Spatial epidemiology and serologic cohorts increase the early detection of leprosy
Barreto et al. BMC Infectious Diseases
Spatial epidemiology and serologic cohorts increase the early detection of leprosy
Josafá Gonçalves Barreto 0 3 4 5
Donal Bisanzio 2 5
Marco Andrey Cipriani Frade 5 9
Tania Mara Pires Moraes 0 4 5
Angélica Rita Gobbo 0 4 5
Layana de Souza Guimarães 5 8
Moisés Batista da Silva 0 1 4 5 6
Gonzalo M. Vazquez-Prokopec 2 5
John Stewart Spencer 5 7
Uriel Kitron 2 5
Claudio Guedes Salgado 0 1 4 5 6
0 Laboratório de Dermato-Imunologia UEPA/UFPA/Marcello Candia , Av. João Paulo II, 113. Bairro Dom Aristides, Marituba CEP: 67200-000, Pará , Brazil
1 Instituto de Ciências Biológicas, Universidade Federal do Pará , Belém, Pará , Brazil
2 Department of Environmental Sciences, Emory University , Atlanta, GA , USA
3 Universidade Federal do Pará , Campus Castanhal, Marituba, Pará , Brazil
4 Laboratório de Dermato-Imunologia UEPA/UFPA/Marcello Candia , Av. João Paulo II, 113. Bairro Dom Aristides, Marituba CEP: 67200-000, Pará , Brazil
5 Abbreviations WHO: World Health Organization; PGL-I: Phenolic glycolipid-I; HHC: Household contacts; IgM: Immunoglobulin M; GIS: Geographic information systems; S: South; W: West; NE: Northeast; SC: School children; T1: First evaluation; T2: Second evaluation; TT: Tuberculoid-tuberculoid; BT: Borderline tuberculoid; BB: Borderline-borderline; BL: Borderline- lepromatous; LL: Lepromatous-lepromatous; PB: Paucibacillary; MB: Multibacillary; DG: Disability grading; ELISA: Enzyme-linked immunosorbent assay; OD: Optical density; SINAN: National Notifiable Diseases Information System; IBGE: Brazilian Institute of Geography and Statistics; GPS: Global positioning system; NNH: Number needed to harm
6 Instituto de Ciências Biológicas, Universidade Federal do Pará , Belém, Pará , Brazil
7 Department of Microbiology, Immunology and Pathology, Mycobacteria Research Laboratories, Colorado State University , Fort Collins, CO , USA
8 Unidade de Referência Especializada em Dermatologia Sanitária Dr. Marcello Candia , Marituba, Pará , Brazil
9 Divison of Dermatology of Internal Medicine Department of Ribeirão Preto Medical School, University of São Paulo , Ribeirão Preto, São Paulo , Brazil
Background: Leprosy remains an important public health problem in some specific high-burden pockets areas, including the Brazilian Amazon region, where it is hyperendemic among children. Methods: We selected two elementary public schools located in areas most at risk (cluster of leprosy or hyperendemic census tract) to clinically evaluate their students. We also followed anti-PGL-I seropositive and seronegative individuals and households for 2 years to compare the incidence of leprosy in both groups. Results: Leprosy was detected in 11 (8.2 %) of 134 school children in high risk areas. The difference in the prevalence was statistically significant (p < .05) compared to our previous findings in randomly selected schools (63/1592; 3.9 %). The 2-year follow-up results showed that 22.3 and 9.4 % of seropositive and seronegative individuals, respectively, developed leprosy (p = .027). The odds of developing overt disease in seropositive people were 2.7 times that of negative people (p < .01), indicating that a follow-up of 10 seropositives has a >90 % probability to detect at least one new case in 2 years. The odds of clinical leprosy were also higher in “positive houses” compared to “negative houses” (p < .05), indicating that a follow-up of ten people living in households with at least one seropositive dweller have a 85 % probability to detect at least one new case in 2 years. Conclusions: Targeted screening involving school-based surveillance planned using results obtained by spatial analysis and targeted household and individual continuous surveillance based on serologic data should be applied to increase the early detection of new leprosy cases.
