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BACKGROUND DOCUMENT 4e

Prevalence surveys of TB disease post-2015 Why, How, Where?

Prepared by: Philippe Glaziou, Irwin Law, Babis Sismanidis,

Ikushi Onozaki, Katherine Floyd

Questions for discussion

1. Do you recommend that national prevalence surveys should be conducted

post-2015?

If yes: 2. Do you agree with the suggested updates to survey methods, in particular:

● Use of Xpert® MTB/RIF (hereafter Xpert) instead of smear microscopy

and culture, with adjustments to survey results until Xpert has equivalent

performance to culture;

● Strengthening of overall governance/oversight mechanisms including

more formal arrangements for survey monitoring and related actions by

implementers and sponsors;

● Ensuring Good Clinical Data Management Practices, including quality

control at each stage of data handling to ensure that all data are reliable

and have been processed correctly;

● Investment of more resources in the work required once results are

finalized, especially to ensure the timely production of survey reports and

effective communication of findings and their implications.

3. Do you have any suggestions for other improvements to survey methods?

4. Do you agree with the proposed criteria for identifying which countries

should consider a national survey post-2015, or would you propose

modifications to these criteria?

5. Should there be any country prioritisation within groups 1 and 2, from a

regional and/or global perspective?

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Introduction

National TB prevalence surveys in 22 global focus countries was one of the three major strategic

areas of work of the Global Task Force on TB Impact Measurement during the period 2008–

2015. An additional 31 countries were identified as eligible to implement a survey, based on

epidemiological criteria agreed by the Task Force in December 2007. In 2010, a major

collaborative effort of Task Force members and other contributors (a total of 50 people from 15

institutions) was used to produce an updated WHO handbook on the design, implementation,

analysis and reporting of prevalence surveys (the “Lime Book”) [1]. The Lime Book built on the

first edition of the handbook (the “Red Book”), which was published in 2007 under the

coordination of WHO’s Western Pacific Regional Office. The Lime Book was able to draw on

experience and lessons learned from a growing number of surveys conducted in Asia as well as a

survey in Eritrea, and a recognized need to expand the content of the book and in some instances

provide more definitive recommendations (e.g. on the screening strategy to be used).

Between 2009 and 2015, 19 countries implemented a survey according to the key methods set out

in the Lime Book. These included 14 of the 22 global focus countries,1 plus 5 other countries.

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further two global focus countries started a survey in 2015 (Bangladesh, Kenya), an additional

three will implement repeat surveys in 2016 (the Philippines) or 2017 (Myanmar, Viet Nam), and

two others will likely commence in 2016 (Mozambique and South Africa). These surveys have

provided a substantial new body of evidence on the burden of TB disease in high TB burden

settings, based on direct measurements (see background documents 4a-c).

Building on the results and lessons learned from surveys implemented 2009–2015 and in the

context of a new era of the Sustainable Development Goals (SDGs) and the End TB Strategy

(background document 1), it is important to consider the role of prevalence surveys in the post-

2015 era: are they still needed, and if yes, where should they be prioritized and should methods

be updated?

This background paper addresses these three questions. It is structured in three parts:

1. Why are prevalence surveys useful? This section explains the rationale for surveys,

illustrated by results and lessons learned from surveys completed 2009–2015.

2. How could survey methods be improved post-2015? This section starts by

summarizing the major challenges faced in surveys implemented 2009–2015, based on

the more detailed assessment provided in background paper 4d. Suggestions for possible

updates to how surveys are done post-2015, to address these challenges and to take

advantage of advances in diagnostic tests and other technologies, are then discussed. The

focus is on four major topics: use of Xpert (or equivalent rapid molecular test) as a

replacement for smear and culture; strengthening overall governance/oversight

mechanisms, especially survey monitoring and the role of the sponsor (funder); ensuring

Good Clinical Data Management Practices; and investment of more resources in the

work required once results are finalized, in particular to ensure the timely production of

survey reports.

3. Where should prevalence surveys be prioritized post-2015? This section proposes

criteria for identifying countries where prevalence surveys should be considered. This is

done separately for a) countries that implemented a survey 2007–2015 and b) countries

that did not implement a survey 2007–2015.

1 In order of when they were implemented: Myanmar (2009-2010); China (2010); Pakistan, Cambodia, Ethiopia

(2011); Thailand, Tanzania, Rwanda and Nigeria (2012); Malawi, Ghana (2013); Indonesia (2013-2014); Zambia

(2014); Uganda (2015). 2 Lao PDR (2010-2011), Gambia (2012), Sudan (2013), Zimbabwe (2014), Mongolia (2015).

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1. Why are prevalence surveys useful?

Measuring the burden of TB disease and monitoring time trends are critical for planning TB

control interventions, assessing their impact on population health and for evaluation of whether

global targets for reductions in disease burden are achieved.

Post-2015 targets for reductions in TB disease burden have been set as part of the United

Nations’ post-2015 SDGs (which cover the period 2016–2030 and replace the MDGs, 2000–

2015) and in WHO’s End TB Strategy (2016–2035). Under the health-related SDG3, target 3.3 is

defined as: “By 2030, end the epidemics of AIDS, TB, malaria and neglected tropical diseases

and combat hepatitis, water-borne diseases and other communicable diseases.” Incidence is the

key indicator that will be used to monitor progress for TB (see also background document 1). The

End TB Strategy includes targets (for 2030 and 2035) and milestones (for 2020 and 2025) for

reductions in TB deaths and TB incidence. The 2030 targets are an 80% reduction in the TB

incidence rate and a 90% reduction in the number of TB deaths, compared with levels in 2015

(for further details, see background document 1).

Although TB disease prevalence is not an indicator explicitly included within the SDGs and End

TB Strategy, WHO guidance on the operationalization of the strategy at country level emphasizes

that measurement of TB prevalence will continue to be relevant in some countries. Part of the

country adaptation that is an explicit principle of the End TB Strategy will need to include

consideration of whether a national (or subnational) prevalence survey is needed to measure TB

disease burden and trends. The rest of this section explains why prevalence surveys will continue

to be relevant post-2015, illustrating the knowledge and insights that they can provide using the

results and lessons learned from surveys implemented 2009–2015 (see also background

documents 4a-c).

