Carga tb poblaciones_en_crisis_review2013

950 www.thelancet.com/infection Vol 12 December 2012

Review

Lancet Infect Dis 2012; 12: 950–65

Faculty of Infectious and Tropical Diseases

(W Kimbrough MD; M Dahab PhD, F Checchi PhD), Faculty of Public Health and Policy (V Saliba MD), London

School of Hygiene and Tropical Medicine, Keppel St, London,

UK; and UN High Commissioner for Refugees, Geneva,

Switzerland (C Haskew MBChB)

Correspondence to:Dr Francesco Checchi, Faculty to Infectious and Tropical Diseases,

London School of Hygiene and Tropical Medicine, Keppel St,

London WC1E 7HT, [email protected]

The burden of tuberculosis in crisis-aff ected populations: a systematic reviewWilliam Kimbrough, Vanessa Saliba, Maysoon Dahab, Christopher Haskew, Francesco Checchi

Crises caused by armed confl ict, forced population displacement, or natural disasters result in high rates of excess morbidity and mortality from infectious diseases. Many of these crises occur in areas with a substantial tuberculosis burden. We did a systematic review to summarise what is known about the burden of tuberculosis in crisis settings. We also analysed surveillance data from camps included in UN High Commissioner for Refugees (UNHCR) surveillance, and investigated the association between confl ict intensity and tuberculosis notifi cation rates at the national level with WHO data. We identifi ed 51 reports of tuberculosis burden in populations experiencing displacement, armed confl ict, or natural disaster. Notifi cation rates and prevalence were mostly elevated; where incidence or prevalence ratios could be compared with reference populations, these ratios were 2 or higher for 11 of 15 reports. Case-fatality ratios were mostly below 10% and, with exceptions, drug-resistance levels were comparable to those of reference populations. A pattern of excess risk was noted in UNHCR-managed camp data where the rate of smear testing seemed to be consistent with functional tuberculosis programmes. National-level data suggested that confl ict was associated with decreases in the notifi cation rate of tuberculosis. More studies with strict case defi nitions are needed in crisis settings, especially in the acute phase, in internally displaced populations and in urban settings. Findings suggest the need for early establishment of tuberculosis services, especially in displaced populations from high-burden areas and for continued innovation and prioritisation of tuberculosis control in crisis settings.

IntroductionWorldwide, tuberculosis remains a leading cause of morbidity and mortality, with about 9·4 million new cases, a prevalence of 11·1 million, and 1·3 million estimated deaths in 2008.1 A substantial proportion of the world’s population is also aff ected by natural disasters (about 230 million per year between 2000 and 20102), armed confl ict (30 wars ongoing as of 20103), and forced displacement (about 15 million refu gees and 25 million internally displaced persons [IDPs; ie, forcibly displaced people who do not cross international boundaries] as of 20104). These events diff er substantially in their eff ects on public health. Recent natural disasters have tended to attract greater humanitarian assistance than armed confl icts, and, possibly due to their shorter duration, seem to feature a lower excess burden of infectious disease.5 Forced displacement into camps has well documented, striking eff ects on public health, but refugees tend to have better health outcomes than IDPs, partly because of better protection and accessibility; and an increasing proportion of both refugees and IDPs are settling in non-camp scenarios, mostly urban environ-ments, where they are less identifi able and more diffi cult to monitor.4 Taken together, however, all of these events, which in this Review we refer generally to as crises, share a potential to cause excess morbidity and mortality due to infectious diseases resulting from risk factors such as overcrowding, acute malnutrition, and disrupted health services; they also feature a similar range of stakeholders, funding mechanisms, and inter ventions, which tend to prioritise emergency humani tarian relief over long-term health-system strengthening.

Tuberculosis is a leading health threat for populations aff ected by crises, and more than 85% of refugees fl ee from and stay in countries with a high burden of

tuberculosis.6 Although some evidence shows that the long-term burden of tuberculosis has increased in modern-era post-confl ict states, the short-term eff ects remain poorly documented.7,8 Diffi culties in measuring the burden of disease in crisis settings are similar to those preventing the inclusion of tuberculosis-control pro grammes in initial humanitarian responses: diagnosis requires implementation of minimum laboratory stand ards for quality smear microscopy, while treatment is lengthy and, in settings of high-drug resistance, can be expensive and reliant on reference laboratories to detect and manage failures to fi rst-line regimens.

Generally, only 5–10% of individuals infected with Mycobacterium tuberculosis will develop active disease and become infectious.1 Various risk factors can trigger disease progression, with HIV infection carrying the highest excess risk.9,10 The active phase of the disease can be insidious with mild symptoms such as cough and fatigue, despite patients already being infectious.11 For this reason, individuals with recent onset of tuberculosis symptoms could go unnoticed by health-care providers in crisis settings. An untreated case of active tuberculosis has a case-fatality ratio (CFR) of about 50% and will transmit infection to ten to 15 contacts annually until death or recovery.12

Several crisis-associated risk factors could lead to increased burden of disease from tuberculosis, including malnutrition, overcrowding, and disruption of health services. Even mild malnutrition can increase the risk of tuberculosis progression and case-fatality;13 lower macro-nutrient and micronutrient intake is a nearly ubiquitous problem in crises and might, therefore, account for a high attributable fraction of excess risk. Overcrowding is also an important risk factor in the onward trans-

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mission of pulmonary tuberculosis:14 in crises, trans-mission could occur because of displacement into camps or increased community and household population density when displaced people settle within host communities. The disruption of existing health services could lead to interruption of tuberculosis treatment, which in the intensive early phase of therapy could cause relapse of active, contagious disease and promote drug resistance and the development of multidrug resistance (MDR).15 Together, these factors would increase the risk of progression to active disease among prevalent latent infections, thereby resulting in a short-term increase in morbidity and mortality; at the same time, community transmission would increase because of a higher prevalence of active disease and greater opportunity for person-to-person spread due to overcrowding, though new in fections resulting from this increase in transmission would only develop as cases of active disease months or years later (fi gure 1).

When crises occur in areas with a high burden of HIV, the epidemiological model becomes more complicated. Screening methods for tuberculosis, including symp-tomatic screening, sputum analysis, tuberculin skin testing, and chest radiography become less sensitive and specifi c as HIV disease progresses.16 Missed cases of tuberculosis contribute signifi cantly to HIV mortality, and HIV infection increases the CFR of tuberculosis.17

Tuberculosis control is not judged to be a top priority in the emergency phase of relief, and, until the publication of recommendations for tuberculosis control in emergencies from WHO and UN High Commissioner for Refugees (UNHCR), it had not been addressed systematically in policy.18 Whereas the theoretical links between the risk factors manifesting themselves in crises and increased tuberculosis burden are biologically plausible, we aimed to synthesise evidence on the actual burden of tuberculosis in these settings, to inform policy. We reviewed published reports from specifi c populations experiencing various types of crisis, investigated tuberculosis notifi cation trends as a function of confl ict in countries where widespread armed confl ict occurred and analysed recent data from refugee camps managed by the UNHCR.

MethodsSearch strategy and selection criteriaWhere relevant, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and checklist in designing and reporting our review.19 We searched the Embase, MedLine, and Global Health databases with the Ovid search engine to identify relevant scientifi c articles published between January, 1980, and October, 2011, in English, French, and Spanish, which presented data on any form of tuberculosis infection within populations aff ected by armed confl ict, natural disaster, displacement, or nutritional crises. We chose tuber culosis search terms

by consulting a MeSH thesaurus, and complemented these with terms used by Cochrane Database reviews of tuberculosis.20,21 Similarly, we iden tifi ed MeSH terms for indicators of burden of disease and types of crises. These three concepts were combined into a search with the general outline of [tuberculosis] AND [burden of disease indicator] AND [type of crisis], with truncated terms where necessary.

To identify studies of tuberculosis burden done in areas of armed confl ict that might not have been captured in the above search, we consulted the Uppsala Confl ict Data Program and International Peace Research Institute, Oslo (UCDP/PRIO) Armed Confl ict Dataset (version 4-2010) to identify all regions of high-intensity confl ict (defi ned as at least 1000 combat-related deaths within a single year) since 1980.3,22 Various denomin ations for these regions were then used in the following search outline: [tuberculosis] AND [burden of disease indicator] AND [country or region experiencing armed confl ict]. We limited the search to publications from the fi rst year of confl ict in that region to Oct, 2011. Lastly, we followed the bibliographic trail of reports identifi ed through the above searches.

To identify unpublished data and grey literature reports, we did a Google search for .pdf, .doc, and .docx documents that had in their titles a combination of tuberculosis (or equivalent words in French or Arabic) and either the name of one of the confl ict-aff ected countries identifi ed above or the same terms for type of crisis used above. The same time limits were applied.

Figure 1: Tuberculosis transmission and disease progression in crisis-aff ected populations

Latent, inactive tuberculosis infections in populationPatients with active tuberculosis who are on treatment

MalnutritionInterruption of tuberculosis treatment servicesOther emotional and physical stressorsHIV co-infection and interruption of antiretroviral treatment

Increased rate of activation of latent tuberculosisinfection to active, contagious diseaseRelapse of previously treated tuberculosis to reactivated tuberculosis with possible drug resistance

Increased tuberculosis transmission because ofovercrowding and higher prevalence of active diseaseIncreased rate of disease progression due tomalnutrition, HIV, and other stressors

Short-term effects (months):Increased morbidity and mortality from active diseaseFurther onward transmission from active, contagiouscases (including drug-resistant strains)

Long-term effects (years):Increased burden of latent infections leading to futureincreases in morbidity and mortalityFuture health-system costs due to higher burden,possibly higher prevalence of multidrug resistant cases


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We also contacted, by email, 23 experts on public health in crisis-aff ected populations as well as tuberculosis at the following agencies: Médecins Sans Frontières, Epicentre, Medical Emergency Relief International, the Inter national Committee of the Red Cross, the United States Centers for Disease Control and Prevention, and WHO. The search strategies and results are detailed in the appendix. WK did peer-reviewed database searches and MD did grey-literature-report database searches, while FC contacted experts and agencies.

Inclusion and exclusion criteria for all searches were as follows: we included reports of the burden of M tuberculosis disease in any crisis-aff ected population. These reports included all forms of tuberculosis. We excluded reports in which data for crisis-aff ected people were not disaggregated from those of the general (or host) population; for which the study population consisted of immigrants or refugees to a high-income, non-crisis-aff ected country, unless they were residing in camps (we excluded these reports so as to focus solely on populations that either continued to reside in the crisis-aff ected region and thus were exposed to risk factors associated with the crisis, or that were exposed to displacement-related overcrowding; a review of tuber-culosis in immigrants to high-income countries has been published23); that described special populations, including military forces, transplant recipients, and prisoners; and that used a case defi nition of tuberculosis that did not include either smear microscopy or a WHO-compliant laboratory or symptom-based diagnosis with appropriate intention to treat.24 Some results for drug resistance were stratifi ed into new and previously treated cases.