Leprosy; Serology; PGL-I; Spatial epidemiology; Geographic information systems; School children
Although the World Health Organization (WHO)
elimination target has been achieved in 2000, with a global
prevalence rate of <1 case/10,000 people, leprosy
remains an important public health problem in some
specific high-burden pockets [
]. In the most recent global
statistics, 206,107 (96 %) of new leprosy cases were
reported from only 14 countries, and among them, India,
Brazil and Indonesia account for more than 80 % of all
new cases [
Brazil has one of the highest annual case detection
rates in the world (15.4/100,000 people), with 31,044
new cases reported in 2013 [
]. Despite the recent
Brazilian economic growth, large pockets of poverty
remain, especially in the North, Central-West and
Northeast of the country, where leprosy is
hyperendemic and underdiagnosed [
]. Approximately half
of the Brazilian cases were detected in high-burden
municipalities that encompass only 17 % of the total
national population .
The problem is historic in the state of Pará, in the
Brazilian Amazon region, north of the country, where
approximately 80,000 new cases were reported during
the last 20 years. In 2012, the annual case detection
rate of this state reached 50/100,000 people, which
was three times the national average (17/100,000)
according to official numbers from the Brazilian
Ministry of Health. Because of the long incubation period
averaging 3–7 years to develop clinical symptoms and
the continued spread of infection from asymptomatic
individuals, the chain of transmission in these areas
continues uninterrupted, with leprosy remaining
hyperendemic among children less than 15 years old
(6.4 % of new cases, or 1996 of 31,044 reported by
WHO for Brazil in 2013 [
]), indicating active foci
of infection in the community [
3, 6, 7
about 50 % of the population is not covered by the
family health strategy, which is responsible for
detecting and treating leprosy cases (http://dab.saude.gov.br/
portaldab/historico_cobertura_sf.php). This fact may
explain the high number of undiagnosed leprosy cases
recently discovered in Pará [
]. Based on our
survey results in eight different municipalities throughout this
state , we estimate that there are approximately 80,000
cases among the 2,000,000 public school students who
are waiting to be diagnosed in Pará, many of them
in difficult to reach areas poorly served by health
There is no laboratory test that detects all forms of
leprosy, but some biomarkers of infection, disease
progression and treatment efficacy have been developed
since the isolation and characterization in the 1980s of
phenolic glycolipid-I (PGL-I), a species-specific antigen
from the M. leprae cell wall [
]. Serology could
potentially be used to detect antibodies against PGL-I to help
classify patients for treatment purposes, monitor
treatment efficacy, identify the risk of relapse and identify the
healthy household contacts (HHC) of leprosy patients
who are most at risk of contracting the disease .
