Download The epidemiological behaviour of leprosy in Brazil

Lepr Rev (2009) 80, 332– 344
The epidemiological behaviour of leprosy in Brazil
MARIA LUCIA FERNANDES PENNA*,
MARIA LEIDE VAN DEL REY DE OLIVEIRA** &
GERSON OLIVEIRA PENNA***
*Consultant to the Brazilian National Hansen’s Disease Control
Program, Secretariat of Health Surveillance, Federal Ministry of
Health, Rio de Janeiro, Brazil
**Coordinator of the Brazilian National Hansen’s Disease Control
Program, Secretariat of Health Surveillance, Federal Ministry of
Health, and Adjunct Professor of the Federal University of Rio de
Janeiro (UFRJ) Medical School, Rio de Janeiro, Brazil
***Secretary of Health Surveillance, Secretariat of Health
Surveillance, Federal Ministry of Health, Adjunct Researcher of the
Tropical Medicine Unit, University of Brası́lia, Brasilia, Brazil
Accepted for publication 07 September 2009
Summary
Background The elimination strategy reduced known leprosy prevalence but the
detection rate remains high in many countries, including Brazil. The high Brazilian
detection rate imposes a limit to the reduction of known prevalence in the short term.
The knowledge of time behaviour and spatial distribution of leprosy statistics will
contribute to decision making for leprosy control.
Method The numbers of newly diagnosed leprosy cases by region and year from
1980 to 2004, and prevalent cases from 1990 to 2007 were fitted as a parabolic
function of time in negative binomial regression models. To detect areas with
increased leprosy detection rates we used spatial scan statistics for cases detected
from 2005 to 2007 in the three regions where leprosy is still a public health problem.
Results All detection rate series except the one for the south region showed
statistically significant regression coefficients for time and time squared, showing an
initial increasing trend. Scan statistics detected 29 statistically significant spatial
clusters. These clusters cover 789 municipalities with a total of 51 904 cases detected.
Conclusion Time behaviour of the detection rate is probably a result of better access
to primary health care. According to spatial scan statistics, Brazil can be divided into
highly endemic areas, containing 11·2% of the total Brazilian population, with a mean
detection rate in 2007 of 76·4 per 100,000 inhabitants, and areas of much lower
endemicity, containing 88·8% of the population with a mean detection rate of 13·2.
Leprosy is concentrated in a small proportion of the Brazilian population.
Correspondence to: Maria Lucia F. Penna, Rua Ministro Raul Fernandes 180 ap. 40122260040 Rio de Janeiro, RJ,
Brazil (Tel: þ 55 21 92327390; e-mail: [email protected])
332
0305-7518/09/064053+13 $1.00
q Lepra
The epidemiological behaviour of leprosy in Brazil
333
Introduction
In 1991, 10 years after the introduction of multi-drug therapy (MDT), the World Health
Organization (WHO) proposed at the 44th World Health Assembly (WHA) the elimination of
leprosy as a public health problem to be achieved by the year 2000. This target reflected an
optimism similar to that which followed the discovery of dapsone in the 1940s. Leprosy is
considered eliminated if the known case prevalence is , 1 per 10 000 inhabitants.
At the beginning of 2005, the elimination target was reached by most of the high burden
countries with the exception of nine countries, including Brazil.1
MDT turned a previously lifelong disease into a curable one, reducing the burden on
health systems. The target of elimination of leprosy as a public health problem through
universal and efficient MDT assumed the reduction of health care period as well as the
reduction in transmission. Unfortunately, after many years of using MDT, there is no
evidence of its impact on lowering transmission. Better knowledge of transmission
mechanisms is still needed to support a control strategy that may produce an important impact
on Mycobacterium leprae transmission.2 – 4
In a scenario where a highly efficacious treatment and government commitment to
provide access to drugs are present, once extensive treatment availability is reached, the
hidden prevalence cases are mainly responsible for transmission and the known prevalence
value variation in time will reflect the detection rate behaviour over time. Hence, reducing
transmission presupposes timely case detection to reduce the duration of the disease prior to
diagnosis. Timely case detection may raise the known prevalence value in the short term.
From that perspective, known point prevalence measures the disease burden to the health
system, not the community, at that point in time and its variation reflects mainly operational,
not epidemiologic, trends.5 – 6
In Brazil, leprosy has been a nationally notifiable disease for many decades. From 1999 a
unique surveillance information system (USIS) was adopted country-wide. Each reported
case is included by the municipality health secretariat in its database that is transmitted to the
Ministry of Heath.
