Mortality of Chagas' disease in Brazil: Spatial patterns and definition of high-risk areas

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DOI: 10.1111/j.1365-3156.2012.03043.x · Source: PubMed
Abstract
Objective: To describe patterns of spatial distribution of mortality associated with Chagas' disease in Brazil. Methods: Nationwide study of all deaths in Brazil from 1999 to 2007, where Chagas' disease was recorded as a cause of death. Data were obtained from the national Mortality Information System of the Ministry of Health. We calculated the mean mortality rate for each municipality of residence in three-year intervals and the entire period. Empirical Bayes smoothing was used to minimise random variation in mortality rates because of the population size in the municipalities. To evaluate the existence of spatial autocorrelation, global and local Moran's I indices were used. Results: The nationwide mean mortality rate associated with Chagas' disease was 3.37/100 000 inhabitants/year, with a maximum of 138.06/100 000 in one municipality. Independently from the statistical approach, spatial analysis identified a large cluster of high risk for mortality by Chagas' disease, involving nine states in the Central region of Brazil. Conclusion: This study defined geographical priority areas for the management of Chagas' disease and consequently reducing disease-associated mortality in Brazil. Different spatial-analytical approaches can be integrated to provide data for planning, monitoring and evaluating specific intervention measures.
Mortality of Chagas’ disease in Brazil: spatial patterns and
definition of high-risk areas
Francisco Rogerla
ˆ
ndio Martins-Melo
1
, Alberto Novaes Ramos Jr
1
, Carlos Henrique Alencar
1,2
, Wolfram Lange
3
and Jorg Heukelbach
1,4
1 Department of Community Health, School of Medicine, Federal University of Ceara
´
, Fortaleza, Brazil
2 Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
3 Institute of Geography, University of Cologne, Ko
¨
ln, Germany
4 Anton Breinl Centre for Public Health and Tropical Medicine, James Cook University, Townsville, Australia
Abstract objective To describe patterns of spatial distribution of mortality associated with Chagas’ disease in
Brazil.
methods Nationwide study of all deaths in Brazil from 1999 to 2007, where Chagas’ disease was
recorded as a cause of death. Data were obtained from the national Mortality Information System of
the Ministry of Health. We calculated the mean mortality rate for each municipality of residence in
three-year intervals and the entire period. Empirical Bayes smoothing was used to minimise random
variation in mortality rates because of the population size in the municipalities. To evaluate the exis-
tence of spatial autocorrelation, global and local Moran’s I indices were used.
results The nationwide mean mortality rate associated with Chagas’ disease was 3.37 100 000
inhabitants year, with a maximum of 138.06 100 000 in one municipality. Independently from the
statistical approach, spatial analysis identified a large cluster of high risk for mortality by Chagas’
disease, involving nine states in the Central region of Brazil.
conclusion This study defined geographical priority areas for the management of Chagas’ disease and
consequently reducing disease-associated mortality in Brazil. Different spatial-analytical approaches can
be integrated to provide data for planning, monitoring and evaluating specific intervention measures.
keywords Chagas’ disease, spatial analysis, ecological studies, epidemiology, mortality, multiple
causes of death, Brazil
Introduction
Chagas’ disease remains a neglected tropical disease and
a public health problem with social significance and
economic implications in most Latin American countries
(Moncayo & Silveira 2009; Coura & Vin
˜
as 2010). Recent
estimates indicate that 10–15 million people are chroni-
cally infected in Latin America, and about 60–100 million
are at risk of becoming infected (WHO 2002, 2010; Dias
2007; Coura & Dias 2009). In Brazil, there are 2–3 million
individuals in the chronic phase, 1 3 of them suffering
from the cardiac and digestive form, which causes high
morbidity and mortality (Akhavan 2000; WHO 2002;
Almeida et al. 2011). In Latin America, about 14 000
people die annually from Chagas’ disease (Schmunis 2007).
Brazil accounts for approximately 43%, with 6000 annual
deaths (Martins-Melo et al. 2012). The chronic form of
the disease causes 97% of Chagas deaths in Brazil,
especially the chronic cardiac form (85%), followed by the
chronic digestive form (9%) (Martins-Melo 2011; Martins-
Melo et al. 2012).
The increasing globalisation of Chagas’ disease owing to
migration of infected people has led to a greater awareness
worldwide (Coura & Vin
˜
as 2010; Schmunis & Yado
´
n
2010), especially in the United States and Europe, where
migration of infected individuals from endemic countries
has become an emerging public health problem (Schmunis
& Yado
´
n 2010).
