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Carga tb poblaciones_en_crisis_review2013


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Carga tb poblaciones_en_crisis_review2013

  1. 1. 950 Vol 12 December 2012ReviewLancet Infect Dis 2012;12: 950–65Faculty of Infectious andTropical Diseases(W Kimbrough MD;M Dahab PhD, F Checchi PhD),Faculty of Public Health andPolicy (V Saliba MD), LondonSchool of Hygiene andTropicalMedicine, Keppel St, London,UK; and UN HighCommissionerfor Refugees, Geneva,Switzerland (C Haskew MBChB)Correspondence to:Dr Francesco Checchi, Faculty toInfectious andTropical Diseases,London School of Hygiene andTropical Medicine, Keppel St,LondonWC1E 7HT, burden of tuberculosis in crisis-affected populations:a systematic reviewWilliam Kimbrough,Vanessa Saliba, Maysoon Dahab, Christopher Haskew, Francesco ChecchiCrises caused by armed conflict, forced population displacement, or natural disasters result in high rates of excessmorbidity and mortality from infectious diseases. Many of these crises occur in areas with a substantial tuberculosisburden. 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 conflict intensity and tuberculosis notification rates at thenational level with WHO data. We identified 51 reports of tuberculosis burden in populations experiencingdisplacement, armed conflict, or natural disaster. Notification rates and prevalence were mostly elevated; whereincidence or prevalence ratios could be compared with reference populations, these ratios were 2 or higher for 11 of15 reports. Case-fatality ratios were mostly below 10% and, with exceptions, drug-resistance levels were comparable tothose of reference populations. A pattern of excess risk was noted in UNHCR-managed camp data where the rate ofsmear testing seemed to be consistent with functional tuberculosis programmes. National-level data suggested thatconflict was associated with decreases in the notification rate of tuberculosis. More studies with strict case definitionsare 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 fromhigh-burden areas and for continued innovation and prioritisation of tuberculosis control in crisis settings.IntroductionWorldwide, tuberculosis remains a leading cause ofmorbidity and mortality, with about 9·4 million newcases, a prevalence of 11·1 million, and 1·3 millionestimated deaths in 2008.1A substantial proportion of theworld’s population is also affected by natural disasters(about 230 million per year between 2000 and 20102),armed conflict (30 wars ongoing as of 20103), and forceddisplacement (about 15 million refugees and 25 millioninternally displaced persons [IDPs; ie, forcibly displacedpeople who do not cross international boundaries] as of20104). These events differ substantially in their effects onpublic health. Recent natural disasters have tended toattract greater humanitarian assistance than armedconflicts, and, possibly due to their shorter duration,seem to feature a lower excess burden of infectiousdisease.5Forced displacement into camps has welldocumented, striking effects on public health, butrefugees tend to have better health outcomes than IDPs,partly because of better protection and accessibility; andan increasing proportion of both refugees and IDPs aresettling in non-camp scenarios, mostly urban environ-ments, where they are less identifiable and more difficultto monitor.4Taken together, however, all of these events,which in this Review we refer generally to as crises, sharea potential to cause excess morbidity and mortality due toinfectious diseases resulting from risk factors such asovercrowding, acute malnutrition, and disrupted healthservices; they also feature a similar range of stakeholders,funding mechanisms, and interventions, which tend toprioritise emergency humanitarian relief over long-termhealth-system strengthening.Tuberculosis is a leading health threat for populationsaffected by crises, and more than 85% of refugees fleefrom and stay in countries with a high burden oftuberculosis.6Although some evidence shows that thelong-term burden of tuberculosis has increased inmodern-era post-conflict states, the short-term effectsremain poorly documented.7,8Difficulties in measuringthe burden of disease in crisis settings are similar tothose preventing the inclusion of tuberculosis-controlprogrammes in initial humanitarian responses:diagnosis requires implementation of minimumlaboratory standards for quality smear microscopy, whiletreatment is lengthy and, in settings of high-drugresistance, can be expensive and reliant on referencelaboratories to detect and manage failures to first-lineregimens.Generally, only 5–10% of individuals infected withMycobacterium tuberculosis will develop active disease andbecome infectious.1Various risk factors can triggerdisease progression, with HIV infection carrying thehighest excess risk.9,10The active phase of the disease canbe insidious with mild symptoms such as cough andfatigue, despite patients already being infectious.11Forthis reason, individuals with recent onset of tuberculosissymptoms could go unnoticed by health-care providersin crisis settings. An untreated case of active tuberculosishas a case-fatality ratio (CFR) of about 50% and willtransmit infection to ten to 15 contacts annually untildeath or recovery.12Several crisis-associated risk factors could lead toincreased burden of disease from tuberculosis, includingmalnutrition, overcrowding, and disruption of healthservices. Even mild malnutrition can increase the risk oftuberculosis progression and case-fatality;13lower macro-nutrient and micronutrient intake is a nearly ubiquitousproblem in crises and might, therefore, account for ahigh attributable fraction of excess risk. Overcrowdingis also an important risk factor in the onward trans-
  2. 2. Vol 12 December 2012 951Reviewmission of pulmonary tuberculosis:14in crises, trans-mission could occur because of displacement into campsor increased community and household populationdensity when displaced people settle within hostcommunities. The disruption of existing health servicescould lead to interruption of tuberculosis treatment,which in the intensive early phase of therapy could causerelapse of active, contagious disease and promote drugresistance and the development of multidrug resistance(MDR).15Together, these factors would increase the riskof progression to active disease among prevalent latentinfections, thereby resulting in a short-term increase inmorbidity and mortality; at the same time, communitytransmission would increase because of a higherprevalence of active disease and greater opportunity forperson-to-person spread due to overcrowding, thoughnew infections resulting from this increase intransmission would only develop as cases of activedisease months or years later (figure 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 skintesting, and chest radiography become less sensitive andspecific as HIV disease progresses.16Missed cases oftuberculosis contribute significantly to HIV mortality,and HIV infection increases the CFR of tuberculosis.17Tuberculosis control is not judged to be a top priority inthe emergency phase of relief, and, until the publicationof recommendations for tuberculosis control inemergencies from WHO and UN High Commissionerfor Refugees (UNHCR), it had not been addressedsystematically in policy.18Whereas the theoretical linksbetween the risk factors manifesting themselves in crisesand increased tuberculosis burden are biologicallyplausible, we aimed to synthesise evidence on the actualburden of tuberculosis in these settings, to inform policy.We reviewed published reports from specific populationsexperiencing various types of crisis, investigatedtuberculosis notification trends as a function of conflictin countries where widespread armed conflict occurredand analysed recent data from refugee camps managedby the UNHCR.MethodsSearch