The National Leprosy Control Programme (LCP) recommends treatment with WHO’s
MDT and distributes it free of charge. The amount of MDT blister packs needed is estimated
based on reported data, which guarantees an approximate relation between cases reported and
cases treated.
Brazil has 5560 municipalities, 26 states plus a Federal District, an estimated population
of 189·6 million inhabitants (2007), an area of 8,514,205 km2, and is divided into five
geographic regions based on climatic and historical aspects – North (N), Northeast (NE),
Southeast (SE), South (S) and Central-West (CW).
In 1989 a health system reform established the basis for a new unified health system (HS),
where the municipalities are the level of government responsible for HS administration. The
Ministry of Health is responsible for a percentage of financial support of the HS and for
setting standards and evaluating different aspects of health care and morbidity. Knowledge of
the spatial distribution of leprosy in the country may help resource distribution between
municipalities and reinforce the political commitment of municipal authorities and civil
society organisations to control leprosy in high-risk areas. Municipalities with a low detection
rate that are located in high-risk areas have to intensify case finding and treatment. Analysis
of the time behaviour of leprosy data allows for short-term prediction and raises explanatory
hypotheses about the past LCP execution and epidemiological behaviour of the disease,
334
M. L. F. Penna et al.
contributing to LCP planning. Our objective is to describe and discuss the time behaviour of
Brazilian leprosy data, and to detect high-risk areas in three geographic regions – N, NE and
CW – where leprosy is still a public health problem.
Material and Methods
DATA SOURCE
A special edition of the National Epidemiological Bulletin,7 published by the Ministry
of Health, provided new case data from 1980 to 1998 broken down by state and the
federal district. National LCP’s USIS database for 1999 to 2007 was used to produce
the number of cases detected by state and municipality. The LCP provided national
known prevalence data from 1990 to 2007 and known prevalence data for each state on
December 1, 2007.
Population data was estimated by the Brazilian Institute of Geography and Statistics
(IBGE) based on demographic census, and published by the Ministry of Health.8 The
geographic coordinates of the town hall were available from the IBGE website.9
STATISTICAL ANALYSIS
The historical data series of numbers of newly diagnosed leprosy cases by region in every
year from 1980 to 2007, and of numbers of national prevalence cases from 1990 to 2007 were
fitted to parabolic functions of time using negative binomial regression models, having
logarithm as a link function and the logarithm of the yearly population as an offset variable,
using Stata 9.0 software.10 The choice of negative binomial models instead of Poisson model
was due to overdispersion of the count data. The use of this statistical approach to deal with
heath surveillance data is well established.11 – 12 A parabolic function of time allows for
alterations in the ascending or descending trends. If the regression coefficient for variables
time and/or time squared did not show statistical significance at a 0·05 level, the series was
fitted to a linear function of time with the same statistical criteria. The fitted curves are
represented in the results as rates, although the statistical models had numbers of cases as a
dependent variable. The inclusion of the logarithm of the yearly population as an offset
variable led to the representation of the models as rates.
To detect areas with increased leprosy detection rate in the N, NE and CW regions we
used spatial scan statistic,13 using Satscan software.14 The sum of leprosy cases detected from
2005 to 2007 among residents and the resident population, expressed as person-years, of each
of the 2709 municipalities from the N, NE and CW regions were spatially allocated at
geographical coordinates of the town hall, placed in the biggest town or city of the
municipality. This approximation of the actual geographical location of the leprosy cases and
population was considered adequate, for these are the points with the highest population
density in each municipality. The polygons of municipal borders were used only for
presentation of the clusters on the map. The method scans the country area for possible
leprosy clusters without a priori specification of their location or size. A circular window
moves though the map with its centre at the coordinates of town halls. At each position the
radius of the circular window varied from zero to 500 km, the limit set by the authors, with
each window including different sets of neighbouring municipalities. A Poisson model
defined the presence of spatial clusters. Under the null hypothesis the expected number of
The epidemiological behaviour of leprosy in Brazil
335
13
cases in each area is proportional to the population in that area, expressed in person-years,
because cases were detected during 3 years. All possible clusters are tested for statistical
significance through log likelihood ratio test statistics accounting for multiple testing.15 The
log likelihood ratio defines the cluster order.
