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BACKGROUND DOCUMENT 3 WHO and IHME estimates of TB disease burden: comparison of methods and results Prepared by: Laura Anderson, Katherine Floyd, Philippe Glaziou, Babis Sismanidis
BACKGROUND DOCUMENT 3
WHO and IHME estimates of TB disease burden:
comparison of methods and results
Prepared by:
Laura Anderson, Katherine Floyd, Philippe Glaziou, Babis Sismanidis
1
Introduction
There are two agencies producing and publishing estimates of the global, regional and national burden
of disease caused by TB: the World Health Organization (WHO) and the Institute of Health Metrics
and Evaluation (IHME) at the University of Washington, Seattle, USA. The teams involved in
producing these estimates discuss their estimates and share related information on a periodic basis, but
the estimates are produced largely independently from each other. WHO estimates are published
annually in global TB reports1 and are also available online, while IHME estimates have been
published in The Lancet (most recently in 20142 and previously in 20123), with additional information
available online. IHME estimates may be updated annually in future.
This background paper compares the methods used by WHO and IHME to produce estimates of TB
disease burden, and the most recently published results. It has 5 major sections:
1. Comparison of methods used by WHO and IHME;
2. Methods used by WHO to compile and analyse IHME datasets for comparative purposes;
3. Comparison of WHO and IHME results: estimates of TB incidence, prevalence and mortality
in 2013;
4. Comparison of WHO and IHME results: estimates of trends in TB incidence, prevalence and
mortality 1990−2013;
5. Analysis of indicators that can assist with assessment and interpretation of results. Five
indicators are considered: prevalence estimates compared with results from national TB
prevalence surveys; estimates of the case fatality ratio and the case detection rate; the rate of
change in TB incidence; and changes in TB incidence relative to changes in TB mortality.
Differences between WHO and IHME estimates of TB disease burden need to be considered in the
context of their published uncertainty ranges and gaps in underlying data. As better quality
surveillance data with more complete coverage become available from notification and vital
registration systems, estimates of TB disease burden produced by different institutions should
converge. For example, it is noticeable that WHO and IHME estimates of TB mortality rates are
relatively close in the Western Pacific and European regions, where the mortality data from national
vital registration systems are of relatively high quality and coverage. The WHO Global Task Force on
TB Impact Measurement is strongly promoting strengthening of surveillance in all countries,
alongside the more interim effort to directly measure TB burden through national TB prevalence
surveys.
1 WHO. Global Tuberculosis Report 2014. Geneva 2014. http://www.who.int/tb/publications/global_report/en/
2 Murray CJL, Ortblad KF, Guinovart C, et al. Global, regional, and national incidence and mortality for HIV,
tuberculosis, and malaria during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013.
Lancet 2014; 6736: 1–66. 3 Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age
groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380:
2095–128.
2
3.1 Comparison of methods used by WHO and IHME
Tables 3.1−3.4 provide a comparative overview of the methods used by WHO and IHME to produce
TB disease burden estimates. The first table compares methods that apply to all three indicators of
disease burden (incidence, prevalence, mortality). The other three tables compare methods that are
specific to incidence, prevalence and mortality.
Table 3.1. Comparison of WHO and IHME methods used to produce TB burden estimates:
methods that apply to all indicators
Variable WHO IHME
Overall analytical
/model framework
Internally consistent models combining
different data sources.
Bayesian model in overall model (Dismod
MR-2.0) that produces disease burden
estimates for multiple conditions/risk
factors; WHO TB burden estimates of the
ratio of notifications to incidence (aka case
detection rate or CDR) are used for some of
the priors.*
Uncertainty
incorporated?
Yes Yes
Time period covered 1990−2013 1990−2013
Frequency of updates Annual Periodic but may become annual; most
recent publications are in 2014 and 2012.
Country consultations? Burden estimates shared with all
countries for review each year prior to
publication in global TB report; other
in-depth reviews via country missions,
regional/global workshops, at-a-
distance communications.
Population estimates
and mortality envelope
(total estimated
number of deaths per
year)
UN Population Division estimates
57 million deaths in 2013
IHME population estimates
53 million deaths in 2010
Published estimates
available
disaggregated by HIV
status?
Yes for incidence and mortality. Yes for incidence and mortality in 2013.
IHME can provide disaggregated estimates
for other years on request.
Documentation of
methods
Online technical appendix that
accompanies the annual global TB
report.
Lancet papers and associated
supplementary material.
Reproducibility by
others (to be
reproducible, complete
datasets and computer
code should be
publicly available)
Not reproducible: some raw data not
published, computer code not
published.
Not reproducible: raw country data and
computer code not publicly available.
