Report of the Intercountry Workshop on Quality of Surveillance and Revision of Estimates of TB Burden. Cairo, Egypt October, 2009

Transcription

1 Report of the Intercountry Workshop on Quality of Surveillance and Revision of Estimates of TB Burden Cairo, Egypt October, 2009

2 Country participants AFGANISTAN, ISLAMIC REPUBLIC OF Dr Said Dauod Mahmoodi Head of Surveillance and M&E National TB Programme Ministry of Public Health Kabul BAHRAIN Dr Said El Safar TB Consultant for Chest Diseases Salmaniyia Medical Complex Ministry of Health Manama Tel: / al_saffarsa@yahoo.com DJIBOUTI Ms Assia Haissama Responsible of the TB Data follow-up National TB Programme Ministry of Health Djibouti assia_haissama@hotmail.com EGYPT Dr Amal Galal Surveillance Officer National TB Control Programme Ministry of Health Cairo Tel: Fax: agalalm@yahoo.com / agalalm@gmail.com IRAQ Dr Mohammed Rahim Abbas Tbena Surveillance Manager National TB Programme Respiratory and Chest Diseases Centre Ministry of Health Baghdad mohemmedtabena@yahoo.com

3 ISLAMIC REPUBLIC OF IRAN Dr Mahshid Nasehi National TB Programme Manager Disease Control Department Ministry of Health and Medical Education Teheran Tel: Fax: / mnasehi@yahoo.com JORDAN Dr Nadia Ismail Abu Sabrah National TB Deputy Manager Surveillance Officer Directorate of Chest Diseases Ministry of Health Amman Tel: Fax: femaletb@yahoo.com LEBANON Dr Mtanios Saade National TB Programme Manager Ministry of Public Health Beirut Tel: Fax: drantoinesaade@hotmail.com LIBYAN ARAB JAMAHIRIYA Dr Mohamed Furjani National TB Manager National Communicable and Infectious Diseases Prevention and Control Centre Tripoli Tel: / furjanim@yahoo.co.uk MOROCCO Dr Naima Benchaikh National TB Programme Manager Directorate of Epidemiology and Diseases Control Ministry of Health 71 Avenu Ibn Sina, Agdul Rabat Tel: Fax:

4 OMAN Dr Ali Al Lawati Specialist & In-charge of TB Control Programme Ministry of Health Muscat Tel: PAKISTAN Dr Razia Kaniz Fatima Monitoring & Evaluation Officer National TB Programme Manager Ministry of Health Islamabad Tel: Fax: QATAR Dr Mohd Mohd Al Hajri Specialist, Community Medicine Programme Primary Health Care Supreme Council of Health Doha Tel: / SAUDI ARABIA Dr Naila Anwar Abu Aljadayel Director of Chest Diseases National TB Manager Ministry of Health Riyadh Tel: Fax: / SOMALIA Dr Rumbidzai Pairamanzi Monitoring & Evaluation Coordinator World Vision International Somalia P. 0. Box Nairobi Tel: rumbidzai_pairamanzi@wvi.org

5 SUDAN Dr Samia Ali Alagaab TB Surveillance Officer National TB Programme Federal Ministry of Health Khartoum Tel: SYRIAN ARAB REPUBLIC Dr Kinaz Shaban Alshaikh Director of TB Department TB Surveillance Focal Point Ministry of Health Damascus Tel: TUNISIA Dr Mohamed Zaher Ahmadi Head of Department Primary Health Care Ministry of Public Health Sidi Bousid Tunis Tel: Fax: UNITED ARAB EMIRATES Dr Kalthoom Mohammed Al Belooshi National TB Manager Ministry of Health RAK P.O.BOX 463 Abu Dhabi Tel: UNITED ARAB EMIRATES (Cont d) Dr Kifah Saleh Al Saqeldi TB Programme Coordinator Ministry of Health Dubai Tel: Fax: / kefah saleh@moh.gov.ae

6 YEMEN, REPUBLIC OF Mr Abdelbari Al Hammadi Monitoring and Evaluation Officer National TB Control Programme Ministry of Public Health and Population Algarda zone, Taif st, Sana a Tel: / Fax: OTHER ORGANIZATIONS Dr Ryuichi Komatsu Team Leader, Strategic Information The Global Fund to Fight AIDS, Tuberculosis and Malaria Ch. Blandonnet Geneva SWITZERLAND Tel: Fax: ryuichi.komatsu@theglobalfund.org Dr Laura Fay Anderson Scientist Epidemiology Health Protection Agency G1 Colindale Avenue London UNITED KINGDOM Tel: laura.anderson@hpa.org Dr Brian Williams TB Consultant 11B chemin Jacques-Attenville, 1218 Grand-Saconnex Geneva SWITZERLAND Tel: williamsbg@me.com WHO Secretariat Dr Akihiro Seita, Coordinator, TB, AIDS and Malaria, WHO/EMRO, Abdul Razak El Sanhouri St., Cairo, Egypt Tel: Fax: seitaa@emro.who.int