Studies on the occurrence of infectious diseases
according to their spatial distribution have become
important for public health, especially for planning and
performance of disease control measures (Nascimento
et al. 2007). In fact, Geographic Information Systems
(GIS) have contributed to the effectiveness of interven-
tions through the analysis of spatial data on health (Khan
et al. 2010). The use of GIS for monitoring vector-borne
diseases such as Chagas’ disease can identify patterns of
spatial distribution of incidence or mortality in defined
Tropical Medicine and International Health doi:10.1111/j.1365-3156.2012.03043.x
volume 17 no 9 pp 1066–1075 september 2012
1066 ª 2012 Blackwell Publishing Ltd
geographic regions. We describe patterns of spatial
distribution and areas of high risk for mortality related to
Chagas’ disease in Brazil.
Materials and methods
Study area
The study was conducted in Brazil, the largest country
in South America and the fifth of the world in size (8.5
million km
2
). Brazil’s population is approximately
190 million, with a density of 22.40 inhabitants km
2
in
2011. The Brazilian economy is the largest in Latin
America and the sixth largest in the world by Gross
Domestic Product (GDP). However, the distribution of
income is extremely unequal, with tremendous differences
between regions and rural urban areas (for details see
United Nations Development Programme - UNDP Brazil;
http://www.pnud.org.br). The country is divided politically
and administratively into 27 federal units (26 States and
one Federal District) and 5565 municipalities (Figure 1).
The Federation is further grouped into five major regions
(North, Northeast, Southeast, South and Central-West)
with different geographic, socio-economic and cultural
characteristics. The municipalities are a territorial area
with legal autonomy, being the smallest autonomous units
of the Federation.
Study population and design
We performed a nationwide ecological study by spatial
analysis using deaths related to Chagas’ disease aggregated
by municipality of residence. We included all deaths in
Brazil from 1999 to 2007, where Chagas’ disease was
recorded as a cause of death. In this study, ‘cause of death’
was defined as the notification of Chagas’ disease in any
line or part of the death certificate, regardless of being
classified as an underlying, primary, secondary or con-
tributing cause of death (so-called multiple causes of death)
(Santo 2009).
We obtained data from all 5565 Brazilian municipalities
through the Mortality Information System (Sistema
Nacional de Mortalidade - SIM) of the Brazilian Ministry of
Health. SIM data are publicly available and were obtained
from the website of the Department of Informatics of the
Unified Health System (DATASUS; http://tabnet.data
sus.gov.br/tabdata/sim/dados/cid10_indice.htm). SIM con-
tains information from death certificates, filled out by
medical professionals. Chagas’ disease as a cause of death
corresponds to the clinical forms included in the category
NORTH
NORTHEAST
CENTRAL-WEST
SOUTHEAST
SOUTH
Peru
Bolivia
Argentina
Colombia
Chile
Paraguay
Venezuela
Guyana
Uruguay
Suriname
French
Guiana
AM
PA
MT
BA
MG
PI
MS
MA
GO
TO
RS
SP
RO
RR
PR
AC
CE
AP
PE
SC
PB
RN
ES
RJ
AL
SE
DF
N
Figure 1 The country of Brazil divided
into 26 states and one Federal District (DF),
situated in South America.
Tropical Medicine and International Health volume 17 no 9 pp 1066–1075 september 2012
F. R. Martins-Melo et al. Mortality of Chagas’ disease in Brazil
ª 2012 Blackwell Publishing Ltd 1067
B57, according to the Tenth Revision of the International
Statistical Classification of Diseases and Related Health
Problems (ICD-10) (WHO 2007).
The process of obtaining data about deaths initially
involved individual data sets for each Federation Unit and
year. Consequently, a total of 243 data sets with about
9 million entries were downloaded and processed (one data
set for each of the 27 federal states and each of the 9 years,
from 1999 to 2007). First, we checked data sets for
completeness in relation to the total number of deaths.
Field codes from different data sets were standardised and
variables not considered in the analysis eliminated. Then,
we identified all death certificates in which Chagas’ disease
was recorded in any line of the certificate as cause of death
(both underlying and associated causes). We created new
variables for causes of death, as in many cases more than
one cause was noted in a line.
