Vol.5, No.10, 1541-1547 (2013) Health
http://dx.doi.org/10.4236/health.2013.510209
Preventable infant mortality: Spatial distribution and
main causes in three Brazilian municipalities
Rosana Rosseto de Oliveira1, Thais Aidar de Freitas Mathias2
1Graduate Nursing Program, State University of Maringá, Maringá, Brazil; rosanarosseto@gmail.com
2Nursing Department, Graduate Nursing Program, State University of Maringá, Maringá, Brazil; tafmathias@uem.br
Received 29 May 2013; revised 28 June 2013; accepted 15 July 2013
Copyright © 2013 Rosana Rosseto de Oliveira, Thais Aidar de Freitas Mathias. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
ABSTRACT
Objective: The objective was to identify spatial
distribution patterns for preventable infant mor-
tality and the main causes of death in three mu-
nicipalities of Paraná state, Brazil. Design and
Sample: Ecological study on infant mortality
among residents of the municipalities of Mar-
ingá, Sarandi and Paiçandu, between 2004 and
2008. Measures: Data were obtained from re-
ports by the Infant Mortality Prevention Com-
mittee, georeferenced in 19 Demographic Ex-
pansion Areas and analyzed statistically using
Local Moran’s Index. Results: Of the 284 deaths
among children under one year of age, 68.7%
were considered preventable, and higher per-
cent ag es were found in outlying area s. The main
causes were illnesses originated during the
perinatal period (73.8%), exter nal c auses (11.3%)
and diseases of the respiratory system (5.1%).
Conclusion: It is necessary to implement ac-
tions and policies on child and prenatal assis-
t ance, in order to redu ce the inequality observed
between the central and outlying areas of the
region under study.
Keywords: Infant Health; Mortality; Public Health;
Population-Based Nursing
1. INTRODUCTION
Infant mortality is an indicator of quality of life and
health, reflects socioeconomic conditions and is also a
parameter to evaluate the organization and quality of
health services and obstetric and neonatal assistance [1].
Knowing the context in which child deaths occur is es-
sential to evaluating local actions with regard to wo-
men’s and children’s health, as well as to promoting
measures against potentially preventable infant morbi-
mortality [2].
Preventable causes of child mortality are defined as
events, fully or partially preventable by effective actions
from health services accessible at a given place and time.
Thus, by using the full set of technology resources and
updated health knowledge, these events should not occur.
The causes of preventable death are sensitive to health
care, but they also respond significantly to improve liv-
ing conditions, access to goods and services, schooling
and income [3].
The precursor of the debate on the term preventability
was David Rutstein, who defined it as a sentinel event—
something that should not occur, whether preventable
illness, unexpected disability or death; its occurrence
serves as a warning sign that the quality of therapy or
promotion/prevention measures must be better analyzed
[4].
A study performed in Brazil and other regions from
1997 to 2006 indicated a 37% reduction in the rate of
infant mortality from preventable causes [3], demon-
strating the positive impact of health services in improv-
ing living conditions for the population. Nevertheless,
infant mortality still reaches high rates—in 2011 it stood
at 21.17 deaths per thousand live births, most of which
were potentially preven table [5]. In the state of Paraná, a
study using results of analyses on child deaths investi-
gated by the Infant Mortality Prevention Committees
(IMPC) showed that 55% of deaths could have been
avoided through adequate care during gestation, delivery
and the neonatal period [6].
Despite its decline, infant mortality remains a health
priority in developing countries given extensive regional
inequalities, between and within cities, which justifies
the analysis of infant mortality and its pr eventab ility on a
regional approach. To that end, geoprocessing techniques
utilize space as an analytical category and use thematic
maps to describe different health phenomena—from the
Copyright © 2013 SciRes. OPEN ACCESS
R. R. de Oliveira, T. A. de F. Mathias / Health 5 (2013) 1541-1547
1542
distribution of morbi-mortality patterns to the allocation
of services [7]. By establishing the location in geogra-
phical space where a phenomenon took place, georefer-
encing contributes to a better understanding of it, making
it possible to identif y characteristic s that rev ea l th e so ci al ,
economic and environmental structure in which a health
event occurs [8].