Anti-PGL-I seropositivity is also a marker of
subclinical infection in healthy subjects [
]. A positive test
for anti-PGL-I IgM is associated with an 8.6-fold higher
risk of leprosy in HHC and a 4.4-fold higher risk in
noncontacts . In our recent school-based surveys
performed in the state of Pará, we recorded a
seroprevalence of 48.8 % among students ranging from 6 to
20 years old, and 4 % of those surveyed were diagnosed
based on well-defined clinical signs and symptoms,
including loss of sensitivity in hypochromic skin lesions,
nerve swelling or pain, and weakness, sensory loss or
loss of function associated with nerve damage in the
extremities such as the hands and feet [
]. Additionally, it
is believed that a healthy carrier, those with M. leprae in
their noses, might be actively involved in transmission
through the shedding of bacilli facilitating its spreading
in endemic regions [
The recent huge growth in spatial epidemiology is
facilitated by improved accessibility of computer-based
geographic information systems (GIS) and personal
computing improvements in processing speed and
userfriendly applications. The combination of these factors
has allowed spatial analysis to reach a large number of
researchers and health policy-makers [
technology and spatial analysis have been applied to identify the
distribution of leprosy at the national, regional and local
]. Indeed, the WHO recently advocated
using GIS as part of the “Final Push” strategy as a
management tool to strengthen capacities in surveillance and
monitoring of new cases and to monitor epidemiological
indicators over time, aiming to identify risk factors and
clusters of substantial endemics and to indicate precisely
where additional resources should be targeted (http://
According to the WHO, the introduction of innovative
case-finding methods in hard-to-reach areas and
population groups, coupled with improved data management,
will result in a large increase in detection of new leprosy
]. 1) In our recent studies, we detected a very
high rate of previously undiagnosed leprosy and
subclinical infection in the state of Pará [
]; and 2) leprosy
cases were spatially clustered in hyperendemic pockets,
even at a fine intra-town spatial scale . Therefore,
the main objective of this study is to describe and
evaluate a targeted screening strategy for the early diagnosis
of leprosy cases, involving school-based active clinical
surveillance in high risk areas determined by spatial
epidemiology, accompanied by regular follow-up of targeted
HHC and families guided by anti-PGL-I IgM serologic
Ethics, consent and permissions
This study adhered to the Declaration of Helsinki and
was approved by the Institute of Health Sciences
Research Ethics Committee at the Federal University of
Pará (protocol number 197/07 CEP-ICS/UFPA). All data
were anonymized. A written informed consent to
publish was obtained from every individual who accepted to
participate in this study.
Our study was performed in two municipalities of the
state of Pará: Castanhal (1.29° S; 47.92° W) and
Oriximiná (1.76° S; 55.86° W); the first is hyperendemic
(annual case detection rate ≥40/100,000 people), and the
second is highly endemic (annual case detection rate of
20 to 39.99/100,000 people) for leprosy. Castanhal is
located 68 km NE of Belém, the capital of Pará, and it is
easily accessed via a paved road. In contrast, Oriximiná
is located 820 km west of the capital, and it is accessible
only by plane or 3 days of travel by boat on the Amazon
and Trombetas Rivers. Table 1 presents some relevant
demographic and epidemiologic characteristics of each
municipality involved in this study.
aSource: Brazilian Institute of Geography and Statistics (IBGE)
bBased on the Brazilian Ministry of Health online database—SINAN
cSeroprevalence of anti-PGL-I IgM detected in our previous cross-sectional study conducted in 2010 [
dNew cases detected based on clinical examination in our previous cross-sectional study [
Sampling design and methods
Based on our previous clinical and serologic
crosssectional studies conducted in Castanhal and Oriximiná
in 2010 (T1) in which we had evaluated 427 HHC and
323 school children (SC) [
], we sampled and
reexamined those clinically healthy subjects who tested
positive or negative to anti-PGL-I 2 years after (T2) the
first evaluation. To be enrolled in the follow-up study,
the subject had to be registered as living in the same
urban area as at the beginning of the study. In addition
to those people evaluated in T1, we included other HHC
(people that share the same house or neighbors with
frequent presence at that house) that were found in the
households at the time of our second visit, although they
had not been examined in T1. The sample size was
determined by the number of people who we could survey
in 1 week of field work during a single visit to each
The subjects were clinically assessed by an experienced
leprologist. Leprosy cases were diagnosed in the field
based on clinical signs, including loss of sensation
associated with obvious skin lesions detectable by assessment
with standard graded Semmes-Weinstein monofilaments
]. For operational reasons, slit skin smears were
not performed. The cases were classified as
indeterminate leprosy, as defined by the Madrid classification [
if there was only a hypopigmented macule but no
detection of nerve involvement, or as one of the five clinical
forms defined by the Ridley and Jopling classification
system (tuberculoid-tuberculoid (TT), borderline
tuberculoid (BT), borderline-borderline (BB),
borderlinelepromatous (BL) or lepromatous-lepromatous (LL))
]. Cases of indeterminate and TT leprosy were
classified as paucibacillary (PB) cases, whereas the other
forms were classified as multibacillary (MB) cases.