The population data for the period were obtained from
the Brazilian Institute of Geography and Statistics (IBGE),
based on a national population census from 2000 and
official estimates for the years between censuses (1999 and
2001–2007) (available at http://www.ibge.gov.br).
Statistical analysis
Spatial analysis methods and GIS techniques were used to
evaluate the geographic distribution of mortality rates
related to Chagas’ disease. We applied the strategy of
analysis areas data (polygons). We adopted the munici-
pality of residence as the unit of analysis, to obtain greater
accuracy of the differences within and between regions and
to reveal the priority areas for interventions.
Two strategies were applied as a basis for the construc-
tion of spatial distribution maps of deaths related to
Chagas’ disease. First, mortality rates were estimated as
three-year means (1999–2001, 2002–2004 and 2005–
2007), and for the total period. Mortality rates were
calculated as follows: the mean number of deaths related to
Chagas’ disease for each period of the triennium or the
total period as the numerator; and the population in the
middle year of each period (2000, 2003, 2005) as the
denominator, by 100 000. Then, to reduce random vari-
ability and to provide greater stability of mortality rates
mainly in small municipalities, we re-estimated mortality
rates by Empirical Bayes Smoothing, to minimise this
variation. This method, when estimating the risk of a small
area, uses information from neighbouring areas that
form the region under study, reducing random fluctuation
of the rates. We used the weighted average between the
measured values and the overall average rates, in which the
weight average is inversely proportional to the population
of the region (Assunc¸a
˜
o et al. 1998).
After descriptive spatial analysis by drawing mortality
maps, we assessed the presence of spatial dependence using
Global Moran’s I index on the smoothed rates, to identify
areas with clusters of similar risks for the outcome of
interest, that is, mortality caused by Chagas’ disease. This
method of global spatial autocorrelation measures the
correlation of a variable with itself in space. Values close to
zero indicate an absence of spatial correlation; positive
values indicate positive spatial autocorrelation, that is, the
existence of similarity between neighbouring municipali-
ties; and negative values show negative spatial autocorre-
lation (Cliff & Ord 1981).
Then, we assessed local autocorrelation [Local Index
of Spatial Association (LISA)] by Local Moran’s index
(Anselin 1995). The Local Moran’s Index determines the
dependence of local data in relation to neighbours and
identifies patterns of spatial association to characterise the
occurrence of clusters of polygons (municipalities) (Anselin
1995).
To identify critical or transition areas, we used the
Moran Scatterplot Map, based on Local Moran’s Index, to
compare the value of each the municipality studied with
neighbouring municipalities and to display spatial depen-
dence and spatial patterns. The quadrants generated in this
technique are interpreted as follows: Q1 (positive values,
positive means) and Q2 (negative values, negative means),
indicating points of positive spatial association or similar
to neighbours, that is, representing municipalities with
high and low mortality rates also surrounded by munici-
palities with high and low coefficients, respectively; Q3
(positive values, negative means) and Q4 (negative values,
negative means), indicating points of negative spatial
association, that is, municipalities with low and high
mortality rates surrounded by municipalities with high and
low rates, respectively. The first two categories represent
areas of agreement and the last two transition areas
(Anselin 1995).
For the spatial representation of the Moran Scatterplot
Map, Moran Maps were used, considering only statisti-
cally significant values (P < 0.05). High risk for mortality
caused by Chagas’ disease areas was considered when
formed by municipalities covered by class Q1 of the Moran
Map.
The neighbourhood matrix was used to estimate the
spatial variability of data, which considers only the
border municipalities (neighbours of first order) for
analysis. The size of the radius used in the analysis of
spatial autocorrelation was defined by the correlation
between the highest z-score of the Global Moran Index
and the distance band. After running the tool from 350 to
800 km, we identified a radius of 640 km to present the
best z-score (ESRI 2010).
Tropical Medicine and International Health volume 17 no 9 pp 1066–1075 september 2012
F. R. Martins-Melo et al. Mortality of Chagas’ disease in Brazil
1068 ª 2012 Blackwell Publishing Ltd
Digital maps were obtained from the cartographic basis
of IBGE, in shape file (.shp) format, compatible with
ArcGIS software version 9.3 (Environmental Systems
Research Institute ESRI, Redlands, CA) and TerraView
software version 4.1 (Instituto Nacional de Pesquisas
Espaciais INPE). These software packages were used for
processing, analysis and presentation of cartographic data
and to calculate the indicators of global and local spatial
autocorrelation as well as for the construction of thematic
maps.