Considering that infant mortality still is high in some
regions of Brazil, that most deaths are considered pre-
ventable, and that few studies have shown this problem
using georeferencing techniques, the object of this study
was to identify patterns of spatial distribution and the
main causes of potentially p reventab le infant mortality in
a region of Paraná state, Brazil. The results are expected
to contribute to the planning, organization and execution
of health actions, and to aid in finding causal explana-
tions for preventable infant mortality, defining priorities
for intervention.
2. METHODS
2.1. Design and Sample
It was an ecological study including all deaths of chil-
dren under one year of age, investigated by the IMPC of
the 15th Regional Health District of the State of Paraná,
among residents of the municipalities of Maringá,
Sarandi and Paiçandu, between January 1, 2004 and De-
cember 31, 2008. Maringá had a population of 357,077
inhabitants in 2010, urbanization rate of 98.2%, Human
Development Index (HDI) of 0.84 for the year 2000 [9],
and is located in northwestern Paraná state. Sarandi, a
municipality that is conurbated with Maringá to the east,
had 82,847 inhabitants in 2010 [9], has the highest
population density (799 inhabitants/km2) among the mu-
nicipalities in this study, urbanization rate of 99.1% and
HDI of 0.76 in 2000, regarded as medium human devel-
opment. Paiçandu, a municipality conurbated with Mar-
ingá to the west, has 35,936 inhabitants, population den-
sity of 210.2 inhabitants/km², urbanization rate of 98.6%,
and HDI of 0.74 [9].
The municipalities were analyzed as a whole, as Ma-
ringá, Paiçandu and Sarandi are conurbated, forming a
single footprint of urban occupation, with a high degree
of socioeconomic and spatial in teraction, representing an
urban agglomeration [10]. They are engaged in intermu-
nicipal flows of commerce and services, with functional
synergy.
2.2. Measures
The classification of prev entab ility an d th e un derlining
cause of death were collected from the Infant Mortality
Investigation System (SIMI), a database containing the
main conclusions of the analyses of child deaths under-
taken by the IMPC of the 15th Regional Health District of
Maringá. IMPC analyzes the history of each death since
gestation, outlining an itinerary up to birth, including
admission data for the mother and newborn, establishes
the responsibility, prevention measures, preventability,
and, when necessary, corrects the underlining cause of
death. Data from SIMI and from the Information System
on Live Births (SINASC) were combined using the Dec-
laration of Live Birth (LBD) number as the identifying
unit.
Demographic Expansion Areas (DEA)—the units of
analysis in this study—were defined by the Brazilian
Institute of Geography and Statistics (IBGE) using data
from the 2000 Census, in order to publish the data by
grouping census sectors, which guarantees statistical and
sociological consistency [10]. A total of 19 DEAs are
defined for the three municipalities: 14 in Maringá, four
in Sarandi, and a single DEA for the entire municipality
of Paiçandu.
2.3. Analytic Strategy
The percentages of preventable deaths were distri-
buted per DEA, using Local Moran’s Index (LISA),
which is a distance decay of the global measure of spatial
correlation, producing a specific value for each area. This
analysis makes it possible to identify and compare the
values of each specific DEA to the values of its neigh-
boring DEA—that is, identify DEA clusters with high
rates of a given variable and neighboring DEAs with
high rates as well (high-high); DEAs with low rates of
the variable and neighboring DEAs with low rates
(low-low); and transition areas, showing DEAs with low
rates and neighboring DEAs with high rates (low-high),
or DEAs with high rates and neighboring DEAs with low
rates (high-low). Non-significant autocorrelation occurs
when the spatial pattern that is different from the whole
is not observed [11]. For the analysis of the spatial dis-
tribution for preventable in fant mortality, the p ercentag es
were presented in quartiles, according to the maximum
and minimum numbers; greyscales were used in the
maps, with the color white representing the lowest and
black representing the highest. The research project was
analyzed and approved by the Research Ethics Commit-
tee of the State University of Maringá, in accordance
with CSN Resolution 196/96 (opinion 413/2009) and by
the Ethics Committee of the State Secretariat of Health
(opinion 120/2009).