Primary neural leprosy was diagnosed if nerve enlargement
associated with functional or sensory loss was detected
but no skin signs were present. If only one nerve was
affected, the case was classified as PB; two or more
enlarged nerves defined the case as MB. The disability
grading (DG) was ranked from 0 to 2 (0 = no disability;
1 = loss of sensation in the hand or foot; 2 = visible
damage or disability) as determined by clinical examination
of the sensory-motor functions using a WHO
standardized neurological evaluation [
The subjects’ antibody titres of anti-PGL-I IgM were
determined by ELISA as described previously using
native PGL-I as the antigen [
]. The ELISA cut-off for the
test to be considered seropositive was established as an
optical density (OD) of 0.295, based on the average plus
3× the standard deviation of the test results from 14
clinically healthy people from the Amazon region, which
would be considered an endemic population. The
subjects were also interviewed to identify their demographic
and socio-economic characteristics. Detailed information
about sampling and eligibility criteria for the first
examination can be found in Barreto et al. [
Additionally, based on the spatial distribution pattern
of leprosy cases described in our previous study in
], we selected two elementary public schools
located in high risk areas, one located within a cluster of
leprosy cases and the other in a hyperendemic census
tract, to survey additional SC. We sent invitation letters
to the parents of students of three or four classes
selected by the director of each of the two schools
(approximately 100 students) to attend a meeting with us in
which they received general information about aspects
of leprosy and an explanation about our project and
experimental procedures. We clinically evaluated and
collected peripheral blood samples from those students
who received permission with a signed consent by a
responsible adult family member. When a new case was
detected in a student, we scheduled a visit to their house
to evaluate their HHC.
Data management and analysis
The spatial distribution pattern of leprosy cases in
Castanhal was determined by combining information
from the National Notifiable Diseases Information
the Brazilian Institute of Geography and Statistics
(IBGE—http://www.ibge.gov.br), and by mapping in the
field. The residences of people affected by leprosy in the
urban area, reported during the last 6 years before our
study, were georeferenced with a handheld GPS device
(Garmin eTrex H, Olathe, KS, USA) to produce detailed
aT1 = First evaluation. T2 = second evaluation performed 2 years later
bThe difference is statistically significant (p = 0.027). Fisher’s exact test
cAt most times, both positive and negative subjects shared the same household
maps of the leprosy distribution. Using GIS (ArcGIS 10
ESRI, Redlands, CA, USA), we drew point pattern maps,
calculated the number of cases and the annual case
detection rate per urban census tract and identified
hyperendemic areas. Additionally, using the software Clusterseer
2.3 (Biomedware, Ann Arbor, MI, USA), we applied
Kulldorff ’s spatial scan statistics [
] to identify
clusters of leprosy (see Barreto et al. for details [
examined SC also had their residential addresses
georeferenced with the GPS device to analyse their
spatial correlation with reported leprosy cases.
We used Fisher’s exact test to compare the proportion
of new cases detected among seropositive and
seronegative people or households and Mann–Whitney U test to
compare the titres of anti-PGL-I IgM among the groups.
We also calculated the odds ratio to analyse the
probability of developing disease and the number needed to
harm (NNH) based on the seropositivity.
Follow-up of individuals
Of the 750 people initially evaluated in T1, we were able
to re-examine 254 (33.8 %) 2 years later (T2). Our
sample included 94 males and 160 females and 143 HHC
and 111 SC. The age of participants ranged from 5 to
80 years (mean = 20, SD = 14.1), with 44 % of individuals
in the age group <15 years. The main reasons for
nonparticipation in the follow-up were: (1) families that had
moved to unknown addresses inside the same city (2),
families that moved to other cities or states and (3)
subjects that were not at home at the time of our visit.
