CHE Seminar 19th January presented by Dr Pete Dodd, ScHARR, The University of Sheffield

1. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
1
Counting children with TB:
the role of modelling.
CHE seminar, York
Thursday, 19 January 2017
Health Economics & Decision Science
School of Health & Related Research
University of Sheffield

2. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
2
Overview
This talk:
• Step through various aspects of TB natural history and epi
• Burden estimation for paediatric TB
• Drug-resistance patterns in children
• Modelling to inform the burden of latent infection
• Modelling the burden of TB mortality in children

3. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
3
TB history
Figure: TB in the UK (credit: HPA)

4. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
4
TB Globally
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
1990 1995 2000 2005 2010 2015
year
GlobalTBincidence
WHO region
AFR
AMR
EMR
EUR
SEA
WPR
WHO GTB estimates, 2015

5. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
5
TB Globally
TB incidence
(per 100,000y)
0 − 50
50 − 100
100 − 200
200 − 300
300 − 400
500 − 852
No data
WHO GTB estimates, 2015

6. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
6
What makes TB different?
Distinction between infection and disease
Most infections do not result in disease.
Figure: Tuberculin skin test for latent M.tb infection
(credit:Greg Knobloch)

7. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
7
What makes TB different?
Delays to disease
Fast and slow. 0 10 20
Age at infection (years)dp(a,
1·000
0·410
0·130
0·086
0·028
0
1 2 3 4 5
Years since ‘conversion’
“Relativerisk”
1·0
0·8
0·6
utum-positive(d+(a))
1951–53
Value used in model
1960–62
1954–56
1963–66
1957–59
1967–69
(b)
(c)
Figure: Vynnycky & Fine, 1997

8. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
8
What makes TB different?
Reinfection
Reinfection puts individuals at renewed risk of TB disease, with
partial protection from previous infection.
Tuberculosis following infection 261
25
1o
~ 2.5
~ 0,5
~01
<
........ ~2.5%
20.02
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970
Calendar Year
Figure 1, Risk of tuberculosis infection in the Netherlands (1880-1970) (showing annual percentage decreases in
og risk).
The incidence of clinical tuherculosis in the Netherlands, 1951 to 1970
Tuberculosis incidence rates are available in the Netherlands for males and females separately
in each of the above age-groups for each of the years 1951 to 1970. Immigrants (non-Dutch
nationals) and cases of tuberculosis among them were excluded from the incidence data from
1965 onwards (the numbers previously were small). The rates are tabulated for all forms of
tuberculosis, for all forms of pulmonary tuberculosis, for bacillary pulmonary tuberculosis and
for smear-positive pu{monary tuberct~losis, The a~alyses below are coniined to two of these
clinical categories, namely to "all forms of pulmonaw tuberculosis', which is the major type of
the disease, and to 'bacillary pulmonary tuberculosis', namely the epidemiologieally important
sub-group of infectious cases.
Figure: Sutherland et al., 1982

9. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
9
What makes TB different?
Diversity of disease
• non-pulmonary TB
• TB pleurisy
• scrofula
• Pott’s disease
• osseous TB
• TB meningitis
• miliary TB
• etc.
• pulmonary TB
• sputum smear positive
• sputum smear negative
0 10 20
Age at infection (years)
1·000
0·410
0·130
0·086
0·028
0
1 2 3 4 5
Years since ‘conversion’
“Relativerisk”
1·0
0·8
0·6
0·4
0·2
0
0 10 20 30 40 50 60 70 80 90
Age (years)Proportionsputum-positive(d+(a))
1951–53
Value used in model
1960–62
1954–56
1963–66
1957–59
1967–69
(b)
(c)
Fig. 2. (a) Relationship between the risk of developing the first primary disease episode and the age at infection assumed in
the model. The relationship (i) between the risk of developing exogenous disease and the age at reinfection, and (ii) between
the risk of developing endogenous disease and the current age of an individual are assumed to follow this basic pattern. Note
that the rates of disease onset for 10–20 year olds can be expressed in terms of those for individuals aged 0–10 years, and
those aged over 20 years. (b) Observed and assumed relationship between the rate at which individuals experience their first
primary episode}exogenous disease in each year following infection}reinfection relative to that during the first year after
infection}reinfection. These were estimated from the distribution of the time interval between ‘tuberculin conversion’ and
disease onset of those who were tuberculin-negative at the start of the UK MRC BCG trial [34]. The ‘relative risk’ for a given
year after ‘conversion’ is taken to be the ratio between: (i) the proportion of the total disease incidence among initially
tuberculin-negative individuals which occurred in that year following ‘conversion’, and (ii) the corresponding proportion
which occurred during the first year after ‘conversion’. (c) Observed and assumed proportion of total respiratory disease
incidence among cases of age a attributable to sputum-positive forms, d+
(a). All lines (excluding the heavy solid line) show
the relative contribution of sputum-positive disease to age-specific notifications of pulmonary tuberculosis in males in
Norway (1951–69). Source: Dr K. Styblo (TSRU) and Dr K. Bjartveit (Norwegian National Health Screening Service).
Figure: Vynnycky & Fine, 1997

10. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
10
What makes TB different?
Duration of disease
Figure: Indian National Tuberculosis Institute, Bull. WHO (51), 1974.

11. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
11
What makes TB different?
TB outcomes without treatment
Tiemersma et al:
• Duration of about 3 years
• Case-fatality of:
• 30% if smear negative
• 70% if smear negative
0 2 4 6 8 10
0.00.20.40.60.81.0
Comparison with 1930s England
time in years
proportion
NTI alive
NTI prevalent
Chakraborty alive
Chakraborty prevalent
India alive fit
India prevalence fit
Tattersal
Thompson
2/5/15yr 50% rule

12. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
12
TB in Africa
q
q
q
q
q
qq
q
q
q
q
q
q
q
q
q
q
q
q
qq
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
Algeria
Angola
Benin
Botswana
Burkina Faso
BurundiCabo Verde
Cameroon
Central African Republic
Chad
Congo
C..te d'Ivoire
Democratic Republic of the Congo
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
GuineaGuinea−Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Rwanda
Sao Tome and PrincipeSenegal
Sierra Leone
South Africa
Swaziland
Togo
Uganda
United Republic of Tanzania
Zambia
Zimbabwe
0
250
500
750
1000
0 25 50 75 100
HIV prevalence in incident TB (%)
TBincidence(per100,000peryear)
1990

13. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
13
HIV
Incidence
Figure: Williams & Dye, 2003

14. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
14
HIV
Disease without TB treatment
• shorter in duration
• more likely to result in death
• less infectious
Outcomes with TB treatment are still good…

15. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
15
What makes TB different?
Summary:
• Distinction between infection and disease:
• most infections do not result in disease
• disease may result after a long delay
• but is more likely after a short one
• with age-dependent characteristics
• Reinfection:
• individuals may be infected again
• but appear to have partial protection from developing
disease
• heterogeneity in infectiousness (& diagnosability)
• interactions with HIV (& diabetes, BMI, smoking, …)
• rareness and difficulty in diagnosing infection →
uncertainty in epi/natural history

16. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
16
Styblo
Figure: Karel Styblo,
1921-1998: the ‘father
of TB control’
The canonical picture
• deaths : incidence : prevalence
in ratio
1 : 2 : 4 for smear positive TB
I.e. CFR≈ 50% & duration ≈ 2 years

17. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
16
Styblo
Figure: Karel Styblo,
1921-1998: the ‘father
of TB control’
The canonical picture
• deaths : incidence : prevalence
in ratio
1 : 2 : 4 for smear positive TB
I.e. CFR≈ 50% & duration ≈ 2 years
• ARI of 1% corresponds with
smr+ incidence 50/100,00 per year
I.e.
β ≈ (1%/y)/(2y × 50 × 10−5
/y) = 10 y−1

18. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
16
Styblo
Figure: Karel Styblo,
1921-1998: the ‘father
of TB control’
The canonical picture
• deaths : incidence : prevalence
in ratio
1 : 2 : 4 for smear positive TB
I.e. CFR≈ 50% & duration ≈ 2 years
• ARI of 1% corresponds with
smr+ incidence 50/100,00 per year
I.e.
β ≈ (1%/y)/(2y × 50 × 10−5
/y) = 10 y−1
• With a 10% lifetime risk of disease, 50%
of it smear positive
1 smr+ case→ 20 infections → 2 cases =
1 smr+ case
I.e. Stable situation, Rn = 1

19. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
17
TB in children‡
‡with James Seddon @ Imperial

20. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
18
Background
Tuberculosis in kids
• Children are at higher risk of developing disease soon after
infection
• Children are at higher risk of extrapulmonary disease,
and are much less infectious
• Difficult diagnosis & low chance of transmission has led to
neglect by public health community
• 1st
time 2012:
GTB @ WHO estimate global burden for kids

21. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
19
TB Globally
among
s been
t of
in
uch
me
burden
or
e first
s of the
490 000
s
methods
needed
nd
o
hildren,
similar results). WHO does not request age-disaggregated data for
relapse cases or those reported as of unknown treatment history; the
number of children in these categories was assumed to be zero.
To estimate TB incidence among children, it was assumed that the
ratio of notified to incident cases at the global level in 2011 (best
estimate 66%, range 64%–69%) was the same for adults and
children. On this basis, TB incidence among children was estimated at
490 000 (range, 470 000–510 000) in 2011, equivalent to about 6%
of the total number of 8.7 million incident cases.
Limitations of the methods used include:
I The assumption that the ratio of notified to incident cases is the
same for adults and children, in the absence of any data on levels
of under-reporting of diagnosed cases for children and adults
separately;
I The assumption that reported cases were true cases of TB.
Misdiagnosis is possible, especially given the difficulties of
diagnosing TB in children; and
I The proportion of cases among children may be different in
countries for which age-disaggregated data are not available.
Figure: WHO TB report 2012

22. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
20
Overview∗

23. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
20
Overview∗
Comments:
• Starting from adult estimates allows comparison with
direct estimates from child notifications.
• Uncertainty in every stage needs to be appropriately
described and propagated.

24. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
21
Overview

25. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
22
Data inputs

26. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
23
Demography
Afghanistan Bangladesh Brazil Cambodia China
Democratic Republic of Congo Ethiopia India Indonesia Kenya
Mozambique Myanmar Nigeria Pakistan Philippines
Russian Federation South Africa Thailand Uganda United Republic of Tanzania
Viet Nam Zimbabwe
0
1000
2000
3000
4000
5000
0
5000
10000
15000
0
5000
10000
15000
0
500
1000
1500
0e+00
5e+04
1e+05
0
3000
6000
9000
0
5000
10000
0
25000
50000
75000
100000
125000
0
5000
10000
15000
20000
25000
0
2000
4000
6000
0
1000
2000
3000
4000
0
1000
2000
3000
4000
5000
0
10000
20000
0
5000
10000
15000
20000
0
3000
6000
9000
0
4000
8000
12000
0
2000
4000
0
2000
4000
0
2000
4000
6000
0
2000
4000
6000
8000
0
2500
5000
7500
0
500
1000
1500
2000
0−4
5−9
10−14
15−19
20−24
25−29
30−34
35−39
40−44
45−49
50−54
55−59
60−64
65−69
70−74
75−79
80−
0−4
5−9
10−14
15−19
20−24
25−29
30−34
35−39
40−44
45−49
50−54
55−59
60−64
65−69
70−74
75−79
80−
Age
Number(thousands)
2010
data: UN ESA Population Division

27. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
24
TB prevalence
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
Afghanistan
Bangladesh
Brazil
Cambodia
China
Democratic Republic of Congo
Ethiopia
India
Indonesia
Kenya
Mozambique
Myanmar
Nigeria
Pakistan
Philippines
Russian Federation
South Africa
Thailand
Uganda
United Republic of Tanzania
Viet Nam
Zimbabwe
0 500 1000
TB prevalence per 100,000
country
2010
data: WHO

28. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
25
Infection risks

29. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
26
Infection risks
0.00
0.05
0.10
0.15
0.20
0 5 10 15
Transmission parameter, β
density
Styblo/β
• Styblo’s rule of thumb was that a
smear-positive case of TB infected
about 10 individuals per year
(β ∼ 10yr−1
)
• Modern reviews of the
ARI:prevalence ratio have found
lower values (e.g. Bourdin Trunz
et al., van Leth et al.).

30. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
27
Progression

31. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
28
Progression
Risks of disease following infection
Separated by 5 age groups and type of disease:
age quantity median LQ UQ
0 probability of disease 0.500 0.298 0.702
1 probability of disease 0.215 0.108 0.360
2-4 probability of disease 0.016 0.002 0.064
5-9 probability of disease 0.001 0.000 0.013
10-14 probability of disease 0.110 0.043 0.219
0 probability disease is EP 0.255 0.112 0.451
1 probability disease is EP 0.295 0.107 0.557
2-4 probability disease is EP 0.060 0.017 0.145
5-9 probability disease is EP 0.085 0.029 0.183
10-14 probability disease is EP 0.000 0.000 0.008
distributions based on Marais et al., 2004 review of the
pre-chemotherapy literature.

32. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
29
BCG
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
Afghanistan
Bangladesh
Brazil
Cambodia
China
Democratic Republic of Congo
Ethiopia
India
Indonesia
Kenya
Mozambique
Myanmar
Nigeria
Pakistan
Philippines
Russian Federation
South Africa
Thailand
Uganda
United Republic of Tanzania
Viet Nam
Zimbabwe
70 80 90 100
% BCG coverage in 2010
country
data: WHO

33. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
30
BCG
Modelling approach
• We modelled BCG protection for EPTB ≥ PTB
quantity median LQ UQ
Protection against EPTB 70% 52% 84%
Protection against PTB 54% 38% 69%
• We considered a model variant where 41% of protection
waned towards the equator.

34. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
31
HIV
q
q
q
q
q
q
q
q
q
Democratic Republic of Congo
Ethiopia
Kenya
Mozambique
Nigeria
South Africa
Uganda
United Republic of Tanzania
Zimbabwe
1 2 3 4
Percent of those aged <15 years with HIV (%)
country
Modelling approach
• Effect modelled as an IRR:
median LQ UQ
20.000 10.118 39.261
based on few studies.
• Age distribution, ART, household
clustering all neglected.
HIV prevalence data: UNAIDS AIDSinfo

35. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
32
Results

36. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
33
TB incidence: top 50 countries, 2014
Russian Federation
Democratic People's Republic of Korea
South Sudan
Canada
C..te d'Ivoire
Brazil
Sudan
Viet Nam
Zambia
Somalia
Uganda
United Republic of Tanzania
Afghanistan
Zimbabwe
Madagascar
Angola
Myanmar
Kenya
Cameroon
Mozambique
Ethiopia
Philippines
Bangladesh
South Africa
Democratic Republic of the Congo
China
Pakistan
Nigeria
Indonesia
India
0 50,000 100,000 150,000 200,000
Total paediatric TB incidence (per year)
country
40
60
80
bcg

37. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
34
Comparison with notifications: 22 HBC, 2010
n adults and
are likely to
uberculosis,
est a global
rediction for
ns that could
ugh not all
e efficacy of
o progress to
e thought to
creening of
ult who has
ment of child
reduce the
se. The large
present the
e to identify
burden are
HO report,6
s of Nelson
e from more
of 1 million
4,25
and the
comparison
gests under-
unger than
notification
e. Further
Figure 3:Violin plot comparing model estimates of paediatric tuberculosis incidence in 2010, with numbers
of cases in each age group reported toWHO by each country
Dots show the numbers of cases reported toWHO.The absence of a dot means paediatric notifications were not
reported by that country in 2010.The violins give a visual representation of the range and distribution of model
estimates for each country on the basis of the community model. DR=Democratic Republic.
Zimbabwe
Vietnam
Tanzania
Uganda
Thailand
South Africa
Russia
Philippines
Pakistan
Nigeria
Burma
Mozambique
Kenya
Indonesia
India
Ethiopia
DR Congo
China
Cambodia
Brazil
Bangladesh
Afghanistan
1 100 10000 1000000 1 100 10000 1000000
Country
Number of new paediatric tuberculosis
cases per year (log scale)
Number of new paediatric tuberculosis
cases per year (log scale)
0–5 5–15
Age group (years)
Suggests a CDR of 35% (IQR 23% - 54%)
notification data: WHO

38. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
35
Pattern by incidence
We did not have data for the a
infection in children, antiretrovir
CD4 cell count in infected indiv
infection as one risk factor uni
children. Exposure to M tubercu
not affected by HIV in our model,
clustering of HIV means that chil
could be expected to have more e
than do children without HIV i
crudeness of the approach to
conclusions for countries with
should be treated with caution,
contributes to 5% of the total inc
issue is likely have little effect on t
The efficacy of BCG vaccinat
recorded variability remain contr
affected by the variation in va
addition to incorporation of unce
characterise the efficacy of BC
pulmonary and extrapulmon
considered structural model va
BCG efficacy by latitude. This ap
interpretation that perceived varia
efficacy is due to masking by h
from non-tuberculous mycobac
estimates were 27% lower u
(infection estimates were unaffec
Neither our model nor the stud
of progression were based
Figure 5: Proportion of total tuberculosis burden that occurs in children and tuberculosis incidence in 2010
for 22 high-burden countries
Proportions are based on mean model estimates from the community method. Incidences are based on WHO
point estimates. DR=Democratic Republic.
Afghanistan
Bangladesh
Brazil
Cambodia
China
DR Congo
Ethiopia
India
Indonesia
Kenya
Mozambique
Burma
Pakistan
Philippines
Russia
South Africa
Thailand
Uganda
Tanzania
Vietnam
Zimbabwe
0 300 600 900
0
5
10
15
20
Proportionoftuberculosisincidenceinchildrenaged<15years(%)
Tuberculosis incidence per 100000 per year

39. Pete Dodd
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Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
36
Results
Summary (2014)
quantity measure median LQ UQ
infection incidence/yr 9.0 million 6.9 million 11.7 million
infection prevalence 62.9 million 49.1 million 81.1 million
disease incidence/yr 827,000 547,000 1,238,000
• Method used in combination with notification-based
approach for WHO estimates in past 2 years.
• Most recently, estimate was 1 million (0.9m - 1.1m) cases in
children.
• Only around 1/3 of children with TB are diagnosed and
notified

40. Pete Dodd
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Overview
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Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
37
Limitations (& opportunities)
Many limitations…
Key limitations
• Inherits any limitations of WHO TB prevalence estimates.
• Uncertainties in parameters around progression and
transmission.
• Variation in BCG efficacy.
• Simplified treatment of HIV.