1.1 Variability in TB burden

Ideally, nationwide disease surveillance systems should provide direct measurements of TB

incidence. However, most countries with a high TB burden do not yet have notification systems

that capture all cases (see also background document 2a and 2d). In particular, cases diagnosed in

the private sector may not be reported, and health systems in many countries lack the reach and

quality required to ensure that all (or virtually all) cases are diagnosed. Routine surveillance may

also include patients misdiagnosed as TB cases. Given gaps in routine surveillance, the only

way to obtain an unbiased estimate of the burden of TB disease in many endemic countries,

and to monitor trends, is to conduct population-based national surveys of the prevalence of TB

disease.

Methods currently used by WHO [2] to estimate TB incidence can be grouped into four major

categories:

1. Case notification data combined with expert opinion about case detection gaps (120

countries in 2015);

2. Results from national TB prevalence surveys (19 countries in 2015);

3. Notifications in high-income countries adjusted by a standard factor to account for under-

reporting and under-diagnosis (73 countries in 2015);

4. Capture-recapture modelling (5 countries in 2015).

Findings from national TB prevalence surveys provide much more robust estimates of TB

incidence (method 2) compared with the first method (currently, the most commonly used

method in endemic countries) based on case notifications and expert opinion about the plausible

range of the case detection gap, which results in biases about the best estimate of incidence and

often, an over-optimistic assessment of uncertainty about it. Figure 1 compares recent estimates

of TB incidence derived from prevalence survey findings to estimates obtained from eliciting

expert opinion about gaps in case detection for the same year.

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Figure 1. Estimates of TB incidence obtained indirectly (in blue) based on case notifications

and expert opinion about detection and reporting gaps prior to a recent national prevalence

survey, compared with estimates derived from prevalence using survey results (in red). Estimates correspond to the survey year (2012–2015), and are based on recent WHO methods [2]. Indirect

estimates were generally provided by experts with over-confident uncertainty ranges and are more heavily

biased than estimates derived from national survey results.

Figure 2. Estimates of TB prevalence (all ages, all forms of TB) for 18 countries, before (in

blue) and after (in red) results from national prevalence surveys became available,

corresponding to the year of the survey. Panels are ordered according to the size of the before−after

difference. The wide uncertainty interval of the post-survey estimate for the United Republic of Tanzania is

because laboratory challenges meant that it was only possible to directly estimate the prevalence of smear-

positive (as opposed to bacteriologically confirmed) TB. Results for Mongolia were not final by the end of

March 2016 and thus are not shown.

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Disease prevalence also has the advantage of being a direct measure of illness caused by TB in a

population [1,3]. Compared with incidence, prevalence captures another facet of disease burden

by accounting for illness duration. The prevalence of infectious cases also determines how much

transmission takes place in any population, because the annual risk of infection, denoted λ, equals

the number of infectious contacts made by each case per person per year (β) multiplied by the

prevalence of infectious cases (P): λ=βP. Given its dependence on the duration of illness,

prevalence responds more rapidly than incidence to improved case finding and drug treatment,

since both interventions shorten disease duration [4].

Large variation in levels of TB prevalence has been documented in prevalence surveys (Figure

2). Furthermore, surveys in Cambodia, Lao PDR, Myanmar and Viet Nam demonstrated that the

burden was much higher than previously estimated using findings from tuberculin surveys, while

those in Nigeria and Indonesia demonstrated a burden much higher than previously estimated

from case notifications and expert opinion about plausible detection gaps. In some other countries

such as the Gambia, prevalence was measured at a lower level than previously estimated.

1.2 Time trends

When repeat surveys are implemented with intervals of approximately 10 years, trends in

prevalence over time can be assessed. Surveys in Cambodia, China, Republic of Korea and the

Philippines have shown that TB prevalence can be halved in a decade (Figure 3).

Figure 3. Time trends in TB prevalence

1.3 Distribution of disease by age and sex

Prevalence surveys can be used to better understand TB epidemiology (e.g. the distribution of

disease by age, sex and geographical variation). The distribution of prevalent cases by age in

recent surveys is shown in Figure 4, and by sex in Figure 5. Findings show that men are

consistently at a higher risk of TB disease across countries and that age-specific rates tend to be

highest in the elderly.

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Figure 4. Age-specific bacteriologically confirmed prevalence rates in countries that

implemented a national TB prevalence survey, 2009–2015.

Figure 5. Sex ratio of prevalent cases, surveys implemented 2009–2015. Tanzania is not shown

because the number of bacteriologically-confirmed cases could not be verified.

1.4 Gaps in detection and reporting

When measurements of prevalence are compared with notifications, prevalence surveys can

identify gaps in detection and reporting. Ratios of prevalent to notified cases (for smear-positive

cases) are shown in Figure 6. Cross-country and male:female comparisons of the prevalence to

notification (P:N) ratio show that in several countries, it should be possible to achieve much more

with strategies and technologies for TB care and control that are already available, and to close

reporting and detection gaps for men. While the burden of TB disease is much higher in men,

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P:N ratios indicate that women are probably accessing available diagnostic and treatment services

in primary care more effectively. They also indicate that cases among older age groups tend to be

detected less effectively, at least in some countries (Figure 7). Reasons for detection and

reporting gaps, particularly among men, such as access barriers, quality of clinical and diagnostic

services, need to be investigated.

Figure 6: Prevalence to notification ratios by sex in countries where prevalence surveys

were implemented 2009–2015. Following the publication of WHO’s Definitions and Reporting

Framework for Tuberculosis (2013 revision), data on smear-positive notifications disaggregated by sex

have not been systematically collected at the global level since 2013 [5]. For this reason, the P:N ratio

could not be calculated for Sudan, Uganda, Zambia and Zimbabwe. For Ghana and Malawi, smear-positive

notifications disaggregated by sex were obtained from the NTP.

Figure 7. Prevalence to notification ratios by age group (years) in four selected countries.

In Vietnam [6] and Indonesia, records of cases on treatment among participants in national

prevalence surveys could be linked with the records of newly detected cases from routine TB

surveillance. This allowed measurement of the magnitude of under-reporting of detected cases.

Under-reporting from the private sector was found to be more important (over 50%) in Indonesia

than previously anticipated, explaining in part the very large P:N ratio. More surveys in the future

could include record-linkage studies of cases detected before survey investigations with records

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from the national TB database, to assess the level of under-reporting, although inventory studies

(see background document 2d) may generally be a better approach since cases can be better

documented.3

In recent surveys in Africa, it has been possible to measure the prevalence of HIV infection

among prevalent TB cases.4 Findings confirmed a lower prevalence of HIV among prevalent

cases compared with newly notified cases (Figure 8). HIV-infected cases may have TB detected

faster, particularly if already enrolled in HIV care programmes. In addition, their illness duration

is shortened by higher case fatality ratios, particularly in people who are immunocompromised

but not on antiretroviral therapy. These results help better understand the dynamics of HIV-

positive TB.