strategy and selection criteriaWhere relevant, we followed the Preferred ReportingItems for Systematic Reviews and Meta-Analyses(PRISMA) statement and checklist in designing andreporting our review.19We searched the Embase,MedLine, and Global Health databases with the Ovidsearch engine to identify relevant scientific articlespublished between January, 1980, and October, 2011, inEnglish, French, and Spanish, which presented data onany form of tuberculosis infection within populationsaffected by armed conflict, natural disaster, displacement,or nutritional crises. We chose tuberculosis search termsby consulting a MeSH thesaurus, and complementedthese with terms used by Cochrane Database reviews oftuberculosis.20,21Similarly, we identified MeSH terms forindicators of burden of disease and types of crises. Thesethree concepts were combined into a search with thegeneral outline of [tuberculosis] AND [burden of diseaseindicator] AND [type of crisis], with truncated termswhere necessary.To identify studies of tuberculosis burden done in areasof armed conflict that might not have been captured inthe above search, we consulted the Uppsala Conflict DataProgram and International Peace Research Institute,Oslo (UCDP/PRIO) Armed Conflict Dataset (version4-2010) to identify all regions of high-intensity conflict(defined as at least 1000 combat-related deaths within asingle year) since 1980.3,22Various denominations forthese regions were then used in the following searchoutline: [tuberculosis] AND [burden of disease indicator]AND [country or region experiencing armed conflict]. Welimited the search to publications from the first year ofconflict in that region to Oct, 2011. Lastly, we followed thebibliographic trail of reports identified through the abovesearches.To identify unpublished data and grey literaturereports, we did a Google search for .pdf, .doc, and .docxdocuments that had in their titles a combination oftuberculosis (or equivalent words in French or Arabic)and either the name of one of the conflict-affectedcountries identified above or the same terms for type ofcrisis used above. The same time limits were applied.Figure 1:Tuberculosis transmission and disease progression in crisis-affectedpopulationsLatent, inactive tuberculosis infections in populationPatients with active tuberculosis who are on treatmentMalnutritionInterruption of tuberculosis treatment servicesOther emotional and physical stressorsHIV co-infection and interruption of antiretroviraltreatmentIncreased rate of activation of latent tuberculosisinfection to active, contagious diseaseRelapse of previously treated tuberculosis toreactivated tuberculosis with possible drug resistanceIncreased tuberculosis transmission because ofovercrowding and higher prevalence of active diseaseIncreased rate of disease progression due tomalnutrition, HIV, and other stressorsShort-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
  3. 3. 952 Vol 12 December 2012ReviewWe also contacted, by email, 23 experts on public healthin crisis-affected populations as well as tuberculosis atthe following agencies: Médecins Sans Frontières,Epicentre, Medical Emergency Relief International, theInternational Committee of the Red Cross, the UnitedStates Centers for Disease Control and Prevention, andWHO. The search strategies and results are detailed inthe appendix. WK did peer-reviewed database searchesand MD did grey-literature-report database searches,while FC contacted experts and agencies.Inclusion and exclusion criteria for all searches wereas follows: we included reports of the burden ofM tuberculosis disease in any crisis-affected population.These reports included all forms of tuberculosis. Weexcluded reports in which data for crisis-affected peoplewere not disaggregated from those of the general(or host) population; for which the study populationconsisted of immigrants or refugees to a high-income,non-crisis-affected country, unless they were residing incamps (we excluded these reports so as to focus solelyon populations that either continued to reside in thecrisis-affected region and thus were exposed to riskfactors associated with the crisis, or that were exposed todisplacement-related overcrowding; a review of tuber-culosis in immigrants to high-income countries hasbeen published23); that described special populations,including military forces, transplant recipients, andprisoners; and that used a case definition of tuberculosisthat did not include either smear microscopy or a WHO-compliant laboratory or symptom-based diagnosis withappropriate intention to treat.24Some results for drugresistance were stratified into new and previouslytreated cases.We extracted data on a Microsoft Excel template; foreach report, variables extracted included the country (oforigin and refuge for refugees), setting (eg, naturaldisaster, refugee camp, camp for IDPs), studypopulation, period of data collection, study design,tuberculosis case definition, and, for each burdenindicator reported, its value, the 95% CI where reportedas part of a survey, and the numerator and denominatorof the indicator where reported.WK applied inclusion criteria to scientific abstracts,short-listed papers, and extracted data; VS independentlyreplicated the above procedures for a random systematicsample of 10% of abstracts (inter-rater agreement wasexcellent; κ=0·85 for key term searches and 0·75 forcountry-based searches) and short-listed reports (fair togood agreement; κ=0·52). FC reviewed reports for whichthere was a discrepant decision on inclusion and replicatedall data extraction. MD applied inclusion criteria to grey-literature reports and extracted data and FC independentlyreplicated all of these.We assessed the quality of all included reports onthe basis of guidelines developed by the UK NationalInstitute for Health and Clinical Excellence for obser-vational studies.25After reviewing the appropriateness ofthe study design, methods of data collection, length offollow-up, definitions of outcomes and evidence ofselection bias and measurement bias, we attributed asummary grade of lower, medium, or higher strength ofevidence to each report. VS did quality assessment; VSand 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 measuresof estimated incidence and period prevalence oftuberculosis from reference populations not affected bycrisis, and compared these with data from studiesincluded in the Review to calculate crude relative risks(incidence or prevalence ratios) of tuberculosis burden incrisis-affected populations versus reference populations.Reference populations were defined as: (1) the populationof the entire country for IDPs and non-displacedpopulations affected by natural disasters or armedconflict, if these populations were only a minority of thetotal population of the country itself; (2) the population ofthe country in the year before the onset of the crisis, ifthe entire country or most its population was affected byarmed conflict; and (3) the populations of both thecountry of origin and the country of refuge for refugees(we provided alternative relative risks using either ofthese reference populations).We obtained reference population estimates for inci-dence or period prevalence of disease from the WHO’sGlobal Tuberculosis Database.12We obtained referencedrug-resistance and MDR prevalences from publishedWHO data for 1994–2007.15Analysis of trends in national tuberculosis notificationsIn addition to reports from individual sites, we alsoinvestigated national level patterns over time in countrieswith widespread armed conflict. We specifically aimed torecord deviations from secular trends in tuberculosisnotifications as a function of occurrence and intensity ofconflict.For this analysis, we included countries that, accordingto the UCDP/PRIO database, had low (25–999 conflict-related violent deaths) or high (≥1000 violent deaths)intensity armed conflict during 1 or more years in theperiod 1980–2010. However, we excluded years duringwhich conflict had been focused within a specificgeographic region comprising less than a fifth of thecountry’s population (this arbitrary cutoff was roughlyestimated on the basis of census figures and historicalaccounts of the conflict provided on the UCDP ConflictEncyclopaedia26). We also excluded instances of short-lived coups d’état not leading to widespread armedconflict (appendix). To be consistent, for each country weadopted the yearly total numbers of tuberculosis casesnotified to WHO (all forms including relapses), asreported in the WHO tuberculosis database.See Online for appendix