*Specifications of prior distributions used in Bayesian models implemented in DisMod-MR 2.0 are not publicly available.
In terms of overall methods, the use of different population estimates and different estimates of the
global mortality envelope mean that WHO and IHME estimates of TB disease burden should be
compared in terms of rates rather than absolute numbers.
The WHO methods described in Tables 3.1−3.4 were reviewed and endorsed by the WHO Global
Task Force on TB Impact Measurement in March 2010, following an 18-month review and related
recommendations from an expert group that was convened under the umbrella of the Task Force and
led by WHO and KNCV.
3
Table 3.2. Comparison of WHO and IHME methods used to produce TB burden estimates:
methods that are specific to HIV-negative TB incidence Variable WHO IHME
Main source of
data
Official TB notifications reported by
countries
Official TB notifications reported by
countries
Modelling
strategy
Case notifications are adjusted for
underreporting of detected cases and under-
diagnosis, using a mixture of expert opinion,
inventory studies, capture-recapture
analysis, mortality data, tuberculin surveys
and covariates such as GDP per capita and
the under-5 mortality ratio. Further details
are provided in background documents 2b,
2d and 5a.
All countries modelled with the same
approach. Notifications used to estimate
incidence with a variable for health system
access used as a proxy for the completeness
of notification. Other regression model
covariates (lag distributed) include income
per capita, malnutrition in children under 5,
cumulative cigarette consumption, smoking
prevalence, diabetes, indoor air pollution,
alcohol, population density, education and
health system access.
Uncertainty Based on expert opinion about plausible
ranges for the ratio of notifications to
incidence. Parameters such as case fatality
ratios or disease duration are modelled using
a probability density function. Errors are
propagated based on the Taylor series
approach or based on simulations.
Based on draws from a regression-
covariance matrix of betas and random
effects distribution, analysis of age-specific
rates and expert opinion about the ratio of
notifications to incidence (case detection
rate). Expert opinion about the CDR is
adjusted so that the minimum interval is
plus or minus 20 percentage points from
values estimated by experts.
Table 3.3. Comparison of WHO and IHME methods used to produce TB burden estimates:
methods that are specific to TB prevalence
Variable WHO IHME
Main sources of
data
National TB prevalence surveys.
National and subnational TB prevalence
surveys.
Adjustments
made to
prevalence survey
data?
Yes, adjustments are made using standard
methods. Prevalence surveys measure
bacteriologically-confirmed pulmonary TB
in adults (≥15 years old), so results need to
be adjusted to include children and
extrapulmonary TB. This is done using data
on the share of TB burden accounted for by
extrapulmonary TB and children.
Not documented.
Modelling
strategy for
countries without
survey data
Prevalence is derived from incidence. Not clear from published documentation.
Uncertainty Standard error propagation methods based
on the Taylor series expansion or
simulation-based approaches, assuming
independence of covariates.
Based on simulations.
4
Table 3.4. Comparison of WHO and IHME methods used to produce TB burden estimates:
methods that are specific to HIV-negative TB mortality
Variable WHO IHME
Main sources of data Publicly available VR data adjusted for
deaths with ill-defined causes and for
incomplete coverage (proportion of
deaths not reported). VR data are used
for 124 countries.
VR data adjusted for deaths with ill-
defined causes and for incomplete
coverage (proportion of deaths not
reported). IHME also uses results from
verbal autopsy surveys that are not
publicly available.
Adjustments made to
mortality data?
South Africa and Zimbabwe excluded
due to important but unquantified
misclassification of HIV and TB deaths.
Adjusted for misclassification of HIV
deaths.
Modelling strategy for
countries or country
years without vital
registration or verbal
autopsy data
TB incidence x case fatality ratio
following disaggregation by case type
(implemented by Avenir Health in
Spectrum in 2014). For countries with
incomplete time-series, VR data are
interpolated for missing data points with
exponential smoothing extrapolation
used at the ends of incomplete time
series.
Cause of Death ensemble modelling
strategy.
Uncertainty Standard error propagation methods
based on the Taylor series expansion or
simulation-based approaches, assuming
independence of covariates.
Based on simulations.
3.2. Methods used by WHO to compile and analyse IHME datasets for
comparative purposes
Some but not all of the data required to compare the WHO and IHME estimates of TB disease burden
are in the public domain (e.g. estimates for 2013). Following a request from WHO, IHME provided
other datasets to WHO. These included numbers and age-standardized rates per 100 000 population
for incidence (HIV-negative), prevalence and mortality (HIV-negative) for the period 1990−2013. For
mortality, country-specific estimates were provided for each year. For incidence and prevalence,
country-specific estimates were provided for 1990, 1995, 2000, 2005, 2010 and 2013. Linear
interpolation was used by WHO to produce complete time-series. Data sets were matched using ISO3
codes. WHO regions were used for regional comparisons.