Figure 8. Prevalence of HIV among prevalent TB cases compared with newly notified cases. The dashed line shows equality. The area of bubbles is proportional to the standard deviation of the survey

estimate. Horizontal segments in blue indicate the mean values of HIV prevalence among prevalent cases

predicted using mixed-effects modelling, and vertical segments their 95% confidence interval.

1.5 Screening for TB

All prevalence surveys indicate a poor sensitivity of symptom screening for TB. Among

bacteriologically confirmed cases, typically 30–50% reported no symptoms meeting survey

screening criteria (Figure 9). This has implications for routine TB detection policies, since many

countries focus their TB detection efforts on chronic coughers.

Commonly used diagnostics, particularly direct microscopic examination of sputum smear

samples, need to be upgraded with better technology, including WHO-approved rapid diagnostics

that are more sensitive and more specific than sputum microscopy. Screening and diagnostic

3 Cases detected before survey investigations are typically not as well documented as survey cases detected during

investigations, particularly in countries where culture or Xpert are not routinely used. 4 HIV testing during field operations was conducted in four countries: Zambia (all participants offered an HIV test);

and Rwanda, Tanzania and Uganda (HIV test offered only to those who screened positive by symptom-based interview

and/or chest X-ray). In Zimbabwe, all bacteriologically confirmed TB cases were offered HIV testing as part of routine

case management, but not directly as part of the survey itself. Instead of offering HIV testing, in Malawi all

participants were asked if they had ever been tested for HIV and, if willing, to state their status. These questions were

also asked in Rwanda, Tanzania and Zimbabwe, but limited to only those participants who screened positive. In

Uganda and Zambia, participants were not asked about a history of being tested for HIV or knowledge of their HIV

status.

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algorithms used in routine clinical care over the past decade need revisiting, and the role of chest

X-ray should be re-evaluated and likely expanded.

Figure 9. Proportion symptom screening negative but chest X-ray positive where

prevalence surveys were implemented 2009–2015.5

1.6 Health care-seeking behaviour

Standardised survey data from multiple countries on patterns of health care-seeking behaviour

(Figure 10, Figure 11) can help to identify actions that NTPs and/or health services in general

could take to shorten the time to TB diagnosis and to ensure prompt provision of high-quality

care. For instance, a large proportion of symptomatic patients took no action (Figure 10) in most

countries, suggesting barriers to access health services. The observed proportion of cases treated

in the private sector (Figure 11) is a useful measure to assess the need for, and coverage of,

public-private mix approaches. All TB care providers need to be engaged, including for case

notification, which should be a mandatory policy that is actively enforced.

Figure 10. Frequency of medical care sought by symptomatic TB cases before the survey

and distribution of types of services

5 Bacteriologically confirmed cases could not be verified in Tanzania.

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Figure 11. Percentage of participants on TB treatment at the time of the survey who were

being treated in the private sector

1.7 Performance of sputum smear microscopy

Sputum smear microscopy was found to be poorly predictive of TB in several surveys (Figure

12), with a large proportion of false positive results even though the population tested had already

been screened for presumptive TB. This highlights the limitation of smear microscopy when used

without confirmation (using another test based on a different technique). In the context of active

case finding, sputum microscopy should be considered an unreliable diagnostic test for TB,

unless high positive predictive values can be demonstrated in the targeted population group.

Figure 12. Frequency of false-positive examination to diagnose TB in selected surveys using

rapid molecular tests. Results are shown for the surveys in which there was systematic use of rapid

molecular tests. In all except one survey, Xpert was used. The exception was Sudan, where LPA was used.

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2. How could survey methods be improved post-2015?

Between 2009 and 2015, 19 national TB prevalence surveys were completed, including 11 in Africa

and 8 in Asia. This is a historically unprecedented effort and achievement, built on substantial

efforts made by countries and their technical and financial partner agencies and advisors, with

overall global guidance and coordination from WHO and with the Lime Book as the key reference

document for all aspects of surveys from design through to reporting of results. Of the 19 surveys,

12 were first-ever national surveys (Ethiopia, Gambia, Lao PDR, Malawi, Mongolia, Nigeria,

Rwanda, Sudan, Tanzania, Uganda, Zambia, Zimbabwe) a further two (Ghana, Indonesia) were the

first in almost 50 years to use the screening methods recommended in the Lime Book, a further one

(Myanmar) was the first using screening methods recommended in the Lime Book and the survey in

Pakistan was the first since 1987. Only Cambodia, China and Thailand had already conducted

surveys in the period since 1990.

A substantial new body of knowledge has been generated by the 19 surveys completed between

2009 and 2015 (section 1). At the same time, given the scale and technical, financial and logistic

demands of conducting surveys with samples sizes typically ranging from 50 000–100 000 people,

and no previous experience of such surveys in most countries, it is inevitable that various challenges

have been faced, despite efforts to standardize methods and to provide as much guidance and

support as possible. For surveys post-2015, it is important that these challenges are clearly

documented, and solutions proposed where possible. Solutions may include, but are not limited to,

the adoption of new technologies.

This section starts by providing a summary of the main challenges that were faced, based on the

more detailed tabular summary shown in background document 4d. This is followed by discussion

of four suggestions for how to improve survey methods post-2015.

2.1 Challenges faced in prevalence surveys implemented 2009–2015

The major challenges faced in surveys implemented 2009–2015, and the number of times they

occurred, are shown in Table 1. Other challenges that occurred but that were not considered to be

“major” challenges are shown in Table 2.6 Counts are based on the more detailed information

shown in background document 4d, for which the main sources were survey monitoring reports and

personal knowledge of the surveys among WHO staff (based on a mixture of first-hand experience

of a survey, information shared during meetings or workshops as well as directly with WHO, the

update of survey status prepared by GTB/TME each quarter, and draft chapters of a book currently

in preparation on prevalence surveys 2009–2015 that includes country-specific chapters within

which major successes and challenges are summarized).7

Major challenges included:

● The time taken to complete survey preparations. This was more than three years in most

countries, with delays primarily linked to delays in the procurement of X-ray equipment, a

need to strengthen laboratory capacity or the time taken to identify a suitable implementing

agency;

● Low participation rates in urban areas;

● Problems with culture examinations. More than 15% of the sputum smear-positive samples

of TB survey cases failed to grow in more than half of all surveys;

● An incomplete data management plan at the time field operations started, and problems

with data management and data cleaning during and after field operations (often linked to

the initial data management plan);

● Delays in case management;

● Delays in producing the final survey report, sometimes (but not always) related to delays in

producing a final, clean dataset.