7 Dr Samiha Baghdadi, Medical Officer Stop TB, WHO/EMRO, Abdul Razak El Sanhouri St., Cairo, Egypt Tel: Fax: Dr Amal Bassili, Technical Officer Stop TB, WHO/EMRO Abdul Razak El Sanhouri St., Cairo, Egypt Tel: Fax: Dr Ana Bierrenbach, Technical Officer, Tuberculosis Monitoring and Evaluation (TME), Stop TB Department, WHO/HQ, Geneva, Switzerland, Tel: Fax: , Dr Philippe Glaziou, Technical Officer, Tuberculosis Monitoring and Evaluation (TME), Stop TB Department, WHO/HQ, Geneva, Switzerland, Tel: Fax: , Dr Mehran Seyed Hosseini, Technical Officer, Tuberculosis Monitoring and Evaluation (TME), Stop TB Department, WHO/HQ, Geneva, Switzerland, Tel: Fax: , Mr Tom Hiatt, Assistant, HQ/TME Tuberculosis Monitoring and Evaluation, Geneva, Switzerland, Tel: , Fax: , Dr Lailuma Nuzhat, TB Officer, WHO/Afghanistan, Dr R.Taghizadeh, National Professional Officer, TB Programme, WHO/Iran,12 th floor, Ministry of Health building, Symaye-e-Iran, Tehran, Iran, Tel: , Dr Peter Metzger, Medical Officer, Stop Tuberculosis, WHO/Pakistan, P.O. Box 1013 NIH Chak Shehzad, Islamabad, Dr Ejaz Qadeer, Stop Tuberculosis, WHO/Pakistan, P.O. Box 1013 NIH Chak Shehzad, Islamabad, Dr Ireneaus Sindani, Medical Officer, Stop Tuberculosis, WHO/Somalia, P.O. BOX , Nairobi, Kenya, Tel: , Dr Ayid Munim, Medical Officer, Stop Tuberculosis, WHO/Sudan, Tel: Fax: Dr Philip Ejikon, Medical Officer, Stop Tuberculosis, WHO/S.Sudan, Tel: , / Mr Essam Ghoneim, Audio Visual Technician, WHO/EMRO, Abdul Razak El Sanhouri St., Cairo, Egypt Tel: ghoneime@emro.who.int

8 Ms Sherine Abdel Malek, Secretary, Division of Communicable Disease Control, WHO/EMRO, Abdul Razak El Sanhouri St., Cairo, Egypt Tel: Fax: Ms Ghada Oraby, Secretary, Division of Communicable Disease Control, WHO/EMRO, Abdul Razak El Sanhouri St., Cairo, Egypt Tel: Fax:

9 Background In October 2009 representatives of 17 countries from the Eastern Mediterranean Region gathered in Cairo to participate in a workshop designed to evaluate detailed tuberculosis (TB) surveillance data and methods for estimating the burden of TB. Prior to the workshop, detailed national and sub-national data were requested from countries. During the workshop, these and other data were analysed and assessed in order to better understand the underlying epidemiology of TB in each country and to make informed estimates of the burden of TB and its trend over time. A major goal of the workshop was to familiarize countries with the methods used by WHO to estimate the TB burden, to revise these estimates using available surveillance data, and to facilitate improved surveillance and data collection at the national and sub-national levels in the future. Methods Using standard Excel templates, representatives from each country were asked to provide national and sub-national data on TB case notifications, infrastructure and case-finding efforts for the years Requested data were as follows: Population by age and sex TB case notifications by case type (smear-positive, smear-negative, extrapulmonary) TB case notifications by age and sex Active case finding efforts Number of new and re-treatment TB cases reported by non-ntp/non-moh providers Number of foreign-born or non-citizen new and re-treatment TB cases reported MDR-TB TB/HIV Lab infrastructure Number of dispensaries and hospitals Number of non-ntp providers and hospitals Number of staff Other (chronic respiratory cases, TB suspects examined, slides examined, % culture-positive/smear-positive) No country was able to provide all of the above data for all years requested. However, data availability has improved in recent years. For details on the data provided by each country, see Annex. Data were then compiled into a single data file using STATA software, and country-specific graphs were generated using R software. These data were then used to complete workbooks designed to answer the following questions: 1. Are TB notifications as complete as possible? 2. Are TB notifications reliable (limited misclassifications)? 3. What proportion of incident cases are missing from routine notifications and why? 4. How have TB notifications changed over time?

10 5. Do changes in notifications over time reflect operational changes (e.g., improved case finding)? 6. Do changes in notifications over time reflect changes in the underlying epidemiology? 7. What specific studies/activities are suggested to improve TB surveillance and programme monitoring & evaluation? Question 1: Completeness of National Data Countries were asked about the completeness of their notifications data in terms of aggregating sub-national reporting data to produce national notifications. Countries provided information on systems used to monitor the completeness of reporting at various administrative levels and to provide reasons for any unusual fluctuations across geographies or over time. For example, there may be programmatic reasons for the variation across administrative units such as differences in the recording and reporting system, lab capacity or case definitions used. There could also be epidemiological reasons for sub-national variation in notifications, such as differences in HIV prevalence and population density. Table 1. Data completeness and reliability Country Are there any missing reports? Are TB cases classified correctly? Yes, but some misclassification problems due to variable Afghanistan No notification policy/practice and EP dx capacity Bahrain No Yes with some variation due to differences in notification policy/practice Djibouti No Yes Egypt No Yes Iran No Yes Iraq No, though in previous years case reports had been tripled Yes Jordan No (case finding has increased in recent years) Yes Morocco No Yes Oman No Yes, but some difference in EP dx capacity and in notification policy/practices Pakistan No Misclassification problems due to dx capacity Qatar No Yes Saudi Arabia No Yes Somalia No, but some fluctuations due to change in recording/reporting system Yes Sudan No Yes, but significant variation over time due to dx capacity and variations in notification policy/practice Syria Changes in recording/reporting system, dx capacity and case definitions led to fluctuations in notifications Tunisia No Yes United Arab Emirates No Some misclassification problems Yemen No, but some changes in case definition Yes Most countries in the region report having systems in place to monitor the completeness of reporting from various administrative levels to the national level. Unusual fluctuations in notifications seem to be due to changes in recording and reporting systems such as the expansion of DOTS and the addition of new reporting units. Countries noting sub-national variations in notifications reported a mixture of reasons - in some cases, true variation in incidence across provinces or administrative units and, in other cases, variation in reporting quality. Question 2: Reliability of Data Following from this, the data were reviewed for their reliability in terms of case classification. Countries were asked about the proportion of all cases that are new, the proportion of new cases that are pulmonary and the proportion of pulmonary cases