  4. 4. Vol 12 December 2012 953ReviewAnalysis of refugee camp dataWe extracted aggregate data on the population, numberof new smear-positive patients with pulmonary tuber-culosis per year, and total number of smear tests done in86 refugee camps located in 17 host countries, managedby UNHCR and covered by the UNHCR HealthInformation System (HIS; a standardised, automatedplatform for collection, analysis, and reporting of healthsurveillance data).27We excluded years during which nosmear tests were done in the camp from the analysis,because this finding suggested a non-functional tuber-culosis programme. Time series of tuberculosis datafrom these camps in the HIS database all ended in Dec,2011, and, depending on the camp, started as early as2006. As above, we computed notification rates of newsmear-positive cases for each camp and compared thesedata with WHO notification rates for the host countryand all countries of origin of refugees living in the camp(the HIS database does not provide tuberculosis cases orpopulations by country of origin).We also investigated whether, across all camps, there wasa general trend in smear-positive incidence rate, smear-testing rate (number of smears per 100000 people peryear), and ratio of new smear-positive patients per smeartest done as a function of increasing time. For this analysis,we included all camps (n=53) for which at least 4 years ofconsecutive data were available, but excluded data beyond4 years because these were sparse and we wanted toanalyse a time series of equal duration for each camp.Statistical analysisCrude relative risks comparing burdens reported in studiesincluded in the Review as well as the UNHCR HIS withthose of reference populations were computed as the ratioof the point estimate of reported incidence (notificationrate) or prevalence in the study reviewed divided byincidence or prevalence in the reference population, bothestimated by WHO (for studies reviewed only) or notifiedto WHO (for studies reviewed and UNHCR HIS data). Weexcluded 2011 HIS data from this comparison since WHOdata were not yet available for this year at the time ofwriting. Where the study period spanned more than 1 yearwith no stratification of data by year, we extracted the meanreference value over that period. WHO estimates areavailable from 1990 and for all-form tuberculosis only; weextracted the lower and upper bound of the WHO estimateto compute a range for the relative risk. Between 1980 and1989 the WHO reports only all-form tuberculosisnotifications: for this decade, reference pulmonarytuberculosis notification rates as reported by the country’snational tuberculosis programme were extracted instead,if published. We only adopted a reference pulmonarytuberculosis notification rate if it matched the casedefinition used in the report being reviewed. As WHOdatabases provide only new (and not relapse) notifiedpulmonary tuberculosis cases, we assessed only new casesfor the reference notification rate.We did not do a meta-analysis of either burdenestimates or relative risk comparisons with referencepopulations, for the following reasons: (1) tuberculosisburden is very heterogeneous on a global scale, and wehad no means of verifying whether studies included inthe Review captured this heterogeneity or were indeedrepresentative of the global crisis-affected populationswhose burden we sought to review; (2) the estimatesthemselves had substantial heterogeneity (data notshown) and reflected various case definitions and caseascertainment methods; and (3) some of the studiesreported a rate but did not contain sufficient informationon the person-time denominator, which is needed formeta-analysis.For the analysis of trends in national tuberculosisnotifications, we fitted two alternative generalised linearmodels to the data, both featuring year as the analysisunit, the natural logarithm of notified cases as thedependent variable (assumed to follow a quasi-Poissondistribution [ie, including an overdispersion parameter]to account for overdispersion) and year as a continuousindependent variable controlling for underlying seculartrends (assumed to be linear).28,29In the first model weincluded conflict intensity as an independent variable,and computed incidence rate ratios (IRRs) comparinglow and high intensity years with the baseline (yearswithout conflict-related violent deaths). In the secondmodel, in which we postulated that conflict also has lageffects, we arbitrarily defined a recovery phase consistingof the 3 years after the end of conflict of either intensity(or shorter if another period of conflict began or the timeseries ended).For the analysis of HIS data as a function of time,after assessing the relative goodness of fit of differentdistributional assumptions and models with and withoutthe rate of smear testing as a covariate, we fitted thefollowing negative binomial generalised linear models:(1) new smear-positive cases (dependent variable), logperson-years of follow-up (offset), time (years 1 andyears 2 vs years 3 and years 4; independent variable), andsmear-testing rate (potential confounder); (2) total smeartests done (dependent), log person-years of follow-up(offset), time as above (independent variable); and (3) newsmear-positive cases (dependent variable), log totalsmears done (offset), time (years 1 and years 2 vs years 3and years 4; independent variable), and smear-testing rate(potential confounder). For all models we computedrobust SEs by specifying camp as a cluster variable.Results51 reports were included in the final analysis (figure 2).Of these, 23 reported on refugees living in camps, five onIDPs, 20 on non-displaced conflict-affected populations,and three on populations affected by a natural disaster.13 studies reported data on incidence (notification rate),ten on prevalence, 18 on CFR, 14 on prevalence of drugresistance, two on the death rate attributable to
  5. 5. 954 Vol 12 December 2012Reviewtuberculosis, three on tuberculosis-proportionalmorbidity, and two on tuberculosis-proportionalmortality. 15 reports described populations in the WHOAfrica region, seven from the Southeast Asia region,13 from the eastern Mediterranean region, five from theEuropean region, two from the region of the Americas,and nine from the western Pacific region.Measured incidences ranged from 21 to 1510 per100000 per year for pulmonary tuberculosis and from82 to 1142 per 100000 per year for all-form tuberculosis,suggesting great variability in burden (table 1). Nearly allthe incidence rate ratios comparing notifications in crisis-affected populations with those in reference populationswere well above 1 (peaking at 27·2); this was also the caseif the comparison was instead made with referenceestimated incidences. The sole exception was BurmeseKaren refugees in Thailand,40for whom notification rateswere lower than estimated incidences but nonethelesshigher than notification rates in both Burma and the hostcountry, Thailand. High relative risks were noted evenwhere the crisis event (eg, displacement) had occurredonly a few years before data collection (table 1). Althoughdata were sparse, this general finding seemed to hold evenif studies with lower strength of evidence were excluded.Longitudinal data