Two major limitations of the available datasets affected the comparative analysis. These were:
1. The IHME datasets for TB incidence and TB mortality 1990−2013 are for HIV-negative TB
only. IHME are willing to share datasets that include HIV-positive estimates, but these were
not available at the time this background paper was prepared. Estimates that include HIV-
positive TB could only be compared for 2013.
2. IHME provided estimates of age-standardised rates. The standard population structure used to
calculate these rates are so far not available to WHO. WHO estimates are not age-
standardised.
Other limitations that affected the comparative analysis were:
1. WHO and IHME use different population estimates as well as different life tables and
associated estimates of the global number of deaths from all causes (Table 3.1). For this
reason, estimates are compared in terms of rates only (not absolute numbers).
2. Both WHO and IHME make adjustments to disease-specific estimates to fit their overall
mortality envelopes (so-called shrinkage). The details of the adjustments made by IHME are
not published. In the past few years, WHO has not applied any shrinkage to TB burden
estimates specifically.
3. 30 countries or territories were excluded from the comparative analysis of incidence and
mortality rates because data were not available from IHME (Table A3.1). However, these
5
countries account for only 0.2% of the total world’s population and 0.07% of the total
estimated number of incident TB cases in 2013.
4. 34 countries or territories were excluded from the comparative analysis of incidence and
mortality rates because data were not available from WHO. This was mainly because
estimates of HIV-positive TB cases were not available from UNAIDS and therefore could not
be subtracted from total incidence (Table A3.2). However, these countries accounted for only
2.1% of the global population and 0.6% of total estimated number of incident TB cases in
2013.
The countries and territories that could not be included in the comparative analysis collectively
accounted for 2.3% of the world`s population and 0.7% of the total estimated incident TB cases in
2013
3.3. Comparison of WHO and IHME results: estimates of TB incidence,
mortality and prevalence in 2013
This section compares WHO and IHME estimates of TB incidence, mortality and prevalence rates in
2013.
3.3.1 WHO and IHME estimates of TB incidence, 2013
Globally, WHO estimates of TB incidence (HIV-positive and HIV-negative) are higher than those of
IHME in 2013 (126 vs. 105 per 100 000 population).
Estimates of HIV-negative TB incidence in 2013 by WHO region are shown in Figure 3.1.
Uncertainty ranges are depicted with a horizontal segment (WHO) and a vertical segment (IHME).
The solid line representing the graph of the function y=x is shown as visual aid to help compare
estimates. If a vertical or horizontal segment depicting uncertainty crosses the line y=x, differences in
estimates can be interpreted as statistically insignificant.
Figure 3.1. WHO and IHME estimates of HIV-negative TB incidence rates in 2013, by WHO region. The 22 high-burden countries (HBCs, see background document 2d) are shown in red.
* IHME rates and WHO rates are not based on the same population estimates
AFR AMR EMR
EUR SEA WPR
0
250
500
750
0
50
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0
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50
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0 50 100 150 100 200 300 400 0 100 200 300 400Incidence rate (WHO)
Incid
ence r
ate
(IH
ME
)
6
The uncertainty intervals for the WHO and IHME estimates overlap for 131/153 of the countries
considered in the analysis. This includes overlapping uncertainty intervals for 16/22 HBCs.
Differences between the WHO and IHME estimates are most apparent in the African and South-East
Asia regions. For 70% of the 153 countries included in the comparative analysis, IHME estimates of
HIV-negative TB incidence are higher than those of WHO.
3.3.2 WHO and IHME estimates of HIV-negative TB mortality, 2013
Globally, WHO estimates of HIV-negative TB mortality are lower than those of IHME (15.9 vs. 19.2
TB deaths per 100 000 population).
Estimates of HIV-negative TB mortality in 2013 by WHO region are shown in Figure 3.2.
Uncertainty ranges are depicted with a horizontal segment (WHO) and a vertical segment (IHME).
Whenever a vertical or horizontal segment depicting uncertainty crosses the solid line y=x,
differences in estimates can be interpreted as statistically insignificant.
Figure 3.2. WHO and IHME estimates of HIV-negative TB mortality rates by WHO region, 2013 (log scale). The 22 high-burden countries (HBCs) are shown in red.
Similarities in the WHO and IHME estimates include:
• Uncertainty intervals overlap for 165/187 countries considered in the analysis. This includes
overlapping uncertainty intervals for 20 of the 22 high burden countries (HBCs).