In T2, 43 people of 254 (16.9 %) were clinically
diagnosed with leprosy. The incidence was significantly
higher (p < 0.05 - Fisher’s exact test) among those who
tested positive to anti-PGL-I in T1 (Table 2). The odds
of developing overt leprosy in seropositive people were
2.7 times higher than for seronegative individuals
(95%CI = 1.29–5.87; p < 0.01), indicating that a follow-up
of 10 seropositives has a >90 % probability to detect at
least one new case in 2 years. Figure 1 shows the
progression of the antibody titration from T1 (no leprosy) to
T2 (diagnosis). Of those 43 new cases, 29 (67.4 %) showed
a significant increase in their IgM titres (mean increase =
110 %, SD = 80 %; median OD value in T1 = 0.333, IQR =
0.251; median value in T2 = 0.686, IQR = 0.353; p < 0.001
by the Mann–Whitney U test). The decrease observed
in the other 14 subjects was not significant (mean
decrease = 30 %, SD = 20 %; median OD value in T1 =
0.956, IQR = 1.755; median value in T2 = 0.723, IQR =
0.947; p > 0.2 by the Mann–Whitney U test).
Considering all 43 new cases, there was a significant
increase (p < 0.01 by the Mann–Whitney U test) in the
anti-PGL-I IgM titres from T1 (median OD value =
0.371, IQR = 0.344) to T2 (median OD value = 0.702,
IQR = 0.542). During the first evaluation, 33 of the 43
(76.7 %) tested positive to anti-PGL-I, whereas at
diagnosis, 39 (90.7 %) were seropositive.
Moreover, the group that did not develop leprosy
during this period of time also demonstrated a significant
increase in their average antibody titres (T1—median
OD = 0.336, IQR = 0.461; T2—median OD = 0.460; IQR
= 0.543; p < 0.05 by the Mann–Whitney U test).
However, the most significant increase in the IgM titres was
observed in the group that developed disease
(T1—median OD = 0.371, IQR = 0.359; T2—median OD = 0.702,
IQR = 0.562; p < 0.05 by the Mann–Whitney U test)
(Fig. 2). Despite this result, 18/148 (12.1 %) of those who
were seropositive at T1 became seronegative after 2
Fig. 1 Anti-PGL-I titres before and at diagnosis for people detected
with leprosy at 2 years follow-up. The red lines/dots represent those
people who showed an increase in their IgM titres (significant increase,
p < 0.001), whereas black lines/dots indicate those who showed a
decrease in their titres (not significant decrease, p > 0.2)
years, and 60/106 (56.6 %) of those who began as
seronegative became seropositive, including seven that were
diagnosed with leprosy.
Seropositive versus seronegative houses
At T2, in addition to the 254 people evaluated twice, we
also examined an additional 324 subjects that were not
examined in T1, including both HHC of leprosy patients
and HHC of seropositive or seronegative students. We
operationally classified households with at least one
seropositive dweller as “positive houses” and those with
only seronegative dwellers as “negative houses”. An
analysis of these additional subjects revealed an additional
48 (14.8 %) new cases for a total of 91. There was a
significant difference (p < 0.05 by Fisher’s exact test) in the
incidence of new cases among people from “positive
houses” compared to those in “negative houses” (Table
3). The odds of finding a new leprosy case in “positive
houses” was 2.6 times higher than in negative houses
(95%CI = 1.18–5.91; p < 0.05), indicating that in a
followup of 10 people living in “positive houses”, the
probability of detecting at least one new case in a period of 2
years is 0.85 (or 85 %).
Survey of students in high risk areas
We also evaluated an additional 134 students, aged 6–14
years (mean = 10.4) from two public elementary schools
located in high-risk areas of Castanhal. Eleven (8.2 %)
new leprosy cases were detected based on clinical signs
and symptoms for disease. Four were classified as PB
leprosy and seven as MB (4 BT and 3 BB). No physical
disability was observed among these 11 cases; 4 (36.3 %)
reported previous contact with at least one leprosy case
(household or close contacts) ranging from 3 to 5 years
long. Three individuals (27.2 %) did not show a BCG
scar. The most frequent skin lesion was hypopigmented
macules with loss of sensation.
