BACKGROUND DOCUMENT 4e. Prevalence surveys of TB disease post-2015 Why, How, Where?

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1 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? 1

2 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 An additional 31 countries were identified as eligible to implement a survey, based on epidemiological criteria agreed by the Task Force in December 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. 2 A 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 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 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 How could survey methods be improved post-2015? This section starts by summarizing the major challenges faced in surveys implemented , 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 and b) countries that did not implement a survey In order of when they were implemented: Myanmar ( ); China (2010); Pakistan, Cambodia, Ethiopia (2011); Thailand, Tanzania, Rwanda and Nigeria (2012); Malawi, Ghana (2013); Indonesia ( ); Zambia (2014); Uganda (2015). 2 Lao PDR ( ), Gambia (2012), Sudan (2013), Zimbabwe (2014), Mongolia (2015). 2

3 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 and replace the MDGs, ) and in WHO s End TB Strategy ( ). 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 (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 underreporting 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. 3

4 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 ( ), 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 smearpositive (as opposed to bacteriologically confirmed) TB. Results for Mongolia were not final by the end of March 2016 and thus are not shown. 4

5 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. 5

6 Figure 4. Age-specific bacteriologically confirmed prevalence rates in countries that implemented a national TB prevalence survey, Figure 5. Sex ratio of prevalent cases, surveys implemented 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, 6

7 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 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 7

8 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 HIVpositive 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. 8

9 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 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. 9

10 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. 10

11 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 Only Cambodia, China and Thailand had already conducted surveys in the period since 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 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 Challenges faced in prevalence surveys implemented The major challenges faced in surveys implemented , 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 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. 11

12 Table 1. Major challenges observed in national prevalence surveys completed Challenges Preparations Time taken to complete survey preparations (i.e. 3 years from decision to implement to start of field operations 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 Number of surveys affected* 14/19 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 5/19 during field operations 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 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; 5/19 restricted sampling frame (>5% of national population in eligible age group) 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 12

13 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 , 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 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 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 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. 13

14 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 14

15 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, 15

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 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 procedures 10 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 16

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 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 incountry 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 goahead 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. 17

18 2.2.3 Ensure Good Clinical Data Management Practices The strengthened monitoring and associated data quality assurance mechanisms described in section 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 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, 18

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 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 Criteria Explanations Group 1 1. Estimated prevalence of smear-positive TB 100 per 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 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 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 or 2. Nationwide survey planned before 2010 Prior survey data allow monitoring of trends. High motivation of NTP to conduct a survey 19

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 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 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 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, * (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 (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. 20

21 Figure 13. Countries that conducted a national prevalence survey, (see Appendix 3). 13 Figure 14. Countries that did not conduct a national prevalence survey 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. 21