were available from only a fewstudies, and did not reveal a uniform pattern (table 1).Notifications gradually decreased from extremely highlevels during the first year after displacement in camps forEthiopian refugees in Sudan, but remained very high.35Notifications remained very high between 1994 and1996 in long-term Tibetan refugees,37and sharplyincreased during the first 3 years of the establishment ofBurmese refugee camps in Thailand, with a gradualdecrease thereafter (although camp populations were verydynamic, with ongoing arrivals and departures).40In theRepublic of Congo, the number of cases notified increasedsubstantially during a period of war despite closure ofmany tuberculosis treatment centres, and peaked in theyear after cessation of hostilities with a higher proportionof smear-negative and extrapulmonary tuberculosis cases.Drug shortages occurred due to the fact that drugs wereprocured on the basis of fairly stable projections of need(ie, drug stocks could not cope with sudden increases incaseload).41In postwar Kosovo, annual all-formtuberculosis declined from 86 per 100000 in 2000 to53 per 100000 in 2005, while new smear-positivepulmonary tuberculosis rates declined from 20 per100000 in 2000 to 11 per 100000 in 2005.42In Nepal, all-form tuberculosis and new smear-positive pulmonarytuberculosis rates increased as conflict persisted.44Inpost-earthquake Kashmir, Pakistan, a reduction innotification rates was apparent in the year after theearthquake (Abrar Ahmad Chughtai, Pakistan NationalTuberculosis Control Programme, Islamabad, Pakistan,personal communication).The prevalence of tuberculosis, mainly measuredthrough exhaustive or sample surveys (ie, with less biasdue to passive reporting), was also higher in crisispopulations than in reference populations, with preva-lence ratios peaking at 20·7 (table 2). The only exceptionwas a camp for Afghans in Iran, where a high burden oftuberculosis at baseline was controlled to near-zero levelsthrough intensive screening.50Prevalence ratios couldnot be calculated for several studies because WHO doesnot supply prevalence estimates of pulmonarytuberculosis only; however, even if reference estimates of1061 abstracts screened901 excluded based onexclusion criteria160 full-text reports retrieved1 duplicate87 no tuberculosisburden measures8 not from crisisaffectedpopulations18 unacceptable casedefinition13 did not disaggregatecrisis-affected group2 special populations2 could not be retrieved29 reports included in review4787 abstracts screened4452 excluded based onexclusion criteria335 full-text reports retrieved57 duplicates fromprimary search98 no tuberculosisburden measures147 not from crisis-affectedpopulations26 unacceptable casedefinition7 did not disaggregatecrisis-affected group5 special populations19 could not be retrieved14 reports included in review43 full-text reports retrieved29 no tuberculosisburden measures8 not from crisis-affectedpopulations1 unacceptable casedefinition3 could not be retrieved2 reports included in review438 hits24 duplicates,190 published in journals133 no tuberculosis burdenmeasures40 not from crisis-affectedpopulations1 unacceptable casedefinition6 did not disaggregatecrisis-affected group13 could not be retrieved28 only cited other studies3 reports included in review12 reports shared6 no tuberculosis burdenmeasures3 not from crisis-affectedpopulations3 reports included in reviewKey term search Country-specific searchBibliographic trail search Web search for grey literatureAgency and expert contactfor grey literatureFigure 2: Literature review
  6. 6. Vol 12 December 2012 955ReviewYear(s) ofdisplacement,war, ordisasterType of study Casedefinition;type of casesNotification rate reported(cases/person-years*)Rate ratio for comparisonwith notification rate inreference populations(reference notification rate)Rate ratio for comparisonwith estimated incidence inreference populations(reference incidence)Strength ofevidenceRefugee campsCambodian refugees inThailand (1981–84)301979 Clinic-basedsurveillanceSmear/WHO;aTB, pTB500 (629/125800) aTB;240 (302/125800) ss+ pTBCambodia (1982–84):313·4 (145) aTB; 2·7 (89) ss+ pTBNA MediumNicaraguan refugeesin Costa Rica (1985)321983–85 Clinic-basedsurveillanceSmear; smear-positive; pTB400 (5/1160) Nicaragua: 8·0 (50)33Costa Rica: 25·0 (16)34NA LowerEthiopian refugees ineastern Sudan(1986–90)351967–83(about 30% ofrefugees);1984–85(about 70%)Clinic-basedsurveillanceSmear; ss+ pTB 1986 (1510); 1987 (790);1988 (630); 1990 (450)‡Ethiopia: NASudan: NANA LowerSomali and Sudaneserefugees in Kenya(1992–93)361991–94 Clinic-basedsurveillanceSmear/WHO;aTB, ss+ pTB1142 (3116/272800) aTB;453 (1235/272800) ss+ pTBKenya: 16·7 (69) aTB,12·9 (35) ss+pTBSomalia: NASudan: 11·4 (101) aTB, NAss+pTBKenya: 7·1–8·8 (130–161) aTBSomalia: 3·0–5·7 (202–383) aTBSudan: 7·2–13·5 (85–160) aTBMediumTibetan refugees inIndia (1994, 96)371959 Clinic-basedsurveillance andcamp screeningSmear/WHO;aTB1994–96 (980[1575/160018]); 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)HigherTibetan refugees inIndia (1994–96)381959 Clinic-basedsurveillance andcamp screeningSmear/WHO;aTB835 (1197/143373) China: 23·2 (36)India: 6·6 (126)China: 5·4–8·0 (105–155)India: 3·4–4·4 (189–246)HigherBhutanese refugees inNepal (1999–2004)391990–98(peak in 1992)NationalprogrammedataSmear/WHO;aTB, ss+ pTB242 (1214/501653) aTB;126 (631/501653) new ss+ pTBBhutan: 1·3 (181) aTB;2·1 (59) new ss+ pTBNepal: 2·0 (119) aTB; 2·3 (55)new ss+ pTBBhutan: 0·9–1·3 (189–278) aTBNepal: 1·2–1·8 (133–197) aTBHigherBurmese refugees inThailand(1987–2005)401984–2004 Clinic-basedsurveillanceWHO; aTB 122 (978/NA); sharp increasefrom 1987 (22) to 1991 (212),then gradual decrease to2005 (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)MediumWar-affected but non-displacedRepublic of Congo(1994–2000)411997–99 Nationalprogrammedata†WHO; aTB, pTB 1997–2000 (186[21886/11758000] aTB;133 [15666/11758000] pTB);1997 (122, 96);1998 (136, 103); 1999(172, 126); 2000 (304, 202)Republic of Congo(1994–96):411·3 (142) aTB,1·2 (111) pTBNA LowerKosovo (2000–01);42much of populationlived in refugee campsin 19991998–99 NationalprogrammedataSmear/WHO;aTB, ss+ pTB82 (3450/4208125) aTB;21 (879/4208125) newss+ pTBSerbia excluding Kosovo:432·4 (34) aTB, 1·0 (20) new ss+pTBNA MediumDang district, Nepal(1998–2003)441996–2003 NationalprogrammedataSmear/WHO;aTB1998–99 (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)MediumNatural disasterEarthquake, Bam city,Iran (2004)45December,2003 (onemonth before)Clinic-basedsurveillanceWHO; aTB 145 (11/7577) Iran: 8·1 (18) Iran: 4·7–6·9 (21–31) LowerEarthquake, AzadJammu and Kashmirprovince, Pakistan(2004–10)§October, 2005 NationalprogrammedataSmear/WHO;aTB ss+ pTB2006–10(111 [21564/19421550]) aTB;31 [6294/19421550] ss+pTB);2006 (102, 29); 2007 (112, 34);2008 (104, 33); 2009 (125, 34);2010 (112, 32)Azad-Jammu-Kashmirprovince (2004–05): 0·9 (127)aTB, 1·0 (33) ss+pTBNA None(insufficientinformation)All rates are per 100000 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 ofCongo.46‡Cases and person-years not reported. §Abrar Ahmad Chughtai, Pakistan NationalTuberculosis Control Programme, Islamabad, Pakistan, personal communication.Table 1: Reports of tuberculosis incidence (notification rate) in crisis-affected populations, 1980–2011
