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 andTropical
Medicine, Keppel St, London,
UK; and UN HighCommissioner
for Refugees, Geneva,
Switzerland (C Haskew MBChB)
Correspondence to:
Dr Francesco Checchi, Faculty to
Infectious andTropical Diseases,
London School of Hygiene and
Tropical Medicine, Keppel St,
LondonWC1E 7HT, UK
francesco.checchi@lshtm.ac.uk
The burden of tuberculosis in crisis-affected populations:
a systematic review
William Kimbrough,Vanessa Saliba, Maysoon Dahab, Christopher Haskew, Francesco Checchi
Crises caused by armed conflict, 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 conflict intensity and tuberculosis notification rates at the
national level with WHO data. We identified 51 reports of tuberculosis burden in populations experiencing
displacement, armed conflict, or natural disaster. Notification 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
conflict was associated with decreases in the notification rate of tuberculosis. More studies with strict case definitions
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.
Introduction
Worldwide, 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 affected by natural disasters
(about 230 million per year between 2000 and 20102
),
armed conflict (30 wars ongoing as of 20103
), and forced
displacement (about 15 million refugees and 25 million
internally displaced persons [IDPs; ie, forcibly displaced
people who do not cross international boundaries] as of
20104
). These events differ substantially in their effects on
public health. Recent natural disasters have tended to
attract greater humanitarian assistance than armed
conflicts, 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 effects 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 identifiable and more difficult
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 interventions, which tend to
prioritise emergency humanitarian relief over long-term
health-system strengthening.
Tuberculosis is a leading health threat for populations
affected by crises, and more than 85% of refugees flee
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-conflict states, the short-term effects
remain poorly documented.7,8
Difficulties in measuring
the burden of disease in crisis settings are similar to
those preventing the inclusion of tuberculosis-control
programmes in initial humanitarian responses:
diagnosis requires implementation of minimum
laboratory standards 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 first-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-

www.thelancet.com/infection Vol 12 December 2012 951
Review
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 infections resulting from this increase in
transmission would only develop as cases of active
disease 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 skin
testing, and chest radiography become less sensitive and
specific as HIV disease progresses.16
Missed cases of
tuberculosis contribute significantly 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 specific populations
experiencing various types of crisis, investigated
tuberculosis notification trends as a function of conflict
in countries where widespread armed conflict occurred
and analysed recent data from refugee camps managed
by the UNHCR.
Methods
Search strategy and selection criteria
Where 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 scientific articles
published between January, 1980, and October, 2011, in
English, French, and Spanish, which presented data on
any form of tuberculosis infection within populations
affected by armed conflict, natural disaster, displacement,
or nutritional crises. We chose tuberculosis search terms
by consulting a MeSH thesaurus, and complemented
these with terms used by Cochrane Database reviews of
tuberculosis.20,21
Similarly, we identified 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 conflict that might not have been captured in
the above search, we consulted the Uppsala Conflict Data
Program and International Peace Research Institute,
Oslo (UCDP/PRIO) Armed Conflict Dataset (version
4-2010) to identify all regions of high-intensity conflict
(defined as at least 1000 combat-related deaths within a
single year) since 1980.3,22
Various denominations for
these regions were then used in the following search
outline: [tuberculosis] AND [burden of disease indicator]
AND [country or region experiencing armed conflict]. We
limited the search to publications from the first year of
conflict in that region to Oct, 2011. Lastly, we followed the
bibliographic trail of reports identified 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 conflict-affected
countries identified 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-affected
populations
Latent, inactive tuberculosis infections in population
Patients with active tuberculosis who are on treatment
Malnutrition
Interruption of tuberculosis treatment services
Other emotional and physical stressors
HIV co-infection and interruption of antiretroviral
treatment
Increased rate of activation of latent tuberculosis
infection to active, contagious disease
Relapse of previously treated tuberculosis to
reactivated tuberculosis with possible drug resistance
Increased tuberculosis transmission because of
overcrowding and higher prevalence of active disease
Increased rate of disease progression due to
malnutrition, HIV, and other stressors
Short-term effects (months):
Increased morbidity and mortality from active disease
Further onward transmission from active, contagious
cases (including drug-resistant strains)
Long-term effects (years):
Increased burden of latent infections leading to future
increases in morbidity and mortality
Future health-system costs due to higher burden,
possibly higher prevalence of multidrug resistant cases