• At regional level, uncertainty intervals overlap only in the Eastern Mediterranean, although
best estimates are relatively close for the European and Western Pacific regions (see also
Figure 3.8).
The most obvious differences between the WHO and IHME estimates are in the African Region,
which has the most limited coverage of national VR systems with cause-of-death data.
3.3.3 WHO and IHME estimates of TB prevalence, 2013
Globally, the WHO estimate of TB prevalence including HIV-positive TB in 2013 was 161 per
100 000 population. IHME estimated a global prevalence excluding HIV-positive TB of 159 per
100 000 population.
Estimates of TB prevalence in 2013 by WHO region are shown in Figure 3.3. Uncertainty ranges are
depicted with a horizontal segment (WHO) and a vertical segment (IHME). Whenever a vertical or
horizontal segment depicting uncertainty crosses the solid line y=x, differences in estimates can be
interpreted as statistically insignificant.
AFR AMR EMR
EUR SEA WPR
1
10
100
1
10
1
100
0.1
1.0
10.0
10
100
1
10
100
1 10 100 0.1 1.0 10.0 1e-41 1e-29 1e-17 1e-05
0.1 1.0 10.0 10 100 1 100Mortality (WHO)
Mort
alit
y (
IHM
E)
7
Figure 3.3. WHO and IHME estimates of TB prevalence by WHO region, 2013 (log scale). The
22 high-burden countries are shown in red.
Uncertainty intervals overlap for 178/187 countries considered in the analysis. This includes
overlapping uncertainty intervals for 20/22 HBCs. At regional level, uncertainty intervals overlap
only in the European and South-East Asia regions (see also Figure 3.12). Finally, propagation of
uncertainty by WHO has produced wider intervals compared to those estimated by IHME.
3.4. Comparison of WHO and IHME results: estimates of trends in TB
incidence, mortality and prevalence, 1990−2013
This section compares WHO and IHME estimates of trends in TB incidence, mortality and prevalence
rates for the period 1990−2013. IHME time-series are shown in red and WHO time-series are shown
in blue.
Figure 3.4. WHO (blue) and IHME (red) estimates of global TB incidence, mortality and
prevalence rates, 1990−2013. Uncertainty intervals are shown by blue and red ribbons.
AFR AMR EMR
EUR SEA WPR
100
1000
10
100
100
10
100
10
100
100 1000 1 100 10 1000
1 10 100 100 1000 10 100 1000Prevalence (WHO)
Pre
vale
nce (
IHM
E)
8
3.4.1 WHO and IHME estimates of HIV-negative TB incidence, 1990−2013
WHO and IHME estimates of global trends in HIV-negative TB incidence rates 1990−2013 are
shown in the first panel of Figure 3.4. Trends for WHO regions, for the 22 HBCs and four countries
considered to have robust surveillance systems are shown in Figure 3.5, Figure 3.6 and Figure 3.7.
Figure 3.5. WHO (blue) and IHME (red) estimates of HIV-negative TB incidence rates by WHO region, 1990−2013. Uncertainty intervals are shown by blue and red ribbons.
Figure 3.6. WHO (blue) and IHME (red) estimates of HIV-negative TB incidence rates, 22 high TB burden countries, 1990−2013. Uncertainty intervals are shown by blue and red ribbons. X symbols
denote case notification rates (HIV-negative).
9
Figure 3.7. WHO (blue) and IHME (red) estimates of HIV-negative TB incidence rates, four countries with robust TB surveillance systems, 1990-2013. Uncertainty intervals are shown by blue and
red ribbons. The X symbol denotes case notification rates (HIV-negative).
Similarities in the WHO and IHME estimates include:
• Globally, the TB incidence rate is estimated to be falling slowly. Although uncertainty
intervals do not overlap, the best estimates are becoming closer over time;
• TB incidence rates are estimated to be falling in five out of six WHO regions. The exception
is Africa, where IHME suggests a slight increase since 2010;
• At regional level, best estimates are quite close for the South-East Asia Region (since 1990),
the Western Pacific Region (since around 2000) and the European Region (since about 2010);
• Among the 22 HBCs, trends in recent years have the same direction for most countries
(downward trend or approximately stable). Exceptions are Kenya, South Africa and
Zimbabwe.
Differences between the WHO and IHME estimates include:
• Uncertainty intervals do not overlap globally or in any region with the exception of 2−3 years
in the European Region. IHME estimates are systematically higher for the African Region
and WHO estimates are systematically higher for the Eastern Mediterranean, South-East Asia
and Western Pacific regions. In the Americas, IHME estimates are lower until around 1997,
and then higher than WHO estimates;
• The trajectories for the European Region are quite distinct.