6 These challenges are not considered “major” in this document either because a) they only affected a few clusters, and/or

b) they could be resolved in a timely manner, and/or c) did not lead to a major bias in final results and estimates. 7 Prior to any wider distribution of the assessment shown in background document 4d, the contents will be reviewed by

survey investigators and those who provided technical assistance.

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Table 1. Major challenges observed in national prevalence surveys completed 2009–2015

Challenges Number of surveys

affected* Preparations Time taken to complete survey preparations (i.e. ≥3 years from decision to

implement to start of field operations 14/19

Delays due to chest X-ray procurement 7/19

Delays due to upgrading laboratory capacity 3/18

Field and central operations Low participation rates (<80%) 5/19

Delays in case management 7/16

Laboratory Low culture confirmation among smear-positive study cases (<85%) 11/19

Failure of biosafety cabinet 1/19

Laboratory protocol violations 6/19

Data management Inadequate data management plan 6/19

Major data management problems during field operations that were not resolved

during field operations 5/19

Considerable time taken to clean data (1+ year) 5/19

Dissemination of results Long delays (1+ year) before survey results accepted by public health authorities 4/19

Long delays in producing the final report of the survey (1+ year) 8/18 * The total number of countries in the denominator varies because information was not available for all surveys at the

time this document was prepared.

Table 2. Other challenges of note that were observed in national prevalence surveys

completed 2009–2015. These challenges are not considered as “major” either because a) they only

affected a few clusters, and/or b) they could be resolved in a timely manner, and/or c) did not lead to a

major bias in final results and estimates.

Challenges Number of surveys

affected Recruitment of survey coordinator and/or data manager 6/19

Access to census data prior to sampling; inaccurate census data or sampling errors;

restricted sampling frame (>5% of national population in eligible age group) 5/19

Issues related to maintenance of X-ray equipment 11/19

Data management problems impacting time to process data, but resolved 5/19

Delays/interruption of 2-3 months during the survey in disbursement of funding 4/19

A few clusters not visited or postponed due to insecurity or adverse weather 10/19

Inadequate corrective action following recommendations of external experts 5/19

Delays in central chest X-ray reading 4/18* * The total number of countries in the denominator varies because information was not available for all surveys at te

time this document was prepared.

It is notable that there were challenges related to data management in each phase of the surveys

(preparations, field operations, post-field operations). These occurred despite an entire chapter

dedicated to the topic in the Lime Book and later updates in the form of web-based material, as

well as direct technical assistance. Examples of problems included data entry errors (including

wrong personal identifiers) and disjointed data tables captured in different systems with no

enforcement of database relations (e.g. laboratory data captured in a database system that was

separate from the system holding clinical data, resulting in an inability to successfully match all

laboratory records with corresponding clinical records), the absence of an audit trail capturing all

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changes made to case report forms and to computer records, and loss of source documents. In a

few cases, these problems required data re-entry in a newly designed database system, leading to

long delays in the production of a final, clean dataset. In a small number of surveys, it was not

possible to check all records against source documents, either because source documents were

not maintained properly and/or because some were lost (e.g. Tanzania), or because identifiers

were wrongly captured in the first place (e.g. Pakistan).

2.2 Possible updates to how surveys could be undertaken post-2015

Given the challenges faced in surveys 2009–2015, consideration of changes to how surveys are

undertaken post-2015 is warranted. Four possible updates that address the biggest challenges

faced are proposed for discussion. These are: 1. Use of Xpert (or equivalent rapid molecular test) instead of smear and culture, alongside

adjustments to results until Xpert tests are comparable to culture;8

2. Strengthening of overall governance/oversight mechanisms including more formal

arrangements for survey monitoring and related actions by implementers and sponsors;

3. Ensuring Good Clinical Data Management Practices[10], including quality control at

each stage of data handling to ensure that all data are reliable and have been processed

correctly;

4. Investment of more resources in the work required once results are finalized, especially

to ensure the timely production of survey reports and effective communication of

findings and their implications.

2.2.1 Use of Xpert (or equivalent rapid molecular test) instead of smear and culture

The gold standard test for prevalence surveys has been culture of M. tuberculosis from sputum

samples. However, culture suffers from four limitations in the context of a survey:

● Test quality is affected by the time to process a sample. Long transportation times from

remote clusters to the designated laboratory put the natural viability of M. tuberculosis at

risk and also increases the risk of sample contamination. Furthermore, if large numbers

of samples are delivered on top of the normal workload for routine (non-survey) testing,

the average processing time may increase. Contamination can be partly controlled

through sample decontamination, but decontamination may kill M. tuberculosis if it is

applied too harshly. Maintaining high quality standards for culture throughout the survey

period and across designated laboratories was problematic in several surveys

(background document 4d);

● Culture quality is generally lower than pre-survey proficiency standards, as illustrated by

the failure to grow more than 15% of the sputum smear-positive samples of TB survey

cases in more than half of recent surveys. This can generate false-negative cases, but it is

difficult to predict in what number or to adjust for this problem after surveys are

completed;9

● Missing culture results are observed in most surveys, creating problems to ascertain

cases. In more recent surveys, such problems have been addressed using Xpert as a

confirmatory test, combined with a panel review of individual cases (including review of

X-rays and other documentation) with missing results.

Xpert was first recommended for use by WHO in December 2010. The use of this test (or

improved versions of Xpert, or equivalent tests) has various advantages compared with culture. It

is rapid, automated, does not require fresh samples to perform optimally and does not require

stringent laboratory containment. Testing without centrifugation has the added advantage of

8 The current WHO policy update for Xpert MTB/RIF states that “Xpert MTB/RIF may be used rather than

conventional microscopy and culture as the initial diagnostic test in all adults suspected of having TB”. Policy

update: Xpert MTB/RIF assay for the diagnosis of pulmonary and extrapulmonary TB in adults and children

http://www.who.int/tb/laboratory/xpert_launchupdate/en/ 9 An extreme case (the only survey where this happened) was Tanzania, where the total number of cases with positive

cultures was less than the number of cases with positive smear results.