3. RESULTS
Of the 284 child deaths investigated by the IMPC
during the study period and classified according to pre-
ventability, 68.7% were considered preventable, with
highlight to DEA 5, represented by the Zones 5 and 6
neighborhoods of Maringá, in which no deaths consid-
Copyright © 2013 SciRes. OPEN ACCESS
R. R. de Oliveira, T. A. de F. Mathias / Health 5 (2013) 1541-1547
Copyright © 2013 SciRes. OPEN ACCESS
1543
ered preventable took place (Table 1).
Of the 195 deaths classified as preventable, 144
(73.8%) showed diseases originated in the perinatal pe-
riod as the underlining cause of death, after investigation.
As the second main underlining causes of death as ana-
lyzed by the IMPC were external causes, with 22 cases
(11.3%); in third place were diseases of the respiratory
system, with 5.1% of deaths (Table 2).
The spatial distribution of the percentages of preven-
table deaths shows that, with the exception of the central
region of Maringá (DEA 5) and a few surro un di n g nei gh-
borhoods (DEA 4, 1, 2, 3, 6, 7, 8, 12 in Maringá and
DEA 17 in Sarandi), most of the maps shows high rates
of preventable deaths—between 63.6% and 84.6% (Fig-
ure 1).
Local Moran’s Index showed a concentration of areas
with “high-high” correlation at DEA 18 in Sarandi,
which features 70% of preventable child deaths;
“low-low” correlation at DEAs 6 and 7, located in cen-
tral areas of Maringá, with percentages of preventable
child deaths of 54.5% an d 50%, respectively; “high-low”
correlation at DEA 10 (area with 65.4% preventable
child deaths); “low-high” correlation at DEA 17 in
Sarandi (55.6% preventabl e deaths) (Figure 2).
4. DISCUSSION
The results of this research show that most child
deaths that occurred in the municipalities under study
(68.7%) were considered preventable; this means it is
essential to seek improvements in prenatal, delivery and
newborn assistance, ensuring access by mother and
newborn to quality services in a timely manner. The re-
sults agree with the findings of a study of municipalities
within the 15th Regional Health District of the State of
Paraná, between 2000 and 2006, in which 70.1% of ana-
lyzed deaths were considered preventable [2]. In that
regard, health and nursing teams must develop actions
that meet the challenges created by the conditions of
morbi-mortality from preventable causes.
The three municipalities in this study, alth oug h located
in the interior of Paraná state, are part of a Metropolitan
Area officially recognized by state law 83/98, currently
consisting of 13 municipalities [10]. Social indicators
and the reality of healthcare in the municipalities under
study are quite similar to conditions found in the ou tlying
areas of large urban centers, where less favorable socio-
economic conditions are found, such as low income and
schooling. This can be confirmed by observing that the
Table 1. Infant mortality by DEA preventability potential. Maringá, Sarandi and Paiçandu, 2004 to 2008.