10
3.4.2 WHO and IHME estimates of HIV-negative TB mortality, 1990−2013
WHO and IHME estimates of global trends in HIV-negative TB mortality rates 1990−2013 are shown
in the second panel of Figure 3.4. Trends for WHO regions, for the 22 HBCs and four countries
considered to have robust surveillance systems are shown in Figure 3.8, Figure 3.9, Figure 3.10 and
Figure 3.11.
Figure 3.8. WHO (blue) and IHME (red) estimates of HIV-negative TB mortality rates by WHO region, 1990−2013. Uncertainty intervals are shown by blue and red ribbons. The global target of a 50%
reduction by 2015 compared with 1990 is shown by dashed lines.
Figure 3.9. WHO and IHME estimates of the distribution of mortality reduction by 2013
compared with the level of 1990 (restricted to countries with a population > 1 million). The red
segments indicate weighted means for each WHO region (using population weights).
Similarities in the WHO and IHME estimates include:
• Globally, TB mortality rates are assessed to be falling. IHME estimates suggest that a 50%
reduction compared with 1990 levels has already been achieved, while WHO projections
suggest a 45% reduction between 1990 and 2013;
• For all 6 WHO regions, IHME and WHO assessments of progress towards the target of a 50%
reduction by 2015 compared with 1990 are broadly consistent. This includes agreement that
------------------------------------------ ------------------------------------------ ------------------------- ------------------------ --------------------- ---------------------
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AFR AMR EMR
EUR SEA WPR
-100
-50
0
50
100
150
-100
-50
0
50
100
150
WHO IHME WHO IHME WHO IHME
Mo
rta
lity
re
du
ctio
n (
%)
11
the target has already been achieved in the Americas and the Western Pacific, and that the
African and European Regions are not on track to do so;
• Uncertainty intervals overlap in the Eastern Mediterranean Region.
Differences between the WHO and IHME estimates include:
• Although incidence estimates for Bangladesh, DR Congo, India and Mozambique are very
similar, mortality estimates are not;
• There are 8 countries where discrepancies in terms of progress towards the 50% reduction
target are apparent: Afghanistan, Bangladesh, Kenya, Nigeria, Pakistan, South Africa,
Tanzania and Uganda.
Figure 3.10. WHO (blue) and IHME (red) estimates of HIV-negative TB mortality rates, 22 high TB burden countries, 1990−2013. Uncertainty intervals are shown by blue and red ribbons. X symbols
denote raw VR data (HIV-negative) before adjustment for ill-defined causes and incomplete coverage. VR data
from South Africa and Zimbabwe were not used by WHO due to the high frequency of misclassification of TB
and HIV causes of death.
Figure 3.11. WHO (blue) and IHME (red) estimates of HIV-negative TB mortality rates, four
countries with robust TB surveillance systems, 1990−2013. Uncertainty intervals are shown by blue
and red ribbons. The X symbol denotes raw VR data on mortality before adjustment for ill-defined causes and
incomplete coverage.
12
3.4.3 WHO and IHME estimates of TB prevalence, 1990−2013
WHO and IHME estimates of global trends in HIV-negative TB prevalence rates 1990−2013 are
shown in the third panel of Figure 3.4. Trends for WHO regions and for the 22 HBCs are shown in
Figure 3.12, Figure 3.13 and Figure 3.14.
Figure 3.12. WHO (blue) and IHME (red) estimates of TB prevalence rates by WHO region, 1990−2013. Uncertainty intervals are shown by blue and red ribbons. The global target of a 50% reduction by
2015 compared with 1990 is shown by dashed lines.
Figure 3.13. WHO and IHME estimates of the distribution of prevalence reduction by 2013
compared with the level of 1990 (restricted to countries with a population > 1 million). The red
segments indicate weighted means for each WHO region (using population weights).
------------------------------------------ ------------------------------------------ ------------------------- ------------------------ --------------------- ---------------------
---------------------------------------------- ---------------------------------------------- --------- --------- -------------- -------------
AFR AMR EMR
EUR SEA WPR
-100
0
100
200
-100
0
100
200
WHO IHME WHO IHME WHO IHME
Pre
va
len
ce
re
du
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n (
%)
13
Figure 3.14. WHO (blue) and IHME (red) estimates of TB prevalence rates, 22 high TB burden countries, 1990−2013. Uncertainty intervals are shown by blue and red ribbons.
Similarities in the WHO and IHME estimates include:
• Globally, TB prevalence is assessed to be falling. Neither IHME or WHO suggests that the
global target of a 50% reduction by 2015 compared with 1990 will be met;
• At regional level, there is some consistency in terms of progress towards the 2015 target of
halving prevalence by 2015 compared with 1990, with neither WHO nor IHME suggesting
that the target will be reached in the African, Eastern Mediterranean or European Regions;
• Estimates for the African Region overlap, as well as in the European and South-East Asia
Regions in recent years.
