Supplementary appendix atic Repub lic of Congo Ethiopia India Indonesia Ken ya Mozambique Myanmar...

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

Supplement to: Dodd PJ, Gardiner E, Coghlan R, Seddon JA. Burden of childhood tuberculosis in 22 high-burden countries: a mathematical modelling study. Lancet Glob Health 2014; published online July 9. http://dx.doi.org/10.1016/S2214-109X(14)70245-1.

Modelling the 22 HBC paediatric TB burden

1

Supplementary Information for ‘Estimating the burden of childhood tuberculosis in the

twenty-two high TB burden countries: a mathematical modelling study’

P.J. Dodd, E. Gardiner, R. Coghlan and J.A. Seddon

Supplementary methods .............................................................................................................................................. 2

Description of data used ........................................................................................................................................... 2

Figure 1: The demography of the 22 HBC populations in 2010. ......................................................................... 2

Figure 2: WHO estimates for total per-capita TB prevalence. ............................................................................. 3

Figure 3: TB notifications by type of TB in 2010 for the 22 HBCs. .................................................................... 4

Figure 4: Types of TB notification in 2010 by age and sex. ................................................................................ 4

Table 1: Those HBC countries with comparable DHS data on household structure. ........................................... 5

Figure 5: DHS household demography - the example of India. ........................................................................... 6

Figure 6: Coverage of BCG vaccination in the 22 HBCs, data from WHO. ........................................................ 7

Figure 7: HIV prevalence in individuals under 15 ............................................................................................... 8

Infection rates ........................................................................................................................................................... 8

Table 2: Literature available used to inform community transmission parameters. ............................................. 9

Figure 8: Log-normal fit to literature estimates of the transmission parameter. ................................................ 10

Table 3: Choice of transmission parameters describing the rates of infection. .................................................. 11

Cohabitation analysis ............................................................................................................................................. 11

Figure 9: TB cohabitation probability (age-dependent model). ......................................................................... 12

Figure 10: TB cohabitation probability (age-uniform model). ........................................................................... 13

Figure 11: Cohabitation probability gradients. ................................................................................................... 14

Disease progression ................................................................................................................................................ 14

Table 4: Choice of parameters for TB disease progression risks. ...................................................................... 15

Protection from BCG .............................................................................................................................................. 15

Table 5: Choice of parameters describing the protective effect of BCG vaccination. ....................................... 16

The effect of HIV ..................................................................................................................................................... 16

Table 6: Choice of parameter describing the IRR for developing TB disease given HIV. ................................ 16

Numerical methods ................................................................................................................................................. 16

Limitations .............................................................................................................................................................. 16

Supplementary results ............................................................................................................................................... 18

Figure 12: Model estimates for all TB incidence in those aged under 15. ......................................................... 19

Table 7: Model estimates for all TB incidence in those aged under 15 years. ................................................... 19

Figure 13: A mosaic plot of the mean proportion of the estimated burden. ....................................................... 20

Table 8: Model estimates of total TB incidence in children 0-4 years and 5-14 years. ..................................... 21

Figure 14: The proportion of the incidence of paediatric TB in each country that is HIV associated. .............. 22

References .................................................................................................................................................................. 23


2

Supplementary methods

Description of data used

All of the 22 high TB burden countries (HBCs) had data available on their demography in 2010 (from UN ESA,

Population Division (1) see Figure 1). Where necessary, 5-year age categories were disaggregated under the

assumption of uniformly distributed ages. These data were used to generate the number of children at risk in each

country by age, and as denominators in the age-weighting of adult prevalence for the age-structured cohabitation

analysis (see below).

Figure 1: The demography of the 22 HBC populations in 2010, data from UN ESA.

Estimates of adult TB prevalence were available for all 22 HBCs in 2010, together with 95% uncertainty bounds

(from (2), see Figure 2). Uncertainty in per-capita prevalence was represented by gamma distributions,

parameterised by taking the quoted ranges defined by the upper and lower bounds as 1.96 x the standard deviation,

and the quoted point estimate as the mean. Similarly, all 22 HBCs reported breakdowns of TB notifications by type,

i.e. smear positive, smear negative, smear unknown, extrapulmonary, other (from WHO (2), see Figure 3). These

data were used to estimate the proportion of incident TB that is smear positive for the community ARI estimate. The

same estimate was used for all countries to avoid bias resulting from different case detection infrastructures etc.