Review
We also contacted, by email, 23 experts on public health
in crisis-affected populations as well as tuberculosis at
the following agencies: Médecins Sans Frontières,
Epicentre, Medical Emergency Relief International, the
International 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-affected population.
These reports included all forms of tuberculosis. We
excluded reports in which data for crisis-affected 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-affected 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-affected 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 definition 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 stratified 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 definition, 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 scientific 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, definitions 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 populations
To 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 affected 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-affected populations versus reference populations.
Reference populations were defined as: (1) the population
of the entire country for IDPs and non-displaced
populations affected by natural disasters or armed
conflict, 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 affected by
armed conflict; 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 notifications
In addition to reports from individual sites, we also
investigated national level patterns over time in countries
with widespread armed conflict. We specifically aimed to
record deviations from secular trends in tuberculosis
notifications as a function of occurrence and intensity of
conflict.
For this analysis, we included countries that, according
to 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 the
period 1980–2010. However, we excluded years during
which conflict had been focused within a specific
geographic region comprising less than a fifth of the
country’s population (this arbitrary cutoff was roughly
estimated on the basis of census figures and historical
accounts of the conflict provided on the UCDP Conflict
Encyclopaedia26
). We also excluded instances of short-
lived coups d’état not leading to widespread armed
conflict (appendix). To be consistent, for each country we
adopted the yearly total numbers of tuberculosis cases
notified to WHO (all forms including relapses), as
reported in the WHO tuberculosis database.
See Online for appendix

Review
Analysis of refugee camp data
We 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, managed
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 finding 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 notification rates of new
smear-positive cases for each camp and compared these
data with WHO notification 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 100000 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 analysis
Crude 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 (notification
rate) or prevalence in the study reviewed divided by
incidence or prevalence in the reference population, both
estimated by WHO (for studies reviewed only) or notified
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 stratification 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
notifications: for this decade, reference pulmonary
tuberculosis notification rates as reported by the country’s
national tuberculosis programme were extracted instead,
if published. We only adopted a reference pulmonary
tuberculosis notification rate if it matched the case
definition used in the report being reviewed. As WHO
databases provide only new (and not relapse) notified
pulmonary tuberculosis cases, we assessed only new cases
for the reference notification 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-affected populations
whose burden we sought to review; (2) the estimates
themselves had substantial heterogeneity (data not
shown) and reflected various case definitions and case
ascertainment methods; and (3) some of the studies
reported a rate but did not contain sufficient information
on the person-time denominator, which is needed for
meta-analysis.
For the analysis of trends in national tuberculosis
notifications, we fitted two alternative generalised linear
models to the data, both featuring year as the analysis
unit, the natural logarithm of notified 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 first model we
included conflict intensity as an independent variable,
and computed incidence rate ratios (IRRs) comparing
low and high intensity years with the baseline (years
without conflict-related violent deaths). In the second
model, in which we postulated that conflict also has lag
effects, we arbitrarily defined a recovery phase consisting
of the 3 years after the end of conflict of either intensity
(or shorter if another period of conflict began or the time
series ended).
For the analysis of HIS data as a function of time,
after assessing the relative goodness of fit of different
distributional assumptions and models with and without
the rate of smear testing as a covariate, we fitted the
following negative binomial generalised linear models:
(1) new smear-positive cases (dependent variable), log
person-years of follow-up (offset), 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
(offset), time as above (independent variable); and (3) new
smear-positive cases (dependent variable), log total
smears done (offset), 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.
Results
51 reports were included in the final analysis (figure 2).
Of these, 23 reported on refugees living in camps, five on
IDPs, 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 drug
resistance, two on the death rate attributable to