11 that are smear-positive. There are data to suggest normal ranges for these proportions. It is expected that about 85-9 of new cases are pulmonary and 65-8 of pulmonary cases are smear-positive. Reasons for any significant deviations from these expected ranges were sought. Again, these factors could be due to reporting issues or epidemiological factors. Regionally, the proportion of all cases that are new is reported to be unusually high at 96% in This pattern has consistently been reflected in the notifications going back to This would suggest insufficient investigation of past treatment history, and it is unclear why the ratio of new cases to all TB cases would be so high in this region compared to others. The reported proportion of all new cases that are pulmonary is lower than expected in the Eastern Mediterranean Region at 78% for Again, similar proportions can be seen in the historical data. This suggests an unusually high level of extra-pulmonary TB in the region, but we have no epidemiological explanation for this phenomenon. Some experts suggest a high rate of M. bovis, but there are little data to support this. One would expect higher levels of extra-pulmonary TB in areas of high HIV prevalence or where children represent a significant proportion of the reported cases, but neither of these is the case in this region. And finally the proportion of pulmonary cases that are smear-positive is remarkably low in the region at 51%. This could be explained by weak lab infrastructure or overuse of clinical diagnosis. This section of the workshop attempted to assess whether the pattern seen in each country was representative of the underlying epidemiology or if the classifications of notifications needed to be corrected. As such, the patterns were analysed at the subnational (where possible), national and regional levels and assessed for reliability in the context of what is known about the natural history of TB and TB epidemiology. Further questions were asked regarding the availability of data on HIV prevalence in TB patients and the prevalence of drug resistance.

12 Table 2. System to monitor TB/HIV and MDR-TB Country TB/HIV MDR-TB Afghanistan No No Bahrain Yes Yes Djibouti Yes Yes Egypt No Yes Iran Yes Yes Iraq Yes No Jordan Yes Yes Morocco Yes Yes Oman Yes Yes Pakistan Yes No Qatar Yes Yes Saudi Arabia Yes Yes Somalia Sudan Yes No Syria No Yes Tunisia Yes Yes United Arab Emirates No No Yemen No No Question 3: Assessment of Missing Cases In order to answer the third question, workshop attendees were guided through the use of "the onion" model which highlights the various layers at which patients may be missed by the surveillance system. TB incidence can theoretically be measured directly from population-based incidence surveys or when the performance of national routine TB surveillance systems is so high that they capture all or almost all incident cases. However, incidence surveys are too resource intensive and impractical for any country to consider, and there are no widely endorsed criteria that allow to know with sufficient confidence whether surveillance systems capture all or almost all incident cases. In some countries and under specific conditions (e.g. existence of a case-based database of TB notified cases), incidence may be indirectly derived from direct measurements of TB mortality (from Vital Registration Systems) or incidence may be derived from direct measurements of TB prevalence (from population-based surveys). However, indirectly derived estimates of incidence are very uncertain. In the past, incidence estimates have often been derived from findings of tuberculin surveys. However, the method is judged too uncertain to be applied: it relies on several key assumptions that are very difficult to validate and the diagnostic value (diagnosis of infection) of the tuberculin test is poor. In all other countries, TB incidence can only be estimated by eliciting expert opinion. To obtain the best informed judgement about incidence, experts are asked to consider the various ways through which TB cases may not be captured by routine surveillance, using the so-called "onion" model. Ideally, a sample of experts should be interviewed using methods that would allow the quantification of systematic biases, but logistical limitations attached to regional workshops result in only one or two national experts being involved, with additional input from WHO experts from Country and Regional Offices.