  7. 7. 956 Vol 12 December 2012Reviewprevalence of all-form tuberculosis were used as thecomparison, all prevalence ratios would be far above 1,with no change in this general finding if lower-strengthevidence was excluded.The current CFR estimate for HIV-positive patientswith tuberculosis on treatment is 10% worldwide;57before availability of highly active antiretroviral therapy,studies in Africa showed CFRs of 16–35% in HIV-positivepatients, and 4–9% in HIV-negative patients on treatmentfor tuberculosis only.17The 1990 estimate of CFR forindustrialised countries was 7%; 15% for eastern Europe,and as high as 20% for developing countries.58In studiesincluded in this Review, CFR was mostly lower than anyof these estimates (table 3), with no obvious chronologicalimprovement or deterioration and a similar pattern iflower-strength evidence was excluded. The only directcomparison was in Khartoum, Sudan, where IDPs fromsouth Sudan had a slightly higher CFR than the localpopulation (4·1% vs 3·7%).64In India, the CFR for HIV-positive patients was seven times higher than for HIV-negative patients over the same time period.68Similarly,the paediatric ward of a hospital in Brazzaville, Republicof Congo, recorded a 20% CFR for 1–2-year-old HIV-positive children during a conflict period compared withno HIV-negative deaths over the same 5 year span.69Inboth studies, antiretroviral therapy was not available.Between 1994 and 2007, 5·3% of all isolates worldwidewere MDR, with much higher rates in eastern Europeand central Asia than in the rest of the world.15Moststudies we reviewed reported a prevalence similar to orlower than reference regional estimates of drug resist-ance prevalence, with notable exceptions (table 4). In theonly direct comparison available, Somali and Sudaneserefugees in Kenya had much higher prevalence of drugresistance than the surrounding host population.72TheMDR prevalence of 42·1% in Laotian Hmong refugees53in Thailand was far above the regional and country-specific prevalences. Studies from the early 1980s inEthiopia and Eritrea show a surprisingly high frequencyof single-drug resistance compared with the WHOregional prevalence of 11·4% from 1994 to 2007. In Haiti,prevalence of MDR seemed to increase from the pre-earthquake to the postearthquake year.82Two studies by Gustafson and colleagues followedcohorts of tuberculosis patients in Bissau city, Guinea-Bissau, from 1997 to 1998, spanning periods before andduring armed conflict.83,84These studies showed anincrease in mortality among patients with tuberculosisYear(s) ofdisplacement,war or disasterType of study Case definition; typeof casesReported prevalence (cases/persons tested)Lower-upper range of ratio forcomparison with estimated prevalencein reference populations (referenceprevalence, lower-upper range)Strength ofevidenceRefugee campsEthiopian refugees inSomalia (1981)471978–80 Household survey Smear; ss+ pTB 2350 (mean of two camps;cases and persons tested notreported)NA LowerVietnamese refugees inThailand (1985–1986)481985–86 Camp entry screening Smear, culture; pTB,ss+ pTB580 (115/19726) pTB100 (20/19726) ss+ pTBNA HigherVietnamese refugees inHong Kong (1992)491975–91 Clinic–based surveillance WHO; aTB, pTB 680 (102/15000) aTB440 (66/15000) pTBVietnam: 1·0–3·8 (178–678) aTBHong Kong: 2·4–11·9 (57–280) aTBMediumAfghan refugees in Iran(1996, 2004)501985 Camp screening Smear/WHO; aTB,pTB, ss+ pTB1996 (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+pTBAfghanistan (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)MediumKosovar refugees inSwitzerland (1999)511998–99 Camp entry screening Smear, culture/WHO;pTB256 (8/3119) NA HigherKosovar refugees in Norway(1999)521998–99 Camp entry screening Smear, culture/WHO;pTB, sc+ pTB, ss+ pTB500 (4/800) pTB125 (1/800) sc+ pTB0 (0/800) ss+ pTBNA HigherLaotian refugees inThailand(2004–05)531975–94 Camp exit screening Smear/WHO; aTB, sc+pTB, ss+ pTB1760 (272/15455) aTB369 (57/15455) sc+TB220 (34/15455) ss+ pTBLaos: 8·0–35·2 (50–219) aTBThailand: 5·5–20·2 (87–321) aTBHigherBurmese refugees inThailand(2007)541984–2007 Camp exit screening Smear; sc+ pTB, ss+pTB598 (28/4686) sc+ pTB150 (7/4686) ss+ pTBNA HigherBhutanese refugees in Nepal(2007–09)551990–98 Camp exit screening Smear/culture; pTB,ss+ pTB644 (151/23459) pTB230 (54/23459) ss+ pTBNA HigherIDPIDP living in hostels inGeorgia (1999)561992–93 Camp screening Smear/WHO; aTB 537 (5/931) Georgia: 2·6–20·7 (26–209) LowerAll prevalences are per 100000 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-affected populations, 1980–2011
  8. 8. Vol 12 December 2012 957Reviewfrom 12 per 100 person-years before war to 34 per100 person-years during wartime, with disruptions tothe antituberculosis drug supply and directly observedtreatment, short course (DOTS) infrastructure judged tobe the main causes. Moreover, the wartime-to-peacetimemortality ratio was 8·2 for HIV-positive patients and1·2 for HIV-negative patients.A few studies measured disease burden in terms ofproportional morbidity and mortality. A hospital-basedstudy set in the Acholi region of northern Uganda between1992 and 1997 (when about 70% of the population wereIDPs in camps) showed that tuberculosis was the thirdleading cause of admission to hospital, accounting for6·2% of all admissions over the study period. Tuberculosiswas also the leading contributor to bed occupancy (24·6%)with an average length of stay of 57·4 days and proportionalmortality 11·3%.85Another hospital-based study in thesame setting reported proportional mortality fromtuberculosis to be 5·7% during conflict compared with4·5% during peacetime (relative risk 1·3).86A1983–85 hospital-based study in Addis Ababa city, Ethiopia,reported tuberculosis to be the cause of 11·2% of alladmissions.66Lastly, tuberculosis sentinel surveillance inApac district, Uganda87(until 2005 a conflict-affecteddistrict with many IDPs) yielded a proportional morbidityin outpatient facilities of 0·52% during January, 2011, toSeptember, 2011, compared with 0·14% in neighbouringdistricts not affected by conflict (relative risk 3·7).Type of study Case definition; type ofcasesCase-fatality rate (deaths/patients) Type oftreatment planusedStrength ofevidenceRefugee campsCambodian refugees inThailand (1981–83)59Clinic-based surveillance Smear; pTB 6·0% (36/615) DOTS LowerCambodian refugees inThailand (1981–84)30Clinic-based surveillance Smear/WHO; aTB, ss+ pTB 5·0% aTB (28/558)3·9% ss+pTB (NA)DOTS MediumCambodian refugees inThailand (1981–90)60Clinic-based surveillance Smear; ss+ pTB 5·0% (46/929) DOTS MediumCambodian refugees inThailand (1984–85)61Clinic-based surveillance WHO; aTB 3·8% (47/1240) DOTS MediumSomali and Sudanese refugees in Kenya(1992–93)36Clinic-based surveillance Smear/WHO; ss+ pTB 2·6% (32/1235) Not specified MediumTibetan refugees in India (1994–96)38Clinic-based surveillanceand camp screeningSmear/WHO; aTB 3·8% (45/1184) Not specified HigherBurundian and Rwandan refugees inTanzania (1995–99)62Clinic-based surveillance Smear; ss+ pTB 10·9% (60/546) DOTS MediumBurmese refugees inThailand (1987–2005)40Clinic-based surveillance WHO; aTB 5·8% (57/978) DOTS MediumSomali refugees in Kenya (2010)63Clinic-based surveillance Smear/WHO; aTB, pTB,ss+ pTB2·7% (11/411) aTB2·2% (7/325) pTB2·3% (4/174) ss+ pTBNot specified None (insufficientinformation)IDPIDP from south Sudan in camps, Khartoum,Sudan (2000)64Clinic-based surveillance WHO; ss+ pTB 4·5% (11/245) for IDP; 3·7% (5/136) forhost populationNot specified MediumNorthern Uganda (1992–2002);65all war-affected, about 70% internally displacedpeople in campsHospital-based surveillance WHO; aTB 10·4% (81/777) Not specified MediumWar-affected but non-displacedAddis Ababa city, Ethiopia (1983–85)66Hospital-based surveillance Smear/WHO; aTB, pTB 7·9% (19/240) aTB8·7% (10/115) pTBNot specified LowerGedo region, Somalia (1994–95)67Hospital-based surveillance Smear/WHO; aTB, pTB,ss+ pTB7·6% (16/211) aTB7·8% (15/192) pTB3·2% (4/125) ss+ pTBDOTS MediumChurachandpur district, India (1998);68district included 39% IDP populationClinic-based surveillance Smear/WHO; aTB, ss+ pTB 2·8% (5/178 aTB [22·2% (4/18) forHIV- positive patients])2·4% (2/85) ss+ pTBDOTS HigherBrazzaville city, Republic of Congo(1999–2004)69Hospital-based surveillance Smear/WHO; pTB (childrenaged 12–23 months)0% (0/45) for HIV-negative patients;20·0% (7/35) for HIV-positive patientsNot specified MediumUpper Nile, south Sudan (2001)70Clinic-based surveillance Smear/WHO; aTB, ss+ pTB 4·3% (7/163) aTB9·1% (3/33) ss+ pTB, all HIV-negativeDOTS (Manyattaregimen)MediumKosovo (2001–04)42Clinic-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 MediumJammu and Kashmir state, India (2003–07)71Hospital-based surveillance Smear; MDR ss+ pTB 21·1% (11/52) DOTS LowerpTB=pulmonarytuberculosis. DOTS=directlyobservedtreatment, short course. aTB=all formsoftuberculosis. ss+=sputum-smear positive. IDPs=internallydisplaced persons. MDR=multidrug resistant. NA=not available.Table 3: Reports of tuberculosis case-fatality ratio in crisis-affected populations, 1980–2011