Review
tuberculosis, three on tuberculosis-proportional
morbidity, and two on tuberculosis-proportional
mortality. 15 reports described populations in the WHO
Africa region, seven from the Southeast Asia region,
13 from the eastern Mediterranean region, five from the
European region, two from the region of the Americas,
and nine from the western Pacific region.
Measured incidences ranged from 21 to 1510 per
100000 per year for pulmonary tuberculosis and from
82 to 1142 per 100000 per year for all-form tuberculosis,
suggesting great variability in burden (table 1). Nearly all
the incidence rate ratios comparing notifications in crisis-
affected 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 notification rates
were lower than estimated incidences but nonetheless
higher than notification 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 finding 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).
Notifications gradually decreased from extremely high
levels during the first year after displacement in camps for
Ethiopian refugees in Sudan, but remained very high.35
Notifications remained very high between 1994 and
1996 in long-term Tibetan refugees,37
and sharply
increased during the first 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 notified 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 100000 in 2000 to
53 per 100000 in 2005, while new smear-positive
pulmonary tuberculosis rates declined from 20 per
100000 in 2000 to 11 per 100000 in 2005.42
In Nepal, all-
form tuberculosis and new smear-positive pulmonary
tuberculosis rates increased as conflict persisted.44
In
post-earthquake Kashmir, Pakistan, a reduction in
notification 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 duplicate
87 no tuberculosis
burden measures
8 not from crisisaffected
populations
18 unacceptable case
definition
13 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 measures
147 not from crisis-affected
populations
26 unacceptable case
definition
5 special populations
29 no tuberculosis
burden measures
populations
1 unacceptable case
definition
438 hits
24 duplicates,
190 published in journals
133 no tuberculosis burden
measures
populations
1 unacceptable case
definition
28 only cited other studies
12 reports shared
6 no tuberculosis burden
measures
populations
Key term search Country-specific search
Bibliographic trail search Web search for grey literature
Agency and expert contact
for grey literature
Figure 2: Literature review

Review
Year(s) of
displacement,
war, or
disaster
Type of study Case
definition;
type of cases
Notification rate reported
(cases/person-years*)
Rate ratio for comparison
with notification rate in
reference populations
(reference notification 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 (629/125800) aTB;
240 (302/125800) 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: NA
Sudan: NA
NA Lower
Somali and Sudanese
refugees in Kenya
(1992–93)36
surveillance
Smear/WHO;
aTB, ss+ pTB
1142 (3116/272800) aTB;
453 (1235/272800) ss+ pTB
Kenya: 16·7 (69) aTB,
12·9 (35) ss+pTB
Somalia: NA
Sudan: 11·4 (101) aTB, NA
ss+pTB
Kenya: 7·1–8·8 (130–161) aTB
Somalia: 3·0–5·7 (202–383) aTB
Sudan: 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
[1575/160018]); 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)
Higher
Tibetan refugees in
India (1994–96)38
1959 Clinic-based
surveillance and
camp screening
Smear/WHO;
aTB
835 (1197/143373) 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
Smear/WHO;
aTB, ss+ pTB
242 (1214/501653) aTB;
126 (631/501653) new ss+ pTB
Bhutan: 1·3 (181) aTB;
2·1 (59) new ss+ pTB
Nepal: 2·0 (119) aTB; 2·3 (55)
new ss+ pTB
Bhutan: 0·9–1·3 (189–278) aTB
Nepal: 1·2–1·8 (133–197) aTB
Higher
Burmese refugees in
Thailand
(1987–2005)40
surveillance
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-affected but non-displaced
Republic of Congo
(1994–2000)41
1997–99 National
programme
data†
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):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
Smear/WHO;
aTB, ss+ pTB
82 (3450/4208125) aTB;
21 (879/4208125) 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 [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-Kashmir
province (2004–05): 0·9 (127)
aTB, 1·0 (33) ss+pTB
NA None
(insufficient
information)
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 of
Congo.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