41. Pete Dodd
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Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
38
Drug-resistant TB
in children*
*with Babis Sismanidis @ WHO & James Seddon @ Imperial

42. Pete Dodd
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Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
39
Drug-resistance
Background
• Around 5% of all incident TB is multi-drug resistant (MDR)
• ∼ 111K of 480K were started on appropriate treatment
• Cost of treatment 50 - 200 × higher than DS-TB
Approach
• Surveillance & survey data on DR in adults
• Sample uncertainty from numbers
• Nearest-neighbour based resampling when data missing

43. Pete Dodd
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TB in kids
Overview
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Results
Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
40
Drug resistance definitions
1st
-line drug resistance types (TB = DS + HMR + RMR + MDR)
• DS - susceptible to isoniazid and rifampicin;
• HMR - isoniazid mono-resistant;
• RMR - rifampicin mono-resistant;
• MDR - multidrug-resistant (resistant to at least isoniazid
and rifampicin);
2nd
-line drug resistance types (MDR = MDR# + FQR + SLR + XDR)
• MDR# - only resistant to isoniazid and rifampicin;
• FQR – MDR# with additional resistance to ≥1
fluoroquinolone but not any second-line injectables;
• SLR – MDR# with additional resistance to ≥1 second-line
injectable but not any fluoroquinolone;
• XDR – MDR# with additional resistance to ≥1
fluoroquinolone and to ≥1 second-line injectable.

44. Pete Dodd
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Drug-resistant TB
Definitions & data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
41
Drug-resistance
RMR HMRMDR# XDR
SLR
FQR
DS
All TB MDR TB
Rifampicin resistant TB Isoniazid resistant TB
Resistant to second−line
injectables
Resistant to
fluoroquinolones

45. Pete Dodd
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TB in kids
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Drug-resistant TB
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Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
42
Approach to TB drug resistance in kids
General approach
• Only up to around 1/3 of TB in children culture confirmed;
hardly any direct data on DR-TB in children
• → use proportion of DR-TB types in treatment-naive adults
as proxy
• analyse data on DR-TB in adults and combine with global
model of infection and disease in children
Other considerations
• Include sample uncertainty from counts
• Consider years 2005-2014

46. Pete Dodd
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Drug-resistant TB
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Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
43
WHO Global Project on Anti-tuberculosis Drug Resistance
Surveillance data†
First-line resistance in treatment-naive patients
• Survey data from 82 countries = 166 country-years with
complete data on HMR, RMR and MDR
• Surveillance data from 87 countries = 627 country- years
with complete data on HMR, RMR and MDR,
• & a further 288 country-years with data on only MDR
resistance
Second-line resistance (90 countries)
• 33 country-years from surveys; 273 country-years from
surveillance
• 227 country-years with complete data
• 40 country-years with only data on XDR and FQR
resistance; 43 country-years with only data on XDR
resistance.
†with thanks for guidance!

47. Pete Dodd
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Definitions & data
Algorithm
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Latent TB
ARI trends
Prevalence
Mortality
Conclusion
44
Algorithm
For each country:
1 data : Bayesian flat Dirichlet-multinomial (including
missing data);
2 data ; ≥ 2 of 5 nearest neighbours : sample neighbour
→ 1);
3 data ; ≥ 2 of 5 nearest neighbours : sample country
from same epidemiological region → 1);
4 data ; ≥ 2 of 5 nearest neighbours ; epidemiological
region : chose a country with data globally → 1).

48. Pete Dodd
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HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
45
Proportions by WHO region
0
25
50
75
100
AFR AMR EMR EUR SEA WPR GLOBAL
WHO region
Proportionofincidenttuberculosisinchildren(%)
DR type
DS
HMR
RMR
MDR
0
25
50
75
100
AFR AMR EMR EUR SEA WPR GLOBAL
WHO region
ProportionofincidentMDRtuberculosisinchildren(%)
MDR type
MDR#
FQR
SLR
XDR
Overall:
• 7.0% (IQR: 6.7% – 7.3%) HMR
• 2.9% (IQR: 2.8% – 3.1%) MDR
• 4.7% (IQR: 4.2% – 5.1%) of MDR is XDR