We extracted data on a Microsoft Excel template; for each report, variables extracted included the country (of origin and refuge for refugees), setting (eg, natural disaster, refugee camp, camp for IDPs), study population, period of data collection, study design, tuberculosis case defi nition, and, for each burden indicator reported, its value, the 95% CI where reported as part of a survey, and the numerator and denominator of the indicator where reported.

WK applied inclusion criteria to scientifi c abstracts, short-listed papers, and extracted data; VS independently replicated the above procedures for a random systematic sample of 10% of abstracts (inter-rater agreement was excellent; κ=0·85 for key term searches and 0·75 for country-based searches) and short-listed reports (fair to good agreement; κ=0·52). FC reviewed reports for which there was a discrepant decision on inclusion and replicated all data extraction. MD applied inclusion criteria to grey-literature reports and extracted data and FC independently replicated all of these.

We assessed the quality of all included reports on the basis of guidelines developed by the UK National Institute for Health and Clinical Excellence for obser-vational studies.25 After reviewing the appropriateness of

the study design, methods of data collection, length of follow-up, defi nitions of outcomes and evidence of selection bias and measurement bias, we attributed a summary grade of lower, medium, or higher strength of evidence to each report. VS did quality assessment; VS and FC reached a consensus grade for each report (appendix).

Comparison with reference populationsTo obtain a measure of excess tuberculosis risk asso-ciated with crises, where available we obtained measures of estimated incidence and period prevalence of tuberculosis from reference populations not aff ected by crisis, and compared these with data from studies included in the Review to calculate crude relative risks (incidence or prevalence ratios) of tuberculosis burden in crisis-aff ected populations versus reference popu lations. Reference populations were defi ned as: (1) the population of the entire country for IDPs and non-displaced populations aff ected by natural disasters or armed confl ict, if these populations were only a minority of the total population of the country itself; (2) the population of the country in the year before the onset of the crisis, if the entire country or most its population was aff ected by armed confl ict; and (3) the populations of both the country of origin and the country of refuge for refugees (we provided alternative relative risks using either of these reference populations).

We obtained reference population estimates for inci-dence or period prevalence of disease from the WHO’s Global Tuberculosis Database.12 We obtained reference drug-resistance and MDR prevalences from published WHO data for 1994–2007.15

Analysis of trends in national tuberculosis notifi cationsIn addition to reports from individual sites, we also investigated national level patterns over time in countries with widespread armed confl ict. We specifi cally aimed to record deviations from secular trends in tuberculosis notifi cations as a function of occurrence and intensity of confl ict.

For this analysis, we included countries that, according to the UCDP/PRIO database, had low (25–999 confl ict-related violent deaths) or high (≥1000 violent deaths) intensity armed confl ict during 1 or more years in the period 1980–2010. However, we excluded years during which confl ict had been focused within a specifi c geographic region comprising less than a fi fth of the country’s population (this arbitrary cutoff was roughly estimated on the basis of census fi gures and historical accounts of the confl ict provided on the UCDP Confl ict Encyclopaedia26). We also excluded instances of short-lived coups d’état not leading to widespread armed confl ict (appendix). To be consistent, for each country we adopted the yearly total numbers of tuberculosis cases notifi ed to WHO (all forms including relapses), as reported in the WHO tuberculosis database.

See Online for appendix


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Analysis of refugee camp dataWe extracted aggregate data on the population, number of new smear-positive patients with pulmonary tuber-culosis per year, and total number of smear tests done in 86 refugee camps located in 17 host countries, man aged by UNHCR and covered by the UNHCR Health Information System (HIS; a standardised, automated platform for collection, analysis, and reporting of health surveillance data).27 We excluded years during which no smear tests were done in the camp from the analysis, because this fi nding suggested a non-functional tuber-culosis programme. Time series of tuberculosis data from these camps in the HIS database all ended in Dec, 2011, and, depending on the camp, started as early as 2006. As above, we computed notifi cation rates of new smear-positive cases for each camp and compared these data with WHO notifi cation rates for the host country and all countries of origin of refugees living in the camp (the HIS database does not provide tuberculosis cases or populations by country of origin).

We also investigated whether, across all camps, there was a general trend in smear-positive incidence rate, smear-testing rate (number of smears per 100 000 people per year), and ratio of new smear-positive patients per smear test done as a function of increasing time. For this analysis, we included all camps (n=53) for which at least 4 years of consecutive data were available, but excluded data beyond 4 years because these were sparse and we wanted to analyse a time series of equal duration for each camp.

Statistical analysisCrude relative risks comparing burdens reported in studies included in the Review as well as the UNHCR HIS with those of reference populations were computed as the ratio of the point estimate of reported incidence (notifi cation rate) or prevalence in the study reviewed divided by incidence or prevalence in the reference population, both estimated by WHO (for studies re viewed only) or notifi ed to WHO (for studies reviewed and UNHCR HIS data). We excluded 2011 HIS data from this comparison since WHO data were not yet available for this year at the time of writing. Where the study period spanned more than 1 year with no stratifi cation of data by year, we extracted the mean reference value over that period. WHO estimates are available from 1990 and for all-form tuberculosis only; we extracted the lower and upper bound of the WHO estimate to compute a range for the relative risk. Between 1980 and 1989 the WHO reports only all-form tuberculosis notifi cations: for this decade, reference pulmonary tuberculosis notifi cation rates as reported by the country’s national tuberculosis pro gramme were extracted instead, if published. We only adopted a reference pulmonary tuberculosis notifi cation rate if it matched the case defi nition used in the report being reviewed. As WHO databases provide only new (and not relapse) notifi ed pulmonary tuberculosis cases, we assessed only new cases for the reference notifi cation rate.

We did not do a meta-analysis of either burden estimates or relative risk comparisons with reference populations, for the following reasons: (1) tuberculosis burden is very heterogeneous on a global scale, and we had no means of verifying whether studies included in the Review captured this heterogeneity or were indeed representative of the global crisis-aff ected populations whose burden we sought to review; (2) the estimates themselves had substantial heterogeneity (data not shown) and refl ected various case defi nitions and case ascertainment methods; and (3) some of the studies reported a rate but did not contain suffi cient information on the person-time denominator, which is needed for meta-analysis.

For the analysis of trends in national tuberculosis notifi cations, we fi tted two alternative generalised linear models to the data, both featuring year as the analysis unit, the natural logarithm of notifi ed cases as the dependent variable (assumed to follow a quasi-Poisson distribution [ie, including an overdispersion parameter] to account for overdispersion) and year as a continuous independent variable controlling for underlying secular trends (assumed to be linear).28,29 In the fi rst model we included confl ict intensity as an independent variable, and computed incidence rate ratios (IRRs) comparing low and high intensity years with the baseline (years without confl ict-related violent deaths). In the second model, in which we postulated that confl ict also has lag eff ects, we arbitrarily defi ned a recovery phase consisting of the 3 years after the end of confl ict of either intensity (or shorter if another period of confl ict began or the time series ended).

For the analysis of HIS data as a function of time, after assessing the relative goodness of fi t of diff erent distributional assumptions and models with and without the rate of smear testing as a covariate, we fi tted the following negative binomial generalised linear models: (1) new smear-positive cases (dependent variable), log person-years of follow-up (off set), time (years 1 and years 2 vs years 3 and years 4; independent variable), and smear-testing rate (potential confounder); (2) total smear tests done (dependent), log person-years of follow-up (off set), time as above (independent variable); and (3) new smear-positive cases (dependent variable), log total smears done (off set), time (years 1 and years 2 vs years 3 and years 4; independent variable), and smear-testing rate (potential confounder). For all models we computed robust SEs by specifying camp as a cluster variable.

Results51 reports were included in the fi nal analysis (fi gure 2). Of these, 23 reported on refugees living in camps, fi ve on IDPs, 20 on non-displaced confl ict-aff ected populations, and three on populations aff ected by a natural disaster. 13 studies reported data on incidence (notifi cation rate), ten on prevalence, 18 on CFR, 14 on prevalence of drug resistance, two on the death rate attributable to


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tuberculosis, three on tuberculosis-pro portional morbidity, and two on tuberculosis-propor tional mortality. 15 reports described populations in the WHO Africa region, seven from the Southeast Asia region, 13 from the eastern Mediterranean region, fi ve from the European region, two from the region of the Americas, and nine from the western Pacifi c region.

Measured incidences ranged from 21 to 1510 per 100 000 per year for pulmonary tuberculosis and from 82 to 1142 per 100 000 per year for all-form tuberculosis, suggesting great variability in burden (table 1). Nearly all the incidence rate ratios comparing notifi cations in crisis-aff ected populations with those in reference populations were well above 1 (peaking at 27·2); this was also the case if the comparison was instead made with reference estimated incidences. The sole exception was Burmese Karen refugees in Thailand,40 for whom notifi cation rates were lower than estimated incidences but none theless higher than notifi cation rates in both Burma and the host country, Thailand. High relative risks were noted even where the crisis event (eg, displacement) had occurred only a few years before data collection (table 1). Although data were sparse, this general fi nding seemed to hold even if studies with lower strength of evidence were excluded.

Longitudinal data were available from only a few studies, and did not reveal a uniform pattern (table 1). Notifi cations gradually decreased from extremely high levels during the fi rst year after displacement in camps for Ethiopian refugees in Sudan, but remained very high.35 Notifi cations remained very high between 1994 and 1996 in long-term Tibetan refugees,37 and sharply increased during the fi rst 3 years of the establishment of Burmese refugee camps in Thailand, with a gradual

decrease thereafter (although camp populations were very dynamic, with ongoing arrivals and departures).40 In the Republic of Congo, the number of cases notifi ed increased substantially during a period of war despite closure of many tuberculosis treatment centres, and peaked in the year after cessation of hostilities with a higher proportion of smear-negative and extrapulmonary tuberculosis cases. Drug shortages occurred due to the fact that drugs were procured on the basis of fairly stable projections of need (ie, drug stocks could not cope with sudden increases in caseload).41 In postwar Kosovo, annual all-form tuberculosis declined from 86 per 100 000 in 2000 to 53 per 100 000 in 2005, while new smear-positive pulmonary tuberculosis rates declined from 20 per 100 000 in 2000 to 11 per 100 000 in 2005.42 In Nepal, all-form tuber culosis and new smear-positive pulmonary tuberculosis rates increased as confl ict persisted.44 In post-earthquake Kashmir, Pakistan, a reduction in notifi cation rates was apparent in the year after the earthquake (Abrar Ahmad Chughtai, Pakistan National Tuberculosis Control Programme, Islamabad, Pakistan, personal communication).