A very high seroprevalence of anti-PGL-I IgM (104/
134; 77.6 %) was observed in this sample of students
(median OD value of seropositive SC was 0.564; IQR =
0.296), but 5 of 11 new cases (45.4 %) tested negative (1
Indeterminate, 1 primary neural, 2 BT and 1 BB). There
was no significant difference (p > 0.2 - Mann–Whitney
U test) between the median OD value of new cases
(0.436; IQR = 0.287) and the median of healthy students
(0.488; IQR = 0.337). We went to the residences of those
SC who were newly diagnosed with leprosy and
examined 42 of their HHC, and another 7 (16.6 %) new cases
were identified with leprosy. Twenty-three of these HHC
(54.7 %) also tested positive to anti-PGL-I (median OD
The spatial distribution of all leprosy cases reported in
the SINAN database from 2004 to February 2010 was
associated with the residence locations of the 134
evaluated SC (Fig. 3). We observed that 22 (16.4 %) were
residing within 50 m of at least one leprosy case, 83 (62 %)
within 100 m and 121 (90.3 %) within 200 m from a
known case. All 11 new SC cases were living within 200
m of at least one case, 6 (54.5 %) of them within 100 m
and 1 (9.1 %) within 50 m. There was a significant
difference (p < 0.05 by Fisher’s exact test) between the
proportion of new cases detected at the schools that were
selected based on the spatial distribution of the reported
cases (11 new cases of 134 SC; 8.2 %) and our previous
] in randomly selected schools (63 new cases
of 1592 SC; 3.9 %).
Overall clinical and epidemiological outcomes
Of the 754 people included in this study, we detected a
total of 109 (14.4 %) new cases; 40 (36.7 %) of these were
*p < 0.05 - Fisher’s exact test
a“Positive house” = household with at least 1 seropositive dweller. “Negative house” = household with only seronegative dwellers
84 (17.4 %)
7 (7.4 %)
91 (15.7 %)
children <15 years old. Of the 109 new cases, 95
(87.2 %) had DG 0 and 14 (12.8 %) had DG 1; 64
(58.7 %) were females; 91 (83.4 %) had at least one BCG
scar. Sixty (55 %) were living in crowded houses (more
than 2 dwellers per bedroom); the average number of
people per household was 5.4, but in 9 houses (9.8 %),
there were 10 or more dwellers. Among these people, 17
individuals (15.6 %) expected to move to another place
in the near future, reflecting the highly mobile nature of
people in this region. Sixteen (14.7 %) reported
starvation at least one time in their lives, as defined by a full
day without meals, because of the absence of resources
to buy food; 55 (50.4 %) had a family income of equal to
or lower than the Brazilian minimum monthly wage
(roughly 250 US dollars) and 77 (70.6 %) received some
type of financial assistance from the federal government,
most often the family or school allowance (Brazilian
official income transfer programs).
Performing targeted screening in selected schools
located in a predefined cluster of leprosy cases or in a
hyperendemic urban census tract of the city resulted in a
two-fold higher detection rate compared to our previous
findings in randomly selected schools [
]. All new cases
detected among SC were from households in close
proximity to reported cases. This spatial correlation can
also help to understand the extremely high prevalence of
subclinical infection observed in this sample of students
because neighbours and extra-domiciliary contacts are
associated with increased risk of leprosy as well [
Simple serological assays used to detect anti-PGL-I IgM
demonstrated their utility as an indicator for the high
rate of infection in hyperendemic cities, and positive
titres to PGL-I have been shown to be a biomarker of
infection at the individual level, as well as a landmark of
households with an increased risk of leprosy. Fine-scale
spatial epidemiology and serology data should be
collected to increase the detection rate in hyperendemic
regions of the globe.