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minimising cross-contamination. Surveys which have used Xpert for all smear-positive samples

include Ghana, Malawi, Indonesia, Uganda, Zambia and Zimbabwe. Current surveys in

Bangladesh, Kenya and the Philippines are now using Xpert alongside culture for all samples.

Two possible designs

There are two possible designs for a survey based on testing with Xpert that maintains the

existing screening algorithm based on symptoms and chest X-ray. These are:

1. Chest X-ray and symptom screening, followed by testing of those meeting screening

criteria with Xpert, with no confirmatory test used for Xpert-positive cases.

2. As for 1, plus confirmatory testing of Xpert-positive cases using a different test (e.g.

culture, or test with Xpert on a second sample).

Design 1 may be particularly useful using the upcoming Xpert® MTB/RIF Ultra test. With the

currently available Xpert tests, Design 2 is preferable to Design 1.

Adjustments required to survey results based on Xpert testing

Until a version of Xpert (or equivalent molecular test) has equivalent performance to culture

under optimum conditions, then if Xpert were to be used in prevalence surveys it would be

necessary to adjust survey results to account for false-positive and false-negative results:

● A test with suboptimal specificity (in this case, compared with culture in optimum

conditions) will generate a greater proportion of false-positive results in a survey of the

general population compared with routine clinical investigations. How to account for this

problem when providing test results to patients requires a decision prior to starting the

survey, based on the expected predicted values for a positive test, and following WHO

recommendations for the clinical use of test results.

● False-positive and false-negative results will generate a bias in the estimate of prevalence

in the tested population. This misclassification bias can be quantified and accounted for

to obtain a bias-corrected prevalence estimate from the observed apparent prevalence,

using a Bayesian modelling approach described immediately below that is simple to

implement .

The adjustments to results that would be needed if either of the two designs that include Xpert

testing for all participants that screen positive (on symptoms or chest X-ray) are illustrated below.

Design 1: Chest X-ray and symptom screening, followed by testing of those meeting

screening criteria with Xpert, with no confirmatory test used for Xpert-positive cases

Suppose that a survey identifies n=5000 survey participants who screen positive using a

combination of chest X-ray signs and reported symptoms based on current WHO

recommendations [1]. Further assume that m=150 among them have an Xpert positive test result.

A recent meta-analysis showed that the pooled sensitivity (se) and the 95% confidence interval

for Xpert was 67% (62% to 71%) while the pooled specificity (sp) was 98% (97% to 99%) [7].

These pooled distributions for sensitivity and specificity values may be updated with data

obtained from recent active case finding activities or from survey results where both Xpert and

culture were used and performed to quality standards. It is suspected that sensitivity may be

decreased to some extent in the context of active case finding, due to a higher expected

proportion of paucibacillary cases.

Let denote apparent prevalence

No sampling design effect is assumed when calculating the 95% confidence interval of (simple

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random sampling is assumed).

Let denote bias-corrected (true) prevalence. is expressed in terms of , se and sp, as follows

(1)

In the above numerical example, the measured (apparent) prevalence is severely biased towards

high values, driven upwards by false-positive results.

Both the numerator and the denominator of the right hand side of equation (1) represent variable

quantities, while se and sp are usually found to be slightly variable between populations and

studies; meta-analyses typically report a 95% confidence interval about these quantities.

Uncertainty about se and sp needs to be propagated as well as uncertainty about that is due to

sampling. Propagating uncertainty can be carried out using a simple Bayesian model

implemented in JAGS [8] or in WinBUGS (see model specification in Appendix 1). To set up the

model, one may assume that se and sp follow a Beta distribution, with parameters obtained using

the method of moments [9]. An uninformative prior is set on . The likelihood function

is obtained by solving equation (1) for

(2)

The conditional probability distribution of 𝜙 is proportional to the product of the likelihood and

the prior,

from which the usual summary statistics are extracted

This example illustrates that the bias correction using equation (1) is not sufficient as it yields an

underestimate of disease prevalence among participants with a positive screening result.

To account for sampling design effects in the measurement of , one may define n’ as the

effective sample size (n’ will usually be smaller than n) and calculate m’ so that the ratio m’/n’ =

m/n. The effective sample size is defined as the number tested divided by the sampling design

effect (deff): n’=n/deff. The design effect equals 1 when simple random sampling is used and is

typically greater than 1 when cluster sampling is used.

Design 2: As for Design 1, plus confirmatory testing of Xpert-positive cases using a different

test (e.g. culture, or test with Xpert on a second sample)

If positive Xpert test results are confirmed with a second test, the modelling approach for Design

1 would be modified as follows:

1. define m as the number of Xpert positive cases and a positive confirmatory test

2. set specificity to 100% (Xpert false-positive results are no longer included in m) and

simplify the likelihood function:

With n=5000 tested individuals, and m=150 confirmed cases,

16

To account for sampling design effects in the measurement of , one may define n’ as the

effective sample size and calculate m’ so that the ratio m’/n’ = m/n, as for Design 1.

The adjustments required for a third design in which there is no chest X-ray nor symptom

screening is shown in Appendix 2. This may have relevance in countries in which widespread

difficulties in transporting mobile chest X-ray equipment to cluster sites are anticipated (e.g.

those with poor roads such as in the Democratic Republic of the Congo, Mozambique and

Nepal).

Biases introduced by the use of a test that does not perform as well as the recommended gold

standard can be accounted for in a Bayesian model at the cost of increased uncertainty about the

adjusted prevalence estimate, as shown in the numerical examples above (calculations of sample

size may be adjusted upwards to account for the loss in precision). The benefits are decreased

survey time, simpler laboratory procedures and improved quality of laboratory data, and less

uncertainty generated by multiple imputation of missing laboratory data.

It should be emphasized that countries that have implemented a national TB prevalence survey in

the past may not benefit as much from adopting updated designs, since this would introduce a

loss of comparability with results from a previous survey. However, direct microscopic

examination of sputum samples of Xpert-positive cases could be considered if Design 1 were

used, or in confirmed cases if Design 2 was used, to allow valid comparisons in estimates of the

prevalence of sputum smear-positive pulmonary TB.