Preventable Non-preventable
DEA Neighborhood N % n % Total
1 Maringá Vila Morangueira 4 57.1 3 42.9 7
2 Maringá Jd. Alvorada 9 60.0 6 40.0 15
3 Maringá Zon a 7 3 60.0 2 40.0 5
4 Maringá Zona 8-Vila St.Antonio 2 33.3 4 66.7 6
5 Maringá Zona 5 and 6 - - 5 100.0 5
6 Maringá Zon a 1, 2 , 3, 4 6 54.5 5 45.5 1 1
7 Maringá A v. Mandacaru 3 50.0 3 50.0 6
8 Maringá Contorno Norte 8 61.5 5 31.3 13
9 M aringá Conjunto Requião 16 84.2 3 15. 8 19
10 Maringá Cidade Alta 17 68.0 8 32.0 25
11 Maringá Liberdade-Aeroporto 16 76.2 5 23. 8 21
12 Maringá Jd. Imperial-Pq Grv. 12 63.2 7 36.8 19
13 Maringá Zona Industrial 6 75.0 2 25.0 8
14 Maringá Olímpico 19 76.0 6 24.0 25
15 Paiçandu 19 73.1 7 26.9 26
16 Sarandi Centro 14 77. 8 4 22.2 18
17 Sarandi Jd. Independência 5 55.6 4 44.4 9
18 S a randi Parque Alvamar 14 70.0 6 30.0 20
19 Sa randi Linha do Trem 22 84.6 4 15.4 26
Total 195 68.7 89 31.3 284
R. R. de Oliveira, T. A. de F. Mathias / Health 5 (2013) 1541-1547
1544
Table 2. Preventable infant deaths according to underlining cause of death. Maringá, Sarandi and Paiçandu, 2004 to 2008.
Total
Underlining causes of death N %
Perinatal 144 73.8
External causes 22 11.3
Respiratory System 10 5.1
Congenital malformations 8 4.1
Diseases of the nervous system 4 2.1
Symptoms, signs and abnormal findings in clinical and laboratory exams, not otherwise classified 3 1.5
Infections and parasite s 2 1.0
Endocrinal, nutritional and metabolic 2 1.0
Total (%) 195 100
Figure 1. Spatial distribution of the rates of preventable child deaths by DEA in Maringá, Sarandi and
Paiçandu, 2004 to 2008.
only region with no potentially preventable child deaths
(DEA 5) and another with lower preventability rates—
between 21.3% and 42.3%—(DEA 4) are located in the
central area of the city of Maringá (Figure 1). The cen-
tral area is characterized by residents with high-income
occupations and greater schooling—that is, occupations
that presuppose specialization and formal education [10].
Also, public services such as clinics and hospitals are
located in the central areas of Maringá, facilitating access
by residents to these services.
Studies developed to evaluate health inequalities in
this same region, using data from SINASC [12,13]
showed the municipalities of Sarandi and Paiçandu as
vulnerable areas, as they featured higher coefficients of
infant mortality and inferior socioeconomic indicators,
with higher rates of teenage mothers, mothers with low
education, children of black or brown color/race, mothers
with insufficient prenatal appointments and newborns
with low vitality scores at birth. These results indicated
vulnerable areas that coincide with the areas with higher
preventability percentages found in the present study.
Recognizing the health inequalities combined with a
discussion on the territorialization of preventable child
deaths and the underlining causes of preventable deaths
Copyright © 2013 SciRes. OPEN ACCESS
R. R. de Oliveira, T. A. de F. Mathias / Health 5 (2013) 1541-1547 1545
Figure 2. Spatial distribution of the percentages of preventable child deaths by Local Moran’s Index
(LISA) and DEA in Maringá, Sarandi and Paiçandu, 2004 to 2008.
in the municipalities of this study can contribute to an
understanding of infant mortality, limits an d possibilities
of health actions [14], and can provide health profession-
als with awareness of the existing inequality among the
different regions of the municipalities.
Although the studied municipalities have lower infant
mortality coefficients when compared to other munici-
palities in different regions of Brazil (9.5, 14.3 and 11.3
deaths per thousand live births in Maringá, Sarandi and
Paiçandu, respectively, between 2004 and 2008), ade-
quate prenatal measures and care measures during deliv-
ery and neonatal stages, such as improving service and
organization and arranging services in networks, would
represent 195 child deaths that could be prevented during
the study period, as these deaths were classified as pre-
ventable according to an analysis by the IMPC.
With regard to the underlining causes, there were a
high percentage of deaths resulting from conditions
originating during the perinatal period, from diseases of
the respiratory system and from external causes, which
combined for 90.2% of deaths. Thes e re sults reve aled the
potential preven tability of these deaths, as perinatal cau s-
es represent fully or partially “preventable deaths” if
adequate care measures were implemented for the mo-
ther during gestation and delivery, and to the newborn
[15].