Incidence rates are calculated by 100,000 inhabitants to allow comparison with other
infectious diseases and to avoid wide perceptual variation being expressed by small numbers.
Prevalence data are calculated by 10 000 inhabitants for the elimination target was set in this
dimension, even though it is unusual to report data in different formats.
Results
Table 1 presents the main leprosy statistics for 2007, for the country and its geographic
regions.
It should be noted that N, NE and CW regions have high detection rates and known point
prevalence above the elimination target. These regions were responsible for 63·5% of all
Brazilian leprosy cases detected in 2007 and 73·3% leprosy cases detected from 2005 to 2007,
while holding only 43·2% of the Brazilian population.
The estimates for the fitted model for detected case series as a function of time by region
can be seen in Table 2.
The observed and the predicted values are presented in Figure 1.
The residual analysis showed proper adjustment of the models.
All series but the one done on the S region showed statistically significant regression
coefficients for time and time squared, with opposite direction. When fitted to a linear
model with a logarithm as a link function, S region data did not show a statistically
significant trend. It is important to notice that data from NE region present the biggest
increase in detection rates over the period of the series, what is represented by its regression
coefficient for time, which is much higher than those for the other regions (Table 2). This
region also reaches the maximum point of the fitted curve later than the others, although
its detection rate values are smaller than those of the N and CW regions in the same
period (Figure 1).
Figure 2 shows the known prevalence graphic from 1990 to 2007 and the curve estimated
by the statistically significant fitted quadratic model (data not shown).
Table 1. Cases in treatment in December, known prevalence, new cases detected, detection rate and Percentage of
multibacillary cases among new cases. Geographic Regions and Brazil, 2007
Region
North
Northeast
Southeast
South
Central-West
Brazil
Percentage of
Cases in treatment Known prevalence per New cases Detection rate per
multibacillary cases
in December
10 000 inhabitants
detected
100,000 inhabitants among new cases
7992
16 471
7209
1461
8416
41 549
5·21
3·16
0·89
0·53
6·23
2·19
8337
16 572
7869
1781
5567
40 126
54·34
31·75
9·76
6·44
41·19
21·19
51·48
51·67
54·64
68·44
58·18
53·86
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M. L. F. Penna et al.
Table 2. Parameters estimated for the negative binomial regression, Brazil and Regions
Parameter
Coefficient
Brasil
t
0·071618
20·00145
t2
Constant
29·12559
Alfa*
0·007038
North region
t
0·055436
20·00121
t2
Constant
27·88732
Alfa
0·014481
Northeast region
t
0·159074
20·00309
t2
Constant
210·01
Alfa
0·006406
Southeast region
t
0·031204
20·00097
t2
Constant
29·07372
Alfa
0·009373
South region
t
20·02293
0·000523
t2
Constant
29·23146
Alfa
0·040008
Central west region
t
0·096484
20·00246
t2
Constant
28·30104
Alfa
0·011545
*
Std. Error
z
P value
95% Conf. Interval
0·008562
0·0003
0·051533
0·001923
8·36
24·82
2177·08
0·000
0·000
0·000
0·054836
20·00203
29·2266
0·004119
0·0884
20·00086
29·02459
0·012024
0·012299
0·000433
0·073759
0·003984
4·51
22·8
2106·93
0·000
0·005
0·000
0·031331
20·00206
28·03189
0·008446
0·079541
20·00036
27·74276
0·024829
0·008257
0·000287
0·05049
0·0018
19·26
210·79
2198·26
0·000
0·000
0·000
0·14289
20·00365
210·109
0·003693
0·175258
20·00253
29·91104
0·011112
0·010054
0·000352
0·060346
0·002577
3·1
22·76
2150·36
0·002
0·006
0·000
0·011498
20·00166
29·19199
0·005469
0·050909
20·00028
28·95544
0·016066
0·020918
0·000729
0·125778
0·010967
21·1
0·72
273·39
0·273
0·473
0·000
20·06393
20·00091
29·47798
0·023378
0·018072
0·001952
28·98493
0·068468
0·010897
0·000382
0·06606
0·003214
8·85
26·45
2125·66
0·000
0·000
0·000
0·075125
20·00321
28·43051
0·006689
0·117842
20·00171
28·17156
0·019924
Alfa ¼ dispersion parameter.