The prevalence of tuberculosis, mainly measured through exhaustive or sample surveys (ie, with less bias due to passive reporting), was also higher in crisis populations than in reference populations, with preva-lence ratios peaking at 20·7 (table 2). The only exception was a camp for Afghans in Iran, where a high burden of tuberculosis at baseline was controlled to near-zero levels through intensive screening.50 Prevalence ratios could not be calculated for several studies because WHO does not supply prevalence estimates of pulmonary tuberculosis only; however, even if reference estimates of

1061 abstracts screened

901 excluded based on exclusion criteria

160 full-text reports retrieved

1 duplicate87 no tuberculosis burden measures 8 not from crisisaffected populations18 unacceptable case definition13 did not disaggregate crisis-affected group 2 special populations 2 could not be retrieved

29 reports included in review

4787 abstracts screened

4452 excluded based on exclusion criteria


57 duplicates from primary search 98 no tuberculosis burden measures147 not from crisis-affected populations 26 unacceptable case definition 7 did not disaggregate crisis-affected group 5 special populations 19 could not be retrieved



29 no tuberculosis burden measures 8 not from crisis-affected populations 1 unacceptable case definition 3 could not be retrieved


438 hits

24 duplicates, 190 published in journals133 no tuberculosis burden measures 40 not from crisis-affected populations 1 unacceptable case definition 6 did not disaggregate crisis-affected group 13 could not be retrieved 28 only cited other studies


12 reports shared

6 no tuberculosis burden measures3 not from crisis-affected populations


Key term search Country-specific search

Bibliographic trail search Web search for grey literatureAgency and expert contact

for grey literature

Figure 2: Literature review


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Year(s) of displacement, war, or disaster

Type of study Case defi nition; type of cases

Notifi cation rate reported (cases/person-years*)

Rate ratio for comparison with notifi cation rate in reference populations (reference notifi cation rate)

Rate ratio for comparison with estimated incidence in reference populations (reference incidence)

Strength of evidence

Refugee camps

Cambodian refugees in Thailand (1981–84)30

1979 Clinic-based surveillance

Smear/WHO; aTB, pTB

500 (629/125 800) aTB; 240 (302/125 800) ss+ pTB

Cambodia (1982–84):31 3·4 (145) aTB; 2·7 (89) ss+ pTB

NA Medium

Nicaraguan refugees in Costa Rica (1985)32

1983–85 Clinic-based surveillance

Smear; smear-positive; pTB

400 (5/1160) Nicaragua: 8·0 (50)33

Costa Rica: 25·0 (16)34

NA Lower

Ethiopian refugees in eastern Sudan (1986–90)35

1967–83 (about 30% of refugees); 1984–85 (about 70%)

Clinic-based surveillance

Smear; ss+ pTB 1986 (1510); 1987 (790); 1988 (630); 1990 (450)‡

Ethiopia: NASudan: NA

NA Lower

Somali and Sudanese refugees in Kenya (1992–93)36


Smear/WHO; aTB, ss+ pTB

1142 (3116/272 800) aTB; 453 (1235/272 800) ss+ pTB

Kenya: 16·7 (69) aTB, 12·9 (35) ss+pTBSomalia: NASudan: 11·4 (101) aTB, NA ss+pTB

Kenya: 7·1–8·8 (130–161) aTBSomalia: 3·0–5·7 (202–383) aTBSudan: 7·2–13·5 (85–160) aTB

Medium

Tibetan refugees in India (1994, 96)37

1959 Clinic-based surveillance and camp screening

Smear/WHO; aTB

1994–96 (980 [1575/160 018]); 1994 (1090); 1995 (1100); 1996 (770)

China: 27·2 (36)India: 7·8 (126)

China: 6·3–9·3 (105–155)India: 4·0–5·2 (189–246)

Higher

Tibetan refugees in India (1994–96)38

1959 Clinic-based surveillance and camp screening

Smear/WHO; aTB

835 (1197/143 373) China: 23·2 (36)India: 6·6 (126)

China: 5·4–8·0 (105–155)India: 3·4–4·4 (189–246)

Higher

Bhutanese refugees in Nepal (1999–2004)39

1990–98 (peak in 1992)

National programme data


242 (1214/501 653) aTB; 126 (631/501 653) new ss+ pTB

Bhutan: 1·3 (181) aTB; 2·1 (59) new ss+ pTBNepal: 2·0 (119) aTB; 2·3 (55) new ss+ pTB

Bhutan: 0·9–1·3 (189–278) aTBNepal: 1·2–1·8 (133–197) aTB

Higher

Burmese refugees in Thailand (1987–2005)40


WHO; aTB 122 (978/NA); sharp increase from 1987 (22) to 1991 (212), then gradual decrease to 2005 (43)

Burma (1990–2005): 1·5 (81)Thailand (1990–2005): 1·7 (71)

Burma (1990–2005): 0·2–0·4 (319–501)Thailand (1990–2005): 0·7–1·2 (106–172)

Medium

War-aff ected but non-displaced

Republic of Congo (1994–2000)41

1997–99 National programme data†

WHO; aTB, pTB 1997–2000 (186 [21 886/11 758 000] aTB; 133 [15 666/11 758 000] pTB); 1997 (122, 96); 1998 (136, 103); 1999 (172, 126); 2000 (304, 202)

Republic of Congo (1994–96):41 1·3 (142) aTB, 1·2 (111) pTB

NA Lower

Kosovo (2000–01);42

much of population lived in refugee camps in 1999

1998–99 National programme data


82 (3450/4 208 125) aTB; 21 (879/4 208 125) new ss+ pTB

Serbia excluding Kosovo:43 2·4 (34) aTB, 1·0 (20) new ss+ pTB

NA Medium

Dang district, Nepal (1998–2003)44

1996–2003 National programme data

Smear/WHO; aTB

1998–99 (90); 2000–01 (194); 2002–03 (208)‡

Nepal (1998–99): 0·8 (110)Nepal (2000–01): 1·6 (120)Nepal (2002–03): 1·7 (119)

Nepal (1998–99): 0·5–0·7 (133–197)Nepal (2000–01): 1·0–1·5 (133–197)Nepal (2002–03): 1·1–1·6 (133–197)

Medium

Natural disaster

Earthquake, Bam city, Iran (2004)45

December, 2003 (one month before)

Clinic-based surveillance

WHO; aTB 145 (11/7577) Iran: 8·1 (18) Iran: 4·7–6·9 (21–31) Lower

Earthquake, Azad Jammu and Kashmir province, Pakistan (2004–10)§

October, 2005 National programme data

Smear/WHO; aTB ss+ pTB

2006–10 (111 [21 564/19 421 550]) aTB; 31 [6294/19 421 550] ss+pTB); 2006 (102, 29); 2007 (112, 34); 2008 (104, 33); 2009 (125, 34); 2010 (112, 32)

Azad-Jammu-Kashmir province (2004–05): 0·9 (127) aTB, 1·0 (33) ss+pTB

NA None (insuffi cient information)

All rates are per 100 000 per year. aTB=all forms of tuberculosis. ss+=sputum-smear positive. pTB=pulmonary tuberculosis. NA=not available. *For some reports, we estimated person-years based on the rate, period of data collection, and number of tuberculosis cases reported. †The report presents only numerators (cases). We calculated rates using US Census Bureau demographic projections for the Republic of Congo.46 ‡Cases and person-years not reported. §Abrar Ahmad Chughtai, Pakistan National Tuberculosis Control Programme, Islamabad, Pakistan, personal communication.

Table 1: Reports of tuberculosis incidence (notifi cation rate) in crisis-aff ected populations, 1980–2011


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prevalence of all-form tuberculosis were used as the comparison, all prevalence ratios would be far above 1, with no change in this general fi nding if lower-strength evidence was excluded.

The current CFR estimate for HIV-positive patients with tuberculosis on treatment is 10% worldwide;57 before availability of highly active antiretroviral therapy, studies in Africa showed CFRs of 16–35% in HIV-positive patients, and 4–9% in HIV-negative patients on treatment for tuber culosis only.17 The 1990 estimate of CFR for industrialised countries was 7%; 15% for eastern Europe, and as high as 20% for developing countries.58 In studies included in this Review, CFR was mostly lower than any of these estimates (table 3), with no obvious chronological im provement or deterioration and a similar pattern if lower-strength evidence was excluded. The only direct comparison was in Khartoum, Sudan, where IDPs from south Sudan had a slightly higher CFR than the local population (4·1% vs 3·7%).64 In India, the CFR for HIV-positive patients was seven times higher than for HIV-negative patients over the same time period.68 Similarly, the paediatric ward of a hospital in Brazzaville, Republic of Congo, recorded a 20% CFR for 1–2-year-old HIV-positive children during a confl ict period compared with

no HIV-negative deaths over the same 5 year span.69 In both studies, antiretroviral therapy was not available.

Between 1994 and 2007, 5·3% of all isolates worldwide were MDR, with much higher rates in eastern Europe and central Asia than in the rest of the world.15 Most studies we reviewed reported a prevalence similar to or lower than reference regional estimates of drug resist-ance prevalence, with notable exceptions (table 4). In the only direct comparison available, Somali and Sudanese refugees in Kenya had much higher prevalence of drug resistance than the surrounding host population.72 The MDR prevalence of 42·1% in Laotian Hmong refugees53 in Thailand was far above the regional and country-specifi c prevalences. Studies from the early 1980s in Ethiopia and Eritrea show a surprisingly high frequency of single-drug resistance compared with the WHO regional prevalence of 11·4% from 1994 to 2007. In Haiti, prevalence of MDR seemed to increase from the pre-earthquake to the postearthquake year.82

Two studies by Gustafson and colleagues followed cohorts of tuberculosis patients in Bissau city, Guinea-Bissau, from 1997 to 1998, spanning periods before and during armed confl ict.83,84 These studies showed an increase in mortality among patients with tuberculosis

Year(s) of displacement, war or disaster


Reported prevalence (cases/persons tested)

Lower-upper range of ratio for comparison with estimated prevalence in reference populations (reference prevalence, lower-upper range)


Refugee camps

Ethiopian refugees in Somalia (1981)47

1978–80 Household survey Smear; ss+ pTB 2350 (mean of two camps; cases and persons tested not reported)

NA Lower

Vietnamese refugees in Thailand (1985–1986)48

1985–86 Camp entry screening Smear, culture; pTB, ss+ pTB

580 (115/19 726) pTB100 (20/19 726) ss+ pTB

NA Higher

Vietnamese refugees in Hong Kong (1992)49

1975–91 Clinic–based surveillance WHO; aTB, pTB 680 (102/15 000) aTB440 (66/15 000) pTB