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

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Figure 2: WHO estimates for total per-capita TB prevalence and 95% uncertainty bounds for the 22 HBCs in

2010.

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Ethiopia

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Russian Federation

South Africa

Thailand

Uganda

United Republic of Tanzania

Viet Nam

Zimbabwe

0 500 1000

TB prevalence per 100,000

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ntr

y

2010


4

Figure 3: TB notifications by type of TB in 2010 for the 22 HBCs, data from WHO.

For 21 of the HBCs, breakdowns of TB notifications in 2010 were also available by age and sex ((2), see Figure 4).

These data were used as the numerator in the age-weighted assignment of prevalence TB cases in the age-structured

cohabitation analysis. Data were not available for Mozambique.

Figure 4: Types of TB notification in 2010 by age and sex for the 22 HBCs, data from WHO.

Afghanistan

Bangladesh

Brazil

Cambodia

China


Ethiopia

India

Indonesia

Kenya

Mozambique

Myanmar

Nigeria

Pakistan

Philippines

Russian Federation

South Africa

Thailand

Uganda


Viet Nam

Zimbabwe

0 25 50 75 100

Proportion (%)

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untr

y

new TB type:

smr+

smr−

smr?

EP

other

Types of new TB notifications in 2010

malemalemalefemalefemalefemale malemalemalefemalefemalefemale malemalemalefemalefemalefemale malemalemalefemalefemalefemale malemalemalefemalefemalefemale




malemalemalefemalefemalefemale malemalemalefemalefemalefemale

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

0−1415−2425−3435−4445−5455−64

65+

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Proportion of 2010 notifications

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5

For 16 of the HBCs (see Table 1), comparable Measure DHS surveys were available (3) including full records of the

ages and sexs living in households, together with formal sample weightings for use in analysis. The DHS survey

data were used to generate synthetic populations with age-, sex- and household-structure representative of each of

these countries, for use in the cohabitation analyses described below. Figure 5 is a summary representation of the

household demography for India. Similar plots are available for all other countries from the authors on request.

Table 1: Those HBC countries with comparable DHS data on household structure, together with the year of

the survey.

Country Year

Bangladesh 2011

Brazil 1996

Cambodia 2010

Democratic Republic of Congo 2007

Ethiopia 2011

India 2006

Indonesia 2007

Kenya 2009

Mozambique 2011

Nigeria 2008

Philippines 2008

South Africa 2003

United Republic of Tanzania 2010

Uganda 2011

Viet Nam 2002

Zimbabwe 2011


6

Figure 5: DHS household demography - the example of India. Each panel represents the age and sex distribution

of those living in a given household size as a pyramid plot.

BCG vaccination coverage estimates were available for all 22 HBCs (from WHO (4), see Figure 6). BCG schedules

(5) all recommended vaccination at birth, with only Nigeria and The Russian Federation including repeat

vaccination of older children. Vaccine strains often differed between countries. The BCG vaccination coverages

were used to determine the fraction of children whose risks of progression from infection to disease were moderated

by BCG (in the manner described in the relevant section below).

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Figure 6: Coverage of BCG vaccination in the 22 HBCs, data from WHO. N.B. the axis displaying coverage

does not begin at 0%.

HIV prevalence estimates in those aged under 15 were available for 9 of the HBCs (source (6), see Figure 7),

together with 95% uncertainty bounds. Countries for which there were not estimates reported from this source were

assumed to have negligible HIV prevalence in those under 15 years of age. Among the 22 HBCs taken to have zero

paediatric HIV prevalence for this reason, Thailand and the Russian Federation had the highest adult prevalences of

HIV, at 1.2% and 1.1% respectively, the rest being 0.6% or lower. Uncertainty in the prevalences was represented

by gamma distributions, parameterised by taking the quoted ranges defined by the upper and lower bounds were

taken as 1.96 x the standard deviation, and the quoted point estimate as the mean. This HIV prevalence was assumed

to be uniform by age in those under 15. Degree of immunosuppression or ART was not considered.