Differences between the WHO and IHME estimates include:
• The WHO estimates indicate that the 50% reduction target has been met in the Americas and
the Western Pacific, while IHME estimates do not;
• There is not much overlap in uncertainty intervals in three of six WHO regions or in several
HBCs;
• Uncertainty ranges in IHME series are very narrow compared with WHO estimates and
compared with 95% confidence intervals from prevalence surveys (Figure 3.15).
3.5. Analysis of indicators that can assist with assessment and
interpretation of results
To further explore the main results presented in section 3.3 and section 3.4, five indicators were
analysed. These were:
1. Estimates of TB prevalence for countries that have recently conducted a national TB
prevalence survey;
2. Estimates of the case fatality ratio (CFR), calculated as estimated mortality divided by
estimated incidence;
3. Estimates of the case detection rate (CDR), calculated as TB notifications divided by
estimated TB incidence;
4. Estimated rates of change in TB incidence;
5. Changes in the TB incidence rate relative to changes in the TB mortality rate.
14
3.5.1 Prevalence survey data compared with prevalence estimates
Estimates of TB prevalence published by WHO and IHME, compared with estimates of the
prevalence of bacteriologically-confirmed pulmonary TB in adults measured in national TB
prevalence surveys, are shown in Figure 3.15. Adjustments to survey measurements are required to
account for childhood TB and extrapulmonary TB (see also Table 3.1 and background paper 7).
Figure 3.15. Prevalence of bacteriologically-confirmed pulmonary TB in adults in national TB
prevalence surveys, plotted against WHO (blue) and IHME (red) estimates of prevalent TB (all forms, all ages) for the year of the survey (log scale). Error bars show sampling uncertainty (grey) and
propagated uncertainty (blue and red).
PHL: Philippines, CHN: China, VNM: Viet Nam, MMR: Myanmar, KHM: Cambodia, ETH: Ethiopia, LAO:
Laos, PAK: Pakistan, GMB: the Gambia, NGA: Nigeria, RWA: Rwanda.
Surveys are ordered according to the year in which they were undertaken.
Statistical adjustments are made by both WHO and IHME, with overlapping uncertainty ranges in 12
out of 15 surveys. Notable exceptions are IHME’s estimates for the Gambia (2012) and Cambodia
(2002). It is possible that IHME did not have access to the survey results from the Gambia at the time
that their estimates were produced.
The uncertainty range of IHME estimates of prevalence is narrow (relative to best estimates) and
compared with the 95% confidence intervals of survey estimates (relative to the survey best estimate).
This suggests that some sources of uncertainty have not been taken into account. Increased relative
uncertainty is expected compared with survey sampling uncertainty, due to additional sources of
uncertainty about childhood TB and extra-pulmonary TB (which are not measured in surveys).
3.5.2 Estimates of the case fatality ratio (CFR)
Estimates of the case fatality ratio (CFR, calculated as estimated TB mortality divided estimated TB
incidence) are shown for the six WHO regions in Figure 3.16. The horizontal and vertical dashed lines
indicate a CFR of 45%, which is the weighted average of the CFR (combining smear positive and
smear negative cases) for untreated TB based on a recent literature review.4 Very large values of the
CFR suggest that mortality is overestimated, and/or incidence is underestimated.
4 Tiemersma EW, van der Werf MJ, Borgdorff MW, Williams BG, Nagelkerke NJD. Natural history of
tuberculosis: duration and fatality of untreated pulmonary tuberculosis in HIV negative patients: a systematic
review. PLoS One 2011; 6: e17601.
RWA 2012
NGA 2012
GMB 2012
PAK 2011
KHM 2011
LAO 2011
ETH 2011
CHN 2010
KHM 2002
MMR 2009
PHL 2007
VNM 2007
CHN 1990
CHN 2000
PHL 1997
100 200 300 400 500 1000 1500 2000
Prevalence per 100,000
15
Figure 3.16. WHO and IHME estimates of the case fatality ratio (CFR) for HIV-negative TB (mortality/incidence), by WHO region. The solid line representing the graph of the function y=x is shown
as visual aid to help compare the estimates.