13 Health system strengthening (HSS) Practical Approach to Lung Health (PAL) Figure 1. The "onion" 6. Cases with no access to health care 5. Cases with access to health services that do not go to health facilities 4. Cases presenting to health facilities, but not diagnosed 3. Cases diagnosed by public or private providers, but not reported HSS to minimize access barriers Communication and social mobilization; contact tracing, active case-finding Improve diagnostic quality or tools Supervision and investment in recording and reporting systems 2. Cases diagnosed by the NTP or by providers collaborating with the NTP, but not recorded/reported 1. Cases recorded in TB notification data Countries were invited to make assessments of the percent of all cases that were unreported at each layer for three years , 2003 and with ranges around each estimated proportion. Although conceptually simple, quantification of the fraction of TB cases missing from each layer is challenging. In some cases, it helps to have an understanding of the overall healthcare system, the distribution of public and private healthcare facilities and providers and how reporting practices differ between public and private sectors. Summing layers 2 to 6 of the onion gives us an estimate of the fraction of incident cases that are not represented within the national notifications, and the upper and lower bounds of these layers is the range of uncertainty around this fraction. The remaining proportion of cases represents an estimated case detection rate for each country. In addition, data on access to health from Demographic and Health Surveys and on the overall performance of health systems as measured by the infant mortality ratio were used to substantiate opinion on the proportion of cases with no or very limited access to health care. For example, the case detection rate for Sudan was adjusted due to the country's low access to health care and its very high infant mortality rate.

14 Country representatives interpreted their data and external evidence with help from facilitators. At the end of the workshop, agreement was reached about estimates for one or more reference years and the trend in incidence for most countries. Table 3. Percentage of undetected TB cases, as estimated by countries, 2008 Country No access Go, but not Have access, diagnosed by but don't go health facility Diagnosed by NTP, but not notified Diagnosed by non-ntp, but not notified Afghanistan 15 (13-17) 5 (3-7) 5 (3-7) 5 (4-6) 15 (14-17) 38 (34-45) Bahrain 0 (0-0) (0-0) Djibouti 6 (5-7) 6 (2-10) 9 (7-11) 3 (2-4) 1 (0-2) 23 (15-30) Egypt 2 (0-5) 1 (0-2) 2 (0-5) 2 (0-5) 13 (5-20) 19 (5-34) Iran 5 (3-7) 5 (3-7) 10 (8-12) 2 (1-3) 21 (19-23) 37 (31-44) Iraq 11 (10-12) 3 (2-4) 4 (2-6) 3 (2-4) 5 (4-6) 24 (19-29) Jordan 1 (0-2) 4 (2-6) 4 (3-6) 0 (0-1) 0 (0-1) 9 (5-15) Lebanon 1 (0-1) 1 (0-2) 5 (0-6) 1 (0-1) 1 (0-2) 9 (0-12) Morocco 2 (0-5) 2 (1-4) 1 (0-2) 1 (0-2) 2 (1-4) 8 (2-16) Oman 0 (0-0) 8 (5-10) 5 (1-5) 0 (0-0) 1.1 (0-2.2) 14 (6-16) Pakistan 10 (5-15) 5 (3-6) 5 (3-6) 10 (5-15) 30 (15-45) 51 (28-70) Qatar 1 (0-1.5) 4 (2.5-7) 13 (7-19) 0 (0-0) 2 (1-3) 19 (10-28) Saudi Arabia 0 (0-0) 5 (1-8) 7 (2-14) 5 (1-10) 5 (1-10) 20 (5-37) Somalia 12 (10-14) 5 (4-6) 3 (2-4) 5 (3-8) 6 (3-8) 28 (20-35) Sudan 11.4 (7-17.6) 3.2 ( ) 3.1 ( ) 2.5 (2-3) 6.1 (4.4-7) 24 (17-33) Syria 7 (5-8) 7 (5-8) 5 (4-6) 3 (2-4) 4 (4-5) 24 (19-28) Tunisia 2 (1-3) 1 (0.5-2) 2 (1-3) 0.5 (0-1) 0.5 (0-1) 6 (2-10) United Arab Emirates 3 (1-5) 9 (6-18) 11 (8-24) 0 (0-0) 15 (7-21) 33 (20-50) Yemen 10 (5-15) 3 (2-5) 7 (5-10) 3 (0-5) 5 (3-7) 27 (14-35) Total Few countries estimated more than 1 of the population without access to healthcare services. Exceptions were Afghanistan, Iraq and Sudan. Also, few countries provided estimates that suggest appropriate diagnosis may be a hurdle to case detection. There is active private sector care in the region which is reflected in the estimates of non-ntp diagnoses that are not reported. Notable countries are Iran and Pakistan. Overall, the estimated fraction of cases that are not reported varies from 5 to 51% by country, with the largest proportion of estimated unreported cases coming from non-ntp diagnoses. In the Eastern Mediterranean Region, resulting estimates of case detection rate ranged from 49 to 94% for 2008, the most recent year of data. The year in which case-finding efforts were assumed to be the most robust - usually the most recent year - was used as the reference year for the new estimate of TB incidence. Applying the case detection rate to reported notifications gives the new incidence estimate for the reference year.

15 Table 4. Case detection rates, based on undetected TB cases as estimated by countries Country Afghanistan Bahrain Djibouti Egypt Iran Iraq Jordan Lebanon Morocco Oman Pakistan Qatar Saudi Arabia Somalia Sudan Syria Tunisia United Arab Emirates Yemen Based on the review of notifications data, countries were asked to indicate sources of data that could be used to assess the extent of TB cases missed in each layer of the onion model. These include mortality data from vital registration and laboratory registries, among other sources. Many countries reported availability of these data for assessment. Table 5. Country availability of data to assess undetected cases Mortality (vital registration) Laboratory registries Separate NTP list Hospital registries HIV notification data with information on TB diagnoses Pharmacy registries Health insurance registries Demographic Health Surveys Country Afghanistan Bahrain x x x x ARI Djibouti x x x x x Egypt x x x x x x Military, universities, prisons, refugees, private providers Iran x x Iraq Jordan Morocco Oman x x x x x Pakistan x x x Prevalence survey Qatar x x x x ARI Saudi Arabia x x x x x x x x Active case finding in high-risk groups Somalia Sudan x x x x Syria x x x Tunisia United Arab Emirates Yemen Additional studies that could help to quantify the missing cases were also suggested. It is hoped that countries will use these data in the future to generate more informed estimates of the fraction of cases that goes unreported each year. Other