Based on new cases detected and reported from 2005 to 2007 in the N, NE and
CW regions, scan statistic detected 29 statistically significant clusters. These clusters contain
789 municipalities, 14·2% of all Brazilian municipalities, with 11·2% of the national
population in 2007 and had 51 904 cases detected in the period, 42·5% of all Brazilian cases
from 2005 to 2007 50·8% of them MB. Table 3 presents some characteristics of each cluster
and their positions are shown in the map in Figure 3.
It is clear from this map that most extensive total area of the clusters lies in the Brazilian
Amazon (BA). The areas in BA correspond to 72·8% of municipalities that fall inside a
cluster. The clusters in the Brazilian Amazon reach into the neighbouring dry savannah area
in the south and in the east of BA, where there are other clusters. Of the clusters outside of the
BA, two along the coastal areas: cluster 7 (Table 3) in the south of the NE region covers
a region with a hot, humid climate with remnants of the Atlantic rainforest and cluster 8,
with a small area, corresponds to Recife’s metropolitan region, the third largest metropolitan
area in Brazil, that attracts poor migrants from dry rural areas of the northeast region.
The southernmost cluster, cluster 13, is in a humid climatic area.
The epidemiological behaviour of leprosy in Brazil
Brazil
30
28
26
24
22
20
18
16
14
12
10
1980 1988 1996 2004
1984 1992 2000
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Southeast region
18
17
16
15
14
13
12
11
10
9
90
North region
80
40
337
Northeast region
35
30
70
25
60
20
50
15
10
40
5
30
1980 1988 1996 2004
1984 1992 2000
0
1980 1988 1996 2004
1984 1992 2000
South region
14
13
12
11
10
9
8
7
6
5
1980 1988 1996 2004
1984 1992 2000
Central-West region
75
70
65
60
55
50
45
40
35
30
25
1980 1988 1996 2004
1984 1992 2000
Figure 1. Leprosy detection rates per 100,000 inhabitants. Observed (dots and dashed lines) and estimated by the
model, Brazil and geographic regions, 1980 to 2007.
Discussion
The models fitted to the historical series show that the increasing rate of detection has been
reduced over time (a positive estimate for t and a negative estimate for t 2). The same happens
in the opposite direction with the decreasing series of prevalent cases.
The reported detection rate reflects operational variations in time, some known and others
unknown. Unplanned operational variation is not stable over time, meaning that it does not
vary in the same direction and in the same proportion year after year, therefore it cannot be
responsible for a two-decade long trend. With innumerable factors influencing the behaviour
of a system in different directions, the resulting variation can best be described through
probability theory16 which allows the interpretation of fitted curves as the actual trends.
A hypothesis to explain time behaviour of the detection rate in Brazil and its regions, with
exception of S region, is the health system reform implemented in the last 20 years. As a
result of the reform, access to primary health care units has improved mainly in rural areas
and small towns, improving the diagnosis of leprosy.17 This fact by itself could explain the
ascendant tendency of the leprosy detection rate in the first part of the series. If this hypothesis
is true, we would have a persistent reduction of the hidden prevalence produced by a shorter
timeframe between the onset of symptoms and diagnosis. However, the decline of the hidden
prevalence has its limits because, even in ideal situations, the diagnosis of skin diseases
depends on the cultural importance given to skin lesions, as well as health-seeking habits
among the population. An example of this is the period of time a patient waits to self-present
for a cutaneous lesion or any other clinical sign that is not interpreted as threatening. Once the
limit in reduction of hidden prevalence is met within the current health system, the new case
338
M. L. F. Penna et al.
22
20
18
16
14
12
10
8
6
4
2
0
1990
1992
1994
1996
1998
2000
2002
2004
2006
Figure 2. Leprosy known prevalence per 10 000 inhabitants. Observed (dots and dashed lines) and estimated by the
model, Brazil, 1990 a 2007.
detection rate tends to reflect the real incidence of the disease. The highest point of the fitted
curves possibly reflects the moment when the access to diagnosis stopped increasing.
The South region is a region that already had good public health care coverage in the early
1980s and it includes two states where leprosy is rare: Santa Catarina and Rio Grande do Sul
have detection rates of 3·6 and 1·7 per 100,000 inhabitants, respectively.