3.5.3 Estimates of the case detection rate (CDR)
Estimates of the CDR (calculated as TB notifications divided by estimated TB incidence) are shown
for the six WHO regions in Figure 3.17. The horizontal and vertical dashed lines indicate a CDR of
100%. Although intensive and wide-scale active case finding interventions that capture a large
proportion of the pool of prevalent cases may occasionally lead to a CDR of over 100%, in general the
CDR is not expected to exceed 100%, particularly in countries with weak health systems and
incomplete coverage of health insurance (or equivalent). Very large values of the CDR indicate either
a problem with over-reporting of notified cases (e.g. TB over-diagnosis is observed in some countries
with large-scale chest X-ray screening programmes and low rates of bacteriological confirmation of
pulmonary disease) or an overestimation of the level of TB incidence.
Figure 3.17. WHO and IHME estimates of the case detection rate (notifications divided by estimated TB incidence), for HIV-negative TB. The solid line representing the graph of the function y=x
is shown as a visual aid to help compare the estimates.
AFR AMR EMR
EUR SEA WPR
0.00
0.25
0.50
0.75
1.00
1.25
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0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.5 0.1 0.2 0.3 0.4
0.0 0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4
CFR (WHO)
CF
R (IH
ME
)
AFR AMR EMR
EUR SEA WPR
25
50
75
100
0
30
60
90
120
40
60
80
100
120
140
25
50
75
100
75
100
125
100
200
25 50 75 100 40 60 80 100 60 80 100
70 80 90 100 40 60 80 100 40 60 80 100
CDR (WHO)
CD
R (IH
ME
)
16
3.5.4 Rates of change in TB incidence, 1990−2013
Estimates of the rate of change in TB incidence, calculated as the year-to-year difference in log-
transformed rates, are shown in Figure 3.18. The horizontal and vertical dashed lines indicate a rate of
change of plus or minus 10% per year. Based on empirical historical observations from countries with
high-quality TB surveillance systems, the absolute value of the annual rate of change in incidence is
usually not expected to exceed 10%.
Figure 3.18. WHO and IHME estimates of rates of change in HIV-negative TB incidence by
WHO region, 1990-2013. The solid line representing the graph of the function y=x is shown as visual aid to
help compare estimates from the two institutions. Coordinates are set equal.
AFR AMR
EMR EUR
SEA WPR
-0.10
-0.05
0.00
0.05
0.10
-0.10
-0.05
0.00
0.05
0.10
-0.10
-0.05
0.00
0.05
0.10
-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3
dI/dt (WHO)
dI/d
t (I
HM
E)
1990
1995
2000
2005
2010
year
17
3.5.5 Changes in the TB incidence rate relative to changes in the TB mortality rate
Changes in the estimated TB incidence rate relative to changes in the estimated HIV-negative TB
mortality rate are shown for IHME and WHO estimates in Figure 3.19 and Figure 3.20. The rate of
change is calculated as the year-to-year difference in log-transformed rates. WHO estimates appear to
exhibit a stronger correlation between time changes in incidence and time changes in mortality
compared with IHME estimates.
Figure 3.19. Change in estimated HIV-negative TB incidence rate relative to change in
estimated HIV-negative TB mortality rate, IHME estimates. The solid line representing the graph of
the function y=x is shown as visual aid to help assess the consistency between the year-to-year change in
incidence and the year-to-year change in mortality. The blue dashed line shows the least-square best fit to the
data.
Figure 3.20. Change in estimated HIV-negative TB incidence rate relative to the change in
estimated HIV-negative TB mortality rate, WHO estimates. The solid line representing the graph of
the function y=x is shown as visual aid to help assess the consistency between the year-to-year change in
incidence and the year-to-year change in mortality. The blue dashed line shows the least-square best fit to the
data.
AFR AMR EMR
EUR SEA WPR
-0.10
-0.05
0.00
0.05
-0.04
0.00
0.04
-0.050
-0.025
0.000
0.025
0.050
-0.05
0.00
0.05
0.10
-0.06
-0.03
0.00
0.03
-0.04
0.00
0.04
0.08
-0.2 -0.1 0.0 0.1 0.2 -0.4 -0.2 0.0 -0.4 -0.3 -0.2 -0.1 0.0 0.1
-0.50 -0.25 0.00 0.25 -0.1 0.0 0.1 -0.4 -0.3 -0.2 -0.1 0.0
dM/dt (IHME)
dI/dt (I
HM
E)
1990
1995
2000
2005
2010
year
AFR AMR EMR
EUR SEA WPR
-0.2
-0.1
0.0
0.1
0.2
0.3
-0.2
-0.1
0.0
0.1
0.2
-0.1
0.0
0.1
-0.2
-0.1
0.0
0.1
0.2
0.3
-0.10
-0.05
0.00
0.05
-0.10
-0.05
0.00
0.05
-1.0 -0.5 0.0 0.5 1.0 -3 -2 -1 0 1 2 3 -1 0 1
-2.5 0.0 2.5 -1 0 1 -2.5 0.0 2.5 5.0
dM/dt (WHO)
dI/dt (W
HO
)
1990
1995
2000
2005
2010
year
18
ANNEX 3.1.