Review
prevalence of all-form tuberculosis were used as the
comparison, all prevalence ratios would be far above 1,
with no change in this general finding 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 tuberculosis 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
improvement 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 conflict 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-
specific 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 conflict.83,84
These studies showed an
increase in mortality among patients with tuberculosis
Year(s) of
displacement,
war or disaster
Type of study Case definition; type
of cases
Reported prevalence (cases/
persons tested)
Lower-upper range of ratio for
comparison with estimated prevalence
in reference populations (reference
prevalence, lower-upper range)
Strength of
evidence
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 (115/19726) pTB
100 (20/19726) ss+ pTB
NA Higher
Vietnamese refugees in
Hong Kong (1992)49
1975–91 Clinic–based surveillance WHO; aTB, pTB 680 (102/15000) aTB
440 (66/15000) pTB
Vietnam: 1·0–3·8 (178–678) aTB
Hong 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) pTB
125 (1/800) sc+ pTB
0 (0/800) ss+ pTB
NA Higher
Laotian refugees inThailand
(2004–05)53
1975–94 Camp exit screening Smear/WHO; aTB, sc+
pTB, ss+ pTB
1760 (272/15455) aTB
369 (57/15455) sc+TB
220 (34/15455) ss+ pTB
Laos: 8·0–35·2 (50–219) aTB
Thailand: 5·5–20·2 (87–321) aTB
Higher
Burmese refugees inThailand
(2007)54
1984–2007 Camp exit screening Smear; sc+ pTB, ss+
pTB
598 (28/4686) sc+ pTB
150 (7/4686) ss+ pTB
NA Higher
Bhutanese refugees in Nepal
(2007–09)55
1990–98 Camp exit screening Smear/culture; pTB,
ss+ pTB
644 (151/23459) pTB
230 (54/23459) 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 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

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
treatment, 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 conflict 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 conflict-affected
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 affected by conflict (relative risk 3·7).
Type of study Case definition; type of
cases
Case-fatality rate (deaths/patients) Type of
treatment plan
used
Strength of
evidence
Refugee camps
Cambodian refugees inThailand (1981–83)59
Clinic-based surveillance Smear; pTB 6·0% (36/615) DOTS Lower
Clinic-based surveillance Smear/WHO; aTB, ss+ pTB 5·0% aTB (28/558)
3·9% ss+pTB (NA)
DOTS Medium
Clinic-based surveillance Smear; ss+ pTB 5·0% (46/929) DOTS Medium
Clinic-based surveillance WHO; aTB 3·8% (47/1240) DOTS Medium
Somali and Sudanese refugees in Kenya
(1992–93)36
Clinic-based surveillance Smear/WHO; ss+ pTB 2·6% (32/1235) Not specified Medium
Tibetan refugees in India (1994–96)38
Clinic-based surveillance
and camp screening
Smear/WHO; aTB 3·8% (45/1184) Not specified Higher
Burundian and Rwandan refugees in
Tanzania (1995–99)62
Clinic-based surveillance Smear; ss+ pTB 10·9% (60/546) DOTS Medium
Burmese refugees inThailand (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) aTB
2·2% (7/325) pTB
2·3% (4/174) ss+ pTB
Not specified None (insufficient
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 specified Medium
Northern Uganda (1992–2002);65
all war-
affected, about 70% internally displaced
people in camps
Hospital-based surveillance WHO; aTB 10·4% (81/777) Not specified Medium
Addis Ababa city, Ethiopia (1983–85)66
Hospital-based surveillance Smear/WHO; aTB, pTB 7·9% (19/240) aTB
8·7% (10/115) pTB
Not specified Lower
Gedo region, Somalia (1994–95)67
Hospital-based surveillance Smear/WHO; aTB, pTB,
ss+ pTB
7·6% (16/211) aTB
7·8% (15/192) pTB
3·2% (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 specified Medium
Upper Nile, south Sudan (2001)70
Clinic-based surveillance Smear/WHO; aTB, ss+ pTB 4·3% (7/163) aTB
9·1% (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=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