16 Table 6. Country plans for studies to assess missing TB cases Inventory studies using existing sources of data Inventory studies using newly collected sources of data Studies of diagnostic procedures performed on TB suspects Yield of patients found as a result of ACSM Yield of patients found following training staff on PAL Yield of patients found while screening high-risk populations Yield of patients found while contact tracing Yield of patients found because of improvements in diagnostic quality or tools Yield of patients found as a result of PPM TB disease prevalence studies Capturerecapture studies Studies of post-mortem registration of TB Country Afghanistan Bahrain x x x x x x Djibouti x x x x x Egypt x x x x x x x x x x Iran x x x x x x Iraq Jordan Morocco Oman x Pakistan x x x x x x x x x x Qatar x x x x x Saudi Arabia x x Somalia Sudan x x x x x x Syria x x x Tunisia United Arab Emirates Yemen Other Questions 4-6: Changes in Notifications over Time The next section of the workbook was designed to assess how the factors already mentioned and others have affected notifications over time in order to understand the trend in TB incidence in each country. In TB epidemiology one would not expect to see incidence change by more than 1 year-over-year, almost regardless of the interventions or lack thereof. As such, where notifications truly reflect underlying incidence, we would not expect to see notifications change by more than this either. However, in addition to changes in true incidence of disease, there are a number of other factors that may cause notifications to change over time. These include the rigor of case-finding efforts, the reach of the NTP and its expansion over time, and other factors mentioned previously such as changes in lab capacity or recording and reporting policies. This section of the workbook was designed to answer 4 questions: 1. Have notifications been increasing, decreasing or stable over time? 2. Were there any changes in case-finding effort and/or recording and reporting that might have affected notifications over time? 3. How have factors that may influence TB incidence changed over time, and have they had any impact on underlying TB incidence? 4. Based on the information above, what is the best assessment of how true underlying incidence changed over time, if at all? Countries were asked about the trends in notifications of new cases over several selfdefined periods of time, including pulmonary, extra-pulmonary, SS+ and SSnotifications. Then they were asked about changes over time in factors that could affect case detection, such as the number of labs doing smear or culture, number of NTP staff, and expenditure on TB control, and the periods during which these factors increased or decreased. From this, the group was able to make judgements regarding the impact these factors might have had on case detection and, therefore, notifications. Then, countries were asked about factors that might influence underlying TB incidence such as HIV prevalence, gross domestic product (GDP) and various risk factors for TB.

17 They were also asked about the age distribution of notified cases as this tells us something about transmission. If the average age of TB cases is increasing, more TB is likely due to reactivation of latent infection rather than new infection/transmission. This information was used to assess whether any changes in notifications might be due to true changes in incidence. Nearly every country in the region noted improvements made in the recording and reporting systems over the years. Also, in most countries the direction of change in notifications has varied over the years - increasing during some periods, decreasing or stable during others. There is no obvious reason why the incidence would be changing in this way, but it may be possible to attribute some of the divergent trends to reporting practices or other factors. However, where the trend has been consistent and epidemiologically plausible over time this may reflect the trend in underlying incidence. Few countries were able to identify any factors that may have attributed to an increase in TB incidence. HIV prevalence is relatively low in the region and so is not thought to be a contributing factor. Changing economic conditions may be leading to reductions in incidence in some cases, while increased immigration may be adding to the disease burden in others. For example, some Gulf countries receive immigrants from countries with a much higher risk of TB than the local population. However, it is difficult to quantify the impact these factors may have had.

18 Table 7. Do changes in TB notifications reflect changes in TB incidence? Country Have TB notifications been increasing, decreasing or stable over time? Were there any changes in case-finding effort and/or recording and reporting that might have affected notifications over time? How have factors that may influence TB incidence changed over time and have they had an impact on underlying TB incidence? Based on the information discussed in questions 1 through 3, how do you think true underlying incidence has changed over time? Afghanistan Increase Increase ( ), Stable ( ), Increase ( ), Bahrain then stable Djibouti Egypt Iran Iraq Improved case detection and expansion of recording and reporting system since 1998 Unknown Unknown Expanded R&R since 2001; began notifying SS- & EP cases in 2003 Decrease ( ), Increase ( ) Labs increased in 2006 Decrease (1995-?), Stable (?- 2003), Decrease ( ) Increase ( ), decrease ( ), stable ( ) Increase ( ), decrease ( ) Expanded R&R and began notifying SS-, EP and pediatric cases in 1995; changed to electronic system and began checking for duplicates and misclassifications in 2004 Improvements in dx capacity, classification of cases and recording and reporting began in 1995 Expanded recording & reporting system, changed to electronic system, increased training; NTP staff and expenditures increased in 2008 Immigration may have increased incidence No GDP changes may have decreased incidence Unknown Jordan Decrease ( ), increase ( ), stable ( ) Morocco Decrease ( ) Active case finding since 2002; expansion of recording & reporting system since 2006 Active case finding, expansion of recording & reporting system since 1991 GDP changes may have decreased incidence Unknown Decreasing among nationals over the last 10 years, but some increase in refugees Oman Decrease ( ), stable ( ), increase ( ) Expansion of recording & reporting system since 1991 No Pakistan Qatar Increase ( ), decrease Expansion of recording & ( ), increase ( ) reporting system Decrease pre 1995, increase ( ), stable ) Unknown Influx of foreign workers may have increased incidence Saudi Arabia Decrease ( ), stable ( ), increase ( ) Active case finding, expansion of recording & reporting system since 1998 No Somalia Increase ( ), decrease ( ) Expansion of recording & reporting system since 2004 Unknown Sudan Increase ( ), decrease ( ) Expansion of recording & reporting system since 1995 Unknown Stable Syria Tunisia United Arab Emirates Increase ( ), decrease ( ) Expansion of recording & reporting system since 2001 Unknown Decrease ), increase ) Unknown Decrease ( ), stable ( ) No No Yemen Decrease ( ) No No