2.2.2 Strengthen governance/oversight mechanisms including more formal arrangements

for survey monitoring and related actions by implementers and sponsors

In terms of governance/oversight mechanisms relevant to surveys (and many clinical research

studies in general), the following roles and responsibilities can be distinguished:

1. The sponsor(s) provides the financing for a survey. Examples include external agencies

such as the Global Fund, development agencies, or the national government, and may

include a mixture of these. Sponsors may request regular reports from survey

implementing agencies, and reports may be linked to periodic release of funds.

2. The Principal Investigator represents all survey investigators, is in charge of the

recruitment of competent staff, and leads the writing of the final report and scientific

papers.

3. Investigators are responsible for survey design (including the development of a protocol

and standard operating procedures, and ethics review and approval), implementation of

field operations including quality control, analysis of results and preparation of a survey

report. During field operations, this includes ensuring the accuracy, completeness,

legibility, and timeliness of the data reported in data collection tools. Data that are

derived from source documents should be consistent with the source documents or

discrepancies should be explained. To achieve maximum data quality, a standard set of

quality assurance procedures10

should be in place [10]. These include checking that

batches of newly entered records are consistent with defined standards.

4. Survey Monitors assess the implementation of survey operations, including checking

protocol modifications and checking for protocol violations. They may conduct batch

checks of data. They advise investigators about their findings and provide

recommendations for corrective actions if needed. They also report to an Independent

Data Monitoring Committee, and may assist the Principal Investigator to prepare the

10 Quality assurance is a process of systematic activities designed to ensure, assess and confirm the quality of the data

collected during a survey. Quality assured data are data that are suitable for their intended purpose. This includes

accuracy, timeliness, accessibility, comparability. A dataset is accurate to the extent that it is free of errors. Information

in database records should exactly match the corresponding information found in case report forms and source

documents and an audit trail should be maintained when updating records. Data are timely if available at the time it is

needed. Accessibility is determined by the relative ease or difficulty of use. Data are comparable if they are the same

from one unit to another, whether that unit of comparison is between individuals, interviewers, clusters, or even

national prevalence surveys

17

final report. In the general context of Good Clinical Practices [10], study monitors

represent the sponsor.

5. An Independent Data Monitoring Committee may be established by the sponsor to

assess the progress of the survey at regular intervals (based on reports from survey

monitors) and to provide recommendations to the sponsor about whether to continue,

modify, or stop the survey.

In relation to these roles and associated responsibilities, the 19 surveys implemented 2009–2015

can be characterized as follows:

● Sponsors included the Global Fund in 15 countries, the US government (via USAID,

PEPFAR and TB CARE) in two countries and national governments (≥50% contribution)

in three countries. Major contributions were provided from other donors in two countries.

● All surveys had at least one Principal Investigator.

● Monitoring was done in a variety of ways. This included independent review of survey

protocols by at least two external experts, and in-country monitoring by one or several

monitors (though they were not called “monitors”), who were either from external

technical agencies (WHO, US-CDC, KNCV, RIT), were people who had previously held

a leading role in a high-quality survey or were independent consultants with extensive

experience and expertise in the topic that they were asked to monitor. Mid-term survey

reviews in which a team of people visited together were undertaken in some countries.

Monitors provided information including recommendations to survey teams, to WHO

and (in some cases) to the sponsor. In most surveys, there was no permanent monitor in-

country for the duration of the survey. Monitors visited regularly and carefully checked

that all recommendations from previous visits had been implemented.

● A formal Independent Data Monitoring Committee was usually not established.

However, all countries established survey steering committees to provide oversight. The

regularity with which these committees met varied between countries. In at least some

countries, the sponsor(s) actively requested formal and regular reporting of findings from

the monitoring of survey operations (e.g. Bangladesh, Cambodia, Myanmar, Pakistan and

Zambia).

In future surveys, possible ways to strengthen governance and oversight mechanisms include:

1. Systematic establishment of a formal Independent Data Monitoring Committee to report

to the sponsor(s), with action taken by the sponsor if recommendations from monitors are

not implemented by investigators.

2. Systematic posting of a permanent external study monitor, who has been trained in Good

Clinical Practices [10], within the survey team at the central level (such as the place

where the survey data are centrally managed) for the duration of the survey until final

reporting. This person would manage monitoring activities, be in regular contact with

Coordinating Investigator(s), ensure that new investigators enrolled during the survey

receive appropriate training and help the Principal Investigator to prepare the final survey

report. The funding required for a permanent study monitor in-country should be covered

by the sponsor (funding for external monitoring is covered by the sponsor). Surveys that

have used an approach similar to this are Cambodia, Indonesia and Lao PDR.

3. Systematic assessment by the sponsor of whether the prerequisites for implementing a

survey (11 prerequisites are clearly defined in the Lime Book) 11

are met, prior to the go-

ahead being given for survey implementation. Based on recent surveys, particular issues

that require scrutiny include the likelihood of sufficiently high participation in urban

areas, especially capital cities; ensuring that all key members of the survey team have the

required qualifications and experience; the data management plan; and access to census

data.

11 These are defined as: 1) strong commitment and leadership from the NTP, Ministry of Health and a core group of

professionals;2) identification of a suitable institute, organization or agency to lead and manage the survey; 3) adequate

laboratory capacity, especially for culture; 4) compliance with the regulations of the national regulatory authority; 5)

reliable and timely procurement and logistics; 6) funding; 7) assurance of security in the field for survey teams and

participants; 8) data management; 9) community participation; 10) expert review and clearance of protocols, including

ethical clearance; 11) external support and technical assistance.

18

2.2.3 Ensure Good Clinical Data Management Practices

The strengthened monitoring and associated data quality assurance mechanisms described in

section 2.2.2 should help to resolve many of the data-related challenges faced in recent surveys.

In addition, use of relational databases, field data entry and barcodes should also help improve

the overall quality of data, including minimizing errors in personal identifiers.

2.2.4 Invest more resources in the work required once results are finalized, especially to

ensure the timely production of survey reports and effective communication of findings and

their implications

It has often taken considerable time to produce a survey report (more than one year in eight

countries). The presence of a permanent full-time survey monitor (suggested in section 2.2.2)

could help to address this challenge, since one of their responsibilities would be to provide

regular reports with material that could subsequently be used in the final survey report. More

generally, more resources (people with the right skills and time for report writing, and funding

for production costs including editing and printing) need to be committed to this task when the

survey budget is first developed and approved.