Ethics
This study is based on secondary data, and all presented
information is public domain. No variables allowed the
identification of individuals.
Results
Spatial distribution of Chagas’ mortality
Between 1999 and 2007, there were 53 930 deaths in
which Chagas’ disease was mentioned on any part of the
death certificate. 55% (3050 5565) of municipalities
reported at least one death related to Chagas’ disease in
this period. The mean mortality in Brazil was 3.37 per
100 000 inhabitants year, ranging from 0.0 to 138.06
deaths per 100 000 inhabitants.
Figure 2 presents spatial distribution of mean mortality
rates in three different periods. The Bayesian method
generated more stable corrected rates, as shown in
Figure 3. In general, both Figures show a clear concen-
tration of municipalities with high mortality in the central
region of Brazil, including the Federal District, major
parts of Goia
´
s state and the so-called Tria
ˆ
ngulo Mineiro
region of north-western of Minas Gerais and northern of
Sa
˜
o Paulo states. Some areas of Mato Grosso do Sul,
Bahia and Tocantins state were also affected. In addition,
there were some small high-mortality areas in the
bordering area of Parana
´
and Sa
˜
o Paulo states, in
southern Piauı
´
and in north-central regions of Bahia state.
The mortality patterns in these regions did not change
considerably over time.
Table 1 details demographic data and death rates of the
24 municipalities with 50 or more deaths caused by
Chagas’ disease per 100 000 inhabitants. Most of these
municipalities are located in the states Minas Gerais (15)
and Goia
´
s (7). The municipality of Abadia dos Dourados
(MG) had the highest mortality related to Chagas disease
with a relative risk of 46 as compared to the country’s
average (Table 1). The 24 municipalities are responsible
for 3.0% of deaths related to Chagas’ disease as multiple
causes of death, but only account for 0.14% of the
Brazilian population.
Spatial clusters of high risk for mortality caused by Chagas’
disease
Spatial autocorrelation, as expressed by Global Moran’s I
index, is depicted in Table 2. For all periods, values for
mortality rates because of Chagas’ disease were highly
significant, evidencing spatial autocorrelation between
municipalities with similar patterns in Brazil.
Moran spatial index corroborated the findings of
clusters of municipalities in the descriptive maps
(Figures 2 and 3). During the progress of Chagas’ disease
mortality in the period, we identified a large cluster of
municipalities with high risk for mortality (Q1 High
High) in central Brazil. This cluster covered almost all
municipalities of Goia
´
s and Minas Gerais, the Federal
District and some municipalities of Sa
˜
o Paulo, Mato
Grosso, Mato Grosso do Sul, Tocantins, Piauı
´
and Bahia
state (Figure 4). Beside this major cluster, we identified
four smaller high-risk areas (Figure 4). Clusters of
municipalities with low mortality rates (Q2 Low Low)
were located in the South, in the Northeast and in the
North regions (Figure 4).
In the observation period, the main cluster was sur-
rounded by small clusters of municipalities with lower
values (Q4 Low High). There were also some munici-
palities with high values (Q3 – High Low) near clusters of
low values (Q2) – transition areas, with reduced mortality
rates. The cluster in southeast Piauı
´
State increased the
number of municipalities with high mortality rates in the
period 2005–2007 (Figure 4).
Discussion
This is the first systematic spatial analysis of Chagas’
disease, using mortality data. We identified spatial clusters
of municipalities with high mortality rates related to
Chagas’ disease in Brazil, in particular an extended risk
area encompassing nine states. Different spatial-analytical
approaches confirmed the geographic extension of this
cluster. We defined priority areas for intervention, appro-
priate management of patients with chronic infection and
the consequent reduction in cases or deaths, considering
epidemiological and operational conditions in the Brazilian
municipalities affected.
The spatial distribution of mortality associated with
Chagas’ disease was heterogeneous with the presence of
deaths in all Brazilian states. The inequality of the risk of
dying of Chagas’ disease among geographical areas was
identified by the presence of groups with high mortality
Tropical Medicine and International Health volume 17 no 9 pp 1066–1075 september 2012
F. R. Martins-Melo et al. Mortality of Chagas’ disease in Brazil
ª 2012 Blackwell Publishing Ltd 1069
rates in the states of Minas Gerais, Goia
´
s, Distrito Federal,
Sa
˜
o Paulo, Parana
´
, Mato Grosso, Mato Grosso do Sul,
Tocantins, Bahia and Piauı
´.