In the present paper we outline areas of high detection in the Brazilian geographic regions
(N, NE and CW) where the elimination target has not been reached. In our first efforts to
detect high-risk clusters for the entire country, the cluster size limit was the inclusion of 50%
of the population at risk, default setting of SatScan software. The resulting most probable
cluster had a radius of 1825·8 km and included the entire N and CW regions, in addition to
part of the NE region. Of course such a cluster reveals less about the spatial distribution of
leprosy than traditional analysis by state and region, but points to the importance of these
three regions relative to the S and SE regions.
In a previous spatial distribution study for the same period18 clusters were defined for the
whole of the country. The result was 10 clusters including 53·5% of all leprosy cases detected
cases in Brazil, 91·3% of them in N, NE and CW regions. The cases outside these regions
were part of a cluster that overlap current cluster 7 and included municipalities in the SE
region in addition to those from the NE region.
While the definition of a cluster is relatively easy, the same is not true for the definition of
its outer limits. Kulldorff11 advises that clusters be made as small as possible, for a high-risk
cluster may sustain the statistical significance when low risk neighbourhoods are included in
the cluster.
The epidemiological behaviour of leprosy in Brazil
339
Table 3. Statistically significant clusters of leprosy defined using spatial scan statistics. North, Northeast and CentralWest regions, 2005–2007
Cluster
Cluster central municipality, state
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
Lajeado Novo, MA
Sta Rita Trivelato, MT
N. Brasilândia Oeste AC
Porangatu, GO
Cariús, CE
Queimada Nova, PI
Porto Seguro, BA
Olinda, PE
Barreiras, BA
Itaituba, PA
Cajazeiras, PB
Edéia, GO
Naviraı́, MS
Sena Madureira, AC
Caroebe, RR
Glória, BA
Mossoró, RN
Umirim, CE
Pinhão, SE
Iaçu, BA
Sobral, CE
Conceição, PB
Morpará, BA
Itabaiana, SE
Bom Jesus da Lapa, BA
Paramoti,CE
Catolé do Rocha, PB
Serra do Navio, AP
Araci, BA
Radius (km)
no. mun*
486·71
482·89
263·94
213·03
29·48
136·81
82·12
5·76
0
0
0
92·79
0
469·57
240·95
8·57
0
0
0
0
0
0
69·87
9·14
19·56
30·22
0
467·94
0
344
134
47
61
3
39
9
2
1
1
1
35
1
31
9
2
1
1
1
1
1
1
4
2
2
5
1
49
1
LLR*
7444·6260
3321·7336
932·4814
773·8566
390·1331
294·2229
273·1821
269·9043
264·2829
185·3388
161·5285
132·4206
127·1899
87·6791
67·7714
57·1981
54·3137
46·1540
41·6441
34·0813
28·5916
22·4099
21·9199
21·2532
18·5710
15·5460
14·6462
12·9477
12·3394
P value
Relative risk
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·001
0·002
0·002
0·003
0·004
2·85
2·89
2·48
2·78
3·98
1·87
2·24
1·55
3·33
3·33
3·94
1·58
4·11
1·45
1·65
2·04
1·71
3·69
6·24
2·7
1·58
2·86
1·71
1·66
1·74
1·5
2·1
1·11
1·73
*
no. mun ¼ number of municipalities in cluster; LLR ¼ log likelihood ratio; relative risk for the clusters
compared to the rest of N, NE and CW regions.
Working within highly endemic regions makes the null hypothesis value higher than
working with the whole country. This allows greater specificity for the cluster limits resulting
in smaller clusters with higher risk. In the previous study, current clusters 2 and 4 (Figure 3)
were included in a single cluster; this also happened with clusters 9, 23 and 25, clusters 5, 6,
11, 22 and 16, and clusters 28 and 10. It is worth noting that the two most probable clusters
overlap in both analyses (clusters 1 and 2 had a larger radius in the analysis including the
whole country, 493·4 and 499·2 km respectively, compared to 486·7 and 482·9 km in this
analysis).