Recent experience in a few countries also highlights the importance of discussing possible survey

results and their implications in advance, and maintaining communication as results emerge with

key decision makers (e.g. planners, policy makers, those with responsibility for communicable

diseases in the Ministry of Health). During discussions, particular emphasis should be given to:

survey validity; quality assurance procedures; monitoring including external monitoring; how

survey findings provide valuable information for decision making on policies, prioritization and

future budgeting for TB control. When to engage with national and local media also needs to be

considered.

The last chapter of the Lime Book, on “Analysis and reporting”, focused on best-practice methods

for the analysis of survey data and how to present results.12

The book does not include a

subsequent chapter on the production of a survey report and communication of results. Such

additional guidance could be valuable to countries implementing surveys in future.

12 This guidance was subsequently updated and published in a journal article: Floyd et al. Analysis of tuberculosis

prevalence surveys: new guidance on best-practice methods. Emerging Themes in Epidemiology 2013, 10:10 013,

http://www.ete-online.com/content/10/1/10

19

3. Where should prevalence surveys be prioritized post-2015?

3.1 Criteria used in 2007

The criteria for prioritizing a prevalence survey agreed by the Task Force in December 2007 and

subsequently published in a WHO Policy Paper (2009) and the Lime Book [1] are shown in Table

3. In 2007, there were 53 countries that met these criteria. Among them, a subset of 22 global

focus countries was identified. These global focus countries were selected on the basis of their

estimated share of global and regional TB disease burden and to ensure inclusion of countries

from different parts of Africa. An important reason for defining a subset of focus countries was

the relatively limited availability of technical expertise and experience to support countries to

implement surveys for the first time. This necessitated prioritization of countries given the

impossibility of providing adequate support to up to 53 countries. Following the implementation

of surveys in 19 countries 2009–2015 alongside concerted efforts to build capacity at global,

regional and national levels including via collaboration between implementing countries, there

are now many more people available to provide technical assistance to countries that have not yet

implemented surveys as well as to support repeat surveys. If adopted, the simplifications to

survey design discussed in section 2 should also make it easier to implement surveys.

Table 3. The criteria used to identify countries eligible to conduct a national survey of the

prevalence of TB disease during the period 2008–2015

Criteria Explanations

Group 1

1. Estimated prevalence of smear-positive TB ≥100

per 100 000 population; and 2. Accounts for ≥1% of the estimated total number of

smear-positive TB cases globally; and 3. Case detection rate (CDR) for smear-positive TB

≤50% or >100%

• Major contribution to global burden of TB • Sample size small enough to make surveys feasible in

terms of cost and logistics • Excludes countries whose contribution to the global

burden of TB is insignificant for the purposes of global

and regional assessments of burden and impact • CDR≤50% or >100% indicates weak reporting

systems and problematic TB estimates, respectively

Group 2

1. Estimated prevalence of smear-positive TB≥70 per

100 000 population; and 2. Accounts for ≥1% of the estimated total number of

smear-positive TB cases globally; and 3. Estimated HIV prevalence rate in the adult

population (15 to 49 years)≥1%

Less stringent criteria for the TB prevalence rate, but

incorporates countries with high HIV prevalence and

therefore where there is potential for a rapid increase

in TB incidence and prevalence rates

Group 3

1. Estimated prevalence of smear-positive TB≥200

per 100 000 population; and 2. Accounts for ≥0.5% of the estimated total number

of smear-positive TB cases globally

Less stringent criteria for a country’s contribution to

the global burden of disease, but incorporates countries

with particularly high TB prevalence rates

Group 4

1. Nationwide survey implemented 2000–2007 or 2. Nationwide survey planned before 2010

Prior survey data allow monitoring of trends. High

motivation of NTP to conduct a survey

20

3.2 Proposed epidemiological criteria for prioritizing a national prevalence survey

post-2015

Post-2015, there are two major dimensions that need to be considered when determining whether

a TB prevalence survey should be prioritized.

1. The added value of survey results. This will be bigger in settings where the available

routine surveillance data on TB (from notification and vital registration systems) are

poorly informative. In countries that have implemented a survey in the past, a repeat

survey will provide not only a point estimate and insights into the current burden of TB

disease and how to address it, but will allow measurement of trends, which can be used

to make inferences about the impact of interventions.

2. Survey feasibility. This includes the required sample size and logistics, expected

participation rate and representativeness, and completeness and quality of data.

These factors and associated criteria can be considered separately for a) countries that conducted

a survey between 2007 and 2015 and b) countries that did not implement a survey between 2007

and 2015.

Table 4 shows the proposed epidemiological criteria for prioritizing a national prevalence survey

post-2015, for these two groups of countries. Figure 13 shows the countries that implemented a

survey between 2007 and 2015. Figure 14 shows the Group 2 countries that did not implement a

national survey between 2007 and 2015 and that meet the criteria for implementing a survey

post-2015. These countries are almost exclusively in Africa, plus India, Afghanistan and Papua

New Guinea.

Table 4. Suggested epidemiological criteria for assessing whether a country could consider

implementing a prevalence survey post-2015, for two major groups of countries

Criteria Explanations

Group 1 - Countries that conducted a national prevalence survey, 2007-2015* (Figure 13)

1. Estimated prevalence of bacteriologically

confirmed TB ≥2.5 per 1000 population aged ≥15

years during the previous survey: and

2. >5 years since the last survey.*

• Sample size small enough (less than 70,000

individuals) to make surveys feasible in terms of cost

and logistics;

• Time between surveys sufficient to allow a

statistically meaningful comparison of prevalence.

Group 2 - Countries that did not implement a national prevalence survey 2007–2015 (Figure 14)

1. Estimated TB incidence** ≥ 1.5 per 1000

population/year (all forms, all ages); and

2. No nationwide vital registration system with

standard coding of causes of deaths; and

3. Infant mortality rate > 10/1000 live births.

• Sample size** small enough (less than 70,000

individuals) to make surveys feasible in terms of cost

and logistics, accounting for added uncertainty due to

the use of rapid molecular tests with performance that

may be inferior to culture;

• No reliable direct measurement of TB disease burden;

• Indirect indicator of low access to quality health

services as defined in the Standards and Benchmarks

for TB surveillance and vital registration [11].