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Pakistan

Philippines

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South Africa

Thailand

Uganda


Viet Nam

Zimbabwe

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% BCG coverage in 2010

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ntr

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8

Figure 7: HIV prevalence in individuals under 15 in those countries reporting HIV prevalence in this age

range to UNAIDS. Other countries were assumed to have negligible HIV prevalence in this age group.

Infection rates

Karel Styblo first empirically estimated the coefficient between the prevalence of smear-positive adult TB and the

annual risk of infection, (7). Using European data on ARIs from TST surveys combined with prevalence surveys,

he inferred from a rate of 10 infections per year for a typical smear positive TB case. This quantity has been

revisited since (8–10), as lower average case severity from improved case-detection, the rise of HIV-related

tuberculosis, and social changes may all have led to departures from Styblo’s estimate; indeed, more recent

estimates tend to be lower. It should be noted that from a modelling perspective, use of this community relationship

between smear positive TB and the ARI does not imply that infections caused by smear negative TB have been

altogether neglected. Rather, it can be thought of as modelling the infections due to smear positive TB cases plus the

product of the number of smear negative cases per smear positive case and the relative infectiousness of a smear

negative case, i.e. as inflated to indirectly include the smear negative contribution to transmission.

We used data from the reviews of Bourdin Trunz and colleagues (10) and van Leth and colleagues (9) (see Table 2)

and fitted a log-normal distribution to these estimates of (see Figure 8 and Table 3), which was used to model the

distribution of values for this coefficient in the community ARI approach to estimating infection incidence.

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Kenya

Mozambique

Nigeria

South Africa

Uganda


Zimbabwe

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Percent of those aged <15 years with HIV (%)

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ntr

y


9

Table 2:Literature available used to inform community transmission parameters.

Country year ARI year smr+ TB

prevalence Ref

Cambodia 2002 2.06 20021 269 7.8 T21

China 1979 1.1 19791 187 5.7 T22

China 1990 1.0 19901 134 7.2 T22

China 2000 0.7 20001 110 6.5 T23

The Philippines 1981-83 2.5 1981-831 660 3.8 T25/5

The Philippines 1997 2.3 19971 310 7.4 T24/5

South Korea 1965 5.3 19651 668 7.9 T26

South Korea 1970 3.9 19701 559 7.0 T26

South Korea 1975 2.3 19751 480 4.8 T26

South Korea 1980 1.8 19801 309 5.8 T26

South Korea 1985 1.2 19851 239 5.0 T26

South Korea 1990 1.1 19901 143 7.7 T26

South Korea 1995 0.5 19951 93 5.4 T26

Kenya 1994-96 1.1 19952 132 8.3 T27

Tanzania 1993-98 0.9 19962 172 5.2 T28

Tanzania 2000 0.68 20002 214 3.1 T29

Malawi 1994 1.0 19952 248 4.0 T30

Madagascar 1991-94 1.2 19952 145 8.4 T31

Egypt 1995-97 0.3 19962 24 13.2 T32

Laos 1996 1.1 19962 165 6.7 T33

India 2000-03 1.5 20022 155 9.6 T34

South Korea 1975 1.78 19751 480 3.7 V7/8

South Korea 1980 1.24 19801 310 4.0 V7/8

South Korea 1985 1.12 19851 240 4.7 V7/8

South Korea 1990 0.6 19901 144 3.2 V7/8

China 1979 0.64 19791 187 3.4 V9

China 1985 0.59 19851 156 3.8 V9

China 1990 0.64 19901 134 4.8 V10


10

China 1996 0.7 19961 121 5.8 V10

The Philippines 1982 2.48 19821 950 2.6 V11

The Philippines 1994 2.30 19941 525 4.4 V11

1. Survey

2. WHO estimate

Tn =Bourdin Trunz and colleagues (10) - ref n; Vn =van Leth and colleagues (9) - ref n;

T21-T32 = (11), (12), (13), (14), (15), (16), (17), (18), (19), (20), (21), (22), (23);

V7-V11= (24), (16), (12), (13), (14)

Figure 8: Log-normal fit to literature estimates of the transmission parameter, beta.