  9. 9. 958 Vol 12 December 2012ReviewThe analysis of tuberculosis notification patterns incountries affected by widespread conflict did not yield auniform pattern, and for most countries associations werenon-significant in either model (appendix). However, therewas an obvious trend towards reduced rates of tuberculosisnotification during years of low-intensity conflict andespecially high-intensity conflict compared with thebaseline. In a model without lag effects, about two-thirdsof countries showed an apparent decline in notificationrates during years of high-intensity conflict (figure 3);negative associations significant to a probability of lessthan 0·10 were estimated for Angola, Azerbaijan, DRCongo, Guinea Bissau, the Solomon Islands, Tajikistan,and Uganda (low-intensity years) and Angola, Azerbaijan,Bosnia and Herzegovina, El Salvador, Iran, Iraq, Kuwait,Serbia and Montenegro, Tajikistan, and Uganda (high-intensity years); significant positive associations were,however, noted for Burundi, Djibouti, Iran, Nepal, andPeru (low-intensity years), and Burundi, Ethiopia, Burma,Nepal, Peru, and Rwanda (high-intensity years).A model including lag effects during recovery periodsafter the end of either intensity conflict showed similarType of study Case definition;type of casesDrug-resistance prevalence (cases/personstested)Drug-resistance prevalence in comparisonpopulationsStrength ofevidenceRefugee campsSomali and Sudanese refugeesin Kenya (1995–96)72Clinic-based survey Smear/WHO;ss+ pTB18·3% (44/241) any drug resistance;2·9% (7/241) MDRHost population in same study: 5·7% (5/88)any drug resistance, 0% (0/88) MDRMediumLaotian (Hmong) refugees inThailand (2005)53Camp exit screening Smear/WHO;aTB57·9% (33/57) any drug resistance;42·1% (24/57) MDRThailand (2006): 20·7% any drug resistance,6·4% MDR; Laos (1994–2007): 4·3% MDRHigherBurmese refugees inThailand(2007)54Camp exit screening Smear/culture;pTB10·7% (3/28) any drug resistance;3·6% (1/28) MDRThailand (2006): 20·7% any drug resistance,6·4% MDR;Thai nationals,Tak province,Thailand (2006–07): 5·7% MDR73HigherBhutanese refugees in Nepal(2007–09)55Camp exit screening Smear/culture;pTB7% (NA) any drug resistance; 2% (NA) MDR Bhutan (2008): 4·2% MDR; Nepal (2007):16·6% any drug resistance, 4·4% MDRHigherWar-affected but non-displacedAddis Ababa city, Ethiopia(1981)74Clinic-based surveillance Smear 23·5% (43/182) single drug resistance, newcasesNA MediumAsmara city, Eritrea (1984)75Clinic-based andhospital-based surveySmear/WHO;pTB56·3% (18/32) any drug resistance NA LowerAddis Ababa and Harar cities,Ethiopia; Asmara city, Eritrea(1986)76Clinic-based survey ss+ pTB 39·1% (108/276) any drug resistance, new NA MediumRwanda (1991–93)77Clinic-based andhospital-based surveyNA 15·4% (46/298) any drug resistance;2·4% (7/298) MDRWHO Africa region (1994–2007): mean13·8% any drug resistance, mean 2·2% MDRLowerBujumbura city, Burundi(2002–03)78Clinic-based andhospital-based surveyWHO; ss+ pTB 16·1% (80/496) any drug resistance, newcases; 30·4% (21/69) any drug resistance,previously treated cases; 1·4% (7/496) MDR,new; 11·6% (8/69) MDR, previously treatedWHO Africa region (1994–2007): mean13·8% any drug resistance, mean 2·2% MDRMediumBasra city, Iraq (2003–04)79Clinic-based survey WHO; pTB 23·1% (24/104) any drug resistance, new;70·8% (48/65) any drug resistance, previouslytreated; 20·0% (13/65) MDR, previouslytreatedIraq (1994–2007): estimated 38·0% MDR,previously treated;WHO EasternMediterranean region (1994–2007): mean13·7% any drug resistance, new; 54·4% anydrug resistance, previously treated; 35·3%MDR, previously treatedMediumAbkhazia, Georgia (2003–05)80Hospital-based survey WHO; ss+ pTB 54·1% (106/196) any drug resistance, new,8·7% (17/196) MDR, new; 68·5% (87/127) anydrug resistance, previously treated,38·6% (49/127) MDR, previously treatedGeorgia (2006): 49·2% any drug resistance,new; 6·8% MDR, new; 66·0% any drugresistance, previously treated; 27·4% MDR,previously treatedHigherJammu and Kashmir state, India(2003–07)71Hospital-based prospectiveobservational cohortWHO; pTB 5·7% (52/910) MDR, 0·9% (8/910) XDR Delhi state, India (1995): 13·3% MDR LowerDohuk province, Iraq(2008–09)81Routine laboratorysurveillanceSmear/WHO;pTB10·5% (4/38) any drug resistance, new;7·9% (3/38) MDR, new; 53·3% (8/15) any drugresistance, previously treated; 46·7% (7/15)MDR, previously treatedWHO Eastern Mediterranean region(1994–2007): mean 13·7% any drugresistance, new; 2·0% MDR, new; 54·4% anydrug resistance, previously treated;35·3% MDR, previously treatedMediumNatural disasterPost-earthquake Haiti (2010)82Routine laboratorysurveillanceSmear/WHO;ss+ pTB5·5% (30/546) MDR Same laboratory (2009): 1·0% MDR MediumAll comparison estimates of drug-resistance prevalence are taken from theWHO’s Anti-tuberculosis Drug Resistance 2008 Report,15unless indicated otherwise. ss+=sputum-smear positive. pTB=pulmonarytuberculosis. 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-affected populations, 1980–2011
  10. 10. Vol 12 December 2012 959Reviewtrends though somewhat more significant associations(appendix). Countries with a reduction in notificationscompared with the baseline during the recovery phaseswere also about twice as common as those with anincrease in notifications.No obvious pattern was identified in the relationbetween the rate of tuberculosis smear-positive notifi-cation in UNHCR-managed refugee camps and thereference notification rates in either the host or origincountries (appendix). Only about half of camps seemedto have a higher burden than reference populations, andcamps within the same host country generally had asimilar relative risk.However, a strong linear correlation was seen at the campand host-country level between the rate of pulmonary-tuberculosis smear-positive notification and the rate ofsmear testing (ie, the number of smears done per100000 people per year), and the latter indicator explainedabout 50% of the variability in smear-positive rate in bothgeneralised linear and ordinary least-squares regressionmodels (data not shown); Chad and Sudan in particularhad low rates of smear testing in nearly all camps.The ratio of new smear-positive cases per smear testdone (data not shown) was significantly higher and muchmore variable in camps where the rate of smear testingwas less than 2000 per 100000 person-years than incamps with a testing rate above that value (median ratios6·0% vs 2·5%, respectively; p=0·002, Kolomogorov-Smirnov test for comparison of medians). This findingsuggests that substantial self-selection of patients typicalof tuberculosis programmes with low population coverageand low case-detection rates occurred below 2000 perAlgeriaAngolaAzerbaijanBosnia and HerzegovinaBurmaBurundiCambodiaCentral African RepublicChadColombia*CongoCôte d’IvoireCroatiaDR CongoDjiboutiEl SalvadorEritreaEthiopiaGeorgiaGhanaGuatemalaGuinea-BissauHaitiIranIraqKuwaitLebanonLiberiaMozambiqueNamibiaNepalNicaraguaPanamaPeruRwandaSerbia and MontenegroSierra LeoneSolomon IslandsSomaliaSyriaTajikistanThailandUgandaYemen0 50 100 150 200–50–100Change in case notification rate (%)Figure 3: Estimated percent change in case reporting rate associated with high-intensity conflict years, by country, based on a model without lag effectsWhiskers indicate 95% CI. *High-intensity years versus low-intensity years.