The strength of the antibody titre has a good
correlation with the bacterial load [
], and patient
responses against PGL-I and other protein antigens, such
as LID-1, have been demonstrated to predict the onset
of leprosy in the armadillo model and in prospective
longitudinal clinical settings [
]. Our analysis
indicates a very high probability (>90 %) that at least 1 of 10
seropositive people will progress to overt disease within
a period of 2 years and that antibody titres will
significantly increase in most of those who will eventually
develop the disease before they are diagnosed. However,
seronegative HHC should not be neglected, especially in
hyperendemic areas because anti-PGL-I serology tests
have poor sensitivity (approximately 50 %) even to detect
those with established PB leprosy .
Moreover, in this study, we showed that 2 years was a
sufficient length of time for some seronegative
individuals to become seropositive and develop clinical
manifestations of leprosy. We observed a slight but
statistically significant increase in the average antibody
titres among those people who did not develop the
disease during this follow-up. There is evidence that
treating the index case in a household results in lower
titres to M. leprae antigens in HHC residing in the
household over time [
], as MDT therapy would cause
a rapid cessation of shedding of viable bacilli, thus
eliminating further exposure of HHC to mycobacterial
antigens. Once the pattern of exposure has been broken,
one would expect that antibody titres would decline in
many of the HHC following successful MDT.
In addition to identifying those specific individuals
with the highest titres to PGL-I, thus establishing their
higher risk to succumb to disease, serologic data also
enabled us to identify those households most at risk of
leprosy. The probability of new cases in “seropositive
houses” is more than two-fold higher compared to
“negative houses”. We calculated that there is an 85 %
probability that at least 1 of 10 people in these “positive
houses” will progress to overt disease in a period of 2
years. Similar findings were obtained by a prospective
study conducted in Cebu (Philippines), where HHC in
approximately 1 of 7 households of MB leprosy patients
developed leprosy during a 7-year period of active
]. Based on their results, those authors
suggested treating antibody-positive high-risk household
contacts, even with no clinical manifestations, with an
MB leprosy treatment regimen to prevent transmission.
However, based on our field experience, this control
approach does not appear practical in hyperendemic
settings such as Pará, Brazil, where there is an extremely
high seroprevalence rate of anti-PGL-I. Some
researchers have tried chemoprophylaxis as an alternative
strategy to interrupt the transmission of M. leprae in
highly endemic settings, and one study showed that
single dose rifampicin therapy provided approximately 60 %
protection against the disease during the first 2 years
]. However, this type of solution is not widely
recommended because there are reservations regarding
how long the protective effect is, the development of
new resistant strains, and its efficacy in such
hyperendemic areas that have a high prevalence of undiagnosed
We were only able to re-evaluate 33.8 % of the original
subjects surveyed in T1, which is a major limitation of
this study. Moreover, more females than males were
included in our surveys because women are frequently in
charge of domestic tasks and were at home when we
visited, whereas men usually work outside the home.
Considering that global epidemiological data historically has
shown a higher incidence of leprosy among males (in
some studies as high as a 2:1 ratio of males to females)
], we likely missed some cases during this study and
underestimated the size of the problem. We classified a
household as a “negative house” based only on dwellers
that we evaluated, but in some cases, we were not able
to examine all residents, which could also be a source of
bias by not detecting possible seropositive individuals in
those “negative houses”.