Papers by Rutherford and colleagues (25) and by Lienhardt and colleagues (26) each described the infection rates

among children who were living with a smear-positive TB case by the time of notification. Rutherford and

colleagues in Indonesia (25) found 48% of child household contacts to be infected by tuberculin skin test (10mm

cut-off) and 51% positive by interferon gamma release assay. Lienhardt and colleagues (26) found 25.8% of child

household contacts positive by TST with 10mm cut-off and 33.6% positive by TST with 5mm cut-off. The

systematic review and meta-analysis of TB contact investigations by Fox and colleagues (27) reported rates of

infection of 45.4% [95% CI: 40.7% to 50.2%] among household contacts of all ages in low- and middle-income

countries, averaged over index smear-status. To interpret these numbers in terms of a transmission rate, one needs to

make an assumption about the duration of household exposure. The assumption of a typical duration before

0.00

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Transmission parameter, b

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sity


11

notification of ~1 year (as in (28); in the context of a ~3 year duration of TB disease without treatment (29))

translates these numbers into rates of 0.65y-1

and 0.71y-1

for (25), and 0.41y-1

and 0.30y-1

for (26). We therefore

modelled the uncertainty in this parameter using a log-normal distribution with an approximate correspondence

between its median and the midpoint of these estimates, and between the distribution’s inter-quartile range their

range, i.e. each model run sampled beta from this distribution (as were other parameters from their distributions).

See Table 3.

Table 3: Choice of transmission parameters describing the rates of infection for a given exposure, modelled as

log-normal distributions.

Quantity mu sigma median LQ UQ

community (y-1) 1.678 0.371 5.354 4.168 6.879

household (y-1) -0.693 0.500 0.500 0.357 0.701

Cohabitation analysis

The cohabitation analyses estimated the risk of infection in children via their risk of sharing a household with a TB

case. To calculate the probability of sharing a household with a TB case we took two approaches. In each approach,

the per-capita estimate of prevalence for the country was used in conjunction with the DHS household data, which

was taken to constitute a synthetic population. In the first, age-dependent cohabitation analysis (labelled ‘hhage’),

the probability of an individual being a TB case was dependent on age and sex, with the distribution being taken

from the smear positive age- and sex-stratified WHO notification data where available. Mozambique, for which

comparable DHS data were available, did not have age-structured notification data and so was not included in this

part of the analysis. Smear negative cases were assumed to follow the same distribution. To calculate the probability

that a child in a particular age group was sharing a household with a TB case, children of that age were repeatedly

sampled from the synthetic population, with weights corresponding to the survey design weights in the DHS sample-

frame. The mean probability of their sharing with a TB case was then calculated as a the mean of:

∏

for each pick, where is the probability of the i-th cohabiting individual being a TB case, and the product is taken

over the other individuals in the household. 10,000 children were sampled at each per-capita population TB

prevalence, and the mean over these used as an estimator for the probability of cohabiting with a TB case.

As the notification data are subject to various biases, and may not fairly reflect the age and sex distribution of

prevalent TB, we carried out a separate age-uniform cohabitation analysis (labelled ‘hhflat’) in which the

distribution of prevalent cases of TB was assumed to be flat across those aged 15 years or older. The procedure was

otherwise identical. Mozambique was included in this analysis.

The results for the probability of cohabiting with a TB case as per-capita population TB prevalence varies between 0

and 1000 per 100,000 are shown in Figure 9 (age-dependent analysis) and Figure 10 (age-uniform analysis). The

probabilities for the different age categories were very similar for a given TB prevalence and, to a good

approximation, linear in the per-capita population TB prevalence. This was used to simplify the onward analysis as

the gradients of the probability with respect to TB prevalence were extracted (compared for both methods in Figure

11), and used in the infection model. Where the gradient was not available for a given country and method, either


12

due to the absence of household data, or the absence of age-stratified notification data for the age-dependent analysis,

it was sampled for each run from those of other countries.

The probability that a cohabiting TB case was smear positive was then taken using the mean proportion of notified

cases that were smear positive from the data presented in Figure 3. Smear negative cases were assumed to be 23% as

infectious per unit time as smear positive cases, based on the molecular studies (30,31).