Review
The analysis of tuberculosis notification patterns in
countries affected by widespread conflict did not yield a
uniform pattern, and for most countries associations were
non-significant in either model (appendix). However, there
was an obvious trend towards reduced rates of tuberculosis
notification during years of low-intensity conflict and
especially high-intensity conflict compared with the
baseline. In a model without lag effects, about two-thirds
of countries showed an apparent decline in notification
rates during years of high-intensity conflict (figure 3);
negative associations significant to a probability 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 Salvador, Iran, Iraq, Kuwait,
Serbia and Montenegro, Tajikistan, and Uganda (high-
intensity years); significant 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 effects during recovery periods
after the end of either intensity conflict showed similar
Type of study Case definition;
type of cases
Drug-resistance prevalence (cases/persons
tested)
Drug-resistance prevalence in comparison
populations
Strength of
evidence
Refugee camps
Somali and Sudanese refugees
in Kenya (1995–96)72
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
42·1% (24/57) MDR
Thailand (2006): 20·7% any drug resistance,
6·4% MDR; Laos (1994–2007): 4·3% MDR
Higher
Burmese refugees inThailand
(2007)54
Camp exit screening Smear/culture;
pTB
3·6% (1/28) MDR
Thailand (2006): 20·7% any drug resistance,
6·4% MDR;Thai nationals,Tak province,
Thailand (2006–07): 5·7% MDR73
Higher
Bhutanese refugees in Nepal
(2007–09)55
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
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
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 theWHO’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-affected populations, 1980–2011

Review
trends though somewhat more significant associations
(appendix). Countries with a reduction in notifications
compared with the baseline during the recovery phases
were also about twice as common as those with an
increase in notifications.
No obvious pattern was identified in the relation
between the rate of tuberculosis smear-positive notifi-
cation in UNHCR-managed refugee camps and the
reference notification 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 notification and the rate of
smear testing (ie, the number of smears done per
100000 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 significantly higher and much
more variable in camps where the rate of smear testing
was less than 2000 per 100000 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 finding
suggests that substantial self-selection of patients typical
of tuberculosis programmes with low population coverage
and low case-detection rates occurred below 2000 per
Algeria
Angola
Azerbaijan
Bosnia and Herzegovina
Burma
Burundi
Cambodia
Central African Republic
Chad
Colombia*
Congo
Côte d’Ivoire
Croatia
DR Congo
Djibouti
El Salvador
Eritrea
Ethiopia
Georgia
Ghana
Guatemala
Guinea-Bissau
Haiti
Iran
Iraq
Kuwait
Lebanon
Liberia
Mozambique
Namibia
Nepal
Nicaragua
Panama
Peru
Rwanda
Serbia and Montenegro
Sierra Leone
Solomon Islands
Somalia
Syria
Tajikistan
Thailand
Uganda
Yemen
0 50 100 150 200–50–100
Change 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 effects
Whiskers indicate 95% CI. *High-intensity years versus low-intensity years.