Vietnam: 1·0–3·8 (178–678) aTBHong Kong: 2·4–11·9 (57–280) aTB

Medium

Afghan refugees in Iran (1996, 2004)50

1985 Camp screening Smear/WHO; aTB, pTB, ss+ pTB

1996 (630 [17/NA] aTB; 593 [16/NA] pTB; 297 [8/NA]; ss+ pTB); 2004 0 (0/1397) aTB, 0 (0/1397) pTB, 0 (0/1397) ss+pTB

Afghanistan (1996): 0·8–3·2 (198–760)Afghanistan (2004): 0 (198–760)Iran (1996): 6·6–26·3 (24–95)Iran (2004): 0 (24–95)

Medium

Kosovar refugees in Switzerland (1999)51

1998–99 Camp entry screening Smear, culture/WHO; pTB

256 (8/3119) NA Higher

Kosovar refugees in Norway (1999)52

1998–99 Camp entry screening Smear, culture/WHO; pTB, sc+ pTB, ss+ pTB

500 (4/800) pTB125 (1/800) sc+ pTB

0 (0/800) ss+ pTB

NA Higher

Laotian refugees in Thailand (2004–05)53

1975–94 Camp exit screening Smear/WHO; aTB, sc+ pTB, ss+ pTB

1760 (272/15 455) aTB369 (57/15 455) sc+ TB220 (34/15 455) ss+ pTB

Laos: 8·0–35·2 (50–219) aTBThailand: 5·5–20·2 (87–321) aTB

Higher

Burmese refugees in Thailand (2007)54

1984–2007 Camp exit screening Smear; sc+ pTB, ss+ pTB

598 (28/4686) sc+ pTB150 (7/4686) ss+ pTB

NA Higher


1990–98 Camp exit screening Smear/culture; pTB, ss+ pTB

644 (151/23 459) pTB230 (54/23 459) ss+ pTB

NA Higher

IDP

IDP living in hostels in Georgia (1999)56

1992–93 Camp screening Smear/WHO; aTB 537 (5/931) Georgia: 2·6–20·7 (26–209) Lower

All prevalences are per 100 000 people. ss+=sputum-smear positive. pTB=pulmonary tuberculosis. NA=not available. aTB=all forms of tuberculosis. sc+=sputum-culture positive. IDPs=internally displaced persons.

Table 2: Reports of tuberculosis prevalence in crisis-aff ected populations, 1980–2011


Review

from 12 per 100 person-years before war to 34 per 100 person-years during wartime, with disruptions to the antituberculosis drug supply and directly observed treat ment, short course (DOTS) infrastructure judged to be the main causes. Moreover, the wartime-to-peacetime mortality ratio was 8·2 for HIV-positive patients and 1·2 for HIV-negative patients.

A few studies measured disease burden in terms of proportional morbidity and mortality. A hospital-based study set in the Acholi region of northern Uganda between 1992 and 1997 (when about 70% of the population were IDPs in camps) showed that tuberculosis was the third leading cause of admission to hospital, accounting for 6·2% of all admissions over the study period. Tuberculosis

was also the leading contributor to bed occupancy (24·6%) with an average length of stay of 57·4 days and proportional mortality 11·3%.85 Another hospital-based study in the same setting reported proportional mortality from tuberculosis to be 5·7% during confl ict compared with 4·5% during peacetime (relative risk 1·3).86 A 1983–85 hospital-based study in Addis Ababa city, Ethiopia, reported tuberculosis to be the cause of 11·2% of all admissions.66 Lastly, tuberculosis sentinel surveillance in Apac district, Uganda87 (until 2005 a confl ict-aff ected district with many IDPs) yielded a proportional morbidity in outpatient facilities of 0·52% during January, 2011, to September, 2011, compared with 0·14% in neighbouring districts not aff ected by confl ict (relative risk 3·7).


Case-fatality rate (deaths/patients) Type of treatment plan used


Refugee camps

Cambodian refugees in Thailand (1981–83)59 Clinic-based surveillance Smear; pTB 6·0% (36/615) DOTS Lower

Cambodian refugees in Thailand (1981–84)30 Clinic-based surveillance Smear/WHO; aTB, ss+ pTB 5·0% aTB (28/558)3·9% ss+pTB (NA)

DOTS Medium

Cambodian refugees in Thailand (1981–90)60 Clinic-based surveillance Smear; ss+ pTB 5·0% (46/929) DOTS Medium

Cambodian refugees in Thailand (1984–85)61 Clinic-based surveillance WHO; aTB 3·8% (47/1240) DOTS Medium


Clinic-based surveillance Smear/WHO; ss+ pTB 2·6% (32/1235) Not specifi ed Medium

Tibetan refugees in India (1994–96)38 Clinic-based surveillance and camp screening

Smear/WHO; aTB 3·8% (45/1184) Not specifi ed Higher

Burundian and Rwandan refugees in Tanzania (1995–99)62

Clinic-based surveillance Smear; ss+ pTB 10·9% (60/546) DOTS Medium

Burmese refugees in Thailand (1987–2005)40 Clinic-based surveillance WHO; aTB 5·8% (57/978) DOTS Medium

Somali refugees in Kenya (2010)63 Clinic-based surveillance Smear/WHO; aTB, pTB, ss+ pTB

2·7% (11/411) aTB2·2% (7/325) pTB2·3% (4/174) ss+ pTB

Not specifi ed None (insuffi cient information)

IDP

IDP from south Sudan in camps, Khartoum, Sudan (2000)64

Clinic-based surveillance WHO; ss+ pTB 4·5% (11/245) for IDP; 3·7% (5/136) for host population

Not specifi ed Medium

Northern Uganda (1992–2002);65 all war-aff ected, about 70% internally displaced people in camps

Hospital-based surveillance WHO; aTB 10·4% (81/777) Not specifi ed Medium


Addis Ababa city, Ethiopia (1983–85)66 Hospital-based surveillance Smear/WHO; aTB, pTB 7·9% (19/240) aTB8·7% (10/115) pTB

Not specifi ed Lower

Gedo region, Somalia (1994–95)67 Hospital-based surveillance Smear/WHO; aTB, pTB, ss+ pTB

7·6% (16/211) aTB7·8% (15/192) pTB3·2% (4/125) ss+ pTB

DOTS Medium

Churachandpur district, India (1998);68 district included 39% IDP population

Clinic-based surveillance Smear/WHO; aTB, ss+ pTB 2·8% (5/178 aTB [22·2% (4/18) for HIV- positive patients])

2·4% (2/85) ss+ pTB

DOTS Higher

Brazzaville city, Republic of Congo (1999–2004)69

Hospital-based surveillance Smear/WHO; pTB (children aged 12–23 months)

0% (0/45) for HIV-negative patients; 20·0% (7/35) for HIV-positive patients

Not specifi ed Medium

Upper Nile, south Sudan (2001)70 Clinic-based surveillance Smear/WHO; aTB, ss+ pTB 4·3% (7/163) aTB9·1% (3/33) ss+ pTB, all HIV-negative

DOTS (Manyatta regimen)

Medium

Kosovo (2001–04)42 Clinic-based surveillance WHO; ss+ pTB 2001 (4·3% [18/421]); 2002 (1·5% [6/402]); 2003 (1·4% [4/292]); 2004 (1·8% [5/272])

DOTS Medium

Jammu and Kashmir state, India (2003–07)71 Hospital-based surveillance Smear; MDR ss+ pTB 21·1% (11/52) DOTS Lower

pTB=pulmonary tuberculosis. DOTS=directly observed treatment, short course. aTB=all forms of tuberculosis. ss+=sputum-smear positive. IDPs=internally displaced persons. MDR=multidrug resistant. NA=not available.

Table 3: Reports of tuberculosis case-fatality ratio in crisis-aff ected populations, 1980–2011


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The analysis of tuberculosis notifi cation patterns in countries aff ected by widespread confl ict did not yield a uniform pattern, and for most countries associations were non-signifi cant in either model (appendix). How ever, there was an obvious trend towards reduced rates of tuberculosis notifi cation during years of low-intensity confl ict and especially high-intensity confl ict compared with the baseline. In a model without lag eff ects, about two-thirds of countries showed an apparent decline in notifi cation rates during years of high-intensity confl ict (fi gure 3); negative associations signifi cant to a prob ability of less

than 0·10 were estimated for Angola, Azerbaijan, DR Congo, Guinea Bissau, the Solomon Islands, Tajikistan, and Uganda (low-intensity years) and Angola, Azerbaijan, Bosnia and Herzegovina, El Salva dor, Iran, Iraq, Kuwait, Serbia and Montenegro, Tajiki stan, and Uganda (high-intensity years); signifi cant positive associations were, however, noted for Burundi, Djibouti, Iran, Nepal, and Peru (low-intensity years), and Burundi, Ethiopia, Burma, Nepal, Peru, and Rwanda (high-intensity years).

A model including lag eff ects during recovery periods after the end of either intensity confl ict showed similar


Drug-resistance prevalence (cases/persons tested)

Drug-resistance prevalence in comparison populations


Refugee camps


Clinic-based survey Smear/WHO; ss+ pTB

18·3% (44/241) any drug resistance; 2·9% (7/241) MDR

Host population in same study: 5·7% (5/88) any drug resistance, 0% (0/88) MDR

Medium

Laotian (Hmong) refugees in Thailand (2005)53

Camp exit screening Smear/WHO; aTB


Thailand (2006): 20·7% any drug resistance, 6·4% MDR; Laos (1994–2007): 4·3% MDR

Higher

Burmese refugees in Thailand (2007)54

Camp exit screening Smear/culture; pTB


Thailand (2006): 20·7% any drug resistance, 6·4% MDR; Thai nationals, Tak province, Thailand (2006–07): 5·7% MDR73

Higher


Camp exit screening Smear/culture; pTB

7% (NA) any drug resistance; 2% (NA) MDR Bhutan (2008): 4·2% MDR; Nepal (2007): 16·6% any drug resistance, 4·4% MDR

Higher


Addis Ababa city, Ethiopia (1981)74

Clinic-based surveillance Smear 23·5% (43/182) single drug resistance, new cases

NA Medium

Asmara city, Eritrea (1984)75 Clinic-based and hospital-based survey

Smear/WHO; pTB

56·3% (18/32) any drug resistance NA Lower

Addis Ababa and Harar cities, Ethiopia; Asmara city, Eritrea (1986)76

Clinic-based survey ss+ pTB 39·1% (108/276) any drug resistance, new NA Medium

Rwanda (1991–93)77 Clinic-based and hospital-based survey

NA 15·4% (46/298) any drug resistance; 2·4% (7/298) MDR

WHO Africa region (1994–2007): mean 13·8% any drug resistance, mean 2·2% MDR

Lower

Bujumbura city, Burundi (2002–03)78

Clinic-based and hospital-based survey

WHO; ss+ pTB 16·1% (80/496) any drug resistance, new cases; 30·4% (21/69) any drug resistance, previously treated cases; 1·4% (7/496) MDR, new; 11·6% (8/69) MDR, previously treated