Figure 9: TB cohabitation probability (age-dependent model). The probability of sharing a household with a

smear positive TB case as TB prevalence increases, by age, and for each of the 15 HBCs for which both age-

structured notification data and a Measure DHS survey was available. This was calculated in a Monte Carlo fashion

using the age-distribution of TB from the smear positive notification data in Figure 4.

Bangladesh Brazil Cambodia Democratic Republic of Congo

Ethiopia India Indonesia Kenya

Nigeria Philippines South Africa United Republic of Tanzania

Uganda Viet Nam Zimbabwe

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Figure 10: TB cohabitation probability (age-uniform model). The probability of sharing a household with a

smear positive TB case as TB prevalence increases, by age, and for each of the 16 HBCs for which a Measure DHS

survey was available. Those aged 15 year or older were assumed equally likely to be a prevalent case of TB.

Bangladesh Brazil Cambodia Democratic Republic of Congo

Ethiopia India Indonesia Kenya

Mozambique Nigeria Philippines South Africa

United Republic of Tanzania Uganda Viet Nam Zimbabwe

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Figure 11: Cohabitation probability gradients. The rate of increase in the probability of a child sharing a

household with a case of TB as the TB prevalence in the population increases. Dots are the gradients according to

the age-neutral model; triangles are the gradients according to the age-structured model.

Disease progression

Disease progression risks were based on the review of the pre-chemotherapy literature by Marais and colleagues

(32). BCG vaccination had not been used in the populations reviewed in this study. We take these risks as

approximating the risk of progression within a year. The risks of developing TB disease in (32) were used to

parameterise the mean of beta distributions for each age category, with the variance taken as 0.125 x mean

(equivalent to taking a range of plausible variation as +/-25% of the mean, and the variance as a quarter of the

plausible range). The risk of extrapulmonary disease (miliary TB or TB meningitis) was modelled as a random

proportion of the risk of any disease, modelled by a beta distribution with mean equal to the range midpoints in (32)

and the variance taken as a quarter of these ranges. Where no ranges were given, +/-25% of the mean was used as

the range. Where the probability was expressed as <x, x/2 was used as the mean. Modelling the two types of disease

in this way ensured that the random total probability for any kind of disease never exceeded unity. These parameters

and their associated modes, lower- and upper-quartiles are presented in Table 4.

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Bangladesh

Brazil

Cambodia


Ethiopia

India

Indonesia

Kenya

Mozambique

Nigeria

Philippines

South Africa


Uganda

Viet Nam

Zimbabwe

4 5 6 7

probability gradient with prevalence

cou

ntr

y


15

Table 4: Choice of parameters for TB disease progression risks, modelled as beta distributions.

quantity alpha beta median LQ UQ

probability TB disease, age [0,1) years 1.500 1.500 0.500 0.298 0.702





proportion TB EP, age [0,1) years 0.960 2.240 0.300 0.112 0.451





Protection from BCG

The reported efficacy of BCG in protecting against different forms of TB disease varies substantially. Rodrigues and

colleagues (33) conducted a meta-analysis of protection against TB meningitis and miliary TB, finding a protection

of 86% [65%,95%] from RCTs and 75% [61%,84%] from case-control studies. Colditz and colleagues (34) meta-

analysis of BCGs protection for infants found protections against all TB of 74% [62%,83%] from RCTs and 52%

[38%,64%] from case-control studies. They found a protection of against TB meningitis of 64% [12%,86%] and a

protection of 78% [58%,88%] against disseminated TB. Bourdin Trunz and colleagues (10) updated these meta-

analyses to arrive at protections of 73% for TB meningitis and 77% for miliary TB. We choose to model the

protection against extra-pulmonary TB by a hazard ratio (i.e. 1-protection) following a beta distribution, with

parameters presented in Table 5. We chose to model the protection against PTB as a certain fraction of the

protection against EPTB, with this fraction beta distributed with parameters given in Table 5. Taken together, this

approach gave a protection against PTB of median [LQ-UQ] = 54% [38%-69%] and a protection against EPTB of

median [LQ-UQ] = 70% [52%-84%].