19 Using the data and expert opinions gathered from countries and the external evidence of health system coverage and other proxies, we assessed, and in some cases challenged, the reliability of country-driven estimates for the reference years. Once estimates of incidence were made for the reference years, we moved on to evaluate trends. In evaluating the possible underlying trend in incidence over time, there were three approaches to calculating the manner in which TB incidence may be changing yearover-year. The first step was to look at the historical notifications. For countries in which notifications were deemed to be a reliable representation of the trend in incidence over several years, the mirror method was used. This simply means that the trend in notifications mirrors the trend in estimated incidence. This trend is applied to the estimate of incidence for the reference year. In most cases, this is used for countries with consistent notifications patterns such as Jordan and Lebanon which both show incrementally declining and then stabilizing rates. Where historical notifications do not appear to reflect underlying incidence, but estimated incidence for the three years evaluated using the onion model is thought be reliable, those three point estimates were used to determine the trend with a mathematically smoothed line characterizing the year-over-year change. This is the method used, for example, in Yemen where reported cases were stable and then declining. Mapping the trend to three years with reasonable estimates shows a steady decline in incidence due to control efforts. And finally, where data were less reliable and the impact of programmatic changes and/or factors that may be affecting the underlying epidemiology were difficult to characterize or quantify, a flat trend was assumed. Having first decided that we had at least one reliable assessment of case detection for a reference year, we then assumed that the incidence rate is not changing over time (or we had no better knowledge to say that it was decreasing or increasing). This is the case in Afghanistan where notifications were not thought to reflect incidence. Here, notifications have been increasing significantly while there have been considerable improvements in casefinding efforts and expansion of the DOTS programme, but there is no information on determinants of TB. In this case, we used the most recent estimate of case detection to estimate incidence and kept the rate constant throughout the years. Another example is Iraq where notifications have varied considerably over the years, but it is difficult to understand the effects of war, internal security issues and displacement on either the functioning of the TB control programme or the epidemiology of the disease in such circumstances. In these situations, no attempt is made to estimate year-over-year changes in incidence and the estimated rate is held constant. However, in the case of Iraq, the best estimate of case detection was for 2003 and this was the reference year used. Prior to 1997, the trend in incidence was assumed to be flat for all countries as we did not analyse data from these years and did not have enough information to inform estimates of trends before this. Confidence intervals were also estimated in an attempt to acknowledge the uncertainty bounds around the estimates. The upper bounds were more uncertain in