* Surveys conducted prior to 2000 may lack comparability with surveys implemented according to the screening and

diagnostic algorithm recommended in the Lime Book. A WHO workshop held in Cambodia in 2012 recommended a

period of 7–10 years between two surveys. Designs 1-3 (section 2.2) of the planned survey may be adapted to include

microscopic examination of smears performed in laboratory confirmed cases to allow comparability of results with the

previous survey. ** Country-specific prevalence estimates may not be published by WHO post-2015, except for countries with

prevalence survey results. For sample size determinations, prevalence in the ≥15 years age group may be predicted

from incidence.

21

Figure 13. Countries that conducted a national prevalence survey, 2007-2015 (see Appendix

3).13

Figure 14. Countries that did not conduct a national prevalence survey 2007–2015 that meet

the proposed Group 2 criteria for prioritizing a survey (see Appendix 3).

For any country that meets the epidemiological criteria shown in Table 4, it is then crucial to

assess the feasibility of a survey, using the prerequisites for implementing a survey defined in the

Lime Book as a framework. For a survey to be feasible, the following are necessary:

1. There is strong commitment and leadership from the NTP, Ministry of Health and a core

group of professionals;

2. A suitable institute, organization or agency to lead and manage the survey can be

identified;

3. There is adequate laboratory capacity;

4. X-ray equipment can comply with the regulations of the national regulatory authority;

5. Reliable and timely procurement and logistics is possible;

6. Funding is available;

7. Security in the field for survey teams and participants can be assured;

8. Data management can be done according to recommended standards;

9. Community participation is likely to be sufficiently high, including in urban areas;

13

It is already recognised that China and Thailand are unlikely to conduct another national survey given

the relatively low burden in terms of rates, the possibility of direct measurement of trends from

surveillance data and the expected low participation among urban/mobile populations. A subnational

survey could be considered e.g. Western China, North Eastern Thailand. Similarly, a repeat survey in

Gambia and Rwanda may also be affected by a low burden of TB.

22

10. External support and technical assistance are available if needed. This is likely to be

especially important for countries implementing a survey for the first time.

With respect to the expected participation rate, it is worth noting that while several countries have

successfully implemented repeat prevalence surveys [12–14], surveys were discontinued in the

Republic of Korea as a result of: (i) lower survey acceptability in an increasingly urbanized and

modern environment and associated reductions in participation rates; and (ii) the large sample

sizes required to obtain a prevalence estimate with satisfactory precision when prevalence had

fallen to much lower levels [14]. If it is anticipated that participation will not be sufficiently high

in urban areas, and especially if the share of the population living in urban areas has increased

since the last survey, then a survey may be ruled out based on this criterion alone. A good recent

example is the survey in Thailand, where participation was very low (below 50%) in Bangkok. If

available, recent observations from active case finding for TB, particularly in urban

environments, may provide an indication of likely participation rates. Other large scale health-

related surveys may also provide an indication of the likely level of survey participation.

23

Appendix 1. JAGS/WinBUGS model specification

Design 1, screening and no confirmation of positive tests

model {

m ~ dbin(theta, n)

theta <- se*phi + (1-sp)*(1-phi)

se ~ dbeta(291.9, 143.8)

sp ~ dbeta(767.34, 15.66)

phi ~ dbeta(1, 1)

}

Designs 2-3, with or without screening, all positive tests are confirmed

model {

m ~ dbin(theta, n)

theta <- se*phi

se ~ dbeta(291.9, 143.8)

phi ~ dbeta(1, 1)

}

24

Appendix 2: Adjustments required to a survey without chest X-ray or symptom

screening that relies on Xpert testing of all survey participants, with confirmatory

test of Xpert positive results 14

Design 3: No chest X-ray or symptom screening, with all participants tested using Xpert

followed by use of a different confirmatory test (or test with Xpert on a second sample) for

participants with Xpert-positive results

A similar analytical approach to that used for Design 2 can be used when X-ray screening is not

feasible or where TB investigations are added to a health survey. The implications of a low

positive predictive value of the test used in a population that is not screened (therefore less likely

to have TB) would likely require the use of confirmatory tests for case management purposes. If

positive Xpert test results are confirmed with a second test, the modelling approach of Design 2

is used:

1. define m as the number of Xpert-positive cases who also had a positive confirmatory test

(using a different testing technique)

2. set specificity to 100% (Xpert false-positive results are no longer included in m) and

simplify the likelihood function:

3. define n’ as the effective sample size of the survey (the effective sample size is the

number of eligible people who were tested with Xpert divided by the sampling design effect) and

calculate m’ so that m’/n’ = m/n

With an effective sample size n’=20000, and m’=120 confirmed cases (adjusted to effective

sample size),

14 Confirmatory tests may not be necessary if the main test is Xpert Ultra or a new rapid test with high specificity.

25

Appendix 3:

3a: Countries that have conducted a national TB prevalence survey from 2007-2015,

and their contribution to the global burden (incidence) of TB.

Country

Proportion (%) of estimated global

incidence (2014)

Bangladesh 3.7

Cambodia 0.6

China 9.6

DPRK 1.1

Ethiopia 2.1

Gambia <0.1

Ghana 0.5

Indonesia 10.5

Kenya 1.1

Lao PDR 0.1

Malawi 0.4

Mongolia 0.1

Myanmar 2.0

Nigeria 5.9

Pakistan 5.2

Philippines 3.0

Rwanda 0.1

Sudan 0.4

Thailand 1.2

Uganda 0.6

UR Tanzania 1.8

Viet Nam 1.3

Zambia 0.7

Zimbabwe 0.4

26

3b: Countries that did not conduct a national prevalence survey during 2007–2015 that

meet the proposed Group 2 criteria for prioritizing a survey, and their contribution to

the global burden (incidence) of TB.

Country

Proportion (%) of estimated global

incidence (2014)

Afghanistan 0.6

Angola 0.9

Bhutan 0.0

Botswana 0.1

Central African Republic 0.2

Cote d'Ivoire 0.4

Cameroon 0.5

DRC 2.5

Congo 0.2

Djibouti 0.1

Micronesia <0.1

Gabon 0.1

Guinea 0.2

Guinea-Bissau 0.1

Equatorial Guinea <0.1

Haiti 0.2

India 22.4

Kiribati <0.1

Liberia 0.1

Lesotho 0.2

Madagascar 0.6

Marshall Islands <0.1

Mozambique 1.6

Namibia 0.1

Nepal 0.5

Papua New Guinea 0.3

Sierra Leone 0.2

Somalia 0.3

Swaziland 0.1

Chad 0.2

Timor-Leste 0.1

Tuvalu <0.1

South Africa 4.7

27

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