This geographical feature was
also observed in intra-regional variations between the
states that had the highest mortality rates for the period.
The state of Goia
´
s, for example, had the highest mortality
rates, but enormous variations between municipalities.
Municipalities with higher risk were usually surrounded by
others with higher rates and or intermediaries. On the
other hand, within groups of municipalities with high
rates, some presented lower rates than the national
average. This reveals that the spatial distribution of
mortality associated with Chagas’ disease is, similar to
other vector-borne diseases, heterogeneous and focal, even
in geographically close areas (Cecere et al. 2004; Vazquez-
Prokopec et al. 2005; Kitron et al. 2006; Atanaka-Santos
et al. 2007; Guimaraes et al. 2008; Mischler 2011; Parise
et al. 2011; Rollemberg et al. 2011).
Differences between municipalities may also have been
caused by underreporting because of problems in the local
health system, such as lack of access to specialised services
in these municipalities leading to migration of patients to
the major urban centres (Martins-Melo et al. 2012), owing
to an inability of health professionals to properly diagnose
Chagas’ disease, and inefficiency of the epidemiological
surveillance services.
2002 – 2004
1999 – 2001
N
N
Crude rate per
100 000 inhabitants
Crude rate per
100 000 inhabitants
0
>0.1 – 3.5
>3.6 – 5.0
>5.0 – 10.0
>10.0 – 15.0
>15.0 – 20.0
>20.0
0 500 1000 km250
0 500 1000 km250
2005 – 2007
1999 – 2007
N
N
Crude rate per
100 000 inhabitants
Crude rate per
100 000 inhabitants
0
>0.1 – 3.5
>3.6 – 5.0
>5.0 – 10.0
>10.0 – 15.0
>15.0 – 20.0
>20.0
0
>0.1 – 3.5
>3.6 – 5.0
>5.0 – 10.0
>10.0 – 15.0
>15.0 – 20.0
>20.0
0
>0.1 – 3.5
>3.6 – 5.0
>5.0 – 10.0
>10.0 – 15.0
>15.0 – 20.0
>20.0
0 500 1000 km250
0 500 1000 km250
Figure 2 Spatial distribution of mean mortality rates related to Chagas’ disease (per 100 000 inhabitants) based on multiple causes of
death by municipality, Brazil, 1999–2007.
Tropical Medicine and International Health volume 17 no 9 pp 1066–1075 september 2012
F. R. Martins-Melo et al. Mortality of Chagas’ disease in Brazil
1070 ª 2012 Blackwell Publishing Ltd
Our data show that spatial patterns of coefficients
between municipalities did not occur randomly. The global
Moran index showed autocorrelation of mortality associ-
ated with Chagas’ disease, and local Moran index identi-
fied clusters of municipalities with high mortality rates and
defined priority areas for surveillance and control of
Chagas’ disease in Brazil. In fact, Chagas’ disease as a focal
disease depends not only on the presence of vector, but also
on certain other conditions, such as access to health
services and poor living conditions (Silveira et al. 2011).
Local Moran statistic identified critical or high priority
areas formed by the municipalities covered by the class Q1
Moran Map. The local spatial autocorrelation analysis
performed revealed a pattern of extreme concentration of
municipalities with high rates of mortality associated with
Chagas’ disease in the central areas of Brazil.
In Brazil, Penna et al. (2009) have recently identified
disease clusters for prioritisation of control measures in
high-risk areas, defining areas with highest detection rates
of leprosy by using spatial scan statistics. Consequently,
the National Leprosy Control Program targeted these
areas as priority regions and is financing research projects
focussing on these areas (Alencar et al. 2012). A similar
approach is proposed to be performed by the National
Chagas’ Disease Control Program.