WHO Africa region (1994–2007): mean 13·8% any drug resistance, mean 2·2% MDR

Medium

Basra city, Iraq (2003–04)79 Clinic-based survey WHO; pTB 23·1% (24/104) any drug resistance, new; 70·8% (48/65) any drug resistance, previously treated; 20·0% (13/65) MDR, previously treated

Iraq (1994–2007): estimated 38·0% MDR, previously treated; WHO Eastern Mediterranean region (1994–2007): mean 13·7% any drug resistance, new; 54·4% any drug resistance, previously treated; 35·3% MDR, previously treated

Medium

Abkhazia, Georgia (2003–05)80 Hospital-based survey WHO; ss+ pTB 54·1% (106/196) any drug resistance, new, 8·7% (17/196) MDR, new; 68·5% (87/127) any drug resistance, previously treated, 38·6% (49/127) MDR, previously treated

Georgia (2006): 49·2% any drug resistance, new; 6·8% MDR, new; 66·0% any drug resistance, previously treated; 27·4% MDR, previously treated

Higher

Jammu and Kashmir state, India (2003–07)71

Hospital-based prospective observational cohort

WHO; pTB 5·7% (52/910) MDR, 0·9% (8/910) XDR Delhi state, India (1995): 13·3% MDR Lower

Dohuk province, Iraq (2008–09)81

Routine laboratory surveillance

Smear/WHO; pTB

10·5% (4/38) any drug resistance, new; 7·9% (3/38) MDR, new; 53·3% (8/15) any drug resistance, previously treated; 46·7% (7/15) MDR, previously treated

WHO Eastern Mediterranean region (1994–2007): mean 13·7% any drug resistance, new; 2·0% MDR, new; 54·4% any drug resistance, previously treated; 35·3% MDR, previously treated

Medium

Natural disaster

Post-earthquake Haiti (2010)82 Routine laboratory surveillance

Smear/WHO; ss+ pTB

5·5% (30/546) MDR Same laboratory (2009): 1·0% MDR Medium

All comparison estimates of drug-resistance prevalence are taken from the WHO’s Anti-tuberculosis Drug Resistance 2008 Report,15 unless indicated otherwise. ss+=sputum-smear positive. pTB=pulmonary tuberculosis. MDR=multidrug resistant. aTB=all forms of tuberculosis. NA=not available. XDR=extensively drug resistant.

Table 4: Reports of tuberculosis drug-resistance prevalence in crisis-aff ected populations, 1980–2011


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trends though somewhat more signifi cant associations (appendix). Countries with a reduction in notifi cations compared with the baseline during the recovery phases were also about twice as common as those with an increase in notifi cations.

No obvious pattern was identifi ed in the relation between the rate of tuberculosis smear-positive notifi -cation in UNHCR-managed refugee camps and the reference notifi cation rates in either the host or origin countries (appendix). Only about half of camps seemed to have a higher burden than reference populations, and camps within the same host country generally had a similar relative risk.

However, a strong linear correlation was seen at the camp and host-country level between the rate of pulmonary-tuberculosis smear-positive notifi cation and the rate of

smear testing (ie, the number of smears done per 100 000 people per year), and the latter indicator explained about 50% of the variability in smear-positive rate in both generalised linear and ordinary least-squares regression models (data not shown); Chad and Sudan in particular had low rates of smear testing in nearly all camps.

The ratio of new smear-positive cases per smear test done (data not shown) was signifi cantly higher and much more variable in camps where the rate of smear testing was less than 2000 per 100 000 person-years than in camps with a testing rate above that value (median ratios 6·0% vs 2·5%, respectively; p=0·002, Kolomogorov-Smirnov test for comparison of medians). This fi nding suggests that substantial self-selection of patients typical of tuberculosis programmes with low population coverage and low case-detection rates occurred below 2000 per

AlgeriaAngola

AzerbaijanBosnia and Herzegovina

BurmaBurundi

CambodiaCentral African Republic

ChadColombia*

CongoCôte d’Ivoire

CroatiaDR Congo

DjiboutiEl Salvador

EritreaEthiopiaGeorgia

GhanaGuatemala

Guinea-BissauHaitiIranIraq

KuwaitLebanon

LiberiaMozambique

NamibiaNepal

NicaraguaPanama

PeruRwanda

Serbia and MontenegroSierra Leone

Solomon IslandsSomalia

SyriaTajikistanThailandUgandaYemen

0 50 100 150 200–50–100Change in case notification rate (%)

Figure 3: Estimated percent change in case reporting rate associated with high-intensity confl ict years, by country, based on a model without lag eff ectsWhiskers indicate 95% CI. *High-intensity years versus low-intensity years.


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100 000 person-years. Indeed, nearly all camps with a smear testing rate of 2000 or more per 100 000 person-years (ie, where tuberculosis programmes could be more safely assumed to achieve a reasonable case detection rate) had notifi cation-rate ratios well above 1 (appendix).

When assessing all camps in a single model, there was no evidence of an association between increasing time and the incidence rate of new smear-positive cases, adjusted for the rate of smear testing (incidence rate ratio [IRR] 0·99 for years 3–4 vs years 1–2, 95% CI 0·77–1·29; p=0·964); weak evidence of a slight increase in smear testing over time (IRR 1·13 for years 3–4 vs years 1–2, 95% CI 0·97–1·32; p=0·121); and no evidence of an association between time and the ratio of new smear-positive patients to smear tests done, ad justed for the rate of smear testing (IRR 1·01 for years 3–4 vs years 1–2, 95% CI 0·78–1·32; p=0·943).

DiscussionMost available reports come from refugees in camps, and data from the 1980s and 1990s are more abundant than for the past decade, at least in published works. Importantly, evidence about the burden of tuberculosis among IDPs and after natural disasters was very sparse.

Results suggest that crises are often associated with up to 20-fold increases in the risk of tuberculosis, although this pattern was more diffi cult to infer for refugee camps managed by UNHCR over the past 5 years. Our fi ndings do not suggest any increase in CFR, while results for drug resistance are somewhat mixed. Despite these broad patterns, both incidence and prevalence showed variability of up to two orders of magnitude. Findings consistently point to a disproportionately high risk of excess mortality among HIV-positive individuals. How-ever, most of the studies included in the Review were done before the era of widespread access to antiretrovirals, which would be expected to moderate excess risk due to HIV in ongoing crises. With notable exceptions, we reported that both high-intensity and low-intensity armed confl ict were mostly associated with reductions in case notifi cation at the national level.

Specifi c studies from displaced populations nearly uniformly suggest an increased burden relative to the reference populations, as postulated by other researchers.88,89 A community-based study of several refugee camps between 1979 and 1985, not included in this Review because its reliance on simple verbal autopsy questionnaires, estimated that tuberculosis caused 26% of all deaths in adults in a camp for IDPs in Somalia 3 years after its establishment, and 50% in a camp in Sudan 10 months after establishment.90 Both populations had high prevalence of acute malnutrition. A similar study of Tibetan refugees in India showed that tuber-culosis was the second most common cause of death (14%).90 In Ethiopia, patients from war-aff ected areas took twice as long to seek treatment as those from unaff ected areas.91 These studies corroborate our broad fi ndings.

Naive analysis of data from UNHCR-managed refugee camps suggests no overall pattern of increased burden; however, if analysis is restricted to camps where tuber-culosis programmes seem to function reasonably well on the basis of the rate of smear testing as a proxy indicator, a clear pattern of higher smear-positive-case incidence emerges when assessing both the host country and any of the countries of origin of camp residents as references. Although the consistency of this fi nding sug gests high excess burden, this inference cannot be substantiated without investigating the alternative ex planation—namely, that tuberculosis programmes in these camps achieve higher detection rates than in reference populations (we could not explore this hypothesis, because information on the rate of smear testing is not available in the WHO country database).

Studies identifi ed in the systematic review contained few longitudinal data with which to ascertain trends over time. Data from a relatively short 4 year time series in a large number of UNHCR camps from around the world did not suggest any trend in either incidence of smear positive cases or new cases per smear test as time progressed.

Analysis of tuberculosis notifi cations to WHO suggests that, at the national level, the occurrence of both low-intensity and high-intensity armed confl ict usually results in reductions in the notifi cation rate, which are sometimes substantial. Furthermore, this eff ect seems to be sustained during the few years immediately after the cessation of armed confl ict. These fi ndings show the potential eff ect of armed confl ict on tuberculosis control programmes, and the extent of the resulting underestimate in the reported burden. How ever, negative associations are not universal, and in some countries confl ict seems to be associated with intensifi ed tuberculosis notifi cations or no relative change. In some of these countries (eg, Somalia,92 Mozambique,93 and Nicaragua94) successful implementation of control pro-grammes, irrespective of war, has been described. Our fi ndings contrast with a previous similar study,95 which, however, examined the incidence of tuberculosis over shorter time series and fewer and diff erent countries.

IDPs account for about 70% of forcibly displaced people worldwide, and most IDPs as well as refugees live not in camps, but rather in urban or rural or dispersed settings; however, data on tuberculosis burden in these popu lations are scarce.4 We postulate that in camps for IDPs, which are usually less covered by relief interventions and more vulnerable to malnutrition, excess tuberculosis risk might be even higher than in refugee camps. IDPs in non-camp settings might experience less overcrowding and have more food security, but are usually dependent on local government health services, and limited access to tuberculosis care because of discrimination and fear of identifi cation, and legal or fi nancial barriers could also result in higher risk.

Similarly, few studies have assessed populations aff ected by natural disasters. Previous re views have


Review

shown that natural disasters by themselves generally lead to infrequent disease outbreaks, with no reports of tuberculosis epidemics.87,96,97 Several factors make the measurement and comparability of disease burden in the post-disaster phase diffi cult. Natural disasters vary in terms of severity, duration, and the extent to which they aff ect underlying health infrastructure.97 The immediate infl ux of hu manitarian aid might result in very high notifi cation rates in the short-term. A resilient health system can eff ectively control tuberculosis in disasters: for example, in Louisiana, USA, after Hurricane Katrina in 2005, federal agencies had located and resumed treatment of the 130 patients with tuberculosis who were displaced by the storm within 6 weeks of evacuation.98 By contrast, the 2010 fl oods in Pakistan displaced approximately 5 million people, many of whom were relocated into makeshift tent camps.99 In such a scenario of large-scale displacement, weaker health systems, and very high baseline tuberculosis burden, there is clearly a high potential for short-term and long-term increases in tuberculosis burden as a result of the disaster.