The intention to locate more accurately the high-risk areas justifies the present strategy,
while the previous result with fewer and more extensive clusters that encompassed two or
more of the present ones, made it easier for the LCP to establish, for the first time, priorities
based on areas of higher risk. Some years ago the municipalities which held higher priority
for the national LCP were defined based on the number of detected cases. Listing Brazilian
municipalities by population, the 250 Brazilian municipalities with the highest population
have 42·5% of leprosy cases detected from 2005 to 2007, the same case proportion of those
340
M. L. F. Penna et al.
N
W
E
S
15
28
5 21
1
10
14
6
3
2
4
12
9
23 e 25
23
26
17
27
11
8
22
16
24 28
29
20
7
13
2000
0
2000
4000 Kilometers
Figure 3. Brazil in South America Map with high detection clusters* (dark grey areas) of North, Northeast and
Central-West regions (light grey areas) and states limits of the Brazilian Amazon (black line). *clusters identified by
rank as in Table 3.
included in high-risk clusters comprising 789 municipalities described in this paper.
The choice to establish national priorities areas based on the number of cases detected, and
not on disease risk, is more attractive given that it involves fewer political players and more
powerful municipalities, such as those within the metropolitan areas. On the other hand,
establishing priorities based on areas with higher risk should produce better results because it
will involve neighbouring municipalities. This should create a larger epidemiological impact
and probably more political commitment from local authorities, since infectious diseases can
propagate into regions independent of geographic limits or political unities. High-risk areas
are, in theory, more vulnerable to leprosy control actions than low-risk areas, considering the
impact on transmission.
What drew our attention in the results was the geographical contiguity of the big clusters
in BA, which strongly suggested the existence of high-risk geographical areas for leprosy.
Therefore, establishing priority areas based on the total number of leprosy cases or based on
risk, even though the areas may have the same number of cases, are parts of different
strategies to reach leprosy control.
According to spatial scan statistics, Brazil can be divided into highly endemic areas,
containing 11·2% of the Brazilian population, with a mean detection rate in 2007 of 76·4 per
100,000 inhabitants, and an extensive, less endemic area, containing 88·8% of the population
with a mean detection rate of 13·2, rate ratio of 5·8. The highly-endemic clusters cover a wide
geographical area, albeit sparsely populated. This suggests a high concentration of leprosy
cases in certain parts of Brazil.
The epidemiological behaviour of leprosy in Brazil
341
For comparison, the highest national detection rate reported to the World Health
Organization in 2006 was from Micronesia with a rate of 136·04 and Papua New Guinea with
63·95 per 100,000 inhabitants.19 It should be highlighted that the total Papua New Guinean
population was 602,000 in 2006 and the Micronesian 111,000, while the Brazilian population
in the cluster areas was 21,185,385 in 2007.
Most of the total area of the clusters is in the Brazilian Amazon that has been recognised
as a highly endemic leprosy area, for some time. In the 19th Century there were reports about
the serious epidemiological situation among native peoples in the state of Pará.20 In 1913,
Oswaldo Cruz, then head of the Brazilian Public Health Division, recognised the unusually
high frequency of leprosy in the Amazon River basin.21 Agrı́cola22 in 1973 pointed that the
state of Acre, situated in the western part of the Brazilian Amazon, had the worst
epidemiological conditions and that Brazilian northeast states, with a semiarid climate, had
the lowest known prevalence rate. Nowadays, states within the Brazilian Amazon still report
the highest detection rate, but the Northeast region was replaced by South region as the one
with the least leprosy.
It is most likely that leprosy was introduced into the Amazon in the 18th Century, when
colonisation settlements started an epidemic among the indigenous population due to
their lack of previous exposure to infection.23 The disease is likely to have spread slowly
throughout the area due to its relative isolation and expansive distances. Until 1970, the
most heavily populated areas of the Amazon could have been described as population
islands because the only possible means of transportation were riverboats or small aircraft.
For this reason, the epidemiological behaviour of leprosy in the Amazon was similar to that
seen in the Pacific islands where similar detection rates were observed. But unlike the Pacific
Islands, the Amazon is a large region which has seen increased population and road
communication with other parts of Brazil and neighbouring countries over the last three
decades.
The first highway to cut through the Amazon rainforest with a distance of 2039 km was
completed in 1974. Today, there are 25 900 km of federal highways in the Brazilian
Amazon.24 Development projects and small-scale strip mining, mostly for gold, brought large
numbers of people to the region. Over the years between 1960 and 1980, the population
contingent of the Amazon increased from 2·6 million to 11 million.