49. Pete Dodd
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Drug-resistant TB
Definitions data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
46
Proportions by region (first-line)
Percent
any resistance
1.4 to 7.5
7.5 to 14
14 to 24
24 to 37
37 to 58
Percent
INH−MR
0.67 to 3.4
3.4 to 5.6
5.6 to 8.8
8.8 to 15
15 to 26
Percent
RIF−MR
0.049 to 0.81
0.81 to 2.7
2.7 to 5.4
5.4 to 9.5
9.5 to 14
Percent
MDR
0.12 to 1.9
1.9 to 4.2
4.2 to 8.7
8.7 to 19
19 to 34

50. Pete Dodd
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Mortality
Conclusion
47
MDR: incidence and proportion
10
20
30
MDR (%)

51. Pete Dodd
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Latent TB
ARI trends
Prevalence
Mortality
Conclusion
48
Results in numbers
Incidence in children by first-line resistance
Region All TB DS HMR RMR MDR
AFR 338,000 [218,000 - 509,000] 309,000 [200,000 - 466,000] 16,900 [10,800 - 25,800] 2,990 [1,910 - 4,590] 8,100 [5,190 - 12,500]
AMR 25,000 [16,100 - 38,500] 22,500 [14,500 - 34,700] 1,400 [870 - 2,220] 391 [210 - 742] 523 [331 - 818]
EMR 75,700 [49,700 - 114,000] 64,000 [42,000 - 96,500] 6,680 [4,310 - 10,100] 1,340 [839 - 2,100] 3,360 [2,170 - 5,250]
EUR 13,500 [8,690 - 21,000] 8,320 [5,310 - 12,900] 1,860 [1,130 - 3,060] 743 [386 - 1,540] 2,100 [1,320 - 3,320]
SEA 294,000 [190,000 - 455,000] 264,000 [171,000 - 410,000] 21,300 [13,700 - 33,000] 1,810 [1,170 - 2,830] 6,370 [4,110 - 9,920]
WPR 91,800 [60,400 - 139,000] 77,200 [50,600 - 117,000] 9,840 [6,440 - 14,900] 1,310 [840 - 2,040] 3,530 [2,340 - 5,410]
GLOBAL 847,000 [558,000 - 1,280,000] 753,000 [497,000 - 1,140,000] 59,100 [38,900 - 89,500] 9,220 [5,990 - 14,100] 24,800 [16,200 - 37,400]
Latent infections:
• 4.9 million (IQR: 3.8 million – 6.3 million) HMR infections
• 2.0 million (IQR: 1.6 million – 2.6 million) MDR infections.

52. Pete Dodd
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Drug-resistant TB
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Latent TB
ARI trends
Prevalence
Mortality
Conclusion
49
Latent TB‡
‡work with Rein Houben @LSHTM

53. Pete Dodd
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Drug-resistant TB
Definitions data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
50
Latent TB
Background motivation
• Common statement that 1/3 of the world’s population is
latently infected with M.tb (LTBI)
• Estimate 20 years old; lots has changed!
• Decreasing prevalence of TB →
increased emphasis on LTBI
• Above estimates in children neglect trends in ARI

54. Pete Dodd
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TB in kids
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Results
Drug-resistant TB
Definitions data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
51
Approach
Reconstruction of historical infection risks
• Two sources of data:
• direct estimates from TST surveys
• indirect estimates: prevalence Styblo’s ratio
• Influences of age-mix, HIV
• Uncertainty
• Gaussian process regression
• Combine with demographic data

55. Pete Dodd
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Drug-resistant TB
Definitions data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
52
Approach: TST surveys
Uncertainty
Older TST surveys don’t report uncertainty in ARI:
LL =
∏
i
Bin(Ni, ki|(1 − e−λai
))
∂2
LL
∂λ2
≲
N¯a
λ
when 1 − exp(−λ.a) ≈ a to conservatively quantify precision
from reported data.

56. Pete Dodd
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Drug-resistant TB
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Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
53
Approach: indirect/Styblo ratio
Prevalence → infection
Driven by WHO prevalence estimates uncertainty, with
age/HIV influencing infectiousness:
• Country/year CDR durations for notified/un-notified ×
HIV ± durations → fraction of prevalent TB HIV ±
• age disaggregations systematic review on smear
positivity by age

57. Pete Dodd
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Results
Drug-resistant TB
Definitions data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
54
Approach: modelling ARI
Gaussian process regression
log(λt) ∼ GP(m(t), k(., .))
m(t) = c0 + c1.t
k(t, t′
) = s2
k exp
(
−
(t − t′
)2
2ℓ2
)
cov(yt, yt′ ) = k(t, t′
) + σ2
t δt,t′
• σ2
t characterises measurement uncertainty for each data
point (see above)
• k(t, t′
) controls smoothing, i.e. influence of data at
different time points