There has been suggestion (e.g. (35,36)) that the observed protective effect of BCG varies with latitude, as a result

of differences in the prevalence of non-tuberculous mycobacteria in the environment. We carried out separate

analysis as a sensitivity analysis where we allowed 41% (36) of the protection from BCG to vary with latitude, so

that the protection at the equator was only 59% of that at the poles. The latitude of each country’s capital was used.


16

Table 5: Choice of parameters describing the protective effect of BCG vaccination, modelled as beta

distributions.

quantity alpha beta median LQ UQ

hazard ratio for EPTB, all ages 1.250 2.500 0.301 0.155 0.484

fractional protection for PTB, all ages 4.000 1.000 0.841 0.707 0.931

The effect of HIV

For each country, the proportion of children who were HIV-infected had their TB incidence multiplied by an

incidence rate ratio (IRR). There is limited evidence around the effect of HIV infection in children on the rate of

progression to TB disease following infection with Mycobacterium tuberculosis. Seddon and colleagues (37)

reported an HIV prevalence of 29% among paediatric TB cases diagnosed in Cape Town during 2007-2009. Taken

with the UNAIDS estimate of 3% HIV prevalence for those under 15 years of age in South Africa, this gives an IRR

of 13, suggesting that children with HIV are 13 times more likely to develop TB than those HIV negative. Madhi

and colleagues (38) estimated a higher incidence rate ratio (RR, 22.5; 95% CI, 13.4–37.6) among children

hospitalised with TB in Johannesberg, South Africa between 1996-1997 as did Hesseling and colleagues (39) (RR

24.2 95% CI, 17–34) among infants aged up to 12 months in the Western Cape of South Africa between 2004-2006.

We modelled the IRR with a log-normal distribution, described in Table 6.

Table 6: Choice of parameter describing the IRR for developing TB disease given HIV infection, modelled as

a log-normal distribution.

quantity mu sigma median LQ UQ

IRR for TB given HIV infection, all ages 2.996 1.000 20.000 10.118 39.261

Numerical methods

The model was run repeatedly to build up a distribution of outputs, every time sampling each the input parameter

from the probability distribution used to characterise its likely value. The reported analyses were based on sample

sizes of 50,000 runs, using a Latin hypercube sampling scheme to improve coverage of the parameter space.

Gaussian kernel density estimation was used to estimate distributions, unless otherwise stated. Paediatric proportions

were calculated using model estimates as the numerator and WHO all-TB estimates as the denominator. All analyses

were carried out using the R environment for statistical computing (40).

Limitations

This section details the limitations of the model, some of the assumptions that have been made and some of the

omissions.

1. The model only includes the 22 highest burden countries. Although these account for approximately 80%

of the burden, the model does not look at the remaining countries of the world. These other countries are

lower burden or smaller countries and it is likely that they have a very different population structure, health

care system, underlying health problems and TB epidemiology.


17

2. The model uses as a starting point the estimated TB prevalence data for adults with TB. These estimates are

themselves based on a simple model of the duration of TB disease; biases in this would reflect in our results.

3. The model uses TB prevalence estimates as an input of burden but uses notifications data as an input of

who in the population gets TB within countries in the age-dependent cohabitation analysis. There may be

systematic differences between the two. For this reason, we included the age-uniform cohabitation model

subvariant, where TB was assumed uniformly distributed across adults by age and sex.

4. It is assumed that the proportions of adults with smear positive and non-smear positive disease in the

notification data is representative of these proportions in the prevalence data.

5. We were not able to access household structure data for China and Russia, which are two of the largest

burden countries and may have very different TB epidemiology and household structure to other countries

due to their political and social history. We were not able to find notifications disaggregated by age for

Mozambique, and so were not able to include this country in the age-structured cohabitation analysis. We

were able to use the ARI approach for these countries, however.

6. We have neglected geographical (and other) heterogeneities within countries; in fact, TB is frequently very

heterogeneous at this scale. Similarly, we have looked at TB only in the general population and not in

special populations such as prisons, mines etc. Although these populations do not usually include children,

adults from these populations are very high risk and when returning home are likely to contribute to

childhood infections.

7. We have assumed that children are only infected by adults and not by other children. Although it is likely

that the majority of infections do result from adults, children, and especially older children, can certainly be

infectious.