It is known that childhood is a particularly vulnerable
life stage, in which biological determinants of morbi-
mortality are strongly linked to external conditions—
either socioeconomic or environmental (housing, diet,
sanitation, hygiene and family relations)—or to the
availability of health services [16]. We must also con-
sider the influence of public health measures; even if
low-cost or easily implementable, these measures are
responsible for a considerable reduction in infant mortal-
ity [17]. Therefore, partnerships with other sectors of
society—such as the Children’s Ministry, for instance—
demonstrate that integration must be considered between
all social levels and public/private partnerships in order
to solve problems that transcend the field of clinical
practice, as in the case of social inequalities [18].
In the present work, the analysis of preventable child
deaths showed areas with “high-high” correlation at DEA
18, in the Sarandi region, meaning that it is an area with
a high rate of preventable deaths surrounded by other re-
gions with high percentages as well. “Low-low” correla-
tions in the central areas of Maringá showed homoge-
nous area in that region, with lower percentages of child
death preventability. “High-low” correlations in the out-
lying areas of Maringá showed an area that needs to be
better evaluated, as it showed percentages that contrast
with the results found in the neighboring DEAs of that
municipality.
The “low-high” correlation for the central area of
Copyright © 2013 SciRes. OPEN ACCESS
R. R. de Oliveira, T. A. de F. Mathias / Health 5 (2013) 1541-1547
1546
Sarandi shows that even within that municipality, there is
a pattern characterized by a nucleus-periphery distribu-
tion, with better health indicators for residents of the
city’s central areas. The results indicate that although all
three municipalities show DEAs with high percentages
of preventable infant mortality, Sarandi has the highest
values. That observation can be partially explained by
the spatial occupation of Sarandi, which has been char-
acterized by people who wish to live in Maringá but
move to neighboring municipalities in search of more
affordable housing, yet maintain their employment, un-
deremployment, studies, shopping and leisure in the
other municipality [10]. Therefore, conditions acknowl-
edged as risks to the mother and newborn—which can be
prevented and intervened by the health sector—com-
bined with unfavorable socioeconomic conditions such
as low family income and low education—can charac-
terize iniquity. Knowing these factors, the responsibility
of managers, health professionals and services is directly
linked to preventable child deaths [19,20].
The results of this study may show biases characteris-
tic of research studies that use secondary databases, ei-
ther in quantity or quality of data, as the information is
subject to data accuracy and thoroughness, which do not
invalidate their use. Information systems with the data
routinely collected in health services constitute valuable
sources of information, the results of which can contrib-
ute to improved quality and coverage of this information
when analyzed. However, we must emphasize the im-
portance of participation and responsibility of munici-
palities for producing, organizing and feeding the data-
bases. It is necessary to eliminate errors in records in
order to obtain reliable data, including address informa-
tion—essential in studies that use georeferenced carto-
graphic parameters [21,22]. In addition to collecting and
forwarding the data, th e municipalities must monitor and
evaluate the quality of the data in health information
systems, by continuously training the agents responsible
for that activity [21].
By using geoprocessing techniques, this analysis adds
information on preventable infant mortality, given that
the location of the event in geographic space was possi-
ble using thematic maps. Using homogenous areas, such
as DEAs, to monitor health events can contribute so that
managers can evaluate each area differently. The results
showed that for the municipalities under analysis, outly-
ing DEAs feature the highest rates of preventable child
deaths. This conclusion leads to the consideration that
these populations are more susceptible to hardship in
accessing medical technologies and adequate health ser-
vices, which would be enough to prevent infant morbi-
mortality.
Another contribution of this study to the territorial-
based analysis of the distribution of potentially prevent-
able deaths and their underlying causes is the use of data
generated from the death investigation analyses by the
IMPCs working in the Regional Health Districts of the
State of Paraná. The analysis of child death prevent-
ability can assist in identifying the areas with the high est
probability of success or failure in preventing these
deaths, in addition to indicating the performance of the
healthcare system and the population’s behavior when
seeking healthcare. This methodology of study can be
reproduced in other localities at different desired units of
analysis. The present study used homogenous areas as
standardized by IBGE according to the National Census,
which guarantees the quality of the methodology.