  11. 11. 960 Vol 12 December 2012Review100000 person-years. Indeed, nearly all camps with asmear testing rate of 2000 or more per 100000 person-years (ie, where tuberculosis programmes could be moresafely assumed to achieve a reasonable case detectionrate) had notification-rate ratios well above 1 (appendix).When assessing all camps in a single model, there wasno evidence of an association between increasing timeand 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 smeartesting 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 anassociation between time and the ratio of new smear-positive patients to smear tests done, adjusted for therate 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, anddata from the 1980s and 1990s are more abundant thanfor the past decade, at least in published works.Importantly, evidence about the burden of tuberculosisamong IDPs and after natural disasters was very sparse.Results suggest that crises are often associated with upto 20-fold increases in the risk of tuberculosis, althoughthis pattern was more difficult to infer for refugee campsmanaged by UNHCR over the past 5 years. Our findingsdo not suggest any increase in CFR, while results fordrug resistance are somewhat mixed. Despite thesebroad patterns, both incidence and prevalence showedvariability of up to two orders of magnitude. Findingsconsistently point to a disproportionately high risk ofexcess mortality among HIV-positive individuals. How-ever, most of the studies included in the Review weredone before the era of widespread access to antiretrovirals,which would be expected to moderate excess risk due toHIV in ongoing crises. With notable exceptions, wereported that both high-intensity and low-intensityarmed conflict were mostly associated with reductions incase notification at the national level.Specific studies from displaced populations nearlyuniformly suggest an increased burden relative tothe reference populations, as postulated by otherresearchers.88,89A community-based study of severalrefugee camps between 1979 and 1985, not included inthis Review because its reliance on simple verbal autopsyquestionnaires, estimated that tuberculosis caused 26%of all deaths in adults in a camp for IDPs in Somalia3 years after its establishment, and 50% in a camp inSudan 10 months after establishment.90Both populationshad high prevalence of acute malnutrition. A similarstudy of Tibetan refugees in India showed that tuber-culosis was the second most common cause of death(14%).90In Ethiopia, patients from war-affected areas tooktwice as long to seek treatment as those from unaffectedareas.91These studies corroborate our broad findings.Naive analysis of data from UNHCR-managed refugeecamps suggests no overall pattern of increased burden;however, if analysis is restricted to camps where tuber-culosis programmes seem to function reasonably well onthe basis of the rate of smear testing as a proxy indicator,a clear pattern of higher smear-positive-case incidenceemerges when assessing both the host country and anyof the countries of origin of camp residents as references.Although the consistency of this finding suggests highexcess burden, this inference cannot be substantiatedwithout investigating the alternative explanation—namely, that tuberculosis programmes in these campsachieve higher detection rates than in referencepopulations (we could not explore this hypothesis,because information on the rate of smear testing is notavailable in the WHO country database).Studies identified in the systematic review contained fewlongitudinal data with which to ascertain trends over time.Data from a relatively short 4 year time series in a largenumber of UNHCR camps from around the world did notsuggest any trend in either incidence of smear positivecases or new cases per smear test as time progressed.Analysis of tuberculosis notifications to WHO suggeststhat, at the national level, the occurrence of both low-intensity and high-intensity armed conflict usuallyresults in reductions in the notification rate, which aresometimes substantial. Furthermore, this effect seems tobe sustained during the few years immediately after thecessation of armed conflict. These findings show thepotential effect of armed conflict on tuberculosis controlprogrammes, and the extent of the resultingunderestimate in the reported burden. However, negativeassociations are not universal, and in some countriesconflict seems to be associated with intensifiedtuberculosis notifications or no relative change. In someof these countries (eg, Somalia,92Mozambique,93andNicaragua94) successful implementation of control pro-grammes, irrespective of war, has been described. Ourfindings contrast with a previous similar study,95which,however, examined the incidence of tuberculosis overshorter time series and fewer and different countries.IDPs account for about 70% of forcibly displacedpeople worldwide, and most IDPs as well as refugees livenot in camps, but rather in urban or rural or dispersedsettings; however, data on tuberculosis burden in thesepopulations are scarce.4We postulate that in camps forIDPs, which are usually less covered by reliefinterventions and more vulnerable to malnutrition,excess tuberculosis risk might be even higher than inrefugee camps. IDPs in non-camp settings mightexperience less overcrowding and have more foodsecurity, but are usually dependent on local governmenthealth services, and limited access to tuberculosis carebecause of discrimination and fear of identification, andlegal or financial barriers could also result in higher risk.Similarly, few studies have assessed populationsaffected by natural disasters. Previous reviews have
  12. 12. Vol 12 December 2012 961Reviewshown that natural disasters by themselves generally leadto infrequent disease outbreaks, with no reports oftuberculosis epidemics.87,96,97Several factors make themeasurement and comparability of disease burden in thepost-disaster phase difficult. Natural disasters vary interms of severity, duration, and the extent to which theyaffect underlying health infrastructure.97The immediateinflux of humanitarian aid might result in very highnotification rates in the short-term. A resilient healthsystem can effectively control tuberculosis in disasters:for example, in Louisiana, USA, after Hurricane Katrinain 2005, federal agencies had located and resumedtreatment of the 130 patients with tuberculosis who weredisplaced by the storm within 6 weeks of evacuation.98Bycontrast, the 2010 floods in Pakistan displacedapproximately 5 million people, many of whom wererelocated into makeshift tent camps.99In such a scenarioof large-scale displacement, weaker health systems, andvery high baseline tuberculosis burden, there is clearly ahigh potential for short-term and long-term increases intuberculosis burden as a result of the disaster.Generally, we identified fewer published reportscovering the past decade than for the 1980s and 1990s,when several landmark epidemiological studies of refu-gee-camp populations were done: this finding mightreflect the decreased accessibility of crisis-affectedpopulations due to the rise of internal displacement and ashift away from camps to more dispersed settings, butsuggests an insufficient effort to document one of theleading causes of morbidity and mortality. In recent years,UNHCR’s HIS has increased the amount of informationavailable for camp-based refugees, but data from HIS aredifficult to interpret without extensive knowledge ofindividual camps, and are greatly dependent on thefunctionality of tuberculosis programmes in these camps.Filling some of the above evidence gaps might requiremore ad-hoc studies (either exhaustive or sample sur-veys) that seek to quantify burden directly (eg, prevalencebased on a representative sample; however, such studieswould be costly because of the large sample size re-quirements needed to accurately estimate tuberculosisprevalence, a numerically rare condition), or indirectly bymonitoring reported incidence and estimating the rate ofcase detection through more statistically efficientapproaches (eg, respondent-driven sampling to detectprevalent cases without the need for a population-basedsurvey). Such studies should also explore other aspects oftuberculosis epidemiology in crises that are directlyrelevant for control programmes (eg, the proportion ofextrapulmonary cases and smear-negative cases; the sexratio and incidence in children), data for which weresparse in the reports included in our study.Our inclusion criterion of smear confirmation orWHO-consistent diagnosis standards resulted in theexclusion of several reports, including many describingtuberculin skin-testing data. Much of what is knownabout the burden of tuberculosis is based on surveys oftuberculin skin testing, to which a mathematical modelis applied so as to extrapolate the region’s tuberculosisincidence, prevalence, and mortality rate. Skin testingbecomes more unreliable as populations become moreunwell due to other infectious diseases, emotional orphysical stress, malnutrition, and HIV. The model usesdata only from small samples of relatively healthy, well-nourished populations in developed countries to modelmortality and incidence.100Conversely, the WHO esti-mates rely on case notifications, national surveys, anddeath-registry systems adjusted by an expert opinion ofproportion of cases detected by these mechanisms.101Publication bias could have affected the results of thisReview in two different ways. About 12 of the studiesincluded were done by aid organisations that could havehad an incentive to report favourable data on theirperformance (eg, on CFR or drug resistance; however,results of these studies did not strikingly differ from therest). Conversely, available reports might be biasedtowards high burden due to a tendency to over-reportalarming findings.Our findings on the excess risk of tuberculosis in crisesare sensitive to potential differences in the sensitivity ofcase definitions and case ascertainment between studiesincluded in the Review and the country-level estimatesand notification data gathered by WHO, which we usedas reference, in the absence of more directly comparabledata. WHO’s estimates before the