Despite the fact that the population increased largely due to an influx of migrants from
less endemic regions, leprosy remains highly endemic and is now concentrated in the areas of
greatest population. Clusters of high leprosy detection in the Amazon overlap areas of mining
and agrarian development projects. Although agricultural development led to the relatively
stable settlements of new groups in the area, the small-scale mining was undertaken by an
extremely mobile population. These strip miners would return seasonally to their home
regions while others would move from one mine to another.25 On the other hand, individuals
involved in unsuccessful land settlement projects often sell their plots and move on to a new
agrarian development project.26 Families successful in land cultivation periodically return to
their city of origin as a sign of social success.27 Certainly, this movement has led to imported
leprosy cases in other regions and raises the question of whether leprosy might re-emerge in
other parts of the country. It is important to note that the NE region clusters are related to
areas where there are currently many agrarian development projects, and that this is the
region with the highest increase of detection since 1980.
The spatial distribution of leprosy in Brazil shows that 88·8% of the Brazilian population
lives in areas where the mean leprosy detection rate in 2007 was 13·16 per 100,000
342
M. L. F. Penna et al.
inhabitants (parts of the country outside the high-risk cluster areas) and 11·20% of the
population lives in areas with a leprosy detection rate in 2007 of 76·4 per 100,000 inhabitants,
i.e., highly endemic areas. To deepen our knowledge on the causes of this spatial behaviour,
the authors are at present analysing these high-risk areas with social determinants of health,
including the study of the distribution of traditional social and economic indicators, such as
literacy, basic sanitation, income, employment, and recent economic development and
ecosystem modifications.
In the cluster areas, the proportion of multibacillary (MB) cases among the total reported
was 50·8%, which means that the lowest mean duration of leprosy treatment in these areas is
0·75 of a year, with the assumption that there is no delay in the treatment timetable and that all
patients will be cured. This treatment duration and a detection rate of 76·4 per 100,000
inhabitants (7·64 per 10 000 population) means that the lowest point prevalence possible is
5·73 per 10 000 inhabitants. In other words, if the present proportion of MB cases remains
stable in these high-risk areas, detection rate must be lower than 13·3 per 100,000 inhabitants
(1·33 per 10 000 population) to reach the target of 1 per 10 000 inhabitants known point
prevalence, meaning a detection rate reduction of 82·6%. Surely such a reduction cannot
happen quickly.
The actual frequency of leprosy in these highly endemic areas in Brazil reinforces the
need of a permanent effort to control the disease. Brazil must continue to invest in leprosy
diagnosis and treatment, mainly in these areas, even though its impact on transmission is still
unknown. This strategy clearly reduces the sources of infection but it is not known if it will
happen soon enough to prevent the majority of secondary infections.28 – 29 Besides the
investments in the development of new strategies, the partners of leprosy control in
government and civil society should bear in mind that socioeconomic development has a
known impact on leprosy’s epidemiological behaviour.30 Evidence about the social
determinants of leprosy may support public policies that affect the social environment and
economic development.
The changes on leprosy magnitude in those areas will probably be apparent only after
many years of work, but it is important to monitor its behaviour. Improvement of the health
information system is an important task to avoid unreliable data.31 Among leprosy statistics,
the prevalence of people undergoing treatment has been used for many decades, but it does
not have an obvious interpretation, as it is the approximate product of treatment duration and
detection rate, whose values can vary simultaneously. Detection rate, the MB proportion
among new cases and effectiveness of leprosy treatment variations will affect the known
prevalence value. While most health workers dealing with leprosy control are familiar with
this frequency measure, its increase or stability does not clearly point to what should be
changed to achieve a better performance. The follow up of cohort treatment results, on the
other hand, is a powerful tool to increase the involvement of health workers responsible for
patient care as part of their work evaluations. A much simpler frequency measure is the
detection rate, although, as already pointed out, it reflects the operational variations of access
to diagnosis. It is easier to interpret a value that is influenced by an action – diagnosis – than
one that is influenced by two – diagnosis and treatment.
In conclusion, the present findings show that leprosy is an important heath problem
for 11·2% of the Brazilian population, requiring patient care to prevent disabilities
and permanent control activities to break the chain of transmission. Health interventions
will be effective only if they involve local authorities, communities and health workers
and are planned based on the local reality and scientific knowledge. Investment in
The epidemiological behaviour of leprosy in Brazil
343
national research is also essential since all national recommendations must be based on solid
scientific evidence.
Competing interest statement
All authors work for the Brazilian Ministry of Health and are committed to leprosy control.
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