It is worth underlining that even though DEAs repre-
sent smaller areas within a municipality, with more ho-
mogenous characteristics, they can still hide different
situations and realities to b e explored.
It is important to highlight the limits of interpretation
of the results of this study, in that the information is sub-
ject to the thoroughness and accuracy of the data regard-
ing the addresses of the mothers of each newborn. This
may compromise the analysis and monitoring of pre-
ventable infant mortality in the geographic realm.
Other studies are still required in order to better know
the population residing in the regions with the highest
rates of preventability, evaluating the conditions of ac-
cess to health services, quality of service, as well as ref-
erence and counter-reference services in caring for wo-
men during gestation and delivery and for newborns.
Lastly, the debate on the responsibility for preventab le
child death must be taken to health services and incorpo -
rated in the work routine, as the occurrence of prevent-
able child death may indicate the need for revision and
suitability of health assistance to the population in their
numerous health needs, beyond those related to mother
and child.
REFERENCES
[1] Ministry of Health (BR) (2009) Departamento de ações
programáticas estratégicas. Manual de vigilância do óbito
infantil e fet al e do comitê de prevenção do óbito infantil
e fetal. MS, Brasília.
[2] Mathias, T.A.F., Assunção, A.M. and Silva, G.F. (2008)
Infant deaths investigated by the Prevention Committee
of Infant Mortality in region of Paraná state. Revista da
Escola de Enfermagem, 42, 445-453.
http://dx.doi.org/10.1590/S0080-62342008000300005
[3] Malta, D.C., Duarte, E.C., Escalante, J.J., Almeida, M.F.,
Sardinha, L.M.V., Macário, E.M., et al. (2010) Avoidable
causes of infant mortality in Brazil, 1997-2006: Contribu-
tions to performance evaluation of the Unified National
Health System. Cadernos de Saúde Pública, 26, 481-491.
http://dx.doi.org/10.1590/S0102-311X2010000300006
[4] Rutstein, D.D., Bereberg, W., Chalmers, T.C., Child, C.G.,
Copyright © 2013 SciRes. OPEN ACCESS
R. R. de Oliveira, T. A. de F. Mathias / Health 5 (201 3) 1541-1547
Copyright © 2013 SciRes. OPEN ACCESS
1547
Fishman, A.P., Perrin, E.B., et al. (1976) Measuring the
quality of medical care: A clinical method. The New
England Journal of Medicine, 294, 582-588.
http://dx.doi.org/10.1056/NEJM197603112941104
[5] Ministry of Health (BR) (2011) Departamento de infor-
mática do SUS. DATASUS, MS, Brasília.
[6] Vianna, R.C.X.F., Moro, C.M.C.B., Moysés, S.J., Car-
valho, D. and Nievola, J.C. (2010) Data mining and cha-
racteristics of infant mortality. Cadernos de Saúde
Pública, 26, 535-542.
http://dx.doi.org/10.1590/S0102-311X2010000300011
[7] Barcellos, C., Ramalho, W.M., Gracie, R., Magalhães,
M.A.F.M., Fontes, M.P. and Skaba, D. (2008) Geocoding
health data in sub-municipal scale: Some Brazilian ex-
periences. Epidemiologia e Serviços de Saúde, 17, 59-70.
[8] Hau, L.C., Nascimento, L.F.C. and Tomazini, J.E. (2009)
Geoprocessing to identify the pattern of birth profile in
Vale do Paraíba. Revista Brasileira de Ginecologia e Ob-
stetrícia, 31, 171-176.