58. Pete Dodd
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Mortality
Conclusion
55
ARI histories (South-East Asia)
qqqq
qqqqqq q q qqq q
q
q q
qq
q
qq
q
qqq
qqqq q
q
q
q
qqq
qq q
qq
qq q q q
q
qq
q
qqq qq qq
q
q qqq
qqqqq qq qqq
qqqqqq qq
qq qq
qq qq qq qqq qqqq
qq
q
q
q
q
q
q
q
q
q
q
q
q
qq
q
q
qq
q
q
qq
q
q qqq qqq q qq qqqqqqq qqq q
qq qq
qq
q
qqq
qqq
q
qq qq
q qqq q
q
q qq
q
q
q q
q
qqq
q
q
q qq qqqq
q
q
q
qq q
q
qq q
q qq qqqqqqqqqq qqqq q qqqqqq q
IDN IND THA
BGD BTN LKA
MMR NPL PRK
−10.0
−7.5
−5.0
−2.5
0.0
−10.0
−7.5
−5.0
−2.5
0.0
−10.0
−7.5
−5.0
−2.5
0.0
1950 1975 2000 1950 1975 2000 1950 1975 2000
Year
log(ARI)
type
q Prevalence estimate
TST survey

59. Pete Dodd
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Conclusion
56
ARI histories (India)
qqq
qqqq q
q
q
q
q
qq
q
q q
qq
qq q
q q
q
IND
−10.0
−7.5
−5.0
−2.5
0.0
1950 1975 2000
Year
log(ARI)
type
q Prevalence estimate
TST survey

60. Pete Dodd
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Mortality
Conclusion
57
Approach
ARI → infection
Cumulative hazard gives all-time infection risk:
Ha =
∫ a
0
da.λa
Pa = 1 − exp(−Ha)
Infection for the first time with time T:
1
PT
a = exp(−Ha−T) − exp(−Ha)
Incidence prevalence of infection (1st
or not):
Ia = λa(α(1 − e−Ha
) + e−Ha
)
PT
a = α(Ha − Ha−T) + (1 − α)(e−Ha−T
− e−Ha
)
(α is the protection from previous infection as a HR)

61. Pete Dodd
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Latent TB
ARI trends
Prevalence
Mortality
Conclusion
58
LTBI prevalence
Latent TB
infection prevalence
0 − 10%
10 − 20%
20 − 30%
30 − 40%
40 − 50%
50%
No data

62. Pete Dodd
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Conclusion
59
LTBI prevalence
AFR AMR EMR
EUR SEA WPR
0%
20%
40%
60%
80%
0%
20%
40%
60%
80%
0−5
5−10
10−15
15−20
20−25
25−30
30−35
35−40
40−45
45−50
50−55
55−60
60−65
65−70
70−75
75−80
80−
0−5
5−10
10−15
15−20
20−25
25−30
30−35
35−40
40−45
45−50
50−55
55−60
60−65
65−70
70−75
75−80
80−
0−5
5−10
10−15
15−20
20−25
25−30
30−35
35−40
40−45
45−50
50−55
55−60
60−65
65−70
70−75
75−80
80−
Age (years)
Percentageinfected

63. Pete Dodd
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ARI trends
Prevalence
Mortality
Conclusion
60
Results
Summary
% LTBI % 15 % 1st
≤2y % ≤2y ‰INH-R ≤ 2y
AFR 22.4 [20.6 - 24.9] 13.3 [11.8 - 14.6] 1.4 [1.2 - 1.6] 1.5 [1.3 - 1.8] 1.1 [0.9 - 1.4]
AMR 11.3 [7.2 - 20.3] 2.2 [1.2 - 3.4] 0.2 [0.1 - 0.2] 0.2 [0.1 - 0.2] 0.1 [0.1 - 0.4]
SEA 30.7 [27.9 - 34.7] 7.1 [6.0 - 8.1] 1.1 [0.8 - 1.5] 1.2 [0.9 - 1.7] 0.6 [0.5 - 0.9]
EMR 16.5 [14.0 - 19.0] 7.7 [6.3 - 9.2] 0.6 [0.5 - 0.9] 0.7 [0.5 - 0.9] 0.5 [0.3 - 0.8]
WPR 26.9 [18.5 - 38.6] 2.5 [1.7 - 3.5] 0.5 [0.3 - 0.6] 0.5 [0.4 - 0.7] 0.7 [0.5 - 1.0]
EUR 13.3 [10.2 - 19.2] 2.0 [1.3 - 2.6] 0.2 [0.2 - 0.3] 0.2 [0.2 - 0.3] 0.6 [0.4 - 1.0]
GLOBAL 22.8 [20.3 - 25.9] 5.8 [4.9 - 6.6] 0.7 [0.6 - 0.8] 0.8 [0.7 - 0.9] 0.7 [0.5 - 0.8]
• Current LTBI → TB incidence of
• 15 / 100,000 y in 2035
• 9 / 100,000 y in 2050
• 10 per million y 2050 elimination target needs to address
LTBI
• 97 m (92m - 103m) children under 15 infected