We found that there was an overlap of high risk for
mortality in areas with high rates of seroreactivity for
Chagas’ disease, and with high rates of infestation by the
previous main vector Triatoma infestans in the past
(Camargo et al. 1984, Passos & Silveira 2011, Silveira
et al. 1984, 2011). This pattern was observed in the area of
the central cluster, the clusters of southeast of Mato
1999 – 2001
N
Smoothed rate per
100 000 inhabitants
0
>0.1 – 3.5
>3.6 – 5.0
>5.0 – 10.0
>10.0 – 15.0
>15.0 – 20.0
>20.0
0
500
1000 km
250
1999 – 2007
N
Smoothed rate per
100 000 inhabitants
0
>0.1 – 3.5
>3.6 – 5.0
>5.0 – 10.0
>10.0 – 15.0
>15.0 – 20.0
>20.0
0
500
1000 km
250
2002 – 2004
N
Smoothed rate per
100 000 inhabitants
0
>0.1 – 3.5
>3.6 – 5.0
>5.0 – 10.0
>10.0 – 15.0
>15.0 – 20.0
>20.0
0
500
1000 km
250
2005 – 2007
N
Smoothed rate per
100 000 inhabitants
0
>0.1 – 3.5
>3.6 – 5.0
>5.0 – 10.0
>10.0 – 15.0
>15.0 – 20.0
>20.0
0
500
1000 km
250
Figure 3 Spatial distribution of mortality rates related to Chagas’ disease after smoothing by Bayesian Local Empirical method, based
on multiple causes of death by municipality, Brazil, 1999–2007.
Tropical Medicine and International Health volume 17 no 9 pp 1066–1075 september 2012
F. R. Martins-Melo et al. Mortality of Chagas’ disease in Brazil
ª 2012 Blackwell Publishing Ltd 1071
Grosso, north-central and Sa
˜
o-Francisco valley of Bahia
and the border between Sa
˜
o Paulo and Parana
´
(Passos &
Silveira 2011; Silveira et al. 2011). Despite the recent
elimination of the most important vector species in Brazil,
in these areas many people are still infected, as evidenced
by these high-risk areas for Chagas’ mortality. However,
these findings may be contradictory and vector presence
and prevalence of infection in humans in the past are
ineffective in predicting whether or not there were changes
in the patterns of mortality indicators in the municipalities
in the study period. This relationship is exemplified by the
observed pattern in Rio Grande do Sul in the extreme south
of Brazil, where the highest rates of infected T. infestans
were found (Passos & Silveira 2011; Silveira 2011; Silveira
et al. 2011), whereas mortality related to Chagas’ disease
in our study showed low values in this region. Future
studies will have to work on regional differences of
transmission, morbidity and mortality.
The interpretation of results must take into consider-
ation that there may be some limitations arising from the
notification and storage of data on mortality (Drumond &
Marcopito 2006). The use of secondary data may show
inconsistency in the quantity and quality of the informa-
tion. Another limitation refers to possible underreporting
of deaths (Santo 2009) that may have occurred despite the
important progress over the period under study, both in
the coverage of the Mortality Information System (SIM)
and in the quality of information of causes of death. We
included multiple causes of death to reduce this error. The
coverage (ratio of deaths reported and estimated) SIM also
Table 1 Municipalities with Chagas’ disease mortality rates 50 deaths 100 000 inhabitants, Brazil 1999 to 2007
Order Municipality (State) Population*
Deaths
(UC)
Deaths
(MC)
Rates
(UC) RR
Rates
(MC) RRà
1 Abadia dos Dourados (MG) 6422 75 80 129.43 46.39 138.06 40.96
2 Joseno
´
polis (MG) 4480 40 40 99.83 35.78 99.83 29.62
3 Carbonita (MG) 9340 82 87 98.96 35.46 105.00 31.15
4 Davino
´
polis (GO) 2069 15 17 79.02 28.32 89.56 26.57
5 Tapiramuta
´
(BA) 17 940 126 140 77.36 27.72 85.95 25.50
6 Cabeceiras (GO) 6734 45 51 73.00 26.16 82.73 24.54
7 Bambuı
´
(MG) 22 119 139 154 70.05 25.10 77.61 23.02
8Sa
˜
o Patrı
´
cio (GO) 1821 11 11 66.60 23.87 66.60 19.76
9 Engenheiro Navarro (MG) 6833 41 45 65.67 23.53 72.08 21.38
10 Coromandel (MG) 28 299 153 173 60.23 21.58 68.10 20.20
11 Nova Ame
´
rica (GO) 2253 12 12 59.65 21.37 59.65 17.70
12 Pedrino
´
polis (MG) 3230 16 18 58.38 20.92 65.68 19.48
13 Paineiras (MG) 4757 25 28 58.01 20.79 64.97 19.27
14 Unaı
´
(MG) 72 768 373 425 57.06 20.45 65.02 19.29
15 Bonfino
´
polis de Minas (MG) 6 213 32 35 56.68 20.31 61.99 18.39
16 Lagamar (MG) 7 577 38 42 55.68 19.95 61.54 18.26
17 Santa Rosa de Goia
´
s (GO) 3380 17 17 55.40 19.85 55.40 16.43
18 Fruta de Leite (MG) 6632 33 34 55.36 19.84 57.04 16.92
19 Jose
´
Gonc¸alves de Minas (MG) 4734 23 25 53.74 19.26 58.41 17.33
20 Sa
˜
o Domingos (GO) 9294 45 48 53.06 19.01 56.60 16.79
21 Biquinhas (MG) 2717 13 16 52.54 18.83 64.66 19.18
22 Sa
˜
o Gonc¸alo do Abaete
ˆ
(MG) 5227 25 30 52.30 18.74 62.76 18.62
23 Santana do Itarare
´
(BA) 5150 24 28 50.31 18.03 58.70 17.41
24 Carmo do Rio Verde (GO) 7656 35 40 50.00 17.92 57.14 16.95
TOTAL 247.645 1438 1596 64.40 23.08 71.48 21.21
UC, underlying cause; MC, multiple causes.