[9] Ipardes (201 1) Caderno est atís tico. Municí pio d e M a r ingá.
http://www.ipardes.gov.br/modules/conteudo/conteudo.ph
p?conteudo=5
[10] Santana, R.G., Udo, M.C.T., Previdelli, I.T.S. and Rodri-
gues, A.L. (2010) Análise da ocupação residencial na
Região Metropolitana de Maringá: A construção e apli-
cação de uma tipologia. In: Rodrigues, A.L., Tonella, C.
and Organizadores, Eds., Retratos da Região Metropoli-
tana de Maringá: Subsídios para a Elaboração de Polí-
ticas Públicas Participativas. Eduem, Maringá, 17-39.
[11] Anselin, L. (1994) Local indicators of spatial association
—LISA. Regional Research Institute, West Virginia Uni-
versity, Virginia.
[12] Predebon, K.M., Mathias, T.A.F., Aidar, T. and Rodrigues,
A.L. (2010) Socio-spatial inequality expressed by indica-
tors from the Information System on Live Births (SI-
NASC). Cadernos de Saúde Pública, 26, 1583-1594.
http://dx.doi.org/10.1590/S0102-311X2010000800012
[13] Oliveira, R.R., Costa, J.R. and Mathias, T.A.F. (2012)
Spatial distribution and autocorrelation of infant mortality
for three cities in Paraná state, Brazil. Geospatial Health,
6, 257-262.
[14] Ventura, R.N., Oliveira, E.M., Silva, E.M., Silva, N.N.
and Puccini, R.F. (2008) Living conditions and infant
mortality in the municipality of Embu, São Paulo, Brazil.
Revista Paulista de Pediatria, 26, 251-257.
http://dx.doi.org/10.1590/S0103-05822008000300009
[15] Malta, D.C. and Duarte, E.C. (2007) Causes of avoidable
mortality through effective healthcare services: A review
of the literature. Ciência & Saúde Coletiva, 3, 765-776.
http://dx.doi.org/10.1590/S1413-81232007000300027
[16] Victora, C.G., Aquino, E.M.L., Leal, M.C., Monteiro,
C.A., Barros, F.C. and Szwarcwald, C.L. (2011) Saúde de
mães e crianças no Brasil: Progressos e desafios. The
Lancet, 6736, 32-46.
[17] Silva, L.R., Christoffel, M.M. and Souza, K.V. (2005)
History, conquests and perspectives of the woman and
child care. Texto C on te xt o-Enferm, 14, 585-593.
http://dx.doi.org/10.1590/S0104-07072005000400016
[18] Silva, A.C.M.A., Villar, M.A.M., Wuillaume, S.M. and
Cardoso, M.H.C.A. (2009) Opinions by physicians from
the Family Health Program on four health care priorities
proposed by the agenda for commitment to comprehend-
sive child health and reduction of infant mortality. Cad-
ernos de Saúde Pública, 25, 349-358.
http://dx.doi.org/10.1590/S0102-311X2009000200013
[19] Vilela, M.B.R., Bonfim, C. and Medeiros, Z. (2008) In-
fant mortality due to infectious and parasitic diseases: A
reflection of the social inequalities in a municipality in
the Northeast Region of Brazil. Revista Brasileira de
Saúde Materno Infantil, 8, 445-461.
[20] Barros, F.C. and Victora, C.G. (2008) Maternal-child
health in Pelotas, Rio Grande do Sul State, Brazil: Major
conclusions from comparisons of the 1982, 1993, and
2004 bi rth cohorts. Cadernos de Saúde Pública, 24, S461-
S467.
http://dx.doi.org/10.1590/S0102-311X2008001500012
[21] Moilmaz, S.A.S., Garbin, C.A.S., Garbin, A.J.I., Zina,
L.G., Yarid, S.D. and Francisco, K.M.S. (2010) Prenatal
information system: Critical analysis of register in a mu-
nicipality of São Paulo State. Revista Brasileira de Enfer-
magem, 63, 385-390.
[22] Grady, S.C. and Enander, H. (2009) Geographic analysis
of low birthweight and infant mortality in Michigan using
automated zoning methodology. International Journal of
Health Geographics, 18, 8-10.