8. We have not included any modelling of the specific issues around HIV and household exposure.

Households that include HIV-infected adults are more likely to both have TB but also have children that are

HIV-infected. The impact of HIV on the infectiousness of the adult has also not been included.

9. We have not included elements of the degree of smear positivity- certainly the more positive the sputum the

more likely children are to become infected. Smear status has been considered binary - smear positive or

smear negative.

10. We have not included any measure of the effects of drug resistance on whether children are more or less

likely to be exposed, become infected or develop disease.

11. When assessing the proportion of household-exposed children who become infected we have used studies

that used infection rates in child contacts at the time of diagnosis of the adult. Converting this into a rate

needed by the model required an assumption about the typical timing of case detection, and the assumption

of a uniform infectiousness through time.

12. In common with almost all infectious disease modelling, we have assumed a consistent and linear

relationship between adult TB prevalence and ARI. In reality this relationship may be non-linear and

variable.

13. The model ignores anergic children. We have used a positive test of infection as a surrogate for TB

infection and assumed that it is a necessary stage to pass through towards disease. However, many children

do not mount response to TST or IGRA and in cohorts of children with TB disease a significant proportion

of children have negative tests of infection.

14. We have looked at HIV status in the child as a binary issue (HIV or no HIV). The impact of degree of

immunosuppression and the use of ART has not been included. HIV prevalence in children has been

assumed not to vary with age.

15. BCG has been challenging to model. The literature surrounding its effects is hugely variable, with a

protective effect described as ranging from 0 to 70%. The implications of geographical location are also

unclear. However, we have included a summary protective effect for both pulmonary and nonpulmonary

disease and also modelled the effect of the latitude, based on the best available data. We have not included

any element of BCG strain used, the mode of delivery, efficacy of cold chain etc. We also neglected the

possibility that BCG vaccination protects against infection, as distinct from disease.


18

16. When modelling the risk of disease progression following infection we have not included factors such as M.

tuberculosis strain type, host genetics, host nutritional status, exposure to sunlight (and vitamin D levels) or

any other factors other than age, HIV status and BCG vaccination history. It is clear that factors other than

these can play a part in risk of disease progression.

17. We have assumed that risk of infection and risk of disease progression once infected are unrelated.

However, in reality, it is likely that the degree of exposure (and likely bacillary load exposed to), will affect

both the risk of infection as well as the risk of disease progression once infected.

18. From an infectious disease modelling perspective we have assumed that all children at the beginning of

their exposure were non-immune and had not been infected before. We have assumed that the risk of

disease progression following infection is the same for a child who has recently been infected for the first

time as it is for a child who has recently been infected but who has also been previously infected.

19. Literature risks of progression refer to a life-long time-frame, whereas we have interpreted these risks as

operating on a timescale shorter than the width of our age categories. This is necessary with the underlying

model structure to allow for age-dependent risks of progression, since we do not represent time-since-

infection. However, where our age-categories are narrowest, the progression is also likely to be most rapid,

which ameliorates this approximation.

20. The model does not include IPT. While IPT significantly reduces the likelihood of disease progression

following infection, in the high burden countries child contacts are rarely identified and IPT is rarely

provided.

21. The most recent demographic data was typically from 2010. We therefore used TB, HIV and BCG

estimates for this year.

Supplementary results

In this section, we present model estimates of the absolute TB incidence aggregated over extra-pulmonary and

pulmonary TB disease, for younger and older age groups ([0,5) and [5-15), respectively). Results from the

community ARI approach (labelled ‘comm’) and the both age-dependent cohabitation (labelled ‘hhage’) and age-

uniform cohabitation (labelled ‘hhflat’) approaches to modelling infection rates are presented. Results are also

presented from the latitude-independent (labelled ‘nolat’) and latitude-dependent (labelled ‘lat’) approaches to

modelling the protection from BCG vaccination. In total then, there are 6 model variants presented: 3 infection

variants x 2 protection BCG variants = (comm, hhage, hhflat) x (lat, nolat).


19

Figure 12: Model estimates for all TB incidence in those aged under 15 years in the 22 HBCs; all model

variants shown. The lines represent a histogram with binwidth 50,000.