Generally, we identifi ed fewer published reports covering the past decade than for the 1980s and 1990s, when several landmark epidemiological studies of refu-gee-camp populations were done: this fi nding might refl ect the decreased accessibility of crisis-aff ected populations due to the rise of internal displacement and a shift away from camps to more dispersed settings, but suggests an insuffi cient eff ort to document one of the leading causes of morbidity and mortality. In recent years, UNHCR’s HIS has increased the amount of information available for camp-based refugees, but data from HIS are diffi cult to interpret without extensive knowledge of individual camps, and are greatly de pendent on the functionality of tuberculosis programmes in these camps.

Filling some of the above evidence gaps might require more ad-hoc studies (either exhaustive or sample sur-veys) that seek to quantify burden directly (eg, prevalence based on a representative sample; however, such studies would be costly because of the large sample size re-quirements needed to accurately estimate tuberculosis prevalence, a numerically rare condition), or indirectly by monitoring reported incidence and estimating the rate of case detection through more statistically effi cient approaches (eg, respondent-driven sampling to detect prevalent cases without the need for a population-based survey). Such studies should also explore other aspects of tuberculosis epidemiology in crises that are directly relevant for control programmes (eg, the proportion of extrapul monary cases and smear-negative cases; the sex ratio and incidence in children), data for which were sparse in the reports included in our study.

Our inclusion criterion of smear confi rmation or WHO-consistent diagnosis standards resulted in the exclusion of several reports, including many describing tuberculin skin-testing data. Much of what is known about the burden of tuberculosis is based on surveys of

tuberculin skin testing, to which a mathematical model is applied so as to extrapolate the region’s tuberculosis incidence, prevalence, and mortality rate. Skin testing becomes more unreliable as populations become more unwell due to other infectious diseases, emotional or physical stress, malnutrition, and HIV. The model uses data only from small samples of relatively healthy, well-nourished populations in developed countries to model mortality and incidence.100 Conversely, the WHO esti-mates rely on case notifi cations, national surveys, and death-registry systems adjusted by an expert opinion of proportion of cases detected by these mechanisms.101

Publication bias could have aff ected the results of this Review in two diff erent ways. About 12 of the studies included were done by aid organisations that could have had an incentive to report favourable data on their performance (eg, on CFR or drug resistance; however, results of these studies did not strikingly diff er from the rest). Conversely, available reports might be biased towards high burden due to a tendency to over-report alarming fi ndings.

Our fi ndings on the excess risk of tuberculosis in crises are sensitive to potential diff erences in the sensitivity of case defi nitions and case ascertainment between studies included in the Review and the country-level estimates and notifi cation data gathered by WHO, which we used as reference, in the absence of more directly comparable data. WHO’s estimates before the initiation of confl ict could also have greatly underesti mated burden, because the estimates are not generally updated to refl ect deteriorating situations in individual countries.100,101 Conversely, refugee populations can have better access to health care than host populations,102,103 which would also have upwardly biased comparison of notifi cation rates.

Where appropriate, a comparison of observed burden with estimated burden in reference populations from the same region or district as the crisis-aff ected popu lation would have been preferable, rather than in the country as a whole, to remove confounding by diff erences between the populations being compared other than exposure to crisis. However, regional level estimates of tuberculosis burden were almost never available, and to ensure a consistent approach we relied on WHO-country-level sources. Displaced populations could have systematically had a higher burden of tuberculosis even before displacement than other communities in their country, thus accounting for some of the excess risk estimated in our study: however, this eff ect does not alter the main practical implication of our fi ndings—namely, that tuberculosis is a major health priority in these populations. On balance, our general fi nding of higher risk of tuberculosis in crises seems plausible because of its consistency across a range of settings and since relative risks exceed one even when comparing notifi cation rates observed in crisis settings with reference estimated incidences, but the size of the excess risk might have been overestimated or under estimated. Similarly, the


Review

CFRs reported in the studies included in this Review could be artifi cially lower than reference levels as a result of shorter intervals of observation in crisis settings.

The analysis of national trends for tuberculosis notifi -cation and its inference entail substantial limitations. Data on tuberculosis notifi cation and confl ict intensity are likely to have substantial error and misclassifi cation (WHO tuberculosis data are in fact missing for crucial confl ict years in some countries, while the PRIO dataset is subject to availability of information on con fl ict deaths). Although useful to detect a global pattern, neither model fully accounts for the specifi cities of each country’s armed confl ict (eg, some health systems are more resilient during wartime and diff erent armed groups exhibit varying behaviour toward existing health structures). Generally, such models can show chrono logical associations but do not convincingly establish causality.

Analysis of UNHCR data is limited by the dearth of detail on individual camps, which could have aided inter-pretation and allowed better adjustment for potential confounders in statistical models: for example, it would have been useful to distinguish between camp popu-lations that had recently arrived and those that had been settled in the camp for a longer period.

Humanitarian responses to crises have traditionally focused fi nite resources on acute diseases perceived as the main crisis-emergent threats (eg, measles, cholera, and other diarrhoeal diseases), leaving more chronic disorders such as tuberculosis for the later stages of humanitarian action.104 Existing WHO/UNHCR recom-mendations for establishing tuberculosis programmes in crises list essential criteria, including that (1) data from the population shows that tuberculosis is an important problem; (2) basic human needs (water, food, shelter, sanitation) have been met; (3) the acute phase of the emergency is over (as defi ned by population death rates); (4) essential services and drugs for common illnesses are available; and (5) basic health services are accessible to a large part of the confl ict-aff ected population.18

Both the Sphere project (a set of guidelines for minimum humanitarian relief standards adhered to by most humanitarian agencies)105 and WHO/UNHCR guidelines agree on the necessity of DOTS and recom-mend 6 month drug regimens with target cure rates of 85%. Conversely, Biot and colleagues106 suggest that the implementation of a 4 month DOTS programme with a more realistic treatment target of 75% in complex emergency settings would decrease the long-term burden of disease, while acknowledging that greater treatment defaulting could lead to increased drug resistance.

In view of our fi ndings, we also believe that recom-mendations might need to be revisited, though an approach that is tailored to the specifi cities of each crisis is needed.

In any acute emergency settings, as part of initial assessments, identifying patients with tuberculosis and ensuring their continuation of treatment as soon as

possible should be a systematic minimum intervention: such patients could be referred to existing host govern-ment programmes or at least registered while basic treatment services are restored.

In settings of natural disaster with limited disruption of the existing health care infrastructure and low exposure to key risk factors such as malnutrition and population displacement, the current WHO/UNHCR and Sphere guidelines, whereby tuberculosis treatment programmes would only be re-established in case of high disease burden once the acute emergency phase is over, are probably appropriate. In displaced populations from high-burden countries, and particularly where HIV co-infection, malnutrition, or both are highly prevalent, earlier, more aggressive re-establishment of active case fi nding and treatment, at least with fi rst-line regimens, might be warranted. We did not assess treatment eff ectiveness. However, alternative regimens, such as the Manyatta regimen, which consists of 4 months of DOTS followed by 3 months of self-administered treat-ment, have been shown to be eff ective in fragile confl ict areas, such as south Sudan.61,70 Regimens requiring shorter duration of observed therapy and unconven tional approaches such as allowing patients to keep an emergency drug supply in case of urgent evacuation could ensure treatment adherence, which is especially important in the early, intensive phase of treatment,107 reduce onward disease transmission, and by pre-empting individuals from seeking care from unregulated sources, could actually prevent the development of drug resistance.106 Although still relatively expensive, rapid tests for tuberculosis could also be deployed more extensively to aid screening of suspect cases.

The current trend of increasingly protracted, within-state crises (eg, in Afghanistan, DR Congo, and Somalia), in which the aff ected population is not necessarily concentrated in a few sites but rather dispersed over wide areas, means that, rather than establishing local ised, ad-hoc tuberculosis treatment programmes, the focus should be on rehabilitating pre-existing national programmes, ensuring prioritisation of tuber culosis in crisis-wide funding appeals, and fosters coordination of humanitarian stakeholders by dissem inating and enforcing common guidelines and standards and integrating tuberculosis referral in NGO-supported health structures. Generally, in any crisis setting there is a need to work towards greater collaboration between national tuberculosis programmes and the NGO or UNHCR-run programmes in aff ected populations. One obvious step forward would be systematic inclusion of refugees and IDPs in applications to the Global Fund to Fight AIDS, Tuberculosis, and Malaria by their host countries, as suggested by Spiegel and colleagues.108

Our fi ndings also lend more urgency to other interventions that can ultimately prevent and control tuberculosis in crises, including ensuring adequate nutrition intake, reducing overcrowding through better


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layout of displaced settlements, and maximising inte-gration of tuberculosis and HIV services in accordance with the recommended minimum HIV service package in emergencies;109 indeed, where HIV burden is high, continued antiretroviral care and active screening for tuberculosis among patients with HIV/AIDS could greatly reduce the burden of tuberculosis (no evidence, however, shows that armed confl ict increases HIV transmission110).

Much is still unknown about the burden of tuber-culosis in crisis settings. Only further studies can improve our understanding of this issue. Better tuberculosis surveillance is needed, especially among IDPs and in urban settings. In view of the available evidence, tuberculosis needs to rank more highly on the list of public health priorities in settings of displacement (including in the acute phase), and public health agencies should consider earlier establishment of treatment programmes. To help establish these programmes, innovative approaches to the traditional DOTS model, requiring shorter regimens and less stringent supervision, should urgently be tested for eff ectiveness and feasibility.ContributorsWK and FC designed the study. WK designed and did the review of peer-reviewed reports, extracted data, and interpreted fi ndings. MD designed and did the review of grey literature, extracted data, and interpreted fi ndings. VS independently validated the peer-reviewed report search strategy, and designed, did, and interpreted quality assessment of reports. FC contacted agencies for grey literature reports; extracted data from papers identifi ed in reviews; designed, did, and interpreted statistical analyses of national and refugee camp data; and helped with quality assessment of reports. CH obtained refugee camp data from the UN High Commissioner for Refugees and interpreted their analysis. WK and FC wrote the paper. All authors contributed to drafts of this report and interpreted fi ndings.

Confl icts of interestWe declare that we have no confl icts of interest.

AcknowledgmentsPhilipp du Cros provided guidance on case defi nition inclusion and exclusion criteria as well as valuable input on the current humanitarian aid guidelines. We thank Maryline Bonnet and Christopher Dye for comments on the draft report and to Abrar Ahmad Chughtai and colleagues at the Pakistan National TB Control Programme for sharing unpublished data.

References 1 WHO. Tuberculosis: fact sheet. http://www.who.int/mediacentre/

factsheets/fs104/en/ (accessed Aug 18, 2010). 2 Centre for Research on the Epidemiology of Disasters.