20 some countries, and bounds around estimates for past years are generally more uncertain. Question 7: Suggestions/Recommendations Countries provided information on planned studies and improvements to the TB surveillance system. Following the detailed analyses, suggestions and recommendations were made regarding additional future studies and improvements to be made to support more robust TB surveillance in the future. Table 8. Planned Activities Afghanistan Bahrain Djibouti Egypt Iran Iraq Jordan Morocco Oman Pakistan Qatar Saudi Arabia Somalia Sudan Syria Tunisia United Arab Emirates Yemen 1. Improve recording and reporting capacity: i. Improve coverage of R&R Yes No Yes Yes No Yes No Yes Yes Yes Yes Yes Yes Yes ii. Improve supervision of R&R activities, from data collection to validation, analysis and findings Yes No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes iii. Introduce a new or improve the existing electronic recording and reporting system Yes No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes 2. Improve capacity to analyse TB notification and other supporting data at the: i. National level Yes No Yes Yes No Yes Yes No Yes Yes Yes Yes Yes Yes Yes ii. Sub-national level Yes No Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes 3. Improve feedback of data analysis and interpretation to TB staff and other healthcare working at the peripheral level Yes No Yes Yes Yes Yes Yes Yes No No Yes Yes Yes Yes Yes 4. Implement a study to identify and eliminate duplicate and misclassified records at the national level No No No Yes No Yes No Yes No Yes Yes Yes 5. Perform data quality assessment (e.g., using data quality assessment tool) Yes No No Yes No Yes No Yes Yes Yes Yes Yes 6. Perform studies comparing number of TB cases with number of suspects examined and/or number of suspects examined with number of chronic respiratory cases attending the healthcare facilities Yes No Yes No No Yes Yes Yes Yes Yes Yes 7. Perform contact investigation studies Yes No Yes Yes No Yes Yes Yes Yes Yes Yes 8. Perform cross-validation of TB notification data with other sources of TB data: i. Pre-existing sources of data No No Yes Yes Yes No Yes No Yes Yes Yes ii. Prospectively collected TB data No No Yes Yes No Yes Yes Yes 9. Capture-recapture studies No No Yes Yes Yes Yes No No Yes Yes No Yes 10. Perform a national survey to estimate the prevalence of drug-resistant TB Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes 11. Perform a national survey of the prevalence of HIV among registered TB patients Yes No Yes Yes Yes Yes No Yes Yes Routine 12. Introduce routine culture and drug susceptibility testing for all new reported cases and link them to the national TB notification system Yes No No Yes Yes No No No Yes 13. Implement routine culture and drug susceptibility testing for all reported retreatment cases and link them to the national TB notification system Yes No Yes No Yes Yes Yes No No Yes Yes Yes Yes 14. Perform a national survey of the prevalence of TB disease Yes Yes No No Yes No No No Yes No Yes 15. Perform studies to assess TB burden in high-risk populations Yes No No No Yes Yes No No Yes Yes Yes 16. Perform studies to quantify the effect of risk factors for TB and their population attributable fraction Yes No Yes No Yes Yes No No No Yes Yes 17. Other No No TB among immigrants No No Detailed country data Country-specific tables and graphs explaining the estimates can be found in the Annex. Conclusions The workshops with country representatives were designed to share information and data on TB epidemiology and the external factors that influence it at the country level. With this information, it is hoped that the estimates of TB burden will improve as more informed analyses are conducted. It is clear from the workshop and discussions that TB surveillance is improving dramatically. Eventually, it is hoped that, as routine surveillance continues to improve, country notifications will provide a more complete and accurate reflection of true incidence. Until that time, the annex that follows

21 outlines the process by which the revised estimates of incidence are made for each country.

22 Annex 1. Afghanistan Summary Considerable increase in notifications year-over-year alongside DOTS expansion and increased case finding efforts Increase in labs ( ), staff ( ), expenditures ( ) Difficult to characterize how epidemiology is changing - not enough robust surveillance data to estimate trend Proportion of all cases that are new is higher than expected Proportion of all TB that is pulmonary is lower than expected Table 1: Data provided by country prior to workshop Data National Sub-national Population Population by age and sex TB case notifications by type TB case notifications by age and sex Active case finding Number of new and re-treatment TB cases reported by non-ntp/non-moh providers Number of new and re-treatment TB cases reported among foreign-born individuals or non-citizens MDR-TB TB/HIV Labs Number of dispensaries and hospitals non-ntp providers and collaborators Staff Other (chronic respiratory cases, TB suspects, slides examined, % culture-positive/smear-positive)

23 Figure 1. Percentage change in new TB case notifications over time (red lines indicate 1 change) Table 2. Contribution of different types of TB in notifications Percentage Country Region Country Region Country Region New / all TB 98% 97% 96% 96% 96% New pulmonary / new TB 74% 76% 7 78% 78% New smear-positive / new pulmonary TB 51% 51% 51% Relapse / all retreatment 93% 10 96% 10 68% Failure / all retreatment 2% 2% 2 Default / all retreatment 93% 1% 11% Other retreat / all retreatment 2% 1% Figure 2. Contribution of different TB case types over time New/All 99% 98% 97% 96% 95% 94% 93% 92% Pulm/New ss+/pulm AFG AFG AFG

24 Table 3: Case detection rate of new TB cases (%) Year Country estimates from the workshop Point estimate (lower-upper bound) (2 3) (27 32) (55 66) Table 4. Comparison of changes in notifications with case finding efforts and TB determinants Assessment Country's assessment Have TB notifications been increasing, decreasing or stable over time? Increasing Were there any changes in case-finding effort that might have affected notifications over time? Yes Were there any changes in recording and reporting that might have affected notifications over time? Yes How have factors that may influence TB incidence changed over time, and have they had an impact on underlying TB incidence? Unknown Table 5. Method to estimate TB incidence and trend Assumptions Method Estimate of incidence for reference year Data source 55% case detection in 2008 Value (per population) 189 Trend in TB incidence Data source Flat trend Table 6. Current versus revised estimate of incidence and case detection rate Estimated rate of new TB cases Estimated rate of new ss+ TB cases Case detection rate, new TB cases Case detection rate, new ss+ TB cases Year Previous Revised Previous Revised Previous Revised Previous Revised

25 Table 7. Revised estimate of new TB cases with the confidence limits Year Estimated incidence rate Lower bound Upper bound Table 8. Estimated incidence rate and notification rate, per population Notification rate Estimated incidence rate Lower bound Upper bound

26 2. Bahrain Summary Considerable variation in notifications year-over-year which cannot be due to changes in incidence alone, though notifications stabilize after 2004 Increased non-ntp notifications since 2000, began notifying retreatment cases in 2002, smear-negative, extrapulmonary and pediatric cases in 2003 Notifications more reliable post-2004 when changes to recording and reporting system were made Notable presence of non-national cases (Nepalese and Ethiopian), though most of these are not notified Proportion of all cases that are new is higher than expected Proportion of pulmonary TB that is smear-positive is lower than expected Table 1: Data provided by country prior to workshop Data National Sub-national Population Population by age and sex TB case notifications by type TB case notifications by age and sex Active case finding Number of new and re-treatment TB cases reported by non-ntp/non-moh providers Number of new and re-treatment TB cases reported among foreign-born individuals or non-citizens MDR-TB TB/HIV Labs Number of dispensaries and hospitals non-ntp providers and collaborators Staff Other (chronic respiratory cases, TB suspects, slides examined, % culture-positive/smear-positive)