Table A3.1. Countries that are not included in IHME estimates
Countries not captured by
IHME
Population % of total
global
population
Estimated
number of
incident cases
% of
total
estimated
global
incidence
American Samoa 55165 0.0008 4 0.0000
Anguilla 14300 0.0002 3 0.0000
Aruba 102911 0.0014 13 0.0001
Bermuda 65341 0.0009 0 0.0000
Bonaire, Saint Eustatius and
Saba
19130 0.0003 0 0.0000
British Virgin Islands 28341 0.0004 1.1 0.0000
Cayman Islands 58435 0.0008 5.8 0.0001
China, Hong Kong SAR 7203836 0.1010 5500 0.0579
China, Macao SAR 566375 0.0079 500 0.0053
Cook Islands 20629 0.0003 2.3 0.0000
Curaçao 158760 0.0022 2.3 0.0000
French Polynesia 276831 0.0039 60 0.0006
Greenland 56987 0.0008 110 0.0012
Guam 165124 0.0023 55 0.0006
Monaco 37831 0.0005 0.79 0.0000
Montserrat 5091 0.0001 0 0.0000
Nauru 10051 0.0001 4.7 0.0000
New Caledonia 256496 0.0036 50 0.0005
Niue 1344 0.0000 0 0.0000
Northern Mariana Islands 53855 0.0008 38 0.0004
Palau 20918 0.0003 9.2 0.0001
Puerto Rico 3688318 0.0517 58 0.0006
Saint Kitts and Nevis 54191 0.0008 0 0.0000
San Marino 31448 0.0004 0.48 0.0000
Saint Maarten (Dutch part) 45233 0.0006 2.3 0.0000
Tokelau 1195 0.0000 0 0.0000
Turks and Caicos Islands 33098 0.0005 2.3 0.0000
Tuvalu 9876 0.0001 23 0.0002
US Virgin Islands 106627 0.0015 8.2 0.0001
Wallis and Futuna Islands 13272 0.0002 1.2 0.0000
Total 13161009 0.1844 6454.67 0.0679
19
Table A3.2. Countries that are not included in WHO estimates of HIV-negative TB incidence
Countries for which
WHO does not have
estimates of HIV-
negative TB incidence
rates
Population % of total global
population
Estimated
number of
incident cases
% of total
estimated global
incidence
Albania 3173271 0.0445 590 0.0062
Andorra 79218 0.0011 5.8 0.0001
Antigua and Barbuda 89985 0.0013 12 0.0001
Bahrain 1332171 0.0187 240 0.0025
Bosnia and
Herzegovina
3829307 0.0537 1700 0.0179
Brunei Darussalam 417784 0.0059 240 0.0025
Comoros 734917 0.0103 250 0.0026
Cyprus 1141166 0.0160 66 0.0007
Dominica 72003 0.0010 3.5 0.0000
Grenada 105897 0.0015 4.3 0.0000
Iraq 33765232 0.4732 15000 0.1578
Jordan 7273799 0.1019 420 0.0044
Kiribati 102351 0.0014 510 0.0054
Kuwait 3368572 0.0472 810 0.0085
Lebanon 4821971 0.0676 760 0.0080
Libya 6201521 0.0869 2500 0.0263
Marshall Islands 52634 0.0007 190 0.0020
Micronesia
(Federated States of)
103549 0.0015 190 0.0020
Qatar 2168673 0.0304 870 0.0092
Saint Lucia 182273 0.0026 10 0.0001
Saint Vincent and the
Grenadines
109373 0.0015 26 0.0003
Samoa 190372 0.0027 33 0.0003
Saudi Arabia 28828870 0.4040 4000 0.0421
Seychelles 92838 0.0013 28 0.0003
Solomon Islands 561231 0.0079 520 0.0055
South Sudan 11296173 0.1583 17000 0.1789
Syrian Arab Republic 21898061 0.3069 3700 0.0389
Former Yugoslav
Republic of
Macedonia
2107158 0.0295 370 0.0039
Timor-Leste 1132879 0.0159 5600 0.0589
Tonga 105323 0.0015 14 0.0001
Turkmenistan 5240072 0.0734 3800 0.0400
United Arab Emirates 9346129 0.1310 160 0.0017
Vanuatu 252763 0.0035 160 0.0017
West Bank and Gaza
Strip
4326295 0.0606 200 0.0021
Total 154503831 2.1652 59982.6 0.6311