2010 Disasters in numbers. Brussels: CRED; 2010. http://cred.be/sites/default/fi les/PressConference2010.pdf (accessed Feb 12, 2012).

3 Themnér L, Wallensteen P. Armed Confl ict, 1946–2010. J Peace Res 2011; 48: 525–36.

4 Spiegel PB, Checchi F, Colombo S, Paik E. Health-care needs of people aff ected by confl ict: future trends and changing frameworks. Lancet 2010; 375: 341–45.

5 Watson JT, Gayer M, Connolly MA. Epidemics after natural disasters. Emerg Infect Dis 2007; 13: 1–5.

6 Connolly M, Gayer M, Ryan M, Salama P, Spiegel P, Heymann D. Communicable diseases in complex emergencies: impact and challenges. Lancet 2004; 364: 1974–83.

7 Barr RG, Menzies R. The eff ect of war on tuberculosis: results of a tuberculin survey among displaced persons in El Salvador and a review of the literature. Tuberc Lung Dis 1994; 75: 251–59.

8 Ghobarah H, Huth P, Russett B. The post-war public health eff ects of civil confl ict. Soc Sci Med 2004; 59: 869–84.

9 Lawn SD, Zumla AI. Tuberculosis. Lancet 2011; 378: 57–72.

10 Davies P, Grange J. Factors aff ecting susceptibility and resistance to tuberculosis. Thorax 2001; 56: 23–29.

11 CDC. Basic TB facts. Atlanta: Center for Disease Control, 2009. 12 WHO. Global tuberculosis database. Geneva: World Health

Organization; 2010. 13 Cegielski JP. The relationship between malnutrition and

tuberculosis: Evidence from studies in humans and experimental animals. Int J Tuberc Lung Dis 2004; 8: 286–98.

14 Beggs C, Noakes C, Sleigh P, Fletcher L, Siddiqi K. The transmission of tuberculosis in confi ned spaces: an analytical review of alternative epidemiological models. Int J Tuberc Lung Dis 2003; 7: 1015–26.

15 WHO. Anti-tuberculosis drug resistance in the world, No.4. Geneva: World Health Organization, 2008.

16 Chamie G, Luetkemeyer A, Charlebois E, Havlir D. Tuberculosis as part of the natural history of HIV infection in developing countries. Clin Infect Dis 2010; 50 (suppl 3): 245–54.

17 Mukadi Y, Maher D, Harries A. Tuberculosis case fatality rates in high HIV prevalence populations in sub-Saharna Africa. AIDS 2001; 15: 143–52.

18 World Health Organization, United Nations High Commissioner for Refugees. Tuberculosis care and control in refugee and displaced populations. Geneva: WHO/UNHCR; 2007. http://whqlibdoc.who.int/publications/2007/9789241595421_eng.pdf (accessed March 16, 2011).

19 Moher D, Liberati A, Tetzlaff J, Altman D, Group TP. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Ann Intern Med 2009; 151: 264–69.

20 Lucas M, Nicol P, McKinnon E, et al. A prospective large-scale study of methods for the detection of latent Mycobacterium tuberculosis infection in refugee children. Thorax 2010; 65: 442–8.

21 Vlassov V, Reze A. Low level laser therapy for treating tuberculosis. Cochrane Database Syst Rev 2002; CD003490.

22 Gleditsch NP, Wallensteen P, Eriksson M, Sollenberg M, Strand H. Armed confl ict 1946-2001: a new dataset. J Peace Res 2002; 39: 615–37.

23 Arshad S, Bavan L, Gajari K, Paget S, Baussano I. Active screening at entry for tuberculosis among new immigrants: a systematic review and meta-analysis. Eur Respir J 2010; 35: 1336–45.

24 WHO. Treatment of tuberculosis: guidelines. Geneva: World Health Organization; 2010.

25 National Institute for Health and Clinical Excellence. The guidelines manual. London: NICE; 2009. http://www.nice.org.uk/guidelinesmanual (accessed Dec 12, 2011).

26 Program UCD. UCDP Confl ict Encyclopaedia. Uppsala: UCDP. http://www.ucdp.uu.se/gpdatabase/search.php (accessed Jan 18, 2012).

27 Haskew C, Spiegel P, Tomczyk B, Cornier N, Hering H. A standardized health information system for refugee settings: rationale, challenges and the way forward. Bull World Health Organ 2010; 88: 792–94.

28 Novoa AM, Perez K, Santamarina-Rubio E, Borrell C. Eff ect on road traffi c injuries of criminalizing road traffi c off ences: a time-series study. Bull World Health Organ 2011; 89: 422–31.

29 Kuhn L, Davidson LL, Durkin MS. Use of Poisson regression and time series analysis for detecting changes over time in rates of child injury following a prevention program. Am J Epidemiol 1994; 140: 943–55.

30 Rieder HL. Tuberculosis in an Indochinese refugee camp: epidemiology, management and therapeutic results: Tubercle 1985; 66: 179–86.

31 Norval PY, San KK, Bakhim T, Rith DN, Ahn DI, Blanc L. DOTS in Cambodia. Directly observed treatment with short-course chemotherapy: Int J Tuberc Lung Dis 1998; 2: 44–51.

32 Diaz T, Achi R. Infectious diseases in a Nicaraguan refugee camp in Costa Rica: Trop Doct 1989; 19: 14–17.

33 Cruz JR, Heldal E, Arnadottir T, Juarez I, Enarson DA. Tuberculosis case-fi nding in Nicaragua: evaluation of routine activities in the control programme. Tuberc Lung Dis 1994; 75: 417–22.

34 Ministerio de la Salud, Direccion de la Vigilancia de la Salud, Costa Rica. Tuberculosis en Costa Rica: Historia y evolucion; 2010. http://www.ministeriodesalud.go.cr/index.php/boletines/doc_download/57-tuberculosis-en-costa-rica-historia-y-evolucion-celebracion-dia-mundial-de-la-tb-2010 (accessed March 3, 2012).

35 Mercer A. Mortality and morbidity in refugee camps in Eastern Sudan: 1985–90. Disasters 1992; 16: 28–42.


Review

36 Sang R, Varaine F. Assessment of the tuberculosis control programmes in the refugee camps of Kenya. Paris: Epicentre, 1994.

37 Bhatia S, Dranyi T, Rowley D. Tuberculosis among Tibetan refugees in India. Soc Sci Med 2002; 54: 423–32.

38 Nelson LJ, Naik Y, Tsering K, Cegielski JP. Population-based risk factors for tuberculosis and adverse outcomes among Tibetan refugees in India, 1994–1996. Int J Tuberc Lung Dis 2005; 9: 1018–26.

39 Bam TS, Enarson DA, Hinderaker SG, Chapman RS. High success rate of TB treatment among Bhutanese refugees in Nepal. Int J Tuberc Lung Dis 2007; 11: 54–58.

40 Minetti A, Camelique O, Hsa Thaw K, et al. Tuberculosis treatment in a refugee and migrant population: 20 Years of experience on the Thai-Burmese border: Int J Tuberc Lung Dis 2010; 14: 1589–95.

41 M’Boussa J, Yokolo D, Pereira B, Ebata-Mongo S. A fl are-up of tuberculosis due to war in Congo Brazzaville: Int J Tuberc Lung Dis 2002; 6: 475–78.

42 Tigani B, Kurhasani X, Adams LV, Zhuri G, Mehmeti R, Cirillo D, et al. DOTS implementation in a post-war, United Nations-administered territory: Lessons from Kosovo: Respir Med 2008; 102: 121–27.

43 Jovanovic D, Skodric-Trifunovic V, Markovic-Denic L, Stevic R, Vlajinac H. Clinical and epidemiological evaluation of tuberculosis in Serbia, 1990–2004. Int J Tuberc Lung Dis 2007; 11: 647–51.

44 Tiwari SK, Love EJ, Sitthi-Amorn C. Tuberculosis treatment services in armed confl ict areas: Does access matter? Int Med J 2006; 13: 163–69.

45 Jafari NJ, Radfar MH, Ghofrani H. Incidence of infectious diseases one month after the Bam earthquake (2004). J Med Sci 2007; 7: 597–602.

46 United States Census Bureau. International Data Base. Washington, DC; 2011. http://www.census.gov/population/international/data/idb/informationGateway.php (accessed Feb 21, 2012).

47 Shears P. Tuberculosis programmes in Somali refugee camps. Disasters 1982; 6: 247–50.

48 Sutter RW, Haefl iger E. Tuberculosis morbidity and infection in Vietnamese in Southeast Asian refugee camps: Am Rev Respir Dis 1990; 141: 1483–86.

49 Taylor WRJ, Zumla A. Referral times of Vietnamese refugees with tuberculosis in camps in Hong Kong. Int J Tuberc Lung Dis 2000; 4: 350–55.

50 Irajian GR, Nassaji M, Ranjbar R, et al. Implementation of directly observed short course therapy for tuberculosis: J Biol Sci 2008; 8: 217–20.

51 Laifer G, Widmer AF, Frei R, Zimmerli W, Fluckiger U. Polymerase chain reaction for Mycobacterium tuberculosis: impact on clinical management of refugees with pulmonary infi ltrates. Chest 2004; 125: 981–86.

52 Rysstad OG, Gallefoss F. TB status among Kosovar refugees: Int J Tuberc Lung Di 2003; 7: 458–63.

53 Oeltmann JE, Varma JK, Ortega L, et al. Multidrug-resistant tuberculosis outbreak among US-bound Hmong refugees, Thailand, 2005: Emerging Infect Dis 2008; 14: 1715–21.

54 United States Centers for Disease Control. Evaluation of tuberculosis program services for Burmese refugees in Thailand resettling to the United States. Atlanta: CDC, 2007. http://ethnomed.org/clinical/tuberculosis/Eval_TB_Burmese.pdf (accessed Dec 18, 2011).

55 Gorbacheva O, Mishra AK, Shapovalov D, Sudtasay S. Prevalence of bacteriologically confi rmed pulmonary tuberculosis in the Bhutanese refugees in Nepal. Results of active case fi nding: Int J Infect Dis 2010; 14: 150.

56 Weinstock DM, Hahn O, Wittkamp M, Sepkowitz KA, Khechinashvili G, Blumberg HM. Risk for tuberculosis infection among internally displaced persons in the Republic of Georgia. Int J Infect Dis 2001; 5: 164–69.

57 WHO. Global tuberculosis control: epidemiology, strategy, fi nancing. Geneva: World Health Organization, 2009.

58 Dolin P, Raviglione M, Kochi A. Estimates of future global tuberculosis morbidity and mortality. JAMA 1994; 271: 739.

59 Miles SH, Maat RB. A successful supervised outpatient short-course tuberculosis treatment program in an open refugee camp on the Thai-Cambodian border: Am Rev Respir Dis 1984; 130: 827–30.