*Average population.
Calculated using the population of the year 2003.
àThe relative risk (RR) refers to the national mean.
Table 2 Global Moran’s index for the mean rates of mortality
related to Chagas’ disease and their significance levels (multiple
causes of death), Brazil 1999–2007
Period Global Moran¢s index P-value
1999–2001 0.3222 <0.0001
2002–2004 0.2725 <0.0001
2005–2007 0.2695 <0.0001
1999–2007 0.3755 <0.0001
Tropical Medicine and International Health volume 17 no 9 pp 1066–1075 september 2012
F. R. Martins-Melo et al. Mortality of Chagas’ disease in Brazil
1072 ª 2012 Blackwell Publishing Ltd
varies between regions in the country, especially in the
North and Northeast. Its coverage in 2001 was 82.3% for
the country as a whole, ranging from 96.1% in Rio Grande
do Sul and 48.8% in Maranha
˜
o (Ministry of Health of Brazil
2004). The proportion of deaths from ill-defined causes is
also unequally distributed between regions, rural and urban
areas, age groups and socio-economic strata (Drumond &
Marcopito 2006). However, we believe that this underre-
porting is at random and not associated with Chagas’
mortality.
We conclude that the use of spatial analysis tools for
defining priority areas is a feasible strategy for surveillance
and control of Chagas’ disease. Control of vector trans-
mission and recognition of Chagas’ disease as a chronic
condition by health authorities is needed. Clinical man-
agement of chronic disease, in primary care and reference
service, needs to be improved, and adequate access to
health services and social care for individuals with chronic
Chagas’ disease should be guaranteed. Integrated care of
chronic patients and intervention measures to reduce
morbidity and mortality should be established, considering
geographical areas of risk.
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Corresponding Author Jo
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Tropical Medicine and International Health volume 17 no 9 pp 1066–1075 september 2012
F. R. Martins-Melo et al. Mortality of Chagas’ disease in Brazil
ª 2012 Blackwell Publishing Ltd 1075
    • "After exclusion of Chagas disease-related deaths, a different risk area pattern was identified, in highly endemic areas for several other NTDs, such as schistosomiasis located mainly on the east coast of the Northeast region, visceral leishmaniasis mainly in endemic areas of the Northeast and North regions, and leprosy and dengue fever in the Central-West region from where most cases have been reported (Martins-Melo et al., 2015a, 2014b, 2015b). Our study confirms findings that the concentration of preventive and control interventions in high-risk areas can be an effective strategy for control of disease burden, not only leading to reduced operational costs, but also contributing to the sustainability of NTD control programmes (Martins-Melo et al., 2014b, 2012b Alencar et al., 2012; Bergquist et al., 2015). In line with current WHO initiatives (WHO, 2010WHO, , 2013WHO, , 2012 Uniting to Combat NTDs, 2012), Brazil launched in 2012 an integrated plan of strategic action (2012–2015) to eliminate important NTDs as a public health problem, such as leprosy, filariasis, schistosomiasis and onchocerciasis ; to eliminate trachoma as an important cause of blindness; and to control more effectively soil-transmitted helminthiasesTable 3 Significant spatiotemporal clusters of NTD-related deaths excluding Chagas disease, defined using space-time scan statistics by municipality of residence, Brazil, (Ministério da Saúde do Brasil, 2012). "
    [Show abstract] [Hide abstract] ABSTRACT: We analysed nationwide trends and spatial distribution of NTD-related mortality in Brazil. We included all death certificates in Brazil from 2000 to 2011, in which NTDs were recorded as any causes of death. A total of 100,814/12,491,280 (0.81%) death certificates were identified, which mentioned at least one NTD. Age-adjusted NTD-related mortality rates showed a significant decrease over time (annual percent change [APC]: − 2.1%; 95% CI: − 2.8 to − 1.3), with decreasing mortality rates in the Southeast, South, and Central-West regions, stability in the Northeast region, and increase in the North region. We identified spatial and spatiotemporal high-risk clusters for NTD-related mortality in all regions, with a major cluster covering a wide geographic range in central Brazil. Despite nationwide decrease of NTD-related mortality in the observation period, regional differences remain, with increasing mortality trends especially in the socioeconomically disadvantaged regions of the country. The existence of clearly defined high-risk areas for NTD-related deaths reinforces the need for integrated prevention and control measures in areas with highest disease burden.