There is strong evidence that HHC and social contacts
(at school, workplace, religious temples, etc.) and
neighbours of leprosy cases have an increased risk of leprosy
26, 27, 41, 42
]. Consequently, it has been suggested that
contact surveys should focus not only on HHC but
should also be extended to entire neighbourhoods or
villages to target a greater spectrum of social contact
networks. However, in a regional scenario where less than
50 % of HHC of reported leprosy cases were examined
in the last 10 years, mainly because of the low coverage
and the inefficiency of the local public health system in
the state of Pará, this goal remains a challenge. More
resources are needed to evaluate all HHC of new leprosy
cases and to extend contact tracing to a wider network
of people at higher risk of leprosy in a sustainable
Spatial targeting has been applied to control various
infectious diseases, including leprosy [
]. Surveys of
school children in high or hyperendemic areas for
leprosy has long been advocated as an important strategy
for early detection since 1947 [
], but it is not generally
recommended by either national and/or regional
control programmes despite some evidence of its efficacy
]. In a recent national student survey, the 2014
Brazilian leprosy campaign concentrated its strategy
on evaluating school children of public schools from
highly endemic municipalities of the country. It
targeted 4.7 million students using an evaluation scheme
in which the parents of the children were in charge
of discovering suspicious skin lesions.
As a result, 199,087 students were clinically examined
by physicians at the basic health units, 354 of them
(0.17 %) were newly diagnosed with leprosy and about
100 new cases were detected among their household
contacts (official data of the Ministry of Health, April
2015). Despite this particularly alarming detection rate
among children, indicating active foci of infection in
their communities, this number may be even higher.
Those referred children were examined by general
doctors, which are not always familiar with leprosy and
therefore may not be skilled to define a child leprosy
case. A child diagnosis is often challenging even for
experienced leprologists. Furthermore, poorer areas and
families are associated with a higher amount of leprosy
cases, and these families have more difficulty finding a
suspicious spot on the children’s skin.
Performing targeted screening involving school-based
active clinical surveillance in high risk areas determined
by spatial epidemiology, accompanied by regular
followup of targeted HHC and families guided by serologic
data, significantly increases the likelihood of early
detection of new leprosy cases.
Based on our findings, we strongly believe that if
largescale school children surveys are performed in specific
spatial clusters of leprosy in each hyperendemic
municipality with well-trained personnel, the detection rate
would be much higher. We advocate such an approach
both for public health reasons and because it will be more
cost-effective than what has been conducted in Brazil.
SD: Standard deviation; IQR: Interquartile range; 95%CI: 95 % confidence
interval; BCG: Bacillus Calmette–Guérin; LID-1: Leprosy Infectious Diseases
Research Institute Diagnostic-1; MDT: Multidrug therapy.
The authors have declared that no competing interests exist.
Conceived and designed the experiments: JGB, MACF, LSG, MBS, JSS, CGS.
Performed the experiments: JGB, MACF, TMPM, ARG, LSG, MBS, CGS.
Analysed the data: JGB, ARG, DB, GVP, UK, CGS. Contributed reagents/
materials/analysis tools: JSS, GVP, UK, CGS. Wrote the paper: JGB, DB, MACF,
TMPM, LSG, MBS, GVP, JSS, UK, CGS. All authors read and approved the final
version of the manuscript.
We would like to thank Márcia Leão, Sabrina Bandeira and André de Sousa
for collecting samples and data from patients, the Castanhal health secretary,
the community health agents and the study participants. This work was
supported by CNPq (481652/2012-4 grant and scholarship for CGS; 448741/
2014-8 grant for JGB and 486183/2013-0 grant for MBS), CAPES (scholarship
for JGB - process 157512–0), CAPES PROAMAZONIA 3288/2013, FAPESPA
077/2013, SESPA, UFPA, FAEPA-HCFMRP-USP, The Order of Malta grants for
leprosy (MALTALEP) to JSS and CGS, a CNPq Science without Borders Visiting
Researcher grant (process 402239/2012-1 to JSS), the National Institutes of
Health (NIH) and National Institutes of Allergy and Infectious Diseases (NIAID)
contract HHSN2010-0516-0005 Mod 9 to JSS and a Fulbright grant to JSS. The
authors thank for PROPESP/UFPA and FADESP for funding the publication
cost of this article. The funders had no role in the study design, data collection
and analysis, decision to publish or preparation of the manuscript.
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