Review
100000 person-years. Indeed, nearly all camps with a
smear testing rate of 2000 or more per 100000 person-
years (ie, where tuberculosis programmes could be more
safely assumed to achieve a reasonable case detection
rate) had notification-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, adjusted 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).
Discussion
Most 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 difficult to infer for refugee camps
managed by UNHCR over the past 5 years. Our findings
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 conflict were mostly associated with reductions in
case notification at the national level.
Specific 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-affected areas took
twice as long to seek treatment as those from unaffected
areas.91
These studies corroborate our broad findings.
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 finding suggests high
excess burden, this inference cannot be substantiated
without investigating the alternative explanation—
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 identified 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 notifications to WHO suggests
that, at the national level, the occurrence of both low-
intensity and high-intensity armed conflict usually
results in reductions in the notification rate, which are
sometimes substantial. Furthermore, this effect seems to
be sustained during the few years immediately after the
cessation of armed conflict. These findings show the
potential effect of armed conflict on tuberculosis control
programmes, and the extent of the resulting
underestimate in the reported burden. However, negative
associations are not universal, and in some countries
conflict seems to be associated with intensified
tuberculosis notifications 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
findings contrast with a previous similar study,95
which,
however, examined the incidence of tuberculosis over
shorter time series and fewer and different 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
populations 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 identification, and
legal or financial barriers could also result in higher risk.
Similarly, few studies have assessed populations
affected by natural disasters. Previous reviews 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 difficult. Natural disasters vary in
terms of severity, duration, and the extent to which they
affect underlying health infrastructure.97
The immediate
influx of humanitarian aid might result in very high
notification rates in the short-term. A resilient health
system can effectively 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 floods 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 identified 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 finding might
reflect the decreased accessibility of crisis-affected
populations due to the rise of internal displacement and a
shift away from camps to more dispersed settings, but
suggests an insufficient effort 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
difficult to interpret without extensive knowledge of
individual camps, and are greatly dependent 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 efficient
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
extrapulmonary 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 confirmation 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 notifications, national surveys, and
death-registry systems adjusted by an expert opinion of
proportion of cases detected by these mechanisms.101
Publication bias could have affected the results of this
Review in two different 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 differ from the
rest). Conversely, available reports might be biased
towards high burden due to a tendency to over-report
alarming findings.
Our findings on the excess risk of tuberculosis in crises
are sensitive to potential differences in the sensitivity of
case definitions and case ascertainment between studies
included in the Review and the country-level estimates
and notification data gathered by WHO, which we used
as reference, in the absence of more directly comparable
data. WHO’s estimates before the initiation of conflict
could also have greatly underestimated burden, because
the estimates are not generally updated to reflect
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 notification rates.
Where appropriate, a comparison of observed burden
with estimated burden in reference populations from the
same region or district as the crisis-affected population
would have been preferable, rather than in the country as
a whole, to remove confounding by differences 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 effect does not alter the main
practical implication of our findings—namely, that
tuberculosis is a major health priority in these
populations. On balance, our general finding 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 notification rates
observed in crisis settings with reference estimated
incidences, but the size of the excess risk might have
been overestimated or underestimated. Similarly, the

Review
CFRs reported in the studies included in this Review
could be artificially 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 notification and conflict intensity
are likely to have substantial error and misclassification
(WHO tuberculosis data are in fact missing for crucial
conflict years in some countries, while the PRIO dataset
is subject to availability of information on conflict deaths).
Although useful to detect a global pattern, neither model
fully accounts for the specificities of each country’s armed
conflict (eg, some health systems are more resilient
during wartime and different armed groups exhibit
varying behaviour toward existing health structures).
Generally, such models can show chronological
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 finite 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 defined 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 conflict-affected 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 findings, we also believe that recom-
mendations might need to be revisited, though an
approach that is tailored to the specificities 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
finding and treatment, at least with first-line regimens,
might be warranted. We did not assess treatment
effectiveness. 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 effective in fragile conflict
areas, such as south Sudan.61,70
Regimens requiring
shorter duration of observed therapy and unconventional
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 affected population is not necessarily
concentrated in a few sites but rather dispersed over wide
areas, means that, rather than establishing localised, ad-
hoc tuberculosis treatment programmes, the focus
should be on rehabilitating pre-existing national
programmes, ensuring prioritisation of tuberculosis in
crisis-wide funding appeals, and fosters coordination of
humanitarian stakeholders by disseminating 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 affected 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 findings 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

Review
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 conflict 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 effectiveness
and feasibility.
Contributors
WK and FC designed the study. WK designed and did the review of
peer-reviewed reports, extracted data, and interpreted findings. MD
designed and did the review of grey literature, extracted data, and
interpreted findings. 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 identified 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 findings.
Conflicts of interest
We declare that we have no conflicts of interest.
Acknowledgments
Philipp du Cros provided guidance on case definition 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.
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