In Figure 12 and Table 7 we compare the results from the different model variants aggregated over all age groups

and all of the 22 HBCs. The cohabitation estimates were similar to each other and less than the community

estimates: medians of around 400,000 compared with 650,000 for the latitude-dependent analysis, 300,000

compared with 500,000 for the latitude-independent analysis. Modes were approximately 20% lower than

corresponding medians. The community ARI model variant consistently gave the highest estimates and the age-

dependent cohabitation analysis the lowest. The latitude-dependent model variants of BCG protection consistently

estimated higher numbers than the latitude-independent model variants. Across all model variants considered, the

lowest lower quartile predicted was approximately 0.2 million cases per year, and the highest upper quartile

predicted was approximately 1 million cases per year.

Table 7: Model estimates for all TB incidence in those aged under 15 years in the 22 HBCs; all model variants

shown. (LQ=lower quartile; UQ=upper quartile.) N.B. from a different sample to results in main paper.

LAT ARI median LQ UQ

lat comm 651,731 424,628 988,953

lat hhage 395,167 271,453 567,114

lat hhflat 437,528 300,365 628,478

nolat comm 473,975 299,379 743,123

nolat hhage 288,140 190,632 428,417

nolat hhflat 319,248 210,991 474,510

0e+00

1e−06

2e−06

0 250000 500000 750000 1000000

22 HBC total paediatr ic TB incidence (per year)

de

nsity

LAT

lat

nolat

method

comm

hhage

hhflat


20

In Figure 13 we present a visualisation of the mean proportions of the total due to ages 0-4 and 5-14, split by type of

disease. The mean estimated incidence in ages 0-4 is similar to that in ages 5-14 (evidenced by similar bar widths).

Extrapulmonary TB is more common in the younger age groups (approximately 25% of incident cases), and makes

up approximately 10% of the total incidence in both age groups. The estimated summaries of model incidence in

each age-group across the 22 HBCs are presented in Table 8.

Figure 13: A mosaic plot of the mean proportion of the estimated burden (for the lat x comm method) that is

pulmonary vs extrapulmonary (here denoted PTB and DTB, respectively), and in younger vs older age groups (0-4

and 5-14). This is a diagrammatic representation of a cross-tabulation: both the widths and heights are meaningful.

age

type

0−4 5−14

PT

BD

TB


21

Table 8: Model estimates of total TB incidence in children 0-4 years and 5-14 years, in the 22 HBCs for the

BCG model variant with no variation of efficacy by latitude, and for the 3 ARI model variants. (LQ=lower quartile;

UQ=upper quartile.) N.B. calculated from a different sample to results in main paper – differences indicate level of

Monte Carlo error.

LAT ARI age median LQ UQ

lat comm 0-4 325,832 206,128 495,726

lat hhage 0-4 199,869 132,741 285,868

lat hhflat 0-4 221,517 146,854 316,775

nolat comm 0-4 227,357 136,076 363,585

nolat hhage 0-4 139,462 86,731 211,336

nolat hhflat 0-4 154,282 95,943 234,306

lat comm 5-14 283,290 134,568 528,877

lat hhage 5-14 172,911 84,577 309,817

lat hhflat 5-14 191,196 93,730 342,941

nolat comm 5-14 205,659 94,449 399,575

nolat hhage 5-14 125,664 59,415 235,267

nolat hhflat 5-14 139,271 65,666 260,711


22

Overall, a small proportion of incident paediatric TB cases were infected with HIV (median [LQ-UQ] = 5.0%

[2.4%-10.1%]), which is lower than the corresponding statistic for adults (13%, (28)); however, this is likely to vary

substantially by country. Figure 14 shows the proportion of incident paediatric TB predicted to be in those with HIV

coinfection for 9 HBC countries with non-zero paediatric HIV prevalence. While in the majority of these countries

the proportion was predicted to be under 20%, for South Africa and Zimbabwe the proportion of coinfected children

was predicted to be over 30%.

Figure 14: The proportion of the incidence of paediatric TB in each country that is HIV associated. In the

other HBCs, HIV prevalence in those aged under 15 was assumed to be zero.


Ethiopia

Kenya

Mozambique

Nigeria

South Africa

Uganda


Zimbabwe

0 20 40

% of paediatr ic TB incidence HIV−associated

cou

ntr

y


23

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