60 Sukrakanchana-Trikham P, Puechal X, Rigal J, Rieder HL. 10-year assessment of treatment outcome among Cambodian refugees with sputum smear-positive tuberculosis in Khao-1-Dang, Thailand. Tuberc Lung Dis 1992; 73: 384–87.

61 Mastro TD, Coninx R. The management of tuberculosis in refugees along the Thai-Kampuchean border. Tubercle 1988; 69: 95–103.

62 Rutta E, Kipingili R, Lukonge H, Assefa S, Mitsilale E, Rwechungura S. Treatment outcome among Rwandan and Burundian refugees with sputum smear-positive tuberculosis in Ngara, Tanzania. Int J Tuberc Lung Dis 2001; 5: 628–32.

63 United Nations High Commissioner for Refugees. United Nations High Commissioner for Refugees. TB and leprosy annual report for Dadaab refugee camp. Nairobi: UNHCR, 2010.

64 Bohler M, Mustafaa SA, Morkve O. Tuberculosis treatment outcome and health services: A comparison of displaced and settled population groups in Khartoum, Sudan. Inter J Tuberc Lung Dis 2005; 9: 32–36.

65 Accorsi S, Fabiani M, Nattabi B, et al. The disease profi le of poverty: Morbidity and mortality in northern Uganda in the context of war, population displacement and HIV/AIDS: Trans Royal Soc Trop Med Hyg 2005; 99: 226–33.

66 Hodes R, Seyoum B. The pattern of tuberculosis in Addis Ababa, Ethiopia. East African Med J 1989; 66: 812–18.

67 Agutu WO. Short-course tuberculosis chemotherapy in rural Somalia. East African Med J 1997; 74: 348–52.

68 Rodger AJ, Toole M, Lalnuntluangi B, Muana V, Deutschmann P. DOTS-based tuberculosis treatment and-control during civil confl ict and an HIV epidemic, Churachandpur District, India. Bull World Health Organ 2002; 80: 451–56.

69 Mabiala-Babela JR, M’Pemba Loufoua AB, Mouko A, Senga P. Pulmonary tuberculosis in infants in Brazzaville, Congo. A review of 117 cases [French]. Medecine Tropicale 2008; 68: 167–72.

70 Keus K, Houston S, Melaku Y, Burling S. Field research in humanitarian medical programmes. Treatment of a cohort of tuberculosis patients using the Manyatta regimen in a confl ict zone in South Sudan. Trans Royal Soc Trop Med Hyg 2003; 97: 614–18.

71 Datta BS, Hassan G, Kadri SM, et al. Multidrug-resistant and extensively drug resistant tuberculosis in Kashmir, India. J Infec Dev Countries 2010; 4: 19–23.

72 Githui WA, Hawken MP, Juma ES, et al. Surveillance of drug-resistant tuberculosis and molecular evaluation of transmission of resistant strains in refugee and non-refugee populations in North-Eastern Kenya: Int J Tuberc Lung Dis 2000; 4: 947–55.

73 Hemhongsa P, Tasaneeyapan T, Swaddiwudhipong W, et al. TB, HIV-associated TB and multidrug-resistant TB on Thailand’s border with Myanmar, 2006–2007. Tropl Med Inter Health 2008; 13: 1288–96.

74 Lemma E, Valdivia Alvarez JA, Gebre-Tsadik G, Echemendia Font M. Drug sensitivity patterns of Mycobacterium tuberculosis isolated in Addis Ababa. Ethiop Med J 1984; 22: 93–96.

75 Lemma E, Gebre-Yohannes A. Drug susceptibility of mycobacterium tuberculosis isolates in Asmara. Ethiopian Medical Journal 1984; 22: 105–6.

76 Wolde K, Lemma E, Abdi A. Primary resistance to the major anti-tuberculosis drugs in Ethiopia. Ethiop Med J 1986; 24: 15–18.

77 Carpels G, Fissette K, Limbana V, Van Deun A, Vandenbulcke W, Portaels F. Drug resistant tuberculosis in sub-Saharan Africa: an estimation of incidence and cost for the year 2000. Tuberc Lung Dis 1995; 76: 480–86.

78 Sanders M, Van Deun A, Ntakirutimana D, et al. Rifampicin mono-resistant Mycobacterium tuberculosis in Bujumbura, Burundi: results of a drug resistance survey. Int J Tuberc Lung Dis 2006; 10: 178–83.

79 Al-Sulami AA, Ali ZM, Al-Maleky DA. Rapid isolation and identifi cation of Mycobacterium tuberculosis from pulmonary tuberculosis patients. Asian Pacifi c J Trop Med 2009; 2: 70–73.

80 Pardini M, Niemann S, Varaine F, et al. Characteristics of drug-resistant tuberculosis in Abkhazia (Georgia), a high-prevalence area in Eastern Europe. Tuberculosis (Edinb) 2009; 89: 317–24.

81 Merza MA, Farnia P, Salih AM, Masjedi MR, Velayati AA. First insight into the drug resistance pattern of Mycobacterium tuberculosis in Dohuk, Iraq: using spoligotyping and MIRU-VNTR to characterize multidrug resistant strains. J Infect Public Health 2011; 4: 41–47.

82 Joseph P. Tuberculosis Among Displaced Haitians: the aftermath of the Haiti earthquake. http://www.bc.lung.ca/association_and_services/documents/03Joseph-TBAmongDisplacedPopulation_AftermathofHaitisEarthquakeFeb2011.pdf (accessed Dec 20, 2011).


Review

83 Gustafson P, Gomes VF, Vieira CS, et al. Tuberculosis mortality during a civil war in Guinea-Bissau. JAMA 2001; 286: 599–603.

84 Gustafson P, Gomes VF, Vieira CS, et al. Clinical predictors for death in HIV-positive and HIV-negative tuberculosis patients in Guinea-Bissau. Infection 2007; 35: 69–80.

85 Accorsi S, Fabiani M, Lukwiya M, Onek PA, Di Mattei P, Declich S. The increasing burden of infectious diseases on hospital services at St. Mary’s Hospital Lacor, Gulu, Uganda. Am J Trop Med Hyg 2001; 64: 154–58.

86 Accorsi S, Fabiani M, Lukwiya M, et al. Impact of insecurity, the aids epidemic, and poverty on population health: Disease patterns and trends in Northern Uganda. Am J Trop Med Hyg 2001; 64: 214–21.

87 Makerere University/University of California at San Francisco collaboration. Uganda Tuberculosis Surveillance Project. 2011. http://www.mu-ucsf.org/tb/reports.aspx (accessed Dec 17, 2011).

88 Spiegel P, Le P, Ververs M, Salama P. Occurrence and overlap of natural disasters, complex emergencies and epidemics during the past decade (1995–2004). Confl Health 2007; 1: 2.

89 Toole MJ, Waldman RJ. An analysis of mortality trends among refugee populations in Somalia, Sudan, and Thailand. Bull World Health Organ 1988; 66: 237–47.

90 Bhatia S, Dranyi T, Rowley D. A social and demographic study of Tibetan refugees in India: Soc Sci Med 2002; 54: 411–22.

91 Gele AA, Bjune GA. Armed confl icts have an impact on the spread of tuberculosis: the case of the Somali Regional State of Ethiopia. Confl Health 2010; 4: 1.

92 Mauch V, Weil D, Munim A, et al. Structure and management of tuberculosis control programs in fragile states—Afghanistan, DR Congo, Haiti, Somalia. Health Policy 2010; 96: 118–27.

93 Salomao MA. The National Tuberculosis Control Programme in Mozambique, 1985-1990. Bull Int Union Tuberc Lung Dis 1991; 66: 175–8.

94 Heldal E, Cruz JR, Arnadottir T, Tardencilla A, Enarson DA. Successful management of a national tuberculosis programme under conditions of war. Int J Tuberc Lung Dis 1997; 1: 16–24.

95 Drobniewski FA, Verlander NQ. Tuberculosis and the role of war in the modern era. Int J Tuberc Lung Dis 2000; 4: 1120–25.

96 Floret N, Viel J, Mauny F, Hoen B, Piarroux R. Negligible risk for epidemics after geophysical disasters. Emerging Infect Dis 2006; 12: 543–48.

97 Khan F, Smith BM, Schwartzman K. Earthquake in Haiti: is the Latin American and Caribbean region’s highest tuberculosis rate destined to become higher? Expert Rev Respir Med 2010; 4: 417–19.

98 DeGraw C, Kimball G, Adams R, et al. Tuberculosis control activities after Hurricane Katrina—New Orleans, Louisiana, 2005. CDC MMWR 2006; 55: 332–35.

99 BBC. Pakistan fl oods prompt mass evacuations in south, 2010. http://www.bbc.co.uk/news/world-south-asia-11105661 (accessed May 15, 2011).

100 Van Leth F, Van Der Werf MJ, Borgdorff MW. Prevalence of tuberculous infection and incidence of tuberculosis; a re-assessment of the Styblo rule. Bull World Health Organ 2008; 86: 20–26.

101 WHO. Global tuberculosis control 2010, Annex 1. Geneva: World Health Organization, 2010.

102 Orach CG, De Brouwere V. Postemergency health services for refugee and host populations in Uganda, 1999–2002. Lancet 2004; 364: 611–12.

103 Hynes M, Sheik M, Wilson HG, Spiegel P. Reproductive health indicators and outcomes among refugee and internally displaced persons in postemergency phase camps. JAMA 2002; 288: 595–603.

104 Coninx R. Tuberculosis in complex emergencies. Bull World Health Organ 2007; 85: 637–40.

105 The Sphere Project. Sphere Handbook. Geneva: The Sphere Project. 2011. www.sphereproject.org (accessed March 5, 2012).

106 Biot M, Chandramohan D, Porter J. Tuberculosis treatment in complex emergencies: are risks outweighing benefi ts? Trop Med Int Health 2003; 8: 211–18.

107 Hehenkamp A, Hargreaves S. Tuberculosis treatment in complex emergencies: south Sudan. Lancet 2003; 362 (suppl): 30–31.

108 Spiegel PB, Hering H, Paik E, Schilperoord M. Confl ict-aff ected displaced persons need to benefi t more from HIV and malaria national strategic plans and Global Fund grants. Confl Health 2010; 4: 2.

109 Inter-Agency Standing Committee. Guidelines for addressing HIV in humanitarian settings. Geneva: IASC; 2010. http://www.humanitarianinfo.org/iasc/downloaddoc.aspx?docID=5145&type=pdf (accessed Feb 12, 2012).

110 Spiegel PB, Bennedsen AR, Claass J, et al. Prevalence of HIV infection in confl ict-aff ected and displaced people in seven sub-Saharan African countries: a systematic review. Lancet 2007; 369: 2187–95.

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