27 Figure 1. Percentage change in new TB case notifications over time (red lines indicate 1 change) Table 2. Contribution of different types of TB in notifications Percentage Country Region Country Region Country Region New / all TB 10 98% 10 96% 99% 96% New pulmonary / new TB 27% 74% 83% 7 61% 78% New smear-positive / new pulmonary TB 55% 51% 55% 51% 55% 51% Relapse / all retreatment 93% 96% 10 68% Failure / all retreatment 2% 2% 2 Default / all retreatment 93% 1% 11% Other retreat / all retreatment 2% 1% Figure 2. Contribution of different TB case types over time New/All 101% 10 99% 98% 97% 96% 95% 94% 93% 92% 91% Pulm/New ss+/pulm BHR BHR BHR

28 Table 3: Case detection rate of new TB cases (%) Country estimates from the workshop Year Point estimate Table 4. Comparison of changes in notifications with case finding efforts and TB determinants Assessment Country's assessment Have TB notifications been increasing, decreasing or stable over time? Alternately increasing and stable Were there any changes in case-finding effort that might have affected notifications over time? No Were there any changes in recording and reporting that might have affected notifications over time? Yes How have factors that may influence TB incidence changed over Shifts in foreign-born population time, and have they had an impact on underlying TB incidence? from countries with high TB prevalence Table 5. Method to estimate TB incidence and trend Assumptions Method Estimate of incidence for reference year Data source 86% case detection in 2008 Value (per population) 46 Trend in TB incidence Data source Mirror notifications since 2004 Table 6. Current versus revised estimate of incidence and case detection rate Estimated rate of new TB cases Estimated rate of new ss+ TB cases Case detection rate, new TB cases Case detection rate, new ss+ TB cases Year Previous Revised Previous Revised Previous Revised Previous Revised

29 Table 7. Revised estimate of new TB cases with the confidence limits Year Estimated incidence rate Lower bound Upper bound Table 8. Estimated incidence rate and notification rate, per population Notification rate Low er bound Estimated incidence rate Upper bound

30 3. Djibouti Summary Large fluctuations in notifications over time In 2001 NTP staff were reduced with resulting reduction in notifications many immigrant workers left which was thought to reduce incidence new TB programme put into place Given fluctuations in notifications and programmatic changes over time, there is insufficient data with which to make an assessment of trend The proportion of new cases that are pulmonary is extraordinarily low, even when compared to the already low regional average The proportion of pulmonary cases that is smear-positive is unusually high Table 1: Data provided by country prior to workshop Data National Sub-national Population Population by age and sex TB case notifications by type TB case notifications by age and sex Active case finding Number of new and re-treatment TB cases reported by non-ntp/non-moh providers Number of new and re-treatment TB cases reported among foreign-born individuals or non-citizens MDR-TB TB/HIV Labs Number of dispensaries and hospitals non-ntp providers and collaborators Staff Other (chronic respiratory cases, TB suspects, slides examined, % culture-positive/smear-positive)

31 Figure 1. Percentage change in new TB case notifications over time (red lines indicate 1 change) 2 15% 1 5% -5% % -2-25% Table 2. Contribution of different types of TB in notifications Percentage Country Region Country Region Country Region New / all TB 94% 98% 95% 96% 93% 96% New pulmonary / new TB 63% 74% % 78% New smear-positive / new pulmonary TB 94% 51% 94% 51% 94% 51% Relapse / all retreatment 6 93% 74% 96% 73% 68% Failure / all retreatment 9% 2% 14% 2% 21% 2 Default / all retreatment 31% 93% 13% 1% 6% 11% Other retreat / all retreatment 2% 1% Figure 2. Contribution of different TB case types over time New/All 10 98% 96% 94% 92% 9 88% 86% Pulm/New ss+/pulm DJI DJI DJI

32 Table 3: Case detection rate of new TB cases (%) Country estimates from the workshop Year Point estimate (lower-upper bound) (53 64) (63 75) (70 85) Table 4. Comparison of changes in notifications with case finding efforts and TB determinants Assessment Country's assessment Have TB notifications been increasing, decreasing or stable over time? Decreasing, then increasing Were there any changes in case-finding effort that might have affected notifications over time? Yes Were there any changes in recording and reporting that might have affected notifications over time? Unknown How have factors that may influence TB incidence changed over time, and have they had an impact on underlying TB incidence? Unknown Table 5. Method to estimate TB incidence and trend Assumptions Method Estimate of incidence for reference year Data source 7 case detection in 2008 Value (per population) 619 Trend in TB incidence Data source Flat Table 6. Current versus revised estimate of incidence and case detection rate Estimated rate of new TB cases Estimated rate of new ss+ TB cases Case detection rate, new TB cases Case detection rate, new ss+ TB cases Year Previous Revised Previous Revised Previous Revised Previous Revised