64. Pete Dodd
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Conclusion
61
TB mortality in children§
(draft not for dissemination)
§work with Courtney Yuen@ Harvard, Babis Sismanidis @ WHO,
James Seddon @Imperial, Helen Jenkins @ Boston U.

65. Pete Dodd
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Conclusion
62
Methods
…redacted until
publication…

66. Pete Dodd
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Conclusion
63
Conclusion

67. Pete Dodd
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64
Acknowledgements
Thanks to collaborators:
• James Seddon @ Imperial
• Rein Houben @ LSHTM
• Babis Sismanidis @ WHO
• Helen Jenkins @ Boston University
• Courtney Yuen @ Harvard
• Andy Prendergast @ QMUL, Beate Kampmann @ Imperial,
Claire Beecroft @ ScHARR
Special thanks also to:
• TB Alliance for funding
• the DRTB surveillance unit at WHO for data, advice and
review

68. Pete Dodd
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Mortality
Conclusion
65
References
Including me …
• P.J. Dodd, A.J. Prendergast, C. Beecroft, B. Kampmann, J.A. Seddon, The impact of HIV and antiretroviral therapy on TB
risk in children: a systematic review and meta-analysis, Thorax, 2017, doi:10.1136/thoraxjnl-2016-209421 [in
press]
• R.C. Harris, P.J. Dodd, RG White, The potential impact of BCG vaccine supply shortages on global paediatric
tuberculosis mortality, BMC Medicine, 2016, doi:10.1186/s12916-016-0685-4
• R.M.G.J. Houben and P.J. Dodd, The global burden of latent tuberculosis infection - a re-estimation using
mathematical modelling, PLOS Medicine, 2016, doi:10.1371/journal.pmed.1002152
• P.J. Dodd, C. Sismanidis, J.A. Seddon, Global burden of drug-resistant tuberculosis in children: a mathematical
modelling study, Lancet Infectious Dis, 2016, doi:10.1016/S1473-3099(16)30132-3
• J.A. Seddon, H.E. Jenkins, L. Liu, T. Cohen, R.E. Black, T. Vos, M.C. Becerra, S.M. Graham, C. Sismanidis, Counting
children with tuberculosis: why numbers matter, P.J. Dodd, Int J Tuberc Lung Dis 2015, doi:10.5588/ijtld.15.0471
• P.J. Dodd, E. Gardiner, R. Coghlan, J.A. Seddon, Estimating the burden of childhood tuberculosis in the twenty-two
high-burden countries using mathematical modelling study, Lancet Global Health, 2014,
doi:10.1016/S2214-109X(14)70245-1
Other:
• Jenkins HE, Yuen CM, Rodriguez CA, Nathavitharana RR, McLaughlin MM, Donald P, Marais BJ, Becerra MC, Mortality in
children diagnosed with tuberculosis: a systematic review and meta-analysis., Lancet Infectious Dis,
2016,doi:10.1016/S1473-3099(16)30474-1
• Yuen CM, Jenkins HE, Rodriguez CA, Keshavjee S, Becerra MC,Global and Regional Burden of Isoniazid-Resistant
Tuberculosis., Pediatrics, 2015,doi:10.1542/peds.2015-0172
• Jenkins HE, Tolman AW, Yuen CM, Parr JB, Keshavjee S, Pérez-Vélez CM, Pagano M, Becerra MC, Cohen T,Incidence of
multidrug-resistant tuberculosis disease in children: systematic review and global estimates., Lancet,
2014,doi:10.1016/S0140-6736(14)60195-1

69. Pete Dodd
TB background
TB today
TB natural history
HIV
TB in kids
Overview
Approach
Results
Drug-resistant TB
Definitions data
Algorithm
Results
Latent TB
ARI trends
Prevalence
Mortality
Conclusion
66
Conclusion
Overall summary points
• TB is still a major threat to health globally
• TB in children has gained increasing recognition as an
important component of this burden
• DRTB is substantial consideration for child TB in some
regions
• A huge number of children are latently infected with TB
• TB in children under 5 is a key missing piece of child
mortality…
• …which improved approaches to correctly identify treat
TB could avoid