initiation of conflictcould also have greatly underestimated burden, becausethe estimates are not generally updated to reflectdeteriorating situations in individual countries.100,101Conversely, refugee populations can have better access tohealth care than host populations,102,103which would alsohave upwardly biased comparison of notification rates.Where appropriate, a comparison of observed burdenwith estimated burden in reference populations from thesame region or district as the crisis-affected populationwould have been preferable, rather than in the country asa whole, to remove confounding by differences betweenthe populations being compared other than exposure tocrisis. However, regional level estimates of tuberculosisburden were almost never available, and to ensure aconsistent approach we relied on WHO-country-levelsources. Displaced populations could have systematicallyhad a higher burden of tuberculosis even beforedisplacement than other communities in their country,thus accounting for some of the excess risk estimated inour study: however, this effect does not alter the mainpractical implication of our findings—namely, thattuberculosis is a major health priority in thesepopulations. On balance, our general finding of higherrisk of tuberculosis in crises seems plausible because ofits consistency across a range of settings and since relativerisks exceed one even when comparing notification ratesobserved in crisis settings with reference estimatedincidences, but the size of the excess risk might havebeen overestimated or underestimated. Similarly, the
  13. 13. 962 Vol 12 December 2012ReviewCFRs reported in the studies included in this Reviewcould be artificially lower than reference levels as a resultof 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 notification and conflict intensityare likely to have substantial error and misclassification(WHO tuberculosis data are in fact missing for crucialconflict years in some countries, while the PRIO datasetis subject to availability of information on conflict deaths).Although useful to detect a global pattern, neither modelfully accounts for the specificities of each country’s armedconflict (eg, some health systems are more resilientduring wartime and different armed groups exhibitvarying behaviour toward existing health structures).Generally, such models can show chronologicalassociations but do not convincingly establish causality.Analysis of UNHCR data is limited by the dearth ofdetail on individual camps, which could have aided inter-pretation and allowed better adjustment for potentialconfounders in statistical models: for example, it wouldhave been useful to distinguish between camp popu-lations that had recently arrived and those that had beensettled in the camp for a longer period.Humanitarian responses to crises have traditionallyfocused finite resources on acute diseases perceived asthe main crisis-emergent threats (eg, measles, cholera,and other diarrhoeal diseases), leaving more chronicdisorders such as tuberculosis for the later stages ofhumanitarian action.104Existing WHO/UNHCR recom-mendations for establishing tuberculosis programmes incrises list essential criteria, including that (1) data fromthe population shows that tuberculosis is an importantproblem; (2) basic human needs (water, food, shelter,sanitation) have been met; (3) the acute phase of theemergency is over (as defined by population death rates);(4) essential services and drugs for common illnesses areavailable; and (5) basic health services are accessible to alarge part of the conflict-affected population.18Both the Sphere project (a set of guidelines forminimum humanitarian relief standards adhered to bymost humanitarian agencies)105and WHO/UNHCRguidelines agree on the necessity of DOTS and recom-mend 6 month drug regimens with target cure rates of85%. Conversely, Biot and colleagues106suggest that theimplementation of a 4 month DOTS programme with amore realistic treatment target of 75% in complexemergency settings would decrease the long-term burdenof disease, while acknowledging that greater treatmentdefaulting could lead to increased drug resistance.In view of our findings, we also believe that recom-mendations might need to be revisited, though anapproach that is tailored to the specificities of each crisisis needed.In any acute emergency settings, as part of initialassessments, identifying patients with tuberculosis andensuring their continuation of treatment as soon aspossible should be a systematic minimum intervention:such patients could be referred to existing host govern-ment programmes or at least registered while basictreatment services are restored.In settings of natural disaster with limited disruptionof the existing health care infrastructure and lowexposure to key risk factors such as malnutrition andpopulation displacement, the current WHO/UNHCRand Sphere guidelines, whereby tuberculosis treatmentprogrammes would only be re-established in case of highdisease burden once the acute emergency phase is over,are probably appropriate. In displaced populations fromhigh-burden countries, and particularly where HIV co-infection, malnutrition, or both are highly prevalent,earlier, more aggressive re-establishment of active casefinding and treatment, at least with first-line regimens,might be warranted. We did not assess treatmenteffectiveness. However, alternative regimens, such asthe Manyatta regimen, which consists of 4 months ofDOTS followed by 3 months of self-administered treat-ment, have been shown to be effective in fragile conflictareas, such as south Sudan.61,70Regimens requiringshorter duration of observed therapy and unconventionalapproaches such as allowing patients to keep anemergency drug supply in case of urgent evacuationcould ensure treatment adherence, which is especiallyimportant in the early, intensive phase of treatment,107reduce onward disease transmission, and by pre-emptingindividuals from seeking care from unregulated sources,could actually prevent the development of drugresistance.106Although still relatively expensive, rapidtests for tuberculosis could also be deployed moreextensively 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 affected population is not necessarilyconcentrated in a few sites but rather dispersed over wideareas, means that, rather than establishing localised, ad-hoc tuberculosis treatment programmes, the focusshould be on rehabilitating pre-existing nationalprogrammes, ensuring prioritisation of tuberculosis incrisis-wide funding appeals, and fosters coordination ofhumanitarian stakeholders by disseminating andenforcing common guidelines and standards andintegrating tuberculosis referral in NGO-supportedhealth structures. Generally, in any crisis setting there isa need to work towards greater collaboration betweennational tuberculosis programmes and the NGO orUNHCR-run programmes in affected populations. Oneobvious step forward would be systematic inclusion ofrefugees and IDPs in applications to the Global Fund toFight AIDS, Tuberculosis, and Malaria by their hostcountries, as suggested by Spiegel and colleagues.108Our findings also lend more urgency to otherinterventions that can ultimately prevent and controltuberculosis in crises, including ensuring adequatenutrition intake, reducing overcrowding through better
  14. 14. Vol 12 December 2012 963Reviewlayout of displaced settlements, and maximising inte-gration of tuberculosis and HIV services in accordancewith the recommended minimum HIV service package inemergencies;109indeed, where HIV burden is high,continued antiretroviral care and active screening fortuberculosis among patients with HIV/AIDS could greatlyreduce the burden of tuberculosis (no evidence, however,shows that armed conflict increases HIV transmission110).Much is still unknown about the burden of tuber-culosis in crisis settings. Only further studies canimprove our understanding of this issue. Bettertuberculosis surveillance is needed, especially amongIDPs and in urban settings. In view of the availableevidence, tuberculosis needs to rank more highly on thelist of public health priorities in settings of displacement(including in the acute phase), and public health agenciesshould consider earlier establishment of treatmentprogrammes. To help establish these programmes,innovative approaches to the traditional DOTS model,requiring shorter regimens and less stringentsupervision, should urgently be tested for effectivenessand feasibility.ContributorsWK and FC designed the study. WK designed and did the review ofpeer-reviewed reports, extracted data, and interpreted findings. MDdesigned and did the review of grey literature, extracted data, andinterpreted findings. VS independently validated the peer-reviewedreport search strategy, and designed, did, and interpreted qualityassessment of reports. FC contacted agencies for grey literature reports;extracted data from papers identified in reviews; designed, did, andinterpreted statistical analyses of national and refugee camp data; andhelped with quality assessment of reports. CH obtained refugee campdata from the UN High Commissioner for Refugees and interpretedtheir analysis. WK and FC wrote the paper. All authors contributed todrafts of this report and interpreted findings.Conflicts of interestWe declare that we have no conflicts of interest.AcknowledgmentsPhilipp du Cros provided guidance on case definition inclusion andexclusion criteria as well as valuable input on the current humanitarian aidguidelines. We thank Maryline Bonnet and Christopher Dye for commentson the draft report and to Abrar Ahmad Chughtai and colleagues at thePakistan National TB Control Programme for sharing unpublished data.References1 WHO. Tuberculosis: fact sheet. (accessed Aug 18, 2010).2 Centre for Research on the Epidemiology of Disasters.2010 Disasters in numbers. Brussels: CRED; 2010. (accessed Feb 12, 2012).3 Themnér L, Wallensteen P. Armed Conflict, 1946–2010. J Peace Res2011; 48: 525–36.4 Spiegel PB, Checchi F, Colombo S, Paik E. Health-care needs ofpeople affected by conflict: future trends and changing frameworks.Lancet 2010; 375: 341–45.5 Watson JT, Gayer M, Connolly MA. 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