    Full-text · Article · Jun 2016
    • "The overall prevalence of CD in Latin America was estimated to be approximately 7–8 million infected people in recent years (Rassi et al., 2010; WHO, 2014 ). In Brazil, about 4.6 million of people are estimated to be infected with T. cruzi (Martins-Melo et al., 2014), with approximately 6000 deaths annually (Martins-Melo et al., 2012). The latest national survey of the seroprevalence of CD in Brazil showed a dramatic reduction in T. cruzi vectorial transmission among children (Ostermayer et al., 2011) 2012). "
    [Show abstract] [Hide abstract] ABSTRACT: Despite the dramatic reduction in Trypanosoma cruzi vectorial transmission in Brazil, acute cases of Chagas disease (CD) continue to be recorded. The identification of areas with greater vulnerability to the occurrence of vector-borne CD is essential to prevention, control, and surveillance activities. In the current study, data on the occurrence of domiciliated triatomines in Brazil (non-Amazonian regions) between 2007 and 2011 were analyzed. Municipalities' vulnerability was assessed based on socioeconomic, demographic, entomological, and environmental indicators using multi-criteria decision analysis (MCDA). Overall, 2275 municipalities were positive for at least one of the six triatomine species analyzed (Panstrongylus megistus, Triatoma infestans, T. brasiliensis, T. pseudomaculata, T. rubrovaria, and T. sordida). The municipalities that were most vulnerable to vector-borne CD were mainly in the northeast region and exhibited a higher occurrence of domiciliated triatomines, lower socioeconomic levels, and more extensive anthropized areas. Most of the 39 new vector-borne CD cases confirmed between 2001 and 2012 in non-Amazonian regions occurred within the more vulnerable municipalities. Thus, MCDA can help to identify the states and municipalities that are most vulnerable to the transmission of T. cruzi by domiciliated triatomines, which is critical for directing adequate surveillance, prevention, and control activities. The methodological approach and results presented here can be used to enhance CD surveillance in Brazil.
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    • "Secondary data may present inconsistencies in the quantity and quality of the information [18]. Despite significant progress in the past years both in coverage and quality of information from SIM and SINAN databases [12,18], the number of cases and deaths related to VL may have been underestimated [12]. We aimed to reduce this source of bias by analyzing multiple causes of death instead of merely the underlying causes. "
    [Show abstract] [Hide abstract] ABSTRACT: BACKGROUND: Visceral leishmaniasis (VL) is a significant public health problem in Brazil and several regions of the world. This study investigated the magnitude, temporal trends and spatial distribution of mortality related to VL in Brazil. METHODS: We performed a study based on secondary data obtained from the Brazilian Mortality Information System. We included all deaths in Brazil from 2000 to 2011, in which VL was recorded as cause of death. We present epidemiological characteristics, trend analysis of mortality and case fatality rates by joinpoint regression models, and spatial analysis using municipalities as geographical units of analysis. RESULTS: In the study period, 12,491,280 deaths were recorded in Brazil. VL was mentioned in 3,322 (0.03%) deaths. Average annual age-adjusted mortality rate was 0.15 deaths per 100,000 inhabitants and case fatality rate 8.1%. Highest mortality rates were observed in males (0.19 deaths/100,000 inhabitants)
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