatients (%)
PT was cost-effective
> 99% CostEffective
> 95% CostEffective
< 95% CostEffective
50% CostEffective
relative to RDT
15
16
100%
RDT was cost-effective
relative to PT
75%
50%
25%
Probability NonMalarial Febrile
Illness is Bacterial (%)
Figure 5. Probability Non-Malarial Febrile Illness is Bacterial
RDTs were more cost-effective as a greater proportion of NMFI was bacterial, with the
potential to become severe. Second-line treatment costs and DALYs incurred increased
relatively more for PT as NMFI always went without appropriate first-line treatment. At
all levels of malaria prevalence commonly found in SSA, RDTs were cost-effective
when more than 20% of NMFI was bacterial. Below 34% malaria prevalence, RDTs
were always cost-effective.
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Malaria Prevalence Among Febrile Outpatients (%) PT was cost-effective
.
> 99% CostEffective
> 95% CostEffective
< 95% CostEffective
50% CostEffective
relative to RDT
16
17
Figure 6. Probability that a patient diagnosed negative for malaria receives an
antibiotic
The cost-effectiveness of RDTs increased as more patients received antibiotics, and
NMFI outcomes therefore became less severe. DALYs followed a similar pattern as
adjustments were made to adherence to antibiotics; however, more DALYs were
incurred when patients did not receive antibiotics at all. Costs of RDTs increased as
antibiotic prescriptions increased – no antibiotics were prescribed under PT, so its costs
remained the same. Absence of first line costs accounted for the high cost-effectiveness
of RDTs at low prevalence and low antibiotic prescription.
100%
75%
50%
25%
0%
20%
40%
60%
80%
Malaria Prevalence Among Febrile Outpatients (%)
Probability NonMalarial Febrile
Illness Recieves
Antibiotic (%)
RDT was cost-effective
relative to PT
.
0%
100%
> 99% CostEffective
> 95% CostEffective
< 95% CostEffective
50% CostEffective
PT was cost-effective
relative to RDT
17
18
Figure 7. Severity of Bacterial Illness
Parameter values used in this sensitivity analysis were given in the following table.
Sensitivity analysis on the severity of bacterial illness
Probability that bacterial illness becomes severe
Patient
Low
Moderately Moderate Moderately High
Age
Low
High
P25 5 or over
10.00%
13.75%
17.50%
21.25% 25.00%
P26 Under 5
20.00%
25.00%
30.00%
35.00% 40.00%
RDTs were more cost-effective as bacterial illness became more severe. Second-line
treatment costs and DALYs incurred increased relatively more for PT as NMFI always
went without appropriate first-line treatment. The effects of variation in these
parameters were stronger in areas where NMFI was more prevalent than malaria.
Moderately High Severity
Moderate Severity
Moderately Low Severity
0%
Low Severity .
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Malaria Prevalence Among Febrile Outpatients (%)
PT was cost-effective
relative to RDT
Probability NMFI
Becomes Severe
High Severity
RDT was cost-effective
relative to PT
> 99% CostEffective
> 95% CostEffective
< 95% CostEffective
50% CostEffective
18
19
Figure 8. Proportion of patients 5 years old or older
Increases in cost and DALYs with increases in the proportion of patients five years old
or older were very slight, and had little impact on the decision to change policy from PT
to RDTs. The cost of PT increased slightly more at low malaria prevalence, leading to
the spreading of the 99% confidence contour. When 60% of the population was 5 years
old or older, the line of 50% certainty corresponded with reference case results at 81%
malaria prevalence.
100%
75%
50%
25%
0%
20%
40%
60%
80%
Malaria Prevalence Among Febrile Outpatients (%)
Probability NonMalarial Febrile
Illness Recieves
Antibiotic (%)
RDT was cost-effective
relative to PT
.
0%
100%
> 99% CostEffective
> 95% CostEffective
< 95% CostEffective
50% CostEffective
PT was cost-effective
relative to RDT
19
20
Figure 9. Ceiling ratio (US$)
The sensitivity of cost-effectiveness to variations in the ceiling ratio (λ) was plotted
according to disease prevalence for each of the three comparisons. Cost-effectiveness
was sensitive to changes in λ, particularly below $150/DALY.
$500
$450
RDT was cost-effective
relative to PT
$350
$300
$250
$200
$150
Valuation of a DALY Averted
$400
.
> 99% CostEffective
> 95% CostEffective
< 95% CostEffective
50% CostEffective
$100
$50
0%
20%
40%
60%
80%
Malaria Prevalence Among Febrile Outpatients (%)
$0
100%
PT was cost-effective
relative to RDT
20